Self-adaptive synchronization method and system for multi-protocol signal transmission

Abstract

The invention discloses a self-adaptive synchronization method and system for multi-protocol signal transmission, and relates to the technical field of signal processing, and the method comprises the steps: receiving and preprocessing a multi-protocol signal, building a state space model based on the preprocessed signal, and carrying out the multi-step prediction of a clock state; future frequency deviation and delay compensation of the signals are calculated based on prediction results, the results are integrated into a calibration parameter set, rolling optimization control is carried out based on the calibration parameter set, and frequency and phase calibration is completed; and outputting the calibrated synchronizing signal and storing the calibrated synchronizing signal in a database. According to the method, the precision and adaptability of signal synchronization are remarkably improved, the frequency deviation and dynamic delay characteristics of the signals can be effectively extracted and utilized, high-precision synchronization of different protocol signals is achieved by combining modal decomposition and correlation weight calculation, the frequency and phase calibration parameters can be adjusted in real time through the self-adaptive rolling optimization control method, and high-precision synchronization of the signals is achieved. And the time reference consistency of the signals is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and in particular 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 multi-channel signals. In application scenarios such as broadcasting, real-time video conferencing, and distributed computing, different devices usually need to synchronously process signals from multiple sources, which often involve multiple transmission protocols. Due to the diversity of protocols, the timestamps, frequency references, and phase characteristics of each signal may be significantly different, resulting in inconsistent time references in signal transmission and processing. This inconsistency can easily cause signal superposition interference, screen switching freezes, or audio and video asynchrony, affecting user experience and system performance. In the prior art, multi-channel signal synchronization methods usually rely on fixed synchronization algorithms. However, fixed algorithms are often difficult to guarantee high-precision synchronization effects when facing multiple transmission protocols, complex channel conditions, and dynamic network environments. For example, traditional time synchronization methods have poor robustness to dynamic delays and sudden interference, and frequency calibration methods have limited filtering capabilities for high-frequency noise and environmental noise. These technical limitations still make it a huge challenge to achieve high-precision synchronization of cross-protocol, multi-channel signals. Summary of the invention

[0003] In view of the problems existing in the above-mentioned existing 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 make it difficult to achieve high-precision synchronization of cross-protocol and multi-channel signals.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides an adaptive synchronization method for multi-protocol signal transmission, comprising: Receive multi-protocol signals and pre-process them, build a state space model based on the pre-processed signals and perform multi-step prediction of clock states; 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; The calibrated synchronization signal is output and stored in the database.

[0006] As a preferred solution of the adaptive synchronization method for multi-protocol signal transmission of the present invention, wherein: the receiving of multi-protocol signals and pre-processing 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 RTC and SRT protocol signals; Initialize the signal frequency reference and phase reference, perform fast Fourier transform on the received signal to obtain the frequency component and phase of the signal, calculate the frequency offset based on the error between the frequency component and the frequency reference, and calculate the phase offset based on the error between the phase and the phase reference.

[0007] As a preferred solution of the adaptive synchronization method for multi-protocol signal transmission of the present invention, wherein: the state space model is constructed based on the preprocessed signal, which means extracting the frequency offset, phase offset and timestamp of each signal and defining them as state variables, and combining the state variables into a state vector; According to the signal characteristics, the state transfer matrix A and control matrix B are constructed. The discrete time state space equation is used to describe the dynamic evolution relationship of the signal. The observable signal characteristics are defined as observation variables and the observation matrix C is constructed. According to the observation matrix, the state variables are mapped to observation values. The initial state of the signal is estimated using the initial characteristics of the preprocessed signal. The Kalman filter is used for real-time state update and covariance update to obtain the real-time state of the signal.

[0008] As a preferred solution of the adaptive synchronization method for multi-protocol signal transmission of the present invention, wherein: 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; The clock state vector is a vector describing the key characteristics of a signal in a state-viewing model, indicating the frequency offset, phase offset and time reference of the signal at a certain moment; Perform time-varying filter empirical mode decomposition on x(k) to generate n intrinsic mode functions and residual components; Set the bandwidth parameters and cutoff frequency of the decomposition, use the iterative algorithm to perform TVF-EMD decomposition, extract the mode functions and residual components to obtain all decomposed modes and residual components; Calculate the energy contribution of each mode function , set the screening threshold W, and screen out the modes whose energy proportion is greater than or equal to the screening threshold W as the key mode function; Combine the selected key mode functions with the residual components to reconstruct the enhanced signal ; The correlation score between each modality and the reconstructed enhanced signal is calculated through the correlation metric, and the correlation score is converted into a modality weight through the attention mechanism; The state space model is enhanced by reconstructing the enhanced signal and filtering the dynamic characteristics of the mode, and a new state evolution formula is obtained; Set the number of forecast periods , taking the next moment state x(k+1) calculated by the enhanced state model as the starting point, initialize the state variable x(k+1); Based on the state transfer matrix A and the observation matrix C, predict the state of the next N steps; Starting from the initial state x(k+1), iteratively predict the future state until the future state is predicted The state of each step is recorded, and the predicted state of each step is outputted, and the state sequence after evolution is output; Define the input increment matrix , indicating the future Cumulative input impact within the step; Calculate the error between the actual state value and the predicted value, and calculate the current control input increment based on the error ; Combine the increment matrix and the control input increment to calculate the control correction for the future state; According to the modal weights and modal functions calculated by the enhanced state model, a modal correction formula is defined; The state evolution, cumulative input increments and dynamic modal features are integrated into the final multi-step prediction formula to obtain the prediction result Y of the clock state.

[0009] As a preferred solution of the adaptive synchronization method for multi-protocol signal transmission described in the present invention, wherein: the calculation of the future frequency offset and delay compensation of the signal based on the prediction results and integrating the results into a calibration parameter set refers to extracting the phase prediction value within the future u cycles from the multi-step prediction results, constructing a linear regression model, taking the phase offset data as the independent variable, taking the actual change value of the frequency reference as the dependent variable, and using the regression model to calculate the future frequency offset trend; Obtain the clock state of each signal in the future u cycles from the multi-step prediction, calculate the cross-correlation function of signal a and signal b in the future prediction cycle, 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; The future frequency offset trend and dynamic delay compensation value are integrated as a calibration parameter set.

[0010] As a preferred solution of the adaptive synchronization method for multi-protocol signal transmission of the present invention, wherein: the rolling optimization control based on the calibration parameter set and completing the frequency and phase calibration refers to using the future frequency offset trend and dynamic delay compensation value in the calibration parameter set to design the objective function J of the synchronization adjustment; Set constraints, use gradient descent to solve the objective function, and calculate the optimal control input for the current cycle ,according to and the frequency offset trend in the calibration parameter set, dynamically adjusting the frequency offset of the signal; Update the frequency state of the signal and gradually align it with the time reference; Dynamically adjust the phase compensation amount of the signal according to the optimized frequency state and delay compensation value; The calculated phase compensation amount is applied to the signal to obtain a calibrated synchronization signal.

[0011] As a preferred solution of the adaptive synchronization method for multi-protocol signal transmission described in the present invention, wherein: the output of the calibrated signal and storing it in a database refers to sorting the synchronized multi-protocol signals according to timestamps and storing them in the database, synchronously backing up the stored data regularly, regularly performing security and integrity checks on the stored data and backup data, and generating a test report.

[0012] In a second aspect, the present invention provides an adaptive synchronization system for multi-protocol signal transmission, comprising: A signal receiving module, used for receiving multi-channel protocol signals and pre-processing the signals; The state space modeling and clock prediction module is used to use the preprocessed signal to build a state space model and perform multi-step prediction to obtain the future clock state of the signal; A calibration parameter set generation module, used to calculate the future frequency offset trend and dynamic delay compensation value of the signal based on the multi-step prediction results, and generate a calibration parameter set; A synchronous calibration module is used to design a synchronous adjustment objective function and dynamically optimize the control input using a calibration parameter set to perform real-time calibration of frequency and phase; The data storage module is used to output the calibrated synchronization signal and store it safely.

[0013] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: 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.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the adaptive synchronization method for multi-protocol signal transmission as described in the first aspect of the present invention.

[0015] 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 the signal, combines modal decomposition with correlation weight calculation, and realizes high-precision synchronization of signals of different protocols. The adaptive rolling optimization control method can adjust the frequency and phase calibration parameters in real time to ensure the consistency of the time base of the signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 Schematic diagram of a flow chart of an adaptive synchronization method for multi-protocol signal transmission in Example 1; Figure 2 Schematic diagram of the structure of the adaptive synchronization system for multi-protocol signal transmission in Example 1. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0019] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0021] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and which provides an adaptive synchronization method for multi-protocol signal transmission. The adaptive synchronization method for multi-protocol signal transmission includes the following steps: S1, receiving multi-protocol signals and preprocessing them, building a state space model based on the preprocessed signals and performing multi-step prediction of clock states; Specifically, receiving the multi-protocol signal and performing preprocessing refers to receiving the multi-protocol signal, performing denoising processing on the signal using a low-pass filter, removing high-frequency noise and interference, and extracting the timestamps of the RTC and SRT protocol signals; Initialize the signal frequency reference and phase reference, perform fast Fourier transform on the received signal to obtain the frequency component and phase of the signal, calculate the frequency offset based on the error between the frequency component and the frequency reference, and calculate the phase offset based on the error between the phase and the phase reference.

[0022] By receiving and preprocessing multi-protocol signals, using low-pass filters to remove noise, FFT to analyze frequency and phase components, and accurate calculation of frequency offset and phase offset, efficient preprocessing and benchmark initialization of multi-channel signals are achieved. The accuracy and stability of signal synchronization are significantly improved, especially for complex scenarios where multiple protocol signals coexist, and the reliability of multi-channel signal transmission and synchronization can be greatly improved.

[0023] Further, constructing a state space model based on the preprocessed signal means extracting the frequency offset, phase offset and time stamp of each signal and defining them as state variables, and combining the state variables into a state vector; According to the signal characteristics, the state transfer matrix A and control matrix B are constructed. The discrete time state space equation is used to describe the dynamic evolution relationship of the signal. The observable signal characteristics (such as frequency, phase, and time measured in real time) are defined as observation variables and the observation matrix C is constructed. The state variables are mapped to observation values ​​according to the observation matrix. The observation matrix enables the model to use real-time data for correction, reduce the deviation between state prediction and reality, improve the accuracy of state update, and enhance the ability to respond to sudden interference. The initial characteristics of the preprocessed signal are used to estimate the initial state of the signal. The Kalman filter is used for real-time state update and covariance update to obtain the real-time state of the signal.

[0024] By constructing a state space model based on preprocessed signals, an efficient dynamic signal synchronization method is proposed. The state variables are extracted and the state vector is defined to achieve a comprehensive characterization of the dynamic characteristics of the signal. The state transfer matrix and control matrix are constructed to describe the dynamic evolution relationship of the signal. The signal state is updated in real time through the Kalman filter, which further improves the robustness and synchronization accuracy of the system.

[0025] Furthermore, performing 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; The clock state vector is a vector describing the key characteristics of a signal in a state-viewing model, indicating the frequency offset, phase offset and time reference of the signal at a certain moment; Perform time-varying filter empirical mode decomposition on x(k) to generate n intrinsic mode functions and residual components:

[0026] In the formula, is the ith eigenmode function, is the residual component, n is the total number of modes; Set the bandwidth parameters and cutoff frequency of the decomposition, use the iterative algorithm to perform TVF-EMD decomposition, extract the mode functions and residual components to obtain all decomposed modes and residual components; By extracting the clock state vector x(k), the dynamic characteristics of the signal in terms of frequency, phase and time basis can be captured, laying the foundation for multi-step prediction. The signal is further decomposed using time-varying filter 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.

[0027] Calculate the energy contribution of each mode function :

[0028] In the formula, is the jth intrinsic mode function, n is the total number of modes, k represents the sequence number of the discrete moment, referring to the current sampling point; By statistically analyzing historical data and setting a screening threshold W, modes with an energy ratio greater than or equal to the screening threshold W are screened out as key mode functions; Combine the selected key mode functions with the residual components to reconstruct the enhanced signal :

[0029] In the formula, is the i-th eigenmode function selected, is the residual component, which represents the low-frequency trend or long-term characteristics of the signal, and m is the number of modes screened out; By calculating the energy proportion of the modal function and setting the screening threshold W, the modal noise with a low energy proportion is effectively eliminated, the key modal function is retained, and the reconstructed enhanced signal is obtained after merging with the residual component. The reconstructed signal has a higher signal-to-noise ratio and dynamic characteristics, which can significantly improve the accuracy and stability of the prediction model.

[0030] The correlation score between each modality and the reconstructed enhanced signal is calculated through the correlation metric, and the correlation score is converted into a modality weight through the attention mechanism; The importance of the modality is quantified by calculating the correlation score between each modality and the reconstructed enhanced signal. The attention mechanism is introduced to convert the correlation score into the modality weight, making the state update process more flexible and intelligent. The dynamic adjustment of the modality weight can adaptively allocate computing resources and improve synchronization efficiency.

[0031] The state space model is enhanced by reconstructing the enhanced signal and filtering the dynamic characteristics of the mode, and a new state evolution formula is obtained:

[0032] In the formula, is the state variable at the next moment, is the current state variable, is the control input, which represents the dynamic adjustment parameter, is the global weight of the reconstructed enhancement signal, indicating its contribution to the state update, obtained by the gradient descent method. is the modal weight, indicating the modality The contribution ratio in the state update is calculated by the attention mechanism; Set the number of forecast periods , taking the next moment state x(k+1) calculated by the enhanced state model as the starting point, initialize the state variable x(k+1); Based on the state transfer matrix A and the observation matrix C, the state of the next N steps is predicted: , In the formula, The future The state prediction result of the step is is the state transfer matrix Power, Description The state evolution after steps, x(k) is the current state variable; Starting from the initial state x(k+1), iteratively predict the future state until the future state is predicted The state of each step is recorded, and the predicted state of each step is outputted, and the state sequence after evolution is output; Define the input increment matrix , indicating the future Cumulative input impact within a step: , In the formula, is the state transfer matrix raised to the power of l, It is the predicted The step before the step; Calculate the error between the actual state value and the predicted value, and calculate the current control input increment based on the error : , In the formula, is the proportional control coefficient, which is used to adjust the influence of the current error on the control input. is the integral control coefficient, which is used to reflect the impact of the cumulative error on the control input. is the differential control coefficient, which is 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, indicating 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, j is the index variable of the integral control part, which is used to represent all historical moments from the initial moment 1 to the current moment k; By reconstructing the signal and key modal characteristics to enhance the state space model, the adaptability of the state model to the dynamic environment is significantly improved. Multi-step prediction uses the state transfer matrix A and the observation matrix C to gradually iterate the future clock state and output an accurate state sequence, providing a reliable basis for subsequent calibration.

[0033] Combining the increment matrix and the control input increment, calculate the control correction for the future state: , In the formula, is the effect of the control input increment on the future state; According to the modal weight and modal function calculated by the enhanced state model, the modal correction formula is defined: , In the formula, is the dynamic correction of the future state by the modal characteristics, is the dynamic modal weight, is the i-th eigenmode function, n is the total number of mode functions; By extracting the phase prediction value in the next u cycles, the linear regression model is used to calculate the frequency offset trend, and the dynamic delay compensation value is obtained by combining the cross-correlation function. These parameters are integrated into a correction parameter set to provide dynamic input for rolling optimization control. The control correction formula generated using the correction parameter set can quickly respond to errors and dynamically adjust the frequency and phase state of the signal.

[0034] The state evolution, cumulative input increments and dynamic modal features are integrated into the final multi-step prediction formula to obtain the prediction result Y of the clock state: ; By accumulating input increments and dynamic modal characteristics, the state evolution formula is optimized and the final prediction result Y is generated. This result can accurately reflect the changing trend of the future state and provide reliable support for signal synchronization calibration.

[0035] By introducing TVF-EMD decomposition, modal weight allocation and dynamic state enhancement technology, high-precision synchronization and dynamic calibration of multi-protocol signals are achieved. The use of state space model and multi-step prediction method not only improves the accuracy of signal synchronization, but also significantly enhances the system's adaptability to dynamic environments. This method has a wide range of application value in the fields of radio and television, real-time communication, multimedia synchronization, etc., and helps to solve the frequency offset and delay compensation problems in multi-protocol signal transmission.

[0036] 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; 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 value within the future u cycles from the multi-step prediction results, constructing a linear regression model, taking the phase offset data as an independent variable, taking the actual change value of the frequency reference as a dependent variable, and using the regression model to calculate the future frequency offset trend; Obtain the clock state of each signal in the future u cycles from the multi-step prediction, calculate the cross-correlation function of signal a and signal b in the future prediction cycle, 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; The future frequency offset trend and dynamic delay compensation value are integrated as a calibration parameter set.

[0037] Based on the multi-step prediction results, the linear regression model and cross-correlation function are used to innovatively realize the calculation of the future frequency offset trend and dynamic delay compensation value of the signal, and through the integration of the correction parameter set, it 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 base alignment of signals in asynchronous transmission. The integrated correction parameter set not only improves the processing efficiency of multi-protocol signals, but also provides accurate input for subsequent rolling optimization control and dynamic calibration.

[0038] Further, performing rolling optimization control based on the calibration parameter set and completing 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: , Where R is the unified time base, Y(k) is the clock state prediction of the current cycle, is the control input increment, Q is the weight matrix, which is used to balance the error term and the control input change amplitude. It is set according to the design requirements of the synchronous system and is usually a positive definite diagonal matrix; The dual objectives of minimizing synchronization error and optimizing input control smoothness are achieved through the design of the objective function J. The introduction of the objective function can dynamically adapt to the characteristics of different signals and effectively balance the synchronization accuracy and system stability.

[0039] Set the constraints as: , In the formula, and are the minimum and maximum values ​​of the control input, usually set according to the physical characteristics of the system; The setting of control input constraints, combined with the physical characteristics of the system, limits the adjustment range of the control input. This mechanism ensures the physical feasibility of synchronous adjustment and avoids the instability of the system caused by excessive input adjustment.

[0040] The objective function is solved by using the gradient descent method. Through fast optimization calculation, the real-time and high efficiency of signal synchronization are guaranteed, and the optimal control input of the current cycle is calculated. ,according to And the frequency offset trend in the calibration parameter set, dynamically adjust the frequency offset of the signal: , In the formula, is the frequency offset of the current cycle, is the dynamic delay compensation value, is the quantized group period; Through the dynamic calibration of frequency offset, the influence of signal frequency reference offset on synchronization accuracy is eliminated, providing a reliable basis for subsequent phase calibration; Update the frequency state of the signal, gradually aligning it with the time reference: , In the formula, is the updated frequency status, is the frequency state of the current cycle; Gradually align with a unified time reference to ensure signal consistency in the frequency dimension, laying the foundation for high-precision signal synchronization; According to the optimized frequency state and delay compensation value, the phase compensation amount of the signal is dynamically adjusted: , , In the formula, is the phase change, is the dynamic delay compensation value, is the phase state after calibration, is the phase state of the current cycle; Through dual calibration of frequency and phase, the comprehensiveness and accuracy of signal synchronization are ensured.

[0041] The calculated phase compensation amount is applied to the signal to obtain a calibrated synchronization signal.

[0042] 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 the existing multi-protocol signal synchronization method are solved. By introducing the quantized group period and gradient descent method, not only the synchronization accuracy and robustness of the system are improved, but also the real-time adjustment capability is significantly enhanced.

[0043] S3, output the calibrated synchronization signal and store it in the database; Specifically, outputting the calibrated signal and storing it in a database means sorting the synchronized multi-protocol signal according to timestamps and storing it in the database, synchronously and regularly backing up the stored data, regularly performing security and integrity checks on the stored data and backup data and generating a test report.

[0044] Through the design of the output and storage of calibration signals, efficient storage, secure backup and dynamic adjustment of signals after synchronization are achieved. Timestamp sorting storage improves the retrieval efficiency of signal data, data backup and detection mechanisms enhance the system's fault tolerance and data security, and the detection report provides a basis for system optimization. The overall design is efficient, reliable and secure, and can be widely used in 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.

[0045] This embodiment also provides an adaptive synchronization system for multi-protocol signal transmission, including: A signal receiving module, used for receiving multi-channel protocol signals and pre-processing the signals; The state space modeling and clock prediction module is used to use the preprocessed signal to build a state space model and perform multi-step prediction to obtain the future clock state of the signal; A calibration parameter set generation module, used to calculate the future frequency offset trend and dynamic delay compensation value of the signal based on the multi-step prediction results, and generate a calibration parameter set; A synchronous calibration module is used to design a synchronous adjustment objective function and dynamically optimize the control input using a calibration parameter set to perform real-time calibration of frequency and phase; The data storage module is used to output the calibrated synchronization signal and store it safely.

[0046] This embodiment also provides a computer device, which is suitable for 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 computer-executable instructions to implement the adaptive synchronization method for multi-protocol signal transmission proposed in the above embodiment.

[0047] The computer device may be a terminal, and 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, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0048] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the adaptive synchronization method for multi-protocol signal transmission proposed in the above embodiment is implemented; 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 (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0049] 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 the signal, and combines modal decomposition and correlation weight calculation to achieve high-precision synchronization of signals of different protocols. The adaptive rolling optimization control method can adjust the frequency and phase calibration parameters in real time to ensure the consistency of the signal's time base.

[0050] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An adaptive synchronization method for multi-protocol signal transmission, characterized in that: include, Receive multi-protocol signals and pre-process them, build a state space model based on the pre-processed signals and perform multi-step prediction of clock states; 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; The calibrated synchronization signal is output and stored in the database.

2. The adaptive synchronization method for multi-protocol signal transmission according to claim 1, characterized in that: Receiving the multi-protocol signal and performing pre-processing refers to receiving the multi-protocol signal, using a low-pass filter to perform denoising on the signal, removing high-frequency noise and interference, and extracting the timestamps of the RTC and SRT protocol signals; Initialize the signal frequency reference and phase reference, perform fast Fourier transform on the received signal to obtain the frequency component and phase of the signal, calculate the frequency offset based on the error between the frequency component and the frequency reference, and calculate the phase offset based on the error between the phase and the phase reference.

3. The adaptive synchronization method for multi-protocol signal transmission according to claim 2, characterized in that: The constructing of the state space model based on the preprocessed signal refers to extracting the frequency offset, phase offset and timestamp of each signal and defining them as state variables, and combining the state variables into a state vector; According to the signal characteristics, the state transfer matrix A and control matrix B are constructed. The discrete time state space equation is used to describe the dynamic evolution relationship of the signal. The observable signal characteristics are defined as observation variables and the observation matrix C is constructed. According to the observation matrix, the state variables are mapped to observation values. The initial state of the signal is estimated using the initial characteristics of the preprocessed signal. The Kalman filter is used for real-time state update and covariance update to obtain the real-time state of the signal.

4. The adaptive synchronization method for multi-protocol signal transmission according to claim 3, characterized in that: 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; The clock state vector is a vector describing the key characteristics of a signal in a state-viewing model, indicating the frequency offset, phase offset and time reference of the signal at a certain moment; Perform time-varying filter empirical mode decomposition on x(k) to generate n intrinsic mode functions and residual components; Set the bandwidth parameters and cutoff frequency of the decomposition, use the iterative algorithm to perform TVF-EMD decomposition, extract the mode functions and residual components to obtain all decomposed modes and residual components; Calculate the energy contribution of each mode function , set the screening threshold W, and screen out the modes whose energy proportion is greater than or equal to the screening threshold W as the key mode function; The selected key mode functions are combined with the residual components to reconstruct the enhanced signal ; The correlation score between each modality and the reconstructed enhanced signal is calculated through the correlation metric, and the correlation score is converted into a modality weight through the attention mechanism; The state space model is enhanced by reconstructing the enhanced signal and filtering the dynamic characteristics of the mode, and a new state evolution formula is obtained; Set the number of forecast periods , taking the next moment state x(k+1) calculated by the enhanced state model as the starting point, initialize the state variable x(k+1); Based on the state transfer matrix A and the observation matrix C, predict the state of the next N steps; Starting from the initial state x(k+1), iteratively predict the future state until the future state is predicted The state of each step is recorded, and the predicted state of each step is outputted, and the state sequence after evolution is output; Define the input increment matrix , indicating the future Cumulative input impact within the step; Calculate the error between the actual state value and the predicted value, and calculate the current control input increment based on the error ; Combine the increment matrix and the control input increment to calculate the control correction for the future state; According to the modal weights and modal functions calculated by the enhanced state model, a modal correction formula is defined; The state evolution, cumulative input increments and dynamic modal features are integrated into the final multi-step prediction formula to obtain the prediction result Y of the clock state.

5. The adaptive synchronization method for multi-protocol signal transmission according to claim 4, characterized in that: The 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 refers to extracting the phase prediction value within the future u cycles from the multi-step prediction results, constructing a linear regression model, taking the phase offset data as an independent variable, taking the actual change value of the frequency reference as a dependent variable, and using the regression model to calculate the future frequency offset trend; Obtain the clock state of each signal in the future u cycles from the multi-step prediction, calculate the cross-correlation function of signal a and signal b in the future prediction cycle, 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; The future frequency offset trend and dynamic delay compensation value are integrated as a calibration parameter set.

6. The adaptive synchronization method for multi-protocol signal transmission according to claim 5, characterized in that: The rolling optimization control based on the calibration parameter set and completing the frequency and phase calibration refers to designing the objective function J of the synchronization adjustment using the future frequency offset trend and dynamic delay compensation value in the calibration parameter set; Set constraints, use gradient descent to solve the objective function, and calculate the optimal control input for the current cycle ,according to and the frequency offset trend in the calibration parameter set, dynamically adjusting the frequency offset of the signal; Update the frequency state of the signal and gradually align it with the time reference; Dynamically adjust the phase compensation amount of the signal according to the optimized frequency state and delay compensation value; The calculated phase compensation amount is applied to the signal to obtain a calibrated synchronization signal.

7. The adaptive synchronization method for multi-protocol signal transmission according to claim 6, characterized in that: Outputting the calibrated signal and storing it in a database means sorting the synchronized multi-protocol signals according to timestamps and storing them in the database, synchronously backing up the stored data regularly, regularly checking the security and integrity of the stored data and the backup data, and generating a test 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: include, A signal receiving module, used for receiving multi-channel protocol signals and pre-processing the signals; The state space modeling and clock prediction module is used to use the preprocessed signal to build a state space model and perform multi-step prediction to obtain the future clock state of the signal; A calibration parameter set generation module, used to calculate the future frequency offset trend and dynamic delay compensation value of the signal based on the multi-step prediction results, and generate a calibration parameter set; A synchronous calibration module is used to design a synchronous adjustment objective function and dynamically optimize the control input using a calibration parameter set to perform real-time calibration of frequency and phase; The data storage module is used to output the calibrated synchronization signal and store it safely.

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, the steps of the adaptive synchronization method for multi-protocol signal transmission described in any one of claims 1 to 7 are implemented.

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

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