Solid-state power supply final amplifier module measurement uncertainty evaluation method and system

Abstract

The application relates to a solid-state power source final-stage power amplifier module measurement uncertainty evaluation method and system, which comprises the following steps: collecting final-stage power amplifier module test data; setting an initial test number M and a tolerance accuracy, randomly extracting a group of the final-stage power amplifier module test data, inputting the data into a measurement uncertainty evaluation model, and generating M groups of power amplifier gain values; according to the M groups of power amplifier gain values, obtaining a gain distribution histogram and calculating an error value; comparing the error value with the tolerance accuracy, if the error value is smaller than the tolerance accuracy, outputting the gain distribution histogram at this time, otherwise, resetting the test number, repeating the above steps, and until the error value is smaller than the tolerance accuracy. The method simulates and considers various uncertainty sources when measuring the amplification gain of the solid-state power source final-stage power amplifier module for an accelerator, and through random extraction and simulation of the uncertainty, the uncertainty of the power amplifier gain measurement can be more comprehensively evaluated.

Description

Technical Field

[0001] This invention relates to a method and system for evaluating the measurement uncertainty of a final stage power amplifier module of a solid-state power source for accelerators, belonging to the field of accelerator technology. Background Technology

[0002] Solid-state power source devices provide radio frequency (RF) energy to accelerators. The final stage power amplifier module is the most important power amplification unit in a solid-state power source; it is composed of multiple modules connected in parallel to achieve high power output. The power gain of the final stage power amplifier module represents the ratio between the amplifier's output power and input power, indicating the amplifier's amplification effect. Accurate power amplifier gain measurement is crucial because it significantly impacts system design, parameter tuning, and performance evaluation. By determining the measurement uncertainty, the reliability of the measurement results can be assessed, and appropriate measures can be taken to reduce the uncertainty, thereby improving the accuracy of the measurement results.

[0003] For the assessment of measurement uncertainty, the ISO (International Organization for Standardization) / IEC (International Electrotechnical Commission) Guide to the Uncertainty in Measurement (GUM) method, also known as the GUM method, is generally adopted. However, for the gain of the final stage power amplifier module in a solid-state power source for accelerators, the measurement system is complex, and the attenuation test of the test coupler introduces mismatch uncertainty, which typically exhibits an asymmetric distribution. When the GUM method is used for complex measurement systems with asymmetric and highly nonlinear determination components, it yields unrealistic coverage intervals, leading to inaccurate measurement results. Summary of the Invention

[0004] To address the aforementioned problems, the present invention aims to provide a method and system for evaluating the measurement uncertainty of a solid-state power source final stage power amplifier module for accelerators. This method simulates and considers various sources of uncertainty when measuring the amplification gain of a solid-state power source final stage power amplifier module for accelerators. By randomly sampling and simulating these uncertainties, the uncertainty of power amplifier gain measurement can be evaluated more comprehensively.

[0005] To achieve the above objectives, the present invention proposes the following technical solution: a method for evaluating the measurement uncertainty of a solid-state power source final stage power amplifier module, comprising the following steps: collecting test data of the final stage power amplifier module; setting an initial number of tests M and tolerance accuracy, randomly selecting a set of test data of the final stage power amplifier module, and inputting it into a measurement uncertainty evaluation model to generate M sets of power amplifier gain values; obtaining a gain distribution histogram based on the M sets of power amplifier gain values, and calculating the error value; comparing the error value with the tolerance accuracy; if the error value is less than the tolerance accuracy, outputting the gain distribution histogram at this time; otherwise, resetting the number of tests, extracting test data of the final stage power amplifier module, inputting it into the measurement uncertainty evaluation model, outputting the gain distribution histogram and the error value for judgment, and repeating this cyclic process until the error value is less than the tolerance accuracy.

[0006] Furthermore, the solid-state power source includes a signal source, a pre-amplifier, a first directional coupler, a final-stage power amplifier module, and a second directional coupler connected in sequence. The signal is output and loaded after passing through the signal source, the pre-amplifier, the first directional coupler, the final-stage power amplifier module, and the second directional coupler in sequence. The first directional coupler is connected to a first probe, the second directional coupler is connected to a second probe, the first probe and the second probe are connected to a power meter, and the final-stage power amplifier module is connected to a power supply.

[0007] Furthermore, the method for collecting test data of the final stage power amplifier module is as follows: the test accuracy of the power meter, the stability of the power supply output voltage, and the uncertainty of the ambient temperature change are assessed; the coupling degree of the directional coupler is obtained by using a vector network analyzer based on the voltage of the first coupler and the second coupler, and the reflection coefficients of the output ports of the preamplifier and the final stage power amplifier module, as well as the reflection coefficients of the ports of the first probe and the second probe, are measured; the readings of the first probe and the second probe channels of the power meter are read.

[0008] Furthermore, the formula for the measurement uncertainty evaluation model is as follows:

[0009] P gain =P out_ran -P in_ran +δP meter +δP mis_cou1 +δP mis_cou2 +δP coupler1_ran +δP coupler2_ran +δP VNA +δP con_PS +δP con_env

[0010] Among them, P gain It is the power output by the model; P out_ran It is the random component of the power output of the final stage power amplifier; P in_ranIt is the random component of the power measurement input of the final stage power amplifier; δP meter It is the measurement uncertainty of the power meter itself; δP mis_cou1 This is the adaptation error caused by the insertion of the first directional coupler; δP mis_cou2 This is the adaptation error caused by the insertion of the second directional coupler; δP coupler1_ran This refers to the measurement repeatability error for the first directional coupler; δP c0upler2_ran This refers to the measurement repeatability error for the second directional coupler; δP VNA This refers to the transmission accuracy error of the vector network analyzer; δP con_PS The uncertainty is caused by the stability of the power supply voltage; δP con_env The uncertainty is caused by changes in ambient temperature.

[0011] Furthermore, P out_ran and P in_ran The mean and standard deviation of the measured values ​​are directly taken as the probability density function parameters of the normal distribution, δP. coupler1_ran and δP coupler2_ran The mean absolute deviation and the root mean square error are used as parameters of the normal distribution probability density function, which is expressed as N(x,u) 2 For the input parameter ξ of the normal distribution, a pseudo-random number r is taken from the standard normal distribution N(0,1), and ξ = x + u(x)r is constructed, where x is the expected value of the normal distribution and u(x) is the standard deviation of the normal distribution.

[0012] Furthermore, the measurement uncertainty δP of the power meter itself... meter And vector network analyzer transmission accuracy error δP VNA All of them are rectangularly distributed, and their probability density function is expressed as R(a1, b1), where a1 and b1 are the upper and lower limits of the rectangular distribution. The calculation formula of the measurement error parameter ξ introduced by it is: ξ=a+(ba)r, where r is a pseudo-random number.

[0013] Furthermore, the adaptation error δP caused by the insertion of the first directional coupler and the second directional coupler... mis_cou1 and δP mis_cou2 The distribution exhibits an arcsine distribution, with its probability density function denoted as U(a2, b2), where a2 and b2 are the upper and lower limits of the distribution, and a2 and b2 are opposites of each other. The formula for calculating the error limit introduced by the mismatch is:

[0014] The distribution exhibits an arcsine distribution, with its probability density function denoted as U(a2, b2), where a2 and b2 are the upper and lower limits of the distribution, and a2 and b2 are opposites of each other. The formula for calculating the error limit introduced by the mismatch is:

[0015]

[0016] Among them, S 11 ,S 12 ,S 21 ,S 22 All are S-parameter matrices of directional couplers. Port 1 is the port connected to the pre-amplifier or final power amplifier module, and port 2 is the port connected to the coupling port, the first probe port, or the second probe port; Γ A Γ is the reflection coefficient at the power output port of the preamplifier or final amplifier. P Let be the reflection coefficient of the first probe port or the second probe port. The formula for calculating the measurement error parameter ξ introduced by ξ is:

[0017]

[0018] r is a pseudo-random number.

[0019] Furthermore, the uncertainty δP caused by the power supply voltage stability con_PS The average value is represented as A. con_PS =10%(P) / V, standard deviation σ con_PS =3%(P) / V; Uncertainty δP caused by changes in ambient temperature con_env The average value is represented as A. con_env = 4% (P) / ℃, standard deviation σ con_env = 1.2%(P) / V, where (P) represents the power level, V is the voltage, and the uncertainty δP caused by the stability of the power supply voltage is... con_PS Uncertainty δP caused by changes in ambient temperature con_env All exhibit a rectangular distribution, with the probability density function denoted as R(a3, b3); where a3 and b3 are the upper and lower limits of the rectangular distribution and are opposites of each other. The formula for calculating b3 is:

[0020]

[0021] Furthermore, the formula for calculating the error value δ is as follows:

[0022]

[0023] Where σ is the standard deviation and M is the number of trials.

[0024] This invention also discloses a measurement uncertainty evaluation system for a solid-state power source final stage power amplifier module, comprising: a data acquisition module for acquiring test data of the final stage power amplifier module; a parameter setting module for setting the initial number of tests M and the tolerance accuracy; a model calculation module for randomly selecting a set of test data of the final stage power amplifier module and inputting it into a measurement uncertainty evaluation model to generate M sets of power amplifier gain values; a result and error output module for obtaining a gain distribution histogram based on the M sets of power amplifier gain values ​​and calculating the error value; and a loop judgment module for comparing the error value with the tolerance accuracy. If the error value is less than the tolerance accuracy, the gain distribution histogram at this time is output; otherwise, the number of tests is reset, the test data of the final stage power amplifier module is extracted, the measurement uncertainty evaluation model is input, and the gain distribution histogram and error value are output for judgment. This loop process is repeated until the error value is less than the tolerance accuracy.

[0025] The technical solution of the present invention has at least the following technical effects or advantages:

[0026] 1. This invention simulates and considers various sources of uncertainty when measuring the amplification gain of the final stage power amplifier module of a solid-state power source for accelerators, such as random measurement errors, inaccuracies of test equipment, mismatch of directional coupler insertion, and changes in environmental conditions. By randomly sampling and simulating these uncertainties, the uncertainty of power amplifier gain measurement can be evaluated more comprehensively.

[0027] 2. This invention can automatically adjust the sampling strategy and select appropriate sampling methods and sample sizes according to the importance and characteristics of the uncertainty sources. This makes the measurement uncertainty assessment more flexible and adaptable, and can more accurately reflect the uncertainty in the measurement of power amplifier gain.

[0028] 3. This invention can generate a large number of random samples. By performing statistical analysis on these samples, indicators such as probability distribution and confidence interval can be obtained to describe the uncertainty of power amplifier gain measurement. Such results are more reliable and can provide more comprehensive information to help evaluators make more accurate decisions. Attached Figure Description

[0029] Figure 1 This is a flowchart of a method for evaluating the uncertainty of gain measurement in a solid-state power source final stage power amplifier module according to an embodiment of the present invention;

[0030] Figure 2 This is a schematic diagram of the structure of a solid-state power source in one embodiment of the present invention;

[0031] Figure 3 This is a logical schematic diagram of a measurement uncertainty evaluation model in one embodiment of the present invention;

[0032] Figure 4This is a histogram of the probability distribution of the amplification gain of the final stage power amplifier module calculated in one embodiment of the present invention. Detailed Implementation

[0033] To enable those skilled in the art to better understand the technical solutions of the present invention, the present invention is described in detail through specific embodiments. However, it should be understood that the specific embodiments are provided only for a better understanding of the present invention and should not be construed as limiting the present invention. In the description of the present invention, it should be understood that the terminology used is for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0034] To address the problem that existing technologies, such as the GUM method, when used in complex measurement systems with asymmetric and highly nonlinear degree components, can yield unrealistic coverage intervals, leading to inaccurate measurement results, this invention proposes a method and system for evaluating the measurement uncertainty of the final stage power amplifier module of an accelerator solid-state power source. First, test data of the final stage power amplifier module and relevant instrument accuracy parameters are collected. Simultaneously, a gain model is established, and the probability density function distribution of the input parameters is evaluated, with the initial number of trials M and tolerance ε set. Then, one input quantity is randomly selected each time, generating M sets of sample points. These M sets of sample points are substituted into the model to calculate M sets of power amplifier gain values, resulting in a gain distribution histogram. The system calculates the error value and then compares it with the expected tolerance value. If the error value is less than the tolerance value, the result is output. If the error value is greater than the tolerance value, the sample size M is readjusted according to the set tolerance and the calculated standard deviation. This process is repeated until the output error is less than the tolerance value, at which point the result is output. This effectively solves the problem encountered by the GUM method in evaluating the uncertainty of the gain measurement system of the final stage power amplifier module of a solid-state power source for accelerators. It can easily, accurately, and quickly obtain the measurement distribution histogram, as well as the average value, standard deviation, and 95% confidence interval. It has advantages such as adaptive adjustment, probability distribution simulation, statistical analysis capabilities, and flexibility, and can provide more accurate and comprehensive uncertainty assessment results. The following detailed description of the invention is provided with reference to the accompanying drawings and embodiments.

[0035] Example 1

[0036] This embodiment discloses a method for evaluating the measurement uncertainty of a 162.5MHz solid-state power source final stage power amplifier module for accelerators, such as... Figure 1 As shown, it includes the following steps:

[0037] S1 collects test data from the final stage power amplifier module.

[0038] like Figure 2As shown, the solid-state power source includes a signal source, a pre-amplifier, a first directional coupler, a final power amplifier module, and a second directional coupler connected in sequence. The signal is output and loaded after passing through the signal source, the pre-amplifier, the first directional coupler, the final power amplifier module, and the second directional coupler in sequence. The first directional coupler is connected to the first probe, the second directional coupler is connected to the second probe, the first probe and the second probe are connected to the power meter, and the final power amplifier module is connected to the power supply.

[0039] The method for collecting test data of the final stage power amplifier module and related technical specifications of the test instruments is as follows:

[0040] S1.1 Detect the test accuracy of the power meter. In this embodiment, the accuracy of the power meter is ±0.2dB. Detect the stability of the power supply output voltage. In this embodiment, the stability of the power supply output voltage is 50V±0.1V. Evaluate the uncertainty of ambient temperature change. In this embodiment, the uncertainty of ambient temperature change is ±1℃.

[0041] S1.2 The coupling degree of the directional coupler is obtained using a vector network analyzer based on the voltages of the first and second couplers. Specifically, the vector network analyzer is used to test the voltage parameters from the input port to the coupling port of both the first and second couplers, and the coupling degree of the directional coupler is obtained from these voltage parameters. The voltage parameters S of the first coupler are: S... 11 =0.07,S 12 =0.316,S 21 =0.316,S 22 =0.06; the voltage parameters S of the second coupler are: S 11 =0.03,S 12 =0.05,S 21 =0.05,S 22 =0.03. The coupling coefficient of the first directional coupler was 20.15dB, 20.1dB, 20dB, and 19.98dB in four tests, respectively. The coupling coefficient of the second directional coupler was 40.1dB, 39.9dB, 40dB, and 40.05dB in four tests, respectively.

[0042] S1.3 Use a vector network analyzer to measure the reflection coefficient Γ at the output ports of the preamplifier and final amplifier modules. A and the reflection coefficient Γ of the first and second probe ports P The tests in steps S1.2 and S1.3 are performed at least four times each. In this embodiment, the reflection coefficient Γ at the output port of the preamplifier is... A The reflection coefficient Γ at the output port of the final stage power amplifier module is 0.13. A The reflection coefficient Γ at the ports of the first and second probes is 0.2. P They are 0.07 and 0.08 respectively.

[0043] S1.4 Read the readings of the first and second probe channels of the power meter. This step should be performed at least six times. The six readings of the first probe are: 19.63dBm, 19.64dBm, 19.63dBm, 19.63dBm, 19.64dBm, and 19.63dBm. The six readings of the second probe are: 60.394dBm, 60.382dBm, 60.386dBm, 60.394dBm, 60.390dBm, and 60.394dBm.

[0044] S2 sets the initial number of tests M and tolerance accuracy, randomly selects a set of test data from the final stage power amplifier module, and inputs it into the measurement uncertainty evaluation model to generate M sets of power amplifier gain values;

[0045] like Figure 3 As shown, the formula for the measurement uncertainty evaluation model is:

[0046] P gain =P out_ran -P in_ran +δP meter +δP mis_cou1 +δP mis_cou2 +δP coupler1_ran +δP coupler2_ran +δP VNA +δP con_PS +δP con_env

[0047] Among them, P gain It is the power output by the model; P out_ran It is the random component of the power output of the final stage power amplifier; P in_ran It is the random component of the power measurement input of the final stage power amplifier; δP meter It is the measurement uncertainty of the power meter itself; δP mis_cou1 This is the adaptation error caused by the insertion of the first directional coupler; δP mis_cou2 This is the adaptation error caused by the insertion of the second directional coupler; δP coupler1_ran This refers to the measurement repeatability error for the first directional coupler; δP coupler2_ran This refers to the measurement repeatability error for the second directional coupler; δP VNA This refers to the transmission accuracy error of the vector network analyzer; δP con_PS The uncertainty is caused by the stability of the power supply voltage; δP con_env The uncertainty is caused by changes in ambient temperature.

[0048] P out_ran and P in_ranIn this embodiment, the mean and standard deviation of the measured values ​​are directly taken as the probability density function parameters of the normal distribution. out_ran and P in_ran The probability density functions are N(60.39, 0.005) and N(0.39, 0.005). 2 ) and N(19.63,0.0052) 2 ). δP coupler1_ran and δP coupler2_ran Using the mean absolute deviation and root mean square error as parameters for the normal distribution probability density function, the normal distribution probability density function is expressed as N(x,u) 2 (x)), in this embodiment, δP coupler1_ran and δP coupler2_ran The normal probability density functions are expressed as N(0, 0.081). 2 ), N(0, 0.085) 2 ).

[0049] For the input parameter ξ of the normal distribution, a pseudo-random number r is taken from the standard normal distribution N(0,1), and ξ = x + u(x)r is constructed, where x is the expected value of the normal distribution and u(x) is the standard deviation of the normal distribution.

[0050] The power meter's own measurement uncertainty δP meter And vector network analyzer transmission accuracy error δP VNA All values ​​exhibit a rectangular distribution, with a probability density function denoted as R(a1, b1), where a1 and b1 are the upper and lower limits of the rectangular distribution, representing the instrument's accuracy range. The calculation formula for the introduced measurement error parameter ξ is: ξ = a + (ba)r, where r is a pseudo-random number. In this embodiment, the power meter accuracy is ±0.2dB, and the vector network analyzer accuracy is ±0.12dB. Therefore, their probability density functions can be expressed as R(-0.2, 0.2) and R(-0.12, 0.12), respectively.

[0051] The adaptation error δP caused by the insertion of the first directional coupler and the second directional coupler mis_cou1 and δP mis_cou2 The distribution exhibits an arcsine distribution, with its probability density function denoted as U(a2, b2), where a2 and b2 are the upper and lower limits of the distribution, and a2 and b2 are opposites of each other. The formula for calculating the error limit introduced by the mismatch is:

[0052]

[0053] Among them, S 11 ,S 12 ,S 21 ,S 22All are S-parameter matrices of directional couplers. Port 1 is the port connected to the pre-amplifier or final power amplifier module, and port 2 is the port connected to the coupling port, the first probe port, or the second probe port; Γ A Γ is the reflection coefficient at the power output port of the preamplifier or final amplifier. P Let be the reflection coefficient of the first or second probe port. The above formula applies to the insertion mismatch error of both the first and second directional couplers. All parameters in the formula are averages of the measured values. Substituting the tested data into the formula yields mismatch errors of 0.024 dB and 0.28 dB, respectively. Therefore, the probability density functions of the insertion mismatch error of the first and second directional couplers can be expressed as U(-0.024, 0.024) and U(-0.28, 0.28), respectively.

[0054] The calculation formula for the measurement error parameter ξ introduced is as follows:

[0055]

[0056] r is a pseudo-random number.

[0057] Assess the uncertainty caused by parameters related to the test conditions. Uncertainty δP caused by power supply voltage stability. con_PS The voltage accuracy needs to be converted into level uncertainty, and its average value is expressed as A. con_PS =10%(P) / V, standard deviation σ con_pS =3%(P) / V; Uncertainty δP caused by changes in ambient temperature cON_ENv The ambient temperature accuracy range needs to be converted into a level distribution range, and its average value is expressed as A. con_env = 4% (P) / ℃, standard deviation σ Con_env = 1.2%(P) / V, where (P) represents the power level, V is the voltage, and δP is the uncertainty caused by the stability of the power supply voltage. con_Ps Uncertainty δP caused by changes in ambient temperature con_env All values ​​exhibit a rectangular distribution, with the probability density function denoted as R(a3, b3); where a3 and b3 are the upper and lower limits of the rectangular distribution, expressed in decibels, and are opposites of each other. The formula for calculating b3 is:

[0058]

[0059] In this embodiment, the measurement uncertainty range limits are 0.045dB and 0.182dB, respectively. Therefore, the probability density functions can be expressed as R(-0.045dB, 0.045dB) and R(-0.182dB, 0.182dB), respectively.

[0060] Based on the probability distribution of the variables, for gX1(ξ)-gXN (ξ) Random sampling generates M sample points for each input quantity X, ensuring that the sample points can well represent the entire sampling space. The evaluation component values ​​of the measurement uncertainty model of the final stage power amplifier module are shown in Table 1.

[0061] S3 obtains the gain distribution histogram based on the gain values ​​of the M groups of power amplifiers and calculates the error value.

[0062] Gain distribution histogram as follows Figure 4 As shown. The error δ of the simulation results within the 95% confidence interval is calculated. The formula for calculating the error value δ is:

[0063]

[0064] Where σ is the standard deviation and M is the number of trials. In this embodiment, the number of trials M = 1000 and the numerical tolerance ε = 0.01 are initially set.

[0065] S4 compares the error value with the tolerance accuracy. If the error value is less than the tolerance accuracy, it outputs the gain distribution histogram at this time. Otherwise, it resets the number of tests, extracts the test data of the final stage power amplifier module, inputs the measurement uncertainty evaluation model, outputs the gain distribution histogram and error value for judgment, and repeats this loop process until the error value is less than the tolerance accuracy.

[0066] If the convergence condition is not met, adjust the sample size to M = (2.58 × σ / ε) based on the output standard deviation. 2 The algorithm is then rounded up to output the final power amplifier gain distribution histogram, and the average value, standard deviation, and 95% confidence interval are read.

[0067] like Figure 4 As shown, the two straight lines on the left and right correspond to the left and right endpoints of the 95% coverage interval, respectively. The average reading amplification gain was 40.703 dB, the standard deviation was σ = 0.284 dB, and the 95% confidence interval was 40.702-41.238 dB. The final number of tests was M = 5399.

[0068] Table 1 Evaluation Components of the Uncertainty Model for Amplification Gain Measurement of the Final Stage Power Amplifier Module

[0069]

[0070] Example 2

[0071] Based on the same inventive concept, this embodiment discloses a measurement uncertainty evaluation system for a solid-state power source final stage power amplifier module, comprising:

[0072] The data acquisition module is used to collect test data from the final stage power amplifier module;

[0073] The parameter setting module is used to set the initial number of tests M and the tolerance accuracy. The model calculation module is used to randomly select a set of test data of the final stage power amplifier module and input it into the measurement uncertainty evaluation model to generate M sets of power amplifier gain values.

[0074] The results and error output module is used to obtain the gain distribution histogram based on the gain values ​​of the M groups of power amplifiers and to calculate the error value.

[0075] The loop judgment module is used to compare the error value with the tolerance accuracy. If the error value is less than the tolerance accuracy, the gain distribution histogram at this time is output. Otherwise, the number of tests is reset, the test data of the final stage power amplifier module is extracted, the measurement uncertainty evaluation model is input, and the gain distribution histogram and error value are output for judgment. This loop process is repeated until the error value is less than the tolerance accuracy.

[0076] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0077] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0078] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0079] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific embodiments of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention. The above content is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the protection scope of the claims.

Claims

1. A method for evaluating the measurement uncertainty of a solid-state power source final stage power amplifier module, characterized in that, Includes the following steps: Collect test data from the final stage power amplifier module; Set the initial number of trials M and the tolerance accuracy. Randomly select a set of test data from the final stage power amplifier module and input it into the measurement uncertainty evaluation model to generate M sets of power amplifier gain values; Based on the M groups of power amplifier gain values, obtain the gain distribution histogram and calculate the error value; The error value is compared with the tolerance accuracy. If the error value is less than the tolerance accuracy, the gain distribution histogram at this time is output. Otherwise, the number of tests is reset, and the test data of the final stage power amplifier module is extracted. The measurement uncertainty evaluation model is input, and the gain distribution histogram and error value are output for judgment. This loop process is repeated until the error value is less than the tolerance accuracy. The solid-state power source includes a signal source, a pre-amplifier, a first directional coupler, a final power amplifier module, and a second directional coupler connected in sequence. The signal is output and loaded after passing through the signal source, the pre-amplifier, the first directional coupler, the final power amplifier module, and the second directional coupler in sequence. The first directional coupler is connected to a first probe, the second directional coupler is connected to a second probe, the first probe and the second probe are connected to a power meter, and the final power amplifier module is connected to a power supply. The formula for the measurement uncertainty evaluation model is: in, It is the power output by the model at its final level; It is the random component of the power output of the final stage power amplifier; It is the random component of the power measurement input of the final stage power amplifier; It is the measurement uncertainty of the power meter itself; The adaptation error is caused by the insertion of the first directional coupler; The adaptation error is caused by the insertion of the second directional coupler; This refers to the measurement repeatability error for the first directional coupler; This refers to the measurement repeatability error for the second directional coupler; This is a transmission accuracy error in the vector network analyzer; The uncertainty is caused by the stability of the power supply voltage; It is the uncertainty caused by changes in ambient temperature.

2. The method for evaluating the measurement uncertainty of the final stage power amplifier module of a solid-state power source as described in claim 1, characterized in that, The method for collecting test data from the final stage power amplifier module is as follows: The test accuracy of the power meter, the stability of the power supply output voltage, and the uncertainty of ambient temperature variation are evaluated. The coupling degree of the directional coupler is obtained by using a vector network analyzer based on the voltage of the first coupler and the second coupler, and the reflection coefficients of the output ports of the pre-amplifier and the final power amplifier module, as well as the reflection coefficients of the ports of the first probe and the second probe, are measured. Read the readings of the first and second probe channels of the power meter.

3. The method for evaluating the measurement uncertainty of the final stage power amplifier module of a solid-state power source as described in claim 2, characterized in that, and The mean and standard deviation of the measured values ​​are directly taken as the probability density function parameters of the normal distribution. and Using the mean absolute deviation and the root mean square error as parameters of the normal distribution probability density function, the normal distribution probability density function is expressed as follows: For the input parameters of a normal distribution Take a pseudo-random number r from the standard normal distribution N(0,1) and construct... , where x is the expected value of the normal distribution and u(x) is the standard deviation of the normal distribution.

4. The method for evaluating the measurement uncertainty of the final stage power amplifier module of a solid-state power source as described in claim 2, characterized in that, The measurement uncertainty of the power meter itself And vector network analyzer transmission accuracy error All values ​​exhibit a rectangular distribution, with a probability density function denoted as R(a1, b1), where a1 and b1 are the upper and lower limits of the rectangular distribution, respectively, and the measurement error parameter introduced by these values ​​is... The calculation formula is: r is a pseudo-random number.

5. The method for evaluating the measurement uncertainty of the final stage power amplifier module of a solid-state power source as described in claim 2, characterized in that, The adaptation error caused by the insertion of the first directional coupler and the second directional coupler and The distribution exhibits an arcsine distribution, with its probability density function denoted as U(a2, b2), where a2 and b2 are the upper and lower limits of the distribution, and a2 and b2 are opposites of each other. The formula for calculating the error limit introduced by the mismatch is: in, , , , All are S-parameter matrices of directional couplers. Port 1 is the port connected to the pre-amplifier or final power amplifier module, and port 2 is connected to the coupling port, the first probe port, or the second probe port. The reflection coefficient is the power output port reflection coefficient of the preamplifier or final amplifier. The reflection coefficient of the first or second probe port, which introduces the measurement error parameter. The calculation formula is: r is a pseudo-random number.

6. The method for evaluating the measurement uncertainty of the final stage power amplifier module of a solid-state power source as described in claim 2, characterized in that, Uncertainty caused by power supply voltage stability The average value is expressed as Standard deviation Uncertainty caused by changes in ambient temperature The average value is expressed as Standard deviation ,in The uncertainty is given in power level form, where V is the voltage, and the uncertainty is caused by the stability of the power supply voltage. Uncertainty caused by changes in ambient temperature All values ​​exhibit a rectangular distribution, with the probability density function denoted as R(a3, b3); where a3 and b3 are the upper and lower limits of the rectangular distribution and are opposites of each other. The formula for calculating b3 is: Where X is the input quantity.

7. The method for evaluating the measurement uncertainty of the final stage power amplifier module of a solid-state power source as described in claim 2, characterized in that, The error value The calculation formula is: in, The standard deviation is... The number of trials.

8. A measurement uncertainty evaluation system for a solid-state power source final stage power amplifier module, characterized in that, include: The data acquisition module is used to collect test data from the final stage power amplifier module; The parameter setting module is used to set the initial number of tests M and the tolerance accuracy. The model calculation module is used to randomly select a set of test data of the final stage power amplifier module and input it into the measurement uncertainty evaluation model to generate M sets of power amplifier gain values. The result and error output module is used to obtain the gain distribution histogram based on the M groups of power amplifier gain values ​​and calculate the error value. The loop judgment module is used to compare the error value with the tolerance accuracy. If the error value is less than the tolerance accuracy, the gain distribution histogram at this time is output. Otherwise, the number of tests is reset, the test data of the final stage power amplifier module is extracted, the measurement uncertainty evaluation model is input, and the gain distribution histogram and error value are output for judgment. This loop process is repeated until the error value is less than the tolerance accuracy. The solid-state power source includes a signal source, a pre-amplifier, a first directional coupler, a final power amplifier module, and a second directional coupler connected in sequence. The signal is output and loaded after passing through the signal source, the pre-amplifier, the first directional coupler, the final power amplifier module, and the second directional coupler in sequence. The first directional coupler is connected to a first probe, the second directional coupler is connected to a second probe, the first probe and the second probe are connected to a power meter, and the final power amplifier module is connected to a power supply. The formula for the measurement uncertainty evaluation model is: in, It is the power output by the model at its final level; It is the random component of the power output of the final stage power amplifier; It is the random component of the power measurement input of the final stage power amplifier; It is the measurement uncertainty of the power meter itself; The adaptation error is caused by the insertion of the first directional coupler; The adaptation error is caused by the insertion of the second directional coupler; This refers to the measurement repeatability error for the first directional coupler; This refers to the measurement repeatability error for the second directional coupler; This is a transmission accuracy error in the vector network analyzer; The uncertainty is caused by the stability of the power supply voltage; It is the uncertainty caused by changes in ambient temperature.

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