Asphalt pavement structure monitoring chip and sensor

Abstract

The invention discloses an asphalt pavement structure monitoring chip and sensor, the chip comprises a power management module, a mechanical signal sensing module, a Bluetooth wireless transmission module and an operation storage module, the power management module is used for providing power for the mechanical signal sensing module, the Bluetooth wireless transmission module and the operation storage module; the mechanical signal sensing module is used for acquiring an original acceleration signal, an original angular velocity signal and an original geomagnetic force signal in the asphalt pavement structure; the Bluetooth wireless transmission module is used for transmitting the collected original acceleration signal, the original angular velocity signal and the original geomagnetic force signal to the IoT gateway; and the operation storage module is used for calculating the dynamic mechanical responsivity in the asphalt pavement structure according to the collected original acceleration signal, the original angular velocity signal and the original geomagnetic force signal. According to the invention, the internal condition of the asphalt pavement structure of the road can be accurately measured, and accurate dynamic mechanical data can be timely obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of road engineering, and in particular to an asphalt pavement structure monitoring chip and a sensor. Background Art

[0002] Asphalt pavement is affected by external vehicle loads, temperature and humidity, and other environmental factors during use, gradually causing fatigue, rutting, cracks and other damage, which seriously affects the use of the pavement. Timely understanding the actual internal state and material properties of the pavement is of great significance for the long-term performance estimation and scientific maintenance decision-making of the pavement.

[0003] The main methods for testing pavement performance, pavement materials and durable structures at home and abroad include manual testing, image recognition, laser measurement, ground penetrating radar and fiber Bragg grating. Manual testing has poor safety, low efficiency and high labor intensity; image recognition and laser measurement mainly collect road surface information such as pavement cracks, potholes, rutting and flatness; ground penetrating radar is often used to detect hidden defects such as pavement thickness and internal voids, but the recognition accuracy is greatly affected by human factors. Summary of the invention

[0004] In view of the defects in the prior art, the present invention provides an asphalt pavement structure monitoring chip and sensor, which can accurately measure the internal conditions of the asphalt pavement structure of the road, so as to obtain accurate dynamic mechanical data in a timely manner. The specific technical solution is as follows:

[0005] An asphalt pavement structure monitoring chip is provided. In a first possible implementation, the chip includes a power management module, a mechanical signal sensing module, a Bluetooth wireless transmission module, and a computing storage module, wherein:

[0006] The power management module is used to provide power to the mechanical signal sensing module, the Bluetooth wireless transmission module and the computing storage module;

[0007] The mechanical signal sensing module is used to collect the original acceleration signal, original angular velocity signal and original geomagnetic signal inside the asphalt pavement structure;

[0008] The Bluetooth wireless transmission module is used to transmit the collected original acceleration signal, original angular velocity signal and original geomagnetic signal to the IoT Internet of Things gateway;

[0009] The operation storage module is used to calculate the dynamic mechanical response degree inside the asphalt pavement structure according to the collected original acceleration signal, original angular velocity signal and original geomagnetic signal.

[0010] In combination with the first possible implementation, in the second possible implementation, the mechanical signal sensing module includes:

[0011] A three-axis acceleration unit is used to collect the original acceleration signals in the three dimensions of x, y, and z inside the asphalt pavement structure;

[0012] A three-axis gyroscope unit is used to collect the original angular velocity signals in the three dimensions of x, y and z inside the asphalt pavement structure;

[0013] The three-axis magnetic sensing unit is used to collect the original geomagnetic signals in the three dimensions of x, y and z inside the asphalt pavement structure.

[0014] In combination with the first implementable manner, in a third implementable manner, the operation storage module includes a filtering processing unit, a fusion processing unit and a calculation unit, wherein:

[0015] The filtering processing unit is used to filter the collected original acceleration signal, original angular velocity signal, and original geomagnetic signal to obtain a reconstructed acceleration signal, a reconstructed angular velocity signal, and a reconstructed geomagnetic signal;

[0016] The fusion processing unit is used to fuse the reconstructed acceleration signal, the reconstructed angular velocity signal and the reconstructed geomagnetic signal to obtain attitude fusion data;

[0017] The calculation unit is used to calculate the dynamic mechanical response degree inside the asphalt pavement structure according to the posture fusion data.

[0018] In combination with the third implementable manner, in a fourth implementable manner, the filtering processing unit includes a decomposition subunit, a filtering subunit and a reconstruction subunit, wherein:

[0019] The decomposition subunit is used to decompose the collected original acceleration signal, angular velocity signal, and geomagnetic signal using a discrete wavelet transform method to obtain a decomposed acceleration signal, a decomposed angular velocity signal, and a decomposed geomagnetic signal;

[0020] The filtering subunit is used to filter the decomposed acceleration signal, the decomposed angular velocity signal and the decomposed geomagnetic signal by using a threshold denoising function to obtain a filtered acceleration signal, a filtered angular velocity signal and a filtered geomagnetic signal;

[0021] The reconstruction subunit is used to reconstruct the filtered acceleration signal, the filtered angular velocity signal and the filtered geomagnetic signal using a signal reconstruction algorithm to obtain a reconstructed acceleration signal, a reconstructed angular velocity signal and a reconstructed geomagnetic signal.

[0022] In combination with the third implementable manner, in a fifth implementable manner, the fusion processing unit is specifically used for:

[0023] The high- and low-pass complementary filtering algorithm is used to fuse the reconstructed acceleration signal, the reconstructed angular velocity signal and the reconstructed geomagnetic signal to obtain attitude fusion data.

[0024] In combination with the third implementable manner, in a sixth implementable manner, the computing unit establishes a dynamic response analysis model according to the posture fusion data using the method shown in the following formula:

[0025]

[0026] Among them, DMRI is the dynamic mechanical response inside the asphalt pavement structure, and ||Q(t)|| is the modulus of the attitude fusion data.

[0027] In combination with the first to sixth possible implementations, in the seventh possible implementation, an asphalt pavement structure monitoring sensor is provided, including an upper protective structure, a lower protective structure, a high-temperature resistant polymer filling material, and also includes an asphalt pavement structure monitoring chip in the first to sixth possible implementations.

[0028] Beneficial effects:

[0029] (1) The asphalt pavement structure monitoring chip and sensor use a Bluetooth signal enhancement circuit, which can achieve 20m long-distance data transmission and penetrate a 10CM asphalt pavement structure layer. It has the advantages of simple burial process, low cost, and good pressure resistance and waterproofness;

[0030] (2) A dynamic response analysis method for asphalt pavement structure was established through the computational storage module. The discrete wavelet transform (DWT) denoising method was used to preprocess the sensor collected data, and the dynamic strain inside the asphalt mixture was calculated. The dynamic mechanical response (DMRI) was used to characterize the intensity of the change in the dynamic mechanical properties of the asphalt pavement structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the specific embodiments of the present invention, the following will briefly introduce the drawings required for the specific embodiments or the prior art description. In all the drawings, each element or part is not necessarily drawn according to the actual scale.

[0032] Figure 1 A schematic diagram of the structure of an asphalt pavement structure monitoring chip provided by one embodiment of the present invention;

[0033] Figure 2 A schematic diagram of the structure of the operation process of an operation storage module in an asphalt pavement structure monitoring chip provided by one embodiment of the present invention;

[0034] Figure 3 A schematic diagram of the structure of an asphalt pavement structure monitoring sensor provided by one embodiment of the present invention;

[0035] Figure 4 A diagram of the installation and layout of an asphalt pavement structure monitoring sensor provided by one embodiment of the present invention;

[0036] Figure 5 A schematic diagram of a data wireless transmission protocol for an asphalt pavement structure monitoring sensor provided by an embodiment of the present invention;

[0037] Reference numerals:

[0038] 100 - asphalt pavement structure monitoring sensor; 110 - upper part of protective structure; 120 - asphalt pavement structure monitoring chip; 130 - lower part of protective structure; 140 - filling material; 111 - first through hole; 112 - second through hole; 113 - third through hole; 114 - fourth through hole; 115 - fifth through hole; 116 - sixth through hole; 117 - seventh through hole. DETAILED DESCRIPTION

[0039] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0040] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0041] In one of the embodiments, an asphalt pavement structure monitoring chip is provided, which includes a power management module, a mechanical signal sensing module, a temperature and humidity measurement module, a Bluetooth wireless transmission module, and a computing storage module.

[0042] Preferably, the power management module is composed of a high temperature resistant battery and a power management unit, and provides power for the mechanical signal sensing module, the Bluetooth wireless transmission module and the computing storage module.

[0043] Preferably, the high temperature resistant battery is a CR2477 button battery with an operating temperature of -40°C to 180°C and a battery capacity of 1000mAh.

[0044] Preferably, the mechanical sensor module includes a three-axis acceleration unit, a three-axis gyroscope unit, and a three-axis magnetic sensor unit, which are used to collect original acceleration signals, original angular velocity signals, and original geomagnetic signals in three dimensions of x, y, and z inside the asphalt pavement structure.

[0045] The mechanical perception module and the operation storage module use the I2C protocol for data communication and control. The power consumption is 0.1mA. The measurement range of the original acceleration signal is ±16g, the measurement range of the original angular velocity signal is ±2000dps, and the measurement range of the original geomagnetic signal is ±30 Gauss.

[0046] Optionally, the chip also includes a temperature and humidity measurement module, which is used to measure the temperature and humidity inside the asphalt pavement structure, and uses the I2C protocol to communicate and control data with the operation and storage module. The temperature measurement range is -40°C to 125°C, the temperature accuracy is 0.1°C, the humidity measurement range is 0 to 100% RH, the humidity accuracy is 1% RH, and the response time is less than 1 second.

[0047] Optionally, a Bluetooth wireless transmission module is used to transmit the collected original acceleration signals, original angular velocity signals, original geomagnetic signals, temperature and humidity data to the IoT gateway via the Bluetooth wireless protocol. The data transmission rate is 1Mbps and the data length of a single packet is 243 bytes.

[0048] Optionally, the operation storage module includes CPU calculation, RAM flash memory, ROM program storage, and E2PROM data storage, which are used for collection control and data preprocessing of raw acceleration signals, raw angular velocity signals, raw geomagnetic signals, temperature and humidity data, as well as data Bluetooth wireless transmission control.

[0049] Combination Figure 2 As shown, in one embodiment, the operation storage module includes a filtering processing unit, a fusion processing unit and a calculation unit, wherein:

[0050] (1) a filtering processing unit, used to filter the collected original acceleration signal, original angular velocity signal, and original geomagnetic signal to obtain a reconstructed acceleration signal, a reconstructed angular velocity signal, and a reconstructed geomagnetic signal;

[0051] Optionally, the present invention uses a filtering processing unit to pre-process the original acceleration signal, the original angular velocity signal and the original geomagnetic signal data.

[0052] Optionally, the filtering processing unit includes:

[0053] ① A decomposition subunit, which is used to decompose the collected original acceleration signal, original angular velocity signal, and original geomagnetic signal using a discrete wavelet transform method to obtain a decomposed acceleration signal, a decomposed angular velocity signal, and a decomposed geomagnetic signal;

[0054] The collected original acceleration signal, original angular velocity signal and original geomagnetic signal are decomposed by using the denoising method of discrete wavelet transform (DWT) to obtain the decomposed acceleration signal, decomposed angular velocity signal and decomposed geomagnetic signal.

[0055] DWT decomposes the dynamic mechanical signal (including the original acceleration signal, the original angular velocity signal and the original geomagnetic signal) into wavelet components of different positions and degrees (the wavelet components here include the decomposed acceleration signal, the decomposed angular velocity signal and the decomposed geomagnetic signal) through the basis function - wavelet function. The wavelet components obtained by DWT have certain signal characteristics, such as signal texture and distribution trend.

[0056] Specifically, the calculation formula of the discrete wavelet transform method is:

[0057]

[0058] Among them, a is the scale factor, τ is the translation factor, is the complex conjugate of the wavelet basis function, x(t) is the collected original acceleration signal, original angular velocity signal and original geomagnetic signal, t is the signal acquisition time, dt is the time-based integral, and wt(a,τ) is the wavelet component.

[0059] In order to make the parameter selection of the wavelet basis function adaptive to the signal characteristics of the original acceleration signal, the original angular velocity signal and the original geomagnetic signal, according to the characteristics of the noise signal in the original acceleration signal, the original angular velocity signal and the original geomagnetic signal, the present invention selects the morlet wavelet basis function as the basis for improvement, designs the improved morlet wavelet basis function, introduces the signal characteristic parameter β, and its calculation formula is:

[0060]

[0061] Among them, β is i is the imaginary unit, is a constant, π is the ratio of pi, t represents the signal acquisition time, and T is the period of the lowest effective frequency of the signal.

[0062] ② A filtering subunit, which is used to filter the decomposed acceleration signal, the decomposed angular velocity signal and the decomposed geomagnetic signal by using a threshold denoising function to obtain a filtered acceleration signal, a filtered angular velocity signal and a filtered geomagnetic signal;

[0063] Specifically, the threshold denoising function calculation formula is:

[0064]

[0065] Among them, w j,k is the wavelet coefficient before processing (the wavelet coefficient before processing here includes the decomposed acceleration signal, the decomposed angular velocity signal and the decomposed geomagnetic signal), μw j,k is the processed wavelet coefficient (here the processed wavelet coefficient includes the filtered acceleration signal, the filtered angular velocity signal and the filtered geomagnetic signal), λ is the threshold, sign is the sign function, j and k are the wavelet decomposition levels.

[0066] ③ A reconstruction subunit, used for reconstructing the filtered acceleration signal, the filtered angular velocity signal and the filtered geomagnetic signal using a signal reconstruction algorithm to obtain a reconstructed acceleration signal, a reconstructed angular velocity signal and a reconstructed geomagnetic signal.

[0067] Optionally, the signal reconstruction algorithm is a wavelet transform reconstruction algorithm, and the calculation formula of the wavelet transform reconstruction algorithm is:

[0068]

[0069] Among them, a is the scale factor, τ is the translation factor, To improve the wavelet basis function, is the processed wavelet coefficient, t is the signal acquisition time, da and dτ are the double integrals of the data, and x'(t) is the reconstructed data signal (the reconstructed data signal here includes the reconstructed acceleration signal, the reconstructed angular velocity signal and the reconstructed geomagnetic signal).

[0070] (2) a fusion processing unit, used to fuse the reconstructed acceleration signal, the reconstructed angular velocity signal and the reconstructed geomagnetic signal to obtain attitude fusion data and fusion displacement data;

[0071] Specifically, since the acceleration signal and geomagnetic signal have high-frequency noise, the instantaneous value error is large; the angular velocity signal has low-frequency noise and is prone to drift. Therefore, the complementary characteristics of the frequency domain can be used to fuse the three signals of reconstructed acceleration signal, reconstructed angular velocity signal and reconstructed geomagnetic signal to improve the measurement accuracy and dynamic characteristics of the attitude.

[0072] Optionally, a high- and low-pass complementary filtering algorithm is used to fuse the reconstructed acceleration signal, the reconstructed angular velocity signal and the reconstructed geomagnetic signal to obtain attitude fusion data.

[0073] Optionally, the reconstructed acceleration signal, the reconstructed angular velocity signal and the reconstructed geomagnetic signal are fused by a high- and low-pass complementary filtering algorithm to obtain quaternion-based attitude fusion data.

[0074] The high-low pass complementary filtering algorithm calculation formula is:

[0075] Q'(t)=(1-γ)·accQ(t)+γ·magQ(t)

[0076]

[0077] Among them, Q(t) is the attitude fusion data based on quaternion; Q'(t) is the attitude quaternion after the fusion of reconstructed acceleration signal and reconstructed geomagnetic signal; γ is the weighting coefficient, which is 0.1; accQ(t) is the attitude quaternion of reconstructed acceleration signal; magQ(t) is the attitude quaternion of reconstructed geomagnetic signal; gyroQ(t) is the attitude quaternion of reconstructed angular velocity signal; e is a natural constant in mathematics, an infinite non-repeating decimal, and a transcendental number, whose value is approximately 2.718281828459045.

[0078] (3) A calculation unit, which is used to calculate the dynamic mechanical response inside the asphalt pavement structure based on the posture fusion data.

[0079] Optionally, the calculation unit calculates the dynamic mechanical response inside the asphalt pavement structure according to the posture fusion data using the method shown in the following formula:

[0080]

[0081] Among them, DMRI is the dynamic mechanical response inside the asphalt pavement structure, and ||Q(t)|| is the modulus of the attitude fusion data.

[0082] In one embodiment, if Figure 3 As shown, the present invention also provides an asphalt pavement structure monitoring sensor, the sensor 100 includes a protective structure upper part 110, a protective structure lower part 130, a filling material 140, the filling material 140 is a high temperature resistant polymer filling material, and also includes an asphalt pavement structure monitoring chip 120 as described in any of the aforementioned embodiments.

[0083] Specifically, seven through holes with a diameter of 2 mm are evenly distributed in the middle of the upper portion 110 of the protective structure (including the first through hole 111; the second through hole 112; the third through hole 113; the fourth through hole 114; the fifth through hole 115; the sixth through hole 116; and the seventh through hole 117), and seven through holes with a diameter of 2 mm are also evenly distributed in the middle of the lower portion 130 of the protective structure.

[0084] Combination Figure 4 As shown, in one embodiment, a method for installing and deploying asphalt pavement structure monitoring sensors is provided. The sensors are buried in the upper layer, middle layer, lower layer, and roadbed of the asphalt pavement structure layer, and data is transmitted to IoT roadside devices via Bluetooth signals, and the IoT roadside devices use 5G networks to transmit data to the cloud platform.

[0085] Combination Figure 5 As shown, in one embodiment, a wireless data transmission protocol for asphalt pavement structure monitoring sensors is provided, the data length is 256, and the data is stored in a little-endian sequence. The first part of the protocol is the Bluetooth MAC address, which occupies 6 bytes. The second part of the protocol is the data type Type, which occupies 1 byte, 0x11 represents the acceleration signal, 0x12 represents the angular velocity signal, 0x13 represents the geomagnetic signal, 0x14 represents the temperature, and 0x15 represents the humidity. The third part of the protocol is the data content, which occupies 240 bytes.

[0086] The present invention proposes an asphalt pavement structure monitoring chip and sensor, which can carry out long-term continuous monitoring of the road traffic load condition, the temperature field distribution law in the road surface and the internal dynamic response of the asphalt layer through the design of the stress-strain data acquisition method, so as to achieve the effect of timely obtaining the actual working status of the asphalt pavement under the natural environment. In addition, the present invention can accurately measure the internal condition of the asphalt pavement structure of the road based on the actual measured information for fatigue life estimation and maintenance scientific decision-making, so as to obtain accurate dynamic mechanical data in a timely manner, and has higher practical application value.

[0087] In the description of the present invention, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0088] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0089] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. An asphalt pavement structure monitoring chip, characterized in that: It includes a power management module, a mechanical signal sensing module, a Bluetooth wireless transmission module and a computing storage module, among which: The power management module is used to provide power to the mechanical signal sensing module, the Bluetooth wireless transmission module and the computing storage module; The mechanical signal sensing module is used to collect the original acceleration signal, original angular velocity signal and original geomagnetic signal inside the asphalt pavement structure; The Bluetooth wireless transmission module is used to transmit the collected original acceleration signal, original angular velocity signal and original geomagnetic signal to the IoT Internet of Things gateway; The operation storage module is used to calculate the dynamic mechanical response degree inside the asphalt pavement structure according to the collected original acceleration signal, original angular velocity signal and original geomagnetic signal.

2. The asphalt pavement structure monitoring chip according to claim 1, characterized in that: The mechanical signal sensing module comprises: A three-axis acceleration unit is used to collect the original acceleration signals in the three dimensions of x, y, and z inside the asphalt pavement structure; A three-axis gyroscope unit is used to collect the original angular velocity signals in the three dimensions of x, y and z inside the asphalt pavement structure; The three-axis magnetic sensing unit is used to collect the original geomagnetic signals in the three dimensions of x, y and z inside the asphalt pavement structure.

3. An asphalt pavement structure monitoring chip as claimed in claim 1 or 2, characterized in that: The operation storage module includes a filtering processing unit, a fusion processing unit and a calculation unit, wherein: The filtering processing unit is used to filter the collected original acceleration signal, original angular velocity signal, and original geomagnetic signal to obtain a reconstructed acceleration signal, a reconstructed angular velocity signal, and a reconstructed geomagnetic signal; The fusion processing unit is used to fuse the reconstructed acceleration signal, the reconstructed angular velocity signal and the reconstructed geomagnetic signal to obtain attitude fusion data; The calculation unit is used to calculate the dynamic mechanical response degree inside the asphalt pavement structure according to the posture fusion data.

4. The asphalt pavement structure monitoring chip according to claim 3, characterized in that: The filtering processing unit includes a decomposition subunit, a filtering subunit and a reconstruction subunit, wherein: The decomposition subunit is used to decompose the collected original acceleration signal, angular velocity signal, and geomagnetic signal using a discrete wavelet transform method to obtain a decomposed acceleration signal, a decomposed angular velocity signal, and a decomposed geomagnetic signal; The filtering subunit is used to filter the decomposed acceleration signal, the decomposed angular velocity signal and the decomposed geomagnetic signal by using a threshold denoising function to obtain a filtered acceleration signal, a filtered angular velocity signal and a filtered geomagnetic signal; The reconstruction subunit is used to reconstruct the filtered acceleration signal, the filtered angular velocity signal and the filtered geomagnetic signal using a signal reconstruction algorithm to obtain a reconstructed acceleration signal, a reconstructed angular velocity signal and a reconstructed geomagnetic signal.

5. The asphalt pavement structure monitoring chip according to claim 3, characterized in that: The fusion processing unit is specifically used for: The high- and low-pass complementary filtering algorithm is used to fuse the reconstructed acceleration signal, the reconstructed angular velocity signal and the reconstructed geomagnetic signal to obtain attitude fusion data.

6. The asphalt pavement structure monitoring chip according to claim 3, characterized in that: The calculation unit calculates the dynamic mechanical response inside the asphalt pavement structure according to the posture fusion data using the method shown in the following formula: Among them, DMRI is the dynamic mechanical response inside the asphalt pavement structure. ||Q(t)|| is the model of the posture fusion data.

7. An asphalt pavement structure monitoring sensor, comprising an upper protective structure, a lower protective structure, and a filling material, and also comprising an asphalt pavement structure monitoring chip as described in any one of claims 1-6.

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