A frequency standard measurement method, device, computer equipment and storage medium

Abstract

The application provides a frequency standard measurement method, device, computer equipment and storage medium, the method comprises: frequency source output measurement signal is carried out frequency division processing, obtains low frequency pulse signal; using high-precision time interval counter to low frequency pulse signal is carried out delay measurement and time interval counting, and is output in the form of time stamp; by setting a sliding window, using sliding average algorithm to time stamp is carried out sliding average filtering processing, all time stamps are carried out sliding difference to obtain time difference, time difference is grouped according to order and is taken average in group to obtain average time difference; by increasing the length of the sliding window, the number of samples is increased to reduce the statistical error; using average time difference, calculate frequency measurement value, relative frequency deviation and allan variance. The method of the application increases the sliding window, and the frequency measurement result obtained by the algorithm of grouping and averaging the time difference has higher measurement precision and stability.

Description

Technical Field

[0001] This invention belongs to the field of time and frequency measurement technology, and specifically relates to a frequency standard measurement method, device, computer equipment, and storage medium. Background Technology

[0002] Time and frequency measurement technology has important applications in modern science and engineering, especially in fields such as communication, navigation, metrology, and scientific research, where the accuracy and stability of frequency sources are extremely important. For example, source crystal oscillators and oven-controlled crystal oscillators (OCXOs), as high-precision frequency sources, are increasingly widely used in communication equipment, satellite navigation systems, and radar systems. These devices have extremely high requirements for the frequency standard (frequency standard) characteristics of the frequency source, such as frequency stability, because frequency stability directly affects the system's performance and reliability. Therefore, frequency standard measurement is a key technology in the field of time and frequency measurement. It mainly measures key characteristic indicators such as frequency accuracy, frequency stability, frequency drift rate, and frequency aging rate of the frequency standard. Among these, frequency accuracy and frequency stability are the fundamental performance indicators characterizing the frequency standard. Frequency accuracy is characterized by the relative frequency deviation between the output frequency (frequency measurement value) of the frequency standard and the nominal frequency (the rated frequency of the oscillator or crystal device, i.e., the ideal operating frequency). Frequency stability is quantified by calculating the Allan variance of the frequency signal to reveal the random fluctuations of the frequency in the form of signal noise, and can be used to evaluate the stability of the frequency standard at different time scales.

[0003] In existing technologies, frequency standard comparators are conventional testing instruments in traditional frequency standard measurement methods. They are used to measure the time-domain and frequency-domain characteristics of frequency standards. By inputting the frequency standard to be measured and a reference frequency standard, they measure the frequency and phase deviation between the two, and then calculate the frequency accuracy, frequency stability, and other indicators of the frequency standard to be measured. Traditional frequency standard measurement techniques mainly employ methods such as the frequency difference multiplication method, the double-mixing time-difference method, and the digital double-mixing time-difference method. These methods measure the average frequency difference or phase difference between two frequency standards using a counter, and then use subsequent processing programs to calculate time-domain technical indicators. Alternatively, they obtain phase difference data by digitally sampling and processing the frequency standard signals. Although these methods can meet measurement requirements to a certain extent, they suffer from problems such as complex circuit configuration, high hardware costs, and difficulty in meeting the design requirements of miniaturized testing devices. In addition, these methods still need improvement in measurement accuracy and stability. For example, while the frequency difference multiplication method and the beat method can achieve high-precision measurement, they have high circuit complexity and strong dependence on hardware equipment. While some simplified methods, such as the combination of direct digital frequency synthesizer (DDS) and phase detector, simplify circuit design to some extent, they still have shortcomings in terms of measurement accuracy and stability.

[0004] Time-to-digital converter (TDC) technology is a high-precision time measurement technique (with time accuracy down to the picosecond level) used to measure the time interval between two signals, thereby calculating their frequency difference. Specifically, it converts the time interval into a digital signal to achieve high-resolution time measurement, featuring high precision, high resolution, and high stability, and is widely used in frequency standard measurement technology. However, directly using conventional TDC technology has drawbacks. Reducing the delay time of delay units to improve resolution leads to a longer delay chain, causing nonlinear accumulation and deterioration of system linearity, resulting in unstable measurement results.

[0005] The development of technology based on the Field Programmable Gate Array (FPGA) platform provides more options beyond frequency standard comparators for achieving high computational efficiency and high accuracy frequency standard measurement. Utilizing the programmable and designable characteristics of FPGAs, the Time-Diverter (TDC) technology is improved. By designing the delay time and employing appropriate algorithms to smooth and average the measurement results, nonlinearity can be effectively corrected. This represents a feasible frequency standard measurement method for achieving the technical goals of improving measurement accuracy and system stability. Summary of the Invention

[0006] To address the aforementioned problems in existing technologies, this invention proposes a frequency standard measurement method, apparatus, computer equipment, and storage medium. By extending the sliding window length corresponding to a single measurement time, and then performing sliding difference and grouped averaging on the measurement results, the measurement accuracy and stability are effectively improved. This method, while maintaining a simple design, significantly enhances the reliability and accuracy of the measurement results, meeting the requirements for high-precision frequency standard measurement.

[0007] A frequency standard measurement method, the specific technical solution of which includes:

[0008] Step 110: The measurement signal output from the frequency source is divided by frequency to obtain a low-frequency pulse signal; the measurement signal is a periodic high-frequency pulse signal.

[0009] Step 120: Use a high-precision time interval counter to perform delay measurement and time interval counting on the low-frequency pulse signal, and convert the counting result into a digital signal, which is then output as a timestamp at fixed time intervals.

[0010] Step 130: The timestamps are filtered using a moving average algorithm to obtain filtered signal data, which is used to suppress high-frequency noise. The moving average algorithm sets a sliding window, calculates the time difference by moving the difference across all timestamps, groups the time differences in order, and averages the results within each group to obtain the average time difference. The length of the sliding window is the maximum number of timestamps in the input sliding window. Increasing the length of the sliding window extends the gate time for a single measurement, thereby increasing the number of samples and reducing statistical errors.

[0011] Step 140: Calculate the frequency measurement value using the average time difference; calculate the relative frequency deviation using the frequency measurement value and the nominal frequency to characterize the accuracy of the frequency standard measurement; calculate the Allan variance using the relative frequency deviation to describe the frequency stability.

[0012] Preferably, the high-precision time interval counter uses a time-to-digital converter and utilizes the programmable and designable capabilities of the FPGA platform to implement steps Step 110-Step 140.

[0013] This invention also protects a frequency standard measurement device, which implements the steps of the aforementioned frequency standard measurement method. The device includes:

[0014] The frequency divider module is used to divide the measurement signal output from the frequency source to obtain a low-frequency pulse signal; the measurement signal is a periodic high-frequency pulse signal.

[0015] The time interval counting module is used to perform delay measurement and time interval counting on the low-frequency pulse signal using a high-precision time interval counter, and convert the counting result into a digital signal, which is then output as a timestamp at fixed time intervals.

[0016] The moving average filtering module is used to perform moving average filtering on timestamps using a moving average algorithm to obtain filtered signal data and suppress high-frequency noise. The moving average algorithm sets a moving average window, calculates the time difference by moving the difference across all timestamps, groups the time differences sequentially, and averages the results within each group to obtain the average time difference. The length of the moving average window is the maximum number of timestamps that can be input into the moving average window. Increasing the length of the moving average window extends the gate time for a single measurement, thereby increasing the number of samples and reducing statistical errors.

[0017] The calculation module is used to calculate the frequency measurement value using the average time difference; calculate the relative frequency deviation using the frequency measurement value and the nominal frequency to characterize the accuracy of the frequency standard measurement; and calculate the Allan variance using the relative frequency deviation to describe the frequency stability.

[0018] Preferably, the frequency standard measurement device is built on an FPGA platform, and the programmable and designable performance of the FPGA is used to realize the function of each module of the device, and the time interval counting module is designed as a time-to-digital converter module.

[0019] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the aforementioned frequency standard measurement method.

[0020] On the other hand, a storage medium is protected on which a computer program is stored, which, when executed by a processor, implements the steps of the aforementioned frequency standard measurement method.

[0021] In summary, this invention proposes a frequency standard measurement method, apparatus, device, and storage medium. Compared with the prior art, the frequency standard measurement method of this invention has the following advantages and beneficial effects:

[0022] (1) The method of the present invention extends the gate time of frequency measurement by increasing the length of the sliding window, thereby increasing the number of samples measured and reducing statistical error. This not only improves the reliability of the measurement results, but also improves the measurement resolution and measurement accuracy.

[0023] (2) This invention performs a moving average filtering process on the timestamp, groups the time difference data obtained by the sliding window to obtain the average time difference, and then uses the average time difference to calculate the frequency measurement value. The accuracy and stability of the frequency standard measurement are evaluated by calculating the relative frequency deviation and Allan variance. Experimental results show that when the sliding window is increased, the frequency measurement results obtained by the grouping and averaging algorithm have higher accuracy and stability. Attached Figure Description

[0024] Figure 1 This is a flowchart of a frequency standard measurement method in the first embodiment of the present invention;

[0025] Figure 2 This is a schematic diagram of the flow framework of a frequency standard measurement method according to the second embodiment of the present invention;

[0026] Figure 3 This is a time difference curve from Experiment 1 when the third embodiment of the present invention was experimentally verified.

[0027] Figure 4 This is a frequency measurement curve from Experiment 1 when the third embodiment of the present invention was experimentally verified.

[0028] Figure 5 The Allan variance curve of Experiment 1 is shown when the third embodiment of the present invention is experimentally verified.

[0029] Figure 6 This is a time difference curve from Experiment 2 when the third embodiment of the present invention was experimentally verified.

[0030] Figure 7 This is the average time difference curve of Experiment 2 when the third embodiment of the present invention was experimentally verified;

[0031] Figure 8 This is a graph showing the relative frequency deviation of Experiment 2 when the third embodiment of the present invention was experimentally verified.

[0032] Figure 9 The Allan variance curve of Experiment 2 is shown when the third embodiment of the present invention is experimentally verified.

[0033] Figure 10 This is a time difference curve from Experiment 3 when the third embodiment of the present invention was experimentally verified.

[0034] Figure 11 This is the average time difference curve of Experiment 3 when the third embodiment of the present invention was experimentally verified.

[0035] Figure 12 This is a graph showing the relative frequency deviation in Experiment 3 when the third embodiment of the present invention was experimentally verified.

[0036] Figure 13 The Allan variance curve of Experiment 3 is shown when the third embodiment of the present invention is experimentally verified.

[0037] Figure 14 A structural framework diagram of a frequency standard measuring device according to the fourth embodiment of the present invention;

[0038] Figure 15 The fifth embodiment of the present invention provides a structural framework diagram of a frequency standard measurement device, wherein the TDC module is a time-to-digital converter module. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0040] To address the problem of frequency standard measurement for frequency sources, this invention provides a frequency standard measurement method. By designing a sliding window to characterize the time of a single measurement and using a moving average algorithm to suppress noise in the measurement results, the method achieves good real-time measurement performance while effectively correcting system nonlinearity, thereby improving the accuracy of frequency standard measurement and the stability of the measurement system.

[0041] In the first embodiment, referring to Figure 1 As shown, the present invention provides a frequency standard measurement method, which specifically includes the following steps:

[0042] Step 110: The measurement signal output from the frequency source is divided by frequency to obtain a low-frequency pulse signal; the measurement signal is a periodic high-frequency pulse signal.

[0043] Step 120: Use a high-precision time interval counter to perform delay measurement and time interval counting on the low-frequency pulse signal, and convert the counting result into a digital signal, which is then output as a timestamp at fixed time intervals.

[0044] Step 130: The timestamps are filtered using a moving average algorithm to obtain filtered signal data, which is used to suppress high-frequency noise. The moving average algorithm sets a sliding window, calculates the time difference by moving the difference across all timestamps, groups the time differences in order, and averages the results within each group to obtain the average time difference. The length of the sliding window is the maximum number of timestamps in the input sliding window. Increasing the length of the sliding window extends the gate time for a single measurement, thereby increasing the number of samples and reducing statistical errors.

[0045] Step 140: Calculate the frequency measurement value using the average time difference; calculate the relative frequency deviation using the frequency measurement value and the nominal frequency to characterize the accuracy of the frequency standard measurement; calculate the Allan variance using the relative frequency deviation to describe the frequency stability.

[0046] Specifically, in Step 110, the process of frequency division of the measurement signal output from the frequency source includes setting the frequency division coefficient. The measurement signal is divided into frequencies of 10 ... low-frequency pulse signal, This is the nominal frequency of the measurement signal. A low-frequency pulse signal is obtained through frequency division to meet the time resolution and measurement accuracy requirements of a high-precision time interval counter. Let the unit time for frequency standard measurement be... The nominal frequency The equal-precision frequency measurement method can be used, that is, by obtaining the unit time... The number of measurement signal pulses (coarse measurement) was obtained by using a time interval counter.

[0047] Furthermore, in Step 120, the high-precision time interval counter is assumed to operate in continuous time. Within, at time intervals Output timestamps, then the number of timestamps output. ,use Represents a timestamp. .

[0048] Specifically, in Step 130, if the length of the sliding window is set to... Then, the gate time (sampling interval) for a single measurement is calculated. , is the time span corresponding to the sliding window. ;

[0049] For all timestamp Perform sliding subtraction to obtain Time difference , The time difference is given by the following formula:

[0050] (1)

[0051] time difference Grouped sequentially, all time differences are divided into... Group, therefore each Group the time differences into one group. This represents the floor operation, for example. .

[0052] In the design of the moving average algorithm, if all... If the time difference can be evenly grouped, then by selecting a suitable... and the length of the sliding window , making It can be an integer, for example, when the time span corresponding to the sliding window is exactly the same as the unit time. If we maintain consistency, we can deduce through substitution and calculation:

[0053] (2)

[0054] Therefore, when the sliding window length does not meet the requirement When the time difference is exactly an integer, it needs to be grouped sequentially using a rounding method, that is, grouping the time differences into integer groups. The time difference is divided into groups. Divided into After grouping, the remaining The time difference is discarded.

[0055] The average of each time difference is obtained. Average time difference Specifically, it is given by the following formula:

[0056] (3)

[0057] Furthermore, in Step 140, the continuous time is calculated using the average time difference. Inside unit time Frequency measurement value , Specifically, it is given by the following formula:

[0058] (4)

[0059] in, It is the nominal frequency;

[0060] Calculate each unit of time using the frequency measurement and nominal frequency. Relative frequency deviation (relative frequency difference) within the range It is given by the following formula:

[0061] , (5)

[0062] Then calculate the Allen variance using the following formula:

[0063] (6)

[0064] in, This indicates the sampling interval.

[0065] The second embodiment of the present invention, as follows: Figure 2 As shown, the programmable and designable capabilities of the FPGA platform are used to implement the steps 110-140 in the first embodiment, and the high-precision time interval counter uses a time-to-digital converter (TDC). This embodiment's method is an improvement on TDC frequency standard measurement technology using an FPGA platform.

[0066] Specifically, by performing frequency division processing on the measurement signal output from the frequency source based on the FPGA platform, precise control of the signal frequency can be achieved, so that the generated low-frequency pulse signal can better adapt to the operating frequency of the TDC and reduce the requirements for circuit design.

[0067] Since the TDC contains a delay chain composed of multiple delay units connected in series, and multiple counters for coarse or fine counting, when the low-frequency pulse signal obtained by frequency division is input into the delay chain of the TDC and propagates within the chain, the TDC will use the counters to measure the time interval based on the system clock frequency inside the FPGA to obtain a coarse count. Based on the coarse count, a fine count is obtained by measuring the position of the signal in the delay chain. The current coarse or fine count of the TDC module is read and stored as a timestamp. This method can obtain higher resolution delay measurement and time interval counting results.

[0068] Furthermore, by leveraging the parallel computing capabilities and configurability of FPGAs, high-speed parallel computing can be achieved in the acquisition of timestamps, the processing of multi-sample data moving averages including sliding windows, and subsequent calculations, thereby improving the efficiency of frequency standard measurement.

[0069] Based on the FPGA platform and TDC of the second embodiment described above, in the third embodiment of the present invention, a temperature-controlled crystal oscillator is selected as the frequency source, and the output... Measurement signals of 5MHz, 10MHz, or 100MHz are frequency-divided to obtain a low-frequency pulse signal of 100Hz. This 100Hz low-frequency pulse signal is input into the delay chain in the TDC for time interval counting, and the time interval is used to... Output timestamps (including coarse and fine counts), selecting consecutive time periods. All timestamps, the number of timestamps obtained. ,use Represents a timestamp. .

[0070] Let the length of the sliding window be... The gate time for a single measurement is the time span corresponding to the sliding window. The unit time for frequency standard measurement is also set to... .

[0071] For all timestamp Perform sliding subtraction to obtain Time difference , And use equation (1) to calculate the 900 time differences. .

[0072] In unit time Within, the time difference Grouped sequentially, all time differences are divided into... Group, each The time difference is divided into groups, and the average time difference of each group is calculated using equation (3). .

[0073] Then, using the average time difference, the frequency measurement value is calculated according to formula (4). , The relative frequency deviation and Allan variance are calculated using formulas (5) and (6), respectively.

[0074] To verify the effectiveness of the frequency standard measurement method of the present invention in improving measurement accuracy and stability, an experiment was conducted on the third embodiment. In the experiment, the time interval of the TDC timestamp output remained unchanged, with the time interval... Output timestamps, and extend the consecutive time range of the timestamps. Number of timestamps The amount of sampled data was increased by adjusting the size of the sliding window. Further verification was made that increasing the length of the sliding window in the moving average algorithm, that is, increasing the measurement gate time accordingly, can improve the accuracy and frequency stability of frequency standard measurement.

[0075] Experiment 1: Without moving average filtering, i.e., the length of the moving window... ;

[0076] Directly to the Each timestamp is used to calculate the number of intervals. Time difference:

[0077] , (7)

[0078] Time difference results as follows Figure 3 As shown.

[0079] Then, the frequency measurement value is calculated using the time difference:

[0080] , (8)

[0081] in, It is the nominal frequency.

[0082] unit time The relative frequency deviation within is given by the following formula:

[0083] , ,

[0084] Frequency measurement results are as follows Figure 4 As shown.

[0085] Finally, the Allen variance is calculated using formula (6), and the Allen variance curve is shown below. Figure 5 As shown, the mean value of the Allen variance is .

[0086] Experiment 2: Setting the length of the sliding window Then perform moving average filtering;

[0087] 9990 time differences were obtained by calculating according to formula (1), and the time difference image is as follows. Figure 6 As shown;

[0088] unit time Within, the 9990 time differences are divided into Group, each Divide them into groups of 10, and discard the remaining 90. Calculate the average time difference according to formula (3). , The mean time difference curve is as follows Figure 7 As shown; then use formula (4) to calculate the frequency measurement value. The relative frequency deviation and Allan variance were calculated using formulas (5) and (6) respectively, and the average value of the Allan variance was obtained. The relative frequency deviation curve in this experiment is shown below. Figure 8 As shown, the Allen variance curve is as follows: Figure 9 As shown.

[0089] Experiment 3: Setting the length of the sliding window Then perform moving average filtering;

[0090] 9900 time differences were obtained by calculating according to formula (1), and the time difference image is as follows. Figure 10 As shown;

[0091] unit time Within, the 9900 time differences are divided into Group, each Divide them into groups and calculate the average time difference according to formula (3). , The mean time difference curve is as follows Figure 11 As shown; then use formula (4) to calculate the frequency measurement value. The relative frequency deviation and Allan variance were calculated using formulas (5) and (6) respectively, and the average value of the Allan variance was obtained. The relative frequency deviation curve in this experiment is shown below. Figure 12 As shown, the Allen variance curve is as follows: Figure 13 As shown.

[0092] Through the above experiments, it can be seen from the experimental results characterized by the relative frequency deviation and Allan variance curves that when the sliding window is increased, the amplitude of the relative frequency deviation ( Figure 4 , Figure 8 and Figure 12 ) and the amplitude and mean of Allen's variance ( Figure 5 , Figure 9 and Figure 13 The fact that all values ​​are decreasing indicates that the frequency measurement results obtained by using the group averaging method have higher accuracy and stability.

[0093] The fourth embodiment of the present invention provides a frequency standard measurement device, such as... Figure 14 As shown, the device is used to implement the steps of the frequency standard measurement method in the foregoing embodiments. The device includes the following modules:

[0094] The frequency divider module is used to divide the measurement signal output from the frequency source to obtain a low-frequency pulse signal; the measurement signal is a periodic high-frequency pulse signal.

[0095] The time interval counting module is used to perform delay measurement and time interval counting on the low-frequency pulse signal using a high-precision time interval counter, and convert the counting result into a digital signal, which is then output as a timestamp at fixed time intervals.

[0096] The moving average filtering module is used to perform moving average filtering on timestamps using a moving average algorithm to obtain filtered signal data and suppress high-frequency noise. The moving average algorithm sets a moving average window, calculates the time difference by moving the difference across all timestamps, groups the time differences sequentially, and averages the results within each group to obtain the average time difference. The length of the moving average window is the maximum number of timestamps that can be input into the moving average window. Increasing the length of the moving average window extends the gate time for a single measurement, thereby increasing the number of samples and reducing statistical errors.

[0097] The calculation module is used to calculate the frequency measurement value using the average time difference; calculate the relative frequency deviation using the frequency measurement value and the nominal frequency to characterize the accuracy of the frequency standard measurement; and calculate the Allan variance using the relative frequency deviation to describe the frequency stability.

[0098] In the fifth embodiment, as Figure 15 As shown, the frequency standard measurement device is built on an FPGA platform, and the programmable and designable performance of the FPGA is used to implement the steps of the aforementioned frequency standard measurement method. The time interval counting module is designed using a TDC module.

[0099] In the foregoing embodiments, the platform for implementing frequency standard measurement using the frequency standard measurement method or device can be other parallel computing measurement system platforms with similar performance to FPGA platforms. The programmable and designable performance of such measurement platforms is used to implement the steps of the aforementioned frequency standard measurement method.

[0100] On the other hand, in one embodiment of the present invention, a computer device is provided, which may be a server. The device includes a processor, memory, a network interface, and a database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores frequency standard measurement data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the frequency standard measurement method.

[0101] Those skilled in the art will understand that the description of the device technical features in the above embodiments does not constitute a limitation on all devices to which the present invention is applied. Specific devices may include more or fewer components, or combinations of certain components, or different component arrangements.

[0102] In another embodiment, a storage medium is provided on which a computer program is stored, which, when executed by a processor, implements the steps of the aforementioned frequency standard measurement method.

[0103] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application 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. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0104] Matters not covered in this invention are common knowledge.

[0105] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0106] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A frequency standard measurement method, characterized in that, include: Step 110: The measurement signal output from the frequency source is divided by frequency to obtain a low-frequency pulse signal; the measurement signal is a periodic high-frequency pulse signal. Step 120: Use a high-precision time interval counter to perform delay measurement and time interval counting on the low-frequency pulse signal, and convert the counting result into a digital signal, which is then output as a timestamp at fixed time intervals. Step 130: The timestamps are filtered using a moving average algorithm to obtain filtered signal data, which is used to suppress high-frequency noise. The moving average algorithm obtains the time difference by setting a sliding window and subtracting all timestamps. The time differences are then grouped in order and the average of the group is taken to obtain the average time difference. The length of the sliding window is the maximum number of timestamps in the input sliding window; by increasing the length of the sliding window, the gate time for a single measurement is extended, thereby increasing the number of samples measured and reducing statistical errors. Step 140: Calculate the frequency measurement value using the average time difference; calculate the relative frequency deviation using the frequency measurement value and the nominal frequency to characterize the accuracy of the frequency standard measurement; The Allan variance is calculated using the relative frequency deviation and is used to describe frequency stability.

2. The frequency standard measurement method according to claim 1, characterized in that, In Step 110, the process of frequency division of the measurement signal output from the frequency source includes setting the frequency division coefficient. The measurement signal is divided into frequencies of 10 ... low-frequency pulse signal, It is the nominal frequency of the measured signal.

3. The frequency standard measurement method according to claim 2, characterized in that, Assume the high-precision time interval counter is based on a time interval. Output timestamps, then in continuous time Number of timestamps output internally ,use Represents a timestamp. Let the unit time for frequency standard measurement be . ; In Step 130, the process of applying a moving average filter to the timestamp using the moving average algorithm includes: Using a length of The sliding window, for the timestamp Perform sliding subtraction to obtain Time difference , The time difference is given by the following formula: ; time difference Grouped sequentially, all time differences are divided into... Group, each Group the time differences into one group. This indicates a floor operation, leaving the remainder. The time difference is discarded; The average of each time difference is taken to obtain Average time difference It is given by the following formula: 。 4. The frequency standard measurement method according to claim 3, characterized in that, In Step 140, The process of calculating frequency measurements using the average time difference includes calculating continuous time. Inside unit time Frequency measurement value , It is given by the following formula: ， in, It is the nominal frequency; The process of calculating the relative frequency deviation using frequency measurements and the nominal frequency includes calculating the deviation for each unit of time. Relative frequency deviation within It is given by the following formula: ， ； Allan variance is calculated using the relative frequency deviation, given by the following formula: , in, This indicates the sampling interval.

5. The frequency standard measurement method according to claim 4, characterized in that, The high-precision time interval counter uses a time-to-digital converter and utilizes the programmable and designable capabilities of the FPGA platform to implement steps Step 110-Step 140.

6. The frequency standard measurement method according to claim 5, characterized in that, The frequency source is a temperature-controlled crystal oscillator, used for output. Measurement signals of 5MHz, 10MHz, or 100MHz are divided to obtain a low-frequency pulse signal of 100Hz. This 100Hz low-frequency pulse signal is then input into the delay chain of the time-to-digital converter for time interval counting, and the time intervals are counted accordingly. Output timestamp; Select continuous time All timestamps, the number of timestamps obtained. Set the length of the sliding window The unit time for frequency standard measurement is set to ,but, In Step 130, the process of applying a moving average filter to the timestamp using the moving average algorithm includes: For all Slide the difference between the timestamps to get Time difference , ; The 900 time differences were grouped into... Group, each Divide the data into groups; calculate the average time difference. ; In Step 140, the frequency measurement value is calculated using the average time difference. , , relative frequency deviation and Allen variance.

7. A frequency standard measuring device, characterized in that, The device is used to implement the steps of the frequency standard measurement method as described in claim 1, wherein the device comprises: The frequency divider module is used to divide the measurement signal output from the frequency source to obtain a low-frequency pulse signal; the measurement signal is a periodic high-frequency pulse signal. The time interval counting module is used to perform delay measurement and time interval counting on the low-frequency pulse signal using a high-precision time interval counter, and convert the counting result into a digital signal, which is then output as a timestamp at fixed time intervals. The moving average filtering module is used to perform moving average filtering on timestamps using a moving average algorithm to obtain filtered signal data and suppress high-frequency noise. The moving average algorithm sets a moving average window, calculates the time difference by moving the difference across all timestamps, groups the time differences sequentially, and averages the results within each group to obtain the average time difference. The length of the moving average window is the maximum number of timestamps that can be input into the moving average window. Increasing the length of the moving average window extends the gate time for a single measurement, thereby increasing the number of samples and reducing statistical errors. The calculation module is used to calculate the frequency measurement value using the average time difference; calculate the relative frequency deviation using the frequency measurement value and the nominal frequency to characterize the accuracy of the frequency standard measurement; and calculate the Allan variance using the relative frequency deviation to describe the frequency stability.

8. The frequency standard measuring device according to claim 7, characterized in that, The frequency standard measurement device is built on an FPGA platform, and the programmable and designable performance of the FPGA is used to realize the function of each module of the device. The time interval counting module is designed as a time-to-digital converter module.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-6.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-6.

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