An Adaptive Synchronization Method and System for Multi-Protocol Signal Transmission

Abstract

The present invention discloses an adaptive synchronization method and system for multi-protocol signal transmission, which relates to the technical field of signal processing. It includes receiving multi-protocol signals and performing preprocessing, constructing a state space model based on the preprocessed signals and performing multi-step prediction of the clock state; calculating the future frequency offset and delay compensation of the signals based on the prediction results, integrating the results into a calibration parameter set, performing rolling optimization control based on the calibration parameter set and completing frequency and phase calibration; outputting the calibrated synchronization signals and storing them in a database. The present invention significantly improves the accuracy and adaptability of signal synchronization, can effectively extract and utilize the frequency offset and dynamic delay characteristics of signals, combines modal decomposition and correlation weight calculation to achieve high-precision synchronization of different protocol signals, and the adaptive rolling optimization control method can adjust the frequency and phase calibration parameters in real time to ensure the consistency of the time reference of the signals.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and particularly to an adaptive synchronization method and system for multi-protocol signal transmission. Background Art

[0002] With the rapid development of multimedia information technology, modern communication systems have put forward higher requirements for the transmission and processing of multiplexed signals. In application scenarios such as radio and television, real-time video conferencing, and distributed computing, different devices usually need to synchronously process signals from multiple sources, and these signals often involve multiple transmission protocols. Due to the diversity of protocols, the timestamps, frequency references, and phase characteristics of each signal may vary significantly, resulting in inconsistent time bases in signal transmission and processing. This inconsistency easily causes problems such as signal superposition interference, stuttering in picture switching, or audio-video asynchronization, affecting the user experience and system performance. In the prior art, multi-channel signal synchronization methods usually rely on fixed synchronization algorithms. However, fixed algorithms often struggle to ensure high-precision synchronization effects when faced with multiple transmission protocols, complex channel conditions, and dynamic network environments. For example, traditional time synchronization methods have poor robustness to dynamic delays and bursty interference, and frequency calibration methods have limited ability to filter out high-frequency noise and environmental noise. These technical limitations still pose a huge challenge to achieving high-precision synchronization of cross-protocol, multi-channel signals. Summary of the Invention

[0003] In view of the problems existing in the above-mentioned prior art adaptive synchronization method and system for multi-protocol signal transmission, the present invention is proposed.

[0004] Therefore, the present invention provides an adaptive synchronization method for multi-protocol signal transmission to solve the problem that the existing technology limitations still pose a huge challenge to achieving high-precision synchronization of cross-protocol, multi-channel signals.

[0005] To solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides an adaptive synchronization method for multi-protocol signal transmission, which includes,

[0007] Receiving multi-protocol signals and performing preprocessing, constructing a state space model based on the preprocessed signals and performing multi-step prediction of the clock state;

[0008] Calculating the future frequency offset and delay compensation of the signals based on the prediction results, integrating the results into a calibration parameter set, and performing rolling optimization control based on the calibration parameter set to complete frequency and phase calibration;

[0009] Outputting the calibrated synchronization signals and storing them in a database.

[0010] As a preferred embodiment of the adaptive synchronization method for multi - protocol signal transmission according to the present invention, wherein: receiving the multi - protocol signal and performing pre - processing means receiving the multi - protocol signal, using a low - pass filter to denoise the signal, removing high - frequency noise and interference, and extracting the timestamps of the RTC and SRT protocol signals;

[0011] Initializing the signal frequency reference and phase reference, performing a fast Fourier transform on the received signal to obtain the frequency components and phase of the signal, calculating the frequency offset according to the error between the frequency components and the frequency reference, and calculating the phase offset according to the error between the phase and the phase reference.

[0012] As a preferred embodiment of the adaptive synchronization method for multi - protocol signal transmission according to the present invention, wherein: constructing a state - space model based on the pre - processed signal means extracting the frequency offset, phase offset, and timestamp of each signal path and defining them as state variables, and combining the state variables into a state vector;

[0013] Constructing a state - transition matrix A and a control matrix B according to the signal characteristics, using a discrete - time state - space equation to describe the dynamic evolution relationship of the signal, defining the observable signal characteristics as observation variables and constructing an observation matrix C, mapping the state variables to observation values according to the observation matrix, estimating the initial state of the signal using the initial characteristics of the pre - processed signal, and using a Kalman filter to perform real - time state update and covariance update to obtain the real - time state of the signal.

[0014] As a preferred embodiment of the adaptive synchronization method for multi - protocol signal transmission according to the present invention, wherein: performing multi - step prediction of the clock state means obtaining the current clock state vector x(k) from the output of the state - space model;

[0015] The clock state vector is a vector that describes the key characteristics of the signal in the state - seeing model, representing the frequency offset, phase offset, and time reference of the signal at a certain moment;

[0016] Performing time - variable filtering empirical mode decomposition on x(k) to generate n intrinsic mode functions and a residual component;

[0017] Setting the decomposition bandwidth parameter and cut - off frequency, performing TVF - EMD decomposition using an iterative algorithm, and extracting the mode functions and residual components to obtain all decomposition modes and residual components;

[0018] Calculating the energy proportion of each mode function , setting a screening threshold W, and screening out the modes with an energy proportion greater than or equal to the screening threshold W as key mode functions;

[0019] Combining the selected key mode functions with the residual component to reconstruct an enhanced signal ;

[0020] Calculate the correlation score between each modality and the reconstructed enhanced signal through correlation measurement, and transform the correlation score into modality weights through the attention mechanism;

[0021] Use the dynamic characteristics of the reconstructed enhanced signal and the selected modalities to enhance the state space model, and obtain a new state evolution formula;

[0022] Set the number of prediction periods , and initialize the state variable x(k + 1) with the next moment state x(k + 1) calculated by enhancing the state model;

[0023] Based on the state transition matrix A and the observation matrix C, predict the state for the next N steps;

[0024] Starting from the initial state x(k + 1), iteratively predict the future state until the state at the th step of the future is predicted, record the predicted state at each step, and output the evolved state sequence;

[0025] Define the input increment matrix , which represents the cumulative input impact within the next steps;

[0026] Calculate the error between the actual state value and the predicted value, and calculate the current control input increment based on the error ;

[0027] Combine the increment matrix and the control input increment to calculate the control correction for the future state;

[0028] Define the modality correction formula according to the modality weights and modality functions calculated by the enhanced state model;

[0029] Integrate the state evolution, cumulative input increment, and dynamic modality features into the final multi-step prediction formula to obtain the prediction result Y of the clock state.

[0030] As a preferred solution of the adaptive synchronization method for multi-protocol signal transmission described in the present invention, wherein: calculating the future frequency offset and delay compensation of the signal based on the prediction result and integrating the results into the calibration parameter set means extracting the phase prediction values within the next u periods from the multi-step prediction result, constructing a linear regression model, using the phase offset data as the independent variable and the actual change value of the frequency reference as the dependent variable, and calculating the future frequency offset trend using the regression model;

[0031] Obtain the clock state of each signal within the next u periods from the multi-step prediction, calculate the cross-correlation function of signals a and b within the future prediction period, set the search range, and find the delay value corresponding to the maximum value of the cross-correlation function as the optimal dynamic delay compensation value;

[0032] Integrate the future frequency offset trend and dynamic delay compensation value as a calibration parameter set.

[0033] As a preferred solution of the adaptive synchronization method for multi-protocol signal transmission according to the present invention, wherein: the rolling optimization control based on the calibration parameter set and the completion of frequency and phase calibration means using the future frequency offset trend and dynamic delay compensation value in the calibration parameter set to design the objective function J of synchronization adjustment;

[0034] Set the constraint conditions, use the gradient descent method to solve the objective function, and calculate the optimal control input for the current period , according to and the frequency offset trend in the calibration parameter set, dynamically adjust the frequency offset of the signal;

[0035] Update the frequency state of the signal and gradually align it with the time reference;

[0036] According to the optimized frequency state and delay compensation value, dynamically adjust the phase compensation amount of the signal;

[0037] Apply the calculated phase compensation amount to the signal to obtain the calibrated synchronization signal.

[0038] As a preferred solution of the adaptive synchronization method for multi-protocol signal transmission according to the present invention, wherein: the outputting of the calibrated signal and storing it in the database means sorting the synchronized multi-protocol signals according to the time stamp and storing them in the database, synchronously backing up the stored data regularly, and regularly detecting the security and integrity of the stored data and the backup data and generating a detection report.

[0039] In a second aspect, the present invention provides an adaptive synchronization system for multi-protocol signal transmission, including,

[0040] A signal receiving module, configured to receive multi-channel protocol signals and preprocess the signals;

[0041] A state space modeling and clock prediction module, configured to use the preprocessed signals to construct a state space model and perform multi-step prediction to obtain the future clock state of the signals;

[0042] A calibration parameter set generation module, configured to calculate the future frequency offset trend and dynamic delay compensation value of the signals based on the multi-step prediction results and generate a calibration parameter set;

[0043] A synchronization calibration module, configured to use the calibration parameter set to design a synchronization adjustment objective function and dynamically optimize the control input to perform real-time calibration of frequency and phase;

[0044] A data storage module, configured to output the calibrated synchronization signal and perform secure storage.

[0045] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, any step of the adaptive synchronization method for multi-protocol signal transmission as described in the first aspect of the present invention is implemented.

[0046] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the adaptive synchronization method for multi-protocol signal transmission as described in the first aspect of the present invention is implemented.

[0047] The beneficial effects of the present invention are as follows: The present invention significantly improves the accuracy and adaptability of signal synchronization, can effectively extract and utilize the frequency offset and dynamic delay characteristics of signals, combines modal decomposition and correlation weight calculation to achieve high-precision synchronization of different protocol signals, and the adaptive rolling optimization control method can adjust the frequency and phase calibration parameters in real time to ensure the consistency of the time reference of the signals. Description of the Drawings

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0049] Figure 1 It is a schematic flowchart of the adaptive synchronization method for multi-protocol signal transmission in Embodiment 1;

[0050] Figure 2 It is a schematic structural diagram of the adaptive synchronization system for multi-protocol signal transmission in Embodiment 1. Detailed Embodiments

[0051] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed embodiments of the present invention in conjunction with the drawings of the specification.

[0052] Many specific details are set forth in the following description in order to fully understand the present invention, but the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0053] Second, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The phrase "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or alternative embodiment that is mutually exclusive with other embodiments.

[0054] Embodiment 1, referring to Figure 1 and Figure 2 , is the first embodiment of the present invention. This embodiment provides an adaptive synchronization method for multi-protocol signal transmission. The adaptive synchronization method for multi-protocol signal transmission includes the following steps:

[0055] S1. Receive multi-protocol signals and perform preprocessing, construct a state space model based on the preprocessed signals, and perform multi-step prediction of the clock state;

[0056] Specifically, receiving multi-protocol signals and performing preprocessing means receiving multi-protocol signals, using a low-pass filter to denoise the signals, removing high-frequency noise and interference, and extracting the timestamps of the RTC and SRT protocol signals.

[0057] Initialize the signal frequency reference and phase reference, perform a fast Fourier transform on the received signals to obtain the frequency components and phases of the signals, calculate the frequency offset according to the error between the frequency components and the frequency reference, and calculate the phase offset according to the error between the phase and the phase reference.

[0058] By receiving multi-protocol signals and performing preprocessing, using a low-pass filter for denoising, FFT analysis of frequency and phase components, and precise calculation of frequency offset and phase offset, efficient preprocessing of multi-channel signals and reference initialization are achieved. The accuracy and stability of signal synchronization are significantly improved, especially suitable for complex scenarios where multiple protocol signals coexist, and the reliability of multi-channel signal transmission and synchronization can be greatly enhanced.

[0059] Furthermore, constructing a state space model based on the preprocessed signals means extracting the frequency offset, phase offset, and timestamp of each channel signal and defining them as state variables, and combining the state variables into a state vector.

[0060] Construct the state transition matrix A and the control matrix B according to the signal characteristics, use the discrete-time state space equation to describe the dynamic evolution relationship of the signal, define the observable signal characteristics (such as the measured frequency, phase, and time in real time) as the observation variables and construct the observation matrix C, map the state variables to the observation values according to the observation matrix, and the observation matrix enables the model to use real-time data for correction, reducing the deviation between the state prediction and the actual situation, improving the accuracy of state update, enhancing the response ability to sudden disturbances, estimate the initial state of the signal using the initial characteristics of the preprocessed signal, and use the Kalman filter for real-time state update and covariance update to obtain the real-time state of the signal.

[0061] By constructing a state space model based on the preprocessed signal, an efficient dynamic signal synchronization method is proposed. Extract the state variables and define the state vector to achieve a comprehensive characterization of the dynamic characteristics of the signal, construct the state transition matrix and the control matrix to describe the dynamic evolution relationship of the signal, and use the Kalman filter to update the signal state in real time, further improving the robustness and synchronization accuracy of the system.

[0062] Furthermore, performing multi-step prediction of the clock state means obtaining the current clock state vector x(k) from the output of the state space model;

[0063] The clock state vector is a vector that describes the key characteristics of the signal in the state space model, representing the frequency offset, phase offset, and time reference of the signal at a certain moment;

[0064] Perform time-varying filtering empirical mode decomposition on x(k) to generate n intrinsic mode functions and a residual component:

[0065]

[0066] where is the i-th intrinsic mode function, is the residual component, and n is the total number of modes;

[0067] Set the decomposition bandwidth parameter and cut-off frequency, use the iterative algorithm to perform TVF-EMD decomposition, and extract the mode functions and the residual component to obtain all the decomposed modes and the residual component;

[0068] By extracting the clock state vector x(k), the dynamic characteristics of the signal in terms of frequency, phase, and time reference can be captured, laying the foundation for multi-step prediction. Further decompose the signal using time-varying filtering empirical mode decomposition (TVF-EMD) to generate intrinsic mode functions and residual components with different characteristics, which helps to separate the high-frequency disturbances and low-frequency trends of the signal.

[0069] Calculate the energy proportion of each mode function :

[0070]

[0071] Wherein, is the jth intrinsic mode function, n is the total number of modes, k represents the serial number of discrete moments, referring to the current sampling point;

[0072] By statistically analyzing historical data, a screening threshold W is set, and the modes with an energy proportion greater than or equal to the screening threshold W are selected as the key mode functions;

[0073] The selected key mode functions are merged with the residual component to reconstruct the enhanced signal :

[0074]

[0075] Wherein, is the ith intrinsic mode function selected, is the residual component, representing the low-frequency trend or long-term characteristics of the signal, and m is the number of selected modes;

[0076] By calculating the energy proportion of the mode functions and setting the screening threshold W, the mode noises with relatively low energy proportions are effectively removed, and the key mode functions are retained. After merging with the residual component, the reconstructed enhanced signal is obtained. The reconstructed signal has a higher signal-to-noise ratio and dynamic characteristics, and can significantly improve the accuracy and stability of the prediction model.

[0077] By calculating the correlation scores between each mode and the reconstructed enhanced signal, and through the attention mechanism, the correlation scores are converted into mode weights;

[0078] By calculating the correlation scores between each mode and the reconstructed enhanced signal, the importance of the modes is quantified. Introducing the attention mechanism to convert the correlation scores into mode weights makes the state update process more flexible and intelligent. The dynamic adjustment of the mode weights can adaptively allocate computing resources and improve the synchronization efficiency.

[0079] Using the dynamic characteristics of the reconstructed enhanced signal and the selected modes to enhance the state space model, a new state evolution formula is obtained:

[0080]

[0081] Wherein, is the state variable at the next moment, is the current state variable, is the control input, representing the dynamically adjusted parameter, is the global weight of the reconstructed enhanced signal, representing its contribution degree to the state update, obtained by the gradient descent method, is the mode weight, representing the mode The contribution ratio in state update is calculated through the attention mechanism;

[0082] Set the number of prediction periods Starting from the next moment state x(k + 1) calculated by the enhanced state model, initialize the state variable x(k + 1);

[0083] Based on the state transition matrix A and the observation matrix C, predict the state for the next N steps:

[0084] ,

[0085] In the formula, is the state prediction result for the th step in the future, is the th power of the state transition matrix, describing the state evolution after steps, and x(k) is the current state variable;

[0086] Starting from the initial state x(k + 1), iteratively predict the future state until predicting the state for the th step in the future, record the predicted state at each step, and output the evolved state sequence;

[0087] Define the input increment matrix , representing the cumulative input influence within the future steps:

[0088] ,

[0089] In the formula, is the lth power of the state transition matrix, is the previous step of the predicted th step;

[0090] Calculate the error between the actual state value and the predicted value, and calculate the current control input increment :

[0091] ,

[0092] In the formula, is the proportional control coefficient, used to adjust the influence of the current error on the control input, is the integral control coefficient, used to reflect the influence of the cumulative error on the control input, is the differential control coefficient, used to reflect the influence of the error change rate on the control input, , and Through experimental adjustment, e(k) is the prediction error at the current moment, representing the deviation between the actual state and the predicted state. e(k - 1) is the prediction error at the previous moment k - 1, and e(j) is the historical error value. is the time step, k represents the serial number of the discrete moment, referring to the current sampling point, and j is the index variable of the integral control part, used to represent all historical moments from the initial moment 1 to the current moment k;

[0093] By reconstructing the signal and the key modal characteristics, the state - space model is enhanced, significantly improving the adaptability of the state model to the dynamic environment. Multi - step prediction uses the state - transition matrix A and the observation matrix C to iteratively step into the future clock state, outputting an accurate state sequence, providing a reliable basis for subsequent calibration.

[0094] Combined with the increment matrix and the control - input increment, calculate the control correction for the future state:

[0095] ,

[0096] In the formula, is the influence of the control - input increment on the future state;

[0097] According to the modal weights and modal functions calculated by the enhanced state model, define the modal - correction formula:

[0098] ,

[0099] In the formula, is the dynamic correction of the modal characteristics to the future state, is the dynamic modal weight, is the i - th eigen - modal function, and n is the total number of modal functions;

[0100] By extracting the phase - prediction values within the next u periods, using a linear - regression model to calculate the frequency - offset trend, and combining with the cross - correlation function to obtain the dynamic - delay compensation value. These parameters are integrated into a calibration - parameter set, providing dynamic input for rolling - optimization control. The control - correction formula generated using the calibration - parameter set can quickly respond to errors and dynamically adjust the frequency and phase states of the signal.

[0101] Integrate the state evolution, the cumulative input increment, and the dynamic modal characteristics into the final multi - step prediction formula to obtain the prediction result Y of the clock state:

[0102] ;

[0103] By accumulating the input increment and the dynamic modal characteristics, optimize the state - evolution formula and generate the final prediction result Y. This result can accurately reflect the change trend of the future state, providing reliable support for signal - synchronization calibration.

[0104] By introducing TVF-EMD decomposition, modal weight allocation, and dynamic state enhancement techniques, high-precision synchronization and dynamic calibration of multi-protocol signals are achieved. Using state space models and multi-step prediction methods not only improves the accuracy of signal synchronization but also significantly enhances the system's adaptability to dynamic environments. This method has broad application value in fields such as radio and television, real-time communication, and multimedia synchronization, and helps to solve frequency offset and delay compensation problems in multi-protocol signal transmission.

[0105] S2. Calculate the future frequency offset and delay compensation of the signal based on the prediction results, integrate the results into a calibration parameter set, perform rolling optimization control based on the calibration parameter set, and complete frequency and phase calibration;

[0106] Specifically, calculating the future frequency offset and delay compensation of the signal based on the prediction results and integrating the results into a calibration parameter set means extracting the phase prediction values within the next u cycles from the multi-step prediction results, constructing a linear regression model, using the phase offset data as the independent variable, using the actual change value of the frequency reference as the dependent variable, and using the regression model to calculate the future frequency offset trend;

[0107] Obtain the clock states of each signal within the next u cycles from the multi-step prediction, calculate the cross-correlation function of signals a and b within the future prediction cycles, set the search range, and find the delay value corresponding to the maximum value of the cross-correlation function as the optimal dynamic delay compensation value;

[0108] Integrate the future frequency offset trend and the dynamic delay compensation value as the calibration parameter set.

[0109] Based on the multi-step prediction results, using a linear regression model and a cross-correlation function, innovatively realizes the calculation of the future frequency offset trend and the dynamic delay compensation value of the signal, and through the integration of the correction parameter set, provides core support for the high-precision synchronization of multi-protocol signals. In the key steps, the introduction of the linear regression model significantly improves the accuracy of signal frequency trend prediction, enabling the system to better adapt to complex channel environments, and the dynamic delay compensation method of the cross-correlation function effectively solves the problem of time reference alignment in asynchronous signal transmission. The integrated correction parameter set not only improves the processing efficiency of multi-protocol signals but also provides accurate inputs for subsequent rolling optimization control and dynamic calibration.

[0110] Furthermore, performing rolling optimization control based on the calibration parameter set and completing frequency and phase calibration means using the future frequency offset trend and the dynamic delay compensation value in the calibration parameter set to design the objective function J of synchronization adjustment:

[0111] ,

[0112] where R is the unified time reference, and Y(k) is the clock state prediction of the current cycle, where $\Delta u$ is the control input increment, and $Q$ is the weight matrix, which is used to balance the error term and the magnitude of the control input change. It is set according to the design requirements of the synchronization system and is usually a positive definite diagonal matrix;

[0113] Through the design of the objective function $J$, the dual objectives of minimizing the synchronization error and optimizing the smoothness of the input control are achieved. The introduction of the objective function can dynamically adapt to the characteristics of different signals, effectively balancing the synchronization accuracy and system stability.

[0114] The constraint conditions are set as:

[0115] ,

[0116] where, and are the minimum and maximum values of the control input, which are usually set according to the physical characteristics of the system;

[0117] The setting of the constraint conditions for the control input, combined with the physical characteristics of the system, limits the adjustment range of the control input. This mechanism ensures the physical feasibility of the synchronization adjustment and avoids the unstable impact on the system caused by excessive input adjustment.

[0118] The gradient descent method is used to solve the objective function. Through fast optimization calculations, the real-time performance and efficiency of signal synchronization are guaranteed, and the optimal control input for the current period is calculated. According to and the frequency offset trend in the calibration parameter set, the frequency offset of the signal is dynamically adjusted:

[0119] ,

[0120] where, is the frequency offset for the current period, is the dynamic delay compensation value, is the quantized group period;

[0121] Through the dynamic calibration of the frequency offset, the influence of the signal frequency reference offset on the synchronization accuracy is eliminated, providing a reliable basis for subsequent phase calibration;

[0122] The frequency state of the signal is updated and gradually aligned with the time reference:

[0123] ,

[0124] where, is the updated frequency state, is the frequency state for the current period;

[0125] Gradually align with the unified time reference to ensure the consistency of the signal in the frequency dimension, laying the foundation for high-precision signal synchronization;

[0126] According to the optimized frequency state and delay compensation value, dynamically adjust the phase compensation amount of the signal:

[0127] ,

[0128] ,

[0129] In the formula, is the phase change amount, is the dynamic delay compensation value, is the calibrated phase state, is the phase state of the current cycle;

[0130] Through the dual calibration of frequency and phase, the comprehensiveness and accuracy of signal synchronization are ensured.

[0131] Apply the calculated phase compensation amount to the signal to obtain the calibrated synchronization signal.

[0132] By performing rolling optimization control and frequency-phase calibration based on the calibration parameter set, the problems of insufficient dynamic adaptability and low synchronization accuracy in existing multi-protocol signal synchronization methods are solved. By introducing the quantified group period and the gradient descent method, not only the synchronization accuracy and robustness of the system are improved, but also the real-time adjustment ability is significantly enhanced.

[0133] S3. Output the calibrated synchronization signal and store it in the database;

[0134] Specifically, outputting the calibrated signal and storing it in the database means sorting the synchronized multi-protocol signals according to the timestamp and storing them in the database, synchronously backing up the stored data regularly, and regularly detecting the security and integrity of the stored data and the backup data and generating a detection report.

[0135] Through the design of the output and storage of the calibrated signal, efficient storage, secure backup and dynamic adjustment after signal synchronization are realized. The timestamp sorting storage improves the retrieval efficiency of signal data, the data backup and detection mechanism enhances the fault tolerance ability and data security of the system, and the detection report provides a basis for system optimization. The overall design has high efficiency, reliability and security, and can be widely applied to technical fields such as multimedia signal processing, real-time communication and distributed data storage, providing a systematic solution for the management and synchronization of multi-protocol signals.

[0136] This embodiment also provides an adaptive synchronization system for multi-protocol signal transmission, including:

[0137] A signal receiving module, configured to receive multiple protocol signals and preprocess the signals;

[0138] A state space modeling and clock prediction module, configured to utilize the preprocessed signals to construct a state space model and perform multi-step prediction to obtain the future clock state of the signals;

[0139] A calibration parameter set generation module, configured to calculate the future frequency offset trend and dynamic delay compensation value of the signals based on the multi-step prediction results, and generate a calibration parameter set;

[0140] A synchronization calibration module, configured to use the calibration parameter set to design a synchronization adjustment objective function and dynamically optimize the control input to perform real-time calibration of frequency and phase;

[0141] A data storage module, configured to output the calibrated synchronization signals and perform secure storage.

[0142] This embodiment also provides a computer device, applicable to the case of an adaptive synchronization method for multi-protocol signal transmission, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the adaptive synchronization method for multi-protocol signal transmission as proposed in the above embodiment.

[0143] This computer device can be a terminal. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the outer shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0144] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the adaptive synchronization method for multi-protocol signal transmission as proposed in the above embodiment. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM for short), electrically erasable programmable read-only memory (EEPROM for short), erasable programmable read-only memory (EPROM for short), programmable read-only memory (PROM for short), read-only memory (ROM for short), magnetic memory, flash memory, a magnetic disk or an optical disc.

[0145] In summary, the present invention significantly improves the accuracy and adaptability of signal synchronization, can effectively extract and utilize the frequency offset and dynamic delay characteristics of signals, combines modal decomposition and correlation weight calculation to achieve high-precision synchronization of different protocol signals, and the adaptive rolling optimization control method can adjust the frequency and phase calibration parameters in real time to ensure the consistency of the time reference of the signals.

[0146] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. An adaptive synchronization method for multi - protocol signal transmission, characterized in that: including Receiving multi-protocol signals and performing preprocessing, constructing a state space model based on the preprocessed signals, and performing multi-step prediction of the clock state; Calculating the future frequency offset and delay compensation of the signals based on the prediction results, integrating the results into a calibration parameter set, performing rolling optimization control based on the calibration parameter set, and completing frequency and phase calibration; Outputting the calibrated synchronization signals and storing them in the database; The multi-step prediction of the clock state refers to obtaining the current clock state vector x(k) from the output of the state space model; Performing time-varying filtering empirical mode decomposition on x(k) to generate n intrinsic mode functions and a residual component; Setting the decomposition bandwidth parameter and cut-off frequency, performing TVF-EMD decomposition using an iterative algorithm, and extracting the mode functions and residual component to obtain all decomposed modes and residual components; Calculate the energy proportion of each modal function , set a screening threshold W, and screen out the modes with an energy proportion greater than or equal to the screening threshold W as key modal functions; Merge the selected key modal functions with the residual components to reconstruct the enhanced signal ; Calculating the correlation score between each mode and the reconstructed enhanced signal through correlation measurement, and converting the correlation score into a mode weight through an attention mechanism; Enhancing the state space model using the reconstructed enhanced signal and the dynamic characteristics of the selected modes to obtain a new state evolution formula; Set the number of prediction periods , starting from the next-state x(k + 1) calculated by the enhanced state model, initialize the state variable x(k + 1); Predicting the state for the next N steps based on the state transition matrix A and the observation matrix C; Starting from the initial state x(k+1), iteratively predict the future states until the state at the step of future prediction is reached, record the predicted state at each step, and output the evolved state sequence; Define the input increment matrix , representing the cumulative input impact within the next steps; Calculate the error between the actual state value and the predicted value, and calculate the current control input increment based on the error ; Combining the increment matrix and the control input increment to calculate the control correction for the future state; Defining a mode correction formula according to the mode weights and mode functions calculated by the enhanced state model; Integrating the state evolution, cumulative input increment, and dynamic mode characteristics into a final multi-step prediction formula to obtain the prediction result Y of the clock state.

2. The adaptive synchronization method for multi-protocol signal transmission according to claim 1, wherein: The receiving multi-protocol signals and performing preprocessing refers to receiving multi-protocol signals, using a low-pass filter to denoise the signals, removing high-frequency noise and interference, and extracting the timestamps of the RTC and SRT protocol signals; Initializing the signal frequency reference and phase reference, performing a fast Fourier transform on the received signals to obtain the frequency components and phases of the signals, calculating the frequency offset according to the error between the frequency components and the frequency reference, and calculating the phase offset according to the error between the phases and the phase reference.

3. The adaptive synchronization method for multi-protocol signal transmission according to claim 2, wherein: The constructing a state space model based on the preprocessed signals refers to extracting the frequency offset, phase offset, and timestamp of each signal path and defining them as state variables, and combining the state variables into a state vector; Constructing the state transition matrix A and the control matrix B according to the signal characteristics, using the discrete-time state space equation to describe the dynamic evolution relationship of the signals, defining the observable signal characteristics as the observation variables and constructing the observation matrix C, mapping the state variables to the observed values according to the observation matrix, estimating the initial state of the signals using the initial characteristics of the preprocessed signals, and performing real-time state update and covariance update using the Kalman filter to obtain the real-time state of the signals.

4. The adaptive synchronization method for multi-protocol signal transmission according to claim 3, wherein: The clock state vector is a vector that describes the key characteristics of the signals in the state space model, representing the frequency offset, phase offset, and time reference of the signals at a certain moment.

5. The adaptive synchronization method for multi-protocol signal transmission according to claim 4, characterized in that: Calculating the future frequency offset and delay compensation of the signal based on the prediction results and integrating the results into a calibration parameter set means extracting the phase prediction values within the next u cycles from the multi-step prediction results, constructing a linear regression model, using the phase offset data as the independent variable and the actual change value of the frequency reference as the dependent variable, and calculating the future frequency offset trend using the regression model; Obtaining the clock state of each signal within the next u cycles from the multi-step prediction, calculating the cross-correlation function of signals a and b within the future prediction cycles, setting the search range, and finding the delay value corresponding to the maximum value of the cross-correlation function as the optimal dynamic delay compensation value; Integrating the future frequency offset trend and the dynamic delay compensation value as the calibration parameter set.

6. The adaptive synchronization method for multi-protocol signal transmission according to claim 5, characterized in that: Performing rolling optimization control based on the calibration parameter set and completing frequency and phase calibration means using the future frequency offset trend and the dynamic delay compensation value in the calibration parameter set to design the objective function J for synchronous adjustment; Set the constraint conditions, use the gradient descent method to solve the objective function, and calculate the optimal control input for the current period , according to and the frequency offset trend in the calibration parameter set, dynamically adjust the frequency offset of the signal; Updating the frequency state of the signal and gradually aligning it with the time reference; Dynamically adjusting the phase compensation amount of the signal according to the optimized frequency state and delay compensation value; Applying the calculated phase compensation amount to the signal to obtain the calibrated synchronous signal.

7. The adaptive synchronization method for multi-protocol signal transmission according to claim 6, wherein: Outputting the calibrated signal and storing it in the database means sorting the synchronized multi-protocol signals according to the timestamp and storing them in the database, regularly backing up the stored data, and regularly detecting the security and integrity of the stored data and the backup data and generating a detection report.

8. An adaptive synchronization system for multi-protocol signal transmission, based on the adaptive synchronization method for multi-protocol signal transmission according to any one of claims 1 to 7, characterized in that: Including, A signal receiving module for receiving multi-protocol signals and preprocessing the signals; A state space modeling and clock prediction module for using the preprocessed signals to construct a state space model and performing multi-step prediction to obtain the future clock state of the signals; A calibration parameter set generation module for calculating the future frequency offset trend and dynamic delay compensation value of the signal based on the multi-step prediction results and generating a calibration parameter set; A synchronous calibration module for using the calibration parameter set to design an objective function for synchronous adjustment and dynamically optimizing the control input to perform real-time calibration of frequency and phase; A data storage module for outputting the calibrated synchronous signal and performing secure storage.

9. A computer device, comprising: A memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, it implements the steps of the adaptive synchronization method for multi-protocol signal transmission according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the adaptive synchronization method for multi-protocol signal transmission according to any one of claims 1 to 7.

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