A method and system for monitoring the performance of long-life pavements

Abstract

The application discloses a long-life road surface performance monitoring method and a monitoring system, and relates to the technical field of road surface performance monitoring. First, current meteorological data, real-time gravity data of a road surface, real-time deformation data of the road surface, and temperature and humidity data are acquired. Then, a first road surface influence coefficient and a second road surface influence coefficient are calculated according to the acquired meteorological data, real-time gravity data of the road surface, real-time deformation data of the road surface, and temperature and humidity data. The first score and the second score are respectively obtained according to the first road surface influence coefficient and the second road surface influence coefficient. The first score and the second score are compared, and the problem with the greatest influence on the road surface at this time is obtained. Then, corresponding processing is performed by a staff member. In this way, the function of long-term performance monitoring of the road surface can be realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of road surface performance monitoring, and particularly relates to a long-life road surface performance monitoring method and a monitoring system. BACKGROUND

[0002] At present, the problem of paying more attention to reconstruction and less attention to maintenance still exists in road engineering projects in China. The whole life cycle management is one of the important methods for solving the problem. In order to realize long-term performance monitoring of road engineering and provide a basis for road maintenance, a method capable of monitoring and recording the construction quality and long-term operation stage condition of road engineering is needed. For the construction process, the compaction quality of roadbed and pavement is one of the most critical indicators for road construction quality control. Therefore, the compaction degree must be monitored and recorded during construction. The traditional compaction control of asphalt pavement is mostly controlled by the experience of on-site construction personnel, and sampling and inspection are performed after construction, which cannot provide real-time guidance and comprehensive data recording for road construction technology.

[0003] In recent years, the intelligent compaction technology appears, which can establish a relationship between the vibration acceleration of the vibration wheel of the vibration roller and the compaction degree, and obtain the compaction degree by processing the vibration acceleration data recorded during construction. However, it is still difficult to establish a reliable correlation model between the two parameters. Therefore, it is a problem to be solved to monitor the long-term performance of the present road pavement. Therefore, the present application provides a long-life road surface performance monitoring method and a monitoring system. SUMMARY

[0004] In view of the deficiencies of the prior art, the purpose of the present application is to provide a long-life road surface performance monitoring method and a monitoring system, which solves the problems in the prior art that

[0005] The purpose of the present application can be achieved by the following technical scheme: a long-life road surface performance monitoring system, comprising a data acquisition module, a data processing module, a data analysis module, an execution module and a control center. The data acquisition module is used to acquire current weather data, real-time gravity data of a road surface, real-time deformation data of the road surface and temperature and humidity data, and send the acquired current weather data, real-time gravity data of the road surface, deformation data of the road surface and temperature and humidity data to the data processing module for processing.

[0006] The data processing module is used to process the current weather data, real-time gravity data of the road surface, deformation data of the road surface and temperature and humidity data received from the data acquisition module, and send the calculated first road surface influence coefficient and second road surface influence coefficient to the data analysis module for analysis.

[0007] The data analysis module is configured to analyze the data after receiving the first road surface influence coefficient and the second road surface influence coefficient sent by the data processing module, obtain the first score and the second score, and compare the first score and the second score. If the first score is greater than the second score, the data analysis module sends a climate influence signal to the execution module, the execution module sends a climate problem to the control center, and the control center feeds back the climate problem to the staff. If the first score is less than the second score, the data analysis module sends a temperature and humidity influence signal to the execution module, the execution module sends a temperature and humidity problem to the control center, and the control center feeds back the temperature and humidity problem to the staff for processing.

[0008] Further, the data collection module comprises a weather collection unit, a dynamic weighing unit, a deformation detection sensor, a temperature sensor and a humidity sensor.

[0009] Further, the collection frequency of the data collection module is 1 time / 10 minutes.

[0010] Further, the processing process of the data processing module comprises the following steps:

[0011] Step 1: mark the current weather data as W i , mark the real-time gravity data of the road surface as G i , mark the deformation data of the road surface as P i , and mark the temperature and humidity data as T i , wherein i is the data collection label, and i = 1, 2, 3,..., n, wherein n is the total number of collection times;

[0012] Step 2: calculate the first road surface influence coefficient using the formula , wherein W0 is the weather influence coefficient, G0 is the gravity influence coefficient, a is the first positive influence coefficient, and b is the first negative influence coefficient;

[0013] Step 3: calculate the second road surface influence coefficient using the formula , wherein T0 is the temperature and humidity influence coefficient, a is the second positive influence coefficient, and β is the second negative influence coefficient, and send the calculated first road surface influence coefficient and the second road surface influence coefficient to the data analysis module for data analysis.

[0014] Further, the analysis process of the data analysis module comprises the following steps:

[0015] Step S1: calculate the first score using the formula , wherein Q0 is the standard influence coefficient, and C is the first score influence coefficient;

[0016] Step S2: calculate the second score using the formula A second score is calculated, wherein D is a second score influence coefficient, and the first score R1 is compared with the second score R2;

[0017] Step S3: If R1>R2, it indicates that the influence of the first road surface influence coefficient in the first score on the road surface is greater than the influence of the second road surface influence coefficient in the second score on the road surface, which shows that the climate at this time has a greater influence on the road surface performance, and thus the data analysis module sends a climate influence signal to the execution module;

[0018] Step S4: If R1

[0019] Further, a long-life road surface performance monitoring method, the method comprising the following steps:

[0020] Step P1: obtaining current weather data, real-time gravity data of the road surface, real-time deformation data of the road surface and temperature and humidity data;

[0021] Step P2: calculating a first road surface influence coefficient and a second road surface influence coefficient according to the obtained weather data, real-time gravity data of the road surface, real-time deformation data of the road surface and temperature and humidity data;

[0022] Step P3: obtaining a first score and a second score according to the calculated first road surface influence coefficient and the second road surface influence coefficient respectively, and comparing the first score with the second score to obtain the problem that has the greatest influence on the road surface at this time, and then a worker performs corresponding processing.

[0023] The present application has the following advantages:

[0024] In the use process of the present application, first, current weather data, real-time gravity data of the road surface, real-time deformation data of the road surface and temperature and humidity data are obtained, then a first road surface influence coefficient and a second road surface influence coefficient are calculated according to the obtained weather data, real-time gravity data of the road surface, real-time deformation data of the road surface and temperature and humidity data, and a first score and a second score are obtained according to the calculated first road surface influence coefficient and the second road surface influence coefficient respectively, and the first score is compared with the second score to obtain the problem that has the greatest influence on the road surface at this time, and then a worker performs corresponding processing, which can realize the function of monitoring the long-term performance of the road surface. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without any creative effort.

[0026] Fig. 1 is a schematic diagram of the overall structure of the embodiment of the present application.

[0027] Fig. 2 is a schematic diagram of the embodiment of the present application. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort fall within the scope of the present application.

[0029] As shown in Figs. 1-2 , a long-life pavement performance monitoring system comprises a data acquisition module, a data processing module, a data analysis module, an execution module and a control center. The data acquisition module is used to acquire current meteorological data, real-time gravity data of a road surface, real-time deformation data of the road surface and temperature and humidity data, and send the acquired current meteorological data, real-time gravity data of the road surface, deformation data of the road surface and temperature and humidity data to the data processing module for processing.

[0030] It needs to be further explained that, in the specific implementation process, the data acquisition module comprises a meteorological acquisition unit, a dynamic weighing unit, a deformation detection sensor, a temperature sensor and a humidity sensor. The acquisition frequency of the data acquisition module is 1 time / 10 minutes.

[0031] The meteorological acquisition unit is used to acquire real-time meteorological data of an observation point. The dynamic weighing unit is used to acquire the total gravity of a vehicle currently passing through a road surface. The deformation detection sensor is used to acquire the deformation of the current road surface. The temperature sensor and the humidity sensor are respectively used to acquire the temperature data and the humidity data of the current road surface.

[0032] It needs to be further explained that in the specific implementation process, the weather collection unit can be real-time, dynamic monitoring, and wireless transmission, the dynamic weighing unit is a piezoelectric dynamic weighing device, which is awakened in advance when the vehicle passes through the road, and accurately collects the axle load, total vehicle weight and vehicle speed when the vehicle passes through, the advantages of the deformation detection sensor, temperature sensor and humidity sensor in use are simple structure, small size, light weight, good frequency response characteristics, can work in harsh conditions, easy to realize miniaturization, integration and variety, the deformation detection sensor is buried in the range of 40-50 cm from the road marking outside the driving lane, and the temperature sensor and humidity sensor are buried in the center line of the driving lane.

[0033] The data processing module is used for processing the current weather data, the real-time gravity data of the road surface, the deformation amount data of the road surface and the temperature and humidity data received by the data collection module; Specifically, the processing process of the data processing module includes the following steps:

[0034] Step one: mark the current weather data as W i , mark the real-time gravity data of the road surface as G i , mark the deformation amount data of the road surface as P i , mark the temperature and humidity data as T i , wherein i is the collection data label, and i = 1, 2, 3,..., n, wherein n is the total number of collection times;

[0035] Step two: calculate the first road surface influence coefficient by using the formula , wherein W0 is the weather influence coefficient, G0 is the gravity influence coefficient, a is the first positive influence coefficient, and b is the first negative influence coefficient;

[0036] Step three: calculate the second road surface influence coefficient by using the formula , wherein T0 is the temperature and humidity influence coefficient, a is the second positive influence coefficient, and β is the second negative influence coefficient, and the first road surface influence coefficient and the second road surface influence coefficient calculated are sent to the data analysis module for data analysis.

[0037] The data analysis module is used for data analysis after receiving the first road surface influence coefficient and the second road surface influence coefficient sent by the data processing module, and specifically, the analysis process of the data analysis module includes the following steps:

[0038] Step S1: calculate the first score by using the formula , wherein Q0 is the standard influence coefficient, and C is the first score influence coefficient;

[0039] Step S2: calculate the second score by using the formula A second score is calculated, where D is a second score influence coefficient, and the first score R1 is compared with the second score R2;

[0040] Step S3: If R1>R2, it indicates that the first road surface influence coefficient in the first score has a greater impact on the road surface than the second road surface influence coefficient in the second score, which means that the climate at this time has a greater impact on the road surface performance, and thus the data analysis module sends a climate influence signal to the execution module;

[0041] Step S4: If R1

[0042] The execution module is configured to feed back and remind the control center after receiving the signal sent by the data analysis module, and the specific process is as follows: after receiving the climate influence signal sent by the data analysis module, the execution module sends a climate problem to the control center, and the control center feeds back the climate problem to the staff; after receiving the temperature and humidity influence signal sent by the data analysis module, the execution module sends a temperature and humidity problem to the control center, and the control center feeds back the temperature and humidity problem to the staff, and the staff processes the problem accordingly.

[0043] A long-life road surface performance monitoring method, the method comprising the following steps:

[0044] Step P1: obtaining current weather data, real-time gravity data of the road surface, real-time deformation data of the road surface, and temperature and humidity data;

[0045] Step P2: calculating a first road surface influence coefficient and a second road surface influence coefficient according to the obtained weather data, real-time gravity data of the road surface, real-time deformation data of the road surface, and temperature and humidity data;

[0046] Step P3: obtaining a first score and a second score according to the calculated first road surface influence coefficient and the second road surface influence coefficient, respectively, and comparing the first score with the second score to obtain the problem that has the greatest impact on the road surface at this time, and then the staff processes the problem accordingly.

[0047] In the description of the specification, the description of the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate way in one or more embodiments or examples.

[0048] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application.

Claims

1. A long-life pavement performance monitoring system, characterized in that: The system comprises a data acquisition module, a data processing module, a data analysis module, an execution module and a control center. The data acquisition module is used to collect current meteorological data, real-time gravity data of the road surface, real-time deformation data of the road surface and temperature and humidity data, and sends the collected current meteorological data, real-time gravity data of the road surface, deformation data of the road surface and temperature and humidity data to the data processing module for processing. The data processing module is used to process the current meteorological data, real-time gravity data of the road surface, deformation data of the road surface and temperature and humidity data sent by the data acquisition module, and send the calculated first road surface influence coefficient and second road surface influence coefficient to the data analysis module for analysis; The processing process of the data processing module includes the following steps: Step 1: Mark the current weather data as W i , the real-time gravity data of the road surface is marked as G i , the road deformation data is marked as P i , temperature and humidity data are marked as T i , where i is the number of collected data, and i = 1, 2, 3, ..., n, where n is the total number of collection times; Step 2: Use the formula The first road surface influence coefficient is calculated, where W0 is the meteorological influence coefficient, G0 is the gravity influence coefficient, a is the first positive influence coefficient, and b is the first negative influence coefficient; Step 3: Use the formula Calculating a second road surface influence coefficient, where T0 is the temperature and humidity influence coefficient, α is the second positive influence coefficient, and β is the second negative influence coefficient, and sending the calculated first road surface influence coefficient and second road surface influence coefficient to a data analysis module for data analysis; The data analysis module is used to perform data analysis after receiving the first road surface influence coefficient and the second road surface influence coefficient sent by the data processing module, obtain a first score and a second score, and compare the first score and the second score. If the first score is greater than the second score, the data analysis module sends a climate impact signal to the execution module, the execution module sends the climate problem to the control center, and the control center then feeds back the climate problem to the staff. If the first score is less than the second score, the data analysis module sends a temperature and humidity impact signal to the execution module, the execution module sends the temperature and humidity problem to the control center, and the control center then feeds back the temperature and humidity problem to the staff for processing.

2. A long-life pavement performance monitoring system according to claim 1, characterized in that: The data acquisition module includes a meteorological acquisition unit, a dynamic weighing unit, a deformation detection sensor, a temperature sensor and a humidity sensor.

3. A long-life pavement performance monitoring system according to claim 2, characterized in that: The data acquisition module has an acquisition frequency of 1 time per 10 minutes.

4. The long-life pavement performance monitoring system according to claim 1, characterized in that: The analysis process of the data analysis module includes the following steps: Step S1: Using the formula The first score is calculated, where Q0 is the standard impact coefficient and C is the first score impact coefficient; Step S2: Using the formula Calculate a second score, where D is the second score influence coefficient, and compare the first score R1 with the second score R2; Step S3: If R1>R2, it means that the first road surface influence coefficient in the first score has a greater impact on the road surface than the second road surface influence coefficient in the second score, indicating that the climate at this time has a greater impact on the road surface performance. Therefore, the data analysis module sends a climate influence signal to the execution module; Step S4: If R1 < R2, it means that the second road surface influence coefficient in the second score has a greater impact on the road surface than the first road surface influence coefficient in the first score, indicating that the temperature and humidity at this time have a greater impact on the road surface performance. Therefore, the data analysis module sends a temperature and humidity influence signal to the execution module.

5. A method for monitoring long-life pavement performance, using a long-life pavement performance monitoring system according to any one of claims 1 to 4, characterized in that: The method comprises the following steps: Step P1: Obtain current meteorological data, real-time gravity data of the road surface, real-time deformation data of the road surface, and temperature and humidity data; Step P2: Calculating a first road surface influence coefficient and a second road surface influence coefficient based on the acquired meteorological data, real-time gravity data of the road surface, real-time deformation data of the road surface, and temperature and humidity data; Step P3: Calculate the first score and the second score based on the calculated first road surface influence coefficient and the second road surface influence coefficient respectively, and compare the first score and the second score to determine the problem that has the greatest impact on the road surface at this time, and then the staff will take corresponding measures.

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