Short-wave time service signal propagation time delay prediction method and device

Abstract

The invention particularly relates to a short-wave time service signal propagation delay prediction method and device, and the method carries out the training of a short-wave time service signal propagation delay prediction model based on prior data, does not need the support of ionosphere data, and only needs several times of simple multiplication and one time of logarithmic operation after a user obtains model parameters. Solving of a differential equation is not involved, and implementation is easy. Besides, since the prediction model fits the time variation of the ionosphere, not only can the higher time delay prediction precision be maintained, but also the parameter model is kept unchanged in the same day time, the signal propagation time delay can be calculated only by acquiring the parameter model once and measuring the signal level in real time, and the parameter model does not need to be frequently acquired.

Description

Technical Field

[0001] This invention relates to the field of shortwave timing technology, specifically to a method and apparatus for predicting the propagation delay of shortwave timing signals, and a computer program product. Background Technology

[0002] In related technologies, shortwave time service is a method of transmitting standard time and frequency using radio frequencies within the shortwave band as carriers. It features long operating distance, simple receiving equipment, and strong resistance to disruption. The BPM shortwave time service system, constructed and maintained by the National Time Service Center of the Chinese Academy of Sciences, is one of my country's earliest major scientific and technological infrastructure projects. Since its completion in 1970, the system has been alternately transmitting national standard time and frequency at four frequency points: 2.5MHz, 5MHz, 10MHz, and 15MHz.

[0003] To achieve long-distance shortwave time synchronization, the shortwave time signal (skywave) transmitted from the transmitting end must undergo continuous refraction and reflection through the ionosphere before reaching the distant receiving end. The ionosphere is not only an important component of the Earth's atmosphere but also the most complex atmospheric region most closely related to shortwave skywave transmission. The instability in skywave propagation caused by the complex changes in the ionosphere is one of the main factors affecting the accuracy of shortwave time synchronization.

[0004] The existing method for calculating the propagation delay of shortwave timing signals with high accuracy is the ray tracing method. This method typically requires obtaining sufficient ionospheric data (such as ionospheric layering structure, electron density, critical frequency, etc.) from the International Reference Ionospheric Model (IRI model), and the ray trajectory calculation process involves solving a large number of differential equations. Therefore, for shortwave timing terminal users who require simple receiving equipment, using this method to calculate the propagation delay of shortwave timing signals presents problems such as difficulty in obtaining ionospheric data and high computational complexity.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] This invention provides a method and apparatus for predicting the propagation delay of shortwave timing signals, as well as a computer program product, which can effectively overcome the defects existing in the prior art.

[0007] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0008] According to a first aspect of the present invention, a method for predicting the propagation delay of a shortwave timing signal is provided, the method comprising: Prior data for the target duration is collected, and the prior data is divided into time periods and preprocessed according to preset rules to obtain sample data corresponding to each time period, and a sample dataset is constructed. Among them, the sample data for each time period includes the propagation delay and signal level of the shortwave time signal corresponding to that time period. The propagation delay prediction model for shortwave timing signals is updated using a sample dataset to determine the updated model hyperparameters. The propagation delay prediction model for shortwave timing signals is constructed based on the carrier frequency characteristics of the shortwave timing signal, the signal level characteristics of the shortwave timing signal at the current time, and the time period characteristics at the current time. The model hyperparameters include the hyperparameters corresponding to the signal level characteristics of the shortwave timing signal at the current time and the time period characteristics at the current time. The updated model hyperparameters are broadcast so that the shortwave time signal receiving terminal can update the propagation delay prediction model of the shortwave time signal according to the updated model hyperparameters, and use the updated model to predict the propagation delay of the shortwave time signal.

[0009] In some exemplary embodiments, the method further includes: pre-constructing a shortwave timing signal propagation delay prediction model based on the carrier frequency characteristics of the shortwave timing signal, the signal level characteristics of the shortwave timing signal at the current time, and the time period characteristics corresponding to the current time, including:

[0010] in, Date serial number d , t Predicted signal propagation delay at time; Date serial number d , t The signal level of the shortwave time signal at a given time is used to represent the signal level characteristics of the shortwave time signal at the current time. for t The time period corresponding to a given moment is used to represent the characteristics of the time period corresponding to the current moment. This indicates the carrier frequency characteristics of the shortwave time synchronization signal; , , These are the model hyperparameters.

[0011] In some exemplary embodiments, the method further includes: configuring the carrier frequency characteristics of the shortwave timing signal according to the carrier frequency of the shortwave timing signal. ,include:

[0012] in, f This indicates the carrier frequency of the shortwave timing signal.

[0013] In some exemplary embodiments, prior data for the target duration is collected, and the prior data is divided into time periods and preprocessed according to preset rules to obtain sample data corresponding to each time period, including: Collect the signal propagation delay measurement value and the signal level measurement value of the shortwave time synchronization signal corresponding to several dates before the current date; Sort the data by date and configure the corresponding date sequence number. d ; Divide each day into M time periods and assign a corresponding time period number λ to each time period; Calculate the arithmetic mean of the signal propagation delay measurements for each time period and configure it as the signal propagation delay for that time period. Calculate the corresponding arithmetic mean based on the signal level measurements for each time period, and configure it as the signal level for that time period.

[0014] In some exemplary embodiments, the method further includes: configuring the range of values ​​for the number of time periods M as follows: And it is divisible by 86400; among which, .

[0015] In some exemplary embodiments, the propagation delay prediction model for shortwave timing signals is updated using a sample dataset to determine the updated model hyperparameters, including: The sample dataset is divided into a training set and a validation set; Define the model parameter vector based on the model hyperparameters. ;in, ; Delay variables are defined based on the signal delay in each time period, and signal delay features are constructed by combining the delay variables and carrier frequency characteristics. And configure the signal delay feature vector according to the signal delay characteristics. ;in, , ; This indicates the carrier frequency characteristics of the shortwave time synchronization signal; Signal level variables are defined based on the signal levels in each time period, and a signal level matrix is ​​constructed by combining the signal level variables and time period characteristics. ;in,

[0016] in, M represents the time period, and M represents the number of time periods; for t The average signal level of the shortwave time signal within the time period; Using the signal level matrix corresponding to the training set Signal delay eigenvector And the pre-configured model order n, the start date number of the training set, and so on. Determine the model parameter vector ,include: ; model parameter vector Substitute the values ​​into the delay prediction model to obtain the corresponding propagation delay prediction values; calculate the error between the delay prediction values ​​and the delay measurement values. Using the validation set to evaluate the model parameter vector To verify this, the model parameter vector corresponding to the minimum mean absolute error is configured as the updated model hyperparameters.

[0017] In some exemplary implementations, the model parameter vector corresponding to the minimum mean absolute error is configured as the updated model hyperparameters, including:

[0018] in, Indicates when When the value is minimized, the model parameter vector used in calculating the predicted time delay is... ; For date serial number , No. Predicted signal propagation delay for a given time period; For date serial number , No. Signal propagation delay during a given time period.

[0019] In some exemplary embodiments, the propagation delay prediction of shortwave timing signals is performed using the updated model, including: Input the shortwave signal level corresponding to the current shortwave time signal and the time period corresponding to the current shortwave time signal into the updated model to obtain the propagation delay of the shortwave time signal corresponding to the current shortwave time signal output by the model.

[0020] According to a second aspect of the present invention, a shortwave timing signal propagation delay prediction device is provided, comprising: Time reproduction equipment is used to provide a reference 10MHz signal and a reference 1PPS for a shortwave time monitoring receiver; The shortwave time synchronization monitoring receiver is used to receive shortwave time synchronization signals and perform timing, time difference, and signal level measurements on the received shortwave time synchronization signals; and outputs the time difference measurement data and signal level measurement data to the host computer; wherein, the time difference measurement data is used to convert the corresponding propagation delay measurement value in the host computer; The host computer is used to collect prior data for the target duration, divide the prior data into time periods and preprocess it according to preset rules to obtain sample data corresponding to each time period, and construct a sample dataset. The sample data for each time period includes the propagation delay measurement value and signal level measurement value of the shortwave timing signal corresponding to that time period. The sample dataset is used to update the parameters of the shortwave timing signal propagation delay prediction model to determine the updated model hyperparameters. The shortwave timing signal propagation delay prediction model is constructed based on the carrier frequency characteristics of the shortwave timing signal, the signal level characteristics of the shortwave timing signal at the current time, and the time period characteristics at the current time. The model hyperparameters include the hyperparameters corresponding to the signal level characteristics of the shortwave timing signal at the current time and the time period characteristics at the current time. The parameter injection unit is used to broadcast the updated model hyperparameters so that the shortwave time signal receiving terminal can update the propagation delay prediction model of the shortwave time signal according to the updated model hyperparameters, and use the updated model to predict the propagation delay of the shortwave time signal.

[0021] According to a third aspect of the present invention, a computer program product is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the above-described method for predicting the propagation delay of shortwave timing signals is implemented.

[0022] According to a fourth aspect of the present invention, an electronic device is provided, comprising: Processor; and Memory for storing the executable instructions of the processor; The processor is configured to implement the shortwave timing signal propagation delay prediction method described above when executing the executable instructions.

[0023] According to a fifth aspect of the present invention, a storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described method for predicting the propagation delay of shortwave timing signals.

[0024] The shortwave time signal propagation delay prediction method provided in the embodiments of the present invention utilizes the carrier frequency characteristics of the shortwave time signal, the signal level characteristics of the shortwave time signal at the current moment, and the time period characteristics of the current moment to construct a shortwave time signal propagation delay prediction model. This prediction model linearly fits the logarithm of the signal propagation delay to the signal level. Furthermore, by using signal level characteristics and time period characteristics in the prediction model, nonlinear fitting of ionospheric time variations is achieved, maintaining high delay prediction accuracy. In addition, by introducing the carrier frequency characteristics of the shortwave time signal into the prediction model, the signal attenuation problem of the time signal during transmission is fully considered, further improving the accuracy of delay prediction at different times. Moreover, the shortwave time signal propagation delay prediction model is trained and its hyperparameters are updated using prior data over a period of time, without requiring ionospheric data support. Furthermore, the parameter model remains unchanged within the same day, allowing users to calculate the signal propagation delay only after obtaining the parameter model once and measuring the signal level in real time, eliminating the need for frequent parameter model acquisition.

[0025] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0026] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0027] Figure 1 The diagram illustrates an exemplary embodiment of the present invention: a method for predicting the propagation delay of a shortwave timing signal. Figure 2 The diagram illustrates a time delay prediction method flow according to an exemplary embodiment of the present invention. Figure 3 This schematic diagram illustrates an exemplary embodiment of the present invention: a shortwave timing signal propagation delay prediction device. Figure 4 This schematic diagram illustrates a data processing method for a propagation delay prediction device according to an exemplary embodiment of the present invention. Figure 5 The schematic diagram illustrates the principle of a data transmission unit according to an exemplary embodiment of the present invention; Figure 6 The diagram illustrates the composition of an electronic device according to an exemplary embodiment of the present invention. Detailed Implementation

[0028] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the invention will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0029] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0030] To address the shortcomings and deficiencies of existing technologies, this exemplary embodiment provides a method for predicting the propagation delay of shortwave timing signals, applied to a shortwave timing system. (Reference) Figure 1 As shown, it can specifically include: Step S11: Collect prior data for the target duration, divide the prior data into time periods and preprocess it according to preset rules to obtain sample data corresponding to each time period, and construct a sample dataset; wherein, the sample data for each time period includes the propagation delay and signal level of the shortwave timing signal corresponding to that time period; Step S12: Update the parameters of the shortwave time signal propagation delay prediction model using the sample dataset to determine the updated model hyperparameters; wherein, the shortwave time signal propagation delay prediction model is constructed based on the carrier frequency characteristics of the shortwave time signal, the signal level characteristics of the shortwave time signal at the current time, and the time period characteristics at the current time; the model hyperparameters include the hyperparameters corresponding to the signal level characteristics of the shortwave time signal at the current time and the time period characteristics at the current time; Step S13: Broadcast the updated model hyperparameters so that the shortwave time signal receiving terminal can update the propagation delay prediction model of the shortwave time signal according to the updated model hyperparameters, and use the updated model to predict the propagation delay of the shortwave time signal.

[0031] This method addresses the problems of difficulty in obtaining ionospheric data and high computational complexity when applying existing technologies to shortwave timing terminal users with simple receiving equipment.

[0032] The following will describe in more detail each step of the shortwave timing signal propagation delay prediction method in this exemplary embodiment, with reference to the accompanying drawings and embodiments.

[0033] In this example implementation, the time delay prediction method may include: pre-constructing a shortwave time signal propagation time delay prediction model based on the carrier frequency characteristics of the shortwave time signal, the signal level characteristics of the shortwave time signal at the current time, and the time period characteristics corresponding to the current time, including:

[0034] in, For date serial number d, t Predicted signal propagation delay at time t, in μs; For date serial number d, t The signal level of the shortwave time signal at time t is used to represent the signal level characteristics of the shortwave time signal at the current time; λ(t) is... t The time period corresponding to a given moment is used to represent the characteristics of the time period corresponding to the current moment. b f This indicates the carrier frequency characteristics of the shortwave time synchronization signal; a 0、 a 1. a i+1 These are the model hyperparameters, and n represents the model order.

[0035] Among them, the carrier frequency characteristics of the shortwave timing signal are configured according to the carrier frequency of the shortwave timing signal. b f , including: b f =145-lg f ;in, f This indicates the carrier frequency of the shortwave timing signal, measured in MHz.

[0036] Specifically, the aforementioned time delay prediction method can be executed by the intelligent terminal of the shortwave time synchronization system. A pre-stored propagation delay model for the shortwave time synchronization signal can be pre-built on the intelligent terminal. This model linearly fits the logarithm of the signal propagation delay to the signal level and nonlinearly fits it to ionospheric time variations. This is achieved by configuring carrier frequency characteristics... b f This can, to some extent, eliminate the need for more decimal points to represent model parameters after signal level amplification, thus requiring more bits for message arrangement; at the same time, by reducing one division operation, it can also reduce the computational complexity for end users.

[0037] Alternatively, in some exemplary embodiments, b can also be configured f =145-lg f Since the model parameters already include constants related to the signal level, the constants in the formula will not affect the model training.

[0038] By using signal level characteristics and time period characteristics corresponding to the current moment in the prediction model, a nonlinear fit to the temporal variation of the ionosphere was achieved. Furthermore, by incorporating the carrier frequency characteristics of the shortwave time synchronization signal into the prediction model, the attenuation of the time synchronization signal during transmission was fully considered. In step S11, prior data for the target duration is collected, and the prior data is divided into time periods and preprocessed according to preset rules to obtain sample data corresponding to each time period, and a sample dataset is constructed; wherein, the sample data for each time period includes the propagation delay and signal level of the shortwave timing signal corresponding to that time period.

[0039] In the example row, step S11 above may include: Step S21: Collect the signal propagation delay measurement value and the signal level measurement value of the shortwave time signal corresponding to several dates before the current date; Step S22: Sort the data by date and configure the corresponding date sequence number d; Step S23: Divide each day into M time periods and assign a corresponding time period number to each time period. ; Step S24: Calculate the corresponding arithmetic mean based on the signal propagation delay measurement values ​​in each time period, and configure it as the signal propagation delay corresponding to that time period; Step S25: Calculate the corresponding arithmetic mean based on the signal level measurement values ​​in each time period, and configure it as the signal level corresponding to that time period.

[0040] Specifically, during a model hyperparameter update cycle, the signal propagation delay (in μs) and signal level (in dBμV) of the shortwave time signal can be collected several days prior to the current date. For example, this could be prior data from the previous three days, obtained by a shortwave time monitoring station deployed far from the shortwave time broadcasting system, which receives and measures the shortwave time signal in real time.

[0041] Specifically, the prior data can be sorted in ascending order by acquisition date, with the corresponding date number denoted by 'd'. The signal propagation delay measurement for each day can be evenly divided into M time periods starting from 0:00:00 UTC, with each time period assigned a time period number. The arithmetic mean of the signal propagation delay measurements within each time period can be calculated, and the result can be used as the signal propagation delay for that time period, thus obtaining the signal propagation delay data table.

[0042] The signal level measurement values ​​for each day are evenly divided into M time periods starting from 0:00:00 UTC time. Each time period is assigned a time period number. The arithmetic mean of the signal level measurement values ​​within each time period is then calculated, and the result is used as the signal level for that time period, thus obtaining the signal level data table.

[0043] In the signal propagation delay data table and the signal level data table, the row number is the date sequence number and the column number is the time period sequence number.

[0044] The signal propagation delay for date d and time period λ in the signal propagation delay data table can be expressed as:

[0045] in, This represents the measured signal propagation delay at time t, with date number d.

[0046] The signal level for date d and time period λ in the signal level data table can be represented as:

[0047] in, This represents the signal level measurement value at time t, with date number d; the initial value of t is UTC time 0:00:00, and the value range is [0, 86399], with the unit being seconds.

[0048] In step S12, the parameters of the shortwave time signal propagation delay prediction model are updated using the sample dataset to determine the updated model hyperparameters. The shortwave time signal propagation delay prediction model is constructed based on the carrier frequency characteristics of the shortwave time signal, the signal level characteristics of the shortwave time signal at the current time, and the time period characteristics at the current time. The model hyperparameters include the hyperparameters corresponding to the signal level characteristics of the shortwave time signal at the current time and the time period characteristics at the current time.

[0049] For example, step S12 described above may include: Step S31: Divide the sample dataset into a training set and a validation set; Step S32: Define the model parameter vector based on the model hyperparameters. A ;in, ; Step S33: Define delay variables based on signal delay for each time period, and construct signal delay features y by combining delay variables and carrier frequency characteristics. λ And configure the signal delay feature vector according to the signal delay characteristics. Y ;in, , ; Step S34: Define signal level variables based on the signal levels of each time period, and construct a signal level matrix V by combining the signal level variables and time period characteristics; Step S35: Utilize the signal level matrix V and signal delay feature vector corresponding to the training set. Y And the pre-configured model order n, the start date number of the training set, and so on. d s Determine the model parameter vector A, including: ; Step S36, convert the model parameter vector A Substitute the values ​​into the delay prediction model to obtain the corresponding propagation delay prediction values; calculate the error between the delay prediction values ​​and the delay measurement values. Step S37, use the validation set to test the model parameter vector. A To verify this, the model parameter vector corresponding to the minimum mean absolute error is configured as the updated model hyperparameters.

[0050] Specifically, signal propagation delay and signal level are selected from the propagation delay data table and the signal level data table, respectively, to construct the first... The sample set for the time period is denoted as ; and the validation set, denoted as And it is expressed as follows:

[0051]

[0052] in, The date sequence number representing the current date. This refers to the date sequence number of the day before the current date.

[0053] express The time period corresponding to a given moment can be represented as:

[0054] Specifically, when updating model hyperparameters, the first sample set is selected... The training set for the time period is denoted as And it is expressed as follows:

[0055] in, , which represents the starting date sequence number of the training set.

[0056] Define the time delay variable as follows:

[0057] in, This refers to the signal delay corresponding to each time period; Based on The calculated geometric mean; Define the signal level variable as follows:

[0058] in, These are the signal levels corresponding to each time period; Based on The calculated arithmetic mean; Define a model parameter vector consisting of the model hyperparameters: ; Define signal delay eigenvector , represented as Among them, signal delay characteristics Constructed based on time delay variables and carrier frequency characteristics, including: ; Combined with signal level variables Time period, define signal level matrix , is represented as:

[0059] in, This indicates a time period, where M represents the maximum value for that time period. for t The average signal level of the shortwave time signal within a given time period.

[0060] Given the order n and the starting date number Then, the corresponding matrix can be obtained through the above formula. sum matrix .

[0061] According to the above and stated The model parameter vector can be calculated, and the formula is expressed as:

[0062] Then, the currently calculated model parameter vector Substituting each element into the prediction model, we can obtain the result when the model order is n and the training set start date is n. At that time, the date sequence number was , No. The predicted signal propagation delay for a given time period is expressed by the formula:

[0063] Adjusting the model order n and start date sequence number The possible values ​​of ; where, nThe adjustment range is from 1 to N , The adjustment range is from 1 to Each adjustment n or Repeat the above method to obtain the corresponding predicted signal propagation delay value.

[0064] Then, the validation set can be used. To verify the optimal model parameters, the minimum mean absolute error criterion was used, and the optimal parameters were obtained, denoted as... , is represented as:

[0065] in, Indicates when When it is at its minimum, the predicted signal propagation delay value The model parameter vector used ; For date serial number , No. Predicted signal propagation delay for a given time period; For date serial number , No. Signal propagation delay during a given time period.

[0066] During training, by adjusting n and ds (different n and ds correspond to different model parameters) ), making the mean absolute error The minimum, and when it is minimized, the corresponding model parameters For optimal model parameters .

[0067] Mean absolute error (MAE) is a regression model evaluation metric in statistics and machine learning. It is used to quantify the average absolute error between predicted and actual values. Its advantages are as follows: 1) The calculation is simple and intuitive. It is obtained by taking the arithmetic mean of the absolute values ​​of the deviations between the predicted and actual values. The calculation process is not complicated. 2) Robust to outliers: Because it uses absolute values, it does not amplify the impact of outliers like the root mean square error. The results better reflect the true average error level of the data. Using it during model training can avoid the impact of large outliers on the training of model parameters.

[0068] For example, in the process of fitting the time-varying ionospheric changes to a polynomial, in order to avoid underfitting and overfitting as much as possible, while also taking into account the computational complexity, N=5 can be configured, that is, the upper limit of the model order n is 5.

[0069] In step S13, the updated model hyperparameters are broadcast so that the shortwave timing receiver terminal can update the propagation delay prediction model of the shortwave timing signal according to the updated model hyperparameters, and use the updated model to predict the propagation delay of the shortwave timing signal.

[0070] For example, using the updated model to predict the propagation delay of shortwave timing signals includes: Input the shortwave signal level corresponding to the current shortwave time signal and the time period corresponding to the current shortwave time signal into the updated model to obtain the propagation delay of the shortwave time signal corresponding to the current shortwave time signal output by the model.

[0071] Specifically, after determining the updated model hyperparameters for the current period, the time synchronization system can broadcast the model hyperparameters, enabling the shortwave time synchronization receiving terminal to receive the updated model hyperparameters for the current period, thereby realizing the local update of the time delay prediction model at the shortwave time synchronization receiving terminal.

[0072] After receiving a shortwave time signal, the shortwave time receiver terminal determines the corresponding signal level value. This indicates that the shortwave terminal user is on the current date. The signal level value measured at any given time.

[0073] Shortwave time synchronization terminal equipment, on the current date of the first day The signal level value is measured at each moment, and then the current signal level value is... The time period corresponding to the current moment Updated model hyperparameters Substituting this into the prediction model, we can calculate the current date's... The predicted signal propagation delay at time t is expressed as:

[0074] in, , , for The elements in.

[0075] The method provided in the embodiments of the present invention is referred to Figure 2As shown, the process includes: S201, acquiring several prior data, including propagation delay measurement data and signal level measurement data of shortwave time synchronization signals; S202, preprocessing the prior data to obtain a propagation delay data table and a signal level data table; S203, constructing a sample set and a validation set composed of signal propagation delay and signal level; S204, constructing a shortwave time synchronization signal propagation delay prediction model, which linearly fits the logarithm of signal propagation delay to the signal level and nonlinearly fits it to ionospheric time variation; S205, selecting a training set from the sample set to train the model parameters and obtain the optimal model parameters; S206, the terminal device calculates the signal propagation delay for each time period of the current date based on the signal level measurement values ​​for each time period of the current date and the optimal model parameters through the prediction model.

[0076] This method trains a shortwave time signal propagation delay prediction model based on prior data, requiring no ionospheric data as support. After obtaining the model parameters, users only need a few simple multiplications and one logarithmic operation, without solving differential equations, making it simple to implement. Furthermore, because the prediction model fits the time-varying nature of the ionosphere, it not only maintains high delay prediction accuracy but also keeps the parameter model unchanged within the same day. Users only need to obtain the parameter model once and measure the signal level in real time to calculate the signal propagation delay, eliminating the need for frequent parameter model acquisition. The shortwave time signal propagation delay prediction method disclosed in this invention is simple to implement and facilitates engineering applications. It not only effectively improves shortwave time synchronization accuracy but also has positive significance for promoting the development of related fields such as shortwave ionospheric monitoring.

[0077] It should be noted that the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may, for example, be executed synchronously or asynchronously in multiple modules.

[0078] Further reference Figure 3 As shown, this example embodiment also provides a shortwave timing signal propagation delay prediction device 30, comprising: Time reproduction device 301 is used to provide a reference 10MHz signal and a reference 1PPS for a shortwave time monitoring receiver; The shortwave timing monitoring receiver 302 is used to receive shortwave timing signals and perform timing, time difference, and signal level measurements on the received shortwave timing signals; and outputs the time difference measurement data and signal level measurement data to the host computer; wherein, the time difference measurement data is used to convert the corresponding propagation delay measurement value in the host computer; The host computer 303 is used to collect prior data for the target duration, divide the prior data into time periods and preprocess it according to preset rules to obtain sample data corresponding to each time period, and construct a sample dataset. The sample data for each time period includes the propagation delay measurement value and signal level measurement value of the shortwave timing signal corresponding to that time period. The sample dataset is used to update the parameters of the shortwave timing signal propagation delay prediction model to determine the updated model hyperparameters. The shortwave timing signal propagation delay prediction model is constructed based on the carrier frequency characteristics of the shortwave timing signal, the signal level characteristics of the shortwave timing signal at the current time, and the time period characteristics at the current time. The model hyperparameters include the hyperparameters corresponding to the signal level characteristics of the shortwave timing signal at the current time and the time period characteristics at the current time. The parameter injection unit 304 is used to broadcast the updated model hyperparameters so that the shortwave time signal receiving terminal can update the propagation delay prediction model of the shortwave time signal according to the updated model hyperparameters, and use the updated model to predict the propagation delay of the shortwave time signal.

[0079] For example, a shortwave time synchronization monitoring station can be provided and deployed remotely from the shortwave time synchronization broadcasting system, including a time reproduction device, a shortwave time synchronization monitoring receiver, a host computer, and a data transmission unit. The time reproduction device provides the shortwave time synchronization monitoring receiver with a reference 10MHz signal and a reference 1PPS required for operation. For instance, the time reproduction device uses a satellite common-view method to achieve time and frequency synchronization with the BPM shortwave time synchronization station.

[0080] refer to Figure 4 As shown, the shortwave time synchronization monitoring receiver is used to receive BPM shortwave time synchronization signals and perform signal acquisition, timing, time difference measurement, and signal level measurement. It then outputs the time difference measurement data and signal level measurement data to the host computer. Timing refers to the shortwave time synchronization receiver performs after acquiring the BPM shortwave time synchronization signal and outputs a local 1PPS. The leading edge of this 1PPS is aligned with the start time of the acquired BPM shortwave time synchronization signal. Time difference measurement refers to the shortwave time synchronization monitoring receiver measuring the time difference between the reference 1PPS and the local 1PPS. This time difference should actually include the fixed channel delay of the shortwave time synchronization monitoring receiver, measured in seconds. Signal level measurement refers to the shortwave time synchronization monitoring receiver measuring the level of the acquired BPM shortwave time synchronization signal.

[0081] The host computer can be deployed at the shortwave time synchronization monitoring station. It may include a data processing unit for constructing a shortwave time synchronization signal propagation delay prediction model, collecting, storing, and preprocessing prior data, and using the prior data to train the delay prediction model and update its hyperparameters to obtain optimal model parameters. The host computer can connect to a data storage server, where the prior data received by the shortwave time synchronization monitoring station can be stored. The host computer can also connect to an industrial control computer to support its operation. In other words, a delay prediction model is deployed at the shortwave time synchronization monitoring station.

[0082] Specifically, the host computer can receive time difference measurement data and signal level measurement data output from the shortwave time synchronization monitoring receiver, convert the time difference measurement data from seconds to microseconds, and then convert it into signal propagation delay measurement data after subtracting the channel delay of the monitoring receiver. The channel delay of the monitoring receiver can be obtained through pre-calibration of the equipment.

[0083] The shortwave time synchronization monitoring station uses time delay data and signal level data to train the prediction model and obtain updated model hyperparameters for the current period. The updated model hyperparameters are then sent to the shortwave time synchronization broadcasting system via the data transmission unit, which broadcasts the updated model hyperparameters.

[0084] For example, refer to Figure 5 As shown, the data transmission unit may include: routers, VPNs, firewalls, network gateways, switches, log auditing components, internet access management components, bastion hosts, and other information technology equipment, forming a secure and reliable data transmission network for remote transmission of prior data. Specifically, routers enable network data access; VPNs establish dedicated communication lines using specially encrypted communication protocols; firewalls ensure the security of internal networks and computers; network gateways disconnect internal and external networks, enabling secure and appropriate application data exchange between networks; switches filter, classify, and forward data packets from different network segments; log auditing components store, monitor, audit, analyze, alarm, respond to, and report logs generated by various information technology devices; internet access management components prevent the malicious spread of illegal information and avoid data leakage, enabling real-time monitoring and management of network resource usage; and bastion hosts protect network data security from intrusion, preventing network and data from being compromised by other visitors.

[0085] Alternatively, in some exemplary embodiments, the host computer of the shortwave time synchronization monitoring station uses a data processing unit to preprocess the time delay data and signal level data of the shortwave time synchronization signal; then, it transmits the preprocessed data to the shortwave time synchronization broadcasting system via a data transmission unit. The shortwave time synchronization broadcasting system receives the time delay data and signal level data, and uses server equipment to train a time delay prediction model on the received data, calculates the updated model hyperparameters for the current period, and then broadcasts the data. That is, a time delay prediction model is deployed on one side of the shortwave time synchronization broadcasting system.

[0086] For example, a shortwave time synchronization broadcasting system may include a parameter injection unit for arranging the updated optimal parameter model into a message according to the format requirements of the shortwave time synchronization broadcasting system and injecting it into the shortwave time synchronization broadcasting system for broadcasting to users.

[0087] For example, updated model hyperparameters can be embedded in the shortwave time signal message. For instance, the model hyperparameters can be compiled and written into a reserved field or an idle field of the message. Upon receiving the message, the shortwave time receiver can parse it and obtain the corresponding model hyperparameters.

[0088] It should be noted that although several modules or units of the device for performing actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0089] Figure 6 A schematic diagram of an electronic device suitable for implementing embodiments of the present invention is shown.

[0090] It should be noted that, Figure 6 The electronic device 1000 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0091] like Figure 6As shown, the electronic device 1000 includes a Central Processing Unit (CPU) 1001, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 1002 or programs loaded from storage section 1008 into Random Access Memory (RAM) 1003. The RAM 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An Input / Output (I / O) interface 1005 is also connected to the bus 1004. Furthermore, the electronic device 1000 also includes an FPGA device and a System-on-a-Chip (SoC) device.

[0092] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. Removable media 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1010 as needed so that computer programs read from them can be installed into storage section 1008 as needed.

[0093] For example, the aforementioned electronic device could be a host computer.

[0094] In particular, according to embodiments of the present invention, the processes described below with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit (CPU) 1001, it performs various functions defined in the system of this application.

[0095] Specifically, the aforementioned electronic devices can be airborne intelligent electronic devices, such as airborne video processing equipment.

[0096] It should be noted that the storage medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein computer-readable program code is carried. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any storage medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0097] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0098] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0099] It should be noted that, as another aspect, this application also provides a storage medium, which may be included in an electronic device or may exist independently without being assembled into the electronic device. The aforementioned storage medium carries one or more programs, which, when executed by an electronic device, cause the electronic device to perform the methods described in the following embodiments. For example, the electronic device may perform... Figure 1 The steps of the method shown.

[0100] In one embodiment, this application provides a computer program product including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0101] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0102] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0103] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for predicting the propagation delay of a shortwave timing signal, characterized in that, The method includes: Prior data for the target duration is collected, and the prior data is divided into time periods and preprocessed according to preset rules to obtain sample data corresponding to each time period, and a sample dataset is constructed. Among them, the sample data for each time period includes the propagation delay and signal level of the shortwave time signal corresponding to that time period. The propagation delay prediction model for shortwave timing signals is updated using a sample dataset to determine the updated model hyperparameters. The propagation delay prediction model for shortwave timing signals is constructed based on the carrier frequency characteristics of the shortwave timing signal, the signal level characteristics of the shortwave timing signal at the current time, and the time period characteristics at the current time. The model hyperparameters include the hyperparameters corresponding to the signal level characteristics of the shortwave timing signal at the current time and the time period characteristics at the current time. The updated model hyperparameters are broadcast so that the shortwave time signal receiving terminal can update the propagation delay prediction model of the shortwave time signal according to the updated model hyperparameters, and use the updated model to predict the propagation delay of the shortwave time signal.

2. The method according to claim 1, characterized in that, The method further includes: pre-constructing a shortwave timing signal propagation delay prediction model based on the carrier frequency characteristics of the shortwave timing signal, the signal level characteristics of the shortwave timing signal at the current time, and the time period characteristics corresponding to the current time, including: in, Date serial number d , t Predicted signal propagation delay at time; Date serial number d , t The signal level of the shortwave time signal at a given time is used to represent the signal level characteristics of the shortwave time signal at the current time. for t The time period corresponding to a given moment is used to represent the characteristics of the time period corresponding to the current moment. This indicates the carrier frequency characteristics of the shortwave time synchronization signal; , , These are the model hyperparameters.

3. The method according to claim 2, characterized in that, The method further includes: configuring the carrier frequency characteristics of the shortwave timing signal according to the carrier frequency of the shortwave timing signal. ,include: in, f This indicates the carrier frequency of the shortwave timing signal.

4. The method according to claim 1, characterized in that, Prior data for the target duration is collected, and the prior data is divided into time periods and preprocessed according to preset rules to obtain sample data corresponding to each time period, including: Collect the signal propagation delay measurement value and the signal level measurement value of the shortwave time synchronization signal corresponding to several dates before the current date; Sort the data by date and configure the corresponding date sequence number. d ; Divide each day into M time periods and assign a corresponding time period number λ to each time period; Calculate the corresponding arithmetic mean based on the signal propagation delay measurements within each time period, and configure it as the signal propagation delay for that time period; Calculate the arithmetic mean of the signal level measurements for each time period and configure it as the corresponding signal level for that time period.

5. The method according to claim 4, characterized in that, The method further includes: configuring the range of values ​​for the number of time periods M as follows: And it is divisible by 86400; among which, .

6. The method according to claim 1, characterized in that, The parameters of the shortwave time signal propagation delay prediction model are updated using a sample dataset to determine the updated model hyperparameters, including: The sample dataset is divided into a training set and a validation set; Define the model parameter vector based on the model hyperparameters. ;in, ; Delay variables are defined based on the signal delay in each time period, and signal delay features are constructed by combining the delay variables and carrier frequency characteristics. And configure the signal delay feature vector according to the signal delay characteristics. ;in, , ; This indicates the carrier frequency characteristics of the shortwave time synchronization signal; Signal level variables are defined based on the signal levels in each time period, and a signal level matrix is ​​constructed by combining the signal level variables and time period characteristics. ;in, in, Indicates the time period, where M represents the maximum time period value; for t The average signal level of the shortwave time signal within the time period; Using the signal level matrix corresponding to the training set Signal delay eigenvector Determine the model parameter vector ,include: ; model parameter vector Substitute the values ​​into the delay prediction model to obtain the corresponding propagation delay prediction values; calculate the error between the delay prediction values ​​and the delay measurement values. Using the validation set to evaluate the model parameter vector To verify this, the model parameter vector corresponding to the minimum mean absolute error is configured as the updated model hyperparameters.

7. The method according to claim 6, characterized in that, Configure the model parameter vector corresponding to the minimum mean absolute error as the updated model hyperparameters, including: in, Indicates when When the value is minimized, the model parameter vector used in calculating the predicted time delay is... ; For date serial number , No. Predicted signal propagation delay for a given time period; For date serial number , No. Signal propagation delay during a given time period.

8. The method according to claim 1, characterized in that, The updated model is used to predict the propagation delay of shortwave timing signals, including: Input the shortwave signal level corresponding to the current shortwave time signal and the time period corresponding to the current shortwave time signal into the updated model to obtain the propagation delay of the shortwave time signal corresponding to the current shortwave time signal output by the model.

9. A device for predicting the propagation delay of a shortwave timing signal, characterized in that, The device includes: Time reproduction equipment is used to provide a reference 10MHz signal and a reference 1PPS for a shortwave time monitoring receiver; The shortwave time synchronization monitoring receiver is used to receive shortwave time synchronization signals and perform timing, time difference, and signal level measurements on the received shortwave time synchronization signals; and outputs the time difference measurement data and signal level measurement data to the host computer; wherein, the time difference measurement data is used to convert the corresponding propagation delay measurement value in the host computer; The host computer is used to collect prior data for the target duration, divide the prior data into time periods and preprocess it according to preset rules to obtain sample data corresponding to each time period, and construct a sample dataset. The sample data for each time period includes the propagation delay and signal level of the shortwave timing signal in that time period. The sample dataset is used to update the parameters of the shortwave timing signal propagation delay prediction model to determine the updated model hyperparameters. The shortwave timing signal propagation delay prediction model is constructed based on the carrier frequency characteristics of the shortwave timing signal, the signal level characteristics of the shortwave timing signal at the current moment, and the time period characteristics at the current moment. The model hyperparameters include the hyperparameters corresponding to the signal level characteristics of the shortwave timing signal at the current moment and the time period characteristics at the current moment. The parameter injection unit is used to broadcast the updated model hyperparameters so that the shortwave time signal receiving terminal can update the propagation delay prediction model of the shortwave time signal according to the updated model hyperparameters, and use the updated model to predict the propagation delay of the shortwave time signal.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the shortwave timing signal propagation delay prediction method as described in any one of claims 1 to 8.

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