A multi-source heterogeneous fusion distributed layered time scale system construction method, system, device and storage medium

Abstract

The application discloses a multi-source heterogeneous fusion distributed layered time scale system construction method, system, equipment and storage medium, and relates to the technical field of time frequency standard and time reference construction. The method comprises the following steps: a distributed layered framework containing a plurality of parallel first-level units in a first layer and one second-level unit in a second layer is constructed, and space, type and affiliation information of time frequency sources are collected; the time frequency sources are grouped according to a grouping strategy, each unit is arranged in different data centers, and a distributed first-level time scale is generated through differential processing; the first-level time scale is represented as a virtual time frequency source and participates in comparison, and is input into the second-level unit; a second-level time scale is generated through virtual comparison; and based on a layered time scale algorithm and an elastic strategy, a final time scale output is dynamically updated and generated. The application can continuously output a time scale meeting stability and continuity requirements, and improves the expandability and elasticity of a time reference system.

Description

Technical Field

[0001] This invention relates to the field of time and frequency standards and time reference construction technology, and in particular to a method, system, device and storage medium for constructing a distributed hierarchical time scale system with multi-source heterogeneous fusion. Background Technology

[0002] In modern science and engineering, highly stable time-frequency references play a crucial role in navigation and positioning, communication synchronization, astronomical observation, and deep space exploration. Existing timescale systems are typically based on a centralized structure, using a unified timescale algorithm to process multiple time-frequency sources involved in constructing the timescale. However, with the increasing number, diversification, and spatial expansion of time-frequency sources, centralized, single-layer timescale systems based on a single time-frequency source type face the following limitations: 1. Difficulty in multi-source fusion and insufficient scalability: Different types of time and frequency sources have different clock error models, noise characteristics, frequency stability and time comparison links. Centralized single-layer time scale systems usually use a unified time scale algorithm to process all time and frequency sources, which makes it difficult to take into account the characteristics of various time and frequency sources at the same time, thus limiting the fusion capability of multi-source heterogeneous time and frequency sources.

[0003] 2. Single point of failure: Centralized time-scale systems typically rely on a single data center for time-scale generation. When this data center fails or experiences communication anomalies, the reliability of the time-scale results may decrease, and even the continuity of time services may be affected.

[0004] 3. Insufficient timescale flexibility and poor resistance to interference and anomalies: In a single-layer timescale system, each time-frequency source usually directly participates in the generation of the final timescale. When the continuity or availability of the time-frequency sources changes, such as when a time-frequency source joins or leaves the clock group or when an anomaly occurs, it can easily directly affect the final timescale output, thereby reducing the stability and continuity of the timescale system.

[0005] Therefore, existing technologies are insufficient to meet the stability, resilience, and scalability requirements of time-scale systems in future environments with globally distributed time and frequency sources and multi-source fusion. Summary of the Invention

[0006] The purpose of this invention is to provide a method, system, device and storage medium for constructing a distributed hierarchical time-scale system with multi-source heterogeneous fusion, aiming to solve or improve at least one of the above-mentioned technical problems.

[0007] To achieve the above objectives, the present invention provides the following solution: A method for constructing a distributed hierarchical time-scale system with multi-source heterogeneous fusion includes: Step S1: Construct a timescale system framework using a distributed hierarchical structure, and collect relevant information about each available time-frequency source participating in the timescale construction; the timescale system framework includes multiple parallel and independent first-level timescale generation units located in the first layer and a second-level timescale generation unit located in the second layer; the relevant information includes spatial distribution information, time-frequency source type information, and affiliated institution information; Step S2: Group the available time and frequency sources according to the time and frequency source grouping strategy. Each group corresponds to a first-level time scale generation unit, and each first-level time scale generation unit is deployed in a different time service data center. Different processing is performed according to the time and frequency source type, time comparison is performed, and distributed first-level time scales are generated in parallel. Step S3: The first-level time scale is characterized as a virtual time-frequency source, and the first-level time scale is used as the input of the second-level time scale generation unit through a time comparison method between virtual time-frequency sources. Step S4: In the secondary time scale generation unit, the virtual time-frequency source is compared in time, and a secondary time scale is generated; Step S5: Generate the time scales of the first and second layers according to the hierarchical time scale algorithm. The hierarchical time scale algorithm updates the time scales based on the state changes of the time-frequency source and the virtual time-frequency source using an elastic strategy, thereby obtaining the output of the time scale system.

[0008] Optionally, representing the first-level time scale as a virtual time-frequency source specifically includes: The clock difference dataset for the first-level time scale is determined based on the clock difference relationship between the first-level time scale and the time-frequency sources involved in constructing the time scale. Virtual time-frequency source state parameters are generated based on the clock difference dataset; the virtual time-frequency source state parameters include the clock difference dataset and the frequency stability dataset. The virtual time-frequency source state parameters are encapsulated into a first-level time scale with a unified time-frequency source state description structure, and participate in the generation process of the second-level time scale in the form of a virtual time-frequency source.

[0009] Optionally, the time comparison method between the virtual time-frequency sources specifically involves the following process: For two virtual time-frequency sources to be compared, reference time-frequency sources that meet the time comparison conditions are determined in the corresponding groups, and the clock difference between the reference time-frequency sources is obtained. Based on the state parameters of the virtual time and frequency sources, the clock difference between the two virtual time and frequency sources and the corresponding reference time and frequency source is determined; Establish clock bias propagation relationship, eliminate reference time and frequency source clock bias, and obtain time comparison results between the two virtual time and frequency sources.

[0010] Optionally, the hierarchical time-scale algorithm includes a first-layer time-scale algorithm, a second-layer time-scale algorithm, and a resilient strategy, wherein: The first-level time-scale algorithm is as follows: based on the type information, stability characteristics and noise characteristics of the time-frequency sources in each first-level time-scale generation unit, an appropriate time-scale algorithm is selected respectively. When the time-frequency source can establish a discrete state-space model, a time-scale algorithm based on Kalman filter or a corresponding variant is used. When the time-frequency source is not suitable for establishing a discrete state-space model, a time-scale algorithm based on weighted average or a corresponding variant is used. The second-level time scale algorithm is: a time scale algorithm based on weighted average or a corresponding variant, and a second-level time scale is generated based on the time comparison results of the virtual time-frequency source; The elastic strategy is as follows: when the time-frequency source and / or virtual time-frequency source joins a group, leaves a group, or experiences an abnormal state, the time-frequency source grouping, the parameters of the first or second layer time-scale algorithm, and the time-scale algorithm type are adjusted to maintain the stability and continuity of the time-scale system output.

[0011] Optionally, the time-frequency source grouping strategy specifically includes the following processing steps: Based on the spatial distribution information, the time-frequency source type information, and the affiliated institution information, the available time-frequency sources are grouped so that the time-frequency sources in the same group meet the set conditions of being close in spatial distribution, having the same or similar type characteristics, and having the same or identical affiliated institutions.

[0012] This invention also provides a multi-source heterogeneous fusion distributed hierarchical time-scale system construction system, comprising: The time-frequency source information management module is used to construct a time-scale system framework using a distributed hierarchical structure and to collect relevant information of each available time-frequency source participating in the time-scale construction. The time-scale system framework includes multiple parallel and independent first-level time-scale generation units located in the first layer and a second-level time-scale generation unit located in the second layer. The relevant information includes spatial distribution information, time-frequency source type information, and affiliated institution information. The first-level time scale generation module is used to group available time and frequency sources according to the time and frequency source grouping strategy. Each group corresponds to a first-level time scale generation unit, and each first-level time scale generation unit is deployed in a different time service data center. It performs time comparison and generates distributed first-level time scales in parallel according to the time and frequency source type differentiation. The time comparison management module is used to characterize the first-level time scale as a virtual time-frequency source, so that the first-level time scale, in the form of a virtual time-frequency source, is used as the input of the second-level time scale generation unit through a time comparison method between virtual time-frequency sources; The secondary time scale generation module is used in the secondary time scale generation unit to perform time comparison on the virtual time-frequency source and generate a secondary time scale; The time scale output module is used to generate the first and second layer time scales according to the hierarchical time scale algorithm. The hierarchical time scale algorithm updates the time scales based on the state changes of the time-frequency source and the virtual time-frequency source using an elastic strategy, thereby obtaining the output of the time scale system.

[0013] The present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the distributed hierarchical time-scale system construction method according to the above-described multi-source heterogeneous fusion.

[0014] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for constructing a distributed hierarchical time-scale system with multi-source heterogeneous fusion as described above.

[0015] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: This invention discloses a method, system, device, and storage medium for constructing a distributed hierarchical timescale system with multi-source heterogeneous fusion. The method includes constructing a distributed hierarchical framework containing multiple parallel first-level units in the first layer and one second-level unit in the second layer; collecting spatial, type, and membership information of time-frequency sources; grouping the time-frequency sources according to a grouping strategy, deploying each unit in different data centers, processing them differently, and generating a distributed first-level timescale; characterizing it as a virtual time-frequency source and participating in comparison as input for the second-level unit; performing virtual comparison to generate the second-level timescale; and dynamically updating and generating the final timescale output based on a hierarchical timescale algorithm and an elastic strategy. This invention can continuously output a timescale that meets the requirements of stability and continuity, improving the scalability and elasticity of the time reference system. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the 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.

[0017] Figure 1 This is a schematic diagram of the distributed hierarchical time-scale architecture of multi-source heterogeneous fusion in this embodiment; Figure 2 This is a flowchart of the virtual time-frequency source generation method in this embodiment; Figure 3This is a schematic diagram of the virtual time-frequency source time comparison method in this embodiment; Figure 4 This is a schematic diagram of the hierarchical timescale algorithm framework in this embodiment. Detailed Implementation

[0018] 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, 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 are within the scope of protection of the present invention.

[0019] The purpose of this invention is to provide a method, system, device and storage medium for constructing a distributed hierarchical time-scale system with multi-source heterogeneous fusion, aiming to solve or improve at least one of the above-mentioned technical problems.

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] As a first aspect, such as Figure 1 As shown, this invention provides a method for constructing a distributed hierarchical time-scale system with multi-source heterogeneous fusion, comprising: Step S1: Construct a timescale system framework using a distributed hierarchical structure and collect relevant information from each available time-frequency source participating in the timescale construction. The timescale system framework includes multiple parallel and independent first-level timescale generation units located in the first layer and one second-level timescale generation unit located in the second layer. The relevant information includes spatial distribution information, time-frequency source type information, and affiliated institution information. Available time-frequency source types include, but are not limited to, ground-based atomic clocks, optical clocks, crystal oscillators, satellite clocks, network time references, astronomical time references, and other time-frequency reference sources that can provide time or frequency standards.

[0022] Step S2: Group the available time and frequency sources according to the time and frequency source grouping strategy. Each group corresponds to a first-level time scale generation unit, and each first-level time scale generation unit is deployed in a different time service data center. The time and frequency sources are processed differently according to their types, and time comparison and parallel generation of distributed first-level time scales are performed.

[0023] The time-frequency source grouping strategy specifically includes the following processing steps: Based on the spatial distribution information, the time-frequency source type information, and the affiliated institution information, the available time-frequency sources are grouped so that the time-frequency sources in the same group meet the set conditions of being close in spatial distribution, having the same or similar type characteristics, and having the same or identical affiliated institutions.

[0024] Step S3: The first-level time scale is characterized as a virtual time-frequency source, and the first-level time scale is used as the input of the second-level time scale generation unit through a time comparison method between virtual time-frequency sources.

[0025] The first-level time scale is characterized as a virtual time-frequency source, and the specific processing steps include: Based on the clock difference relationship between the first-level time scale and the time-frequency sources participating in the construction of the time scale, the clock difference dataset of the first-level time scale is determined; virtual time-frequency source state parameters are generated based on the clock difference dataset; the virtual time-frequency source state parameters include the clock difference dataset and the frequency stability dataset; the virtual time-frequency source state parameters are encapsulated into a first-level time scale with a unified time-frequency source state description structure, and participate in the generation process of the second-level time scale in the form of a virtual time-frequency source.

[0026] Furthermore, the time comparison method between virtual time-frequency sources involves the following specific processing steps: For two virtual time-frequency sources to be compared, reference time-frequency sources that meet the time comparison conditions are determined in the corresponding groups, and the clock difference between the reference time-frequency sources is obtained; based on the state parameters of the virtual time-frequency sources, the clock difference between the two virtual time-frequency sources and the corresponding reference time-frequency sources is determined; a clock difference propagation relationship is established, the clock difference of the reference time-frequency sources is eliminated, and the time comparison result between the two virtual time-frequency sources is obtained.

[0027] Step S4: In the secondary time scale generation unit, the virtual time-frequency source is compared in time, and a secondary time scale is generated.

[0028] Step S5: Generate the time scales of the first and second layers according to the hierarchical time scale algorithm. The hierarchical time scale algorithm updates the time scales based on the state changes of the time-frequency source and the virtual time-frequency source using an elastic strategy, thereby obtaining the output of the time scale system.

[0029] The hierarchical time-scale algorithm includes a first-layer time-scale algorithm, a second-layer time-scale algorithm, and an elastic strategy.

[0030] The first-level time-scale algorithm is as follows: based on the type information, stability characteristics and noise characteristics of the time-frequency sources in each first-level time-scale generation unit, an appropriate time-scale algorithm is selected. When the time-frequency source can establish a discrete state-space model, a time-scale algorithm based on Kalman filter or a corresponding variant is used. When the time-frequency source is not suitable for establishing a discrete state-space model, a time-scale algorithm based on weighted average or a corresponding variant is used.

[0031] The second-level time scale algorithm is: a time scale algorithm based on weighted average or a corresponding variant, and generates a second-level time scale based on the time comparison results of the virtual time-frequency source.

[0032] The elastic strategy is as follows: when the time-frequency source and / or virtual time-frequency source joins a group, leaves a group, or experiences an abnormal state, the time-frequency source grouping, the parameters of the first or second layer time-scale algorithm, and the time-scale algorithm type are adjusted to maintain the stability and continuity of the time-scale system output.

[0033] Based on the above technical solution, the following embodiments are provided.

[0034] Example 1: Distributed hierarchical time-scale architecture like Figure 1 As shown, this embodiment of the invention provides a distributed hierarchical timescale system with multi-source heterogeneous fusion. The timescale system adopts a distributed hierarchical structure, including a first layer and a second layer.

[0035] The first layer comprises multiple parallel and independent first-level timescale generation units, each corresponding to a set of time-frequency sources. These first-level timescale generation units are deployed in different time service data centers and can generate first-level timescales based on the time-frequency sources within their corresponding groups. By having multiple first-level timescale generation units operate in parallel, a distributed first-level timescale generation structure can be formed.

[0036] Specifically, since different time-frequency sources differ in spatial distribution, source type, and affiliated organization, this embodiment first groups the available time-frequency sources according to a preset grouping strategy, so that time-frequency sources with similar characteristics are assigned to the same group. Each group corresponds to a first-level time scale generation unit, thereby enabling the use of appropriate time scale algorithms for time-frequency sources in different groups.

[0037] Furthermore, each primary timescale generation unit adopts an appropriate time comparison method based on the time-frequency source type and time comparison link, and generates a primary timescale based on a hierarchical timescale algorithm.

[0038] In this embodiment, the first-level timescale generated in the first layer needs to be characterized as a virtual time-frequency source before participating in the generation of the second-level timescale. The virtual time-frequency source is a data object used to uniformly represent a time reference. It characterizes the temporal characteristics of the first-level timescale by constructing a state description structure consistent with the physical time-frequency source. In this way, the first-level timescale can maintain consistency with the physical time-frequency source in data structure, thereby enabling processing within a unified timescale algorithm framework.

[0039] The second layer includes a secondary timescale generation unit. The secondary timescale generation unit takes multiple virtual time-frequency sources from the first layer as input and generates secondary timescales based on the time comparison method of the virtual time-frequency sources and the hierarchical timescale algorithm, thereby obtaining the final output timescale of the system.

[0040] Furthermore, during the operation of the time-scale system, when a time-frequency source and / or virtual time-frequency source joins or leaves a clock group, or experiences an abnormal state, the system can adjust the time-scale generation process according to a preset elastic strategy. This includes adjusting the time-frequency source grouping, adjusting time-scale algorithm parameters, or changing the specific time-scale algorithm to maintain the stability and continuity of the time-scale output. The specific implementation methods of the time-frequency source grouping method, the first-level time-scale generation method, the virtual time-frequency source generation method, the virtual time-frequency source time comparison method, and the elastic strategy will be further explained in subsequent embodiments.

[0041] Through the above structure, this embodiment constructs a distributed hierarchical timescale system with multi-source heterogeneous fusion, which can continuously output a stable and continuous timescale under the conditions of changes in the number and type of time and frequency sources, stability fluctuations, and changes in time transfer conditions.

[0042] Example 2: Time-Frequency Source Grouping and First-Level Time Scale Generation This invention provides a method for grouping time-frequency sources and generating first-level time scales. In this embodiment, information from multiple available types of time-frequency sources is acquired and grouped. A first-level time scale is then generated within the first-level time scale generation unit corresponding to each group, providing input for subsequent virtual time-frequency source generation and second-level time scale generation. The method mainly includes the following steps: S1: Time and Frequency Source Information Acquisition The system first acquires information from available time and frequency sources. In this embodiment, the physical time and frequency sources involved in the generation of the time scale include ground-based atomic clocks, optical clocks, crystal oscillators, satellite clocks, network time references, astronomical time references, and other time and frequency reference sources that can provide time or frequency standards.

[0043] In a specific engineering application scenario, the system can connect to the following time-frequency sources: 20 cesium atomic clocks, 30 rubidium atomic clocks, 30 hydrogen atomic clocks, 50 GNSS satellite atomic clocks, 40 low-orbit satellite high-stability crystal oscillators, 6 network time sources, and 7 pulsar time references.

[0044] The aforementioned time and frequency sources are connected to the system via receivers, networks, or dedicated time transfer links, and the time and frequency source information management module uniformly acquires information from each time and frequency source as well as time observation data.

[0045] In this embodiment, the information to be collected includes the spatial distribution of time-frequency sources, source types, affiliated institutions, time comparison conditions, and other information that may affect time-frequency source grouping and time comparison. This information can be obtained through publicly available and accessible domain knowledge bases, standardized data publishing platforms, or public data interfaces of relevant time-frequency monitoring networks. This information constitutes essential public reference data in the time-frequency domain, and its acquisition complies with data sharing practices and interoperability specifications advocated by authoritative organizations such as the International Bureau of Weights and Measures (BIPM). It is important to note that the core of this invention lies in performing subsequent grouping, time comparison, and timescale generation based on this publicly available data, rather than involving unauthorized acquisition or use of proprietary data from specific institutions.

[0046] S2: Time-frequency source grouping After obtaining information on all available time-frequency sources, the system groups the time-frequency sources according to a preset grouping strategy to construct multiple parallel and independent time-frequency source groups. The information includes the spatial distribution of the time-frequency reference sources, the type of time-frequency source, the affiliated organization, and other information that may affect the grouping of time-frequency sources. The grouping strategy groups time-frequency sources that are spatially close, have the same or similar type characteristics, and belong to the same or identical organizations. The grouping strategy can be further optimized based on the characteristics of the time service data center.

[0047] In one possible implementation, time and frequency service data centers can be grouped based on their capabilities. For example, some time service data centers primarily access and process ground-based atomic clock or optical clock data, while others primarily access and process pulsar time reference data. Therefore, different time and frequency sources, such as atomic clocks, optical clocks, and pulsars, can be assigned to different groups according to the functional characteristics of the data centers. Furthermore, within the same type of time and frequency source, further subdivision can be made according to spatial distribution or affiliated system. For example, GNSS satellite atomic clocks and ground station atomic clocks can be divided into different groups, or GPS, BeiDou, and Galileo satellite atomic clocks can be divided into different groups according to different navigation systems.

[0048] It should be noted that this invention does not limit the specific grouping method, as long as it can achieve reasonable and effective organization of time and frequency sources and support subsequent time scale generation.

[0049] S3: Generation on a first-level timescale After the time-frequency source is grouped, a corresponding first-level time scale is generated within each group.

[0050] Specifically, each time-frequency source group corresponds to a first-level time-scale generation unit. Multiple first-level time-scale generation units are deployed in different time service data centers and operate in parallel and independently, thus forming a distributed time-scale generation structure.

[0051] In a specific engineering application scenario, the distributed first-level timescale generation unit can be: 1. The first-level time scale generation unit 1 is deployed in the time service data center A, and includes 3 cesium atomic clocks and 8 rubidium atomic clocks; 2. The first-level time scale generation unit 2 is deployed in the time service data center B and contains 40 high-stability crystal oscillators; 3. The first-level time scale generation unit 3 is deployed in the time service data center C, which includes 9 network time references and 7 local timekeeping laboratory cesium atomic clocks; 4. The first-level timescale generation unit 4 is deployed in the time service data center D and contains 7 pulsars; 5. The first-level timescale generation unit 5 is deployed in the time service data center E and contains 14 ion optical clocks; The primary timescale generation unit performs time comparisons between time-frequency sources within a group based on the type information and time comparison conditions of the time-frequency sources within the group, using a time comparison module. The time comparison methods include, but are not limited to, satellite two-way time comparison, time comparison based on GNSS common-view method and precise single-point positioning, time comparison based on fiber optic links, time comparison based on network protocols, time comparison based on laser ranging, and time comparison based on astronomical observations. These methods can achieve time comparisons between various types of time-frequency sources involved in this invention, and are all mature technologies known in the art, not the focus of this invention's innovation; therefore, they will not be described in detail here.

[0052] Based on this, the time scale generation module selects a time scale algorithm suitable for each type of time-frequency source according to the type information, stability characteristics, and noise characteristics of the time-frequency source within each first-level time scale generation unit.

[0053] In one possible implementation, available time-scale algorithms include, but are not limited to, weighted average-based time-scale algorithms and their variants (such as ALGOS, AT1, and AT2 algorithms) and Kalman filter-based time-scale algorithms and their variants (such as weighted Kalman time-scale algorithms and Kred time-scale algorithms). When the time-frequency source can establish a discrete state-space model, a Kalman filter-based time-scale algorithm and its variants can be used; when the time-frequency source is not suitable for establishing a discrete state-space model, a weighted average-based time-scale algorithm and its variants can be used. In this way, differentiated processing can be performed for different types of time-frequency sources, thereby achieving multi-source heterogeneous fusion of time-frequency sources and improving the stability and scalability of time-scale generation. The above-mentioned time-scale algorithms are all mature technologies known in the art and are not the focus of innovation in this invention; therefore, they will not be described in detail here.

[0054] Through the above processing, each time-frequency source group can generate a corresponding first-level time scale. This first-level time scale has better stability and continuity compared to a single time-frequency source, and can resist the impact of anomalies or failures of a single time-frequency source on the system to a certain extent.

[0055] The generated primary timescale then provides input data for the generation of subsequent virtual time-frequency sources and secondary timescales. The virtual time-frequency source generation method will be described in detail in Example 3.

[0056] The method described in this embodiment can be used to group and fuse multiple types of time-frequency sources to generate a stable and reliable distributed first-level time scale, providing basic support for building a hierarchical time scale system.

[0057] Example 3: Virtual Time-Frequency Source Generation Method like Figure 2 As shown, this embodiment provides a virtual time-frequency source generation method to achieve unified representation and processing of physical time-frequency sources and the first-level paper time reference (first-level time scale) generated in a hierarchical time-scale system. This converts the first-level time scale generated in Embodiment 2 into a virtual time-frequency source, enabling it to participate in the generation of the second-level time scale in a form consistent with the physical time-frequency source. The physical time-frequency source refers to a real-world time-frequency source, while the paper time reference refers to a time reference generated by an algorithm that does not have corresponding time-frequency features. The virtual time-frequency source generation method mainly includes the following steps: S1: Determine the clock difference dataset at the first-level time scale. In this step, the clock difference dataset of the first-level time scale is determined based on the clock difference relationship between the first-level time scale and the multiple time-frequency sources involved in constructing the first-level time scale.

[0058] Specifically, within the first-level timescale generation unit, the clock difference sequence of the timescale relative to each time-frequency source in the group can be obtained according to the physical meaning of the timescale algorithm. This sequence is used to describe the time deviation of the timescale relative to the set of time-frequency sources that participated in generating the timescale.

[0059] The clock error dataset is a collection of clock error sequences relative to each time-frequency source within the group at the first-level time scale. It is used to characterize the time state features of the first-level time scale, enabling this time scale to participate in subsequent processing in a manner similar to that of a single physical time-frequency source.

[0060] Generally, taking the first-level timescale generation unit as an example, in one possible implementation, for a first-level timescale generation unit using a weighted average-based timescale algorithm, its basic timescale equation is: For a first-level timescale generation unit using a timescale algorithm based on Kalman filtering, the basic timescale equation is: in, Indicates time, This indicates the number of time-frequency sources in the group. This indicates that the first-level timescale generated by this timescale generation unit is in The clock face reading of the hour. This indicates that the number within the generation unit of this timescale is The clock face reading of the physical time and frequency source. This indicates that the number is The weights of the physical time-frequency sources, This indicates the Kalman filter pair in the generation unit of this time scale. The predicted value. In the fundamental equation of the time scale, This refers to the time offset of the time scale relative to the time-frequency source numbered 1 that participated in generating the time scale. The clock difference sequence of the time scale relative to all time-frequency sources within the group can be obtained through the basic equation of the time scale. The set of all clock difference sequences is the clock difference dataset of the time scale.

[0061] S2: Generate virtual time-frequency source state parameters After obtaining the clock difference dataset at the first-level time scale, the system further generates state parameters of the virtual time-frequency source to describe the time-frequency characteristics of that first-level time scale.

[0062] In this embodiment, the state parameters of the virtual time-frequency source may include, but are not limited to, clock bias datasets and frequency stability datasets. The clock bias dataset describes the time deviation state at this time scale; the frequency stability dataset characterizes the stability features at this time scale.

[0063] In one possible implementation, the frequency stability dataset of the virtual time-frequency source can be obtained through the frequency stability variance. Based on the clock difference dataset, the clock difference sequence of the first-level time scale relative to the time-frequency sources within the group can be obtained, and thus the frequency stability variance sequence relative to the time-frequency sources within the group can be obtained. The set of frequency stability variance sequences of the first-level time scale relative to all time-frequency sources within the group is used as the frequency stability dataset. The frequency stability variance includes, but is not limited to, Allan variance, modified Allan variance, Hadamard variance, and modified Hadamard variance.

[0064] By generating the aforementioned state parameters, the first-level time scale can possess describable time-frequency characteristics consistent with the physical time-frequency source, thus enabling it to be uniformly incorporated into the time-scale calculation framework.

[0065] S3: Encapsulate virtual time-frequency source data object After generating the virtual time-frequency source state parameters, these parameters are encapsulated into a data object with a unified time-frequency source state description structure, thereby forming a virtual time-frequency source.

[0066] The data object is used to uniformly represent the state information of physical time-frequency sources and virtual time-frequency sources, so that different time references can be processed within the same time-scale algorithm framework.

[0067] In one implementation, the virtual time-frequency source can be described using a unified data object, for example: Virtual time-frequency source: { Virtual time-frequency source number; Clock difference dataset; Frequency stability dataset; } By adopting a unified state description structure, clock error datasets and frequency stability datasets can be acquired and updated in real time, enabling physical and virtual time-frequency sources to use consistent data processing methods in time comparison, weight calculation, and time scale updates.

[0068] After the virtual time-frequency source is generated, each first-level time scale can participate in the generation process of the next-level time scale through the virtual time-frequency source. Subsequently, through time comparison and fusion calculation between virtual time-frequency sources, a higher-level time scale is generated.

[0069] The method described in this embodiment can convert a distributed, first-level time scale into a virtual time-frequency source that can be processed uniformly, thereby enabling the fusion processing of multi-source heterogeneous time references within the same time scale algorithm framework.

[0070] Example 4: Virtual Time-Frequency Source Time Comparison Method: like Figure 3 As shown, this embodiment provides a virtual time-frequency source time comparison method based on a reference time-frequency source. By establishing a clock difference propagation relationship, indirect time comparison between virtual time-frequency sources is achieved. The method mainly includes the following steps.

[0071] S1: Determine the reference time and frequency source and obtain the corresponding clock difference For two virtual time-frequency sources to be compared, reference time-frequency sources that meet the time comparison conditions are determined in their respective time-frequency source groups, and the clock difference between the reference time-frequency sources is obtained.

[0072] Specifically, in one possible implementation, when considering a first-order time scale... and first-order time scale When performing time comparison on the generated virtual time-frequency source, a first-level time scale is set. The corresponding time-frequency source group is A, the first-level time scale. The corresponding time-frequency source group is B. Two reference time-frequency sources with time comparison links are selected respectively, and a reference time-frequency source is selected from group A. Select a reference time-frequency source in group B. You can obtain both. t The time comparison results at each moment are .

[0073] The time comparison between the reference time and frequency sources can be obtained through satellite two-way time comparison, GNSS common view, fiber optic time transfer or network time transfer, etc. The above methods are all mature technologies in this field and will not be described in detail here.

[0074] S2: Determine the clock difference between the virtual time-frequency source and the reference time-frequency source. After determining the reference time-frequency source, the clock difference between the virtual time-frequency source and the corresponding reference time-frequency source is calculated based on the state parameters of the virtual time-frequency source.

[0075] Specifically, in Example 3, when generating the virtual time-frequency source, the system has already obtained the time deviation sequence of each time-frequency source within its group at the first-level time scale, and stored it in the state parameters of the virtual time-frequency source's clock difference dataset. Therefore, it is possible to obtain the time deviation sequence of two virtual time-frequency sources relative to their corresponding reference time-frequency sources. t The time comparison results at each moment are and .

[0076] S3: Establish clock difference propagation relationships and calculate the time difference between virtual time and frequency sources. After obtaining the clock difference between the reference time and frequency sources and the clock difference of the virtual time and frequency source relative to the reference time and frequency source, a clock difference propagation relationship can be established to eliminate the influence of the reference time and frequency source, thereby obtaining the time comparison result between the two virtual time and frequency sources.

[0077] Specifically, this can be obtained through the clock bias propagation relationship: Through the aforementioned clock difference propagation relationship, time comparison of virtual time-frequency sources can be achieved. The time comparison results between virtual time-frequency sources obtained based on the above method can be used as input data for subsequent secondary time scale generation algorithms, the specific implementation of which will be further explained in Example 5.

[0078] Example 5: Hierarchical Time Scale Algorithm: like Figure 4 As shown, in this embodiment, in order to achieve heterogeneous fusion of multiple types of time-frequency sources and ensure the elasticity of the time-scale system in a dynamic operating environment, a hierarchical time-scale algorithm and elasticity strategy are proposed.

[0079] The hierarchical time-scale algorithm includes a first-level time-scale algorithm, a second-level time-scale algorithm, and a flexible strategy. The first-level time-scale algorithm generates first-level time scales within each first-level time-scale generation unit; the second-level time-scale algorithm generates second-level time scales based on a virtual time-frequency source; and the flexible strategy dynamically adjusts the time-scale generation process when the operating environment changes.

[0080] This method mainly includes the following steps: S1: First-level time-scale algorithm Because the time and frequency sources involved in this invention have obvious multi-source heterogeneous characteristics, such as atomic clocks, optical clocks, crystal oscillators, GNSS satellite clocks, network time references, and astronomical time references, different types of time and frequency sources have significant differences in stability, noise characteristics, and time propagation conditions. Therefore, in the first-level time scale generation process, each first-level time scale generation unit selects an appropriate time scale algorithm to generate a first-level time scale based on the type information, stability characteristics, and noise characteristics of the time and frequency sources within its group.

[0081] In one possible implementation, the time scale algorithm selection strategy for first-level time scale adaptation is as follows: when the time frequency source can establish a discrete state space model, a time scale algorithm based on Kalman filter or its variant can be used to generate the time scale; when the time frequency source is not suitable for establishing a discrete state space model, a time scale algorithm based on weighted average or its variant can be used to generate the time scale.

[0082] Specifically, available time-scale algorithms include, but are not limited to: time-scale algorithms based on weighted averages and their variants, such as ALGOS, AT1, and AT2 algorithms; and time-scale algorithms based on Kalman filtering and their variants, such as weighted Kalman time-scale algorithms and Kred time-scale algorithms.

[0083] S2: Second-level time-scale algorithm In this embodiment, the second-layer time-scale algorithm is used to generate a secondary time scale based on the time comparison results between virtual time-frequency sources. Since the virtual time-frequency sources have been uniformly described in Embodiment 3, the second-layer time-scale algorithm can fuse time references from different first-level time-scale generation units within a unified framework.

[0084] In one possible implementation, the second-level time scale is generated using a weighted average-based time scale algorithm or a variant thereof. This is primarily based on the following considerations.

[0085] 1. Different virtual time-frequency sources may originate from different types of time-frequency source groups, and their internal time-scale algorithms may also differ. For example, some first-level time scales may be generated using a Kalman filter-based time-scale algorithm, while others may be generated using a weighted average-based time-scale algorithm. Therefore, the time-frequency characteristics of virtual time-frequency sources are difficult to model and describe, making Kalman filter-based time-scale algorithms unsuitable.

[0086] 2. The input to the second-layer timescale generation process consists of multiple virtual time-frequency sources generated in Example 3. These virtual time-frequency sources are essentially stable time references resulting from the fusion of multiple physical time-frequency sources. Therefore, compared to the first layer which directly processes the original time-frequency sources, the second layer typically processes fewer virtual time-frequency sources, and their stability has already been significantly improved during the first-layer timescale generation process, leaving little room for further stability improvement.

[0087] Therefore, in the hierarchical timescale system of this invention, the first layer generates a stable first-level timescale by selecting an appropriate timescale algorithm for multi-source heterogeneous time-frequency sources, while the second layer fuses multiple first-level timescales using a weighted average timescale algorithm. This improves the overall stability and robustness of the timescale system while ensuring controllable computational complexity. Through this design, the first-level timescale algorithm focuses on modeling and fusing multi-source heterogeneous time-frequency sources, while the second-level timescale algorithm focuses on the stable fusion of multiple first-level timescales, thus forming a hierarchical timescale generation structure.

[0088] Through the above processing, multiple distributed first-level time scales can be further integrated into a unified system time scale, thereby improving the stability and anomaly resistance of the time scale.

[0089] S3: Flexible Strategy During the operation of the time-scale system, the operating status of the time-frequency source and / or virtual time-frequency source may change, such as the time-frequency source joining or leaving a group, changes in stability, or equipment malfunctions. To ensure the stability and continuity of the time-scale output, this embodiment introduces a flexible strategy based on the hierarchical time-scale algorithm.

[0090] The elastic strategy is used to dynamically adjust the time scale generation process when the system's operating state changes. Its triggering conditions may include, but are not limited to: Time-frequency source joins group; time-frequency source leaves group; time-frequency source operating status is abnormal; virtual time-frequency source stability decreases; time comparison link changes.

[0091] When the above situation is detected, the system can perform the following adjustment operations: 1. Adjust the time-frequency source grouping structure When a new time-frequency source is added to the system or an existing time-frequency source leaves the system, the system can regroup the time-frequency sources so that the new time-frequency source can participate in the generation process of the corresponding first-level time scale.

[0092] 2. Adjust the time scale algorithm parameters When the stability of certain time-frequency sources changes, the system can dynamically adjust the weight parameters in the time-scale algorithm to reduce the impact of abnormal time-frequency sources on the time-scale results.

[0093] 3. Adjust the time scale algorithm type When the type of time-frequency source within a group changes significantly, the system can switch between a Kalman filtering algorithm and a weighted average algorithm to adapt to the new time-frequency source characteristics.

[0094] 4. Adjust the weights of the virtual time-frequency source. During the generation of the second-level time scale, if the stability of a virtual time-frequency source decreases or the time comparison quality is reduced, its weight in the generation of the second-level time scale can be reduced.

[0095] Through the above-mentioned flexible strategy, the time scale generation process can be dynamically adjusted when the system operating environment changes, thereby ensuring that the time scale system can continue to output stable and continuous time scales even in complex dynamic environments.

[0096] In one possible implementation, a hierarchical timescale system can be constructed as follows. The system connects to multiple types of time and frequency sources, including: 10 cesium atomic clocks, 20 rubidium atomic clocks, 5 hydrogen atomic clocks, 30 GNSS satellite atomic clocks, 6 network time references, and 7 optical clocks.

[0097] The system divides the aforementioned time-frequency sources into multiple groups and generates a first-level time scale within each group. For example: Group 1: Cesium atomic clock and rubidium atomic clock group Includes: 10 cesium atomic clocks and 20 rubidium atomic clocks.

[0098] The time-frequency sources within this group exhibit high stability and relatively stable noise models, thus allowing for the establishment of discrete state-space models. Within this group, a Kalman-filter-based time-scale algorithm can be used to generate a first-level time scale, enabling dynamic estimation of the states of each atomic clock.

[0099] Group 2: Hydrogen Atomic Clock Group Includes: 5 hydrogen atomic clocks. Hydrogen atomic clocks have good short-term stability, so a weighted Kalman timescale algorithm can be used to generate a first-level timescale to fully utilize its short-term stability advantage.

[0100] Group 3: GNSS Satellite Clock Group Includes: 30 GNSS satellite atomic clocks.

[0101] There are many such time and frequency sources with varying stability, and the probability of satellite clock anomalies is high. Therefore, the Kred time scale algorithm can be used to generate a first-level time scale to reduce the impact of the divergence speed of the Kalman filter on the time scale.

[0102] Group 4: Network Time Reference Source Group Includes: 6 network time references.

[0103] Since network time sources are greatly affected by link noise and it is difficult to establish a stable state-space model, a weighted average time scale algorithm can be used to generate a first-level time scale.

[0104] Group 5: Optical Clock Group Includes: 7 light clocks Optical clocks have extremely high long-term stability and can generate a first-level time scale using a weighted Kalman timescale algorithm.

[0105] After the generation of the first-level time scale is completed, each first-level time scale is converted into a virtual time-frequency source using the method described in Example 3, and the time comparison results between the virtual time-frequency sources are obtained using the virtual time-frequency source time comparison method described in Example 4.

[0106] Subsequently, during the generation of the second-level time scale, the system uses a weighted average-based time scale algorithm to fuse multiple virtual time-frequency sources based on the time comparison results between the virtual time-frequency sources, thereby generating a unified second-level time scale.

[0107] Through the above-described hierarchical construction process, appropriate time-scale algorithms can be adopted for different types of time-frequency sources to achieve effective fusion of multi-source heterogeneous time-frequency sources and improve the stability and robustness of the time-scale system.

[0108] In summary, this method has at least the following beneficial effects: (1) This application constructs a distributed hierarchical time scale system, enabling multiple first-level time scale generation units to operate in parallel and independently, thereby reducing the risk of single-point failure in the centralized time scale system and improving the system reliability and scalability. (2) This application introduces a virtual time-frequency source mechanism to achieve the unification of the data structure and processing method between the first-level time scale and the physical time-frequency source, so that time reference objects at different levels can be processed under a unified time scale algorithm framework, simplifying the process of multi-source fusion. (3) This application improves the elasticity of the output by using a hierarchical time scale algorithm and a flexible strategy design to enable the time scale system to adapt to complex environments such as changes in the number of time and frequency sources, fluctuations in stability and changes in time comparison conditions. (4) This application can support the fusion of multiple types of time and frequency sources such as ground atomic clocks, optical clocks, crystal oscillators, satellite clocks, network time references and astronomical time references, and is suitable for large-scale time scale construction in multi-source heterogeneous environments.

[0109] Without departing from the technical concept of this application, those skilled in the art can make various modifications or substitutions to the above technical solutions, and all equivalent substitutions or improvements should fall within the protection scope of this application.

[0110] As a second aspect, the present invention also provides a multi-source heterogeneous fusion distributed hierarchical time-scale system construction system, comprising: The time-frequency source information management module is used to construct a time-scale system framework using a distributed hierarchical structure and to collect relevant information of each available time-frequency source participating in the time-scale construction. The time-scale system framework includes multiple parallel and independent first-level time-scale generation units located in the first layer and a second-level time-scale generation unit located in the second layer. The relevant information includes spatial distribution information, time-frequency source type information, and affiliated institution information. The first-level time scale generation module is used to group available time and frequency sources according to the time and frequency source grouping strategy. Each group corresponds to a first-level time scale generation unit, and each first-level time scale generation unit is deployed in a different time service data center. It performs time comparison and generates distributed first-level time scales in parallel according to the time and frequency source type differentiation. The time comparison management module is used to characterize the first-level time scale as a virtual time-frequency source, so that the first-level time scale, in the form of a virtual time-frequency source, is used as the input of the second-level time scale generation unit through a time comparison method between virtual time-frequency sources; The secondary time scale generation module is used in the secondary time scale generation unit to perform time comparison on the virtual time-frequency source and generate a secondary time scale; The time scale output module is used to generate the first and second layer time scales according to the hierarchical time scale algorithm. The hierarchical time scale algorithm updates the time scales based on the state changes of the time-frequency source and the virtual time-frequency source using an elastic strategy, thereby obtaining the output of the time scale system.

[0111] As a third aspect, the present invention also provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to cause the electronic device to perform the distributed hierarchical timescale system construction method according to the above-described multi-source heterogeneous fusion.

[0112] As a fourth aspect, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for constructing a distributed hierarchical time-scale system with multi-source heterogeneous fusion as described above.

[0113] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0114] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for constructing a distributed hierarchical time-scale system with multi-source heterogeneous fusion, characterized in that, include: Step S1: Construct a time-scale system framework using a distributed hierarchical structure, and collect relevant information from each available time-frequency source participating in the time-scale construction; The timescale system framework includes multiple parallel and independent first-level timescale generation units located in the first layer and a second-level timescale generation unit located in the second layer; the relevant information includes spatial distribution information, time-frequency source type information, and affiliated institution information. Step S2: Group the available time and frequency sources according to the time and frequency source grouping strategy. Each group corresponds to a first-level time scale generation unit, and each first-level time scale generation unit is deployed in a different time service data center. Different processing is performed according to the time and frequency source type, time comparison is performed, and distributed first-level time scales are generated in parallel. Step S3: The first-level time scale is characterized as a virtual time-frequency source, and the first-level time scale is used as the input of the second-level time scale generation unit through a time comparison method between virtual time-frequency sources. Step S4: In the secondary time scale generation unit, the virtual time-frequency source is compared in time, and a secondary time scale is generated; Step S5: Generate the time scales of the first and second layers according to the hierarchical time scale algorithm. The hierarchical time scale algorithm updates the time scales based on the state changes of the time-frequency source and the virtual time-frequency source using an elastic strategy, thereby obtaining the output of the time scale system.

2. The method for constructing a distributed hierarchical time-scale system based on multi-source heterogeneous fusion according to claim 1, characterized in that, The process of characterizing the first-level time scale as a virtual time-frequency source specifically includes: The clock difference dataset for the first-level time scale is determined based on the clock difference relationship between the first-level time scale and the time-frequency sources involved in constructing the time scale. Virtual time-frequency source state parameters are generated based on the clock difference dataset; the virtual time-frequency source state parameters include the clock difference dataset and the frequency stability dataset. The virtual time-frequency source state parameters are encapsulated into a first-level time scale with a unified time-frequency source state description structure, and participate in the generation process of the second-level time scale in the form of a virtual time-frequency source.

3. The method for constructing a distributed hierarchical time-scale system based on multi-source heterogeneous fusion according to claim 2, characterized in that, The time comparison method between the virtual time-frequency sources specifically involves the following process: For two virtual time-frequency sources to be compared, reference time-frequency sources that meet the time comparison conditions are determined in the corresponding groups, and the clock difference between the reference time-frequency sources is obtained. Based on the state parameters of the virtual time and frequency sources, the clock difference between the two virtual time and frequency sources and the corresponding reference time and frequency source is determined; Establish clock bias propagation relationship, eliminate reference time and frequency source clock bias, and obtain time comparison results between the two virtual time and frequency sources.

4. The method for constructing a distributed hierarchical time-scale system based on multi-source heterogeneous fusion according to claim 1, characterized in that, The hierarchical time-scale algorithm includes a first-layer time-scale algorithm, a second-layer time-scale algorithm, and a resilient strategy, wherein: The first-level time-scale algorithm is as follows: based on the type information, stability characteristics and noise characteristics of the time-frequency sources in each first-level time-scale generation unit, an appropriate time-scale algorithm is selected respectively. When the time-frequency source can establish a discrete state-space model, a time-scale algorithm based on Kalman filter or a corresponding variant is used. When the time-frequency source is not suitable for establishing a discrete state-space model, a time-scale algorithm based on weighted average or a corresponding variant is used. The second-level time scale algorithm is: a time scale algorithm based on weighted average or a corresponding variant, and a second-level time scale is generated based on the time comparison results of the virtual time-frequency source; The elastic strategy is as follows: when the time-frequency source and / or virtual time-frequency source joins a group, leaves a group, or experiences an abnormal state, the time-frequency source grouping, the parameters of the first or second layer time-scale algorithm, and the time-scale algorithm type are adjusted to maintain the stability and continuity of the time-scale system output.

5. The method for constructing a distributed hierarchical time-scale system based on multi-source heterogeneous fusion according to claim 1, characterized in that, The time-frequency source grouping strategy specifically includes the following processing steps: Based on the spatial distribution information, the time-frequency source type information, and the affiliated institution information, the available time-frequency sources are grouped so that the time-frequency sources in the same group meet the set conditions of being close in spatial distribution, having the same or similar type characteristics, and having the same or identical affiliated institutions.

6. A distributed hierarchical time-scale system for constructing a multi-source heterogeneous fusion architecture, characterized in that, include: The time-frequency source information management module is used to construct a time-scale system framework using a distributed hierarchical structure and to collect relevant information of each available time-frequency source participating in the time-scale construction. The time-scale system framework includes multiple parallel and independent first-level time-scale generation units located in the first layer and a second-level time-scale generation unit located in the second layer. The relevant information includes spatial distribution information, time-frequency source type information, and affiliated institution information. The first-level time scale generation module is used to group available time and frequency sources according to the time and frequency source grouping strategy. Each group corresponds to a first-level time scale generation unit, and each first-level time scale generation unit is deployed in a different time service data center. It performs time comparison and generates distributed first-level time scales in parallel according to the time and frequency source type differentiation. The time comparison management module is used to characterize the first-level time scale as a virtual time-frequency source, so that the first-level time scale, in the form of a virtual time-frequency source, is used as the input of the second-level time scale generation unit through a time comparison method between virtual time-frequency sources; The secondary time scale generation module is used in the secondary time scale generation unit to perform time comparison on the virtual time-frequency source and generate a secondary time scale; The time scale output module is used to generate the time scales of the first and second layers according to the hierarchical time scale algorithm. The hierarchical time scale algorithm updates the time scales based on the state changes of the time-frequency source and the virtual time-frequency source using an elastic strategy, thereby obtaining the output of the time scale system.

7. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program and the processor runs the computer program to enable the electronic device to perform the method for constructing a distributed hierarchical time-scale system based on multi-source heterogeneous fusion according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the method for constructing a distributed hierarchical time-scale system with multi-source heterogeneous fusion as described in any one of claims 1-5.

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