Method and system for automatically controlling bias point of photoelectric modulator

Abstract

The invention discloses an automatic control method and system for a bias point of a photoelectric modulator, and relates to the field of signal processing, and the method comprises the steps: generating a composite disturbance voltage signal through a digital signal processor; the bias electrode is injected into the photoelectric modulator and used for collecting the optical signal output by the photoelectric modulator and converting the optical signal into an electric signal; extracting a fundamental component amplitude and phase difference data of a second harmonic component, a third harmonic component and a fundamental wave; dynamic characteristic parameters are calculated through spectral analysis, and the mapping relation between the characteristic parameters and the offset of the offset points is established; outputting the offset direction and the offset level of the offset point through a deep learning model; a bias voltage compensation amount is generated through a fuzzy PID (Proportion Integration Differentiation) controller; and adjusting the bias electrode voltage, smoothing the filter, and eliminating high-frequency jitter. The method has the advantages that high-precision and stable bias point adjustment is realized through the composite disturbance signal, real-time spectrum analysis and deep learning optimization decision in combination with fuzzy PID control and a dynamic closed-loop mechanism, and the system performance and stability are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of signal processing, and in particular to a method and system for automatically controlling a bias point of an optoelectronic modulator. Background Art

[0002] The optoelectronic modulator (OEM) is a crucial component in optical communication systems, responsible for converting electrical signals into optical signals and performing modulation. Its performance directly impacts the stability and transmission efficiency of the communication system. With the advancement of communication technology and the diversification of application scenarios, the research and application of automatic bias point control methods for OEMs provide a solid foundation and support for further innovation and application of optical communication technology.

[0003] Currently, the automatic control methods for the bias point of optoelectronic modulators on the market mostly rely on single-frequency disturbance signals or simple control algorithms. This design often cannot provide sufficient dynamic response and precise adjustment capabilities when faced with complex working environments or frequently changing optical signal characteristics, resulting in unstable system performance under high-frequency jitter or noise interference. Secondly, some methods use relatively simple algorithms in the process of spectrum analysis and feature parameter extraction, lacking targeted optimization and deep learning decision support, and cannot effectively handle the dynamic adjustment requirements in complex data scenarios, affecting the real-time and adaptive capabilities of the system. In addition, some methods on the market are relatively conservative in the selection of control strategies and fail to fully utilize advanced technologies such as fuzzy PID controllers to optimize compensation quantities, which limits the reliability and sustained performance of the system under long-term stable operation and extreme working conditions. Summary of the Invention

[0004] To improve existing methods and systems, a method and system for automatic bias point control of an optoelectronic modulator are provided. This method achieves high-precision and stable bias point adjustment through composite disturbance signals, real-time spectrum analysis, and deep learning optimization decision-making, combined with fuzzy PID control and a dynamic closed-loop mechanism, significantly improving system performance and stability.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is: A method for automatically controlling a bias point of an electro-optical modulator, comprising: Generate a composite disturbance voltage signal by a digital signal processor, wherein the signal is composed of a linear superposition of a low-frequency sinusoidal signal and a broadband pseudo-random code signal; The composite disturbance signal is injected into the bias electrode of the photoelectric modulator, the optical signal output by the photoelectric modulator is collected in real time, and converted into an electrical signal through a photodetector; Perform real-time spectrum analysis on electrical signals, and synchronously extract the fundamental component amplitude, second harmonic component amplitude, and phase difference data between the third harmonic component and the fundamental wave through bandpass filtering, analog-to-digital conversion, and fast Fourier transform; Based on the acquired spectrum analysis data, dynamic characteristic parameters are calculated and a mapping relationship between the characteristic parameters and the bias point offset is established; The dynamic feature parameters are input into the pre-trained deep learning model, the offset direction and offset level of the current bias point relative to the target working point are output, and the quantitative data of the bias point offset is obtained; A fuzzy PID controller is driven based on the offset direction and offset level to generate an offset voltage compensation amount; The bias voltage compensation amount is added to the DC bias terminal of the modulator, the bias electrode voltage is adjusted, and high-frequency jitter is eliminated by applying a voltage smoothing filter. The above steps are repeated to perform dynamic closed-loop control of the bias point.

[0006] Preferably, the composite disturbance voltage signal is generated by a digital signal processor, wherein the composite disturbance voltage signal is formed by linear superposition of a low-frequency sinusoidal signal and a broadband pseudo-random code signal, and specifically includes: Generate a low-frequency sinusoidal signal and a broadband pseudo-random code signal through a digital signal processor based on the low-frequency operating frequency range of the optoelectronic modulator; The low-frequency sinusoidal signal and the broadband pseudo-random code signal are linearly superimposed to form a composite disturbance voltage signal, wherein the composite signal includes a low-frequency steady disturbance and a high-frequency random disturbance; Based on the superimposed signal, the amplitude of the composite disturbance signal is adjusted so as to be within the working range of the optoelectronic modulator; The generated composite disturbance signal is output through a digital signal processor.

[0007] Preferably, injecting the composite disturbance signal into the bias electrode of the photoelectric modulator, collecting the optical signal output by the photoelectric modulator in real time, and converting the optical signal into an electrical signal through a photoelectric detector specifically includes: Converting the generated composite disturbance voltage signal into an analog signal through a digital-to-analog converter, and injecting the composite disturbance signal into the bias electrode of the optoelectronic modulator; The optoelectronic modulator adjusts the bias point based on the injected composite disturbance signal, thereby modulating the optical signal passing through it and outputting it; The optical signal is collected in real time by a photodetector and converted into an electrical signal based on the change of light intensity; The converted electrical signal is amplified by a low-noise amplifier, and high-frequency noise is removed by a filter to obtain a clear electrical signal.

[0008] Preferably, the real-time spectrum analysis of the electrical signal, and the synchronous extraction of the fundamental component amplitude, the second harmonic component amplitude, and the phase difference data between the third harmonic component and the fundamental wave through bandpass filtering, analog-to-digital conversion, and fast Fourier transform specifically include: The acquired clear electrical signal is converted into a digital signal through an analog-to-digital converter; Processing the digital signal through fast Fourier transform, converting the time domain signal into a frequency domain signal, and generating a spectrum diagram, wherein the spectrum diagram includes amplitude and phase data of the signal at different frequency points; Based on the obtained spectrum, the amplitude of the fundamental component is obtained by searching for the amplitude value corresponding to the frequency point; The second harmonic frequency is twice the fundamental frequency. Find the amplitude value of the second harmonic frequency point in the spectrum to obtain the amplitude of the second harmonic component; The third harmonic frequency is three times the fundamental frequency. Find the amplitude value of the third harmonic frequency point in the spectrum and obtain the amplitude of the third harmonic component. The fundamental wave phase and the harmonic phase are obtained at the fundamental wave frequency point, and the phase difference between the fundamental wave and the harmonic is calculated.

[0009] Preferably, the calculating of dynamic characteristic parameters based on the acquired spectrum analysis data and the establishment of a mapping relationship between the characteristic parameters and the bias point offset specifically include: Calculating dynamic characteristic parameters based on the acquired fundamental wave component amplitude, second harmonic component amplitude, and phase difference data between the third harmonic component and the fundamental wave; The calculated dynamic characteristic parameters are converted into linear representations of bias point offsets through a logarithmic mapping function.

[0010] Preferably, the step of inputting the dynamic feature parameters into a pre-trained deep learning model, outputting the offset direction and offset level of the current bias point relative to the target working point, and obtaining the bias point offset quantitative data specifically includes: The dynamic feature parameters are combined into a standardized input vector, and the input vector is scanned in the time domain through a one-dimensional convolution kernel. Each convolution kernel extracts local features and outputs a feature map. The feature map is input into the LSTM unit in time series to generate a hidden state vector, and the original decision vector is output; The classification model outputs the offset level, the regression model outputs the continuous offset value, and the offset quantitative data of the bias point is obtained through integration.

[0011] Preferably, the driving of the fuzzy PID controller based on the offset direction and the offset level to generate the offset voltage compensation specifically includes: The fuzzy logic rule set is set based on the obtained offset point offset quantitative data, including the control action corresponding to the offset direction and level; The actual bias point offset is converted into a fuzzy set through fuzzification, the corresponding control output is generated according to the designed fuzzy control rules, and then the control quantity of the fuzzy PID controller is obtained through defuzzification; Adjusting the bias voltage based on the signal of the control quantity of the fuzzy PID controller; Based on the real-time offset direction and offset level, the fuzzy logic rules and the parameters of the fuzzy PID controller are updated to optimize the control performance and generate the offset voltage compensation amount.

[0012] Preferably, the step of adding the bias voltage compensation amount to the DC bias terminal of the modulator, adjusting the bias electrode voltage, eliminating high-frequency jitter by applying a voltage smoothing filter, and repeating the above steps to perform dynamic closed-loop control on the bias point specifically includes: Based on the obtained bias voltage compensation amount, it is added to the DC bias terminal of the modulator; By adjusting the bias electrode voltage of the modulator, the bias current or voltage is changed to achieve fine adjustment of the bias point; In the process of adjusting the compensation voltage and the bias electrode voltage, a smoothing filter is added to the circuit to eliminate high-frequency jitter and noise; Monitor the system's bias voltage and output signal in real time, and adjust the compensation voltage and bias electrode voltage based on the actual measurement results; Through the feedback control mechanism, the bias voltage is adjusted according to the changes in the output signal to maintain the stability of the bias point.

[0013] Preferably, the photoelectric modulator is a Mach-Zehnder type lithium niobate modulator, and the bias electrodes include: a main bias electrode for coarse adjustment of the operating point, with a voltage range of -5V to +5V; a fine-tuning electrode for dynamic compensation, with a voltage range of -0.5V to +0.5V; the compensation amount acts preferentially on the fine-tuning electrode, and when the cumulative compensation amount exceeds the threshold, it switches to the main bias electrode.

[0014] Furthermore, an automatic control system for the bias point of an optoelectronic modulator is proposed, comprising: Composite disturbance signal generation module: This module generates a composite disturbance signal of a low-frequency sinusoidal signal and a broadband pseudo-random code signal through a digital signal processor, providing a disturbance signal for the bias electrode; Signal injection and optical signal acquisition module: This module injects the composite disturbance signal into the bias electrode of the optoelectronic modulator, collects the optical signal output by the optoelectronic modulator in real time and converts it into an electrical signal; Spectrum analysis module: This module performs fast Fourier transform analysis on the electrical signal, extracts the fundamental wave, second harmonic, third harmonic components and phase difference data, and obtains spectrum analysis results; Characteristic parameter calculation module: This module calculates dynamic characteristic parameters based on spectrum data and establishes a mapping relationship with the bias point offset; Deep learning decision module: This module inputs dynamic feature parameters into a pre-trained deep learning model, outputs the offset direction and offset level of the bias point, and obtains quantitative data of the bias point offset; Fuzzy PID control module: This module calculates the offset voltage compensation amount through a fuzzy PID controller based on the offset direction and offset level, and generates a control signal: Closed-loop control module: This module adds the bias voltage compensation to the DC bias terminal of the modulator, adjusts the bias electrode voltage, and eliminates high-frequency noise through a smoothing filter to perform closed-loop control; Processor: The processor is used to process the calculation process of each formula and the construction calculation process of each model.

[0015] Compared with the prior art, the advantages of the present invention are: By introducing a composite disturbance voltage signal, combined with a low-frequency sinusoidal signal and a broadband pseudo-random code signal, the limitations of a single-frequency disturbance are effectively avoided, thereby providing a more comprehensive bias point adjustment capability. Secondly, real-time spectrum analysis and a precise decision-making process based on a deep learning model make the dynamic adjustment of the bias point more precise, and can monitor and adapt to changes in the working environment in real time. In addition, the use of a fuzzy PID controller to optimize the compensation amount, combined with a dynamic closed-loop control mechanism, ensures that the bias point of the modulator can continuously maintain the optimal working state, avoids interference from high-frequency jitter and noise, and further improves the stability and reliability of the system. In particular, in Mach-Zehnder lithium niobate modulators, the joint adjustment of the main bias electrode and the fine-tuning electrode can efficiently cope with the wide range of adjustment and fine-tuning requirements of the bias point, ensuring high-precision working results. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of the method proposed in the present invention; Figure 2 This is a schematic diagram of generating a composite disturbance voltage signal proposed by the present invention; Figure 3 This is a schematic diagram of the conversion electrical signal proposed by the present invention; Figure 4 This is a schematic diagram of spectrum analysis data proposed by the present invention; Figure 5 This is a schematic diagram of the calculation dynamic characteristic parameters proposed by the present invention; Figure 6 This is a schematic diagram of obtaining bias point offset quantization data proposed by the present invention; Figure 7 This is a schematic diagram of generating a bias voltage compensation amount proposed by the present invention; Figure 8 This is a schematic diagram of the dynamic closed-loop control proposed by the present invention. DETAILED DESCRIPTION

[0017] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0018] Automatic control system for bias point of optoelectronic modulator, including: Composite disturbance signal generation module: This module generates a composite disturbance signal of a low-frequency sinusoidal signal and a broadband pseudo-random code signal through a digital signal processor, providing a disturbance signal for the bias electrode; Signal injection and optical signal acquisition module: This module injects the composite disturbance signal into the bias electrode of the optoelectronic modulator, collects the optical signal output by the optoelectronic modulator in real time and converts it into an electrical signal; Spectrum analysis module: This module performs fast Fourier transform analysis on the electrical signal, extracts the fundamental wave, second harmonic, third harmonic components and phase difference data, and obtains spectrum analysis results; Characteristic parameter calculation module: This module calculates dynamic characteristic parameters based on spectrum data and establishes a mapping relationship with the bias point offset; Deep learning decision module: This module inputs dynamic feature parameters into a pre-trained deep learning model, outputs the offset direction and offset level of the bias point, and obtains quantitative data of the bias point offset; Fuzzy PID control module: This module calculates the offset voltage compensation amount through a fuzzy PID controller based on the offset direction and offset level, and generates a control signal: Closed-loop control module: This module adds the bias voltage compensation to the DC bias terminal of the modulator, adjusts the bias electrode voltage, and eliminates high-frequency noise through a smoothing filter to perform closed-loop control; Processor: The processor is used to process the calculation process of each formula and the construction calculation process of each model.

[0019] See Figure 1 As shown, the method for automatically controlling the bias point of an electro-optical modulator includes: Step 1: Generate a composite disturbance voltage signal by a digital signal processor, wherein the signal is composed of a linear superposition of a low-frequency sinusoidal signal and a broadband pseudo-random code signal; Step 2: Inject the composite disturbance signal into the bias electrode of the photoelectric modulator, collect the optical signal output by the photoelectric modulator in real time, and convert it into an electrical signal through a photodetector; Step 3: Perform real-time spectrum analysis on the electrical signal, and synchronously extract the fundamental component amplitude, second harmonic component amplitude, and phase difference data between the third harmonic component and the fundamental wave through bandpass filtering, analog-to-digital conversion, and fast Fourier transform; Step 4: Based on the acquired spectrum analysis data, calculate the dynamic characteristic parameters and establish a mapping relationship between the characteristic parameters and the bias point offset; Step 5: Input the dynamic feature parameters into the pre-trained deep learning model, output the offset direction and offset level of the current bias point relative to the target working point, and obtain the bias point offset quantitative data; Step 6: Drive the fuzzy PID controller based on the offset direction and offset level to generate the offset voltage compensation amount; Step 7: Add the bias voltage compensation to the DC bias terminal of the modulator, adjust the bias electrode voltage, and eliminate high-frequency jitter by applying a voltage smoothing filter. Repeat the above steps to perform dynamic closed-loop control of the bias point.

[0020] See Figure 2 As shown, a composite disturbance voltage signal is generated by a digital signal processor, and the signal is composed of a linear superposition of a low-frequency sinusoidal signal and a broadband pseudo-random code signal, specifically including: Generate a low-frequency sinusoidal signal and a broadband pseudo-random code signal through a digital signal processor based on the low-frequency operating frequency range of the optoelectronic modulator; The low-frequency sinusoidal signal and the broadband pseudo-random code signal are linearly superimposed to form a composite disturbance voltage signal, wherein the composite signal includes a low-frequency steady disturbance and a high-frequency random disturbance; Based on the superimposed signal, the amplitude of the composite disturbance signal is adjusted so as to be within the working range of the optoelectronic modulator; The generated composite disturbance signal is output through a digital signal processor.

[0021] Specifically, the generation of the low-frequency sinusoidal signal can be realized by a digital signal processor, and the frequency of the low-frequency sinusoidal signal is set to , the signal is expressed in the time domain as:

[0022] in, is the voltage value of the low-frequency sinusoidal signal, is the amplitude of the low-frequency sinusoidal signal, is the frequency of the low-frequency sinusoidal signal, is the initial phase of the signal; Wideband pseudo-random coded signals are usually generated by pseudo-random sequence generation algorithms, such as pseudo-random binary sequences (PRBS). The low-frequency sinusoidal signal and the broadband pseudo-random code signal are linearly superimposed to obtain a composite disturbance signal. In order to ensure that the amplitude of the composite disturbance signal is within the operating range of the optoelectronic modulator, the amplitude of the signal needs to be adjusted. Finally, the adjusted composite disturbance signal is output through the digital signal processor.

[0023] See Figure 3As shown, the composite disturbance signal is injected into the bias electrode of the photoelectric modulator, the optical signal output by the photoelectric modulator is collected in real time, and converted into an electrical signal through a photodetector. Specifically, the following steps are performed: Converting the generated composite disturbance voltage signal into an analog signal through a digital-to-analog converter, and injecting the composite disturbance signal into the bias electrode of the optoelectronic modulator; The optoelectronic modulator adjusts the bias point based on the injected composite disturbance signal, thereby modulating the optical signal passing through it and outputting it; The optical signal is collected in real time by a photodetector and converted into an electrical signal based on the change of light intensity; The converted electrical signal is amplified by a low-noise amplifier, and high-frequency noise is removed by a filter to obtain a clear electrical signal.

[0024] Specifically, the composite disturbance signal is converted from a digital signal into an analog voltage signal through a digital-to-analog converter, which is suitable for injection into the bias electrode of the optoelectronic modulator. The composite disturbance signal converted by the digital-to-analog converter is injected into the bias electrode of the optoelectronic modulator. The bias electrode of the optoelectronic modulator is used to adjust its operating point. Based on the input disturbance signal, the optoelectronic modulator adjusts the modulation depth of the optical signal, thereby affecting the intensity of the optical signal passing through the modulator. The optoelectronic modulator adjusts its optical signal output according to the input composite disturbance signal. The output optical signal intensity formula of the modulator is:

[0025] in, is the optical signal intensity output by the optoelectronic modulator, is the static light intensity of the electro-optical modulator, is the modulation sensitivity of the modulator, is the voltage signal on the bias electrode; The modulated light signal is collected by a photodetector. The photodetector converts the light signal into an electrical signal. To enhance the collected electrical signal and remove high-frequency noise, the electrical signal is amplified by a low-noise amplifier. A filter is then used to remove high-frequency noise, resulting in a clear electrical signal.

[0026] See Figure 4 As shown, real-time spectrum analysis is performed on the electrical signal. The fundamental component amplitude, second harmonic component amplitude, and phase difference data between the third harmonic component and the fundamental wave are synchronously extracted through bandpass filtering, analog-to-digital conversion, and fast Fourier transform. Specifically, the following data are included: The acquired clear electrical signal is converted into a digital signal through an analog-to-digital converter; Processing the digital signal through fast Fourier transform, converting the time domain signal into a frequency domain signal, and generating a spectrum diagram, wherein the spectrum diagram includes amplitude and phase data of the signal at different frequency points; Based on the obtained spectrum, the amplitude of the fundamental component is obtained by searching for the amplitude value corresponding to the frequency point; The second harmonic frequency is twice the fundamental frequency. Find the amplitude value of the second harmonic frequency point in the spectrum to obtain the amplitude of the second harmonic component; The third harmonic frequency is three times the fundamental frequency. Find the amplitude value of the third harmonic frequency point in the spectrum and obtain the amplitude of the third harmonic component. The fundamental wave phase and the harmonic phase are obtained at the fundamental wave frequency point, and the phase difference between the fundamental wave and the harmonic is calculated.

[0027] Specifically, the time domain signal is converted into a frequency domain signal through fast Fourier transform, and the spectrum is obtained through fast Fourier transform processing, which contains the amplitude and phase information of each frequency point; The amplitudes of the fundamental wave, second harmonic, and third harmonic components are obtained based on the spectrum diagram, and the phase difference is calculated based on the phase information of the extracted fundamental wave and harmonics; In order to avoid the influence of low-frequency noise or high-frequency interference in the spectrum, a bandpass filter is used to extract the frequency band. The design of the bandpass filter adjusts the passband of the filter according to the fundamental frequency and harmonic frequency range.

[0028] See Figure 5 As shown, based on the acquired spectrum analysis data, the dynamic characteristic parameters are calculated, and the mapping relationship between the characteristic parameters and the bias point offset is established, specifically including: Calculating dynamic characteristic parameters based on the acquired fundamental wave component amplitude, second harmonic component amplitude, and phase difference data between the third harmonic component and the fundamental wave; The calculated dynamic characteristic parameters are converted into linear representations of bias point offsets through a logarithmic mapping function.

[0029] Specifically, the dynamic characteristic parameter calculation formula is:

[0030] in, is the dynamic characteristic parameter, is the amplitude of the fundamental component, is the amplitude of the second harmonic component, is the third harmonic phase difference, is the phase sensitivity coefficient; In order to make the relationship between the characteristic parameters and the bias point offset more consistent with the response characteristics of the actual system, a logarithmic mapping function is used to convert the dynamic characteristic parameters into the bias point offset. The logarithmic mapping function can convert the nonlinear changes of the dynamic characteristic parameters into linear changes of the bias point, which makes the adjustment of the bias voltage smoother and avoids excessive fluctuations caused by nonlinear changes. The formula is:

[0031] in, is the bias point offset, is the target characteristic parameter, is the modulator sensitivity coefficient.

[0032] See Figure 6 As shown, the dynamic feature parameters are input into the pre-trained deep learning model, and the offset direction and offset level of the current bias point relative to the target working point are output. The quantitative data of the bias point offset is obtained specifically including: The dynamic feature parameters are combined into a standardized input vector, and the input vector is scanned in the time domain through a one-dimensional convolution kernel. Each convolution kernel extracts local features and outputs a feature map. The feature map is input into the LSTM unit in time series to generate a hidden state vector, and the original decision vector is output; The classification model outputs the offset level, the regression model outputs the continuous offset value, and the offset quantitative data of the bias point is obtained through integration.

[0033] See Figure 7 As shown, the fuzzy PID controller is driven based on the offset direction and offset level to generate the offset voltage compensation amount, specifically including: The fuzzy logic rule set is set based on the obtained offset point offset quantitative data, including the control action corresponding to the offset direction and level; The actual bias point offset is converted into a fuzzy set through fuzzification, the corresponding control output is generated according to the designed fuzzy control rules, and then the control quantity of the fuzzy PID controller is obtained through defuzzification; Adjusting the bias voltage based on the signal of the control quantity of the fuzzy PID controller; Based on the real-time offset direction and offset level, the fuzzy logic rules and the parameters of the fuzzy PID controller are updated to optimize the control performance and generate the offset voltage compensation amount.

[0034] Specifically, the bias point offset of the input signal is obtained and quantized into discrete values. By measuring the offset of the system, the current offset, offset direction, and level are obtained, such as large, medium, small, positive, and negative. Fuzzy control rules are designed based on the system's operating characteristics and requirements. The rules map the direction and level of the offset to the control action. The actual offset data is fuzzified to obtain the membership of the fuzzy set. Based on the fuzzification results and the designed fuzzy rules, fuzzy inference methods are used to generate the control output. For each fuzzy rule, the fuzzy result of each control action is obtained based on the input membership and the rule result. The results of fuzzy reasoning are converted into specific control output quantities. The bias voltage is adjusted according to the control quantity obtained by defuzzification. During the control process, the system needs to monitor the changes in offset direction and offset level in real time, and adjust the fuzzy rules and PID controller parameters according to the new data to optimize the control performance.

[0035] See Figure 8 As shown, the bias voltage compensation amount is added to the DC bias terminal of the modulator, the bias electrode voltage is adjusted, and high-frequency jitter is eliminated by applying a voltage smoothing filter. The above steps are repeated to perform dynamic closed-loop control of the bias point. Specifically, the following steps are performed: Based on the obtained bias voltage compensation amount, it is added to the DC bias terminal of the modulator; By adjusting the bias electrode voltage of the modulator, the bias current or voltage is changed to achieve fine adjustment of the bias point; In the process of adjusting the compensation voltage and the bias electrode voltage, a smoothing filter is added to the circuit to eliminate high-frequency jitter and noise; Monitor the system's bias voltage and output signal in real time, and adjust the compensation voltage and bias electrode voltage based on the actual measurement results; Through the feedback control mechanism, the bias voltage is adjusted according to the changes in the output signal to maintain the stability of the bias point.

[0036] Specifically, the bias voltage compensation is used to adjust the DC bias point of the modulator, with the goal of adjusting the modulator's operating state to an ideal operating point. Before obtaining this compensation, it is necessary to first measure the current bias voltage and compare it with the target value. By adding the calculated compensation to the DC bias terminal of the modulator, the modulator's bias point is adjusted. The bias voltage adjustment is usually achieved through the power supply module, that is, the voltage applied to the power supply terminal of the modulator adjusts the modulator's operating state. The adjustment of the bias electrode voltage is used to fine-tune the bias current or voltage of the modulator, thereby further optimizing the operating point. The relationship between the bias electrode voltage and the bias current or voltage is defined by the input-output characteristics of the modulator and is usually adjusted through a linear or nonlinear relationship. During the real-time monitoring process, a closed-loop feedback control algorithm is used to adjust the bias voltage in real time to maintain its stability and precisely regulate the bias voltage so that the modulator output signal always remains near the target value.

[0037] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0038] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0039] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for automatically controlling the bias point of an electro-optical modulator, characterized in that: include: Generate a composite disturbance voltage signal by a digital signal processor, wherein the signal is composed of a linear superposition of a low-frequency sinusoidal signal and a broadband pseudo-random code signal; The composite disturbance signal is injected into the bias electrode of the photoelectric modulator, the optical signal output by the photoelectric modulator is collected in real time, and converted into an electrical signal through a photodetector; Perform real-time spectrum analysis on electrical signals, and synchronously extract the fundamental component amplitude, second harmonic component amplitude, and phase difference data between the third harmonic component and the fundamental wave through bandpass filtering, analog-to-digital conversion, and fast Fourier transform; Based on the acquired spectrum analysis data, dynamic characteristic parameters are calculated and a mapping relationship between the characteristic parameters and the bias point offset is established; The dynamic feature parameters are input into the pre-trained deep learning model, the offset direction and offset level of the current bias point relative to the target working point are output, and the quantitative data of the bias point offset is obtained; A fuzzy PID controller is driven based on the offset direction and offset level to generate an offset voltage compensation amount; The bias voltage compensation amount is added to the DC bias terminal of the modulator, the bias electrode voltage is adjusted, and high-frequency jitter is eliminated by applying a voltage smoothing filter. The above steps are repeated to perform dynamic closed-loop control of the bias point.

2. The method for automatically controlling the bias point of an electro-optical modulator according to claim 1, wherein: The method of generating a composite disturbance voltage signal by a digital signal processor, wherein the composite disturbance voltage signal is formed by linear superposition of a low-frequency sinusoidal signal and a broadband pseudo-random code signal, specifically includes: Generate a low-frequency sinusoidal signal and a broadband pseudo-random code signal through a digital signal processor based on the low-frequency operating frequency range of the optoelectronic modulator; The low-frequency sinusoidal signal and the broadband pseudo-random code signal are linearly superimposed to form a composite disturbance voltage signal, wherein the composite signal includes a low-frequency steady disturbance and a high-frequency random disturbance; Based on the superimposed signal, the amplitude of the composite disturbance signal is adjusted so as to be within the working range of the optoelectronic modulator; The generated composite disturbance signal is output through a digital signal processor.

3. The method for automatically controlling the bias point of an electro-optical modulator according to claim 1, wherein: The step of injecting the composite disturbance signal into the bias electrode of the photoelectric modulator, collecting the optical signal output by the photoelectric modulator in real time, and converting the optical signal into an electrical signal through a photoelectric detector specifically includes: Converting the generated composite disturbance voltage signal into an analog signal through a digital-to-analog converter, and injecting the composite disturbance signal into the bias electrode of the optoelectronic modulator; The optoelectronic modulator adjusts the bias point based on the injected composite disturbance signal, thereby modulating the optical signal passing through it and outputting it; The optical signal is collected in real time by a photodetector and converted into an electrical signal based on the change of light intensity; The converted electrical signal is amplified by a low-noise amplifier, and high-frequency noise is removed by a filter to obtain a clear electrical signal.

4. The method for automatically controlling the bias point of an electro-optical modulator according to claim 1, wherein: The real-time spectrum analysis of the electrical signal is performed to synchronously extract the fundamental component amplitude, the second harmonic component amplitude, and the phase difference data between the third harmonic component and the fundamental wave through bandpass filtering, analog-to-digital conversion, and fast Fourier transform. Specifically, the following steps are performed: The acquired clear electrical signal is converted into a digital signal through an analog-to-digital converter; Processing the digital signal through fast Fourier transform, converting the time domain signal into a frequency domain signal, and generating a spectrum diagram, wherein the spectrum diagram includes amplitude and phase data of the signal at different frequency points; Based on the obtained spectrum, the amplitude of the fundamental component is obtained by searching for the amplitude value corresponding to the frequency point; The second harmonic frequency is twice the fundamental frequency. Find the amplitude value of the second harmonic frequency point in the spectrum to obtain the amplitude of the second harmonic component; The third harmonic frequency is three times the fundamental frequency. Find the amplitude value of the third harmonic frequency point in the spectrum and obtain the amplitude of the third harmonic component. The fundamental wave phase and the harmonic phase are obtained at the fundamental wave frequency point, and the phase difference between the fundamental wave and the harmonic is calculated.

5. The method for automatically controlling the bias point of an electro-optical modulator according to claim 1, wherein: The step of calculating the dynamic characteristic parameters based on the acquired spectrum analysis data and establishing a mapping relationship between the characteristic parameters and the bias point offset specifically includes: Calculating dynamic characteristic parameters based on the acquired fundamental wave component amplitude, second harmonic component amplitude, and phase difference data between the third harmonic component and the fundamental wave; The calculated dynamic characteristic parameters are converted into linear representations of bias point offsets through a logarithmic mapping function.

6. The method for automatically controlling the bias point of an electro-optical modulator according to claim 1, wherein: Inputting the dynamic feature parameters into the pre-trained deep learning model, outputting the offset direction and offset level of the current bias point relative to the target working point, and obtaining the bias point offset quantitative data specifically include: The dynamic feature parameters are combined into a standardized input vector, and the input vector is scanned in the time domain through a one-dimensional convolution kernel. Each convolution kernel extracts local features and outputs a feature map. The feature map is input into the LSTM unit in time series to generate a hidden state vector, and the original decision vector is output; The classification model outputs the offset level, the regression model outputs the continuous offset value, and the offset quantitative data of the bias point is obtained through integration.

7. The method for automatically controlling the bias point of an electro-optical modulator according to claim 1, wherein: The step of driving the fuzzy PID controller based on the offset direction and the offset level to generate the offset voltage compensation specifically includes: The fuzzy logic rule set is set based on the obtained offset point offset quantitative data, including the control action corresponding to the offset direction and level; The actual bias point offset is converted into a fuzzy set through fuzzification, the corresponding control output is generated according to the designed fuzzy control rules, and then the control quantity of the fuzzy PID controller is obtained through defuzzification; Adjusting the bias voltage based on the signal of the control quantity of the fuzzy PID controller; Based on the real-time offset direction and offset level, the fuzzy logic rules and the parameters of the fuzzy PID controller are updated to optimize the control performance and generate the offset voltage compensation amount.

8. The method for automatically controlling the bias point of an electro-optical modulator according to claim 1, wherein: The method of adding the bias voltage compensation amount to the DC bias terminal of the modulator, adjusting the bias electrode voltage, eliminating high-frequency jitter by applying a voltage smoothing filter, and repeating the above steps to perform dynamic closed-loop control on the bias point specifically includes: Based on the obtained bias voltage compensation amount, it is added to the DC bias terminal of the modulator; By adjusting the bias electrode voltage of the modulator, the bias current or voltage is changed to achieve fine adjustment of the bias point; In the process of adjusting the compensation voltage and the bias electrode voltage, a smoothing filter is added to the circuit to eliminate high-frequency jitter and noise; Monitor the system's bias voltage and output signal in real time, and adjust the compensation voltage and bias electrode voltage based on the actual measurement results; Through the feedback control mechanism, the bias voltage is adjusted according to the changes in the output signal to maintain the stability of the bias point.

9. The method for automatically controlling the bias point of an electro-optical modulator according to claim 1, wherein: The photoelectric modulator is a Mach-Zehnder lithium niobate modulator, and the bias electrodes include: a main bias electrode for coarse adjustment of the operating point, with a voltage range of -5V to +5V; a fine-tuning electrode for dynamic compensation, with a voltage range of -0.5V to +0.5V; the compensation amount is preferentially applied to the fine-tuning electrode, and when the accumulated compensation amount exceeds a threshold, it switches to the main bias electrode.

10. An electro-optical modulator bias point automatic control system, used to implement the electro-optical modulator bias point automatic control method according to any one of claims 1 to 9, characterized in that: include: Composite disturbance signal generation module: This module generates a composite disturbance signal of a low-frequency sinusoidal signal and a broadband pseudo-random code signal through a digital signal processor, providing a disturbance signal for the bias electrode; Signal injection and optical signal acquisition module: This module injects the composite disturbance signal into the bias electrode of the optoelectronic modulator, collects the optical signal output by the optoelectronic modulator in real time and converts it into an electrical signal; Spectrum analysis module: This module performs fast Fourier transform analysis on the electrical signal, extracts the fundamental wave, second harmonic, third harmonic components and phase difference data, and obtains spectrum analysis results; Characteristic parameter calculation module: This module calculates dynamic characteristic parameters based on spectrum data and establishes a mapping relationship with the bias point offset; Deep learning decision module: This module inputs dynamic feature parameters into a pre-trained deep learning model, outputs the offset direction and offset level of the bias point, and obtains quantitative data of the bias point offset; Fuzzy PID control module: This module calculates the offset voltage compensation amount through a fuzzy PID controller based on the offset direction and offset level, and generates a control signal: Closed-loop control module: This module adds the bias voltage compensation to the DC bias terminal of the modulator, adjusts the bias electrode voltage, and eliminates high-frequency noise through a smoothing filter to perform closed-loop control; Processor: The processor is used to process the calculation process of each formula and the construction calculation process of each model.

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