Time-frequency system integrity monitoring method, device, equipment and storage medium

Abstract

The present invention relates to a method, device, equipment and storage medium for monitoring the integrity of a time-frequency system. The method comprises: obtaining the time difference measurement result of each time-frequency signal link and the temperature measurement result of the time-frequency system. The time difference measurement result and the temperature measurement result are input into a time difference measurement combination estimation model to generate a temperature variation coefficient and a noise normalization coefficient. The temperature-removed time difference measurement result is calculated according to the temperature variation coefficient and the temperature measurement result to obtain a first time difference measurement result, and a factor graph model is constructed according to the first time difference measurement result and a preset sliding window length. The time difference prediction deviation and the time difference frequency deviation of each time-frequency signal link are estimated according to the factor graph model, and the RAIM algorithm is used to monitor the integrity of the time-frequency system according to the time difference prediction deviation, the time difference frequency deviation, the noise normalization coefficient and the preset integrity monitoring parameters. The use of this method can improve the deviation alarm efficiency and accuracy of the time-frequency system.

Description

Technical Field

[0001] The present invention relates to the technical field of navigation time and frequency system monitoring, and in particular to a time and frequency system integrity monitoring method, device, equipment and storage medium. Background Art

[0002] The high-precision and high-reliability time and frequency system (hereinafter referred to as the time and frequency system) is the information cornerstone for the Global Navigation Satellite System (GNSS) to provide users with navigation, positioning and timing services. The health of the time and frequency system directly determines the performance of the navigation, positioning and timing services of the GNSS. Failure of the time and frequency system will cause great damage to the operation of the GNSS. In order to ensure the long-term stable and uninterrupted service of the system, the time and frequency system must be robust and reliable, and the integrity of the time and frequency signals output by the time and frequency system needs to be monitored. At present, there are mutual comparisons of the output signals of the time and frequency sources, and the phase, frequency deviation and frequency stability of the comparison results are monitored; or a system for maintaining the consistency of the main and backup time and frequency sources is built to predict the atomic clock error and calculate the frequency deviation to ensure the accuracy and stability of the time and frequency signals when the main and backup time and frequency sources are switched; or a set of real-time monitoring systems for time and frequency systems is designed and built, but this monitoring system only monitors significant phase jumps and frequency jumps of the time and frequency signals, and it is difficult to detect and identify minor faults, the alarm time is long, and the integrity monitoring performance is weak. Therefore, there is an urgent need to improve the integrity monitoring performance of the time-frequency system. Summary of the invention

[0003] Based on this, it is necessary to provide a time-frequency system integrity monitoring method, device, equipment and storage medium that can improve the alarm accuracy in response to the above technical problems.

[0004] A time-frequency system integrity monitoring method, the method comprising:

[0005] Obtain the time difference measurement results of each time-frequency signal link and the temperature measurement results of the time-frequency system.

[0006] The time difference measurement result and the temperature measurement result are input into the time difference measurement combination estimation model to generate the temperature variation coefficient and the noise normalization coefficient.

[0007] The temperature-removed time difference measurement result is calculated according to the temperature variation coefficient and the temperature measurement result to obtain a first time difference measurement result, and a factor graph model is constructed according to the first time difference measurement result and a preset sliding window length.

[0008] The time difference prediction deviation and time difference frequency deviation of each time-frequency signal link are estimated according to the factor graph model. The RAIM algorithm is used to monitor the integrity of the time-frequency system based on the time difference prediction deviation, time difference frequency deviation, noise normalization coefficient and preset integrity monitoring parameters.

[0009] A time-frequency system integrity monitoring device, the device comprising:

[0010] The measurement result acquisition module is used to obtain the time difference measurement result of each time-frequency signal link and the temperature measurement result of the time-frequency system.

[0011] The coefficient generation module is used to input the time difference measurement result and the temperature measurement result into the time difference measurement combination estimation model to generate the temperature variation coefficient and the noise normalization coefficient.

[0012] The factor graph model building module is used to calculate the temperature-removed time difference measurement result according to the temperature variation coefficient and the temperature measurement result to obtain the first time difference measurement result, and to build the factor graph model according to the first time difference measurement result and the preset sliding window length.

[0013] The integrity detection module is used to estimate the time difference prediction deviation and time difference frequency deviation of each time-frequency signal link according to the factor graph model, and use the RAIM algorithm to monitor the integrity of the time-frequency system according to the time difference prediction deviation, time difference frequency deviation, noise normalization coefficient and preset integrity monitoring parameters.

[0014] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0015] Obtain the time difference measurement results of each time-frequency signal link and the temperature measurement results of the time-frequency system.

[0016] The time difference measurement result and the temperature measurement result are input into the time difference measurement combination estimation model to generate the temperature variation coefficient and the noise normalization coefficient.

[0017] The temperature-removed time difference measurement result is calculated according to the temperature variation coefficient and the temperature measurement result to obtain a first time difference measurement result, and a factor graph model is constructed according to the first time difference measurement result and a preset sliding window length.

[0018] The time difference prediction deviation and time difference frequency deviation of each time-frequency signal link are estimated according to the factor graph model. The RAIM algorithm is used to monitor the integrity of the time-frequency system based on the time difference prediction deviation, time difference frequency deviation, noise normalization coefficient and preset integrity monitoring parameters.

[0019] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0020] Obtain the time difference measurement results of each time-frequency signal link and the temperature measurement results of the time-frequency system.

[0021] The time difference measurement result and the temperature measurement result are input into the time difference measurement combination estimation model to generate the temperature variation coefficient and the noise normalization coefficient.

[0022] The temperature-removed time difference measurement result is calculated according to the temperature variation coefficient and the temperature measurement result to obtain a first time difference measurement result, and a factor graph model is constructed according to the first time difference measurement result and a preset sliding window length.

[0023] The time difference prediction deviation and time difference frequency deviation of each time-frequency signal link are estimated according to the factor graph model. The RAIM algorithm is used to monitor the integrity of the time-frequency system based on the time difference prediction deviation, time difference frequency deviation, noise normalization coefficient and preset integrity monitoring parameters.

[0024] The above-mentioned time-frequency system integrity monitoring method, device, equipment and storage medium, first, by obtaining the time difference measurement results and temperature measurement results of each time-frequency signal link, using these data to input into the time difference measurement combination estimation model, generate temperature variation coefficient and noise normalization coefficient. The introduction of temperature variation coefficient can effectively compensate for the influence of ambient temperature fluctuation on the time difference measurement results, while the noise normalization coefficient dynamically optimizes the measurement error and improves the accuracy and robustness of the measurement from the data source. Secondly, the time difference measurement results are de-temperatured by the temperature variation coefficient to obtain a more stable first time difference measurement result, which significantly reduces the measurement deviation caused by environmental interference. This provides high-quality input data for the construction of subsequent factor graph models. The factor graph model is constructed using the first time difference measurement result and the sliding window length, so that the system can improve the ability to capture minor faults through time series correlation analysis. The factor graph model accurately estimates the time difference forecast deviation and frequency deviation through comprehensive probabilistic inference, providing an important basis for subsequent monitoring and identification. Finally, combined with the RAIM (Receiver Autonomous Integrity Monitoring) algorithm, the time difference prediction deviation, frequency deviation, noise normalization coefficient and preset integrity monitoring parameters are integrated to perform real-time integrity monitoring of the time and frequency system. The use of the RAIM algorithm can not only quickly detect minor faults in the system, but also improve the accuracy and response speed of the alarm. At the same time, the overall design of the system also embeds a multi-layer data verification and redundancy analysis mechanism, which further improves the monitoring performance and system stability. In summary, comprehensive improvement measures are provided at the three levels of data preprocessing, model construction and algorithm optimization, breaking through the technical bottleneck of traditional methods in minor fault detection and alarm response. By optimizing measurement accuracy, improving alarm efficiency and strengthening integrity monitoring performance, it provides solid technical support for the high-reliability operation of the time and frequency system, and effectively achieves the technical effects of rapid alarm and accurate fault identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of a flow chart of a method for monitoring integrity of a time-frequency system in one embodiment;

[0026] Figure 2 A schematic diagram of a flow chart of a time-frequency system integrity monitoring step in one embodiment;

[0027] Figure 3 A factor graph model framework diagram in one embodiment;

[0028] Figure 4 is a structural block diagram of a time-frequency system integrity monitoring device in one embodiment;

[0029] Figure 5It is a structural block diagram of a time-frequency system integrity monitoring device in another embodiment;

[0030] Figure 6 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0032] In one embodiment, Figure 1 As shown, a time-frequency system integrity monitoring method is provided, comprising the following steps:

[0033] Step 102, obtaining the time difference measurement result of each time-frequency signal link and the temperature measurement result of the time-frequency system.

[0034] Step 104: Input the time difference measurement result and the temperature measurement result into a time difference measurement combination estimation model to generate a temperature variation coefficient and a noise normalization coefficient.

[0035] Step 106, calculating the temperature-removed time difference measurement result according to the temperature variation coefficient and the temperature measurement result to obtain a first time difference measurement result, and constructing a factor graph model according to the first time difference measurement result and a preset sliding window length.

[0036] Step 108, estimate the time difference prediction deviation and the time difference frequency deviation of each time-frequency signal link according to the factor graph model, and use the RAIM (Receiver Autonomous Integrity Monitoring) algorithm to monitor the integrity of the time-frequency system according to the time difference prediction deviation, the time difference frequency deviation, the noise normalization coefficient and the preset integrity monitoring parameters.

[0037] In the above-mentioned time-frequency system integrity monitoring method, first, by obtaining the time difference measurement results and temperature measurement results of each time-frequency signal link, these data are input into the time difference measurement combination estimation model to generate the temperature variation coefficient and the noise normalization coefficient. The introduction of the temperature variation coefficient can effectively compensate for the influence of ambient temperature fluctuation on the time difference measurement results, while the noise normalization coefficient dynamically optimizes the measurement error and improves the accuracy and robustness of the measurement from the data source. Secondly, the time difference measurement results are de-temperatured by the temperature variation coefficient to obtain a more stable first time difference measurement result, which significantly reduces the measurement deviation caused by environmental interference. This provides high-quality input data for the construction of the subsequent factor graph model. The factor graph model is constructed using the first time difference measurement result and the sliding window length, so that the system can improve the ability to capture minor faults through time series correlation analysis. The factor graph model accurately estimates the time difference forecast deviation and frequency deviation through comprehensive probabilistic inference, providing an important basis for subsequent monitoring and identification. Finally, combined with the RAIM (Receiver Autonomous Integrity Monitoring) algorithm, the time difference prediction deviation, frequency deviation, noise normalization coefficient and preset integrity monitoring parameters are integrated to perform real-time integrity monitoring of the time and frequency system. The use of the RAIM algorithm can not only quickly detect minor faults in the system, but also improve the accuracy and response speed of the alarm. At the same time, the overall design of the system also embeds a multi-layer data verification and redundancy analysis mechanism, which further improves the monitoring performance and system stability. In summary, comprehensive improvement measures are provided at the three levels of data preprocessing, model construction and algorithm optimization, breaking through the technical bottleneck of traditional methods in minor fault detection and alarm response. By optimizing measurement accuracy, improving alarm efficiency and strengthening integrity monitoring performance, it provides solid technical support for the high-reliability operation of the time and frequency system, and effectively achieves the technical effects of rapid alarm and accurate fault identification.

[0038] In one embodiment,

[0039] Run the high-precision time interval measurement module, access the time-frequency signals of all different links, select the time-frequency signal output by any link as the reference link time-frequency signal, and measure the time difference measurement results of each time-frequency signal link based on the reference link time-frequency signal. At the same time, run the high-resolution temperature measurement module to obtain the temperature measurement results of the time-frequency system.

[0040] In one embodiment, the temperature change result is obtained by subtracting the temperature measurement result from the preset reference temperature, and the temperature change result and the time difference measurement result are input into the time difference measurement combined estimation model for measurement estimation to obtain the temperature change coefficient and the time difference measurement noise of each time-frequency signal link. After calculating the standard deviation of the time difference measurement noise, the noise normalization coefficient is obtained.

[0041] In one embodiment, the time difference measurement result and the temperature change result are input into the time difference measurement combined estimation model, and the influence of the temperature change on the time difference measurement result is separated by the time difference measurement combined estimation model, and the fixed delay difference, combined temperature change coefficient, time difference measurement noise and time difference frequency deviation of each time-frequency signal link are estimated. The time difference measurement combined estimation model is expressed as:

[0042] ;

[0043] in, m ( t ) represents the time difference measurement result, m d Indicates the fixed delay difference of each time-frequency signal link, A represents the combined temperature variation coefficient, ΔT Indicates temperature change, Indicates the time difference frequency deviation, t Indicates time, n ( t ) represents the time difference measurement noise.

[0044] In one embodiment, the time difference measurement result, the temperature change coefficient and the product of the temperature change result are subtracted to obtain a first time difference measurement result, and a factor graph model is constructed according to the first time difference measurement result and a preset sliding window length. Figure 3 As shown in Figure 1, the factor graph model includes: estimated variables, state factors, and observation factors. Among them, the estimated variables estimate the time difference between each time-frequency signal link and the frequency deviation corresponding to the time difference. The time difference measurement result is used as the observation value of the observation factor. The state factor satisfies the following state equation:

[0045] ;

[0046] ;

[0047] ;

[0048] Among them, x k represents the state variable, represents the state noise term, represents the covariance of the driving noise, represents the state transfer matrix, Indicates the time, represents the frequency random walk noise variance, represents the frequency white noise variance, Represents the time interval when the state is transferred. The observation factor satisfies the following observation equation:

[0049] ;

[0050] in, represents the actual observation at the current moment, and H represents the observation matrix , represents the observation noise term, represents the covariance of the observation noise , σ Represents the noise normalization coefficient of the time-frequency signal link.

[0051] In one embodiment, the time difference is subtracted from the time difference measurement result according to the factor graph model to obtain the time difference prediction deviation, and the time difference prediction deviation is normalized:

[0052] ;

[0053] in, represents the normalized time difference forecast deviation, pb i Indicates i The time difference prediction deviation of the time-frequency link, σ i Indicates i The noise normalization coefficient of the time-frequency link. And the frequency deviation corresponding to the time difference is normalized:

[0054] ;

[0055] in, represents the frequency deviation corresponding to the normalized time difference, Indicates i The estimated frequency deviation of the time-frequency link, Represents the reference value of the time difference frequency deviation estimation. The normalized time difference forecast deviation and the frequency deviation corresponding to the time difference are respectively input into the integrity monitoring model as the observed values ​​of the state estimation results. The integrity monitoring model uses the RAIM algorithm to perform consistency monitoring according to the preset integrity monitoring parameters to obtain the time difference consistency result and the frequency deviation consistency result.

[0056] In one embodiment, a parity vector is used to construct a test statistic, and the time difference consistency result and the frequency deviation consistency result are detected according to the test statistic:

[0057] ;

[0058] ;

[0059] ;

[0060] ;

[0061] in,r i Indicates i The test statistic of the links, represents the transposed matrix of the parity vector p, Represents an even-odd space matrix No. i List, Represents the detection threshold, P FA represents the false alarm probability, represents the normalized equivalent measurement error.

[0062] In one embodiment, if Figure 2 As shown, a time-frequency system integrity monitoring step is provided, which specifically includes the following contents:

[0063] Step 1, the user sets the factor graph model parameter integrity monitoring parameters;

[0064] The user sets the fault judgment threshold and related parameters in step 1. The related parameters include factor graph model parameters: factor graph sliding window length, generally 50s; integrity monitoring parameters in step 1 include: false alarm probability, generally 1E-5; missed detection probability, generally 1E-4; normalized alarm limit, generally 1; normalized equivalent measurement error, generally 1; reference value of time difference frequency deviation estimation, generally 3E-16.

[0065] Step 2: Run the high-precision time interval measurement module and the high-resolution temperature measurement module to obtain the time difference measurement results and temperature measurement results of each time-frequency signal link;

[0066] In order to ensure the accuracy and stability of the measurement results, it is necessary to remove the measurement results of the initial operation of the high-precision time interval measurement module and the temperature measurement module for a period of time, generally 15 minutes. Use the high-precision time interval measurement module to measure the time difference of the time-frequency signal before and after passing through the link, select the time-frequency signal output by a link as a reference, and measure the time difference between the time-frequency signal of each link and the time-frequency signal of the reference link. The measurement results of the high-precision time interval measurement module and the temperature measurement module are calculated and stored by relevant software and sent to the main control computer.

[0067] Step 3, according to the temperature measurement result in step 2, set the reference temperature and calculate the temperature change result.

[0068] When processing data in the main control computer, a reference temperature is selected, generally 25°C, and the temperature measurement result obtained in step 3 is subtracted from the reference temperature to calculate the temperature change result.

[0069] Step 4, according to the time difference measurement result in step 2, the temperature change result in step 3 and the time difference measurement combination estimation model in the patent "Time-frequency system fault diagnosis method, device, equipment and storage medium", obtain the temperature change coefficient and noise normalization coefficient of each time-frequency signal link of the time-frequency system;

[0070] When processing data in the main control computer, a reference temperature is selected, generally 25°C, and the temperature measurement result obtained in step 3 is subtracted from the reference temperature to calculate the temperature change result; at the same time, the time difference measurement result obtained by the high-precision time interval measurement module is obtained.

[0071] The temperature variation coefficient and the time difference measurement noise are estimated by using the temperature variation result and the time difference measurement result and the time difference measurement combination estimation model. The square root of the time difference measurement noise is calculated and used as the noise normalization coefficient.

[0072] Step 5, based on the time difference measurement result of step 2, the temperature change result of step 3 and the temperature change coefficient of step 4, calculate the time difference measurement result that eliminates the influence of temperature change.

[0073] The time difference measurement result is subtracted from the product of the temperature change result and the temperature change coefficient to calculate the time difference measurement result that eliminates the influence of temperature change.

[0074] Step 6, constructing a factor graph model according to the factor graph model parameters in step 1 and the time difference measurement result in step 5;

[0075] The factor graph model in step 6 includes: estimation variables, state factors and observation factors; the estimation variables estimate the time difference and frequency deviation between each time-frequency signal link;

[0076] The state factor satisfies the following state equation:

[0077] (1)

[0078] The observation factor satisfies the following observation equation:

[0079] (2)

[0080] The observation value in the observation factor is the time difference measurement result in step 5; if the previous time difference integrity monitoring result is normal, then the observation factor of the variable at the current moment is added, otherwise it is not added;

[0081] Step 7: Estimate the time difference prediction deviation and time difference frequency deviation of each time-frequency signal link based on the factor graph model of step 6.

[0082] The time difference prediction deviation in step 7 is the difference between the time difference result calculated in step 5 and the time difference result estimated in step 6 for each time-frequency link; the time difference frequency deviation is the time difference frequency deviation estimated in step 6.

[0083] Step 8: Monitor the integrity of the time-frequency system based on the integrity monitoring parameters in step 1, the noise normalization coefficient in step 4, the time difference prediction bias and time difference frequency bias in step 7, and the RAIM algorithm based on parity vectors in Section 4.4 of "Principles and Methods of Satellite Navigation System Integrity".

[0084] The integrity monitoring of the time-frequency system in step 7 includes: using the noise normalization coefficient, the time difference prediction deviation, the false alarm probability, the missed detection probability, the normalized alarm limit, the normalized equivalent measurement error and the RAIM algorithm to realize the integrity monitoring of the time difference of the time-frequency system; using the time difference frequency deviation, the reference value of the time difference frequency deviation estimation, the false alarm probability, the missed detection probability, the normalized alarm limit, the normalized equivalent measurement error and the RAIM algorithm to realize the integrity monitoring of the frequency deviation of the time-frequency system. When the integrity monitoring result is abnormal, it is necessary to warn the time-frequency system user to display the faulty link and cause of the time-frequency system.

[0085] It should be understood that although Figure 1-Figure 2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1-Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0086] In one embodiment, Figure 4 As shown, a time-frequency system integrity monitoring device is provided, including: a measurement result acquisition module 402, a coefficient generation module 404, a factor graph model construction module 406 and an integrity detection module 408, wherein:

[0087] The measurement result acquisition module 402 is used to obtain the time difference measurement result of each time-frequency signal link and the temperature measurement result of the time-frequency system.

[0088] The coefficient generation module 404 is used to generate a temperature variation coefficient and a noise normalization coefficient according to the time difference measurement result and the temperature measurement result input into the time difference measurement combination estimation model.

[0089] The factor graph model building module 406 is used to calculate the temperature-removed time difference measurement result according to the temperature variation coefficient and the temperature measurement result to obtain the first time difference measurement result, and to build a factor graph model according to the first time difference measurement result and a preset sliding window length.

[0090] The integrity detection module 408 is used to estimate the time difference prediction deviation and the time difference frequency deviation of each time-frequency signal link according to the factor graph model, and use the RAIM algorithm to monitor the integrity of the time-frequency system according to the time difference prediction deviation, the time difference frequency deviation, the noise normalization coefficient and the preset integrity monitoring parameters.

[0091] In one embodiment, if Figure 5 As shown, a device for detecting the integrity of a time-frequency system is provided, which can receive signals and data from the time-frequency system. The delay calibration device includes: a high-precision time interval measurement module 1, a high-resolution temperature measurement module 2, and a main control computer 3. The high-precision time interval measurement module 1 accesses the time-frequency signals output by each time-frequency signal link in the time-frequency system, selects a link time-frequency signal as a reference, measures the time difference between other links and the reference link, the high-resolution temperature measurement module 2 measures the temperature result, and the main control computer 3 receives the time difference and temperature results measured by the high-precision time interval measurement module 1 and the high-resolution temperature measurement module 2, eliminates the influence of temperature change on the time difference measurement result, completes the integrity monitoring of the time difference and frequency deviation, and then completes the integrity monitoring of the time-frequency system.

[0092] For the specific limitations of the time-frequency system integrity monitoring device, please refer to the limitations of the time-frequency system integrity monitoring method above, which will not be repeated here. Each module in the above-mentioned time-frequency system integrity monitoring device can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0093] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for monitoring the integrity of a time-frequency system is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0094] Those skilled in the art will understand that Figure 4-Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0095] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0096] Obtain the time difference measurement results of each time-frequency signal link and the temperature measurement results of the time-frequency system.

[0097] The time difference measurement result and the temperature measurement result are input into the time difference measurement combination estimation model to generate the temperature variation coefficient and the noise normalization coefficient.

[0098] The temperature-removed time difference measurement result is calculated according to the temperature variation coefficient and the temperature measurement result to obtain a first time difference measurement result, and a factor graph model is constructed according to the first time difference measurement result and a preset sliding window length.

[0099] The time difference prediction deviation and time difference frequency deviation of each time-frequency signal link are estimated according to the factor graph model. The RAIM algorithm is used to monitor the integrity of the time-frequency system based on the time difference prediction deviation, time difference frequency deviation, noise normalization coefficient and preset integrity monitoring parameters.

[0100] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0101] Obtain the time difference measurement results of each time-frequency signal link and the temperature measurement results of the time-frequency system.

[0102] The time difference measurement result and the temperature measurement result are input into the time difference measurement combination estimation model to generate the temperature variation coefficient and the noise normalization coefficient.

[0103] The temperature-removed time difference measurement result is calculated according to the temperature variation coefficient and the temperature measurement result to obtain a first time difference measurement result, and a factor graph model is constructed according to the first time difference measurement result and a preset sliding window length.

[0104] The time difference prediction deviation and time difference frequency deviation of each time-frequency signal link are estimated according to the factor graph model. The RAIM algorithm is used to monitor the integrity of the time-frequency system based on the time difference prediction deviation, time difference frequency deviation, noise normalization coefficient and preset integrity monitoring parameters.

[0105] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0106] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0107] The above-mentioned embodiments only express several implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A method for monitoring the integrity of a time-frequency system, characterized in that: The method comprises: Obtain the time difference measurement results of each time-frequency signal link and the temperature measurement results of the time-frequency system; Inputting the time difference measurement result and the temperature measurement result into a time difference measurement combination estimation model to generate a temperature variation coefficient and a noise normalization coefficient; Calculating a temperature-removed time difference measurement result according to the temperature variation coefficient and the temperature measurement result to obtain a first time difference measurement result, and constructing a factor graph model according to the first time difference measurement result and a preset sliding window length; The time difference prediction deviation and the time difference frequency deviation of each time-frequency signal link are estimated according to the factor graph model, and the integrity of the time-frequency system is monitored using the RAIM algorithm according to the time difference prediction deviation, the time difference frequency deviation, the noise normalization coefficient and preset integrity monitoring parameters.

2. The method according to claim 1, characterized in that: Obtain the time difference measurement results of each time-frequency signal link and the temperature measurement results of the time-frequency system, including: Run the high-precision time interval measurement module, access the time-frequency signals of all different links, select the time-frequency signal output by any link as the reference link time-frequency signal, and measure the time difference measurement result of each time-frequency signal link according to the reference link time-frequency signal; At the same time, the high-resolution temperature measurement module is run to obtain the temperature measurement results of the time-frequency system.

3. The method according to claim 2, characterized in that Inputting the time difference measurement result and the temperature measurement result into a time difference measurement combination estimation model to generate a temperature variation coefficient and a noise normalization coefficient, including: Subtracting the temperature measurement result from a preset reference temperature to obtain a temperature change result, inputting the temperature change result and the time difference measurement result into a time difference measurement combined estimation model for measurement estimation, and obtaining a temperature change coefficient and a time difference measurement noise for each time-frequency signal link; After calculating the standard deviation of the time difference measurement noise, a noise normalization coefficient is obtained.

4. The method according to any one of claims 1 to 3, characterized in that: The time difference measurement result and the temperature change result are input into a time difference measurement combined estimation model, and the influence of temperature change on the time difference measurement result is separated by the time difference measurement combined estimation model, and the fixed delay difference, combined temperature change coefficient, time difference measurement noise and time difference frequency deviation of each time-frequency signal link are estimated; wherein the time difference measurement combined estimation model is expressed as: ； in, m ( t ) represents the time difference measurement result, m d Indicates the fixed delay difference of each time-frequency signal link, A represents the combined temperature variation coefficient, ΔT Indicates temperature change, Indicates the time difference frequency deviation, t Indicates time, n ( t ) represents the time difference measurement noise.

5. The method according to claim 4, characterized in that The method further comprises: calculating a temperature-removed time difference measurement result according to the temperature variation coefficient and the temperature measurement result to obtain a first time difference measurement result, and constructing a factor graph model according to the first time difference measurement result and a preset sliding window length, including: Subtracting the time difference measurement result, the temperature change coefficient and the product of the temperature change result to obtain a first time difference measurement result, and constructing a factor graph model according to the first time difference measurement result and a preset sliding window length; The factor graph model includes: estimation variables, state factors and observation factors; wherein the estimation variables estimate the time difference between each of the time-frequency signal links and the frequency deviation corresponding to the time difference; and the time difference measurement result is used as the observation value of the observation factor; The state factor satisfies the following state equation: ； ； ； Among them, x k represents the state variable, represents the state noise term, represents the covariance of the driving noise, represents the state transfer matrix, Indicates the time, represents the frequency random walk noise variance, represents the frequency white noise variance, Indicates the time interval when the state is transferred; The observation factor satisfies the following observation equation: ； in, represents the actual observation at the current moment, and H represents the observation matrix , represents the observation noise term, represents the covariance of the observation noise , σ Represents the noise normalization coefficient of the time-frequency signal link.

6. The method according to claim 5, characterized in that The method includes estimating the time difference prediction deviation and the time difference frequency deviation of each time-frequency signal link according to the factor graph model, and using the RAIM algorithm to perform integrity monitoring on the time-frequency system according to the time difference prediction deviation, the time difference frequency deviation, the noise normalization coefficient and the preset integrity monitoring parameters, including: According to the factor graph model, the time difference is subtracted from the time difference measurement result to obtain a time difference prediction deviation, and the time difference prediction deviation is normalized: ； in, represents the normalized time difference forecast deviation, pb i Indicates i The time difference prediction deviation of the time-frequency link, σ i Indicates i The noise normalization coefficient of the time-frequency link; And the frequency deviation corresponding to the time difference is normalized: ； in, represents the frequency deviation corresponding to the normalized time difference, Indicates i The estimated frequency deviation of the time-frequency link, represents the reference value of the time difference frequency deviation estimate; The normalized time difference prediction deviation and the frequency deviation corresponding to the time difference are respectively input into the integrity monitoring model as the observation quantities of the state estimation results. The integrity monitoring model uses the RAIM algorithm to perform consistency monitoring according to the preset integrity monitoring parameters to obtain the time difference consistency result and the frequency deviation consistency result.

7. The method according to claim 6, characterized in that After the integrity monitoring of the time-frequency system is performed using the RAIM algorithm according to the time difference prediction deviation, the time difference frequency deviation, the noise normalization coefficient and the preset integrity monitoring parameters, the method further includes: A test statistic is constructed using a parity vector, and the time difference consistency result and the frequency deviation consistency result are detected according to the test statistic: ； ； ； ； in, r i Indicates i The test statistic of the links, represents the transposed matrix of the parity vector p, Represents an even-odd space matrix No. i List, Represents the detection threshold, P FA represents the false alarm probability, represents the normalized equivalent measurement error.

8. A time-frequency system integrity monitoring device, characterized in that: The device comprises: A measurement result acquisition module is used to obtain the time difference measurement result of each time-frequency signal link and the temperature measurement result of the time-frequency system; A coefficient generation module, used for inputting the time difference measurement result and the temperature measurement result into the time difference measurement combination estimation model to generate a temperature variation coefficient and a noise normalization coefficient; A factor graph model building module, used to calculate the temperature-removed time difference measurement result according to the temperature variation coefficient and the temperature measurement result to obtain a first time difference measurement result, and to build a factor graph model according to the first time difference measurement result and a preset sliding window length; The integrity detection module is used to estimate the time difference prediction deviation and the time difference frequency deviation of each time-frequency signal link according to the factor graph model, and use the RAIM algorithm to monitor the integrity of the time-frequency system according to the time difference prediction deviation, the time difference frequency deviation, the noise normalization coefficient and the preset integrity monitoring parameters.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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