Intelligent non-pneumatic tire damage diagnosis method and system based on wavelet packet energy

Abstract

The invention relates to the technical field of intelligent driving, in particular to an intelligent non-pneumatic tire damage diagnosis method and system based on wavelet packet energy. Vibration signals of the intelligent non-pneumatic tire in the health state and different local damage states are obtained; the method comprises the following steps: processing an original vibration signal by adopting a wavelet signal de-noising method, selecting a proper wavelet basis function according to the characteristics of the signal and the characteristics needing to be highlighted, carrying out wavelet decomposition on the original vibration signal, and solving a wavelet coefficient of each node; calculating the energy of each node and the total energy, and solving the energy ratio of each node; and establishing a wavelet packet total energy change rate WER, comparing RES values, and judging the damage degree of the intelligent non-pneumatic tire. According to the invention, the precision and efficiency of damage identification of the intelligent non-pneumatic tire are improved, and powerful technical support is provided for application of the non-pneumatic tire in the field of intelligent driving.

Description

Technical Field

[0001] The present invention relates to the field of intelligent driving technology, and in particular to an intelligent non-pneumatic tire damage diagnosis method and system based on wavelet packet energy. Background Art

[0002] The damage information of the structure is always contained in the signal. By measuring and calculating certain vibration parameters in the structure that are sensitive to damage, the damage state of the structure can be identified. For this type of signal analysis and processing technology, time and frequency are two important factors of the signal. However, in previous processing methods, time or frequency is often two singular functions, and there is no connection between them. The emergence of Fourier transform has established a bridge between time and frequency, but its limitation of being unable to analyze local area signals has also limited its application. The emergence of wavelet transform has made up for the shortcomings of Fourier transform to a certain extent, and can locally analyze the details of the signal in the time domain and frequency domain. Based on wavelet transform, wavelet packet transform can analyze and process the signal more finely without redundancy and omission. This makes wavelet packet transform widely used in earthquake recognition, speech recognition, mechanical fault recognition and engineering structure damage recognition.

[0003] Regarding tire loss identification, although some scholars and research institutions have proposed damage identification methods for traditional pneumatic tires based on tire pressure, acceleration and other signals, there is still a lack of research on non-pneumatic tire damage identification, and no effective identification method has yet appeared. From the perspective of signal analysis, local damage to non-pneumatic tires will inevitably lead to changes in some frequency components, which is manifested as an increase or decrease in the energy of some frequency bands in wavelet packet analysis. Therefore, the present invention proposes an intelligent non-pneumatic tire loss identification method based on wavelet packet energy. Summary of the invention

[0004] In view of the above problems existing in the prior art, the present invention is proposed.

[0005] Therefore, the present invention provides an intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy, which can solve the problem that traditional signal processing methods, such as Fourier transform, can establish a connection between the frequency domain and the time domain, but cannot effectively analyze the local area of ​​the signal. For non-pneumatic tires, there are relatively few studies at present, and most of the existing damage identification methods are based on the characteristics of pneumatic tires and cannot be directly applied to non-pneumatic tires. In addition, traditional methods are insufficient in signal noise processing and damage identification accuracy.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a smart non-pneumatic tire damage diagnosis method based on wavelet packet energy, comprising: obtaining the vibration signal of the smart non-pneumatic tire in a healthy state and in different local damage states; processing the original vibration signal by a wavelet signal denoising method, performing signal decomposition, and obtaining the wavelet coefficient of each node; calculating the energy of each node, the total energy and the energy proportion of each node, establishing the wavelet packet total energy change rate WER, comparing the RES values, and determining the damage degree of the smart non-pneumatic tire.

[0007] As a preferred solution of the intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy described in the present invention, the wavelet signal denoising method is used to process the original vibration signal, including selecting Meyer wavelet as the wavelet basis function to perform wavelet packet decomposition, and setting the Meyer wavelet through the spectrum of a group of conjugate orthogonal mirror filter groups. The frequency domain formula is expressed as follows:

[0008]

[0009] v(x)=x 4 (35-84x+70x 2 -20x 3 )x∈[0,1]

[0010] Among them, v(x) is the auxiliary function, ω is the frequency, ψ(·) is the wavelet function, j is the number of wavelet packet layers, sin(·) is the sine function, and cos(·) is the cosine function.

[0011] As a preferred solution of the intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy of the present invention, the signal decomposition includes three-layer decomposition of Meyer wavelet, and the signal f(t) is decomposed by j layers of wavelet packets, which is publicly expressed as:

[0012]

[0013] in, is the wavelet packet grouping signal, is the wavelet packet coefficient, ψ j,k,i (t) is the wavelet basis function, i is the group index, t is the time, and k is the sample index value.

[0014] As a preferred solution of the intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy described in the present invention, wherein: the wavelet coefficients of each node are obtained, the wavelet packet coefficients are the wavelet packet coefficients of each node after reconstruction, and the scaling function and the wavelet function formula are expressed as follows:

[0015]

[0016] Among them, h k , gk are the filter coefficients in different multi-resolution analyses, φ(t) is the scaling function, and ψ(t) is the wavelet function.

[0017] As a preferred solution of the intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy of the present invention, wherein: the scaling function and the wavelet function include a recursive relationship satisfying the function, which is expressed as:

[0018]

[0019] Among them, w n (t) is the wavelet packet function family determined by the basis function w0(t) = φ(t);

[0020] The recursive expression of wavelet packet decomposition is:

[0021]

[0022] Among them, l is the index value of the wavelet packet decomposition layer;

[0023] The recursive expression of wavelet packet reconstruction is:

[0024]

[0025] in, is the j-th layer wavelet packet.

[0026] As a preferred solution of the intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy described in the present invention, wherein: the calculation of each node energy, total energy and energy proportion of each node includes the total wavelet packet energy of the signal f(t) after being decomposed by j layers of wavelet packets It is expressed as,

[0027]

[0028] Among them, m and n are different subband indexes respectively;

[0029] According to the orthogonality of wavelet packets, the energy of the i-th group of signal f(t) at the j-level of wavelet packet decomposition is expressed as:

[0030]

[0031] in, is the energy of the i-th group of signal f(t) at the j-level of wavelet packet decomposition.

[0032] As a preferred solution of the intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy described in the present invention, wherein: the determination of the damage degree of the intelligent non-pneumatic tire includes establishing a calculation formula for the wavelet packet total energy change rate WER, comparing the RES value, and obtaining the wavelet coefficient after the original vibration signal is decomposed by the wavelet packet. According to the wavelet packet energy component at the node and the total energy of the wavelet packet, the total energy change rate of the wavelet packet is obtained, and the formula is expressed as follows:

[0033]

[0034] in, is the total energy of the wavelet packet under damage state.

[0035] Another object of the present invention is to provide an intelligent non-pneumatic tire damage diagnosis system based on wavelet packet energy, so as to solve the shortcomings of traditional methods in signal local feature extraction, noise resistance, and accuracy and reliability of non-pneumatic tire damage identification. The existing technology mainly focuses on damage detection of pneumatic tires, and relatively little research is done on non-pneumatic tires. Due to the special structure of non-pneumatic tires, local damage can cause significant changes in frequency components. The present invention obtains vibration signals of intelligent non-pneumatic tires in healthy and different local damage states, uses wavelet signal denoising to process the original signal, and calculates the energy proportion of each node and the total energy change rate through wavelet packet decomposition, thereby identifying and judging the degree of tire damage. The method of the present invention can effectively improve the accuracy and reliability of damage identification, and meet the needs of the intelligent driving field for non-pneumatic tire damage diagnosis.

[0036] As a preferred solution of the intelligent non-pneumatic tire damage diagnosis system based on wavelet packet energy described in the present invention, it includes: a data acquisition module, a signal processing module and a damage determination module;

[0037] The data acquisition module is responsible for acquiring vibration signals of the intelligent non-pneumatic tire in a healthy state and in different local damage states, and ensures the effectiveness of subsequent analysis through accurate signal acquisition;

[0038] The signal processing module processes the original vibration signal by using a wavelet signal denoising method, decomposes the signal, and obtains the wavelet coefficients of each node;

[0039] The damage determination module calculates the energy of each node, the total energy and the energy proportion of each node, establishes the wavelet packet total energy change rate WER, and determines the damage degree of the intelligent non-pneumatic tire by comparing the RES value.

[0040] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of any one of the methods for intelligent non-pneumatic tire damage diagnosis based on wavelet packet energy are implemented.

[0041] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of any one of the methods for intelligent non-pneumatic tire damage diagnosis based on wavelet packet energy are implemented.

[0042] Beneficial effects of the present invention: By adopting the wavelet packet energy spectrum analysis method, the damage diagnosis of non-pneumatic tires is made more accurate and reliable. Wavelet packet transform has good time-frequency localization characteristics, and can perform fine analysis on the signal in the time domain and frequency domain, so as to extract effective information from the tire vibration signal, especially in the presence of noise interference, the significant characteristics of the signal can still be retained. The present invention establishes the wavelet packet total energy change rate WER, compares the energy changes under different damage states, and effectively identifies the degree of tire damage. The method has strong anti-noise ability and can adapt to complex road conditions and operating environments. The present invention not only improves the accuracy and efficiency of intelligent non-pneumatic tire damage identification, but also provides strong technical support for the application of non-pneumatic tires in the field of intelligent driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0044] Figure 1 A schematic flow chart of an intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy provided by an embodiment of the present invention.

[0045] Figure 2 A schematic diagram of three-layer wavelet packet decomposition of an intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy provided by an embodiment of the present invention.

[0046] Figure 3 A diagram showing energy proportions of various frequency bands of acceleration signals at different damage degrees in an intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy provided by an embodiment of the present invention.

[0047] Figure 4 A graph showing the proportion of total energy of acceleration signals at different damage degrees for an intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy provided by an embodiment of the present invention.

[0048] Figure 5 The WER diagram of acceleration signals under different damage degrees of an intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. 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 ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0050] Example 1, reference Figure 1-Figure 5 , which is the first embodiment of the present invention, provides an intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy, comprising:

[0051] S1: Obtain vibration signals of the smart non-pneumatic tire in healthy state and different local damage states.

[0052] It should be noted that this embodiment simulates the working condition of an intelligent non-pneumatic tire running steadily at a speed of 60 km / h under a vertical load of 5000 N, and collects the vibration signal of the non-pneumatic tire through a vibration sensor embedded on the inner side of the tread of the intelligent non-pneumatic tire.

[0053] First, draw the wavelet packet color coefficient diagram. The x-axis represents the number of sampling points of the signal. Corresponding to 1 second, each point represents 1 / 5900 second. On the y-axis, the numbers displayed correspond to the nodes in the wavelet packet tree. Starting from the bottom, the order is nodes 7, 8, 10, 9, and 11. The sampling frequency is 1000Hz. According to the sampling theorem, the Nyquist sampling frequency is 500Hz. The number of decomposition layers is 3 layers, and the last layer is 2^3=8 frequency bands. The frequency interval of each frequency band is 500 / 8=62.5Hz. The wavelet packet analysis basis function is Meyer wavelet, such as Figure 2 Shown is a three-layer exploded view.

[0054] S2: Process the original vibration signal through the wavelet signal denoising method, decompose the signal, and obtain the wavelet coefficients of each node.

[0055] It should be noted that the wavelet signal denoising method is used to process the original vibration signal. According to the characteristics of the signal and the features that need to be highlighted, for example, white noise cannot be used. An appropriate wavelet basis function is selected, the original vibration signal is decomposed by wavelet, and the wavelet coefficients of each node are obtained.

[0056] Furthermore, the wavelet basis functions to be selected include Hear wavelet, Daubechies (dbN) wavelet, Morlet wavelet, Mexican Hat wavelet, and Meyer wavelet.

[0057] Hear wavelet is the earliest orthogonal wavelet function with the simplest structure. Its support domain is t∈[0,1], which is a single rectangular wave and can be expressed analytically as follows:

[0058]

[0059] Furthermore, Daubechies wavelet is a wavelet function constructed by Ingrid Daubechies, a famous French wavelet analysis scholar, abbreviated as dbN (N represents the order of the wavelet, N = 2, .., 10). When N = 1, it is the hear wavelet. Except for this special case, the dbN wavelet is not symmetrical and has no clear expression. The dbN wavelet is an orthogonal and tightly supported wavelet function. Its support area is 2N-1, and its vanishing moment is N. As the order increases, the localization of the frequency domain is enhanced and the frequency band division effect is better, but it will weaken the support of the time domain and increase the amount of calculation.

[0060] Furthermore, the Morlet wavelet is a single-frequency sine function under a Gaussian envelope, has no scaling function, and is a non-orthogonal decomposition. The formula is:

[0061]

[0062] Among them, C is the normalization parameter, ω0 is the center frequency of the wavelet;

[0063] The normalized Fourier transform is expressed as,

[0064]

[0065] Furthermore, the MexicanHat wavelet is the second-order derivative of the Gauss function. It has good localization in both time and frequency domains, but there is no scaling function, so this wavelet function is not orthogonal. The expression of the MexicanHat wavelet is,

[0066]

[0067] It should be noted that the wavelet signal denoising method for processing the original vibration signal includes selecting Meyer wavelet as the wavelet basis function to perform wavelet packet decomposition. The Meyer wavelet has good symmetry and orthogonality. The Meyer wavelet is set by a spectrum of a group of conjugate orthogonal mirror filter groups. The frequency domain formula is expressed as:

[0068]

[0069] v(x)=x 4 (35-84x+70x 2 -20x 3 )x∈[0,1]

[0070] Among them, v(x) is the auxiliary function, ω is the frequency, ψ(·) is the wavelet function, j is the number of wavelet packet layers, sin(·) is the sine function, and cos(·) is the cosine function.

[0071] The signal decomposition includes three-layer decomposition of Meyer wavelet, and the signal f(t) is decomposed by j-layer wavelet packets, which is expressed as:

[0072]

[0073] in, is the wavelet packet grouping signal, is the wavelet packet coefficient, ψ j,k,i (t) is the wavelet basis function, i is the group index, t is the time, and k is the sample index value.

[0074] The wavelet coefficients of each node are obtained, including wavelet packet coefficients, which are the wavelet packet coefficients of each node after reconstruction. The scaling function and wavelet function formula are expressed as follows:

[0075]

[0076] Among them, h k , g k are the filter coefficients in different multi-resolution analyses, φ(t) is the scaling function, and ψ(t) is the wavelet function.

[0077] The above formula satisfies the following recursive relationship, expressed as,

[0078]

[0079] Among them, w n (t) is the wavelet packet function family determined by the basis function w0(t) = φ(t);

[0080] The expression of wavelet packet reconstruction can generally be obtained by recursion as follows:

[0081]

[0082] Among them, l is the index value of the wavelet packet decomposition layer;

[0083] The expression of wavelet packet reconstruction can generally be obtained by recursion as follows:

[0084]

[0085] in, is the j-th layer wavelet packet.

[0086] S3: Calculate the energy of each node, the total energy and the energy proportion of each node, establish the total energy change rate WER of the wavelet packet, compare the RES values, and determine the damage degree of the intelligent non-pneumatic tire.

[0087] It should be noted that local damage to the smart non-pneumatic tire will inevitably lead to changes in the frequency components of its vibration signal. This change will be manifested as an increase or decrease in the energy of some frequency bands in wavelet packet analysis. The total energy of the wavelet packet after the signal f(t) is decomposed by j layers of wavelet packets is It is expressed as,

[0088]

[0089] Among them, m and n are different subband indexes respectively;

[0090] According to the orthogonality of wavelet packets, the energy of the i-th group of signal f(t) at the j-level of wavelet packet decomposition is expressed as:

[0091]

[0092] in, is the energy of the i-th group of signal f(t) at the j-th level of wavelet packet decomposition.

[0093] The determination of the damage degree of the intelligent non-pneumatic tire includes establishing a calculation formula for the total wavelet packet energy change rate WER, comparing RES values, decomposing the original vibration signal through wavelet packets to obtain wavelet coefficients, and obtaining the total wavelet packet energy change rate (totalwaveletpacketenergychangerate, WER) according to the wavelet packet energy components at the nodes and the total wavelet packet energy. The formula is expressed as follows:

[0094]

[0095] in, is the total energy of the wavelet packet under damage state.

[0096] like Figure 3 As shown in the figure, the energy proportion of the acceleration signal at each node in the third layer after Meyer wavelet decomposition is calculated. The energy distribution is basically concentrated in the first four nodes. The total energy of the third layer after wavelet packet decomposition under each damage condition is counted. The results are shown in the figure. Figure 4 , with the increase of damage degree, the total energy of wavelet packet has a significant trend of increasing. Figure 5 As shown, according to the calculated wavelet packet total energy change rate WER, it can be seen that:

[0097] Under the condition of constant load and speed, WER increases with the increase of damage degree;

[0098] Under the condition that the load and damage degree remain unchanged, WER decreases with the increase of cable length;

[0099] Under the condition that the speed and damage degree remain unchanged, WER decreases with the increase of cable tension.

[0100] In general, the WER index is damage sensitive and can well characterize the degree of damage.

[0101] The above is a schematic scheme of the intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy in this embodiment. It should be noted that the technical scheme of the intelligent non-pneumatic tire damage diagnosis system based on wavelet packet energy and the technical scheme of the intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy belong to the same concept. The details of the technical scheme of the intelligent non-pneumatic tire damage diagnosis system based on wavelet packet energy in this embodiment that are not described in detail can all be referred to the description of the technical scheme of the intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy.

[0102] The intelligent non-pneumatic tire damage diagnosis system based on wavelet packet energy in this embodiment includes: a data acquisition module, a signal processing module and a damage determination module;

[0103] The data acquisition module is responsible for acquiring vibration signals of the intelligent non-pneumatic tire in a healthy state and in different local damage states, and ensures the effectiveness of subsequent analysis through accurate signal acquisition;

[0104] The signal processing module processes the original vibration signal by using a wavelet signal denoising method, decomposes the signal, and obtains the wavelet coefficients of each node;

[0105] The damage determination module calculates the energy of each node, the total energy and the energy proportion of each node, establishes the wavelet packet total energy change rate WER, and determines the damage degree of the intelligent non-pneumatic tire by comparing the RES value.

[0106] This embodiment further provides a computing device, which is applicable to the case of an intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy, including:

[0107] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0108] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0109] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0110] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0111] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy, characterized in that: include, Obtain vibration signals of intelligent non-pneumatic tires in healthy state and different local damage states; The original vibration signal is processed by wavelet signal denoising method, the signal is decomposed, and the wavelet coefficients of each node are obtained; The energy of each node, the total energy and the energy proportion of each node are calculated, the total energy change rate WER of the wavelet packet is established, and the RES values ​​are compared to determine the damage degree of the intelligent non-pneumatic tire.

2. The intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy according to claim 1, characterized in that: The method of processing the original vibration signal by using the wavelet signal denoising method includes selecting Meyer wavelet as the wavelet basis function to perform wavelet packet decomposition, and setting the Meyer wavelet through the spectrum of a group of conjugate orthogonal mirror filter groups. The frequency domain formula is expressed as: v(x)=x 4 (35-84x+70x 2 -20x 3 )x∈[0,1] Among them, v(x) is the auxiliary function, ω is the frequency, ψ(·) is the wavelet function, j is the number of wavelet packet layers, sin(·) is the sine function, and cOS(·) is the cosine function.

3. The intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy according to claim 2, characterized in that: The signal decomposition includes three-layer decomposition of Meyer wavelet, and the signal f(t) is decomposed by j-layer wavelet packets, which is expressed as: in, is the wavelet packet grouping signal, is the wavelet packet coefficient, ψ j，k，i (t) is the wavelet basis function, i is the group index, t is the time, and k is the sample index value.

4. The intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy according to claim 3, characterized in that: The wavelet coefficients of each node are obtained, including wavelet packet coefficients, which are the wavelet packet coefficients of each node after reconstruction. The scaling function and wavelet function formula are expressed as follows: Among them, h k , g k are the filter coefficients in different multi-resolution analyses, φ(t) is the scaling function, and ψ(t) is the wavelet function.

5. The intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy according to claim 4, characterized in that: The scaling function and the wavelet function include a recursive relationship satisfying the function, expressed as, Among them, w n (t) is the wavelet packet function family determined by the basis function w0(t) = φ(t); The recursive expression of wavelet packet decomposition is: Among them, l is the index value of the wavelet packet decomposition layer; The recursive expression of wavelet packet reconstruction is: in, is the j-th layer wavelet packet.

6. The intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy according to claim 5, characterized in that: The calculation of each node energy, total energy and energy proportion of each node includes the total wavelet packet energy after the signal f(t) is decomposed by j layers of wavelet packets. It is expressed as, Among them, m and n are different subband indexes respectively; According to the orthogonality of wavelet packets, the energy of the i-th group of signal f(t) at the j-level of wavelet packet decomposition is expressed as: in, is the energy of the i-th group of signal f(t) at the j-th level of wavelet packet decomposition.

7. The intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy according to claim 6, characterized in that: The damage degree of the intelligent non-pneumatic tire is determined by establishing a calculation formula for the total energy change rate WER of the wavelet packet, comparing the RES value, decomposing the original vibration signal through the wavelet packet to obtain the wavelet coefficient, and obtaining the total energy change rate of the wavelet packet according to the wavelet packet energy component at the node and the total energy of the wavelet packet. The formula is expressed as follows: in, is the total energy of the wavelet packet under damage state.

8. A system based on the intelligent non-pneumatic tire damage diagnosis method based on wavelet packet energy according to any one of claims 1 to 7, characterized in that: It includes a data acquisition module, a signal processing module and a damage determination module; The data acquisition module is responsible for acquiring vibration signals of the intelligent non-pneumatic tire in a healthy state and in different local damage states, and ensures the effectiveness of subsequent analysis through accurate signal acquisition; The signal processing module processes the original vibration signal by using a wavelet signal denoising method, decomposes the signal, and obtains the wavelet coefficients of each node; The damage determination module calculates the energy of each node, the total energy and the energy proportion of each node, establishes the wavelet packet total energy change rate WER, and determines the damage degree of the intelligent non-pneumatic tire by comparing the RES value.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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