A pavement performance monitoring integrated system
The road performance monitoring system, which links the axle load meter with the trigger function module, only triggers the sensor to collect data when a vehicle passes by. Combined with multi-dimensional sensor deployment and data processing center, it solves the problems of high power consumption and a lot of invalid data when the sensor is working continuously, and achieves efficient and accurate road performance monitoring.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Utility models(China)
- Current Assignee / Owner
- ROADMAINT CO LTD
- Filing Date
- 2025-06-16
- Publication Date
- 2026-07-07
AI Technical Summary
In existing road performance monitoring systems, sensors consume a lot of power while operating continuously and collect a lot of invalid data, resulting in high energy consumption, high maintenance costs, and low data analysis efficiency.
By linking the axle load meter with the triggering function module, the sensors on the road surface are triggered to collect data only when a vehicle passes by. Combined with the deployment of multi-dimensional sensors and the data processing center, on-demand monitoring and efficient data transmission are achieved.
It reduced system energy consumption, improved data validity and monitoring accuracy, reduced invalid data collection, lowered maintenance costs, and improved data analysis efficiency and pavement performance assessment accuracy.
Smart Images
Figure CN224468274U_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of monitoring systems, and more particularly to an integrated system for monitoring road performance. Background Technology
[0002] With increasing traffic volume and more complex vehicle loads, road performance monitoring is crucial for driving safety and road lifespan. Existing monitoring systems based on embedded sensors suffer from the following drawbacks: the sensors require continuous operation, leading to high power consumption, and the collection of numerous invalid data points results in high energy consumption for the monitoring system. Utility Model Content
[0003] In view of this, the purpose of this application is to propose an integrated system for road performance monitoring to solve the problems of high power consumption and excessive invalid data collection by sensors in current road performance monitoring systems.
[0004] To achieve the above objectives, this application provides an integrated system for monitoring road surface performance, comprising:
[0005] An axle load meter, installed on the road surface, is used to monitor vehicle load.
[0006] Sensors, buried in the road surface, are used to monitor road surface performance;
[0007] A trigger function module is connected to the axle load cell and the sensor. It is used to trigger the sensor to monitor road surface performance after receiving a tire contact signal. The tire contact signal is the signal emitted by the axle load cell when the vehicle wheel travels to the axle load cell.
[0008] Optionally, the sensor includes a stress sensor, and multiple stress sensors are provided for monitoring the stress on the road surface.
[0009] Optionally, a temperature sensor is also installed inside the road surface to monitor the road surface temperature.
[0010] Optionally, a road surface displacement sensor is also installed inside the road surface to monitor the displacement of the road surface.
[0011] Optionally, the axle load meter is a non-embedded structure and is installed on the road surface.
[0012] Optionally, the pavement performance monitoring integrated system also includes a data processing center connected to the sensors for receiving, storing and analyzing pavement performance data and generating pavement performance evaluation reports.
[0013] Optionally, the road performance monitoring integrated system also includes a communication module connected between the data processing center and the sensor, for transmitting the data collected by the sensor to the data processing center.
[0014] Optionally, the pavement performance monitoring integrated system also includes an early warning module, which is connected to the data processing center and is used to provide real-time early warnings for abnormal pavement performance based on pavement performance data and preset thresholds.
[0015] Optionally, the pavement performance monitoring integrated system also includes a remote monitoring terminal, which is connected to the data processing center and the early warning module to display pavement performance data, receive early warning information, and remotely control and set parameters for the system.
[0016] Optionally, the road performance monitoring integrated system also includes a vehicle information acquisition module, used to collect vehicle license plate information, vehicle type information, and driving speed information. The vehicle information acquisition module is connected to the data processing center, which combines vehicle information and road performance data for comprehensive analysis to assess the impact of vehicle behavior on road performance.
[0017] As can be seen from the above, the pavement performance monitoring integrated system provided in this application includes an axle load meter, sensors, and a triggering function module. When a vehicle wheel travels onto the axle load meter, the axle load meter not only measures the vehicle load but also triggers the sensors in the pavement through the triggering function module. On the one hand, this avoids data redundancy caused by continuous high-frequency sampling by the sensors in the pavement, collecting pavement response data only as needed when a vehicle passes by, thus improving monitoring efficiency and data validity. On the other hand, when the axle load meter detects the vehicle wheel rolling over it, it triggers the sensors to synchronously collect the mechanical response (such as stress, load, etc.) of each structural layer of the pavement, realizing real-time correlation analysis of pavement performance data. This allows for the prediction of the impact of different load levels on pavement life by combining historical data, providing a scientific basis for pavement maintenance and design. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0019] Figure 1 This is a schematic diagram showing the position of the axle load meter in an embodiment of this application;
[0020] Figure 2 A top view of the stress sensor is shown for an embodiment of this application;
[0021] Figure 3 This is a schematic diagram showing the connection of each module in an embodiment of this application.
[0022] Reference numerals: 01, Road surface; 011, First cement-aggregate layer; 012, Second cement-aggregate layer; 013, Coarse asphalt concrete layer; 014, High-modulus asphalt concrete layer; 015, Road surface layer; 02, Subgrade; 1, Axle load meter; 2, Sensor; 21, Stress sensor; 22, Temperature sensor; 23, Humidity sensor; 24, Road surface displacement sensor; 3, Trigger function module; 4, Communication module; 5, Data processing center; 6, Early warning module; 7, Remote monitoring terminal; 8, Vehicle information collection module. Detailed Implementation
[0023] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.
[0024] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.
[0025] As mentioned in the background, with the exponential growth of traffic volume and the increasing complexity of vehicle loads, pavement performance monitoring has become a key technological link in ensuring driving safety, optimizing road maintenance strategies, and extending road service life. Currently, although pavement performance monitoring systems based on embedded sensors are widely used, many technical bottlenecks still need to be addressed:
[0026] From an energy consumption perspective, traditional embedded sensors operate continuously, resulting in high overall system energy consumption. On one hand, the sensors run continuously when vehicles are not passing, consuming a large amount of electrical energy and accelerating the aging of batteries and circuit components, significantly increasing later maintenance costs. On the other hand, due to the lack of an effective triggering mechanism, the sensors collect a large amount of invalid data. This data not only occupies a lot of storage resources but also requires additional computing power for processing and filtering, further exacerbating system energy consumption.
[0027] Regarding data validity, existing monitoring systems collect invalid data accounting for as much as 60%-80%, severely impacting the efficiency and accuracy of data analysis. The redundant accumulation of invalid data not only reduces the extraction efficiency of key pavement performance parameters (such as stress, strain, and fatigue damage) but may also interfere with the assessment of the actual pavement condition, leading to delayed or misjudged maintenance decisions. Furthermore, continuously operating sensors operate in complex pavement environments (such as high temperature, high humidity, and heavy-load compaction), causing their monitoring accuracy to rapidly decline over time, further reducing data reliability.
[0028] From a system maintenance perspective, the frequent battery replacements or external power supply cabling due to high energy consumption pose significant challenges in practical operation. In remote areas or busy traffic zones, maintenance personnel not only need to invest substantial manpower and resources but also must interrupt traffic to perform operations, resulting in high safety risks and economic costs. Furthermore, the continuous accumulation of invalid data can easily lead to data storage device capacity saturation, increasing the difficulty of data management and backup.
[0029] In summary, how to effectively reduce system energy consumption and minimize invalid data collection while ensuring monitoring accuracy has become a pressing technical challenge in the field of road performance monitoring.
[0030] The following is in conjunction with the appendix Figure 1-3 The embodiments of this application will be described in detail below.
[0031] like Figure 1 , Figure 2 and Figure 3 As shown, a pavement performance monitoring integrated system includes:
[0032] Axle load meter 1, installed on road surface 01, is used to monitor vehicle load;
[0033] Sensor 2 is buried in road surface 01 to monitor the performance of road surface 01;
[0034] Trigger module 3 is connected to the axle load meter 1 and the sensor 2. It is used to trigger the sensor 2 to monitor the performance of the road surface 01 after receiving the tire contact signal. The tire contact signal is the signal emitted by the axle load meter 1 when the vehicle wheel travels to the axle load meter 1.
[0035] Specifically, a roadbed 02 is provided under the road surface 01. The road surface 01 has a multi-layer structure, including a first cement-aggregate layer 011, a second cement-aggregate layer 012, a coarse asphalt concrete layer 013, a high-modulus asphalt concrete layer 014, and a road surface layer 015. Multiple sensors 2 are provided and buried in the road surface 01 to monitor the performance parameters of the road surface 01 and provide data support for the performance of the road surface 01.
[0036] In addition, the trigger function module 3 can use a microcontroller (such as Arduino or STM32) as the core component to build a trigger circuit, which is connected to the sensor in the axle load meter 1 and the sensor 2 buried in the road surface 01 respectively. The microcontroller collects the tire contact signal of the sensor on the axle load meter 1, generates a trigger signal, and communicates with the sensor 2 in the road surface 01 to trigger the sensor 2 to monitor the performance of the road surface 01. Specifically, when the vehicle has not yet reached the axle load meter 1, the meter has no pressure, the trigger module 3 outputs a "not triggered" signal, and the sensor 2 in the road surface 01 is in standby mode. At this time, the sensor 2 in the road surface 01 does not collect road surface 01 data or collects road surface 01 data at a low frequency. When the vehicle wheel travels onto the axle load meter 1, the sensor 2 on the axle load meter 1, such as a strain gauge, experiences a change in resistance due to pressure, causing the bridge output voltage signal to rise, which sends a tire contact signal to the trigger module 3. After receiving the tire contact signal, the trigger module 3 sends a trigger signal (such as a "start collection" command) to the sensor 2 in the road surface 01. When the vehicle passes the axle load meter 1, the pressure in the axle load meter 1 decreases, the trigger module 3 stops sending signals, and the sensor 2 ends the current data collection. Through this mechanism, the system can avoid data redundancy caused by continuous high-frequency sampling by the sensor 2 in the road surface 01, and only collect road surface 01 response data when the vehicle passes, improving monitoring efficiency and data validity.
[0037] In this embodiment, the pavement performance monitoring integrated system includes an axle load meter 1, a sensor 2, and a trigger function module 3. The axle load meter 1 is not only used to measure vehicle load, but also links the sensor 2 in the pavement 01 through the trigger function module 3. On the one hand, it avoids data redundancy caused by continuous high-frequency sampling by the sensor 2 in the pavement 01, and only collects pavement 01 response data as needed when a vehicle passes by, thereby improving monitoring efficiency and data validity. On the other hand, when the axle load meter 1 detects vehicle wheel rolling, it triggers the sensor 2 to synchronously collect the mechanical response (such as stress, load, etc.) of the pavement 01, realizing real-time correlation analysis of pavement 01 performance data. This allows for the prediction of the impact of different load levels on the pavement 01 life by combining historical data, providing a scientific basis for pavement 01 maintenance and design.
[0038] In some embodiments, the sensor 2 includes a stress sensor 21, and multiple stress sensors 21 are provided for monitoring the stress of the road surface 01.
[0039] In addition, a temperature sensor 22 is installed inside the road surface 01 to monitor the temperature of the road surface 01. A road surface displacement sensor 24 is also installed inside the road surface 01 to monitor the displacement of the road surface 01.
[0040] Specifically, stress sensors 21 are mainly installed at the bottom of the asphalt layer (coarse asphalt concrete layer 013) to monitor the stress of the asphalt layer and the structural layers above it. For example, four sets of stress sensors 21 are provided, one set arranged horizontally and the other three sets arranged vertically (e.g., ...). Figure 2 As shown, temperature sensors 22 are installed in a gradient layout to comprehensively detect road surface stress. Vertically, a set of temperature sensors 22 is buried at intervals (e.g., 50 cm) from the road surface 015 downwards to monitor temperature changes at different depths. Horizontally, a monitoring section is set at intervals (e.g., 50 meters) along the road's longitudinal direction. For example, each section is equipped with 3-5 sets of sensors 2 to analyze the temperature distribution differences in different areas of the road surface 01, as well as the impact of diurnal and seasonal changes on the performance of the road surface 01. Humidity sensors 23 are installed within the roadbed 02 to monitor the humidity of the roadbed 02 and prevent road surface 01 settlement.
[0041] In this embodiment, the deployment of multi-dimensional sensors 2 enables comprehensive coverage of all structural layers and different areas of the road surface 01, achieving accurate monitoring of performance indicators such as stress, temperature, humidity, and displacement of the road surface 01. Multi-parameter fusion analysis further improves the accuracy and reliability of the monitoring data, allowing road management departments to more clearly and accurately grasp the actual performance status of the road surface 01.
[0042] In some embodiments, the axle load meter 1 is a non-embedded structure and is installed on the road surface layer 015.
[0043] Specifically, the axle load meter 1 adopts an embedded structure and can be fixed with bolts to improve the stability and installation efficiency of the axle load meter 1 on the road surface 01.
[0044] In this embodiment, the axle load meter 1 is a non-embedded structure and is installed on the surface layer 015 of the road surface to avoid causing major damage to the road surface 01 and affecting the normal function of the road surface 01.
[0045] In some embodiments, such as Figure 3 As shown, the road performance monitoring integrated system also includes a data processing center 5, which is connected to the sensor 2 and is used to receive, store and analyze road performance data and generate a road performance evaluation report.
[0046] In addition, the road performance monitoring integrated system also includes a communication module 4, which is connected between the data processing center 5 and the sensor 2, for transmitting the data collected by the sensor 2 to the data processing center 5.
[0047] Specifically, data processing center 5 can use a central processing unit (CPU) chip to process large amounts of pavement performance data, such as preliminary analysis and calculation of stress and temperature data. It can also use a field-programmable gate array (FPGA) to perform preliminary screening, noise reduction, and feature extraction on the raw data collected by sensor 2, reducing data transmission volume. Edge computing nodes are set up near the monitored road sections to analyze pavement stress data in real time. The cloud is responsible for in-depth data mining, long-term storage, and complex model calculations, such as training pavement performance prediction models based on historical data, achieving efficient division of labor in data processing and reducing network transmission pressure.
[0048] Communication module 4 supports dual-mode communication of 5G and LPWAN (such as NB-IoT, LoRa). In areas with high traffic volume and high data transmission demand, 5G network is used to achieve high-speed, low-latency transmission of sensor 2 data, ensuring real-time monitoring of road surface performance changes. In remote road sections or for sensors 2 with high power consumption requirements (such as battery-powered temperature and humidity sensors), LPWAN technology is used to ensure stable data transmission with its advantages of low power consumption and long-distance transmission, building a communication network covering the entire road section and avoiding data transmission blind spots.
[0049] In this embodiment, the data processing center 5 can centrally receive and systematically store a large amount of pavement 01 performance data collected from various sensors 2 (such as stress, temperature, and humidity sensors 23), avoiding data dispersion and chaos, and achieving unified data management. Simultaneously, the data analysis function can uncover potential information behind the data, such as analyzing the changing trends of pavement 01 performance over time through long-term data accumulation, and the impact of different environmental factors on pavement 01 performance, providing comprehensive data support for road management. The communication module 4, as a key bridge for data transmission, ensures that the data collected by the sensors 2 can be transmitted quickly and stably to the data processing center 5.
[0050] In some embodiments, the pavement performance monitoring integrated system further includes an early warning module 6, which is connected to the data processing center 5 and is used to provide real-time early warnings for abnormal pavement performance based on pavement 01 performance data and preset thresholds.
[0051] In addition, the road performance monitoring integrated system also includes a remote monitoring terminal 7, which is connected to the data processing center 5 and the early warning module 6 to display road performance data and receive early warning information, and to remotely control and set parameters for the system.
[0052] Specifically, the early warning module 6 uses a microcontroller (such as the STM32 series) or a small single-board computer (such as a Raspberry Pi) to quickly process the pavement 01 performance data transmitted from the data processing center 5, and makes logical judgments based on preset thresholds or algorithms. Furthermore, the early warning module 6 can combine historical pavement 01 performance data and real-time traffic flow to construct a dynamic early warning model. For example, when the data processing center 5 analyzes that a certain road section has recently experienced a significant increase in traffic flow and the pavement 01 temperature exceeds a threshold, even if the stress data has not yet reached the preset threshold, the early warning module 6 will issue a preventative warning in advance, indicating a potential risk of pavement 01 fatigue or thermal expansion cracking. The early warning module 6 classifies pavement 01 performance anomalies into multiple warning levels (such as general warning, severe warning, and emergency warning), with different warning methods and response procedures for different levels. For example, a general warning is pushed to the road maintenance department via an APP, while a severe warning simultaneously triggers SMS and voice calls to the responsible person, and is highlighted with a flashing red indicator on the remote monitoring terminal 7. The remote control terminal can be a computer or mobile phone, which displays the real-time operating status of each system component (such as sensor 2, communication module 4, and data processing center 5). When abnormal data is detected from sensor 2 or unstable signal from communication module 4, the terminal automatically generates a fault diagnosis report to analyze possible causes of the fault (such as hardware damage or network interruption).
[0053] In this embodiment, the early warning module 6 analyzes the performance data of road surface 01 in real time based on preset thresholds, and can issue timely warnings at the initial stage of abnormalities in road surface 01, preventing traffic accidents caused by road surface 01 damage and ensuring the safety of vehicles and pedestrians. The remote monitoring terminal 7 presents the road surface 01 performance data in intuitive charts, maps, and other forms, making it convenient for managers to have a comprehensive understanding of road conditions. Combined with the early warning information, managers can analyze the data and formulate more scientific and reasonable road maintenance plans and traffic management strategies.
[0054] In some embodiments, the road performance monitoring integrated system further includes a vehicle information acquisition module 8, which is used to collect vehicle license plate information, vehicle type information and driving speed information. The vehicle information acquisition module 8 is connected to the data processing center 5. The data processing center 5 combines vehicle information and road performance data for comprehensive analysis to evaluate the impact of vehicle behavior on road performance.
[0055] Specifically, the vehicle information collection module 8 adopts a multi-modal fusion solution of "high-definition camera + millimeter-wave radar + lidar". The high-definition camera uses image recognition technology to accurately collect license plate and vehicle model information; the millimeter-wave radar monitors vehicle speed, distance, and angle in real time, especially compensating for the shortcomings of the camera in adverse weather conditions such as rain and fog; the lidar constructs a 3D model of the vehicle through point cloud data to assist in identifying special vehicle models (such as oversized or overweight vehicles) and abnormal loading conditions, achieving comprehensive and high-precision collection of vehicle information. The vehicle information collection module 8 performs in-depth analysis of vehicle speed, acceleration, braking frequency, and other data to identify dangerous driving behaviors such as rapid acceleration, sudden braking, and frequent lane changes. It correlates these behavioral data with road surface performance data to study the impact of abnormal vehicle driving on road surface impact, providing a reference for road design optimization. At the same time, it can feed back dangerous driving information to traffic management departments for traffic violation monitoring.
[0056] In this embodiment, traditional road surface performance monitoring often only focuses on the physical characteristics of the road surface itself, such as smoothness, skid resistance, and load-bearing capacity. However, this embodiment incorporates vehicle license plate information, vehicle model information, and driving speed information into the evaluation system through the vehicle information acquisition module 8, thereby more comprehensively analyzing the impact of external behavioral environment on road surface performance.
[0057] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application is limited to these examples; under the concept of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in detail for the sake of brevity.
[0058] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.
[0059] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.
[0060] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the claims of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.
Claims
1. A pavement performance monitoring integrated system, characterized in that, include: Axle load meter (1) is installed on the road surface (01) to monitor vehicle load; Sensor (2) is buried in the road surface (01) to monitor the performance of the road surface (01); The trigger function module (3) is connected to the axle load meter (1) and the sensor (2) to receive the tire contact signal and trigger the sensor (2) to monitor the performance of the road surface (01). The tire contact signal is the signal emitted by the axle load meter (1) when the vehicle wheel travels to the axle load meter (1).
2. The integrated system for monitoring road performance according to claim 1, characterized in that, The sensor (2) includes a stress sensor (21), and multiple stress sensors (21) are provided for monitoring the stress of the road surface (01).
3. The integrated system for monitoring road surface performance according to claim 1, characterized in that, A temperature sensor (22) is also installed inside the road surface (01) to monitor the temperature of the road surface (01).
4. The integrated system for monitoring road surface performance according to claim 1, characterized in that, A road surface displacement sensor (24) is also installed inside the road surface (01) to monitor the displacement of the road surface (01).
5. The integrated system for monitoring road performance according to claim 1, characterized in that, The axle load meter (1) is a non-embedded structure and is installed on the surface of the road (016).
6. The integrated system for monitoring road performance according to claim 1, characterized in that, It also includes a data processing center (5), which is connected to the sensor (2) for receiving, storing and analyzing pavement (01) performance data and generating pavement (01) performance evaluation reports.
7. The integrated system for monitoring road performance according to claim 6, characterized in that, It also includes a communication module (4), which is connected between the data processing center (5) and the sensor (2) for transmitting the data collected by the sensor (2) to the data processing center (5).
8. The integrated system for monitoring road performance according to claim 7, characterized in that, It also includes an early warning module (6), which is connected to the data processing center (5) and is used to provide real-time early warning of abnormal performance of the road surface (01) based on the road surface (01) performance data and preset thresholds.
9. The integrated system for monitoring road performance according to claim 8, characterized in that, It also includes a remote monitoring terminal (7), which is connected to the data processing center (5) and the early warning module (6) to display road surface (01) performance data and receive early warning information, and to remotely control and set parameters of the system.
10. The integrated system for monitoring road performance according to claim 9, characterized in that, It also includes a vehicle information collection module (8) for collecting vehicle license plate information, vehicle type information and driving speed information. The vehicle information collection module (8) is connected to the data processing center (5). The data processing center (5) combines vehicle information and road surface (01) performance data for comprehensive analysis to evaluate the impact of different vehicle behaviors on road surface (01) performance.