A dual-factor combined weight ALGOS comprehensive atomic time algorithm improvement method and system

Abstract

The application discloses a double-factor combined weight setting ALGOS comprehensive atomic time algorithm improvement method, which introduces the frequency prediction deviation of current period atomic clock data and the historical atomic clock data frequency prediction deviation to form double factors for combined weight setting in weight design, so as to improve the stability and accuracy of the atomic time algorithm. The innovation of the method is to optimize the weight scheme of the classical ALGOS algorithm, and the frequency prediction deviation of the current period atomic clock is integrated into the weight calculation process. The frequency prediction deviation of the current period atomic clock is obtained by subtracting the average value of the calculated frequency value and the predicted frequency value of the time transfer data. The design can effectively suppress the interference of the atomic clock data with good historical data frequency predictability and poor current period frequency predictability on the final time scale, and improve the short-term stability of the time scale.

Description

Technical Field

[0001] This invention belongs to the field of comprehensive atomic time algorithms, specifically involving an improved method and system for ALGOS comprehensive atomic time algorithm with dual-factor joint weighting. Background Technology

[0002] The ALGOS (Algorithm Générale Observer System) integrated atomic time algorithm is the core algorithm used by the International Bureau of Weights and Measures (BIPM) to establish and maintain International Atomic Time (TAI). Based on observational data from over 500 atomic clocks provided by more than 80 timekeeping laboratories worldwide, this algorithm generates free atomic time (EAL) through post-processing with weighted averaging. This free atomic time, after frequency calibration and correction, yields the International Atomic Time (TAI), providing crucial support for cutting-edge scientific fields such as global satellite navigation systems, high-precision astronomical observations, and deep space exploration.

[0003] However, the classic ALGOS algorithm relies entirely on the frequency prediction bias of long-term historical data of atomic clocks when determining the weights of each atomic clock, without fully considering the predictability of atomic clocks within the current calculation cycle. Therefore, while the classic ALGOS algorithm can effectively ensure the long-term stability of the time reference, it performs poorly in short-term stability and cannot be adapted to application scenarios with high requirements for short-term stability. Summary of the Invention

[0004] The purpose of this invention is to provide an improved method for the ALGOS comprehensive atomic time algorithm with two-factor joint weighting. By optimizing and improving the classic ALGOS algorithm, short-term stability is improved while maintaining its long-term stability advantage, thereby expanding its applicability.

[0005] According to one aspect of the present invention, an improved method for a two-factor joint weighted ALGOS synthetic atomic time algorithm is provided, comprising:

[0006] Step 1: Obtain the basic data required for free atomic time calculation, and calculate the clock difference of each atomic clock relative to free atomic time and geocentric coordinate time;

[0007] Step 2: Determine the calculation period and calculation interval;

[0008] Step 3: In the current calculation interval, based on the clock difference of each atomic clock relative to free atomic time and geocentric coordinate time, calculate the clock frequency, clock drift, and atomic clock prediction value of the atomic clock respectively.

[0009] Step 4: Calculate the frequency prediction deviation of the atomic clock data in the current calculation cycle, and calculate the frequency prediction deviation of the historical atomic clock data based on the clock frequency and clock drift obtained in Step 3.

[0010] Step 5: Calculate the joint weight of the two factors based on the frequency prediction deviation of the atomic clock data in the current calculation cycle and the frequency prediction deviation of the historical atomic clock data;

[0011] Step 6: Calculate the weighted average result based on the time transfer data, atomic clock predictions, and dual-factor joint weights in the basic data.

[0012] Step 7: Based on the weighted average calculation result, calculate the deviation between the actual frequency and the predicted frequency of each atomic clock's weighted average calculation result, remove atomic clocks whose deviation exceeds the preset range, return to step 5 for re-iteration calculation, and after the set number of iterations is reached, exit the iteration and return to step 3 to enter the calculation of the next calculation interval.

[0013] Step 8: After completing the calculations for all calculation intervals in the current calculation cycle, smooth the results at each moment of the current calculation cycle to obtain the final calculation result for the current calculation cycle.

[0014] As a further technical solution, step 3, which involves calculating the atomic clock frequency, clock drift, and atomic clock prediction value, includes:

[0015] The clock frequency is calculated based on the clock difference of each atomic clock relative to the free atom.

[0016] Clock drift is calculated based on the clock difference of each atomic clock relative to the geocentric coordinates;

[0017] The predicted value of the atomic clock at any moment in the current calculation cycle is calculated based on the quadratic model.

[0018] As a further technical solution, step 4, calculating the frequency prediction deviation of the atomic clock data in the current calculation period, includes:

[0019] Based on the time transfer data in the basic data obtained in step 1, the deviation between the actual frequency and the predicted frequency of the time transfer data is calculated, where the predicted frequency is calculated from the clock frequency and clock drift obtained in step 3.

[0020] The calculated deviation is subjected to mean removal to eliminate the frequency deviation of the reference clock, thereby obtaining the frequency prediction deviation of the atomic clock data in the current calculation period.

[0021] As a further technical solution, step 4, calculating the frequency prediction deviation of historical atomic clock data, includes:

[0022] The frequency prediction deviation of historical atomic clock data is obtained by subtracting the cumulative frequency change calculated by clock drift from the difference in clock frequencies between two adjacent preset time periods.

[0023] As a further technical solution, the calculation cycle in step 2 is determined based on the number of days in each month, and each calculation cycle is divided into segments according to the set calculation interval.

[0024] As a further technical solution, the basic data mentioned in step 1 includes satellite-to-ground two-way time transfer data, PPP time transfer data, and historical atomic clock data.

[0025] According to one aspect of the present invention, an improved system for a two-factor joint weighted ALGOS synthetic atomic time algorithm is provided, comprising:

[0026] The first main module is used to obtain the basic data required for free atom time calculation and to calculate the clock difference of each atomic clock relative to free atom time and geocentric coordinate time.

[0027] The second main module is used to determine the calculation cycle and calculation interval;

[0028] The third main module is used to calculate the clock frequency, clock drift, and predicted value of the atomic clocks based on the clock difference of each atomic clock relative to free atomic time and geocentric coordinates at the current calculation interval.

[0029] The fourth main module is used to calculate the frequency prediction deviation of the atomic clock data in the current calculation cycle, and to calculate the frequency prediction deviation of the historical atomic clock data based on the obtained clock frequency and clock drift.

[0030] The fifth main module is used to perform the following iterative calculations within the current calculation interval: Calculate the dual-factor joint weight based on the frequency prediction deviation of the atomic clock data in the current calculation cycle and the frequency prediction deviation of the historical atomic clock data; calculate the weighted average result based on the time transfer data, atomic clock prediction values, and dual-factor joint weight in the basic data; calculate the deviation between the actual frequency and the predicted frequency of each atomic clock's weighted average calculation result based on the calculated weighted average result, remove atomic clocks whose deviation exceeds a preset range, perform a new iterative calculation, and exit the iteration after a set number of iterations, entering the calculation of the next calculation interval;

[0031] The sixth main module is used to smooth the results at each moment of the current calculation cycle after completing the calculations for all calculation intervals in the current calculation cycle, and use this as the final calculation result for the current calculation cycle.

[0032] According to one aspect of the present invention, an improved device for a two-factor joint weighted ALGOS comprehensive atomic time algorithm is provided, comprising a memory and a processor, wherein the memory stores program instructions that are executed by the processor, and the processor invokes the program instructions to execute the improved method for a two-factor joint weighted ALGOS comprehensive atomic time algorithm.

[0033] According to one aspect of the present invention, a non-transitory computer-readable storage medium is provided, the non-transitory computer-readable storage medium storing computer instructions that cause the computer to execute the improved method of the ALGOS synthesis atomic time algorithm with two-factor joint weighting.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0035] 1. Based on the calculated and predicted frequencies of time-transfer data, this invention effectively removes the frequency deviation of the reference clock through difference calculation and mean elimination, thereby accurately obtaining the estimated value of the frequency prediction deviation of the atomic clock in the current period.

[0036] 2. The dual-factor joint weighting scheme proposed in this invention takes into account the influence of the frequency prediction deviation of the atomic clock in the current time period, which is not considered in the classic ALGOS integrated atomic time algorithm, and incorporates it into the weight calculation to improve the short-term stability of the time scale.

[0037] 3. The dual-factor joint weighting scheme proposed in this invention retains the weighting calculation logic based on prediction bias of long-term historical data in the classic ALGOS integrated atomic time algorithm, which improves the short-term stability of the time scale while ensuring long-term stability. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a flowchart illustrating an improved method for a two-factor joint weighted ALGOS synthetic atomic time algorithm provided in an embodiment of the present invention. Detailed Implementation

[0040] In the classic ALGOS integrated atomic time algorithm's weighting scheme, the weight calculation is determined solely by the frequency prediction deviation of long-term historical atomic clock data, without considering the predictability of the current time period data in the integrated weighting formula. Atomic clocks with frequency deviations greater than 5 ns / day are only removed during algorithm iteration. Therefore, this invention introduces a dual-factor approach in the weighting design, combining the frequency prediction deviation of the current time period atomic clock data and the frequency prediction deviation of historical atomic clock data, to jointly determine the weights, thereby improving the stability and accuracy of the atomic time algorithm.

[0041] The core innovation of this invention lies in optimizing the weighting scheme of the classic ALGOS algorithm. It incorporates the frequency prediction deviation of the atomic clock in the current time period into the weighting calculation process. The frequency prediction deviation of the atomic clock in the current time period is obtained by subtracting and averaging the calculated frequency value and the predicted frequency value from the time-transferred data. This design effectively suppresses the interference of atomic clock data with "good predictability of historical data frequency but poor predictability of current period frequency" on the final time scale, thus improving the short-term stability of the time scale. Simultaneously, the proposed two-factor joint weighting scheme retains the weighting logic based on prediction deviations from long-term historical data in the classic ALGOS integrated atomic time algorithm, ensuring that the long-term stability of the time scale is not significantly affected.

[0042] The technical process of the method of this invention mainly includes:

[0043] Step 1: Obtain the basic data required for the calculation of the free atomic time (EAL) and perform preliminary calculations to obtain the clock difference (EAL-clocks) and TT-clocks of each atomic clock relative to the free atomic time (EAL) and the geocentric coordinate time (TT).

[0044] Step 2: Determine the length of the calculation cycle and the calculation interval for each calculation cycle.

[0045] Step 3: Calculate the historical clock frequency and clock drift based on the EAL-clocks and T-clocks obtained in Step 1, and then calculate the predicted value of the atomic clock based on the quadratic model.

[0046] Step 4: Based on the time transfer data obtained in Step 1, calculate the deviation between the actual frequency and the predicted frequency of the time transfer data. The predicted frequency is calculated from the historical clock frequency and clock drift obtained in Step 3. Then, the deviation is averaged and removed to eliminate the frequency deviation of the reference clock, obtaining the estimated frequency prediction deviation of the atomic clock for the current period. The frequency prediction deviation of the atomic clock's historical data is calculated based on the clock frequency and clock drift obtained in Step 3. Here, the current period refers to the current calculation cycle.

[0047] Step 5: Calculate the joint weight of the two factors based on the frequency prediction deviation between the historical atomic clock data and the current time period data obtained in Step 4.

[0048] Step 6: Calculate the final weighted average result based on the time transfer data, atomic clock predictions, and dual-factor joint weights obtained in steps 1, 3, and 5 respectively.

[0049] Step 7: Based on the weighted average result calculated in Step 6, calculate the deviation between the actual frequency and the predicted frequency of the weighted average result of each atomic clock, remove atomic clocks with large deviations and re-iterate the calculation, and exit the iteration after 4 iterations.

[0050] Step 8: After the iteration of step 7 is completed, the calculation of the next calculation interval is performed.

[0051] Step 9: After completing all interval calculations for the current calculation cycle in Step 8, smooth the results obtained at each moment of the current cycle to obtain the final calculation result for the current cycle.

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. In addition, the technical features of the various embodiments or individual embodiments provided by the present invention can be arbitrarily combined to form new technical solutions. Such combinations are not bound by the order of steps and / or structural composition patterns, but must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0053] Reference Figure 1 As shown in the figure, this invention provides an implementation example of an improved ALGOS comprehensive atomic time algorithm with dual-factor joint weighting, including the following steps:

[0054] Step (1): Obtain the basic data required for calculating the Free Atomic Time (EAL) from the BIPM website (https: / / www.bipm.org / ), including satellite-to-ground two-way time transfer data, PPP time transfer data, and historical data for the past 12 months such as UTC-UTC(k), UTC(k)-clocks, EAL-TAI, TT-EAL, and TAI-UTC. Calculate the clock difference (EAL-clocks) of each atomic clock relative to the Free Atomic Time (EAL) using UTC-UTC(k), UTC(k)-clocks, EAL-TAI, and TAI-UTC. Calculate the clock difference (TT-clocks) of each atomic clock relative to the geocentric coordinate time (TT) using EAL-clock and TT-EAL.

[0055] Step (2): Determine the calculation period T for free atoms. The calculation period is determined based on the number of days in each month, and can be 25, 30, or 35 days. Each period is divided into segments with a 5-day time interval. The start time of each period is recorded as... The end time is recorded as The intermediate time is recorded as .

[0056] Step (3), extracting EAL-clocks based on the results obtained in step (1) The EAL-clocks of a given time are denoted as The subscript i indicates the clock number. This indicates the calculation period. The clock frequency is obtained by differential calculation based on EAL-clocks and TT-clocks respectively. , For clock drift calculations, hydrogen atomic clocks are calculated using a quadratic difference averaging method, while cesium and rubidium atomic clocks are calculated using linear fitting. Clock drift is calculated every four months of data. The clock frequency and predicted clock drift for the current period are denoted as follows: , subscript Indicates a prediction. , The calculation formula is , Then, based on the quadratic model... Calculate the atomic clock prediction value at time t in the current period. .

[0057] Step (4), based on the time transfer data obtained in step (1) Calculate its frequency Where i and j represent clock numbers, and j is the clock number of the reference clock. Then, the clock frequency calculated in step (3) is combined with the clock frequency. And Zhong Piao The frequency prediction bias of time-transfer data is calculated as follows: ,in , These represent the lengths of the previous and current calculation cycles, respectively. The mean-removal calculation is performed to obtain the frequency prediction deviation of the atomic clock for the current time period. Based on the clock frequency and clock drift calculated in step (3), the frequency prediction deviation of historical atomic clock data is calculated. The calculation method is as follows Atomic clocks require a availability of 5-12 months. Only then can it participate in EAL calculations when atoms are free.

[0058] Step (5): Calculate the dual-factor joint weight based on the current time period atomic clock frequency prediction deviation and the historical atomic clock data frequency prediction deviation obtained in step (4). The calculation method is as follows: , ,in Indicates the month number. This represents the prediction bias after weighted averaging. This is a two-factor joint weighting, where N is the number of atomic clocks. .

[0059] Step (6) Based on the time transfer data obtained in steps (1), (3), and (5) respectively Atomic clock prediction values Two-factor joint weighting Using the weighted average formula and The difference between the clock face reading of each atomic clock and the EAL value was calculated. .

[0060] Step (7): Based on the results calculated in step (6), calculate the deviation between the actual frequency and the predicted frequency of the weighted average result of each atomic clock, and remove atomic clocks with a deviation greater than 5 ns / day for re-iteration calculation. The weight of each iteration uses the weight of the previous iteration. After removing the data, it needs to be re-normalized. After 4 iterations, the iteration ends.

[0061] After the iterations of steps (8) and (7) are completed, the calculation of the next calculation interval is performed, and the calculation method is the same as that of steps (3) to (7).

[0062] Step (9): Based on the calculation results of step (8), smooth the EAL-clock result of each clock in the current cycle to obtain the final calculation result of the current cycle.

[0063] The implementation of the various embodiments of this invention is based on programmed processing through a device with processor functionality. Therefore, in practical engineering, the technical solutions and functions of the various embodiments of this invention are encapsulated into various modules. Based on this reality, and building upon the above embodiments, the embodiments of this invention provide an improved system for a two-factor joint weighting ALGOS comprehensive atomic time algorithm. This system is used to execute an improved method for a two-factor joint weighting ALGOS comprehensive atomic time algorithm as described in the above method embodiments.

[0064] The system includes: a first main module for acquiring the basic data required for free atomic time calculation and calculating the clock difference of each atomic clock relative to free atomic time and geocentric coordinates; a second main module for determining the calculation period and calculation interval; a third main module for calculating the clock frequency, clock drift, and atomic clock prediction value of each atomic clock relative to free atomic time and geocentric coordinates within the current calculation interval; a fourth main module for calculating the frequency prediction deviation of atomic clock data in the current calculation period and calculating the frequency prediction deviation of historical atomic clock data based on the obtained clock frequency and clock drift; and a fifth main module for performing the following iterative calculations within the current calculation interval: based on the current calculation period... The frequency prediction deviations of the current atomic clock data and the frequency prediction deviations of historical atomic clock data are used to calculate the joint weight of the two factors. Based on the time transfer data, atomic clock prediction values, and joint weight of the two factors in the basic data, a weighted average result is calculated. Based on the calculated weighted average result, the deviation between the actual frequency and the predicted frequency of each atomic clock weighted average calculation result is calculated. Atomic clocks with deviations exceeding the preset range are eliminated, and the calculation is re-iterated. After the set number of iterations is reached, the iteration ends and the calculation of the next calculation interval begins. The sixth main module is used to smooth the result of each moment in the current calculation cycle after completing the calculation of all calculation intervals in the current calculation cycle, and use it as the final calculation result of the current calculation cycle.

[0065] This invention provides an improved system for a two-factor joint weighted ALGOS integrated atomic time algorithm. While the classic ALGOS algorithm can effectively guarantee the long-term stability of time references, it performs poorly in short-term stability and cannot adapt to application scenarios with high requirements for short-term stability. By using the aforementioned modules, the classic ALGOS algorithm is optimized and improved, thereby enhancing its short-term stability while maintaining its long-term stability advantage, thus expanding its applicability.

[0066] It should be noted that the system embodiments provided by the present invention are used not only to implement the methods in the above method embodiments, but also to implement the methods in other method embodiments provided by the present invention. The only difference is that corresponding functional modules are set. The principle is basically the same as that of the above system embodiments provided by the present invention. As long as those skilled in the art can improve the modules in the above system embodiments by referring to the specific technical solutions in other method embodiments and combining technical features to obtain corresponding technical means and technical solutions composed of these technical means, on the basis of the above system embodiments, and on the premise of ensuring the practicality of the technical solutions, they can obtain corresponding system-like embodiments for implementing the methods in other method-like embodiments.

[0067] Based on the same inventive concept as any of the foregoing embodiments, this embodiment of the invention provides an improved device for a two-factor joint weighted ALGOS comprehensive atomic time algorithm, including a memory and a processor. The memory stores program instructions that are executed by the processor, and the processor calls the program instructions to execute the improved method for a two-factor joint weighted ALGOS comprehensive atomic time algorithm.

[0068] In embodiments of the present invention, the memory can be non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), or it can be volatile memory, such as random-access memory (RAM). Memory is any other medium capable of carrying or storing desired program code having an instruction or data structure form and accessible by a computer, but is not limited thereto. The memory in embodiments of the present invention can also be a circuit or any other device capable of implementing a storage function for storing program instructions and / or data.

[0069] In this embodiment of the invention, the processor may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in this embodiment of the invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in this embodiment of the invention can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0070] Based on the same inventive concept as any of the foregoing embodiments, this embodiment of the invention provides a non-transitory computer-readable storage medium storing computer instructions. These computer instructions cause the computer to execute the following steps of the improved ALGOS comprehensive atomic time algorithm method for two-factor joint weighting:

[0071] Step 1: Obtain the basic data required for free atomic time calculation, and calculate the clock difference of each atomic clock relative to free atomic time and geocentric coordinate time;

[0072] Step 2: Determine the calculation period and calculation interval;

[0073] Step 3: In the current calculation interval, based on the clock difference of each atomic clock relative to free atomic time and geocentric coordinate time, calculate the clock frequency, clock drift, and atomic clock prediction value of the atomic clock respectively.

[0074] Step 4: Calculate the frequency prediction deviation of the atomic clock data in the current calculation cycle, and calculate the frequency prediction deviation of the historical atomic clock data based on the clock frequency and clock drift obtained in Step 3.

[0075] Step 5: Calculate the joint weight of the two factors based on the frequency prediction deviation of the atomic clock data in the current calculation cycle and the frequency prediction deviation of the historical atomic clock data;

[0076] Step 6: Calculate the weighted average result based on the time transfer data, atomic clock predictions, and dual-factor joint weights in the basic data.

[0077] Step 7: Based on the weighted average calculation result, calculate the deviation between the actual frequency and the predicted frequency of each atomic clock's weighted average calculation result, remove atomic clocks whose deviation exceeds the preset range, return to step 5 for re-iteration calculation, and after the set number of iterations is reached, exit the iteration and return to step 3 to enter the calculation of the next calculation interval.

[0078] Step 8: After completing the calculations for all calculation intervals in the current calculation cycle, smooth the results at each moment of the current calculation cycle to obtain the final calculation result for the current calculation cycle.

[0079] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0080] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0081] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0082] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0083] In summary, this invention proposes an improved ALGOS integrated atomic time algorithm with dual-factor joint weighting. This method incorporates the frequency prediction deviation of the current time period's atomic clock data and the frequency prediction deviation of historical atomic clock data into the weighting design, forming a dual-factor joint weighting to improve the stability and accuracy of the atomic time algorithm. The core innovation of this method lies in optimizing the weighting scheme of the classic ALGOS algorithm, integrating the frequency prediction deviation of the current time period into the weighting calculation process. The frequency prediction deviation of the current time period's atomic clock is obtained by subtracting and averaging the calculated frequency value and the predicted frequency value from the time-transferred data. This design can effectively suppress the interference of atomic clock data with "good historical frequency predictability but poor current period frequency predictability" on the final time scale, improving the short-term stability of the time scale. Simultaneously, the dual-factor joint weighting scheme proposed in this invention retains the weighting logic based on long-term historical data prediction deviation in the classic ALGOS integrated atomic time algorithm, ensuring that the long-term stability of the time scale is not significantly affected.

[0084] Unless otherwise specified, all of the above technologies are publicly known technologies.

[0085] The terms “comprising” and “having”, and any variations thereof, in the specification, claims, and accompanying drawings of this invention are intended to cover a non-exclusive inclusion, such as a process, method, system, product, or apparatus that includes a series of steps or units, not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. An improved method for a two-factor joint weighted ALGOS comprehensive atomic time algorithm, characterized in that, include: Step 1: Obtain the basic data required for free atomic time calculation, and calculate the clock difference of each atomic clock relative to free atomic time and geocentric coordinate time; Step 2: Determine the calculation period and calculation interval; Step 3: In the current calculation interval, based on the clock difference of each atomic clock relative to free atomic time and geocentric coordinate time, calculate the clock frequency, clock drift, and atomic clock prediction value of the atomic clock respectively. Step 4: Calculate the frequency prediction deviation of the atomic clock data in the current calculation cycle, and calculate the frequency prediction deviation of the historical atomic clock data based on the clock frequency and clock drift obtained in Step 3. Step 5: Calculate the joint weight of the two factors based on the frequency prediction deviation of the atomic clock data in the current calculation cycle and the frequency prediction deviation of the historical atomic clock data; Step 6: Calculate the weighted average result based on the time transfer data, atomic clock predictions, and dual-factor joint weights in the basic data. Step 7: Based on the weighted average calculation result, calculate the deviation between the actual frequency and the predicted frequency of each atomic clock's weighted average calculation result, remove atomic clocks whose deviation exceeds the preset range, return to step 5 for re-iteration calculation, and after the set number of iterations is reached, exit the iteration and return to step 3 to enter the calculation of the next calculation interval. Step 8: After completing the calculations for all intervals in the current calculation cycle, smooth the results at each moment of the current calculation cycle to obtain the final calculation result for the current calculation cycle.

2. The improved method of ALGOS comprehensive atomic time algorithm with dual-factor joint weighting according to claim 1, characterized in that, Step 3, which involves calculating the atomic clock frequency, clock drift, and predicted atomic clock value, includes: The clock frequency is calculated based on the clock difference of each atomic clock relative to the free atom. Clock drift is calculated based on the clock difference of each atomic clock relative to the geocentric coordinates; The predicted value of the atomic clock at any moment in the current calculation cycle is calculated based on the quadratic model.

3. The improved method of ALGOS comprehensive atomic time algorithm with dual-factor joint weighting according to claim 1, characterized in that, Step 4, calculating the frequency prediction bias of the atomic clock data for the current calculation period, includes: Based on the time transfer data in the basic data obtained in step 1, the deviation between the actual frequency and the predicted frequency of the time transfer data is calculated, where the predicted frequency is calculated from the clock frequency and clock drift obtained in step 3. The calculated deviation is subjected to mean removal to eliminate the frequency deviation of the reference clock, and the frequency prediction deviation of the atomic clock data in the current calculation period is obtained.

4. The improved method of ALGOS comprehensive atomic time algorithm with dual-factor joint weighting according to claim 1, characterized in that, Step 4, calculating the frequency prediction bias of historical atomic clock data, includes: The frequency prediction deviation of historical atomic clock data is obtained by subtracting the cumulative frequency change calculated by clock drift from the difference in clock frequencies between two adjacent preset time periods.

5. The improved method of ALGOS comprehensive atomic time algorithm with dual-factor joint weighting according to claim 1, characterized in that, The calculation cycle described in step 2 is determined based on the number of days in each month, and each calculation cycle is divided into segments according to the set calculation intervals.

6. The improved method of ALGOS comprehensive atomic time algorithm with dual-factor joint weighting according to claim 1, characterized in that, The basic data mentioned in step 1 includes satellite-to-ground two-way time transfer data, PPP time transfer data, and historical atomic clock data.

7. An improved system for a two-factor joint weighted ALGOS comprehensive atomic time algorithm, characterized in that, include: The first main module is used to obtain the basic data required for free atom time calculation and to calculate the clock difference of each atomic clock relative to free atom time and geocentric coordinate time. The second main module is used to determine the calculation cycle and calculation interval; The third main module is used to calculate the clock frequency, clock drift, and predicted value of the atomic clocks based on the clock difference of each atomic clock relative to free atomic time and geocentric coordinates at the current calculation interval. The fourth main module is used to calculate the frequency prediction deviation of the atomic clock data in the current calculation cycle, and to calculate the frequency prediction deviation of the historical atomic clock data based on the obtained clock frequency and clock drift. The fifth main module is used to perform the following iterative calculation within the current calculation interval: calculate the joint weight of the two factors based on the frequency prediction deviation of the atomic clock data in the current calculation cycle and the frequency prediction deviation of the historical atomic clock data; Based on the time transfer data, atomic clock predictions, and dual-factor joint weights in the basic data, a weighted average result is calculated. Based on the calculated weighted average result, the deviation between the actual frequency and the predicted frequency of each atomic clock's weighted average calculation result is calculated. Atomic clocks with deviations exceeding a preset range are removed, and the calculation is re-iterated. After the set number of iterations is reached, the iteration ends and the calculation begins in the next calculation interval. The sixth main module is used to smooth the results at each moment of the current calculation cycle after completing the calculations for all calculation intervals in the current calculation cycle, and use this as the final calculation result for the current calculation cycle.

8. An improved device for a two-factor joint weighted ALGOS integrated atomic time algorithm, characterized in that, The system includes a memory and a processor, wherein the memory stores program instructions that are executed by the processor, and the processor invokes the program instructions to execute an improved method for a two-factor joint weighting ALGOS synthetic atomic time algorithm as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions that cause the computer to execute the improved ALGOS synthetic atomic time algorithm method for two-factor joint weighting as described in any one of claims 1 to 6.

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