Non-intrusive permanent magnet synchronous servo motor bearing fault online diagnosis method and system

Abstract

The invention discloses a non-intrusive permanent magnet synchronous servo motor bearing fault on-line diagnosis method and system, and the method comprises the steps: injecting a chirp current signal in a tuning stage, calculating a frequency response function through combining with a rotating speed feedback signal, and determining a resonant frequency according to the frequency response function; calculating a theoretical characteristic order according to the motor bearing size parameter; in a motor operation stage, collecting a motor rotating speed signal and determining a theoretical characteristic frequency according to the theoretical characteristic order; determining an optimal resonance demodulation frequency band according to the resonance frequency and the theoretical characteristic frequency, and performing band-pass filtering on the motor rotating speed signal according to the optimal resonance demodulation frequency band; an envelope signal of the filtered signal is calculated online through an orthogonal demodulation algorithm, the frequency spectrum of the envelope signal is obtained through FFT analysis, threshold comparison is conducted on the frequency spectrum of the envelope signal and the amplitude corresponding to the theoretical characteristic frequency, and fault diagnosis result judgment is completed. According to the invention, on-line diagnosis of the bearing fault of the permanent magnet synchronous servo motor under the condition of low-performance chip resources is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of fault diagnosis, and in particular to a non-intrusive permanent magnet synchronous servo motor bearing fault online diagnosis method and system. Background Art

[0002] In recent years, permanent magnet synchronous servo motors have been widely used in industrial automation, new energy vehicles, industrial robots and other fields due to their high power density and efficiency, high torque-to-volume ratio, and excellent dynamic performance. However, when they run for a long time under harsh conditions, various reliability problems will inevitably occur in their key components. As one of the key mechanical components of permanent magnet synchronous servo motors, motor bearings have a high probability of failure, so online diagnosis and monitoring of motor bearings is of great significance.

[0003] The vibration signal analysis method is one of the most commonly used bearing fault diagnosis methods in the industry. This method requires the installation of additional sensors to collect signals, and the diagnostic cost is high. On the other hand, bearing fault diagnosis based on the vibration signal analysis method requires accurate identification of the optimal resonant demodulation frequency band. The identification of the optimal resonant demodulation frequency band usually requires the participation of equipment such as hammering devices or exciters, and the identification process is complicated. Although the identification of the optimal resonant demodulation frequency band can also be achieved through algorithms such as fast kurtosis spectrograms, the implementation of advanced signal processing methods requires high chip computing power, which is not conducive to implementation in embedded systems. At present, some scholars have also performed motor bearing fault diagnosis through motor current signal analysis, but the sensitivity of the current signal is low, and the effect is usually only obvious when the fault is more serious. In order to meet the needs of high-performance motion control, servo motors have their own high-precision encoders. Although scholars have also conducted relevant research based on servo motor speed signals, the identification of the optimal resonant demodulation frequency band is mainly based on complex signal processing methods, and the existing diagnostic schemes are difficult to deploy under low-performance chip resource conditions. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] Based on the above problems, the present invention provides a non-intrusive permanent magnet synchronous servo motor bearing fault online diagnosis method and system to solve the problem that it is difficult to perform permanent magnet synchronous servo motor bearing fault online diagnosis under low-performance chip resource conditions.

[0006] (II) Technical solution

[0007] Based on the above technical problems, the present invention provides a non-intrusive online diagnosis method for permanent magnet synchronous servo motor bearing faults, comprising the following steps:

[0008] S1. Calculation parameters of the configuration diagnostic algorithm during the tuning phase:

[0009] S11, in the inverter tuning stage, using the inverter to inject a chirp current signal into the quadrature axis of the motor, calculating a frequency response function in combination with a speed feedback signal, and determining the resonant frequency according to the frequency response function;

[0010] S12, calculating the theoretical characteristic order according to the motor bearing size parameters;

[0011] S13, recording the resonance frequency and the theoretical characteristic order in the driver;

[0012] S2, running stage online running diagnosis algorithm:

[0013] S21, during the normal operation stage of the motor, collecting the motor speed signal, and determining the theoretical characteristic frequency according to the theoretical characteristic order;

[0014] S22, determining an optimal resonance demodulation frequency band according to the resonance frequency and the theoretical characteristic frequency, performing band-pass filtering on the motor speed signal according to the optimal resonance demodulation frequency band to obtain a filtered speed signal, and calculating an envelope signal of the filtered speed signal online by an orthogonal demodulation algorithm;

[0015] S23, analyzing the envelope signal by Fourier transform to obtain a spectrum of the envelope signal;

[0016] S24, performing threshold comparison on the frequency spectrum of the envelope signal and the amplitude corresponding to the theoretical characteristic frequency to achieve fault diagnosis result determination.

[0017] Further, in S11, the injected chirp current signal is:

[0018]

[0019] The formula for calculating the frequency response function in combination with the speed feedback signal is:

[0020]

[0021] The calculation formula for determining the resonant frequency according to the frequency response function is:

[0022]

[0023] In the formula, I c (t) represents the chirp current signal, I N Indicates the rated current of the motor, f 0 Indicates the injection start frequency, f 1 represents the injection termination frequency, t 1 is the injection duration, t represents the current time, F{·} represents the Fourier transform of the signal, and w i(t) represents the speed feedback signal, H(ω) represents the frequency response function, and f c Indicates the resonant frequency.

[0024] Further, in S12, the formula for calculating the theoretical characteristic order according to the motor bearing size parameters is:

[0025]

[0026] In the formula, O BI , O BO , O BE , O CA They represent the theoretical characteristic orders of the inner ring, outer ring, ball and cage failure of the bearing respectively, D represents the cage diameter, d represents the ball body diameter, and a represents the ball body contact angle.

[0027] Further, in S21, the method for determining the theoretical characteristic frequency according to the theoretical characteristic order is:

[0028]

[0029] In the formula, f BI 、f BO 、f BE 、f CA They represent the theoretical characteristic frequencies of the inner ring, outer ring, ball and cage failures of the bearing, respectively, and w ave It is the average value of the speed signal collected during the normal operation of the motor.

[0030] Further, in S22, the formula for determining the optimal resonant demodulation frequency band according to the resonant frequency and the theoretical characteristic frequency is:

[0031]

[0032] Where B w Indicates the determined optimal resonant demodulation frequency band;

[0033] The method for calculating the envelope signal of the filtered speed signal online by using an orthogonal demodulation algorithm comprises: comparing the filtered speed signal with the carrier signal cos(2πf c t) and obtain the in-phase component I(t) through a low-pass filter LPF; the filtered speed signal and the orthogonal phase carrier sin(2πf c t) and obtain the orthogonal component Q(t) through a low-pass filter LPF; the envelope signal is calculated:

[0034]

[0035] Further, in S24, the fault diagnosis result determination method includes:

[0036] Note that the spectrum of the envelope signal is related to the theoretical characteristic frequency f BI 、f BO 、f BE 、f CA The corresponding characteristic amplitudes are A BI , A BO , A BE , A CA The alarm thresholds corresponding to the four fault types of outer ring, inner ring, rolling element and cage are CI 1 , CI 2 , CI 3 , CI 4 ,but:

[0037] If the characteristic amplitude A BI >Alarm threshold CI 1 , it is judged to be a bearing inner ring fault;

[0038] If the characteristic amplitude A BO >Alarm threshold CI 2 , it is judged to be a bearing outer ring failure;

[0039] If the characteristic amplitude A BE >Alarm threshold CI 3 , it is judged as a rolling element failure;

[0040] If the characteristic amplitude A CA >Alarm threshold CI 4 , it is judged as a cage failure;

[0041] If the amplitudes corresponding to the theoretical characteristic frequencies do not exceed the threshold, it is determined that there is no bearing fault.

[0042] Furthermore, the starting frequency f of the chirp current signal is 0 is 10Hz, the injection termination frequency f 1 The injection frequency is one-fifth of the inverter switching frequency, and the injection duration is between 2 and 4 seconds.

[0043] The present invention also discloses a non-intrusive permanent magnet synchronous servo motor bearing fault online diagnosis system, comprising:

[0044] at least one processor; and at least one memory in communication with the processor, wherein:

[0045] The memory stores program instructions that can be executed by the processor, and the processor can execute the method by calling the program instructions.

[0046] The present invention also discloses a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions enable the computer to execute the method.

[0047] (III) Beneficial effects

[0048] The above technical solution of the present invention has the following advantages:

[0049] (1) The present invention injects a chirp current signal into the quadrature axis of the motor to accurately identify the resonance frequency and determine the optimal resonance demodulation frequency band. No hammering device or advanced signal processing method is required. The motor speed signal is analyzed, and an envelope signal is obtained through bandpass filtering and orthogonal demodulation. The spectrum of the envelope signal is then obtained through Fourier transform. Finally, the amplitude corresponding to the spectrum of the envelope signal is threshold-compared to achieve online diagnosis of four types of faults. No additional diagnostic sensors or additional MCU chips are required. The chip computing power of the driver is sufficient. The overall method is simple and reliable, low-cost, and the diagnostic scheme can be implemented online, with low requirements on chip computing power.

[0050] (2) The present invention improves the reliability and safety of the servo motor operation through online diagnosis of fault types. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The features and advantages of the present invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the present invention in any way. In the accompanying drawings:

[0052] Figure 1 This is a flow chart of a non-intrusive online diagnosis method for permanent magnet synchronous servo motor bearing faults according to an embodiment of the present invention;

[0053] Figure 2 A control principle diagram of a frequency response function calculation method according to an embodiment of the present invention;

[0054] Figure 3 A time domain waveform diagram of an injected chirp current signal and a corresponding speed feedback signal according to an embodiment of the present invention;

[0055] Figure 4 is a waveform diagram of a frequency response function curve of an embodiment of the present invention;

[0056] Figure 5 It is a time domain waveform diagram of the speed signal when the motor is operating normally according to an embodiment of the present invention;

[0057] Figure 6 is a schematic diagram of a method for calculating an envelope signal of a rotation speed signal according to an embodiment of the present invention;

[0058] Figure 7 FIG. 4 is a FFT spectrum waveform diagram of the envelope signal according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0060] An embodiment of the present invention is a non-invasive online diagnosis method for permanent magnet synchronous servo motor bearing faults. The method makes full use of the signal source and computing power resources of the driver itself. In the inverter tuning stage, the frequency response function is calculated by the chirp current signal injection method to obtain the resonance frequency, and the optimal resonance demodulation frequency band is determined in combination with the theoretical characteristic frequency of the bearing fault. In the normal operation stage of the motor, the motor speed signal is bandpass filtered based on the determined optimal resonance demodulation frequency band, and the envelope of the filtered signal is calculated online through the orthogonal demodulation algorithm. Finally, the envelope spectrum is calculated by FFT and compared with the amplitude corresponding to the theoretical characteristic frequency to determine the fault diagnosis result. This method has high economic value for improving the operating reliability of the servo drive system. Figure 1 As shown, the specific steps include:

[0061] S1. Tuning stage - configure the calculation parameters of the diagnostic algorithm: In the motor tuning stage, the resonant frequency is obtained by calculating the frequency response function, the theoretical characteristic order of the bearing fault is calculated according to the bearing size parameters, and the resonant frequency and theoretical characteristic order are recorded in the driver.

[0062] S11, in the inverter tuning stage, using the inverter to inject a chirp current signal into the quadrature axis of the motor, calculating a frequency response function in combination with a speed feedback signal, and determining the resonant frequency according to the frequency response function;

[0063] Figure 2 This is the control principle diagram of frequency response function calculation. The chirp current signal is injected into the motor quadrature axis by the frequency converter, and the corresponding speed feedback signal is collected to calculate the frequency response function. The injected chirp current signal can be specifically:

[0064]

[0065] Where I c (t) represents the chirp current signal, I N Indicates the rated current of the motor, f 0 Indicates the injection start frequency, f 1 represents the injection termination frequency, t 1 is the injection duration, and t represents the current time. The starting frequency of the injected chirp current signal is f 0 Usually 10Hz is used, and the injection termination frequency f 1 Usually one fifth of the inverter switching frequency is taken, and the injection duration is usually around 2 to 4 seconds.

[0066] Figure 3 It is the time domain waveform of the injected chirp current signal and the corresponding speed feedback signal. Figure 4 It is the time domain waveform of the frequency response function curve H(ω). The formula for calculating the frequency response function in combination with the speed feedback signal can be specifically:

[0067]

[0068] Where F{·} represents the Fourier transform of the signal, w i (t) represents the speed feedback signal, and H(ω) represents the frequency response function.

[0069] The step of determining the resonant frequency according to the frequency response function may specifically be:

[0070]

[0071] Where f c Indicates the resonant frequency. Figure 4 The frequency corresponding to the point with the highest amplitude is the resonant frequency f c .

[0072] S12, calculating the theoretical characteristic order according to the motor bearing size parameters;

[0073] The specific steps for calculating the theoretical characteristic order based on the motor bearing size parameters can be:

[0074]

[0075] Where O BI , O BO , O BE , O CA They represent the theoretical characteristic order of the inner ring, outer ring, ball and cage fault of the bearing respectively, D represents the cage diameter, d represents the ball body diameter, and a represents the ball body contact angle. The bearing size parameters and other information can be obtained by mapping before the motor leaves the factory, or by querying the database based on the bearing model.

[0076] S13, recording the resonance frequency and the theoretical characteristic order in the driver.

[0077] The parameters such as the resonance frequency and theoretical characteristic order are recorded in the driver to complete the configuration of parameters related to the tuning phase and the diagnostic algorithm.

[0078] If the tuning is successful, the motor will enter the normal operation stage. Otherwise, adjust the parameters until the tuning is successful.

[0079] S2. Operation phase - online operation diagnosis algorithm: During the normal operation phase of the motor, determine the optimal resonance demodulation frequency band, use the orthogonal demodulation algorithm to calculate the envelope of the signal after bandpass filtering online, use FFT to calculate the envelope spectrum and compare the threshold with the amplitude corresponding to the theoretical characteristic frequency to achieve fault diagnosis result judgment.

[0080] S21, during the normal operation stage of the motor, collecting the motor speed signal, and determining the theoretical characteristic frequency according to the theoretical characteristic order;

[0081] Figure 5 is the time domain waveform of the speed signal collected during the normal operation of the motor. ave The specific steps for determining the theoretical characteristic frequency by combining the theoretical characteristic order recorded in the driver during the tuning phase can be as follows:

[0082]

[0083] Where f BI 、f BO 、f BE 、f CA They represent the theoretical characteristic frequencies of the inner ring, outer ring, ball and cage failures of the bearing, respectively, and w ave It is the average value of the speed signal collected during the normal operation of the motor.

[0084] S22, determining an optimal resonance demodulation frequency band according to the resonance frequency and the theoretical characteristic frequency, performing band-pass filtering on the motor speed signal according to the optimal resonance demodulation frequency band to obtain a filtered speed signal, and calculating an envelope signal of the filtered speed signal online by an orthogonal demodulation algorithm;

[0085] Figure 6 The schematic diagram of the orthogonal demodulation algorithm for calculating the envelope signal includes the steps of first filtering the signal with a bandpass filter BPF and then calculating the envelope signal of the filtered signal online with the orthogonal demodulation algorithm.

[0086] The specific steps of determining the optimal resonant demodulation frequency band according to the resonant frequency and the theoretical characteristic frequency may be:

[0087]

[0088] Where B w Indicates the determined optimal resonant demodulation frequency band. According to the optimal demodulation frequency band, the frequency range of the bandpass filter can be determined.

[0089] The step of online calculating the envelope signal of the filtered speed signal by using an orthogonal demodulation algorithm comprises:

[0090] The filtered speed signal is combined with the carrier signal cos(2πf c t) and obtain the in-phase component I(t) through a low-pass filter LPF;

[0091] The filtered speed signal is combined with the carrier sin(2πf c t) and obtain the orthogonal component Q(t) through a low-pass filter LPF;

[0092] The envelope signal calculation step may be:

[0093]

[0094] S23, analyzing the envelope signal by Fourier transform FFT to obtain the spectrum of the envelope signal; Figure 7 It is the FFT spectrum waveform of the envelope signal.

[0095] S24, performing threshold comparison on the frequency spectrum of the envelope signal and the amplitude corresponding to the theoretical characteristic frequency to achieve fault diagnosis result determination.

[0096] Note that the spectrum of the envelope signal is related to the theoretical characteristic frequency f BI 、f BO 、f BE 、f CA The corresponding characteristic amplitude is A BI , A BO , A BE , A CA The alarm thresholds corresponding to the four fault types of outer ring, inner ring, rolling element and cage are CI 1 , CI 2 , CI 3 , CI 4 The fault diagnosis result is determined by comparing the characteristic amplitude with the corresponding alarm threshold. The specific determination rules are:

[0097] If the characteristic amplitude A BI >Alarm threshold CI 1 , it is judged to be a bearing inner ring fault;

[0098] If the characteristic amplitude A BO >Alarm threshold CI 2 , it is judged to be a bearing outer ring failure;

[0099] If the characteristic amplitude A BE >Alarm threshold CI 3 , it is judged as a rolling element failure;

[0100] If the characteristic amplitude A CA >Alarm threshold CI 4, it is judged as a cage failure;

[0101] If the amplitudes corresponding to the theoretical characteristic frequencies do not exceed the threshold, it is determined that there is no bearing fault.

[0102] The fault alarm threshold can also be set based on the test value of a healthy motor under the same diagnostic algorithm.

[0103] Finally, it should be noted that the above-mentioned diagnostic method can be converted into software program instructions, which can be implemented by using a diagnostic system including a processor and a memory, or by computer instructions stored in a non-transitory computer-readable storage medium. The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform some steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0104] In summary, the above-mentioned non-intrusive permanent magnet synchronous servo motor bearing fault online diagnosis method and system have the following beneficial effects:

[0105] (1) The present invention injects a chirp current signal into the quadrature axis of the motor to accurately identify the resonance frequency and determine the optimal resonance demodulation frequency band. No hammering device or advanced signal processing method is required. The motor speed signal is analyzed, and an envelope signal is obtained through bandpass filtering and orthogonal demodulation. The spectrum of the envelope signal is then obtained through Fourier transform. Finally, the amplitude corresponding to the spectrum of the envelope signal is threshold-compared to achieve online diagnosis of four types of faults. No additional diagnostic sensors or additional MCU chips are required. The chip computing power of the driver is sufficient. The overall method is simple and reliable, low-cost, and the diagnostic scheme can be implemented online, with low requirements on chip computing power.

[0106] (2) The present invention improves the reliability and safety of the servo motor operation through online diagnosis of fault types.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the embodiments of the present invention are described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations shall fall within the scope defined by the appended claims.

Claims

1. A non-intrusive online diagnosis method for permanent magnet synchronous servo motor bearing fault, characterized in that: The following steps are involved: S1. Calculation parameters of the configuration diagnostic algorithm during the tuning phase: S11, in the inverter tuning stage, using the inverter to inject a chirp current signal into the quadrature axis of the motor, calculating a frequency response function in combination with a speed feedback signal, and determining the resonant frequency according to the frequency response function; S12, calculating the theoretical characteristic order according to the motor bearing size parameters; S13, recording the resonance frequency and the theoretical characteristic order in the driver; S2, running stage online running diagnosis algorithm: S21, during the normal operation stage of the motor, collecting the motor speed signal, and determining the theoretical characteristic frequency according to the theoretical characteristic order; S22, determining an optimal resonance demodulation frequency band according to the resonance frequency and the theoretical characteristic frequency, performing band-pass filtering on the motor speed signal according to the optimal resonance demodulation frequency band to obtain a filtered speed signal, and calculating an envelope signal of the filtered speed signal online by an orthogonal demodulation algorithm; S23, analyzing the envelope signal by Fourier transform to obtain a spectrum of the envelope signal; S24, performing threshold comparison on the frequency spectrum of the envelope signal and the amplitude corresponding to the theoretical characteristic frequency to achieve fault diagnosis result determination.

2. The non-intrusive online diagnosis method for permanent magnet synchronous servo motor bearing fault according to claim 1 is characterized in that: In S11, the injected chirp current signal is: The formula for calculating the frequency response function in combination with the speed feedback signal is: The calculation formula for determining the resonant frequency according to the frequency response function is: In the formula, I c (t) represents the chirp current signal, I N represents the rated current of the motor, f0 represents the injection start frequency, f1 represents the injection end frequency, t1 represents the injection duration, t represents the current time, F{·} represents the Fourier transform of the signal, and w i (t) represents the speed feedback signal, H(ω) represents the frequency response function, and f c Indicates the resonant frequency.

3. The non-intrusive online diagnosis method for permanent magnet synchronous servo motor bearing fault according to claim 2 is characterized in that: In S12, the formula for calculating the theoretical characteristic order according to the motor bearing size parameters is: In the formula, O BI , O BO , O BE , O CA They represent the theoretical characteristic orders of the inner ring, outer ring, ball and cage failure of the bearing respectively, D represents the cage diameter, d represents the ball body diameter, and a represents the ball body contact angle.

4. The non-intrusive online diagnosis method for permanent magnet synchronous servo motor bearing fault according to claim 3 is characterized in that: In S21, the method for determining the theoretical characteristic frequency according to the theoretical characteristic order is: In the formula, f BI 、f BO 、f BE 、f CA They represent the theoretical characteristic frequencies of the inner ring, outer ring, ball and cage failures of the bearing, respectively, and w ave It is the average value of the speed signal collected during the normal operation of the motor.

5. The non-intrusive online diagnosis method for permanent magnet synchronous servo motor bearing fault according to claim 4 is characterized in that: In S22, the formula for determining the optimal resonant demodulation frequency band according to the resonant frequency and the theoretical characteristic frequency is: Where B w Indicates the determined optimal resonant demodulation frequency band; The method for calculating the envelope signal of the filtered speed signal online by using an orthogonal demodulation algorithm comprises: comparing the filtered speed signal with the carrier signal cos(2πf c t) and obtain the in-phase component I(t) through a low-pass filter LPF; the filtered speed signal and the orthogonal phase carrier sin(2πf c t) and obtain the orthogonal component Q(t) through a low-pass filter LPF; the envelope signal is calculated:

6. The non-intrusive online diagnosis method for permanent magnet synchronous servo motor bearing fault according to claim 5 is characterized in that: In S24, the fault diagnosis result determination method includes: Note that the spectrum of the envelope signal is related to the theoretical characteristic frequency f BI 、f BO 、f BE 、f CA The corresponding characteristic amplitudes are A BI , A BO , A BE , A CA , the alarm thresholds corresponding to the four fault types of outer ring, inner ring, rolling element and cage are CI1, CI2, CI3 and CI4 respectively, then: If the characteristic amplitude A BI > alarm threshold CI1, it is judged as a bearing inner ring fault; If the characteristic amplitude A BO > alarm threshold CI2, it is judged as a bearing outer ring failure; If the characteristic amplitude A BE >Alarm threshold CI3, it is judged as rolling element failure; If the characteristic amplitude A CA > alarm threshold CI4, it is judged as cage failure; If the amplitudes corresponding to the theoretical characteristic frequencies do not exceed the threshold, it is determined that there is no bearing fault.

7. The non-intrusive online diagnosis method for permanent magnet synchronous servo motor bearing fault according to claim 2 is characterized in that: The starting frequency f0 of the chirp current signal is 10 Hz, the injection termination frequency f1 is one fifth of the inverter switching frequency, and the injection duration is between 2 and 4 seconds.

8. A non-intrusive permanent magnet synchronous servo motor bearing fault online diagnosis system, characterized in that: include: at least one processor; and at least one memory in communication with the processor, wherein: The memory stores program instructions executable by the processor, and the processor can execute the method according to any one of claims 1 to 7 by calling the program instructions.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, which cause the computer to execute the method according to any one of claims 1 to 7.

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