Signal receiving method based on double identities

Abstract

The invention discloses a signal receiving method based on double identities, and particularly relates to the field of communication, and the method comprises the following steps: S1, setting a sensor module, a signal processing module, an environment monitoring module and a data analysis module; s2, inputting a signal acquired by the sensor module into a digital signal processor through an analog-to-digital converter, processing the signal by using an adaptive filtering algorithm in the digital signal processor, and eliminating direct current bias and noise in real time; and S3, monitoring the intensity of the output signal through regular sampling, and comparing the intensity with the set target intensity based on a PID control algorithm. The system has the advantages of intelligence, enhanced signal processing capability, improved system efficiency, excellent expandability and the like, the performance of the system in coping with a complex environment is far better than that of a traditional scheme due to the advantages, the ever-increasing requirements of a modern sensor network are met, and the overall performance and reliability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the field of communications, and more particularly, to a signal receiving method based on dual identities. Background Art

[0002] Distributed fiber optic sensing is a technology that uses the one-dimensional spatial continuity of optical fibers for sensing. The optical fiber serves both as a sensing element and a transmission element, and can continuously measure environmental parameters distributed along the optical fiber throughout the length of the optical fiber, while obtaining the spatial distribution state of the measured quantity and information varying with time. Since the phase, intensity, and polarization state of the light propagating in the optical fiber are affected by the physical fields around the optical fiber, such as temperature, pressure, vibration, etc., these physical quantities can be restored by detecting the parameters of the light. This technology has extensive applications and broad prospects in various fields such as perimeter security, aerospace, shipbuilding industry, power industry, petrochemical industry, and medicine.

[0003] Distributed fiber optic sensing technology requires receiving and processing weak backscattered light signals. Traditional processing methods either have a long sensing detection distance, up to dozens of kilometers, but low spatial resolution; or have a high spatial resolution, up to 5 - 10 meters, but the sensing detection range is only 1 - 2 kilometers. The reason for this problem is that as the laser propagates in the optical fiber, its intensity decays exponentially with distance, and the longer the distance, the weaker the light. Moreover, the backscattered light is much smaller than the light propagating forward, resulting in very small light energy received at the receiving end. To enhance the energy of the laser pulse, its pulse width can be increased, but this sacrifices resolution and sensitivity.

[0004] To solve this problem and improve the detection distance and sensitivity of distributed fiber optic sensing, some in the industry adopt the method of time gain control (TGC), such as the wide - area all - fiber disturbance positioning signal time gain control device disclosed in the patent application with the application number 200810024484.8, which makes the gain of the optical receiver increase exponentially with time, so as to achieve balanced amplification of the exponentially decaying backscattered light; some adopt the automatic gain control method of comparing the signal intensity with a preset threshold, reducing the amplification factor when the signal is too strong and increasing the amplification factor when the signal is too small, such as the wide - area all - fiber sensing system continuous - wave adaptive large - dynamic - range signal processing method disclosed in the patent application with the application number 201110286454.6.

[0005] However, these methods do not eliminate the DC bias of the output signal of the optoelectronic conversion device. Performing time gain control without eliminating the DC bias will exponentially amplify the DC voltage as well, causing the subsequent operational amplifier or analog-to-digital converter to saturate and drowning the optical sensing information. Performing automatic gain control without eliminating the DC bias will result in the DC bias voltage being included in the signal strength as well, making the gain control incorrect and losing the dynamic range. Additionally, these methods do not take into account the actual operation of the system. The backward scattered light on the optical fiber is modulated by the surrounding environmental conditions and is not a clean exponentially decaying signal but a superposition of an exponentially decaying signal and a disturbance signal. For gain equalization, it is necessary to extract the mathematical parameters of the exponentially decaying signal. Only on the premise of no disturbance signal can the extraction of mathematical parameters be accurate. These methods do not consider the influence of the disturbance signal on the gain equalization control.

[0006] To solve the above problems, a technical solution is provided now. Summary of the Invention

[0007] To overcome the above defects of the prior art, an embodiment of the present invention provides a signal receiving method based on dual identities to solve the problems raised in the above background art.

[0008] To achieve the above object, the present invention provides the following technical solutions: A signal receiving method based on dual identities, comprising the following steps: Step S1, set a sensor module, a signal processing module, an environmental monitoring module, and a data analysis module; Step S2, the signal collected by the sensor module is input into the digital signal processor through an analog-to-digital converter, and the adaptive filtering algorithm is used in the digital signal processor to process the signal, eliminating the DC bias and noise in real time; Step S3, by periodically sampling, monitor the intensity of the output signal, compare it with the set target intensity, and based on the PID control algorithm, calculate the gain adjustment value and dynamically adjust the gain setting of the amplifier to ensure that the output signal remains within a predetermined range; Step S4, use wavelet transform to extract the spectral features of the signal, screen out important parameters through feature pattern recognition, and combine the environmental data collected by the environmental monitoring module with the sensor signal and give feedback.

[0009] In a preferred embodiment, step S1 specifically includes the following content: The sensor module is used to collect the corresponding optical fiber signal, the signal processing module is used for signal reception, signal processing, and gain control, the environmental monitoring module is used to monitor the surrounding environmental parameters in real time and collect the factors that can affect the signal, and the data analysis module is used for signal feature extraction and model optimization.

[0010] In a preferred embodiment, step S2 specifically includes the following content: When using the adaptive filtering algorithm, update the filter coefficients according to the error between the output result of each time and the target signal, so as to adjust the filter in real time to adapt to the input signal, realize the adaptive update mechanism, and enable the filter to adjust in real time to cope with environmental changes.

[0011] In a preferred embodiment, step S3 specifically includes the following content: Analyze the received signal strength in real time through the signal processing module, digitize the signal through the analog-to-digital converter, design a sensitive automatic gain control logic, and compare the current signal strength with the preset value through the difference calculator; Set a target level, dynamically adjust the gain according to the feedback signal, combine the PID control theory, and set the gain, integral and differential parameters to ensure the stability of the system and a rapid response to signal changes.

[0012] In a preferred embodiment, step S4 specifically includes the following content: Perform high-frequency sampling on the received signal to ensure obtaining sufficient samples according to the Nyquist theorem, and then perform quantization to convert it into a digital signal; Introduce wavelet transform or Fourier transform for spectrum analysis, calculate wavelet coefficients and evaluate their sharpness and stability, and extract important signal features; Design a pattern recognition algorithm to identify the difference between the valid signal and the background noise, and evaluate the effect of the pattern recognition algorithm through the accuracy rate and recall rate; Use the confusion matrix to test the recognition result, ensure that the difference between the valid signal and the background noise can be effectively captured, verify the accuracy of the data, and ensure that the extracted signal features are substantially helpful for gain control.

[0013] In a preferred embodiment, regularly collect and analyze the gain adjustment effect, and optimize the parameters using the Kalman filter; Train a machine learning model through historical data to predict the best gain setting under different environmental conditions and apply it to future gain adjustments.

[0014] In a preferred embodiment, use MATLAB / Simulink to build a mathematical model and a simulation environment to verify the gain control effect and its impact on signal quality.

[0015] In a preferred embodiment, conduct tests in a real environment, evaluate the actual effect, and gradually optimize the algorithm and parameter settings.

[0016] The technical effects and advantages of a signal reception method based on dual identities of the present invention: 1. By analyzing the sensor output and environmental conditions (such as temperature, humidity, light changes, etc.) in real time, the system can dynamically adjust the gain setting. This intelligent feature enables the system to maintain high efficiency in complex environments, improves the flexibility and adaptability of the system, and can handle changes in various optical fiber signals to ensure signal quality and maximize the system dynamic range, effectively avoiding signal distortion caused by environmental changes; 2. Enhanced signal processing capability Adaptive filtering can adjust itself according to the changes of input signals and remove DC bias and noise in real time, while Kalman filtering provides the best estimate of signal state, significantly improving signal clarity and maintaining relatively good performance under different working conditions, reducing the impact on signal quality and ensuring the accuracy of subsequent signal processing and analysis; 3. By continuously learning historical data, the gain control model can be gradually improved to make it more adaptable. Since the system can continuously optimize itself based on data feedback, resource utilization efficiency is improved, operation and maintenance costs are reduced, and equipment failures caused by signal saturation and excessive gain are avoided; 4. The modular gain control and signal processing system allows the subsequent addition of more sensors and data analysis modules to adapt to future technological development needs. It does not rely on specific hardware, making the solution flexible and promoting the future application development of technology, such as the introduction of new sensor types or algorithms, promoting long-term updates and maintenance of the system, and facilitating system iteration and upgrades based on new technological developments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a flow chart of a signal receiving method based on dual identities of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example 1 Figure 1 The present invention provides a signal receiving method based on dual identity, which specifically includes the following steps: Step S1, setting a sensor module, a signal processing module, an environment monitoring module and a data analysis module; Step S2: The signals collected by the sensor module are input into the digital signal processor through an analog-to-digital converter. In the digital signal processor, an adaptive filtering algorithm is used to process the signals to eliminate the DC bias and noise in real time. Step S3: By performing regular sampling, monitor the intensity of the output signal and compare it with the set target intensity. Based on the PID control algorithm, calculate the gain adjustment value and dynamically adjust the gain setting of the amplifier to ensure that the output signal remains within the predetermined range. Step S4: Use wavelet transform to extract the spectral features of the signal. Through characteristic pattern recognition, screen out the important parameters, and combine the environmental data collected by the environmental monitoring module with the sensor signals and provide feedback.

[0020] Step S1 specifically includes the following: The sensor module is used to collect the corresponding optical fiber signals. The signal processing module is used for signal reception, signal processing, and gain control. The environmental monitoring module is used to monitor the surrounding environmental parameters in real time and collect the factors that can affect the signals. The data analysis module is used for signal feature extraction and model optimization.

[0021] It should be added that a modular design is adopted to ensure clear decoupling between modules. Standardized APIs (Application Programming Interfaces) are designed for each module to provide clear inputs and outputs, making the interaction between modules clear. Define the data format, communication protocol, and call method to make the subsequent module integration simple and flexible. The open interfaces facilitate the flexible integration of new hardware or software modules in the future. Implement comprehensive system monitoring to ensure the coordinated operation between modules and the overall stability of the system.

[0022] Reasonably allocate the resources of the processor, memory, and database to ensure that each module still runs smoothly during the peak load period. Implement dynamic parameter adjustment and quickly respond to changes in external conditions based on the information provided by the environmental monitoring module.

[0023] Different from traditional integrated systems, the present invention adopts a modular design to ensure decoupling between functional modules, enabling the modules to be independently developed, tested, and updated, reducing the impact on and risks of the entire system. Based on open APIs and standard protocols, the system can easily integrate new hardware or software modules without redesigning the existing architecture, significantly enhancing the scalability of the system.

[0024] Adopt the modular design concept. Through clear module division and open interfaces, it lays a foundation for future system expansion. In addition, optimize resource management, exception handling, and fast response mechanisms to ensure that the system can still operate stably when the signals change, improving the performance and reliability of the entire system. These new features make this design significantly different from current traditional systems.

[0025] Step S2 specifically includes the following content: When using the adaptive filtering algorithm, the filter coefficients are updated according to the error between each output result and the target signal, so as to adjust the filter in real time to adapt to the input signal, realize the adaptive update mechanism, and enable the filter to be adjusted in real time to cope with environmental changes.

[0026] It should be added that the appropriate adaptive filtering algorithm NLMS is selected. The algorithm is simple and easy to implement, and is suitable for situations where the signal changes little and the solution speed is fast. Through normalization processing, the robustness to the amplitude fluctuation of the input signal is improved. For NLMS, choosing an appropriate step size is the key. A too large step size will cause the system to be unstable, while a too small step size will reduce the convergence speed. Realize the adaptive update mechanism, and enable the filter to be adjusted in real time to cope with environmental changes.

[0027] Compared with the traditional adaptive filtering technology, the present invention can more intelligently dynamically adjust the algorithm parameters based on the signal change situation, improving the system's adaptive ability. By combining the adaptive filtering with the environmental monitoring module, an overall system is formed, allowing for a higher level of coordination. The system has better performance in coping with signal changes in real time and meets higher application requirements.

[0028] The present invention realizes the dynamic update mechanism, designs a modular and flexible system, and ensures that the system can respond to signal changes in a timely and stable manner. These optimization measures are significantly different from the prior art and help to improve the performance of the overall system.

[0029] Step S3 specifically includes the following content: The received signal strength is analyzed in real time through the signal processing module, the signal is digitized through the analog-to-digital converter, a sensitive automatic gain control logic is designed, and the current signal strength is compared with the preset value through the difference calculator; A target level is set, the gain is dynamically adjusted according to the feedback signal, and the gain, integral, and differential parameters are set in combination with the PID control theory to ensure the stability of the system and a rapid response to signal changes.

[0030] It should be added that signal delay and environmental uncertainty are considered in the control algorithm to improve the real-time control ability. The prior art often adopts a single control path, while the present invention enables the system to flexibly adjust the gain under different signal conditions through multi-level feedback, avoiding signal loss or excessive amplification.

[0031] Through the method of the present invention, the automatic gain control system can effectively cope with the change of signal strength, achieving stable performance and rapid response. These innovative features make the new AGC technology significantly different from the prior art and help to improve the overall performance of the communication system.

[0032] Step S4 specifically includes the following content: Perform high-frequency sampling on the received signal to ensure obtaining sufficient samples according to the Nyquist theorem, and then perform quantization to convert it into a digital signal; Introduce wavelet transform or Fourier transform for spectrum analysis, calculate wavelet coefficients and evaluate their sharpness and stability, and extract important signal features; Design a pattern recognition algorithm to identify the difference between the valid signal and background noise, and evaluate the effect of the pattern recognition algorithm through accuracy and recall rate; Use a confusion matrix to test the recognition result, ensure that the difference between the valid signal and background noise can be effectively captured, verify the accuracy of the data, and ensure that the extracted signal features are substantially helpful for gain control.

[0033] It should be added that the present invention can have an adaptive model, enabling it to adjust parameters according to environmental changes and dynamic updates of signal features. For example, when the signal environment changes, the AGC can learn new features in real time and quickly initiate a new pattern recognition process.

[0034] Compared with traditional spectrum analysis methods, the present invention combines wavelet transform and Fourier transform to achieve a finer-grained time-frequency analysis and better process non-stationary signals. The pattern recognition algorithm combines machine learning methods for adaptive learning, enabling the system to better adapt to different signal environments and improve the recognition accuracy. Ensure that the system can not only quickly respond to signal changes but also adapt to newly emerging signal types and noise environments, enhancing the intelligent level of gain adjustment.

[0035] Regularly collect and analyze the gain adjustment effect, and optimize the parameters using Kalman filtering; Train a machine learning model through historical data to predict the optimal gain setting under different environmental conditions and apply it to future gain adjustments.

[0036] Use MATLAB / Simulink to build a mathematical model and simulation environment to verify the gain control effect and its impact on signal quality.

[0037] Conduct tests in a real environment, evaluate the actual effect, and gradually optimize the algorithm and parameter settings.

[0038] It should be added that by regularly collecting data, the gain adjustment situation in the actual operation of the system can be continuously obtained, and analyzing these data can reveal the problems existing in the current gain setting. As an efficient recursive filter, the Kalman filter can utilize the dynamic model of the system and measurement data to optimally estimate the system state, thereby more accurately optimizing the gain parameters and reducing errors. It can track the changes of the system in real time, timely adjust the gain parameters to adapt to the dynamic characteristics of the system, keep the system running stably under various working conditions, and avoid system oscillation or instability caused by improper gain setting. During the process of optimizing the parameters, the Kalman filter can effectively process the noise in the measurement data, filter out the noise interference, extract a purer signal, thereby improving the system's response ability to the real signal and enhancing the overall performance of the system.

[0039] By using machine learning models to learn and analyze a large amount of historical data, the potential relationship between the gain setting and environmental conditions can be mined. Thus, it is possible to predict the optimal gain setting in advance according to the environmental conditions that may occur in the future, realizing intelligent gain adjustment without manual trial and adjustment. Different environmental conditions often have different requirements for the system gain. In this way, the system can automatically adapt to the changes in the environment, quickly find the appropriate gain setting in various complex and changeable environments, and ensure the stability and reliability of the system performance. Accurate gain prediction can make the system more reasonable in resource allocation. It avoids the situation of resource waste or insufficiency caused by unreasonable gain setting, improves the utilization efficiency of system resources, and reduces the operating cost.

[0040] MATLAB / Simulink provides rich libraries and tools, which can conveniently and quickly build various complex mathematical models and intuitively display the structure and dynamic characteristics of the system. It can easily model and analyze different gain control strategies, evaluate their impact on the signal quality, and provide a theoretical basis for the design and optimization of the actual system. Verifying in the simulation environment can quickly verify the feasibility and effectiveness of different gain control schemes without building an actual physical system. It greatly reduces the number of experiments and the development cycle, reduces the R & D cost, and improves the development efficiency. Through simulation, potential problems and risks can be discovered in advance, such as signal distortion and system instability caused by gain setting. Repeatedly debugging and optimizing the model before actual application reduces the risk of the actual system malfunctioning or having poor performance, and improves the reliability and safety of the system.

[0041] There are various complex factors and uncertainties in the real environment. By testing in the real environment, the feasibility and effectiveness of the algorithm and gain control strategy in practical applications can be verified, ensuring that the system can achieve the expected performance indicators during actual operation. Testing in the real environment can obtain the most real and accurate data, which reflect the true performance of the system under actual working conditions. Based on the analysis and optimization of these data, the algorithm and parameter settings can be made more in line with the actual situation, further improving the performance of the system. Gradually optimizing the algorithm and parameters according to the actual test results can enable the system to continuously adapt to the changes and requirements of the real environment, achieving continuous performance improvement. This optimization process based on actual feedback can keep the system in the best operating state at all times, improving user satisfaction and the competitiveness of the system.

[0042] As described above, it is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claimed rights.

[0043] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A signal receiving method based on dual identity, characterized in that: The steps include: Step S1, setting a sensor module, a signal processing module, an environment monitoring module and a data analysis module; Step S2, the signal collected by the sensor module is input into a digital signal processor through an analog-to-digital converter, and an adaptive filtering algorithm is used in the digital signal processor to process the signal to eliminate DC bias and noise in real time; Step S3, monitoring the intensity of the output signal by periodic sampling and comparing it with the set target intensity, calculating the gain adjustment value and dynamically adjusting the gain setting of the amplifier based on the PID control algorithm to ensure that the output signal remains within a predetermined range; Step S4, using wavelet transform to extract the spectral characteristics of the signal, through characteristic pattern recognition, screen out important parameters, combine the environmental data collected by the environmental monitoring module with the sensor signal and provide feedback.

2. A signal receiving method based on dual identity according to claim 1, characterized in that: Step S1 specifically includes the following contents: The sensor module is used to collect the corresponding optical fiber signals, the signal processing module is used for signal reception, signal processing and gain control, the environmental monitoring module is used to monitor the surrounding environmental parameters in real time and collect factors that can affect the signals, and the data analysis module is used for signal feature extraction and model optimization.

3. A signal receiving method based on dual identity according to claim 2, characterized in that: Step S2 specifically includes the following contents: When using an adaptive filtering algorithm, the filter coefficients are updated based on the error between each output result and the target signal, so that the filter is adjusted in real time to adapt to the input signal, realizing an adaptive update mechanism so that the filter can be adjusted in real time to cope with environmental changes.

4. The signal receiving method based on dual identity according to claim 3, characterized in that: Step S3 specifically includes the following contents: The signal processing module analyzes the received signal strength in real time, digitizes the signal through the analog-to-digital converter, designs a sensitive automatic gain control logic, and compares the current signal strength with the preset value through the difference calculator; Set a target level, dynamically adjust the gain according to the feedback signal, combine PID control theory, set the gain, integral and differential parameters to ensure the system is stable and responds quickly to signal changes.

5. A signal receiving method based on dual identity according to claim 4, characterized in that: Step S4 specifically includes the following contents: The received signal is sampled at high frequency to ensure that enough samples are obtained according to the Nyquist theorem, and then quantized to convert it into a digital signal; Introduce wavelet transform or Fourier transform for spectrum analysis, calculate wavelet coefficients and evaluate their sharpness and stability to extract important signal features; Design pattern recognition algorithms to identify the difference between valid signals and background noise, and evaluate the effectiveness of pattern recognition algorithms through precision and recall; The confusion matrix is ​​used to test the recognition results to ensure that the difference between the effective signal and the background noise can be effectively captured, verify the accuracy of the data, and ensure that the extracted signal features are of substantial help to the gain control.

6. A signal receiving method based on dual identity according to claim 5, characterized in that: Regularly collect and analyze gain adjustment effects and use Kalman filtering to optimize parameters; The machine learning model is trained with historical data to predict the optimal gain settings under different environmental conditions for future gain adjustments.

7. A signal receiving method based on dual identity according to claim 6, characterized in that: Use MATLAB / Simulink to build mathematical models and simulation environments to verify the gain control effect and its impact on signal quality.

8. The signal receiving method based on dual identity according to claim 7, characterized in that: Conduct tests in real environments, evaluate actual results, and gradually optimize algorithms and parameter settings.

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