Communication noise reduction optimization system based on time-frequency analysis

Abstract

The present invention relates to the technical field related to control signal transmission, and specifically includes a communication noise reduction optimization system based on time-frequency analysis, including: collecting U digital signals preprocessed by an ADC converter and a signal conditioning circuit, and establishing a time-frequency analysis module by using a combined time-frequency analysis logic; from the perspective of time domain and frequency domain, combining the module to perform multi-level decomposition of digital signals, and respectively obtaining time domain and frequency domain component sequences; introducing a signal quality index, using fuzzy logic to reconstruct the signal, and generating a control instruction, thereby solving the technical problem that noise interference exists in the communication transmission process and the accuracy of the control signal cannot be guaranteed during the control signal transmission process, and achieving the technical effect of effectively reducing the influence of time domain and frequency domain noise on the control signal in a complex communication environment, improving the safety of the control signal, introducing a signal quality index, improving the anti-interference ability of the control signal, and enhancing the credibility and stability of the control signal transmission process.

Description

Technical Field

[0001] The present invention relates to the technical field related to control signal transmission, and in particular to a communication noise reduction optimization system based on time-frequency analysis. Background Art

[0002] The communication system will be affected by various noise interferences during the transmission process. Most of the noise interferences are caused by environmental factors, which lead to the degradation of signal quality. In order to improve this situation, advanced signal processing technology is needed to improve the clarity and reliability of the signal. On the one hand, in the face of non-stationary noise environment, although low-pass filters, band-pass filters, etc. can reduce some noise, they cannot fully adapt to the changes in noise characteristics. In addition, there are physical limitations of hardware, such as the nonlinear characteristics of amplifiers, which may introduce additional noise and distortion. On the other hand, the control signal is the key to device control, and its stability is related to the continuous and stable operation of the equipment. Therefore, it is necessary to make in-depth improvements to the existing signal processing technology and hardware design to fully ensure the credibility of control signal transmission.

[0003] In summary, the prior art has the technical problem that noise interference exists in the communication transmission process and the accuracy of the control signal cannot be guaranteed during the control signal transmission process. Summary of the invention

[0004] The present application provides a communication noise reduction optimization system based on time-frequency analysis, aiming to solve the technical problems in the prior art that noise interference exists in the communication transmission process and the accuracy of the control signal cannot be guaranteed during the control signal transmission process.

[0005] In view of the above problems, the technical solution to implement this application is:

[0006] The present application provides a communication noise reduction optimization system based on time-frequency analysis, wherein the communication noise reduction optimization system based on time-frequency analysis is deployed in a control signal transmission device, and the control signal transmission device includes an ADC converter, a signal sending port, and a signal receiving port, including:

[0007] A signal collection unit, connected to the signal sending port of the control signal transmission device, to collect U digital signals limited by the sampling step, wherein the digital signals are signals preprocessed by the ADC converter and the signal conditioning circuit;

[0008] A time-frequency analysis module establishment unit is used to, at the same time, establish a time-frequency analysis module in combination with the U digital signals from the time-frequency analysis combination analysis logic composed of the STFT time-frequency analysis method, the wavelet transform time-frequency analysis method, and the FFT time-frequency analysis method;

[0009] A time domain characteristic multi-level decomposition unit, used to perform time domain characteristic multi-level decomposition from a time domain perspective on the time domain distribution characteristics of the U digital signals in combination with the time-frequency analysis module to obtain a first decomposition sequence, wherein the first decomposition sequence includes the time domain components of the U digital signals;

[0010] A frequency domain characteristic multi-level decomposition unit, used to perform frequency domain characteristic multi-level decomposition from a frequency domain perspective on the frequency domain distribution characteristics of the U digital signals in combination with the time-frequency analysis module to obtain a second decomposition sequence, wherein the second decomposition sequence includes the frequency domain components of the U digital signals;

[0011] A signal reconstruction unit, configured to introduce a signal quality index according to the first decomposition sequence and the second decomposition sequence, and use fuzzy logic control logic to set an adaptive filtering interval and perform signal reconstruction to obtain a noise reduction reconstructed signal, wherein the signal quality index includes a signal-to-noise ratio and a signal distortion;

[0012] The control operation unit is used to import the noise reduction and reconstruction signal into the signal receiving port of the control signal transmission device to generate a control operation instruction.

[0013] In summary, one or more technical solutions provided in the present application solve the technical problem that noise interference exists in the communication transmission process and the accuracy of the control signal cannot be guaranteed during the control signal transmission process. It achieves the technical effect of effectively reducing the influence of time domain and frequency domain noise on the control signal in a complex communication environment, improving the security of the control signal, introducing signal quality indicators, improving the anti-interference ability of the control signal, and enhancing the credibility and stability of the control signal transmission process. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A structural schematic diagram of a communication noise reduction optimization system based on time-frequency analysis is provided for this application;

[0015] Figure 2 The present application provides a flowchart of the execution steps of obtaining a first decomposition sequence of a time domain characteristic multi-level decomposition unit in a communication noise reduction optimization system based on time-frequency analysis.

[0016] Explanation of the reference numerals: signal collection unit M100, time-frequency analysis module establishment unit M200, time domain characteristic multi-level decomposition unit M300, frequency domain characteristic multi-level decomposition unit M400, signal reconstruction unit M500, control operation unit M600. DETAILED DESCRIPTION

[0017] Example

[0018] The present application is described in detail below with reference to the accompanying drawings. Figure 1As shown, the present application provides a communication noise reduction optimization system based on time-frequency analysis, wherein the communication noise reduction optimization system based on time-frequency analysis is deployed in a control signal transmission device, and the control signal transmission device includes an ADC converter, a signal sending port, and a signal receiving port, including:

[0019] The signal collection unit M100 is used to connect to the signal sending port of the control signal transmission device to collect U digital signals limited by the sampling step, and the digital signals are signals preprocessed by the ADC converter and the signal conditioning circuit.

[0020] Before executing the corresponding execution steps of the embodiment, the communication noise reduction optimization system based on time-frequency analysis is deployed on a control signal transmission device. Specifically, the various components of the noise reduction optimization system are physically connected to the corresponding ports of the control signal transmission device, for example, the signal collection unit is connected to the signal sending port to collect digital signals; the relevant parameters of the communication noise reduction optimization system based on time-frequency analysis, including the sampling frequency, etc., are configured in the operating system or embedded software of the control signal transmission device; after completing the hardware connection and software configuration, a system integration test is performed to ensure the operational stability of the communication noise reduction optimization system based on time-frequency analysis.

[0021] The signal collection unit implements the steps, including establishing a physical connection between the signal transmission ports of the control signal transmission device, including operations such as plugging and unplugging cables and docking connectors; after the connection is established, the signal collection unit M100 needs to be configured with necessary parameters, including setting the sampling step length, that is, determining the time interval between each digital signal. The selection of the sampling step length should be based on the frequency characteristics of the signal and the required signal resolution. Once the parameter configuration is completed, the digital signal is received from the signal transmission port. The digital signal is obtained by converting the analog signal into a digital signal by the ADC converter (analog to digital converter), and then pre-processing (such as filtering, amplification, etc.) by the signal conditioning circuit; the received digital signal needs to be temporarily stored in the internal memory of the signal collection unit M100 for subsequent processing and analysis.

[0022] The time-frequency analysis module establishing unit M200 is used to simultaneously establish a time-frequency analysis module from the time-frequency analysis combination analysis logic composed of the STFT time-frequency analysis method, the wavelet transform time-frequency analysis method, and the FFT time-frequency analysis method, in combination with the U digital signals.

[0023] The implementation steps of the time-frequency analysis module establishment unit include: clarifying the specific requirements of the time-frequency analysis module, including the signal type to be analyzed, analysis accuracy, real-time requirements, etc.; according to the requirements, selecting appropriate algorithms or algorithm combinations from time-frequency analysis methods such as STFT, wavelet transform, FFT, etc. Each method has unique advantages and scope of application. For example, STFT is suitable for analyzing the time-frequency characteristics of non-stationary signals, wavelet transform provides variable time-frequency resolution, and FFT is suitable for frequency domain analysis of stationary signals; in-depth research on the principles, implementation steps and key parameters of the selected time-frequency analysis algorithm, determine the advantages and disadvantages of each time-frequency analysis method, and its application in practical applications. performance in; prepare the development tools and library files required for implementation. For example, if you choose to implement it in MATLAB, you need to ensure that the necessary toolboxes are installed; if you choose to implement it in an embedded system, you need to prepare the corresponding compilation environment and hardware support. Then, design the overall architecture and interface of the time-frequency analysis module, determine the functional module division of the module, the data flow processing flow and the interaction method with other modules, and implement it according to the module design. The specific steps include initializing algorithm parameters, reading digital signals, calling the time-frequency analysis algorithm for processing, storing and analyzing results, etc. In the implementation process, attention should be paid to the readability and maintainability of the code.

[0024] The time domain characteristic multi-level decomposition unit M300 is used to perform multi-level decomposition of the time domain characteristics from a time domain perspective on the time domain distribution characteristics of the U digital signals in combination with the time-frequency analysis module to obtain a first decomposition sequence, wherein the first decomposition sequence includes the time domain components of the U digital signals.

[0025] The implementation steps of the time domain characteristic multi-level decomposition unit include receiving U digital signals as input, the U digital signals include different changes in the time domain, and further, before performing the time domain characteristic multi-level decomposition, using the time-frequency analysis module to pre-process the input digital signal, the purpose of which is to identify the main frequency components in the signal and their changes over time, so as to provide a basis for the subsequent time domain characteristic decomposition. The initial parameters of the multi-level decomposition are set, including the number of decomposition levels, the required filter design, etc., and the first level of time domain characteristic decomposition is performed on the U digital signals, which involves decomposing the signal into different time domain components, such as amplitude components, phase components, etc.; according to the set decomposition level, each component obtained by the previous level of decomposition is further decomposed; after the multi-level decomposition, a first decomposition sequence containing the time domain components of the U digital signals is obtained, and the first decomposition sequence describes the different components of the original signal in the time domain.

[0026] Through the above steps, the time domain characteristic multi-level decomposition unit M300 effectively performs multi-level decomposition of the digital signal from the time domain perspective, extracts the key time domain characteristics of the signal, and provides strong support for subsequent signal processing.

[0027] The frequency domain characteristic multi-level decomposition unit M400 is used to perform multi-level decomposition of the frequency domain characteristics of the U digital signals from a frequency domain perspective in combination with the time-frequency analysis module to obtain a second decomposition sequence, wherein the second decomposition sequence includes the frequency domain components of the U digital signals.

[0028] The implementation steps of the frequency domain characteristic multi-level decomposition unit include receiving U digital signals as input, the U digital signals include different changes in the frequency domain, and further, before performing the frequency domain characteristic multi-level decomposition, using the time-frequency analysis module to pre-process the input digital signals to accurately identify the main components of the signal in the frequency domain and the changes over time. Set the initial parameters of the multi-level decomposition, including the number of decomposition levels, the decomposition method of each level (such as FFT, wavelet transform, etc.), filter design (if necessary), etc., for the frequency domain characteristic decomposition process; perform the first level frequency domain characteristic decomposition on the U digital signals, involving decomposing the signal into different frequency domain components, such as components of different frequencies, power spectrum density components, etc., and also include converting the signal from the time domain to the frequency domain, and dividing the frequency domain into different sub-bands or frequency bands; further decompose each frequency domain component obtained by the previous level decomposition according to the set decomposition level; after the multi-level decomposition, obtain a second decomposition sequence containing the frequency domain components of the U digital signals, and the second decomposition sequence describes in detail the different components of the original signal in the frequency domain.

[0029] Through the above steps, the frequency domain characteristic multi-level decomposition unit M400 can effectively perform multi-level decomposition of the digital signal from the frequency domain perspective, extract the key frequency domain characteristics of the signal, and provide strong support for subsequent signal processing.

[0030] The signal reconstruction unit M500 is used to introduce signal quality indicators according to the first decomposition sequence and the second decomposition sequence, and use fuzzy logic control logic to set the adaptive filtering interval and perform signal reconstruction to obtain a noise reduction reconstructed signal. The signal quality indicators include signal-to-noise ratio and signal distortion. The control operation unit M600 is used to import the noise reduction reconstructed signal into the signal receiving port of the control signal transmission device to generate a control operation instruction.

[0031] The signal reconstruction unit implements the steps, including: analyzing the input first decomposition sequence and the second decomposition sequence, calculating the signal-to-noise ratio (SNR) and the signal distortion, the signal-to-noise ratio measures the ratio of effective information to noise in the signal, and the signal distortion measures the degree of loss of the signal during transmission or processing; according to the calculated signal-to-noise ratio and signal distortion, using fuzzy logic control logic to determine the parameters of the adaptive filtering interval; the fuzzy logic control logic can handle uncertainty and ambiguity, and is suitable for situations where the signal quality index has a large range of variation; according to the output of the fuzzy logic control logic, setting the threshold and range of the adaptive filtering interval; the adaptive filtering interval is used to determine the signal part that should be focused on during the signal reconstruction process.

[0032] In the adaptive filtering interval, the first decomposition sequence and the second decomposition sequence are reconstructed, and appropriate signal processing techniques such as filtering and interpolation are used to restore the original characteristics of the signal and reduce the noise to obtain a noise-reduced reconstructed signal. The noise-reduced reconstructed signal effectively reduces the noise while maintaining the characteristics of the original signal. Adaptive filtering and signal reconstruction are performed according to the signal quality index to ensure the quality of the control signal.

[0033] The control operation unit implements the steps, including identifying key information in the noise reduction and reconstruction signal, such as amplitude, frequency, phase, etc.; extracting characteristic parameters useful for control operation according to the results of signal analysis, including specific waveforms, change trends, threshold trigger conditions, etc. of the signal; generating corresponding control operation instructions according to the set control strategy, including switch control, parameter adjustment, execution of specific actions, etc.; sending the generated control operation instructions to the target device through the signal sending port of the control signal transmission device, and receiving the execution feedback of the control operation instructions from the target device to verify the correctness and effectiveness of the instructions. Convert the noise reduction and reconstruction signal into control operation instructions to achieve precise control of the equipment.

[0034] Furthermore, if Figure 2 As shown, the time domain characteristic multi-level decomposition unit M300 is used to perform the following method:

[0035] From the time domain perspective, the time domain distribution characteristics of the U digital signals are extracted, and the time domain distribution characteristics are used to identify effective signals and time domain noise components; using the energy-aware routing protocol and the distance vector routing protocol, in comparison with the time domain distribution characteristics, combined with the time-frequency analysis module, multi-level decomposition of the time domain characteristics is performed to obtain multi-level time domain components; based on the multi-level time domain components, combined with the time domain characteristic decomposition level, a first decomposition sequence is obtained.

[0036] Specifically, the time domain distribution characteristics refer to the changing rules and characteristics of the signal on the time axis, including the amplitude, period, pulse width, etc. of the signal, which help to identify valid signals and noise; the energy-aware routing protocol is a protocol that selects data transmission paths based on the energy status of nodes, aiming to improve network energy efficiency and extend network life; the distance vector routing protocol is a dynamic routing protocol, in which routers update routing tables by regularly exchanging routing information to determine the best path to the destination.

[0037] Execution steps: from the perspective of time domain, for the time domain distribution characteristics of the U digital signals, the time domain characteristics are multi-level decomposed in combination with the time-frequency analysis module to obtain a first decomposition sequence, including using a time domain analysis tool (such as an oscilloscope, a signal analyzer, etc.) to extract the time domain distribution characteristics from the U digital signals, the time domain distribution characteristics are used to distinguish between effective signals and time domain noise components, and the time domain distribution characteristics include the change of the amplitude, frequency, phase, etc. of the signal over time; the energy-aware routing protocol can evaluate the energy distribution of the signal, and give priority to the path with concentrated energy and high signal-to-noise ratio for signal transmission to improve the transmission quality of the signal. At the same time, the distance vector routing protocol can determine the shortest path for signal transmission, reduce the delay and attenuation during the transmission process, and improve the timeliness of the signal; combined with the energy-aware routing protocol and the distance vector routing protocol, the data transmission path and node selection are optimized in the signal processing network to ensure effective processing of the signal, and then, in comparison with the previously extracted time domain distribution characteristics, the time domain characteristics are multi-level decomposed in combination with the time-frequency analysis module, and the signal is decomposed into components on different time scales through multi-level decomposition, and each time domain component reflects the characteristics of the signal on a specific time scale. From the time domain perspective, we can gain an in-depth understanding of the characteristics of U digital signals, effectively identify effective signals and time domain noise components, and obtain multi-level time domain components through multi-level decomposition. The multi-level time domain components represent the characteristics of the signal at different time scales, which helps to further analyze the nature of the signal and provide support for subsequent analysis.

[0038] Furthermore, the communication noise reduction optimization system based on time-frequency analysis is used to perform the following method:

[0039] Monitor a communication channel state, wherein the communication channel state includes a multipath effect; and obtain a first calibration constraint based on the communication channel state with the goal of reducing the impact of time domain noise, wherein the first calibration constraint is used to adjust the adaptive filtering interval.

[0040] Specifically, the multipath effect refers to the fact that in wireless communications, signals may reach the receiving end through different paths, including direct radiation, reflection, diffraction, etc., causing the receiving end to receive multiple copies of the signal with different delays and phases, thereby causing signal distortion and interference; in the communication noise reduction system, the adaptive filtering algorithm will dynamically adjust the filter parameters according to the characteristics of the signal and the noise level. The adaptive filtering range refers to the signal range or frequency band that the filter considers when adjusting the parameters.

[0041] Execution steps: using energy-aware routing protocol and distance vector routing protocol, comparing the time domain distribution characteristics, combining the time-frequency analysis module to perform multi-level decomposition of time domain characteristics, and obtaining multi-level time domain components, and also including real-time monitoring of the state of the communication channel, including parameters such as signal transmission quality, signal strength, signal attenuation, and multipath effect. Further, the multi-path propagation phenomenon caused by encountering obstacles or reflectors during the transmission process will cause the signal to diffuse in time and selectively fade in frequency at the receiving end. The monitored multipath effect is analyzed in detail, including the strength, delay, phase and other characteristics of the multipath signal; the impact of the multipath effect on communication performance is evaluated, such as signal interference, increased bit error rate, etc.; the time domain noise component introduced by the multipath effect is analyzed, including the intensity, frequency distribution, duration, etc. of the noise; the impact of the time domain noise on the signal quality is evaluated, and how it interferes with the accurate reception and decoding of the signal; with the goal of reducing the impact of the time domain noise, a first calibration constraint is formulated, and the first calibration constraint involves adjusting the parameters of the adaptive filtering interval, such as the bandwidth, order, weight, etc. of the filter, so as to more effectively suppress the time domain noise and reduce the impact of the time domain noise on the signal. Constraints are formulated and calibrated with the goal of reducing the impact of noise, aiming to optimize communication performance and improve signal reception quality and decoding accuracy.

[0042] Furthermore, the communication noise reduction optimization system based on time-frequency analysis is used to perform the following method:

[0043] Acquire location coordinate information of a target device connected to the control signal transmission device; acquire a second calibration constraint based on the location coordinate information and in combination with corresponding communication quality in an environment, wherein the second calibration constraint is used to adjust the adaptive filtering interval.

[0044] Specifically, the location coordinate information of the target device refers to the specific location of the device connected to the control signal transmission device in geography or space, and is usually output in the form of longitude and latitude, or XYZ coordinates.

[0045] Execution steps: The control signal transmission device needs to establish a communication connection with the target device, select a suitable positioning technology, such as Wi-Fi positioning, Bluetooth positioning, etc., obtain the precise location coordinates of the target device, and resolve them into a coordinate format recognizable by the system, such as converting them into a unified coordinate system or map projection output. The location coordinate information of the target device is correlated with the communication quality data to find out possible correlations or influencing patterns, including obstacles, distances, etc. The communication quality can be evaluated by measuring indicators such as signal strength, signal-to-interference ratio, and bit error rate; then, the second calibration constraint is set, including adjusting the signal transmission power, changing the signal transmission frequency, optimizing the signal encoding method, etc. For example, in areas with poor communication quality, the bandwidth of the filter may need to be increased to reduce noise interference; in areas with good communication quality, the bandwidth can be reduced to improve signal resolution. Through accurate location information and real-time communication quality analysis, calibration constraints are formulated, and the adaptive filtering interval is adjusted accordingly to achieve optimal communication performance.

[0046] Furthermore, the communication noise reduction optimization system based on time-frequency analysis is used to perform the following method:

[0047] The U digital signals are subjected to data encryption processing, and potential security threats are predicted at the same time; risk level scores are performed based on the potential security threats, and a security mechanism is set; after the security mechanism is triggered, delay synchronization correction is performed in accordance with each communication link.

[0048] Specifically, data encryption processing is to convert the original data (U digital signals) through an encryption algorithm to prevent unauthorized access or leakage; potential security threats include malware threats, network attack threats and other categories; risk level scoring is to quantitatively evaluate the potential security threats based on the predicted severity and possibility, and assign corresponding risk levels; the security mechanism is an emergency response plan to protect the system from security threats.

[0049] Execution steps: Select a suitable encryption algorithm, such as AES or RSA, to encrypt U digital signals to ensure the security of data during transmission or storage. At the same time, generate the key required for encryption and ensure its secure storage and transmission. For symmetric encryption algorithms, it is necessary to ensure the secure distribution of keys; for asymmetric encryption algorithms, it is necessary to manage public and private keys; use the selected encryption algorithm and key to encrypt U digital signals to generate ciphertext data. Through network monitoring, log analysis and other means, collect threat intelligence related to the network environment, and conduct in-depth analysis to identify factors that may pose a threat to the system; based on historical data and current threat intelligence, build a prediction model to predict possible security threats in the future; based on the severity, scope of impact, probability of occurrence and other factors of the predicted potential security threats, evaluate the degree of impact on the system and the possibility of occurrence. In combination with the rating standards, the risk level can be scored by referring to industry specifications or other scoring standards. According to the risk level score, set up corresponding security mechanisms to deal with potential security threats. The security mechanisms may include firewalls, data backup and recovery plans, etc.; regularly update the security mechanism to adapt to the ever-changing security environment; after the security mechanism is triggered, it is necessary to perform delay synchronization correction against each communication link; delay synchronization correction aims to ensure that the data transmission delay in multiple communication links remains consistent to avoid security issues caused by delay differences.

[0050] Furthermore, the communication noise reduction optimization system based on time-frequency analysis is used to perform the following method:

[0051] Monitor the signal transmission status of each communication link; verify the signal transmission status of each communication link to determine whether a signal loss occurs; if a signal loss occurs, send an FRR request to perform forward error correction.

[0052] Specifically, the signal transmission status refers to the quality and efficiency of signal transmission in the communication link, including parameters such as signal strength, stability, and bit error rate. During the communication process, if the receiving end fails to correctly receive the signal sent by the sending end, or the signal is lost or damaged during the transmission process, it is called signal loss. FRR (Fast Reroute) is usually used for fast rerouting of network routing. In signal transmission, FRR request refers to a request sent by fast reroute to initiate recovery measures such as forward error correction (FEC). Forward error correction (FEC) is a technology that adds redundant information to the transmitted data so that the receiving end can correct errors that occur during the transmission process to a certain extent.

[0053] Execution steps: after triggering the safety mechanism, perform delay synchronization correction against each communication link, and also include monitoring the signal transmission status of each communication link, such as key indicators such as signal strength and bit error rate; detect whether the data packet is erroneous or lost during the transmission process through methods such as parity check; if data transmission errors, data packet loss or abnormal signal strength reduction are found, it is determined that the signal is missing; once the signal is missing, an FRR request is immediately sent to trigger the recovery mechanism; after receiving the FRR request, the missing signal is restored by forward error correction, and the lost or damaged data part is attempted to be restored through the redundant information in the data. After the error correction is completed, the error correction step is jumped out, and then, the delay synchronization correction is performed against each communication link. Through the above steps, the signal missing problem in the communication link is effectively detected and restored to ensure the continuity and integrity of the signal.

[0054] Furthermore, the frequency domain characteristic multi-level decomposition unit M400 is used to perform the following method:

[0055] From the frequency domain perspective, the frequency domain distribution characteristics of the U digital signals are extracted, and the frequency domain distribution characteristics are used to identify effective signals and frequency domain noise components; using the energy-aware routing protocol and the distance vector routing protocol, in comparison with the frequency domain distribution characteristics, combined with the time-frequency analysis module, the frequency domain characteristics are multi-level decomposed to obtain multi-level frequency domain components; based on the multi-level frequency domain components, combined with the frequency domain characteristic decomposition level, a second decomposition sequence is obtained.

[0056] Specifically, the frequency domain distribution characteristics refer to the performance of the signal in the frequency domain, including the amplitude, phase and other information of each frequency component, which can reflect the intensity and distribution of different frequency components in the signal; the multi-level frequency domain components are the frequency components of each level obtained by multi-level decomposition of the frequency domain characteristics.

[0057] Execution steps: From the frequency domain perspective, for the frequency domain distribution characteristics of the U digital signals, the time-frequency analysis module is combined to perform multi-level decomposition of the frequency domain characteristics to obtain the second decomposition sequence, including performing frequency domain transformation on the U digital signals, such as fast Fourier transform (FFT), converting them from the time domain to the frequency domain, and extracting the frequency domain distribution characteristics of each signal, including the main frequency, spectrum width, spectrum shape, etc.; the frequency domain distribution characteristics are used to identify effective signals and frequency domain noise components. Effective signals usually have obvious and regular frequency distributions, while frequency domain noise components are manifested as spurious and irregular frequency distributions. Energy-aware routing protocols and distance vector routing protocols are introduced. Energy-aware routing protocols are used to dynamically select the optimal path for data transmission according to the energy status of network nodes to ensure the efficiency and stability of data transmission. At the same time, distance vector routing protocols are used to dynamically establish routing tables by periodically exchanging routing information to determine the shortest path for data transmission. In contrast to the extracted frequency domain distribution characteristics, the signal is decomposed in the multi-level frequency domain using the time-frequency analysis module to obtain multi-level frequency domain components. Multi-level decomposition means that the frequency domain characteristics of the signal are subdivided at different levels to obtain more detailed frequency domain components. Each level of decomposition will decompose the signal into more detailed frequency components, including the main frequency, amplitude, etc., which helps to understand the frequency domain characteristics of the signal more deeply. Furthermore, the multi-level frequency domain components are combined with the level of frequency domain characteristic decomposition to construct a second decomposition sequence, which reflects the characteristics of the signal at different frequency domain levels. In-depth understanding of the characteristics of U digital signals from the frequency domain perspective, effective identification of effective signals and frequency domain noise components, and obtaining more detailed frequency domain information through multi-level decomposition of frequency domain characteristics help to understand the frequency domain structure of the signal more deeply and provide support for subsequent analysis.

[0058] Furthermore, the control operation unit M600 is used to execute the following method:

[0059] The target device is connected to obtain a linkage control signal, wherein the linkage control signal includes a speed control signal and a direction control signal; through the linkage control signal, in combination with the first decomposition sequence and the second decomposition sequence, synchronous coordinated operation control is performed with the target device.

[0060] Specifically, the linkage control signal is a signal used to control the action of the target device, including a speed control signal and a direction control signal, which are used to instruct the target device to make corresponding speed and direction adjustments.

[0061] Execution step: importing the noise reduction and reconstruction signal into the signal receiving port of the control signal transmission device, and then, based on the interface compatibility, communication distance, data transmission rate and other factors of the target device, selecting an appropriate interface (such as a serial port, an Ethernet interface, etc.) to connect the target device to ensure smooth communication; obtaining a linkage control signal from the target device, exemplarily, if the target device is an autonomous driving vehicle, the linkage control signal includes a speed control signal and a direction control signal, which are used to guide the acceleration, deceleration, steering and other motion operations of the target device; combining the linkage control signal with the first decomposition sequence and the second decomposition sequence for use, for example, in the autonomous driving vehicle scenario, correspondingly, the driving speed and driving direction are compensated in real time according to the road conditions and the vehicle status; preferably, the speed control is optimized by the second decomposition sequence corresponding to the frequency domain characteristics and the linkage control signal, and the direction control is optimized by the first decomposition sequence corresponding to the time domain characteristics and the linkage control signal; the processed control signal is sent to the target device, and at the same time, the response of the target device is monitored, and real-time adjustments are made according to the actual situation to achieve synchronous and coordinated operation control.

[0062] In summary, the beneficial effects of the embodiments of the present application are:

[0063] Through time-frequency analysis, the change of signals over time can be described more accurately, and noise components can be effectively identified and removed, thereby improving the quality of received signals.

[0064] 2. For control signals, the influence of time domain and frequency domain noise is effectively reduced, the anti-interference ability of communication is significantly improved, and the accuracy of control signals during communication transmission is ensured.

[0065] 3. Since the target device is connected, the linkage control signal is obtained, and the linkage control signal includes a speed control signal and a direction control signal; through the linkage control signal, the first decomposition sequence and the second decomposition sequence are combined to perform synchronous and coordinated operation control with the target device. The first decomposition sequence corresponding to the time domain characteristics and the linkage control signal are used to optimize the direction control; the processed control signal is sent to the target device, and at the same time, the response of the target device is monitored, and real-time adjustments are made according to the actual situation to achieve synchronous and coordinated operation control.

[0066] In summary, any step can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor, and no unnecessary restrictions are made here.

[0067] Furthermore, the above-mentioned technical scheme only reflects the preferred technical scheme of the technical scheme of the embodiment of the present application. Some changes that may be made to some parts thereof by technicians in this technical field all reflect the novel principles of the embodiment of the present application. Obviously, technicians in this field can make various changes and modifications to the present application without departing from the scope of the present application.

Claims

1. A communication noise reduction optimization system based on time-frequency analysis, characterized in that: The communication noise reduction optimization system based on time-frequency analysis is deployed in a control signal transmission device, and the control signal transmission device includes an ADC converter, a signal sending port, and a signal receiving port, including: A signal collection unit, connected to the signal sending port of the control signal transmission device, to collect U digital signals limited by the sampling step, wherein the digital signals are signals preprocessed by the ADC converter and the signal conditioning circuit; A time-frequency analysis module establishment unit is used to, at the same time, establish a time-frequency analysis module in combination with the U digital signals from the time-frequency analysis combination analysis logic composed of the STFT time-frequency analysis method, the wavelet transform time-frequency analysis method, and the FFT time-frequency analysis method; A time domain characteristic multi-level decomposition unit, used to perform time domain characteristic multi-level decomposition from a time domain perspective on the time domain distribution characteristics of the U digital signals in combination with the time-frequency analysis module to obtain a first decomposition sequence, wherein the first decomposition sequence includes the time domain components of the U digital signals; A frequency domain characteristic multi-level decomposition unit, used to perform frequency domain characteristic multi-level decomposition from a frequency domain perspective on the frequency domain distribution characteristics of the U digital signals in combination with the time-frequency analysis module to obtain a second decomposition sequence, wherein the second decomposition sequence includes the frequency domain components of the U digital signals; A signal reconstruction unit, configured to introduce a signal quality index according to the first decomposition sequence and the second decomposition sequence, and use fuzzy logic control logic to set an adaptive filtering interval and perform signal reconstruction to obtain a noise reduction reconstructed signal, wherein the signal quality index includes a signal-to-noise ratio and a signal distortion; A control operation unit, used for importing the noise reduction and reconstruction signal into the signal receiving port of the control signal transmission device to generate a control operation instruction; From a time domain perspective, for the time domain distribution characteristics of the U digital signals, the time domain characteristics are multi-level decomposed in combination with the time-frequency analysis module to obtain a first decomposition sequence, including: From a time domain perspective, extracting time domain distribution characteristics of the U digital signals, wherein the time domain distribution characteristics are used to identify effective signals and time domain noise components; Using the energy-aware routing protocol and the distance vector routing protocol, comparing the time domain distribution characteristics, and combining the time-frequency analysis module to perform multi-level decomposition of the time domain characteristics, obtaining multi-level time domain components, including: Monitoring a communication channel state, wherein the communication channel state includes a multipath effect; According to the communication channel state, with the goal of reducing the influence of time domain noise, obtaining a first calibration constraint, wherein the first calibration constraint is used to adjust the adaptive filtering interval; Based on the multi-level time domain components, combined with the time domain characteristic decomposition level, a first decomposition sequence is obtained; Acquiring location coordinate information of a target device connected to the control signal transmission device; A second calibration constraint is acquired according to the position coordinate information and in combination with corresponding communication quality in an environment, and the second calibration constraint is used to adjust the adaptive filtering interval.

2. The communication noise reduction optimization system based on time-frequency analysis according to claim 1, characterized in that: include: Performing data encryption processing on the U digital signals and predicting potential security threats; Based on the potential security threats, risk level scoring is performed and security mechanisms are set up; After the safety mechanism is triggered, delay synchronization correction is performed against each communication link.

3. The communication noise reduction optimization system based on time-frequency analysis as claimed in claim 2, characterized in that: Delay synchronization correction is performed against each communication link. Previously, it also included: Monitoring the signal transmission status of each communication link; Verify the signal transmission status of each communication link and determine whether there is signal loss; If signal loss occurs, an FRR request is sent for forward error correction.

4. The communication noise reduction optimization system based on time-frequency analysis according to claim 1, characterized in that: From a frequency domain perspective, for the frequency domain distribution characteristics of the U digital signals, the time-frequency analysis module is combined to perform multi-level decomposition of the frequency domain characteristics to obtain a second decomposition sequence, including: From a frequency domain perspective, extracting frequency domain distribution characteristics of the U digital signals, wherein the frequency domain distribution characteristics are used to identify effective signals and frequency domain noise components; Using the energy-aware routing protocol and the distance vector routing protocol, comparing the frequency domain distribution characteristics, and combining the time-frequency analysis module to perform multi-level decomposition of the frequency domain characteristics, to obtain multi-level frequency domain components; Based on the multi-level frequency domain components, combined with the frequency domain characteristic decomposition level, a second decomposition sequence is obtained.

5. The communication noise reduction optimization system based on time-frequency analysis according to claim 4, characterized in that: The noise reduction and reconstruction signal is introduced into the signal receiving port of the control signal transmission device, and then, the method further includes: Connecting to the target device and acquiring a linkage control signal, wherein the linkage control signal includes a speed control signal and a direction control signal; Through the linkage control signal, in combination with the first decomposition sequence and the second decomposition sequence, synchronous coordinated operation control is performed with the target device.

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