CN115235985A - Tire anti-skid sensing early warning method, device and storage medium based on triboelectricity - Google Patents

Abstract

The invention relates to a tire anti-skid sensing pre-warning method based on triboelectricity, comprising the following steps: obtaining calibration voltage signals output by implanted sensing units under different friction coefficients; performing data processing on the calibration voltage signals to obtain calibration data; determining calibration data According to the correlation between the amplitude characteristics and the road friction coefficient; determine the amplitude threshold between each warning level according to the friction coefficient and the corresponding relationship between the amplitude; obtain the detection voltage signal output by the implanted sensing unit in real time ; Perform data processing on the detection voltage signal to obtain detection data; compare the amplitude of the detection data with each warning level threshold to determine the warning level. Compared with the prior art, the present invention has the advantages of fast calculation speed, accurate perception and the like.

Description

Tire anti-skid sensing early warning method, device and storage medium based on triboelectricity

technical field

The invention relates to the field of pavement anti-skid technology, in particular to a tire anti-skid sensing early warning method, device and storage medium based on triboelectricity.

Background technique

The development of intelligent transportation systems and vehicle-road coordination has put forward higher requirements for sensing equipment. Sensors with functions such as vehicle speed measurement, traffic volume monitoring, and vehicle emission monitoring are gradually being applied to road infrastructure. greatly increase. In addition, when the road surface is slippery, uneven and severely damaged, it will affect the adhesion between the vehicle roads to varying degrees, so that the vehicle cannot quickly judge the safe driving speed in the state of low anti-skid coefficient, unevenness and serious damage. And the safe following distance, which leads to traffic accidents, brings serious challenges to the realization of intelligent transportation systems.

Triboelectric nanogenerator (TENG) utilizes the coupling between triboelectricity and electrostatic induction to convert mechanical energy into electrical energy. The type of sensing device is applied to the traffic system to monitor traffic information such as tire pressure and vehicle speed. Patent CN114324144A discloses a pavement self-driven anti-slip detection device and method based on origami-tribo nano-power generation technology. The self-driven origami-tribo nano-power anti-slip detection structure is used to detect triboelectricity, and the collected current is detected. The signal is analyzed, and the friction coefficient of different road surfaces is obtained. However, because the detection unit of the origami structure is thick, the detection signal effect is poor, and the current signal has a certain delay. When the vehicle speed is fast, the response speed is slow, which cannot meet the needs.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a tire anti-skid sensing pre-warning method based on triboelectricity, so as to improve the response speed and realize the real-time and accurate anti-skid pre-warning.

The object of the present invention can be realized through the following technical solutions:

A tire anti-skid sensing early warning method based on triboelectricity, comprising the following steps:

Obtain the calibration voltage signal output by the implanted sensing unit under different friction coefficients;

Perform data processing on the calibration voltage signal to obtain calibration data;

Determine the correlation between the amplitude characteristics of the calibration data and the friction coefficient of the road surface;

Determine the amplitude threshold between each warning level according to the friction coefficient and its corresponding relationship with the amplitude;

Obtain the detection voltage signal output by the implanted sensing unit in real time;

Data processing is performed on the detection voltage signal to obtain detection data;

The amplitude of the detection data is compared with each warning level threshold to determine the warning level.

The data processing includes the following steps:

Sampling the voltage signal according to a preconfigured sampling frequency to obtain a sampling signal;

Filter the sampled signal to reduce the 50Hz ambient noise;

Perform fast Fourier transform and wavelet transform on the sampled signal respectively to obtain the amplitude and phase characteristics of the sampled signal.

The warning level includes a first warning level and a second warning level, wherein the friction coefficient corresponding to the first warning level is less than or equal to 0.3, and the friction coefficient corresponding to the second warning level is greater than 0.3 and less than or equal to 0.5.

The correlation between the amplitude characteristics of the calibration data and the friction coefficient of the road surface is obtained by means of function fitting. First, the electrical signal amplitudes of different friction coefficients under different road anti-slip grades are measured based on the grade division of the road surface anti-skid performance. The corresponding relationship between the specific friction coefficient and the amplitude is obtained, and the corresponding relationship between the specific friction coefficient and the amplitude is obtained; then the deep learning model is used to perform function fitting to obtain the correlation between the amplitude feature and the pavement friction coefficient. to complete the calibration.

The voltage signal acquisition condition is that the vehicle motion speed is greater than 0.

A tire anti-skid sensing early warning device based on triboelectricity, comprising:

Low-power wireless transmission unit, the low-power wireless transmission unit is composed of low-power rectifiers, diodes, capacitors and inductors to build energy management circuits, ultra-low-power ULP wireless Bluetooth, and buck-boost converters to achieve wireless data transmission;

Also includes:

an implantable sensing unit, the implanted sensing unit uses an elastic half-lobed sphere as a spacer layer and an elastic conductive material as an electrode layer, collects information based on the triboelectric nano-power generation effect, and outputs a voltage signal waveform;

A data processing unit includes a memory, a processor, and a program stored in the memory, and the processor implements the method described above when the program is executed.

Under the action of no external force, the elastic half-lobe balls isolate the positive and negative friction layers; under the action of force, the elastic half-lobe balls are deformed by force to realize the contact between the positive and negative friction layers.

The information collected by the implanted sensing unit includes the road surface anti-skid performance, tire wear degree, vehicle speed, acceleration and tire pressure signals in a wet and slippery state.

The implanted sensing unit is integrated on the tire.

A storage medium on which a program is stored, and when the program is executed, the method as described above is implemented.

Compared with the prior art, the present invention has the following advantages:

(1) The implanted sensing unit of the present invention has a thinner structure, better flexibility, and can be better attached to the tire surface.

(2) The present invention uses the voltage signal as the signal to be processed. Compared with the current signal, the voltage signal has no delay, and the collected road surface characteristics can be fed back to the output end in time. When the vehicle speed is fast, it can also be quickly Respond and realize early warning.

(3) The data processing method adopted in the present invention can play a good signal amplification effect on the small signal collected by the implanted sensing unit, and the calculation speed is fast, which can meet the needs of fast response.

Description of drawings

Fig. 1 is the method flow chart of the present invention;

2 is a system structure diagram of an embodiment of the present invention, wherein 1—implantable sensing unit, 2—lightweight data acquisition and processing unit, 3—low-power wireless transmission unit, and 4—WeChat terminal early warning unit;

3 is a schematic structural diagram of the sensing unit of a woven-based triboelectric smart tire in an embodiment of the present invention; wherein, 5—positive friction layer, 6—top electrode, 7—elastic half-lobed ball, 8—bottom electrode, 9—negative friction layer ;

FIG. 4 is a result diagram of the usability test of the implanted perception unit in the embodiment of the present invention;

5 is a graph showing the results of a durability performance test of an implanted sensing unit in an embodiment of the present invention;

6 is an output waveform diagram under the rough road surface of the implanted sensing unit in the embodiment of the present invention;

Fig. 7 is the output waveform diagram under the smooth road table of the implanted sensing unit in the embodiment of the present invention;

8 is a schematic diagram of a wavelet transform result in an embodiment of the present invention;

FIG. 9 is a graph showing the evaluation result of the anti-skid warning level in the embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

A tire anti-skid sensing early warning device based on triboelectricity, the structure of which is shown in Figure 2, including:

(1) Implantable sensing unit

The implanted sensing unit uses the elastic half-lobed ball as the spacer layer and the elastic conductive material as the electrode layer, collects information based on the triboelectric nano-power generation effect, and outputs a voltage signal waveform, the structure of which is shown in Figure 3.

Under the action of no external force, the elastic half-lobe balls isolate the positive and negative friction layers; under the action of force, the elastic half-lobe balls are deformed by force to realize the contact between the positive and negative friction layers.

Compared with the traditional anti-slip performance sensing device, the implanted sensing unit utilizes the coupling between triboelectricity and electrostatic induction, which not only solves the problem of energy supply, but also has excellent sensing ability; The addition of the valve ball isolation layer, on the one hand, ensures a large distance between the positive and negative electrodes of the sensing element, and on the other hand converts the flat friction layer into a curved surface, which significantly increases the friction surface area and increases the electric field strength. According to the relationship of "potential difference = electric field intensity × distance between positive and negative electrodes", it is not difficult to obtain that the introduction of elastic half-lobed spheres will significantly improve the output electrical signal of the sensing device and indirectly improve the sensing accuracy.

The implanted sensing unit is integrated on the tire and collects information including road surface anti-skid performance in wet conditions, tire wear level, vehicle speed, acceleration and tire pressure signals.

The usability test of the implanted sensing unit, according to the results in Figure 4, shows that the tested open circuit voltage remains almost unchanged as the excitation frequency increases. The main reason is that the circuit is in a short-circuit state when the open-circuit voltage is tested, resulting in that the voltage is only related to the separation distance of the triboelectric nanogenerator structure, and has nothing to do with the rate of contact and separation of the two surfaces.

In order to further characterize the durability of the triboelectric sensing element to ensure the normal use of the sensing element. The results from Figure 5 show that under the frequency excitation of 14 Hz, after about 11,000 reciprocating pressings, the output voltage remains almost unchanged, indicating that the protected sensing element can maintain good power generation performance and has excellent durability.

(2) The data processing unit realizes the data processing of the output signal of the implanted sensing unit, and obtains the warning result.

The data processing unit implements a tire anti-skid sensing early warning method based on triboelectricity, including the following steps:

Obtain the calibration voltage signal output by the implanted sensing unit under different friction coefficients;

Sampling the voltage signal according to a preconfigured sampling frequency to obtain a sampling signal;

Filter the sampled signal to reduce the 50Hz ambient noise;

Perform fast Fourier transform and wavelet transform on the sampled signal respectively to obtain the amplitude and phase characteristics of the sampled signal;

Determine the correlation between the amplitude characteristics of the calibration data and the friction coefficient of the road surface;

Determine the amplitude threshold between each warning level according to the friction coefficient and its corresponding relationship with the amplitude;

Obtain the detection voltage signal output by the implanted sensing unit in real time;

Data processing is performed on the detection voltage signal to obtain detection data;

The amplitude of the detection data is compared with each warning level threshold to determine the warning level.

The warning level includes a first warning level and a second warning level, wherein the friction coefficient corresponding to the first warning level is less than or equal to 0.3, and the friction coefficient corresponding to the second warning level is greater than 0.3 and less than or equal to 0.5.

The correlation between the amplitude characteristics of the calibration data and the friction coefficient of the road surface is obtained by means of function fitting. First, the electrical signal amplitudes of different friction coefficients under different road anti-slip grades are measured based on the grade division of the road surface anti-skid performance. The corresponding relationship between the specific friction coefficient and the amplitude is obtained, and the corresponding relationship between the specific friction coefficient and the amplitude is obtained; then the deep learning model is used to perform function fitting to obtain the correlation between the amplitude feature and the pavement friction coefficient. to complete the calibration.

The voltage signal acquisition condition is that the vehicle motion speed is greater than 0.

In this example, according to the Technical Specification for Highway Asphalt Pavement Maintenance (JTG 5142-2019), a pendulum friction coefficient tester is used to test the friction coefficients of rough and smooth road surfaces respectively, to verify the perception of anti-skid performance based on the perception module of friction nano-power generation effect capacity, pendulum values of the prepared rough and smooth pavement panels are shown in Table 1.

Table 1 Results of different road table pendulum values (BPN)

According to the specification JTG 5142-2019, the result of dividing the pendulum value BPN by 100 is the friction coefficient. Therefore, in combination with Table 1, the friction coefficients corresponding to the rough road surface and the smooth road surface are 0.88 and 0.66, respectively.

Based on the above two road surfaces, the test result of the sensing element under the rough road surface in this embodiment is shown in FIG. 6 , and the test result under the smooth road surface is shown in FIG. 7 . It is obvious that the electrical signal on the rough road surface is "full-wave", while on the smooth road surface, the electrical signal is "half-wave", mainly because the frictional resistance of the rough road surface to the tire is much greater than that of the smooth road surface, and when the Under the influence of friction, only part of the sensing elements can effectively act on the road surface, while the other part leaves the road surface due to the push of the rolling of the wheels, resulting in the appearance of a half-wave signal. At the same time, the above results also verify the feasibility of the protected anti-skid performance sensing based on the triboelectric nanopower effect.

Considering that the friction coefficient of the road surface is related to the positive pressure F of the tire/road contact, the tire speed v and other parameters, in order to quantify and analyze the difference of the waveform more intuitively, FFT and wavelet transform are used to carry out data mining, and the lightweight data is constructed by Matlab software. The analysis and processing system processes the current and voltage data obtained from the test with FFT or wavelet transform, and obtains a more intuitive and quantifiable electrical signal waveform image. Figure 7 is an example of the result of wavelet transform.

The friction coefficient values of road surfaces with different roughness levels were tested and divided into four grades: rough, rough, smooth and smooth. The results are shown in Table 2:

Table 2 Different road surface grades and friction coefficient values

road table type road table rating friction coefficient Grooved Cement Pavement rough 0.7 Asphalt road rougher 0.6 Grooved Marble Pavement smoother 0.5 smooth marble pavement smooth 0.3

Figure 8 shows the results of evaluating the anti-skid performance of the tire anti-skid sensing early warning system under different road surfaces in this embodiment.

(3) Low-power wireless transmission unit

The low-power wireless transmission unit is composed of low-power rectifiers, diodes, capacitors and inductors to build energy management circuits, ultra-low-power ULP wireless Bluetooth, and buck-boost converters to realize wireless data transmission.

(4) WeChat terminal early warning unit

In order to better realize the application, a small program terminal is built through independent programming to receive data processing signals in real time and provide drivers with anti-skid warning information in a visual way. When the road surface is at the "smooth" level, the terminal module will issue a reminder of "drive carefully to prevent slippery roads", and when the road surface is at the "smooth" level, the terminal module will issue "the road surface is slippery, it is not recommended to travel" warning.

The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and changes according to the concept of the present invention without creative efforts. Therefore, all technical solutions that can be obtained by those skilled in the art through logical analysis, reasoning or limited experiments on the basis of the prior art according to the concept of the present invention shall fall within the protection scope determined by the claims.

Claims (10)

1. a tire anti-skid sensing early warning method based on triboelectricity, is characterized in that, comprises the following steps: Obtain the calibration voltage signal output by the implanted sensing unit under different friction coefficients; Perform data processing on the calibration voltage signal to obtain calibration data; Determine the correlation between the amplitude characteristics of the calibration data and the friction coefficient of the road surface; Determine the amplitude threshold between each warning level according to the friction coefficient and its corresponding relationship with the amplitude; Obtain the detection voltage signal output by the implanted sensing unit in real time; Data processing is performed on the detection voltage signal to obtain detection data; Compare the amplitude of the detection data with each warning level threshold to determine the warning level. 2. A kind of tire anti-skid sensing early warning method based on triboelectricity according to claim 1, is characterized in that, described data processing comprises the following steps: Sampling the voltage signal according to a preconfigured sampling frequency to obtain a sampling signal; Filter the sampled signal to reduce the 50Hz ambient noise; Perform fast Fourier transform and wavelet transform on the sampled signal respectively to obtain the amplitude and phase characteristics of the sampled signal. 3. A tire anti-skid sensing early warning method based on triboelectricity according to claim 1, wherein the early warning level comprises a first warning level and a second warning level, wherein the friction corresponding to the first warning level The coefficient is less than or equal to 0.3, and the friction coefficient corresponding to the second warning level is greater than 0.3 and less than or equal to 0.5. 4. A tire anti-skid sensing early warning method based on triboelectricity according to claim 1, wherein the correlation between the amplitude feature of the calibration data and the road surface friction coefficient is obtained by function fitting , First of all, based on the classification of pavement anti-skid performance grades, the amplitude characteristics of electrical signals of different friction coefficients under different pavement anti-skid grades are measured. Repeated tests are carried out for each pavement structure, and abnormal conditions are eliminated to obtain specific friction coefficients and amplitudes. Then use the deep learning model to perform function fitting to obtain the correlation between the amplitude feature and the road friction coefficient, and complete the calibration. 5 . The tire anti-skid sensing early warning method based on triboelectricity according to claim 1 , wherein the voltage signal acquisition condition is that the vehicle speed is greater than 0. 6 . 6. A tire anti-skid sensing early warning device based on triboelectricity, comprising: Low-power wireless transmission unit, the low-power wireless transmission unit is composed of low-power rectifiers, diodes, capacitors and inductors to build energy management circuits, ultra-low-power ULP wireless Bluetooth, and buck-boost converters to achieve wireless data transmission; It is characterized in that it also includes: an implantable sensing unit, the implanted sensing unit uses an elastic half-lobed sphere as a spacer layer and an elastic conductive material as an electrode layer, collects information based on the triboelectric nano-power generation effect, and outputs a voltage signal waveform; A data processing unit includes a memory, a processor, and a program stored in the memory, and the processor implements the method according to any one of claims 1-8 when the processor executes the program. 7. A tire anti-skid sensing early warning device based on triboelectricity according to claim 6, characterized in that, under the action of no external force, the elastic half-lobe ball isolates the positive and negative friction layers; under the action of force , the elastic half-lobe ball is deformed by force to realize the contact between the positive and negative friction layers. 8 . The tire anti-skid sensing early warning device based on triboelectricity according to claim 6 , wherein the collected information of the implanted sensing unit includes the anti-skid performance of the road surface, tire wear degree, Vehicle speed, acceleration and tire pressure signals. 9 . The tire anti-skid sensing early warning device based on triboelectricity according to claim 6 , wherein the implanted sensing unit is integrated on the tire. 10 . 10. A storage medium on which a program is stored, characterized in that, when the program is executed, the method according to any one of claims 1-5 is implemented.

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