Asphalt pavement real-time paving auxiliary monitoring system based on Internet of Things

Abstract

The invention discloses an asphalt pavement real-time paving auxiliary monitoring system based on the Internet of Things, and belongs to the technical field of asphalt paving, and the asphalt pavement real-time paving auxiliary monitoring system comprises a temperature monitoring module used for obtaining asphalt temperature information during asphalt paving; the thickness monitoring module is used for acquiring the thickness information of the pavement after the asphalt is paved; the Internet of Things analysis platform is used for receiving the monitored temperature and thickness information and analyzing and processing the monitored temperature and thickness information to generate a paving condition value so as to judge whether paving is abnormal or not according to the paving condition value; and the adjusting module is used for adjusting the moving speed of the paver when the paving is judged to be abnormal. According to the method, the paving moving speed of the next adjusting period can be adjusted in time according to the paving condition value after the paving abnormity is found, meanwhile, the environmental factors are considered, and the moving speed of the paver can be dynamically adjusted according to the actual condition, so that the subsequent paving quality is ensured in time.

Description

Technical Field

[0001] The present invention belongs to the technical field of asphalt paving, and in particular relates to a real-time asphalt pavement paving auxiliary monitoring system based on the Internet of Things. Background Art

[0002] With the acceleration of highway construction projects, the demand for pavement construction is growing. Pavement construction is an important part of highway transportation infrastructure construction, and its quality directly affects traffic safety and comfort. Asphalt pavement is widely used on highways due to its good driving comfort and durability.

[0003] In existing asphalt paving, asphalt is generally delivered to a designated road surface by a paver for paving operations, and the paving quality is mostly monitored manually. However, since asphalt is affected by many factors during paving, manual monitoring is difficult to achieve real-time and comprehensive monitoring of the paving process. Moreover, when paving asphalt, the moving speed of the paver is set in advance, but it is affected by factors such as the actual environment. If the paving operation is performed at a specified speed, it is easy to cause unequal paving thickness, thereby affecting the paving quality. Summary of the Invention

[0004] The purpose of the present invention is to provide an asphalt pavement real-time paving auxiliary monitoring system based on the Internet of Things to solve the problems faced in the above-mentioned background technology.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A real-time asphalt pavement paving auxiliary monitoring system based on the Internet of Things, the monitoring system comprising:

[0007] A temperature monitoring module, which is used to obtain temperature information of asphalt during asphalt paving;

[0008] A thickness monitoring module, which is used to obtain the thickness information of the road surface after asphalt paving;

[0009] An IoT analysis platform, which receives the monitored temperature and thickness information and performs analysis and processing to generate a paving condition value, thereby determining whether paving is abnormal based on the paving condition value;

[0010] An adjustment module adjusts the moving speed of the paver when it is determined that an abnormality occurs in the paving.

[0011] Furthermore, the temperature monitoring module includes an infrared thermometer installed on the paver and embedded temperature sensors distributed inside the paving layer. The infrared thermometer is used to obtain the temperature of the asphalt mixture surface, and the embedded temperature sensor is used to obtain the temperature of the deep asphalt layer.

[0012] Furthermore, it is characterized in that the method for obtaining the paving condition value is:

[0013] There is an adjustment cycle to obtain the temperature T monitored by the embedded temperature sensor in real time. B And the temperature T monitored by the infrared thermometer V , so through the formula T=α1*T B +α2*T V The actual temperature value T is obtained, where α1 and α2 are proportional coefficients, and a curve function T(t) of the actual temperature value over time during the adjustment period is formulated;

[0014] During the adjustment period, the pavement thickness L is obtained by a laser scanner, and the paving thickness change curve function L(t) over time is developed;

[0015] By formula

[0016] Obtain the paving condition value PA;

[0017] Among them, t1 is the start time of the adjustment cycle, t2 is the end time of the adjustment cycle, T0(t) is the curve function of the standard actual temperature value over time proposed by the system, L0(t) is the curve function of the standard paving thickness over time proposed by the system, β1 and β2 are weight coefficients, μ T and μ L They are the set temperature comparison value and thickness comparison value respectively.

[0018] Furthermore, the method for the IoT analysis platform to determine whether paving is abnormal is as follows:

[0019] After obtaining the paving condition value PA within the adjustment period, compare it with the preset paving condition judgment threshold PA x1 、PA x2 To compare:

[0020] when When , it is judged that there is an abnormality in the paving during the adjustment period.

[0021] Furthermore, the method for the adjustment module to adjust the moving speed of the paver is:

[0022] When it is determined that paving is abnormal, the adjustment module generates speed increase instructions and speed decrease instructions;

[0023] When PA<PA x1 When , a speed increase instruction is generated, and the formula

[0024] Adjust the moving speed of the next adjustment cycle to Vadd ;

[0025] When PA>PA x2 When , a speed reduction instruction is generated, and the formula

[0026] Adjust the moving speed of the next adjustment cycle to V reduce ;

[0027] Among them, V0 is the current moving speed, V max is the maximum speed adjusted for the system operation, τ is the speed conversion coefficient, T emp is the ambient temperature, T th is the preset ambient control temperature, S emp is the ambient humidity, S th is the preset environmental reference humidity, ρ1 and ρ2 are weight coefficients.

[0028] Furthermore, the IoT analysis platform is also used to further analyze the acquired relevant parameter information to generate an equipment abnormality risk value, and perform a risk assessment on the paver condition based on the equipment abnormality risk value.

[0029] Furthermore, the method for the Internet of Things analysis platform to perform risk assessment on the paver condition is:

[0030] In m adjustment cycles, obtain the paving status value PA of each adjustment cycle i , through the formula Obtain the equipment abnormality risk value R;

[0031] When R>R th If the equipment is abnormal, it is judged that there is a risk, and the paver should be repaired and maintained;

[0032] Where k is the number of paving anomalies in m adjustment cycles, |min PA| is the absolute value of the minimum paving condition value in m adjustment cycles, |max PA| is the absolute value of the maximum paving condition value in m adjustment cycles, and i∈[1,m], R th It is the preset risk judgment threshold.

[0033] Beneficial effects of the present invention:

[0034] The present invention uses Internet of Things technology to monitor the pavement paving of the paver in real time, without the need for manual monitoring, so that the paving process can be controlled in real time. At the same time, according to the difference between the pavement thickness after paving and the expected standard thickness and the difference between the temperature of the asphalt during paving and the standard temperature, it is judged whether there is any abnormality in the paving within the adjustment period. The paving condition can be accurately judged. Once an abnormality occurs, timely adjustment can be made to ensure the subsequent paving quality.

[0035] After discovering paving abnormalities, the present invention can timely adjust the paving movement speed of the next adjustment cycle according to the paving status value. At the same time, taking environmental factors into consideration, the movement speed of the paver can be dynamically adjusted according to actual conditions, thereby timely ensuring the subsequent paving quality.

[0036] The present invention can determine whether there is a risk of abnormal operation of the paver based on a comprehensive analysis of the fluctuations in the acquired paving condition values, the number of paving anomalies, and the extreme values of the paving condition values. Once there is a risk of abnormal operation, the paver can be repaired and maintained in a timely manner to ensure the quality of subsequent paving.

[0037] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0041] In one embodiment, a real-time asphalt pavement paving auxiliary monitoring system based on the Internet of Things is disclosed. Figure 1 As shown, the monitoring system includes:

[0042] Temperature monitoring module, which is used to obtain the temperature information of asphalt during paving. The temperature monitoring module includes an infrared thermometer installed on the paver and embedded temperature sensors distributed within the paving layer. The infrared thermometer is used to obtain the surface temperature of the asphalt mixture, and the embedded temperature sensor is used to obtain the temperature of the deep asphalt layer;

[0043] Thickness monitoring module, which is used to obtain the thickness information of the road surface after asphalt paving;

[0044] The IoT analysis platform is used to receive the monitored temperature and thickness information, analyze and process it to generate paving status values, and then determine whether the paving is abnormal based on the paving status values;

[0045] The adjustment module adjusts the moving speed of the paver when it determines that there is an abnormality in paving.

[0046] Through the above scheme, this application uses the Internet of Things technology to monitor the pavement paving of the paver in real time, without the need for manual monitoring, so that the paving process can be controlled in real time. At the same time, the temperature information during paving is obtained through the temperature monitoring module, and the pavement thickness information after paving is obtained through the thickness monitoring module. Combined with this information, a comprehensive analysis is performed to determine whether there is any abnormality in the paving. After determining that there is an abnormality in the paving, the moving speed of the paver can be adjusted in time to ensure the quality of subsequent paving.

[0047] The method for obtaining the paving condition value is as follows: an adjustment cycle is set up to obtain the temperature T monitored by the embedded temperature sensor in real time. B And the temperature T monitored by the infrared thermometer V , so through the formula T=α1*T B +α2*T V The actual temperature value T is obtained, where α1 and α2 are proportional coefficients, and a curve function T(t) of the actual temperature value over time during the adjustment period is formulated;

[0048] During the adjustment period, the pavement thickness L is obtained by a laser scanner, and the paving thickness change curve function L(t) over time is developed;

[0049] By formula Obtain the paving condition value PA;

[0050] Among them, t1 is the start time of the adjustment cycle, t2 is the end time of the adjustment cycle, T0(t) is the curve function of the standard actual temperature value over time proposed by the system, L0(t) is the curve function of the standard paving thickness over time proposed by the system, β1 and β2 are weight coefficients, μ T and μ L They are the set temperature comparison value and thickness comparison value respectively;

[0051] The method for the IoT analysis platform to determine whether paving is abnormal is as follows: after obtaining the paving condition value PA within the adjustment period, it is compared with the preset paving condition judgment threshold PA x1 、PA x2 To compare:

[0052] when When , it is judged that there is an abnormality in the paving during the adjustment period.

[0053] The above technical solution provides a specific method for the IoT analysis platform to determine whether paving is abnormal. Since the quality of asphalt paving is easily affected by multiple factors, such as asphalt temperature, high temperature reduces the viscosity of the asphalt mixture and improves its fluidity, which will affect the compaction effect. On the other hand, low temperature significantly reduces the fluidity of the asphalt mixture, making it difficult to spread evenly to form a smooth road surface. This will cause problems such as unevenness and looseness on the road surface, affecting the smoothness and driving comfort of the road surface. For example, if the paving thickness is different from the original thickness, it will also affect the paving quality. Therefore, this application first sets a paving adjustment cycle based on paving experience, and obtains the temperature T monitored by the embedded temperature sensor in real time during the adjustment cycle. B And the temperature T monitored by the infrared thermometer V , so through the formula T=α1*T B +α2*T V The actual temperature value T is obtained, where α1 and α2 are proportional coefficients, which are determined based on empirical data. The surface temperature and the inner layer temperature of the asphalt mixture are comprehensively analyzed to obtain a representative actual temperature value, which can more accurately represent the comprehensive actual temperature of the asphalt mixture. Based on the actual temperature value T, a curve function T(t) of the actual temperature value over time in the adjustment period is formulated. At the same time, during the adjustment period, the paving thickness L of the pavement after paving is obtained through a laser scanner, thereby formulating a curve function L(t) of the paving thickness over time.

[0054] Obtain the paving condition value PA; formula It is expressed as the difference between the temperature of asphalt and the standard temperature, and the formula It indicates the difference between the thickness of the paved road surface and the standard thickness. Generally speaking, if the difference between the temperature of the asphalt and the standard temperature of the paving process and the difference between the thickness of the paved road surface and the standard thickness is closer to zero, the better the overall paving quality is. If it is too large or too small, it indicates that the paving is abnormal. Therefore, after obtaining the paving condition value PA within the adjustment period, it is compared with the preset paving condition judgment threshold PA. x1 、PA x2 Compare: When In this way, the difference between the pavement thickness after paving and the expected standard thickness, as well as the difference between the asphalt temperature during paving and the standard temperature, can be used to determine whether there is an abnormality in the paving during the adjustment period. This allows for accurate judgment of the paving condition, and if an abnormality is found, timely adjustments can be made to ensure subsequent paving quality.

[0055] In the above technical solution, the standard actual temperature value versus time curve function T0(t) and the standard paving thickness versus time curve function L0(t) proposed by the system can be determined based on the standard data and empirical data of the relevant industry, and the set temperature comparison value and thickness comparison value μ T and μ L , weight coefficients β1 and β2 and the preset paving condition judgment threshold PA x1 、PA x2 It can be formulated based on the historical operating data of the paver and empirical data, which will not be described in detail here.

[0056] The method for the adjustment module to adjust the moving speed of the paver is as follows: when it is determined that paving is abnormal, the adjustment module generates a speed increase instruction and a speed decrease instruction;

[0057] When PA<PA x1 When , a speed increase instruction is generated, and the formula

[0058] Adjust the moving speed of the next adjustment cycle to V add ;

[0059] When PA>PA x2 When , a speed reduction instruction is generated, and the formula

[0060] Adjust the moving speed of the next adjustment cycle to V reduce ;

[0061] Among them, V0 is the current moving speed, V max is the maximum speed adjusted for the system operation, τ is the speed conversion coefficient, T emp is the ambient temperature, T th is the preset ambient control temperature, S emp is the ambient humidity, S th is the preset environmental reference humidity, ρ1 and ρ2 are weight coefficients.

[0062] The above scheme provides a specific method for the adjustment module to adjust the moving speed of the paver. When it is judged that the paving is abnormal, in order to ensure the subsequent paving quality, it is necessary to adjust the running speed of the paver in time. Therefore, when it is judged that the paving is abnormal, the adjustment module generates a speed increase instruction and a speed decrease instruction. When PA<PA x1 When , it indicates that the asphalt temperature is too low relative to the standard temperature or the paving thickness is too high relative to the standard thickness. In order to ensure the normal subsequent paving, it is necessary to increase the moving speed of the paver appropriately, and a speed increase instruction is generated. At this time, the formula Adjust the moving speed of the next adjustment cycle to Vadd ; When adjusting, the speed adjustment is not only related to the size of the paving condition value obtained, but also to the environmental factors, such as ambient temperature and ambient humidity. The ambient temperature will correspondingly affect the temperature of the asphalt, thereby affecting the pavement paving quality. In a high humidity environment, the moisture content in the air is high, which may cause the surface of the asphalt mixture after paving to absorb moisture quickly, thereby accelerating its cooling speed. Rapid cooling will affect the compaction effect, resulting in a decrease in the quality of the final pavement. In a high dryness environment, the temperature of the asphalt mixture dissipates relatively slowly, and it can maintain good fluidity for a long time. In order to ensure the compaction effect, the paving speed can be appropriately increased. Therefore, the environmental factors are taken into consideration and the formula is used. Perform comprehensive analysis and adjust the moving speed of the next adjustment cycle to V add , it can be seen from the formula that when PA x1 -The larger the value of PA is, the more speed it needs to increase. Similarly, when the ambient temperature T emp The lower the ambient humidity S emp The lower the speed, the more it needs to be increased. The speed adjustment should be within the appropriate range and cannot be increased or decreased blindly. Therefore, based on historical data and experience, the maximum speed V for the system to be adjusted is formulated. max Limit to ensure paving quality; similarly, when PA>PA x2 When , it means that the temperature of the asphalt is too high relative to the standard temperature or the paving thickness is too low relative to the standard thickness. In order to ensure the normal subsequent paving, it is necessary to appropriately reduce the moving speed of the paver, and generate a speed reduction instruction, and use the formula Adjust the moving speed of the next adjustment cycle to V reduce ; From the formula, we can see that when PA-PA x2 The larger the value, the more the speed needs to be reduced. When the ambient temperature T emp The higher the ambient humidity S emp The higher it is, the more it needs to reduce its speed, and finally the moving speed of the next adjustment cycle is adjusted to V reduce In this way, after discovering paving abnormalities, the paving movement speed of the next adjustment cycle can be adjusted in time according to the paving status value. At the same time, environmental factors can be taken into consideration and the movement speed of the paver can be dynamically adjusted according to actual conditions, thereby ensuring the subsequent paving quality in a timely manner.

[0063] It should be noted that the speed conversion coefficient τ and the ambient reference temperature T th , environmental reference humidity S th The weight coefficients ρ1 and ρ2 can be determined based on historical data combined with empirical data, and the ambient temperature T emp and ambient humidity S empIt is obtained through relevant sensors and transmitted to the Internet of Things analysis platform.

[0064] The IoT analysis platform is also used to further analyze the relevant parameter information obtained to generate an equipment abnormality risk value, and to conduct a risk assessment of the paver condition based on the equipment abnormality risk value. The assessment method is: within m adjustment cycles, obtain the paving condition value PA of each adjustment cycle. i , through the formula

[0065] Obtain the equipment abnormality risk value R;

[0066] When R>R th If the equipment is abnormal, it is judged that there is a risk, and the paver should be repaired and maintained;

[0067] Where k is the number of paving anomalies in m adjustment cycles, |min PA| is the absolute value of the minimum paving condition value in m adjustment cycles, |max PA| is the absolute value of the maximum paving condition value in m adjustment cycles, and i∈[1,m], R th It is the preset risk judgment threshold.

[0068] The above technical solution provides a specific method for the Internet of Things analysis platform to conduct risk assessment on the operating status of the paver. First, within m adjustment cycles, the paving status value PA of each adjustment cycle is obtained. i , through the formula

[0069] Obtain the equipment abnormal risk value R; formula It represents the fluctuation of the paving condition value in m cycles. The larger its value is, the greater the fluctuation between the paving condition values obtained each time, which means that the paver has a greater risk of abnormal operation. Similarly, k is the number of paving abnormalities in m adjustment cycles, |min PA| is the absolute value of the minimum paving condition value in m adjustment cycles, and |max PA| is the absolute value of the maximum paving condition value in m adjustment cycles. The larger its value is, the greater the risk of abnormal operation of the paver. Therefore, combined with the formula Perform comprehensive analysis to obtain the equipment abnormality risk value R, and then compare it with the preset risk judgment threshold R th For comparison, the preset risk judgment threshold R th According to empirical data, when R>R thWhen the device is abnormal, it is judged that there is a risk of abnormal operation, and the paver needs to be repaired and maintained. In this way, based on the fluctuation of the obtained paving status value, the number of paving abnormalities, and the extreme values of the paving status value, a comprehensive analysis can be performed to determine whether the paver has an abnormal operation risk. If there is an abnormal operation risk, the paver can be repaired and maintained in time to ensure the quality of subsequent paving.

[0070] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. A real-time asphalt pavement paving auxiliary monitoring system based on the Internet of Things, characterized by: The monitoring system comprises: A temperature monitoring module, which is used to obtain temperature information of asphalt during asphalt paving; A thickness monitoring module, which is used to obtain the thickness information of the road surface after asphalt paving; An IoT analysis platform, which receives the monitored temperature and thickness information and performs analysis and processing to generate a paving condition value, thereby determining whether paving is abnormal based on the paving condition value; An adjustment module adjusts the moving speed of the paver when it is determined that an abnormality occurs in the paving.

2. The asphalt pavement real-time paving auxiliary monitoring system based on the Internet of Things according to claim 1 is characterized in that: The temperature monitoring module includes an infrared thermometer installed on the paver and embedded temperature sensors distributed inside the paving layer. The infrared thermometer is used to obtain the temperature of the asphalt mixture surface, and the embedded temperature sensor is used to obtain the temperature of the deep asphalt layer.

3. The real-time asphalt pavement paving auxiliary monitoring system based on the Internet of Things according to claim 2 is characterized in that: The method for obtaining the paving condition value is: There is an adjustment cycle to obtain the temperature T monitored by the embedded temperature sensor in real time. B And the temperature T monitored by the infrared thermometer V , so through the formula T=α1*T B +α2*T V The actual temperature value T is obtained, where α1 and α2 are proportional coefficients, and a curve function T(t) of the actual temperature value over time during the adjustment period is formulated; During the adjustment period, the pavement thickness L is obtained by a laser scanner, and the paving thickness change curve function L(t) over time is developed; By formula Obtain the paving condition value PA; Among them, t1 is the start time of the adjustment cycle, t2 is the end time of the adjustment cycle, T0(t) is the curve function of the standard actual temperature value over time proposed by the system, L0(t) is the curve function of the standard paving thickness over time proposed by the system, β1 and β2 are weight coefficients, μ T and μ L They are the set temperature comparison value and thickness comparison value respectively.

4. The real-time asphalt pavement paving auxiliary monitoring system based on the Internet of Things according to claim 3 is characterized in that: The method used by the IoT analysis platform to determine whether paving is abnormal is as follows: After obtaining the paving condition value PA within the adjustment period, compare it with the preset paving condition judgment threshold PA x1 、PA x2 To compare: when When , it is judged that there is an abnormality in the paving during the adjustment period.

5. The asphalt pavement real-time paving auxiliary monitoring system based on the Internet of Things according to claim 4 is characterized in that: The method for the adjustment module to adjust the moving speed of the paver is: When it is determined that paving is abnormal, the adjustment module generates speed increase instructions and speed decrease instructions; When PA<PA x1 When , a speed increase instruction is generated, and the formula Adjust the moving speed of the next adjustment cycle to V add ; When PA>PA x2 When , a speed reduction instruction is generated, and the formula Adjust the moving speed of the next adjustment cycle to V reduce ; Among them, V0 is the current moving speed, V max is the maximum speed adjusted for the system operation, τ is the speed conversion coefficient, T emp is the ambient temperature, T th is the preset ambient control temperature, S emp is the ambient humidity, S th is the preset environmental reference humidity, ρ1 and ρ2 are weight coefficients.

6. The real-time asphalt pavement paving auxiliary monitoring system based on the Internet of Things according to claim 4 is characterized in that: The IoT analysis platform is also used to further analyze the acquired relevant parameter information to generate an equipment abnormality risk value, and to conduct a risk assessment of the paver condition based on the equipment abnormality risk value.

7. The asphalt pavement real-time paving auxiliary monitoring system based on the Internet of Things according to claim 6 is characterized in that: The method for the IoT analysis platform to perform risk assessment on the paver condition is as follows: In m adjustment cycles, obtain the paving status value PA of each adjustment cycle i , through the formula Obtain the equipment abnormality risk value R; When R>R th If the equipment is abnormal, it is judged that there is a risk, and the paver should be repaired and maintained; Where k is the number of paving anomalies in m adjustment cycles, |min PA| is the absolute value of the minimum paving condition value in m adjustment cycles, |max PA| is the absolute value of the maximum paving condition value in m adjustment cycles, and i∈[1,m], R th It is the preset risk judgment threshold.

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