Accurate clock synchronization parameter tracking method based on augmented Kalman neural network

Abstract

The invention relates to a precise clock synchronization parameter tracking method based on an augmented Kalman neural network, and belongs to the field of time synchronization. According to the method, for a multi-hop network scene with environment temperature change and asymmetric link transmission delay, the influence of the accumulated asymmetric link transmission delay in a multi-hop network on clock synchronization precision is considered, and a multi-hop network clock observation equation containing the accumulated asymmetric link delay is obtained; establishing a recursive state equation of the clock frequency offset and the temperature change based on a quadratic polynomial model of the temperature and the clock frequency offset; and modeling an evolution process of accumulated asymmetric delay and an unknown temperature coefficient into a first-order linear difference equation, augmenting the first-order linear difference equation to a clock parameter tracking state space model, and realizing joint tracking of clock phase offset and clock frequency offset of a slave node by using an augmented Kalman neural network algorithm. According to the method, joint tracking of clock phase offset and frequency offset is carried out by adopting the idea of data and model joint driving, and the accuracy and robustness of clock parameter tracking are improved.

Description

Technical Field

[0001] The invention belongs to the field of time synchronization and relates to an accurate clock synchronization parameter tracking method based on an augmented Kalman neural network. Background Art

[0002] Time synchronization provides a unified time reference for many time-critical applications in the network, such as data fusion, resource scheduling, energy management, and collaborative measurement, and is a basic requirement for ensuring the normal operation of the network. Traditional time synchronization protocols include: Global Positioning System (GPS), Network Time Protocol (NTP), Precise Time Protocol (PTP), etc. The Precise Time Protocol has become one of the most commonly used protocols in distributed networks due to its obvious advantages of few restrictions, high cost performance, and the ability to ensure efficient distributed interaction in the network.

[0003] The key to precise clock synchronization is to eliminate the clock phase offset between the reference clock, which is corrected by collecting hardware timestamps. However, due to the accumulation of clock frequency offset, the clock may still experience unbounded clock phase offset after calibration, resulting in frequent clock resynchronization. Therefore, in order to extend the synchronization interval, it is necessary to track both the clock phase offset and the clock frequency offset. Most of the existing studies on the joint tracking of clock phase offset and clock frequency offset for precise clock synchronization in multi-hop networks assume that the uplink and downlink delays are symmetrical, so the traditional Kalman filter is used to jointly track the clock phase offset and frequency offset. However, these studies still have the following shortcomings: 1) In actual multi-hop networks, due to the influence of routing asymmetry, line speed asymmetry, and variable processing delays of each intermediate device, the uplink and downlink delays of the master clock and the clock to be synchronized are asymmetric. 2) Affected by the variable temperature of the network deployment environment, the temperature change will affect the inherent characteristics of the clock crystal oscillator, causing the output frequency of the clock crystal oscillator to change, thereby generating an unbounded clock phase offset. 3) In actual multi-hop networks, the observation noise covariance is difficult to be given through experience or the given observation noise covariance is not accurate, while the traditional Kalman filter is very sensitive to the observation noise covariance. The unknown observation noise covariance will lead to a decrease in the tracking performance of the Kalman filter for the clock parameters.

[0004] Based on the above shortcomings, the present invention urgently needs to solve the problem of joint reliable tracking of precise clock phase offset and frequency offset of unknown observation noise covariance under the influence of accumulated delay asymmetry and temperature change in multi-hop networks. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide an accurate clock synchronization parameter tracking method based on an augmented Kalman neural network. In a multi-hop network composed of a source node, a target node and a plurality of intermediate nodes, the influence of the accumulated asymmetric link transmission delay in the multi-hop network on the clock synchronization accuracy is considered, and the multi-hop network clock observation equation containing the accumulated asymmetric link delay is derived. A recursive state equation of clock frequency offset and temperature change is established based on a quadratic polynomial model of temperature and clock frequency offset; the evolution process of the accumulated asymmetric delay and the unknown temperature coefficient is modeled as a first-order linear difference equation, and augmented to a clock parameter tracking state space model, and the augmented Kalman neural network algorithm is used to realize the joint tracking of the clock phase deviation and clock frequency deviation of the slave node. The present invention takes into account the influence of the accumulated asymmetric link delay and temperature change in the multi-hop network, and augments the accumulated asymmetric link delay and the unknown temperature coefficient to the state space vector through a quadratic augmented Kalman filter, compensating for the influence of asymmetric delay and temperature change on tracking clock phase offset and frequency offset. In addition, the present invention further introduces a deep learning method to learn the Kalman gain through a neural network, thereby overcoming the problem of unknown observation noise covariance in a multi-hop network and significantly improving the accuracy and robustness of clock parameter tracking.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A method for accurately tracking clock synchronization parameters based on an augmented Kalman neural network, the method specifically comprising the following steps:

[0008] S1: According to the precise clock protocol in the multi-hop network, synchronization messages are sent in a point-to-point mode. Considering the accumulated asymmetric delay, the target node establishes a clock observation equation based on the received synchronization message; considering the influence of temperature on the clock crystal oscillator, a recursive model of the node clock frequency offset and temperature is established, and based on the two-state clock model, the clock state equation is established;

[0009] S2: Combine the clock observation equation and the clock state equation to establish a clock state space model, model the unknown temperature coefficient and the accumulated asymmetric delay as a first-order linear difference equation, and expand the unknown temperature coefficient and the accumulated asymmetric delay to the state space vector;

[0010] S3: The augmented Kalman neural network method is used to track the clock parameters. The observed values ​​and true values ​​of the clock state parameters are used as training sets, and the clock parameters are trained using a recurrent neural network to obtain a prediction model. The prediction gain of the prediction model output is obtained by inputting the posterior estimate of the previous moment and the observed value of the current moment into the prediction model. The prediction gain is used to weigh the trust of the observed value and the predicted value.

[0011] Furthermore, in S1, the observation equation and state equation of precise clock synchronization of multi-hop network under the influence of cumulative asymmetric delay and ambient temperature are established, and the specific steps are as follows:

[0012] S11: Intermediate node i calculates the clock offset from the master clock based on the received timestamp It is expressed as:

[0013]

[0014] in, Indicates the time when the master clock sends the Sync message. Indicates the downstream transparent clock TC i The time when the Sync message is received; It is expressed as:

[0015]

[0016] in, represents the link delay between transparent clock i and node i-1, Indicates the residence time of the synchronization message in node i-1, Represents the total delay from the previous node i to the master clock, the initial value is zero; the residence time of node i-1 can be calculated as:

[0017]

[0018] where ε (i-1,i-1) is a random variable, usually assumed to be Gaussian white noise, representing the timestamp contribution associated with different clock readings;

[0019] S12: In the kth round of synchronization, assuming that the clock offset between node i and node i-1 is And further assuming that the clock phase offset remains constant over sufficiently short time intervals, we have:

[0020]

[0021] in, and They represent the uplink and downlink fixed delays between nodes i and i-1 respectively, and their relationship is: in represents the fixed delay asymmetry measure between node i and node i-1 in the kth synchronization cycle;

[0022] S13: Assume that the average link delay is but It can be expressed as:

[0023]

[0024] S14: The formula for measuring the clock offset between the master clock and the transparent clock i is:

[0025]

[0026] in, Represents the measurement of clock offset, and They correspond to the cumulative residence time and the cumulative unknown asymmetric delay of the synchronization information between the master clock and the transparent clock i, respectively; represents the entire observation noise and is expressed as:

[0027]

[0028] in represents the measurement error of the i-th node in the k-th round of synchronization; in a multi-hop network, It is difficult to obtain through experience. is a Gaussian noise signal with zero mean and unknown covariance;

[0029] S15: The measured value of the clock frequency deviation between the master clock and the transparent clock i The clock phase deviation can be measured gives:

[0030]

[0031] S16: Establish a parabolic model of unknown coefficients related to clock frequency offset and temperature change, and regard interference as noise input. If the sampling interval is small enough, the recursive equation of clock frequency offset is modeled as:

[0032]

[0033] in, is the unknown relationship between the current clock frequency offset and temperature change of the intermediate node i;

[0034] S17: For curves with unknown parabolic coefficients, The change is related to the central axis of the parabola, which is the reference temperature. Celsius, approximately:

[0035]

[0036] in, represents the unknown temperature coefficient of node i at the kth round of synchronization, which is an unknown value that fluctuates within a small range;

[0037] Then the recursive form of the clock phase offset can be obtained as:

[0038]

[0039] S18: When designing the state equation, use a two-state clock model to determine the clock phase offset to describe the behavior of the clock at consecutive synchronization moments; the clock recursive state equation is as follows:

[0040]

[0041] Further, in S2, a clock state space model is established, the unknown temperature coefficient and the accumulated asymmetric delay are modeled as a first-order linear difference equation, and the unknown temperature coefficient and the accumulated asymmetric delay are expanded to a state space vector. The specific steps are:

[0042] S21: Combining the clock state equation and clock observation equation of the intermediate node i, the clock state space equation is:

[0043]

[0044] in B=[1 0] T ,

[0045] S22: The accumulated asymmetric delay between the master clock and the transparent clock i is expressed as:

[0046]

[0047] The asymmetric delay between two adjacent transparent clocks is modeled as a first-order difference equation:

[0048]

[0049] represents Gaussian noise with zero mean;

[0050] S23: The recursive form of accumulating asymmetric delays is obtained:

[0051]

[0052] Due to the accumulation of multiple zero-mean Gaussian noises, the covariance is difficult to obtain accurately;

[0053] S24: Similarly, the unknown temperature coefficient is modeled as a first-order difference equation:

[0054]

[0055] S25: The accumulated asymmetric delay and the unknown temperature coefficient are expanded to the state space vector, and the expanded clock state space model is obtained as follows:

[0056]

[0057] in,

[0058] Furthermore, in S3, the clock parameter tracking is performed using an augmented Kalman neural network method, and the specific steps are as follows:

[0059] S31: Calculate the prior state estimate of transparent node i according to the augmented state space model The calculation formula is:

[0060]

[0061] in is the posterior estimate of node i at the previous moment;

[0062] S32: Use the measurement value of node i at time k and the prior estimate Residual is calculated as:

[0063]

[0064] S33: Augmented Kalman Neural Network-based Method Using Neural Networks to Learn Kalman Gains Through learning Perform execution status update, expressed as:

[0065]

[0066] S34: Kalman gain The calculation method is to optimize the mean square error loss function of the parameter Θ through end-to-end training; the dataset consists of N sequences of length m, expressed as in is the observation vector sequence of trajectory j, is the true value sequence of trajectory j; the recursion is extended to m time steps, and a sequence-to-sequence supervised training scheme is obtained; for each trajectory j, the empirical minimum mean square error loss function is measured as:

[0067]

[0068] Among them, Θ (.) is the output of the recurrent neural network, Θ is its trainable parameter, and α is the regularization coefficient;

[0069] S35: To optimize the parameter Θ, an adaptive momentum stochastic optimization method is adopted, which combines the second-order momentum with stochastic gradient descent. For each batch of trajectories indexed by r, L < N training indices are randomly selected, and the batch loss ξ r (Θ) is as follows:

[0070]

[0071] The beneficial effects of the present invention are as follows:

[0072] (1) The present invention fully considers the influence of the cumulative uplink and downlink transmission delay asymmetry on the clock phase offset and the influence of temperature change on the clock frequency offset in the actual multi-hop network, and uses the augmented Kalman neural network algorithm to jointly track the clock phase offset and frequency offset in the multi-hop network, significantly reducing the influence of temperature change and cumulative asymmetric delay on the clock parameter tracking accuracy in the multi-hop network.

[0073] (2) The present invention uses the neural network to learn the Kalman gain, and weighs the trust degree of the estimated value and the measured value through the learned gain, overcoming the problem of the decline in the joint tracking accuracy of the clock phase offset and frequency offset caused by the unknown observation noise covariance, and significantly improving the accuracy and robustness of the clock parameter tracking.

[0074] Other advantages, objectives and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be described in detail preferably with reference to the accompanying drawings, where:

[0076] Figure 1 is the schematic diagram of clock synchronization information interaction in the multi-hop network of the present invention;

[0077] Figure 2 is the flow chart of the accurate clock synchronization parameter tracking method based on the augmented Kalman neural network in the present invention;

[0078] Figure 3 is the performance comparison diagram of the accurate clock phase offset tracking method based on the augmented Kalman neural network in the present invention;

[0079] Figure 4 is the performance comparison diagram of the accurate clock frequency offset tracking method based on the augmented Kalman neural network in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0080] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0081] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0082] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0083] See also Figure 1 to Figure 4 , Figure 1 This is a schematic diagram of time message interaction in the multi-hop network precise clock synchronization provided by the present invention, such as Figure 1 As shown in the figure, the master clock and the slave clock exchange dedicated synchronization messages for precise clock synchronization through multiple intermediate transparent clock nodes. The specific process is as follows:

[0084] 1) In the kth round of synchronization messages, the master clock is The Sync message is sent at all times and forwarded by the intermediate transparent clock to the slave clock, which records the time when the message is received. 2) After the Sync message is sent, the master clock sends the Forwarded to the slave clock by the middle n-1 cascaded transparent clocks; 3) Transparent clock TC i-1 After receiving the Sync message, record its reception time The message is then forwarded by other active ports to the downstream transparent clock TC i , TC i Record the time of receiving And record its forwarding time as At the same time, the transparent clock TC i-1 Calculate the residence time of the Sync message as Indicates transparent clock TC i-1 The processing delay from receiving the Sync message to forwarding the message. And record it cumulatively in the correctionField field of the Follow_Up message; 4) Transparent clock TC i The synchronization message is obtained by point-to-point delay measurement in TC i-1 With TC i Specifically, TC i Send a Pdelay_Req message to the upstream clock and record its sending time as After the upstream clock receives the message, it records the receiving time as and in Time, to TC i Reply to Pdelay_Res message. TC i After receiving the message, record the receiving time as Subsequently, the upstream clock sends the Pdelay_Res_Follow_Up message to Send to TC i At this time, the transparent clock TC i Collected Four timestamps, and under the assumption that the link delay is symmetrical, TC is calculated. i-1 With TC i The average link transmission delay between the two. At the same time, it is recorded in the correctionField field of the Follow_Up message;

[0085] Intermediate node i calculates the clock offset from the master clock based on the received timestamp It can be expressed as:

[0086]

[0087] in represents the total path delay from the master clock to the intermediate transparent clock node i. It can be expressed as:

[0088]

[0089] in, represents the link delay between transparent clock i and node i-1, Indicates the residence time of the synchronization message at node i-1, Represents the total delay from the previous node i to the master clock, the initial value is zero. The residence time of node i-1 can be calculated as:

[0090]

[0091] where ε (i-1,i-1) is a random variable, usually assumed to be Gaussian white noise, representing the contribution of timestamps associated with different clock readings.

[0092] In the kth round of synchronization, node i collects the correctionField field. Timestamp, which can calculate the clock offset between node i and node i-1 for

[0093]

[0094] in, and They represent the uplink and downlink fixed delays between nodes i and i-1 respectively, and their relationship is: in It represents the fixed delay asymmetry measure between node i and node i-1 in the kth synchronization cycle.

[0095] Assume that the average link delay is Subtracting the two formulas in (4), and further assuming that the clock phase offset remains constant in a sufficiently short time interval, the average link delay can be calculated as

[0096]

[0097] Combining formulas (1), (2), (3), and (5), the clock phase offset formula between the master clock and transparent clock i can be obtained:

[0098]

[0099] in, Represents the measurement of clock offset, and They correspond to the cumulative residence time and the cumulative asymmetric link delay of the synchronization information between the master clock and the transparent clock i, respectively. represents the entire observation noise and can be expressed as:

[0100]

[0101] in represents the measurement error of the i-th node in the k-th round of synchronization. It is difficult to obtain empirically, so it is assumed is an unknown noise signal with zero mean and covariance.

[0102] The measured value of the clock frequency deviation between the master clock and the transparent clock i The value can be measured by clock phase offset The calculation results in:

[0103]

[0104] Furthermore, a recursive form of clock frequency offset in a multi-hop model is established. The specific steps are:

[0105] A parabolic model of unknown coefficients related to clock frequency offset and temperature change is established, and interference is regarded as noise input. If the sampling interval is small enough, the recursive equation of clock frequency offset can be modeled as:

[0106]

[0107] in, is the unknown relationship between the current clock frequency offset of intermediate node i and the temperature change.

[0108] For a curve with unknown parabolic coefficients, The change of the central axis of the parabola (i.e. the reference temperature Celsius), can be approximated as:

[0109]

[0110] in, It represents the unknown temperature coefficient of node i at the kth round of synchronization, which is an unknown value fluctuating in a small range.

[0111] Combining equations (9) and (10), the recursive form of the clock phase offset can be obtained as:

[0112]

[0113] When designing the state equation, a two-state clock model is used to determine the clock phase offset to describe the behavior of the clock at continuous synchronization moments. Combined with equation (11), the clock recursive state equation is as follows:

[0114]

[0115] Furthermore, a clock state space model is established to model the unknown temperature coefficient and the accumulated asymmetric delay as a first-order linear difference equation, and the unknown temperature coefficient and the accumulated asymmetric delay are expanded to the state space vector. The specific steps are as follows:

[0116] Combining equations (6), (8) and (12), the clock state space equation of intermediate node i is:

[0117]

[0118] in B=[1 0] T ,

[0119] Then, the accumulated asymmetric link delay and the unknown temperature coefficient are augmented. First, the accumulated asymmetric delay between the master clock and the transparent clock i is expressed as:

[0120]

[0121] The asymmetric delay between two adjacent transparent clocks can be modeled as a first-order difference equation:

[0122]

[0123] represents Gaussian noise with zero mean.

[0124] Combining equations (14) and (15), the recursive form of the accumulated asymmetric delay can be obtained:

[0125]

[0126] Due to the accumulation of multiple zero-mean Gaussian noises, the covariance is difficult to obtain accurately.

[0127] Similarly, the unknown temperature coefficient can be modeled as a first-order difference equation:

[0128]

[0129] The accumulated asymmetric delay and unknown temperature coefficient are expanded to the state space vector, and the expanded clock state space model is obtained as follows:

[0130]

[0131] in,

[0132] Furthermore, an accurate clock synchronization parameter tracking method based on an augmented Kalman neural network is adopted, and the specific steps are as follows:

[0133] Calculate the prior state estimate of node i according to the augmented state space model The calculation formula is:

[0134]

[0135] wherein is the posterior estimate value of node i at the previous moment.

[0136] Using the measurement value of node i at time k and the prior estimate value the residual can be calculated as:

[0137]

[0138] The method based on the augmented Kalman neural network uses the Kalman gain learned by the neural network to perform state updates, expressed as:

[0139]

[0140] The Kalman gain is calculated by optimizing the mean square error loss function of the parameter Θ through end-to-end training. The dataset consists of N sequences of length m, expressed as wherein is the observed vector sequence of trajectory j, is the true value sequence of trajectory j. The recursion is extended to m time steps, resulting in a sequence-to-sequence supervised training scheme. For each trajectory j, the empirical minimum mean square error loss function metric is:

[0141]

[0142] wherein, Ψ Θ (.) is the output of the recurrent neural network, Θ is its trainable parameter, and α is the regularization coefficient.

[0143] To optimize the parameter Θ, an adaptive momentum stochastic optimization method is adopted, which combines second-order momentum with stochastic gradient descent and has the advantages of simple implementation, high computational efficiency, and small memory overhead. For each batch of trajectories indexed by r, L < N training indices are randomly selected, and the batch loss ξ r (Θ) is

[0144]

[0145] Figure 2 is the flow chart of the accurate clock synchronization parameter tracking method based on the augmented Kalman neural network in this embodiment. This embodiment provides a joint tracking method for clock phase offset and frequency offset applicable to unknown observation noise covariance under the influence of temperature change and link delay asymmetry in a multi-hop network, as Figure 2 shown, specifically including the following steps:

[0146] P1: The precise clock synchronization parameter tracking process based on the augmented Kalman neural network begins;

[0147] P2: Initialization based on the augmented Kalman neural network method;

[0148] P3: The slave clock interacts with the master clock for precise clock synchronization through the intermediate transparent clock, and the slave clock records the relevant timestamps;

[0149] P4: Obtain the observed values ​​of clock phase offset and frequency offset according to the recorded timestamp;

[0150] P5: Obtain the temperature at time K through the temperature sensor and establish the clock state equation;

[0151] P6: Use the clock state model to predict the prior state estimate of the clock phase offset and frequency offset at time K;

[0152] P7: Calculate the filter residual based on the observed value and the predicted value;

[0153] P8: Input the observation value at the current moment and the posterior estimate value at the previous moment, and use the neural network to calculate the Kalman gain;

[0154] P9: Use the gains calculated by the neural network to weight the predicted values ​​and observed values ​​and update the posterior estimate;

[0155] P10~P12: Determine whether the synchronization round has reached the preset value. If it has, the clock phase offset tracking process ends; otherwise, the synchronization round is increased by 1 and enters process P3 to continue the clock parameter tracking process.

[0156] Figure 3 and Figure 4 A performance comparison chart of the precise clock synchronization parameter tracking method based on the augmented Kalman neural network provided in this embodiment is given. Figure 3 and Figure 4 It can be seen that the augmented Kalman neural network method can effectively eliminate the impact of accumulated asymmetric link delays and compensate for rapid temperature changes in the environment on the precise tracking of clock phase offsets and frequency offsets, and can more accurately and reliably track the true values ​​of clock phase offsets and frequency offsets, proving the practicality of the precise clock synchronization parameter tracking method based on the augmented Kalman neural network provided by the present invention.

[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution 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 solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A precise clock synchronization parameter tracking method based on augmented Kalman neural network, characterized in that: The method specifically comprises the following steps: S1: According to the precise clock protocol in the multi-hop network, synchronization messages are sent in a point-to-point mode. Considering the accumulated asymmetric delay, the target node establishes a clock observation equation based on the received synchronization message; considering the influence of temperature on the clock crystal oscillator, a recursive model of the node clock frequency offset and temperature is established, and based on the two-state clock model, the clock state equation is established; S2: Combine the clock observation equation and the clock state equation to establish a clock state space model, model the unknown temperature coefficient and the accumulated asymmetric delay as a first-order linear difference equation, and expand the unknown temperature coefficient and the accumulated asymmetric delay to the state space vector; S3: The augmented Kalman neural network method is used to track the clock parameters. The observed values ​​and true values ​​of the clock state parameters are used as training sets, and the clock parameters are trained using a recurrent neural network to obtain a prediction model. The prediction gain of the prediction model output is obtained by inputting the posterior estimate of the previous moment and the observed value of the current moment into the prediction model. The prediction gain is used to weigh the trust of the observed value and the predicted value.

2. The method for accurate clock synchronization parameter tracking based on augmented Kalman neural network according to claim 1, characterized in that: In S1, the observation equation and state equation for precise clock synchronization of a multi-hop network under the influence of cumulative asymmetric delay and ambient temperature are established. The specific steps are as follows: S11: Intermediate node i calculates the clock offset from the master clock based on the received timestamp It is expressed as: in, Indicates the time when the master clock sends the Sync message. Indicates the downstream transparent clock TC i The time when the Sync message is received, and represents the total path delay from the master clock to the intermediate transparent clock node i; It is expressed as: in, represents the link delay between transparent clock i and node i-1, Indicates the residence time of the synchronization message in node i-1, Represents the total delay from the previous node i to the master clock, the initial value is zero; the residence time of node i-1 is calculated as: where ε (i-1,i-1) is a random variable, assumed to be Gaussian white noise, representing the timestamp contribution associated with different clock readings; S12: In the kth round of synchronization, let the clock offset between node i and node i-1 be Assuming that the clock phase offset remains constant in a sufficiently short time interval, we have: in, and They represent the uplink and downlink fixed delays between nodes i and i-1 respectively, and their relationship is: in represents the fixed delay asymmetry measure between node i and node i-1 in the kth synchronization cycle; S13: Assume that the average link delay is but It is expressed as: S14: The formula for measuring the clock offset between the master clock and the transparent clock i is: in, Represents the measurement of clock offset, and They correspond to the cumulative residence time and the cumulative unknown asymmetric delay of the synchronization information between the master clock and the transparent clock i, respectively; represents the entire observation noise and is expressed as: in represents the measurement error of the i-th node in the k-th round of synchronization; in a multi-hop network, let is a Gaussian noise signal with zero mean and unknown covariance; S15: The measured value of the clock frequency deviation between the master clock and the transparent clock i By measuring the clock phase deviation gives: S16: Establish a parabolic model of unknown coefficients related to clock frequency offset and temperature change, and regard interference as noise input. If the sampling interval is small enough, the recursive equation of clock frequency offset is modeled as: in, is the unknown relationship between the current clock frequency offset and temperature change of the intermediate node i; S17: For curves with unknown parabolic coefficients, The change is related to the central axis of the parabola, which is the reference temperature. Celsius, approximately: in, represents the unknown temperature coefficient of node i at the kth round of synchronization, which is an unknown value that fluctuates within a small range; The recursive form of the clock phase offset is: S18: When designing the state equation, use the two-state clock model to determine the clock phase offset to describe the behavior of the clock at continuous synchronization time; the clock recursive state equation is as follows: Where τ represents the sampling interval.

3. The method for accurate clock synchronization parameter tracking based on augmented Kalman neural network according to claim 2, characterized in that: In S2, a clock state space model is established, the unknown temperature coefficient and the accumulated asymmetric delay are modeled as a first-order linear difference equation, and the unknown temperature coefficient and the accumulated asymmetric delay are expanded to a state space vector. The specific steps are: S21: Combining the clock state equation and clock observation equation of the intermediate node i, the clock state space equation is: in B=[1 0] T , S22: The accumulated asymmetric delay between the master clock and the transparent clock i is expressed as: The asymmetric delay between two adjacent transparent clocks is modeled as a first-order difference equation: represents Gaussian noise with zero mean; S23: The recursive form of accumulating asymmetric delays is obtained: Due to the accumulation of multiple zero-mean Gaussian noises, the covariance is difficult to obtain accurately; S24: Similarly, the unknown temperature coefficient is modeled as a first-order difference equation: S25: The accumulated asymmetric delay and the unknown temperature coefficient are expanded to the state space vector, and the expanded clock state space model is obtained as follows: in, 4. The method for accurate clock synchronization parameter tracking based on augmented Kalman neural network according to claim 3, characterized in that: In S3, the clock parameter tracking is performed using an augmented Kalman neural network method, and the specific steps are as follows: S31: Calculate the prior state estimate of transparent node i according to the augmented state space model The calculation formula is: in is the posterior estimate of node i at the previous time; S32: Use the measurement value of node i at time k and the prior estimate Residual is calculated as: S33: Augmented Kalman Neural Network-based Method Using Neural Networks to Learn Kalman Gains Through learning Perform execution status update, expressed as: S34: Kalman gain The calculation method is to optimize the mean square error loss function of the parameter Θ through end-to-end training; the dataset consists of N sequences of length m, expressed as in is the observation vector sequence of trajectory j, is the true value sequence of trajectory j; Extending the recursion to m time steps yields a sequence-to-sequence supervised training scheme; for each trajectory j, the empirical minimum mean square error loss function is measured as: Among them, Θ (.) is the output of the recurrent neural network, Θ is its trainable parameter, and α is the regularization coefficient; S35: To optimize the parameter Θ, an adaptive momentum stochastic optimization method is adopted, which combines the second-order momentum with stochastic gradient descent. For each batch of trajectories indexed by r, L < N training indices are randomly selected, and the batch loss ξ r (Θ) is as follows:

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