Signal processing method and signal transmission system

Abstract

The present application is applicable to the field of signal processing technology, and provides a signal processing method and a signal transmission system, the method comprising: obtaining sampling signals transmitted by multiple optical fiber transmission channels; determining the target wavelet basis function and the target decomposition layer number corresponding to each of the sampling signals; performing discrete wavelet transform on each of the sampling signals based on the target wavelet basis function and the target decomposition layer number corresponding to each of the sampling signals; performing autocorrelation function analysis on each of the sampling signals after discrete wavelet transform to obtain the time delay information of each of the sampling signals; aligning the timestamps of the multiple sampling signals based on the time delay information. Through the above method, time synchronization can be performed on signals transmitted through different transmission channels.

Description

Technical Field

[0001] The present application belongs to the field of communication technology, and in particular, relates to a signal processing method and a signal transmission system. Background Art

[0002] In modern science and technology, time and frequency are two basic and extremely critical quantities, which are widely used in measurement and control in many fields such as astronomy, physics, earth science, transportation, etc. In various application scenarios, it is necessary to transmit time or frequency signals between different locations or systems, such as satellite time synchronization, radar array time synchronization, traffic signal timing control, and biological clock synchronization in medical equipment.

[0003] Traditional time and frequency signal transmission usually adopts a cable-based wired method or a navigation satellite-based wireless method. However, the transmission distance of wired transmission is limited and the signal attenuation is large. Wireless transmission, especially the navigation satellite-based method, may be affected by signal blocking, interference and other problems. These problems may cause the instability of time and frequency signal transmission, thus failing to meet the needs of some high-precision measurement and control application scenarios.

[0004] Fiber optic transmission technology can achieve high-speed, long-distance, and low-noise signal transmission. If signal transmission can be based on optical fiber, the time-frequency signal transmission can be stable.

[0005] However, when transmitting time-frequency signals based on optical fiber transmission technology, due to transmission distance or other reasons, there may be a certain time difference between signals transmitted by different transmission channels, resulting in time asynchrony. Summary of the invention

[0006] In view of this, an embodiment of the present application provides a signal processing method and a signal transmission system for performing time synchronization on signals transmitted through different transmission channels.

[0007] A first aspect of an embodiment of the present application provides a signal processing method, including:

[0008] Acquire sampling signals transmitted by multiple optical fiber transmission channels;

[0009] Determine the target wavelet basis function and the target decomposition level number corresponding to each of the sampled signals;

[0010] Based on the target wavelet basis function and the target decomposition level number corresponding to each of the sampled signals, performing discrete wavelet transform on each of the sampled signals;

[0011] Performing autocorrelation function analysis on each sampled signal after discrete wavelet transformation to obtain time delay information of each sampled signal;

[0012] Based on the delay information, time stamps of the plurality of sampling signals are aligned.

[0013] A second aspect of an embodiment of the present application provides a signal transmission system, including: a signal sending device, an optical fiber transmission channel, and a signal receiving device, wherein:

[0014] The signal sending device is used to convert the time-frequency signal of the signal source from an electrical time-frequency signal to an optical time-frequency signal, and send the optical time-frequency signal to the optical fiber transmission channel;

[0015] The optical fiber transmission channel is used to transmit the optical time-frequency signal to the signal receiving device;

[0016] The signal receiving device is used to receive the optical time-frequency signal transmitted by the optical fiber transmission channel, and perform signal processing on the optical time-frequency signal based on the method described in the first aspect above.

[0017] A third aspect of an embodiment of the present application provides a signal processing device, including:

[0018] A sampling signal acquisition module is used to acquire sampling signals transmitted by multiple optical fiber transmission channels;

[0019] A discrete wavelet transform parameter determination module, used to determine the target wavelet basis function and the target decomposition layer number corresponding to each of the sampled signals;

[0020] A discrete wavelet transform module, used for performing discrete wavelet transform on each of the sampled signals based on the target wavelet basis function and the target decomposition level number corresponding to each of the sampled signals;

[0021] A time delay information determination module is used to perform autocorrelation function analysis on each sampled signal after discrete wavelet transformation to obtain the time delay information of each sampled signal;

[0022] A time synchronization module is used to align the timestamps of the plurality of sampling signals based on the delay information.

[0023] A fourth aspect of an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the computer program.

[0024] A fifth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect above is implemented.

[0025] A fifth aspect of the embodiments of the present application provides a computer program product. When the computer program product is run on a terminal device, the terminal device executes the method described in the first aspect.

[0026] Compared with the prior art, the embodiments of the present application have the following advantages:

[0027] The method in the embodiment of the present application can process signals transmitted using optical fiber transmission channels. The terminal device can obtain multiple sampling signals transmitted by multiple optical fiber transmission channels. When processing the signal transmitted by each optical fiber transmission channel, the terminal device can perform a wavelet transform on the sampled signal. When performing a wavelet transform, the target wavelet basis function and the target decomposition layer number corresponding to the sampled signal can be determined, and then the sampled signal is subjected to a wavelet transform based on the target wavelet basis function and the target decomposition layer number, and the sampled signal after the discrete wavelet transform is subjected to an autocorrelation function analysis to obtain the time delay information of each of the sampled signals. After the terminal device determines the time delay information of each sampled signal, the timestamps of the multiple sampled signals can be aligned according to the time delay information, thereby achieving time synchronization of the signals. Based on the method in the embodiment of the present application, the time synchronization of the signals of different optical fiber transmission channels can be ensured while ensuring the stability of the transmission of the time-frequency signal based on optical fiber transmission, so that the time-frequency signal based on optical fiber transmission can be parsed and used by the receiving device. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art.

[0029] Figure 1 is a schematic diagram of a signal transmission system provided in an embodiment of the present application;

[0030] Figure 2 It is a schematic diagram of the steps of a signal processing method provided in an embodiment of the present application;

[0031] Figure 3 is a schematic diagram of a signal processing device provided in an embodiment of the present application;

[0032] Figure 4 It is a schematic diagram of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] In the following description, specific details such as specific system structures, technologies, etc. are proposed for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from hindering the description of the present application.

[0034] In modern science and technology, time and frequency are two basic and extremely critical quantities, which are widely used in measurement and control in many fields such as astronomy, physics, earth science, transportation, etc. In various application scenarios, it is necessary to transmit time or frequency signals between different locations or systems, such as satellite time synchronization, radar array time synchronization, traffic signal timing control, and biological clock synchronization in medical equipment.

[0035] Traditional time and frequency signal transmission usually adopts cable-based wired transmission or navigation satellite-based wireless transmission. However, these transmission methods have problems such as limited transmission distance, signal attenuation, signal blocking or interference, resulting in unstable time and frequency signal transmission and unable to meet the needs of high-precision measurement and control.

[0036] Fiber optic transmission technology can achieve high-speed, long-distance, and low-noise signal transmission. If signal transmission can be based on optical fiber, the time-frequency signal transmission can be stable.

[0037] Based on this, the solution in the embodiment of the present application provides a signal transmission system, which can use an optical fiber transmission channel to transmit time-frequency signals. The signal transmission system may include: a signal sending device, an optical fiber transmission channel and a signal receiving device, wherein:

[0038] A signal sending device, used to convert the time-frequency signal of the signal source from an electrical time-frequency signal to an optical time-frequency signal, and send the optical time-frequency signal to an optical fiber transmission channel;

[0039] An optical fiber transmission channel for transmitting optical time-frequency signals to a signal receiving device;

[0040] The signal receiving device is used to receive the optical time-frequency signal transmitted by the optical fiber transmission channel and perform signal processing on the optical time-frequency signal.

[0041] Figure 1 Schematic diagram of a signal transmission system provided in an embodiment of the present application. Figure 1 As shown, the signal transmission system may include a sending device and a receiving device, wherein the sending device may be the above-mentioned signal sending device, and the receiving device may be the above-mentioned signal receiving device. Figure 1As shown, the sending device can be connected to the signal source, the laser and the optical fiber transmitter, and the sending device can control the signal source, the laser and the optical fiber transmitter. The signal source can be any device or equipment that emits a time-frequency signal, such as a Beidou or GPS satellite navigation system, a frequency synthesizer, a clock, etc. The sending device can control the signal source to send the electrical time-frequency signal to the laser, and control the laser to generate an optical time-frequency signal based on the electrical time-frequency signal. The sending device can control the laser to send the optical time-frequency signal to the optical fiber transmission channel, and the optical fiber transmission channel can send the optical time-frequency signal to the receiving device. The optical fiber transmission channel is an optical fiber used to transmit signals, which can be a single-mode optical fiber or a multi-mode optical fiber. Different types of optical fibers are selected according to the transmission distance and bandwidth requirements.

[0042] like Figure 1 As shown, the receiving device can be connected to the modem, the photoelectric converter and the signal processing module, and control the modem, the photoelectric converter and the signal processing module. The receiving device can control the photoelectric converter to receive the optical fiber signal of the optical fiber transmission channel and convert the optical fiber signal into an electrical signal. The receiving device can control the photoelectric converter to send the electrical signal to the modem for signal demodulation, thereby obtaining a sampled signal. The receiving device can control the receiving device to send the sampled signal to the signal processing module, and the signal processing module can perform signal processing on the sampled signal. Among them, the signal processing module can use the signal processing method provided in the embodiment of the present application to process the sampled signal.

[0043] The signal transmission system in this application can be used for high-precision timestamp synchronization and transmission, as well as data collection and processing in distributed sensor networks. The signal transmission system in this application can achieve high-speed, stable and reliable signal transmission by adopting optical fiber transmission technology, avoiding problems such as signal attenuation and delay in traditional methods, and has strong anti-interference ability against environmental interference, electromagnetic interference, etc., and is especially suitable for special areas with complex electromagnetic environments, deep sea and dense forests, etc. .

[0044] Signals are used to transmit information. In order to obtain accurate information, it is generally necessary to obtain the frequency and phase of the signal. In order to ensure time synchronization of the signal, the timestamp of the signal needs to be aligned. Therefore, for signals transmitted through optical fiber transmission channels, signal processing is required to align the timestamp of the signal and determine the frequency and phase of the signal.

[0045] Based on this, the present application provides a signal processing method based on time-frequency analysis, thereby ensuring that the time-frequency signal stably transmitted based on the optical fiber transmission channel can be used for measurement and control.

[0046] Figure 2A schematic flow chart of a signal processing method provided in an embodiment of the present application is shown, which may specifically include the following steps:

[0047] S201, obtaining sampling signals transmitted by multiple optical fiber transmission channels.

[0048] The execution subject of the embodiment of the present application may be a terminal device, and the terminal device may be a signal receiving device.

[0049] The time-frequency signal sending device can convert the time-frequency signal into an optical signal and send it to the optical fiber transmission channel. The optical fiber transmission channel is an optical fiber used to transmit signals. It can be a single-mode optical fiber or a multi-mode optical fiber. Different types of optical fibers are selected according to the transmission distance and bandwidth requirements. The signal receiving device can receive the optical fiber signal transmitted by the optical fiber transmission channel and convert the optical fiber signal into an electrical signal. After adjusting and demodulating the electrical signal, a sampling signal can be obtained.

[0050] S202, determining a target wavelet basis function and a target decomposition level number corresponding to each of the sampled signals.

[0051] Wavelet transform (WT) can provide a "time-frequency" window that changes with frequency, and is an ideal tool for signal time-frequency analysis and processing. In the embodiment of the present application, the terminal device can process the sampled signal based on wavelet transform. The wavelet transform method used in the present application can be discrete wavelet transform (Discretewavelettransform), which discretizes the scale and translation of the basic wavelet.

[0052] When performing discrete wavelet transform, it is necessary to determine the wavelet basis function to be used and the number of decomposition layers for performing wavelet transform on the signal in discrete wavelet transform.

[0053] When determining the wavelet basis function used for discrete wavelet transform, the attribute information of the sampled signal can be determined, and the attribute information is used to indicate whether the sampled signal belongs to a continuous signal or a discrete signal. If the sampled signal is a continuous signal, the target wavelet basis function can be determined to be a Morlet wavelet or a Mexican Hat wavelet, wherein the Morlet wavelet is a complex non-orthogonal wavelet, and the Mexican Hat wavelet is a symmetrical, compactly supported non-orthogonal wavelet. If the sampled signal is a discrete signal, the target wavelet basis function is determined to be a Daubechies wavelet. The Daubechies wavelet is an orthogonal wavelet.

[0054] When determining the number of decomposition layers used for discrete wavelet transform, the length information of the sampled signal can be determined. If the length of the sampled signal is less than a preset value, the target number of decomposition layers can be determined to be the first number of layers; if the length of the sampled signal is greater than or equal to the preset value, the target number of decomposition layers can be determined to be the second number of layers, wherein the second number of layers is greater than the first number of layers. For example, if the length of the sampled signal is short, the target number of decomposition layers can be any one of 1 to 4 layers; if the length of the sampled signal is long, the target number of decomposition layers can be 4 to 10 layers, and if the sampled signal has multi-scale characteristics, the target number of decomposition layers can be a value greater than 10 layers.

[0055] S203 , performing discrete wavelet transform on each of the sampled signals based on the target wavelet basis function and the target decomposition level number corresponding to each of the sampled signals.

[0056] After determining the target wavelet basis function and the target number of decomposition layers corresponding to the sampled signal, the terminal device can perform discrete wavelet transform on each sampled signal.

[0057] In discrete wavelet transform, discrete wavelet transform coefficients may be determined, and the discrete wavelet transform coefficients may include a scale factor and a translation factor. Exemplarily, the discrete wavelet transform coefficients may be determined by the following formula:

[0058] W(a,b)=∑x[n]·ψ*(a·nb)

[0059] Among them, a is the scale factor, n is the time factor, b is the translation factor, ψ* is the conjugate of the target wavelet basis function, and x[n] is the sampled signal.

[0060] Based on the discrete wavelet transform coefficients and the target decomposition level, discrete wavelet transform is performed on each sampled signal.

[0061] After performing discrete wavelet transform on the sampled signal, the frequency value and phase value of the sampled signal can be determined according to the maximum value of the discrete wavelet transform coefficient and the corresponding scale factor and translation factor.

[0062] Since there is an inverse relationship between the scale factor and the frequency, the formula for converting the scale value to the frequency value is:

[0063] f=f s / (a·k)

[0064] Where f is the frequency of the sampling signal, f s is the sampling frequency of the sampled signal, a is the scale factor, and k is a constant related to the selected wavelet basis function.

[0065] The formula for converting the translation factor to a phase value is:

[0066]

[0067] in, is the corresponding phase value in the sampled signal, f is the frequency value, b is the translation factor, and fs is the sampling frequency.

[0068] That is, the terminal device can calculate the frequency and phase values ​​of the sampling signal by the following formula:

[0069] f=f s / (a·k)

[0070]

[0071] Where f is the frequency of the sampling signal, f s is the sampling frequency of the sampled signal, a is the scale factor, k is a constant, is the phase value of the sampling signal, and b is the translation factor.

[0072] The phase value and frequency value obtained based on discrete wavelet transform can be used to analyze the signal to obtain the data contained in the signal.

[0073] S104, performing autocorrelation function analysis on each sampled signal after discrete wavelet transformation to obtain time delay information of each sampled signal.

[0074] After performing a discrete wavelet transform on a sampled signal, the transformed sampled signal can be subjected to autocorrelation function analysis. Autocorrelation function analysis can determine repetitive patterns in a signal based on the autocorrelation function. The autocorrelation function, sometimes called serial correlation in the discrete time case, is the correlation of a signal with a delayed copy of itself as a function of the delay. Informally speaking, it is the similarity between observations of a random variable as a function of the time lag between them. Informally speaking, the autocorrelation function is the similarity between two observations as a function of the time difference between them.

[0075] In the embodiment of the present application, the autocorrelation function is:

[0076] R(τ)=∑x[n]·x[n-τ]

[0077] Wherein, n is the time factor, x[n] is the sampling signal, and τ is the delay information.

[0078] When the autocorrelation function R(τ) reaches its maximum value, the corresponding τ value is the time delay information of the sampling signal.

[0079] S105: Align the timestamps of the plurality of sampling signals based on the delay information.

[0080] For the sampling signals of multiple optical fiber transmission channels, the timestamps of each sampling signal can be aligned according to the delay information of the sampling signal of each channel and the delay information τ_i of each channel signal. The specific operation of aligning the timestamps of each sampling signal is as follows: for each signal channel, the sampling signal can be moved forward or backward by τ_i time units according to the delay information τ_i, so that all sampling signals are aligned at the same timestamp. After the timestamp alignment, the synchronization and measurement of each signal channel can be realized.

[0081] Through the above steps, the signal processing method based on time-frequency analysis provided by this application can effectively extract the frequency, phase and delay information of the signal, and realize high-precision measurement and control. Combined with optical fiber transmission technology, the time-frequency signal transmission system and signal processing method provided by this application can realize long-distance, high-precision, stable and reliable signal transmission and processing, meeting the needs of various application scenarios.

[0082] The signal processing method based on time-frequency analysis provided by the present application can adopt a combination of wavelet transform and autocorrelation function to achieve high-precision measurement and control. In an embodiment of the present application, when the terminal device performs signal processing on the sampled signal, it can select appropriate wavelet basis functions, decomposition layers and other parameters according to different application scenarios and signal characteristics. For example, when processing non-stationary signals, wavelet basis functions with good locality such as Morlet wavelet or Mexican Hat wavelet can be selected; and for stationary signals, wavelet basis functions with compact support properties such as Daubechies wavelet can be selected. In addition, when the terminal device performs signal processing on the sampled signal, it can also adjust the parameters in the wavelet transform and autocorrelation function calculation according to the characteristics of the signal such as the bandwidth and delay range to achieve higher-precision measurement and control.

[0083] The signal transmission system and signal processing method provided in this application are applicable to various time-frequency signal transmission and processing requirements, can flexibly select signal sources, optical fiber transmission channels and receivers according to specific application scenarios, and are easy to expand and adjust.

[0084] The signal transmission system and signal processing method provided by the present application can be applied to time synchronization and transmission of distributed radar systems. The signal transmission system and signal processing method provided by the present application can be used to achieve precise synchronization and data transmission between radar arrays during time synchronization and transmission of distributed radar systems, thereby improving the positioning accuracy and performance of the radar system.

[0085] The signal transmission system and signal processing method provided by the present application can be applied to the timing control of the traffic signal system. The signal transmission system and signal processing method provided by the present application can achieve precise time synchronization between traffic signal devices, thereby improving the accuracy and efficiency of traffic signal control and reducing traffic congestion and accidents.

[0086] The signal transmission system and signal processing method provided by the present application can be applied to the synchronization of biological clocks of medical equipment. In the process of medical diagnosis and treatment, the biological clock can be accurately synchronized and controlled based on the signal transmission system and signal processing method provided by the present application to improve the performance and accuracy of the equipment.

[0087] The signal transmission system and signal processing method provided by the present application can be applied to data collection and processing in the fields of industrial automation and control. The signal transmission system and signal processing method provided by the present application can be applied to achieve high-precision time synchronization between industrial equipment, thereby improving the automation degree and control accuracy of the production process.

[0088] In summary, the signal transmission system and signal processing method provided in this application have broad application prospects in the fields of communication, sensing and measurement, and can achieve long-distance, high-precision, stable and reliable signal transmission and processing, and can meet the needs of various application scenarios.

[0089] It should be noted that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0090] Reference Figure 3 , shows a schematic diagram of a signal processing device provided in an embodiment of the present application, which may specifically include a sampling signal acquisition module 31, a discrete wavelet transform parameter determination module 32, a discrete wavelet transform module 33, a delay information determination module 34 and a time synchronization module 35, wherein:

[0091] The sampling signal acquisition module 31 is used to acquire the sampling signals transmitted by multiple optical fiber transmission channels;

[0092] A discrete wavelet transform parameter determination module 32 is used to determine the target wavelet basis function and the target decomposition layer number corresponding to each of the sampled signals;

[0093] A discrete wavelet transform module 33 is used to perform discrete wavelet transform on each of the sampled signals based on the target wavelet basis function and the target decomposition level number corresponding to each of the sampled signals;

[0094] The time delay information determination module 34 is used to perform autocorrelation function analysis on each sampled signal after discrete wavelet transformation to obtain the time delay information of each sampled signal;

[0095] The time synchronization module 35 is used to align the time stamps of the plurality of sampling signals based on the time delay information.

[0096] In a possible implementation, the discrete wavelet transform parameter determination module includes:

[0097] An information determination submodule, used to determine attribute information and length information of the sampled signal, wherein the attribute information is used to indicate whether the sampled signal is a continuous signal or a discrete signal;

[0098] A target wavelet basis function determination submodule is used to determine the target wavelet basis function according to the attribute information;

[0099] The target decomposition layer number determination submodule is used to determine the target decomposition layer number according to the length information.

[0100] In a possible implementation, the target wavelet basis function determination submodule includes:

[0101] A first wavelet basis function determining unit, configured to determine, if the sampled signal is a continuous signal, that the target wavelet basis function is a first wavelet basis function, wherein the first wavelet basis function is a Morlet wavelet or a Mexican Hat wavelet;

[0102] The second wavelet basis function determining unit is used to determine that the target wavelet basis function is a second wavelet basis function if the sampling signal is a discrete signal, and the second wavelet basis function is a Daubechies wavelet.

[0103] In a possible implementation, the discrete wavelet transform module 33 includes:

[0104] A discrete wavelet transform coefficient determination submodule, used to determine discrete wavelet transform coefficients, wherein the discrete wavelet transform coefficients include a scale factor and a translation factor;

[0105] A discrete wavelet transform submodule, used for performing discrete wavelet transform on each of the sampled signals based on the discrete wavelet transform coefficients and the target decomposition level number;

[0106] The coefficients of the discrete wavelet transform are determined by the following formula:

[0107] W(a,b)=∑x[n]·ψ*(a·nb)

[0108] Wherein, a is the scale factor, n is the time factor, b is the translation factor, ψ* is the conjugate of the target wavelet basis function, and x[n] is the sampled signal.

[0109] In a possible implementation, the above device further includes:

[0110] The frequency value and phase value of the sampling signal are calculated by the following formula:

[0111] f=f s / (a·k)

[0112]

[0113] Wherein, f is the frequency value of the sampling signal, f s is the sampling frequency of the sampling signal, a is the scale factor, k is a constant, is the phase value of the sampling signal, and b is the translation factor.

[0114] In a possible implementation, in the step of performing autocorrelation function analysis on each sampled signal after discrete wavelet transformation to obtain the time delay information of each sampled signal, the autocorrelation function used is:

[0115] R(τ)=∑x[n]·x[n-τ]

[0116] Wherein, n is the time factor, x[n] is the sampling signal, and τ is the delay information.

[0117] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiment part.

[0118] Figure 4 This is a schematic diagram of the structure of a terminal device provided in an embodiment of the present application. Figure 4 As shown, the terminal device 400 of this embodiment includes: at least one processor 40 ( Figure 4 Only one is shown in the figure), a memory 41, and a computer program 42 stored in the memory 41 and executable on the at least one processor 40, wherein the processor 40 implements the steps of any of the above-mentioned method embodiments when executing the computer program 42.

[0119] The terminal device may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will appreciate that Figure 4 It is only an example of the terminal device 400 and does not constitute a limitation on the terminal device 400. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, etc.

[0120] The processor 40 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0121] In some embodiments, the memory 41 may be an internal storage unit of the terminal device 400, such as a hard disk or memory of the terminal device 400. In other embodiments, the memory 41 may also be an external storage device of the terminal device 400, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal device 400. Further, the memory 41 may also include both an internal storage unit of the terminal device 400 and an external storage device. The memory 41 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 41 may also be used to temporarily store data that has been output or is to be output.

[0122] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0123] An embodiment of the present application provides a computer program product. When the computer program product runs on a terminal device, the terminal device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0124] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application is described in detail with reference to the above-mentioned embodiments, a person skilled in the art should understand that the technical solutions described in the above-mentioned embodiments can still be modified, or some of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A signal processing method, characterized in that: For signal receiving equipment, including: Acquire sampling signals transmitted by multiple optical fiber transmission channels, wherein the sampling signals are obtained by the signal receiving device after receiving the optical fiber signals transmitted by the optical fiber transmission channels and converting the optical fiber signals into electrical signals, and adjusting and demodulating the electrical signals; Determine the target wavelet basis function and the target decomposition level number corresponding to each of the sampled signals; Based on the target wavelet basis function and the target decomposition level number corresponding to each of the sampled signals, performing discrete wavelet transform on each of the sampled signals; Performing autocorrelation function analysis on each of the sampled signals after discrete wavelet transformation to obtain time delay information of each of the sampled signals; Based on the delay information, aligning timestamps of a plurality of the sampling signals; Wherein, the step of determining the target wavelet basis function and the target decomposition level number corresponding to each of the sampled signals comprises: Determining attribute information and length information of the sampled signal, wherein the attribute information is used to indicate whether the sampled signal is a continuous signal or a discrete signal; Determining the target wavelet basis function according to the attribute information; The target decomposition level is determined according to the length information.

2. The method according to claim 1, characterized in that Determining the target wavelet basis function according to the attribute information includes: If the sampled signal is a continuous signal, determining that the target wavelet basis function is a first wavelet basis function, wherein the first wavelet basis function is a Morlet wavelet or a Mexican Hat wavelet; If the sampled signal is a discrete signal, the target wavelet basis function is determined to be a second wavelet basis function, and the second wavelet basis function is a Daubechies wavelet.

3. The method according to claim 1, characterized in that The step of performing discrete wavelet transform on each of the sampled signals based on the target wavelet basis function and the target decomposition level number corresponding to each of the sampled signals comprises: Determining discrete wavelet transform coefficients, wherein the discrete wavelet transform coefficients include a scale factor and a translation factor; Based on the discrete wavelet transform coefficients and the target decomposition level number, performing discrete wavelet transform on each of the sampled signals respectively; The coefficients of the discrete wavelet transform are determined by the following formula: W(a,b)=∑x[n]·ψ*(a·nb) Wherein, a is the scale factor, n is the time factor, b is the translation factor, ψ* is the conjugate of the target wavelet basis function, and x[n] is the sampled signal.

4. The method according to claim 3, characterized in that After performing discrete wavelet transform on each of the sampled signals based on the target wavelet basis function and the target decomposition level number corresponding to each of the sampled signals, the method further includes: The frequency value and phase value of the sampling signal are calculated by the following formula: f=f s / (a·zk) Wherein, f is the frequency value of the sampling signal, f s is the sampling frequency of the sampling signal, a is the scale factor, k is a constant, is the phase value of the sampling signal, and b is the translation factor.

5. The method according to claim 1, characterized in that In the step of performing autocorrelation function analysis on each sampled signal after discrete wavelet transformation to obtain the time delay information of each sampled signal, the autocorrelation function used is: R(τ)=∑x[n]·x[n-τ] Wherein, n is the time factor, x[n] is the sampling signal, and τ is the delay information.

6. A signal transmission system, characterized in that: include: Signal sending equipment, optical fiber transmission channels and signal receiving equipment, including: The signal sending device is used to convert the time-frequency signal of the signal source from an electrical time-frequency signal to an optical time-frequency signal, and send the optical time-frequency signal to the optical fiber transmission channel, and the sampling signal is obtained according to the optical fiber signal transmitted by the optical fiber transmission channel; The optical fiber transmission channel is used to transmit the optical time-frequency signal to the signal receiving device; The signal receiving device is used to receive the optical time-frequency signal transmitted by the optical fiber transmission channel, and perform signal processing on the optical time-frequency signal based on the method described in any one of claims 1-5.

7. A signal processing device, characterized in that: For signal receiving equipment, including: A sampling signal acquisition module, used to acquire sampling signals transmitted by multiple optical fiber transmission channels, wherein the sampling signals are obtained by the signal receiving device after receiving the optical fiber signals transmitted by the optical fiber transmission channels and converting the optical fiber signals into electrical signals, and adjusting and demodulating the electrical signals; A discrete wavelet transform parameter determination module, used to determine the target wavelet basis function and the target decomposition layer number corresponding to each of the sampled signals; A discrete wavelet transform module, used for performing discrete wavelet transform on each of the sampled signals based on the target wavelet basis function and the target decomposition level number corresponding to each of the sampled signals; A time delay information determination module is used to perform autocorrelation function analysis on each sampled signal after discrete wavelet transformation to obtain the time delay information of each sampled signal; A time synchronization module, used for aligning the timestamps of the plurality of sampling signals based on the delay information; Wherein, the discrete wavelet transform module is used to perform: Determining attribute information and length information of the sampled signal, wherein the attribute information is used to indicate whether the sampled signal is a continuous signal or a discrete signal; Determining the target wavelet basis function according to the attribute information; The target decomposition level is determined according to the length information.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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