Methods, devices, equipment, and storage media for predicting the clock bias of atomic clocks

Abstract

This invention discloses a method, apparatus, device, and storage medium for predicting the clock bias of an atomic clock. The method includes: acquiring historical clock bias data output by the atomic clock at the current time and historical times, and segmenting the historical clock bias data into multiple segments; determining the average value and segment weight corresponding to each segment of clock bias data; the segment weight includes data weight, flag weight, and statistical weight; the data weight is used to characterize the availability of the segmented clock bias data; the flag weight is used to characterize whether all segmented clock bias data is missing; the statistical weight is used to characterize the uncertainty of the clock bias data; and determining the clock bias data of the atomic clock at a target time based on the average value and segment weight of each segment. The technical solution disclosed in this invention solves the problem of low accuracy in predicting the clock bias data of atomic clocks in the prior art, and improves the accuracy of clock bias data prediction.

Description

Technical Field

[0001] This invention relates to the field of atomic clock technology, and in particular to a method, apparatus, device, and storage medium for predicting the clock bias of an atomic clock. Background Technology

[0002] With the rapid development of science and technology, navigation, positioning, measurement, and astronomy all require time and frequency standards with higher stability and accuracy. As a time and frequency standard, the atomic time scale represents a nation's technological level and is a core manifestation of its competitiveness. Atomic clock bias prediction is a crucial link in the atomic clock time scale and its operation; the accuracy of atomic clock bias prediction directly affects the quality of the atomic time scale and the operational capability of the atomic clock. Therefore, the impact of clock bias prediction on the atomic time scale is paramount.

[0003] Currently, the Timekeeping Laboratory of the National Institute of Metrology of China is conducting data processing on the clock difference data characteristics of different atomic clocks, atomic clock groups, ground clocks and satellite clocks in order to build the Chinese atomic time standard system. However, the prediction process does not take into account the individual differences in the output data of atomic clocks, resulting in low accuracy of the predicted data. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and storage medium for predicting the clock bias of an atomic clock, thereby solving the problem of low accuracy in predicting the clock bias data of atomic clocks in the prior art and improving the accuracy of clock bias data prediction.

[0005] In a first aspect, embodiments of the present invention provide a method for predicting the clock bias of an atomic clock, the method comprising:

[0006] The atomic clock's current time and historical clock difference data are obtained, and the historical clock difference data is segmented to obtain multiple segments of segmented clock difference data.

[0007] Determine the segmented average value and segmented weight corresponding to each segmented clock difference data; the segmented weight includes data weight, flag weight, and statistical weight; the data weight is used to characterize the availability of the segmented clock difference data; the flag weight is used to characterize whether all the segmented clock difference data are missing; the statistical weight is used to characterize the uncertainty of the clock difference data;

[0008] The clock difference data of the atomic clock at the target time is determined based on the average value of each segment and the weight of each segment.

[0009] Optionally, determining the segmented average value corresponding to each of the segmented clock difference data includes:

[0010] Obtain the data values ​​corresponding to each time point in the segmented clock difference data, and perform average processing on the data values ​​to obtain the segmented average value of the segmented clock difference data.

[0011] Optionally, the method for determining the statistical weights includes:

[0012] Obtain a preset uncertainty calculation expression, and determine the uncertainty of the segmented clock difference data based on the uncertainty expression, each of the data values, and the segmented average value;

[0013] The statistical weights of the segmented clock difference data are determined based on a preset statistical weight expression and the uncertainty.

[0014] Optionally, the method for determining the flag weight includes:

[0015] Determine whether all segmented clock difference data of the current segment is missing; if the segmented clock difference data of the current segment is missing, then determine the first preset weight value as the flag bit weight; if the segmented clock difference data of the current segment is not missing, then determine the second preset weight value as the flag bit weight.

[0016] Optionally, the method for determining the data weights includes:

[0017] Obtain the data expression of the segmented clock difference data, and determine the data weight of the segmented clock difference data based on the data expression and the limiting conditions of the data expression.

[0018] Optionally, determining the segment weights corresponding to each of the segmented clock difference data includes:

[0019] The data weights, the flag weights, and the statistical weights are multiplied together, and the resulting weights are used as the segment weights of the segmented clock difference data.

[0020] Optionally, determining the clock difference data of the atomic clock at the target time based on the average value of each segment and the weight of each segment includes:

[0021] Obtain a preset clock error prediction expression, and determine the clock error data of the atomic clock at the target time based on the clock error prediction expression, the average value of each segment, and the weight of each segment.

[0022] Secondly, embodiments of the present invention also provide a clock bias prediction device for an atomic clock, the device comprising:

[0023] The segmented clock difference data acquisition module is used to acquire the historical clock difference data output by the atomic clock at the current time and historical time, and to segment the historical clock difference data to obtain multiple segments of segmented clock difference data.

[0024] The segmented average and segmented weight determination module is used to determine the segmented average and segmented weight corresponding to each segmented clock difference data. The segmented weight includes data weight, flag weight, and statistical weight. The data weight is used to characterize the availability of the segmented clock difference data. The flag weight is used to characterize whether all the segmented clock difference data is missing. The statistical weight is used to characterize the uncertainty of the clock difference data.

[0025] The clock difference data determination module is used to determine the clock difference data of the atomic clock at the target time based on the average value of each segment and the weight of each segment.

[0026] Thirdly, embodiments of the present invention also provide an electronic device, comprising:

[0027] At least one processor; and

[0028] A memory communicatively connected to the at least one processor; wherein,

[0029] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the atomic clock bias prediction method according to any embodiment of the present invention.

[0030] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions for causing a processor to execute and implement the clock bias prediction method of an atomic clock according to any embodiment of the present invention.

[0031] The technical solution of this invention obtains historical clock difference data output by the atomic clock at the current time and historical times, and segments the historical clock difference data to obtain multiple segments of segmented clock difference data; determines the segment average value and segment weight corresponding to each segment of clock difference data; and determines the clock difference data of the atomic clock at the target time based on the segment average value and segment weight. This technical solution improves the accuracy of data prediction by predicting the clock difference data of the atomic clock at the target time based on the average value and segment weight corresponding to different segments of historical clock difference data. Furthermore, it does not compensate for missing data in the segmented clock difference data, avoiding the problem of reduced prediction accuracy due to data overfitting caused by compensation, thus further improving the accuracy of data prediction.

[0032] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a flowchart of a clock bias prediction method for an atomic clock according to Embodiment 1 of the present invention;

[0035] Figure 2 This is a schematic diagram of the structure of a clock error prediction device for an atomic clock according to Embodiment 2 of the present invention;

[0036] Figure 3 This is a schematic diagram of the structure of an electronic device that implements the clock error prediction method of an atomic clock according to an embodiment of the present invention. Detailed Implementation

[0037] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0038] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in sequences other than those illustrated or described herein.

[0039] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0040] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0041] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0042] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0043] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0044] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0045] Example 1

[0046] Figure 1 This document provides a flowchart of a method for predicting the clock bias of an atomic clock according to Embodiment 1 of the present invention. This embodiment is applicable to predicting the clock bias data of atomic clocks, and optionally, it is also applicable to predicting linear continuous and discontinuous data in other fields. This method can be executed by an atomic clock bias prediction device, which can be implemented in hardware and / or software, and can be configured in a smart terminal or a cloud server. Figure 1 As shown, the method includes:

[0047] S110. Obtain the current time and historical clock difference data output by the atomic clock, and segment the historical clock difference data to obtain multi-segmented clock difference data.

[0048] In this embodiment of the invention, an atomic clock can be understood as a timing device. Specifically, an atomic clock can be understood as a device that uses the energy difference between atoms and molecules as a reference signal to calibrate the frequency of a crystal oscillator or laser, so that it outputs a standard frequency signal. It utilizes the optical signal generated by atomic energy level transitions, which, after photoelectric conversion and signal processing, obtains a negative feedback correction signal used to correct the frequency of the crystal oscillator or laser, resulting in a stable output frequency. This output frequency can be used to accurately measure time. Clock bias can be understood as the frequency difference or time difference between atomic clocks. Correspondingly, clock bias data can be understood as the time data or relative frequency deviation output by the atomic clock at different times.

[0049] In practical applications, the clock difference data output by the atomic clock is read. Specifically, the historical clock difference data output by the atomic clock at the current time and at historical times can be read. It should be noted that the number of historical times can be determined according to actual needs. For example, the number of historical times can be determined based on the number of times of subsequent predicted times. This embodiment does not limit this.

[0050] To facilitate subsequent prediction and processing of atomic clock error data, historical clock error data is segmented based on the acquired data. Specifically, the historical clock error data can be segmented according to time intervals. For example, if the acquired historical clock error data is for a single day, the segmentation time interval can be preset to one hour. Based on this time interval, the clock error data is segmented into 24 segments.

[0051] Since atomic clocks are not constantly running, the acquired historical clock difference data may contain missing and outlier data. Existing technologies, when encountering data anomalies, first remove the outlier data points, then compensate for the missing data in the same way as the missing data, before proceeding to the next data prediction step. These data points can be understood as any segmented clock difference data in this embodiment. The aforementioned data processing method of removing and compensating data can introduce errors in prediction and data compensation due to inappropriate model parameter selection. For example, the most commonly used linear regression method, least squares, finds the best function match for the data by minimizing the sum of squared errors. Its purpose is to find the functional relationship between the dependent and independent variables. When data points are missing, the least squares method may suffer from overfitting or underfitting.

[0052] To address the issue of low accuracy in data prediction caused by data compensation, this embodiment does not perform data compensation when it is determined that there are missing data in each segment of clock difference data. Instead, it assigns segment weights that are different from those for data without missing data. This allows for the prediction of the clock difference data of the atomic clock at the target time based on the different segment weights corresponding to different segments of clock difference data, thereby improving the accuracy of data prediction.

[0053] S120. Determine the segment average and segment weight corresponding to each segment of clock difference data; the segment weight includes data weight, flag weight and statistical weight.

[0054] In this embodiment of the invention, due to the internal structure of the atomic clock, there may be missing data and abnormal output data when the atomic clock outputs time data. In this embodiment, abnormal output data is directly discarded, and the time data at that point in time is marked as missing data to avoid the problem of low accuracy in subsequent data prediction results due to abnormal data. Therefore, the historical clock difference data output by the atomic clock will contain different numbers of data points in each segmented clock difference data after data processing and segmentation; that is, the number of time data points contained in each segmented clock difference data is different. For example, the first segmented clock difference data contains 50 data points, the second segmented clock difference data contains 60 data points, and the third segmented clock difference data contains 0 data points.

[0055] Based on this, the method for determining the segmented average value corresponding to the segmented clock difference data in this embodiment may include: obtaining the data values ​​corresponding to each time moment in the segmented clock difference data, averaging the data values, and obtaining the segmented average value of the segmented clock difference data.

[0056] Taking any segmented clock difference data as an example, the data value corresponding to each moment can be understood as the time data corresponding to each data point in the current clock difference data. Then, by averaging the above time data, the average time data obtained is the segmented average value of the current segmented clock difference data.

[0057] Based on this, the segment weights of each segment of clock difference data are determined so that subsequent clock difference data can be predicted based on the segment average and segment weights.

[0058] In this embodiment of the invention, the prediction expression for clock bias data can be expressed in the form of Expression 1. For example, Expression 1 is shown below:

[0059]

[0060] in, H(i) represents the clock difference data at the target time; H(i) represents the segment weight of the segmented clock difference data at historical times; f(ni) represents the segmented average value of the segmented clock difference data corresponding to the segment weight.

[0061] As shown in Expression 1 above, the corresponding segment average can be determined based on the time data in each segment of the clock difference data. Furthermore, by determining the segment weights of each segment of the clock difference data based on the time data and the segment average, the clock difference data for the target time can be determined based on the segment clock difference data.

[0062] In this embodiment, the segment weights include data weights, flag weights, and statistical weights. The data weights characterize the availability of the segmented clock difference data; the flag weights characterize whether all segmented clock difference data is missing; and the statistical weights characterize the uncertainty of the clock difference data. Therefore, the segment weights can be obtained from the data weights, flag weights, and statistical weights.

[0063] Optionally, the method for determining segment weights in this embodiment may include: multiplying the data weights, flag weights, and statistical weights, and using the weights obtained after multiplication as the segment weights of the segmented clock difference data.

[0064] Specifically, the expression for the segment weight H(i) can be given in the form of Expression 2. For example, Expression 2 is shown below:

[0065] H(i)=h(i)w(i)c(i) (2)

[0066] Where H(i) represents segment weight; h(i) represents data weight; w(i) represents flag weight; and c(i) represents statistical weight.

[0067] Optionally, in this embodiment, the method for determining statistical weights includes: obtaining a preset uncertainty calculation expression, and determining the uncertainty of segmented clock difference data based on the uncertainty expression, each data value, and the segmented average value; and determining the statistical weights of segmented clock difference data based on a preset statistical weight expression and the uncertainty.

[0068] In practical applications, the uncertainty of segmented clock difference data can be characterized based on the standard deviation of each time data point within the segmented clock difference data. Specifically, the standard deviation can be determined based on a pre-defined standard deviation calculation expression. For example, the standard deviation calculation expression can be shown in the following expression 3:

[0069]

[0070] Where σ represents the standard deviation of the segmented clock error data, i.e., the uncertainty; K represents the number of time data points, K≤n-1; x i This represents the i-th time data; This represents the segmented average value of the segmented clock difference data;

[0071] Based on the above, the uncertainty of the piecewise clock difference data is substituted into a preset statistical weighting expression to obtain the statistical weights of the piecewise clock difference data. For example, the preset statistical weighting expression can be expressed as shown in expression 4 below:

[0072]

[0073] Where c(i) represents the statistical weight of the i-th segment clock difference data; σ i represents the standard deviation of the i-th segment clock difference data; K represents the number of time data in the segment clock difference data.

[0074] Specifically, if there are no missing data in the current segmented clock difference data, then the statistical weight of the segmented clock difference data can be expressed as follows:

[0075] It should be noted that in this embodiment, an FIR (finite impulse response) filter is used for the prediction of subsequent clock difference data. Since the structure of this filter and the original data output by the atomic clock contain white noise, flicker noise and random walk noise, the average value and uncertainty of the segmented clock difference data are calculated, and the weight of the segmented clock difference data is determined based on the above data. This can reduce the impact of zero-mean Gaussian white noise on the subsequent prediction of clock difference data.

[0076] Optionally, the method for determining the flag weight in this embodiment includes: determining whether all segmented clock difference data of the current segment is missing; if the segmented clock difference data of the current segment is missing, then determining the first preset weight value as the flag weight; if the segmented clock difference data of the current segment is not missing, then determining the second preset weight value as the flag weight.

[0077] It should be noted that in this embodiment, the complete absence of segmented clock difference data indicates that the atom did not output time data within the current segment, or did not output valid time data. Therefore, using this segmented clock difference data for data prediction may lead to a decrease in the accuracy of the prediction. Thus, in this embodiment, a low-weight flag is assigned to the segmented clock difference data with completely missing data to reduce its presence in the prediction and avoid affecting the accuracy of the prediction.

[0078] In practical applications, after obtaining segmented clock difference data from historical clock difference data, it is determined whether all time data in each segment is missing. If so, the weight of the flag bit of that segment is set to a first preset weight value, i.e., a lower weight value, such as 0.1; of course, to further ensure the accuracy of subsequent settings, the first preset weight value can also be set to 0. Conversely, if not all time data in the segmented total difference data is missing, the weight of the flag bit of that segment is set to a second weight value, i.e., a higher weight value, such as 0.9; of course, to further ensure the accuracy of subsequent settings, the second preset weight value can also be set to 1.

[0079] Optionally, the method for determining data weights in this embodiment includes: obtaining the data expression of the segmented clock difference data, and determining the data weights of the segmented clock difference data based on the data expression and the limiting conditions of the data expression.

[0080] In this embodiment, the data expression for the segmented clock difference data can be represented in the form of Expression 5. For example, Expression 5 is:

[0081] f(n) = f0 + an + f r (n) (5)

[0082] Where f0 represents the initial frequency deviation of the atomic clock; a represents the frequency drift factor of the atomic clock system, f r This represents the random perturbation of an atomic clock.

[0083] Specifically, the random perturbations of atomic clocks are assessed from a large amount of measured data from atomic clocks, and are included in the prediction process of atomic clock systems. r This is an optional option. Therefore, expression 5 above can be simplified to the form of expression 6 below. An exemplary expression 6 is:

[0084] f(n)=f0+an (6)

[0085] Based on the above embodiments, since the data expressed by expressions 1 and 6 are both clock difference data, combining expressions 1 and 6 yields the following expression 7. For example, let... Then expression 7 is:

[0086]

[0087] Specifically, by integrating the zeroth-order and first-order terms of expression 7 above, we obtain expressions 8 and 9 respectively. For example, expression 8 is shown below:

[0088]

[0089] An example expression 9 is shown below:

[0090] Furthermore, expression 10 can be obtained from expression 8 and the 0th-order constraint. For example, expression 10 is shown below:

[0091]

[0092] Furthermore, from expression 9 and the first-order constraint, we can obtain expression 11. For example, expression 11 is shown below:

[0093]

[0094] Assuming the noise factors are independently distributed, the noise gain of the filter can be expressed in the form of the following expression 12. For example, expression 12 is shown below:

[0095]

[0096] The Lagrange operator, combined with constraints g0 and g1, is used to optimize the noise gain F(h(1),…h(i)) to its minimum. The Lagrange algorithm can then be expressed as expression 13. For example, expression 13 is shown below:

[0097]

[0098] By ensuring that the partial derivatives of all parameters of L are 0, we can obtain expressions 14, 15, and 16.

[0099] For example, expression 14 is shown below:

[0100]

[0101] For example, expression 15 is shown below:

[0102]

[0103] For example, expression 16 is shown below:

[0104]

[0105] Furthermore, based on the above implementation method, solving the above expression yields parameters λ0 and λ1. For example, parameters λ0 and λ1 can be expressed by expression 17. For example, expression 17 is as follows:

[0106]

[0107] because, Furthermore, the superscript parameter l = 0, 1, or 2; w(i) and c(i) can be obtained from the measured data. Therefore, based on expression 17, the data weight h(i) in the segmented weight can be calculated. For example, the expression for h(i) is shown in expression 18 below:

[0108]

[0109] Based on this, the data weight h(i), flag weight w(i), and statistical weight c(i) of the segmented clock difference data can be multiplied, and the weight obtained after multiplication can be used as the segment weight of the current segmented clock difference data.

[0110] S130. Determine the clock difference data of the atomic clock at the target time based on the average value of each segment and the weight of each segment.

[0111] In this embodiment of the invention, the preset clock difference prediction expression can be understood as Expression 1 above. Specifically, based on obtaining the segmented average value and segmented weight of each segmented clock difference data, these values ​​are substituted into Expression 1 above to obtain the clock difference data of the atom at the target time.

[0112] The technical solution of this invention obtains historical clock difference data output by the atomic clock at the current time and historical times, and segments the historical clock difference data to obtain multiple segments of segmented clock difference data; determines the segment average value and segment weight corresponding to each segment of clock difference data; and determines the clock difference data of the atomic clock at the target time based on the segment average value and segment weight. This technical solution improves the accuracy of data prediction by predicting the clock difference data of the atomic clock at the target time based on the average value and segment weight corresponding to different segments of historical clock difference data. Furthermore, it does not compensate for missing data in the segmented clock difference data, avoiding the problem of reduced prediction accuracy due to data overfitting caused by compensation, thus further improving the accuracy of data prediction.

[0113] Example 2

[0114] Figure 2 This is a schematic diagram of the structure of a clock error prediction device for an atomic clock provided in Embodiment 2 of the present invention. Figure 2 As shown, the device includes: a segmented clock difference data acquisition module 210, a segmented average value and segmented weight determination module 220, and a clock difference data determination module 230;

[0115] The segmented clock difference data acquisition module 210 is used to acquire the historical clock difference data output by the atomic clock at the current time and historical time, and to segment the historical clock difference data to obtain multiple segments of segmented clock difference data.

[0116] The segmented average and segmented weight determination module 220 is used to determine the segmented average and segmented weight corresponding to each segment of clock difference data. The segmented weight includes data weight, flag weight, and statistical weight. The data weight is used to characterize the availability of segmented clock difference data. The flag weight is used to characterize whether all segmented clock difference data is missing. The statistical weight is used to characterize the uncertainty of clock difference data.

[0117] The clock difference data determination module 230 is used to determine the clock difference data of the atomic clock at the target time based on the average value of each segment and the weight of each segment.

[0118] Based on the above implementation method, optionally, the segmented average value and segmented weight determination module 220 includes:

[0119] The segmented average value determination unit is used to obtain the data values ​​corresponding to each time moment in the segmented clock difference data, and to perform average processing on the data values ​​to obtain the segmented average value of the segmented clock difference data.

[0120] Based on the above implementation method, optionally, the segmented average value and segmented weight determination module 220 includes:

[0121] The uncertainty determination unit is used to obtain a preset uncertainty calculation expression and determine the uncertainty of the segmented clock difference data based on the uncertainty expression, each data value, and the segmented average value.

[0122] The statistical weight determination unit is used to determine the statistical weights of the segmented clock difference data based on a preset statistical weight expression and uncertainty.

[0123] Based on the above implementation method, optionally, the segmented average value and segmented weight determination module 220 includes:

[0124] The data missing judgment unit is used to determine whether all segmented clock difference data of the current segment are missing;

[0125] The first flag bit weight determination unit is used to determine the first preset weight value as the flag bit weight if the current segment segmented clock difference data is missing.

[0126] The second flag weight determination unit is used to determine the second preset weight value as the flag weight if the current segmented clock difference data is not missing.

[0127] Based on the above implementation method, optionally, the segmented average value and segmented weight determination module 220 includes:

[0128] The data weight determination unit is used to obtain the data expression of the segmented clock difference data, and determine the data weight of the segmented clock difference data based on the data expression and the limiting conditions of the data expression.

[0129] Based on the above implementation method, optionally, the segmented average value and segmented weight determination module 220 includes:

[0130] The segment weight determination unit is used to multiply the data weight, flag weight, and statistical weight, and use the weight obtained after multiplication as the segment weight of the segmented clock difference data.

[0131] Based on the above implementation method, optionally, the clock difference data determination module 230 includes:

[0132] The clock error data prediction unit is used to obtain a preset clock error prediction expression, and determine the clock error data of the atomic clock at the target time based on the clock error prediction expression, the average value of each segment, and the weight of each segment.

[0133] The atomic clock error prediction device provided in this embodiment of the invention can execute the atomic clock error prediction method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0134] Example 3

[0135] Figure 3 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0136] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0137] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0138] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the clock bias prediction method for atomic clocks.

[0139] In some embodiments, the atomic clock bias prediction method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the atomic clock bias prediction method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the atomic clock bias prediction method by any other suitable means (e.g., by means of firmware).

[0140] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0141] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0142] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0143] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0144] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0145] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0146] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0147] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for predicting the clock bias of an atomic clock, characterized in that, include: The atomic clock's current time and historical clock difference data are obtained, and the historical clock difference data is segmented to obtain multiple segments of segmented clock difference data. No data compensation will be performed if there are missing data in any of the segmented clock difference data. Determine the segment average value and segment weight corresponding to each segmented clock difference data; set different segment weights for segmented clock difference data with missing data compared to segmented clock difference data without missing data; the segment weights include data weights, flag weights, and statistical weights; the data weights are used to characterize the availability of the segmented clock difference data; The flag weight is used to characterize whether all the segmented clock difference data is missing; the statistical weight is used to characterize the uncertainty of the clock difference data. The clock difference data of the atomic clock at the target time is determined based on the average value of each segment and the weight of each segment.

2. The method according to claim 1, characterized in that, Determining the segmented average value corresponding to each of the segmented clock difference data includes: Obtain the data values ​​corresponding to each time point in the segmented clock difference data, and perform average processing on the data values ​​to obtain the segmented average value of the segmented clock difference data.

3. The method according to claim 2, characterized in that, The method for determining the statistical weights includes: Obtain a preset uncertainty calculation expression, and determine the uncertainty of the segmented clock difference data based on the uncertainty expression, each of the data values, and the segmented average value; The statistical weights of the segmented clock difference data are determined based on a preset statistical weight expression and the uncertainty.

4. The method according to claim 1, characterized in that, The method for determining the weight of the flag bit includes: Determine whether all segmented clock difference data of the current segment is missing; if the segmented clock difference data of the current segment is missing, then determine the first preset weight value as the flag bit weight; if the segmented clock difference data of the current segment is not missing, then determine the second preset weight value as the flag bit weight.

5. The method according to claim 1, characterized in that, The method for determining the data weights includes: Obtain the data expression of the segmented clock difference data, and determine the data weight of the segmented clock difference data based on the data expression and the limiting conditions of the data expression.

6. The method according to claim 1, characterized in that, Determining the segment weights corresponding to each of the segmented clock difference data includes: The data weights, the flag weights, and the statistical weights are multiplied together, and the resulting weights are used as the segment weights of the segmented clock difference data.

7. The method according to claim 1, characterized in that, The clock difference data of the atomic clock at the target time is determined based on the average value of each segment and the weight of each segment, including: Obtain a preset clock error prediction expression, and determine the clock error data of the atomic clock at the target time based on the clock error prediction expression, the average value of each segment, and the weight of each segment.

8. A clock bias prediction device for an atomic clock, characterized in that, include: The segmented clock difference data acquisition module is used to acquire the historical clock difference data output by the atomic clock at the current time and historical time, and to segment the historical clock difference data to obtain multiple segments of segmented clock difference data; no data compensation is performed if there is data loss in any of the segmented clock difference data. The segmented average value and segmented weight determination module is used to determine the segmented average value and segmented weight corresponding to each segmented clock difference data; to set segmented clock difference data with missing data with different segmented clock difference data than segmented clock difference data without missing data; the segmented weight includes data weight, flag weight and statistical weight; the data weight is used to characterize the availability of the segmented clock difference data; The flag weight is used to characterize whether all the segmented clock difference data is missing; the statistical weight is used to characterize the uncertainty of the clock difference data. The clock difference data determination module is used to determine the clock difference data of the atomic clock at the target time based on the average value of each segment and the weight of each segment.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the clock bias prediction method for the atomic clock according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the clock bias prediction method of the atomic clock according to any one of claims 1-7.

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