Automated Testing System and Method for Microwave Components

Abstract

The present invention relates to the field of electrical parameter testing technology, specifically to a microwave component automated testing system and method, comprising a node density identification module, an energy level control module, a matching gradient extraction module, a dynamic test execution module, and a calibration path switching module. In the present invention, a three-dimensional state coordinate is constructed by frequency injection response and a density classification label is established by dividing the frequency block into units, so that the frequency domain state evolution has a visual identification basis, and then the signal combination screening is performed by the minimum difference and maximum change rate of the response fluctuation amplitude and the phase offset dynamics, completing the adaptation of the excitation granularity and energy level for different frequency bands, solving the problem of insufficient resolution capability existing in the previous frequency domain response adjustment. After the signal parameters are matched, the path frequency step direction and the response gradient direction are combined to judge, and the microstructure device is gradually tuned to ensure the continuous acquisition of the response parameters and avoid the path deviation caused by the misjudgment of the tuning direction.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical parameter testing, and in particular to an automated testing system and method for microwave components. Background Art

[0002] The field of electrical parameter testing encompasses the techniques used to measure, evaluate, and test the various electrical properties of electrical equipment and its components. Core elements of this technology include the precise measurement of voltage, current, resistance, capacitance, inductance, power, impedance, and other parameters. This technology is applicable to circuit elements, signal transmission paths, radio frequency components, high-frequency devices, and other objects.

[0003] The microwave component automated test system refers to a test system that uses program control and automated measurement processes to measure the electrical performance parameters of electronic components operating within the radio frequency band and possessing specific microwave signal transmission characteristics. This system acquires the electrical parameters of microwave components under different operating conditions, covering technical aspects such as test signal source configuration, measurement port impedance matching, frequency sweep control, power setting, standing wave ratio detection, and amplitude and phase response recording. Specifically, it controls output frequency and power through a programmable signal source, controls component port connections through an automatic switching network, acquires amplitude and phase characteristic data through a vector network analyzer, and utilizes an automated test program to control measurement steps and data acquisition, ensuring the continuity and accuracy of the test process.

[0004] Existing technologies perform full-band sweeps at a fixed step frequency during the test process. When recording the amplitude and phase responses and setting the power of components, they lack a mechanism for identifying and classifying the internal state density in the frequency domain. This prevents regional clustering and classification management of frequency domain response characteristic variations. In areas of drastic response fluctuations, the fluctuation boundaries cannot be automatically identified, and signal configuration can only be adjusted based on global average metrics. This results in excessive excitation in low-variance areas and insufficient excitation in high-variance areas. In sections where the component reflection coefficient and phase response change rates are unstable, path switching requires manually preset judgment thresholds. In practice, due to significant differences in responses across different frequency bands, manually preset thresholds are not universal, leading to frequent misjudgment of path adjustment nodes, causing misaligned control of tuning devices, and thus compromising the continuity and validity of test results. During dynamic testing at multiple frequency points, this fixed structural configuration-based processing approach cannot adapt to the frequency domain state evolution characteristics in real time. It lacks a control channel for the response gradient direction, and it is difficult to establish a synchronization mechanism for micro-step tuning operations and acquisition logic, limiting the system's practical capabilities in high-frequency precision control scenarios. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a microwave component automatic testing system and method.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: The microwave component automatic testing system includes:

[0007] The node density identification module obtains the data of microwave components on the test platform, constructs a coordinate map and divides it into equally spaced blocks, calculates the distribution density of state nodes within the blocks, and obtains the frequency band state distribution coordinate set;

[0008] The energy level control module selects a signal parameter combination that meets the differentiation condition according to the state node distribution density of the frequency band state distribution coordinate set, and generates a partition signal excitation configuration group;

[0009] The matching gradient extraction module implements signal injection into each frequency band of the microwave component according to the frequency step value and excitation energy level set for each block in the partitioned signal excitation configuration group, marks the frequency points that meet the conditions, and generates a frequency point tuning response annotation table;

[0010] The dynamic test execution module performs micro-step value adjustment of the series inductor or the parallel capacitor based on the frequency points marked in the frequency point tuning response annotation table to generate a frequency point response matching data group.

[0011] As a further solution of the present invention, the frequency band state distribution coordinate set includes a frequency interval identifier, a corresponding block density level label, and a three-dimensional coordinate set of the state point. The partitioned signal excitation configuration group is specifically the signal frequency granularity, the signal energy level, and the block corresponding excitation parameter index. The frequency point tuning response annotation table includes a frequency point position label, a matching gradient direction identifier, and a signal parameter association index. The frequency point response matching data group specifically refers to the reflection coefficient record value, the amplitude response data column, and the phase response data column.

[0012] As a further solution of the present invention, the node density identification module includes:

[0013] The state parameter construction submodule obtains the three types of parameter data of the microwave component under each frequency injection in the test platform: response amplitude, phase offset, and temperature rise. It divides the frequency into multiple continuous frequency blocks according to the frequency step. The three types of response parameters corresponding to each frequency block are used as input variables of the amplitude axis, phase axis, and temperature rise axis respectively. The coordinate representation of the state point in the three-dimensional response space is constructed, and each state point is marked in the frequency block to which it belongs to generate a state response coordinate set.

[0014] The density calculation submodule extracts the amplitude response sequence and phase offset sequence of all state points in each frequency block based on the number of state points in the state response coordinate set, using the formula:

[0015] ;

[0016] Calculation frequency block Normalized node density value of , sort the density values by frequency blocks to obtain the normalized density sequence of frequency band nodes;

[0017] in, is a frequency block The number of state nodes in is a frequency block The standard deviation of the internal amplitude response sequence, is the maximum value of the standard deviation of the amplitude response in all frequency blocks, is a frequency block Middle The phase response value of the state point, is a frequency block Middle The phase response value of the state point, is a frequency block The total number of state nodes in is the maximum value among the phase change average values in all frequency blocks, is the index of the state point in the sequence, is the total number of state nodes in the frequency block.

[0018] The density classification submodule extracts the density values of various positions in the sequence as the demarcation criteria to divide the corresponding density areas according to the normalized density value of each frequency block in the normalized density sequence of the frequency band node, summarizes the frequency range, attribution level, and three-dimensional coordinates of the state point of each type of block, and generates a frequency band state distribution coordinate set.

[0019] As a further solution of the present invention, the energy level control module includes:

[0020] The amplitude difference calculation submodule extracts the amplitude response values of all state points within the frequency range covered by each density block based on the frequency range and coordinate point distribution of the density block in the frequency band state distribution coordinate set, obtains the amplitude difference of each pair of state points, and identifies the minimum amplitude response difference in the amplitude difference set;

[0021] The phase difference extraction submodule reads the phase response value of the state point in each frequency block from the state coordinate point, arranges them in ascending order of frequency, and then compares the phase values of adjacent state points in turn to determine the maximum value, thereby obtaining the maximum adjacent phase offset change value;

[0022] The signal excitation screening submodule calls all signal energy level and frequency granularity combination items set in the signal library, compares the frequency granularity with the minimum amplitude response difference, and screens out combination items with granularity values greater than the minimum amplitude difference. The signal energy level and phase difference are compared with the maximum adjacent phase offset change value to perform excitation threshold judgment, and the combination items with excitation capabilities lower than the current maximum phase offset are screened out. The remaining combination items are normalized and matched by frequency and energy dimensions, using the formula:

[0023] ;

[0024] Calculate the Stimulus adaptation score of signal combination , sort the incentive adaptation scores and establish a partition signal incentive configuration group;

[0025] in, Indicates the The signal combination The difference between the frequency granularity item and the median frequency of the excitation frequency interval, Indicates the The signal combination The energy level value of the signal, Indicates the The amplitude response difference of the state points, Indicates the The phase difference of the state points, Indicates the number of signal parameter items in the combination to be filtered.

[0026] As a further solution of the present invention, the matching gradient extraction module includes:

[0027] The injection scanning submodule performs a signal injection operation on the frequency band of the microwave component according to the frequency step value and excitation energy level set for each block in the partitioned signal excitation configuration group. During the frequency point scanning process, the input impedance value and output impedance value of the corresponding frequency point are collected in real time at set time intervals to obtain an impedance monitoring data set;

[0028] The matching coefficient calculation submodule is based on the impedance monitoring data set and calls the input impedance value and output impedance value of each frequency point in order of frequency points, using the formula:

[0029] ;

[0030] Calculate frequency points The corresponding matching coefficient , integration obtains the matching coefficient sequence;

[0031] in, Indicates frequency point The input impedance value, Indicates frequency point The output impedance value, Indicates the input properties. Indicates output terminal attributes;

[0032] The tuning annotation generation submodule extracts the matching coefficient differences between adjacent frequency points as matching gradient values based on the matching coefficient sequence, classifies the matching gradient values by block to form a gradient sequence set, compares the matching gradient value corresponding to each frequency point with the set tuning trigger threshold, marks the frequency points that meet the tuning trigger threshold conditions, and generates a frequency point tuning response annotation table.

[0033] As a further solution of the present invention, the dynamic test execution module includes:

[0034] The signal excitation configuration extraction submodule extracts the signal excitation configuration parameters of the corresponding frequency points based on the frequency points marked in the frequency point tuning response annotation table, loads the signal source configuration, including information such as signal energy level and frequency granularity, and loads the corresponding current settings of the inductor and capacitor tuning modules based on the direction of the matching gradient, reads the set values of the inductor and capacitor modules, detects whether the tuning modules need to be adjusted, and obtains the inductor and capacitor tuning settings;

[0035] The response recording and adjustment submodule loads the inductor and capacitor tuning settings, selects an adjustment mode by determining the direction of the matching gradient, and executes a microstep value increment operation for the series inductor if the matching gradient direction is positive; and executes a microstep value decrement operation for the parallel capacitor if the matching gradient direction is negative. The reflection coefficient, amplitude response, and phase response data of the corresponding frequency point after each adjustment are recorded to obtain a frequency point response matching data set.

[0036] As a further solution of the present invention, a calibration path switching module is further included, which obtains the frequency response matching data group, selects a configuration that is better than the current path as a new path, and generates a frequency path optimization adjustment list.

[0037] As a further solution of the present invention, the frequency point path optimization adjustment list includes a fluctuation boundary point index, a path optimization parameter group, and a post-replacement path setting label.

[0038] As a further solution of the present invention, the calibration path switching module includes:

[0039] The fluctuation detection marking submodule obtains the phase values of three consecutive frequency points in the frequency point response matching data group, calculates the change slope, and the difference between two adjacent slopes as the fluctuation rate, determines whether the fluctuation rate exceeds the phase slope mutation threshold, and if so, marks the frequency point as a fluctuation boundary point, retrieves the test path information and the available path candidate group to which the fluctuation boundary point belongs, and obtains the fluctuation boundary point path distribution information set;

[0040] The path switching optimization submodule compares the frequency granularity and response resolution parameters of the paths in the available path alternative group based on the fluctuation boundary point path distribution information set, selects configuration paths with finer granularity or higher resolution as alternative options for the current path, establishes corresponding alternative relationships, and generates a frequency point path optimization adjustment list.

[0041] A microwave component automated testing method, which is performed based on the above-mentioned microwave component automated testing system, comprises the following steps:

[0042] S1: Obtain data of microwave components on the test platform, construct a coordinate map and divide it into equally spaced blocks, calculate the distribution density of state nodes within the blocks, and obtain the frequency band state distribution coordinate set;

[0043] S2: Screening signal parameter combinations that meet the differentiation conditions based on the state node distribution density of the frequency band state distribution coordinate set to generate a partitioned signal excitation configuration group;

[0044] S3: According to the frequency step value and excitation energy level set for each block in the partitioned signal excitation configuration group, perform signal injection on each frequency band of the microwave component, mark the frequency points that meet the conditions, and generate a frequency point tuning response annotation table;

[0045] S4: Based on the frequency points marked in the frequency point tuning response annotation table, performing micro-step value adjustment of the series inductor or the parallel capacitor to generate a frequency point response matching data set;

[0046] S5: Acquire the frequency response matching data group, select a configuration that is better than the current path as a new path, and generate a frequency path optimization adjustment list.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, three-dimensional state coordinates are constructed through frequency injection response and density classification labels are established based on frequency block division, so that the frequency domain state evolution has a visual identification basis. Then, the signal combination screening is performed through the minimum difference and maximum change rate of the response fluctuation amplitude and phase offset dynamics, and the adaptation of the excitation granularity and energy level of different frequency bands is completed, which solves the problem of insufficient resolution capability in the previous frequency domain response adjustment. After the signal parameters are matched, the path frequency step direction and the response gradient direction are combined to judge and perform step-by-step tuning of the microstructure device to ensure the continuous collection of response parameters, avoid the path offset caused by misjudgment of the tuning direction, and fully collect the three types of reflection coefficient, amplitude, and phase. After the response results are obtained, the phase slope difference between multiple frequency points is extracted as the fluctuation rate indicator based on the slope fluctuation trend of the response curve. The fluctuation boundary point in the frequency domain is located and mapped to the path structure. The frequency granularity of the alternative path is compared with the response resolution data to screen the alternative path. A path optimization adjustment mechanism based on real-time fluctuation identification is constructed to complete the construction of the path replacement plan. It has the ability to dynamically switch when the joint response state of multiple frequency points changes drastically. The processing chain constructs an overall closed loop with state point density, fluctuation speed, directional gradient, and tuning structure as continuous processes, so that parameter tuning and path reconstruction have a data-driven response adjustment mechanism, which improves the tuning accuracy and path interchangeability in frequency domain testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a system flow chart of the present invention;

[0050] Figure 2 This is a flow chart of the node density identification module of the present invention;

[0051] Figure 3 This is a flow chart of the energy level control module of the present invention;

[0052] Figure 4 This is a flow chart of the matching gradient extraction module of the present invention;

[0053] Figure 5 This is a flow chart of the dynamic test execution module of the present invention;

[0054] Figure 6 This is a flow chart of the calibration path switching module of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present 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 only used to explain the present invention and are not intended to limit the present invention.

[0056] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0057] See also Figure 1 The present invention provides a technical solution: a microwave component automated testing system comprising:

[0058] The node density identification module obtains the response amplitude, phase offset, and temperature rise of the microwave component under each frequency injection in the test platform, constructs a three-dimensional state coordinate map, divides it into equally spaced blocks according to the set frequency span, calculates the state node distribution density within the block, compares the node distribution density with the density cutoff value group, performs block classification operations, and records the coordinate points of the corresponding state distribution to obtain the frequency band state distribution coordinate set;

[0059] The energy level control module extracts the minimum response amplitude difference and the maximum adjacent phase offset change value between state points in the corresponding frequency range according to the frequency range and coordinate point distribution of the frequency band state distribution coordinate concentration density block. It calls the signal energy level and frequency granularity combination items in the signal library, and selects the signal parameter combination that meets the differentiation conditions according to the standard that the frequency granularity is less than the minimum amplitude spacing and the energy level is higher than the maximum phase difference required for excitation, and generates a partitioned signal excitation configuration group.

[0060] The matching gradient extraction module implements signal injection into each frequency band of the microwave component based on the frequency step value and excitation energy level set for each block in the partitioned signal excitation configuration group. During the frequency point scanning process, the input and output impedance values are recorded in real time, the matching coefficients are calculated according to the frequency sequence, and the difference between the matching coefficients of adjacent frequency points is extracted as the matching gradient value. The matching gradient sequence is grouped by frequency partition, and the matching gradient change amplitude is compared with the set tuning trigger threshold. The frequency points that meet the conditions are marked, and the corresponding signal configuration and state distribution coordinates are associated to generate a frequency point tuning response annotation table.

[0061] The dynamic test execution module extracts the associated signal excitation configuration parameters based on the frequency points marked in the frequency point tuning response annotation table, loads the current settings of the corresponding inductor and capacitor tuning modules, matches the corresponding gradient direction, performs microstep adjustments of the series inductor or shunt capacitor according to the matching direction, records the reflection coefficient, amplitude response, and phase response values at the corresponding frequency points, and generates a frequency point response matching data set;

[0062] The calibration path switching module obtains the phase values of three consecutive frequency points in the frequency response matching data group, calculates the change slope, and the difference between two adjacent slopes as the fluctuation rate. It determines whether the fluctuation rate exceeds the phase slope mutation threshold. If it does, it marks the frequency point as a fluctuation boundary point, retrieves the test path information to which the fluctuation boundary point belongs and the group of available path alternatives, compares the frequency granularity and response resolution parameters of the alternative paths, selects the configuration that is better than the current path as the new path, and generates a frequency point path optimization adjustment list;

[0063] The frequency band state distribution coordinate set includes the frequency interval identifier, the corresponding block density level label, and the three-dimensional coordinate set of the state point. The partition signal excitation configuration group specifically includes the signal frequency granularity, signal energy level, and block corresponding excitation parameter index. The frequency point tuning response annotation table includes the frequency point position label, matching gradient direction identifier, and signal parameter association index. The frequency point response matching data group specifically refers to the reflection coefficient record value, amplitude response data column, and phase response data column. The frequency point path optimization adjustment list includes the fluctuation boundary point index, path optimization parameter group, and post-replacement path setting label.

[0064] See also Figure 2 , the node density identification module includes:

[0065] The state parameter construction submodule obtains the three types of parameter data of the microwave component under each frequency injection in the test platform: response amplitude, phase offset, and temperature rise. It divides the frequency into multiple continuous frequency blocks according to the frequency step. The three types of response parameters corresponding to each frequency block are used as input variables of the amplitude axis, phase axis, and temperature rise axis respectively. The coordinate representation of the state point in the three-dimensional response space is constructed, and each state point is marked in the frequency block to which it belongs to generate a state response coordinate set.

[0066] Obtain three types of response data of the microwave component under each frequency injection in the test platform: response amplitude, phase offset, and temperature rise. Specifically, within the specified frequency range, the injection frequency points are set in sequence with a step of 2 MHz, and the frequency control unit is called to increase the microwave frequency from 8.0 GHz to 12.0 GHz. The amplitude response (in dBm), phase response (in degrees), and temperature rise response (in degrees C) of the microwave component at each frequency point are recorded. The amplitude and phase are sampled in real time based on the frequency point. 100 samples are collected at each frequency point to improve data accuracy. The average method is used to eliminate random disturbances. For example, when the injection frequency is 9.0 GHz, the recorded amplitude samples are [-22.3, -22.5, -22.4, ..., -22.6] dBm, with an average value of -22.45 dBm, and the phase samples are [35.2, 35.4, 35.3, ..., 35.6] degrees, with an average value of 3 The three types of response quantities at the frequency point of 9.0 GHz were collected. Based on this, the entire frequency range was divided into 40 frequency blocks of 100 MHz. For all frequency points within each frequency block, a list was created and numbered according to the corresponding relationship between injection frequency, amplitude response, phase response, and temperature rise response. For example, if the frequency block [9.0 GHz, 9.1 GHz] contains 50 frequency points, the three-dimensional response coordinate information of the 50 groups of frequency points in this block is recorded. A three-dimensional state space coordinate system was constructed with frequency as the horizontal axis, with frequency as the x-axis, amplitude response as the y-axis, and phase response as the z-axis. Temperature rise was bound to each three-dimensional coordinate point as a label attribute. All three-dimensional coordinate points in the frequency block were labeled as state points and labeled according to the block range to which the state point frequency belonged, forming a state response coordinate set.

[0067] The density calculation submodule extracts the amplitude response sequence and phase offset sequence of all state points in each frequency block based on the number of state points contained in the state response coordinate set, using the formula:

[0068] ;

[0069] Calculation frequency block Normalized node density value of , sort the density values by frequency blocks to obtain the normalized density sequence of frequency band nodes;

[0070] in, is a frequency block The number of state nodes in is a dimensionless positive integer, is a frequency block The standard deviation of the internal amplitude response sequence reflects the degree of amplitude dispersion, and the unit is consistent with the response amplitude. is the maximum value of the standard deviation of the amplitude response in all frequency blocks, used as a normalized reference benchmark, is a frequency block Middle The phase response value of each state point is expressed in degrees or radians. is a frequency block Middle The phase response value of the state point is adjacent, It is Individual and The absolute value of the phase difference between the state points, is a frequency block The total number of state nodes in It is a frequency block From the first To The phase difference of each state point is summed up. It is the maximum value among the phase change average values in all frequency blocks and serves as the reference for normalizing the phase change. is the index of the state point in the sequence, is the total number of state nodes in the frequency block.

[0071] First, the amplitude response sequence is extracted from each frequency block, and the standard deviation is calculated to measure the response fluctuation. The formula ,in, Indicates frequency block Middle The amplitude response of each state point (in decibel milliwatts), is the mean value of the amplitude response in the block, is the number of state points. For example, if the amplitude responses of the five state points in the block [9.000GHz, 9.100GHz) are -22.3, -22.6, -22.1, -22.2, and -22.5 dBm, respectively, then the average value is : ; Among them, the standard deviation unit is still decibel milliwatt, which is used to measure the discreteness of the amplitude response; at the same time, the phase response values of adjacent state points in the frequency block are subtracted to obtain the absolute value, and the average value is calculated to represent the phase response variation. For example, if the phase responses of the five state points in the block are 35.1, 35.4, 35.2, 35.3, and 35.5 degrees, the phase difference sequence is 0.3, 0.2, 0.1, and 0.2, and the average value is Repeat this process for all blocks, and the maximum value of the standard deviation of the amplitude response in all frequency blocks is 0.315 dBm, and the maximum value of the mean of all phase changes is 0.45 degrees. These are used as normalized reference values, and the current block value is normalized accordingly. The amplitude normalization value is , the phase normalized value is , combined with the number of state points , substitute into the formula: , thereby calculating the normalized node density index of the frequency block, and repeating this process to complete the normalized density calculation of all blocks.

[0072] The density classification submodule extracts the density values of various positions in the sequence as the demarcation criteria to divide the corresponding density areas according to the normalized density value of each frequency block in the normalized density sequence of the frequency band node. It summarizes the frequency range, attribution level, and three-dimensional coordinates of the state point of each type of block to generate a frequency band state distribution coordinate set.

[0073] First, the density values of all blocks are arranged in ascending order, and the density values corresponding to the 25%, 50%, and 75% positions in the arranged sequence are extracted as the three-level density division thresholds. In the example, the 8 density values are [0.842, 1.213, 1.458, 2.017, 2.784, 3.092, 3.923, 4.110]. The 25th percentile is the second item: 1.213, the 50th percentile is the fourth item: 2.017, and the 75th percentile is the sixth item: 3.092, which are set as low density, medium density, and high density division thresholds respectively. The normalized density value of each frequency block is The judgment is made in sequence. If the density value is less than 1.213, it is classified as a low-density block. For example, the density value of the [8.2–8.3) block is 0.842, which is less than the low-density threshold of 1.213. If the density value is between 1.213 and 3.092, it is classified as a medium-density block, such as the [8.0–8.1) block with a density of 1.458. If the density is greater than 3.092, it is classified as a high-density block, such as the [8.1–8.2) block with a density of 3.923. This result shows that after the density classification is completed, the state distribution differences in different frequency bands can be clearly identified, and a frequency band state distribution coordinate set can be formed.

[0074] See also Figure 3 , the energy level control module includes:

[0075] The amplitude difference calculation submodule extracts the amplitude response values of all state points within the frequency range covered by each density block based on the frequency range and coordinate point distribution of the density block in the frequency band state distribution coordinate set, obtains the amplitude difference of each pair of state points, and identifies the minimum amplitude response difference in the amplitude difference set;

[0076] According to the frequency range and coordinate point distribution of the frequency band state distribution coordinate concentration density block, the amplitude response values of all state points in each frequency block are obtained, and they are sorted from small to large according to the frequency. The amplitude response values of each pair of adjacent state points are subjected to a difference operation. During the refinement execution, if the frequency point sequence contained in the block [9.0GHz, 9.1GHz) is [9.000, 9.002, 9.004, 9.006, 9.008]GHz, the corresponding amplitude response is [-22.3, -22.5, -22.4, -22.1, -22.6]dBmW. After sorting, the adjacent differences are 0.2, 0.1, 0.3, and 0.5 respectively. The minimum value judgment operation is used to obtain the minimum difference of 0.1dBmW. The judgment operation is completed by traversing the above difference array and calling the numerical comparison instruction to obtain the minimum term. In order to ensure the repeatability of the operation, a mapping index table corresponding to the amplitude difference vector and the frequency sequence is constructed. Identify the frequency combination of the difference source point. For example, the difference of 0.1 comes from the combination of frequency points 9.002GHz and 9.004GHz. In the implementation process, the impact of thermal drift on amplitude change needs to be considered. In the actual test, the temperature rise difference threshold is set to 0.2 degrees Celsius as the point pair screening condition. This setting is based on the statistical results of the temperature rise disturbance from 100 repeated injection tests of the same frequency point on the platform. The temperature rise difference samples are as follows: the temperature rise of point pairs 9.002 and 9.004 is respectively The temperature rises of point pairs 9.004 and 9.006 are 2.93°C and 2.92°C respectively, with a difference of 0.01°C; the temperature rises of point pairs 9.006 and 9.008 are 2.92°C and 2.94°C respectively, with a difference of 0.02°C, all within 0.2°C. Therefore, in actual screening, the amplitude difference calculation and minimum value extraction operations are performed only on the point pairs whose temperature rise difference does not exceed the threshold, and the minimum response amplitude difference is finally obtained.

[0077] The phase difference extraction submodule reads the phase response value of the state point in each frequency block from the state coordinate point, arranges them in ascending order of frequency, and compares the phase values of adjacent state points in turn to determine the maximum value, thereby obtaining the maximum adjacent phase offset change value;

[0078] The phase response values of the recorded state points in the density block are called, sorted in ascending order of frequency, and then the adjacent state point pairs are extracted point by point. The absolute value difference operation is performed on the phase values of each pair of state points. For example, the state point frequency sequence is [9.000, 9.002, 9.004, 9.006] GHz, and the corresponding phase values are [35.2, 35.4, 35.1, 35.3] degrees. The adjacent differences are |35.4-35.2|=0.2, |35.1-35.4|=0.3, and |35.3-35.1|=0.2. After obtaining all the phase differences, the maximum value recognition operation is used to select the maximum phase offset difference of 0.3 degrees. During the implementation process, the phase response value is collected in angular units. The platform's fast sampling device is used to perform 100 consecutive measurements at each frequency point and then take the average value. The data used in the difference calculation are all average values. In addition, to eliminate the influence of outliers caused by faults or spike jumps, the phase jump upper threshold is set to 3.0 degrees. If the difference is greater than this threshold, it is considered an invalid point pair and does not participate in the maximum value judgment. This threshold refers to the maximum phase response change amplitude observed in continuous testing of the same component at different frequency points. Sample data shows that the phase values of the frequency point pair 9.002 and 9.004 are 35.4 and 35.1, and the difference is 0.3 degrees, which is valid; the phase values of the point pair 9.004 and 9.006 are 35.1 and 35.3, and the difference is 0.2 degrees, which is valid; the phase values of the point pair 9.006 and 9.008 are 35.3 and 38.6, and the difference is 3.3 degrees, which is invalid. Finally, only the valid point pairs are retained to perform the maximum value operation to obtain the maximum adjacent phase offset change value.

[0079] The signal excitation screening submodule calls all signal energy level and frequency granularity combination items set in the signal library, compares the frequency granularity with the minimum amplitude response difference, and screens out combination items with granularity values greater than the minimum amplitude difference. The signal energy level and phase difference are used to determine the excitation threshold with the maximum adjacent phase offset change value, and the combination items with excitation capabilities lower than the current maximum phase offset are screened out. The remaining combination items are normalized and matched by frequency and energy dimensions using the formula:

[0080] ;

[0081] Calculate the Stimulus adaptation score of signal combination , sort the incentive adaptation scores and establish a partition signal incentive configuration group;

[0082] in, Indicates the The signal combination The difference between the frequency granularity item and the median frequency of the excitation frequency interval, Indicates the The signal combination The energy level value of the signal, Indicates the The amplitude response difference of the state points, Indicates the The phase difference of the state points, Indicates the number of signal parameter items in the combination to be filtered.

[0083] First, the minimum response amplitude difference in the amplitude difference calculation submodule is obtained, which is 0.1 dBm here. Then, the maximum adjacent phase offset change value in the phase difference extraction submodule is called, which is 0.3 degrees here. 0.1 dBm is used as the frequency granularity judgment threshold, and the granularity items ΔF in the signal combinations are compared one by one to see if they are less than the threshold. For example, if the frequency granularity array is [0.05, 0.15, 0.1, 0.2] MHz, only the combination items with ΔF of 0.05 are retained. Then, 0.3 degrees is used as the excitation threshold, and the energy level array is set to [0.2, 0.4, 0.5, 0.8] mW. According to the energy and phase excitation matching relationship, the excitation energy required for the current maximum phase offset of 0.3 degrees is 0.35 mW. Based on this, the combination items with E ≥ 0.35 are retained, that is, the corresponding combinations of 0.4, 0.5, and 0.8. Finally, the scoring function is called on the remaining combination items to calculate the fitness, using the following formula:

[0084] ;

[0085] in, =[0.05, 0.08, 0.09, 0.07] MHz, =[0.4, 0.5, 0.5, 0.8] mW, = [0.1, 0.2, 0.3, 0.15] dBm, =[0.3, 0.25, 0.2, 0.28] degrees, substitute into the formula for sub-item calculation as follows:

[0086] Item 1 is ;

[0087] Item 2 is ;

[0088] Item 3 is ;

[0089] Item 4 is .

[0090] The sum is 3.736, and the average is: .

[0091] The final excitation adaptation score for this combination was 0.934. After sorting the scores of the remaining combinations in ascending order, the combinations with lower scores were selected to establish the partitioned signal excitation configuration group. The formula's benefit lies in incorporating both signal frequency granularity and energy level, establishing a denominator ratio relationship with the actual response offset amplitude and angle of the state point. This balances signal capability and response difficulty in a normalized dimension, enhancing the accuracy of combination differentiation.

[0092] See also Figure 4 , the matching gradient extraction module includes:

[0093] The injection scanning submodule performs signal injection operations on each frequency band of the microwave component according to the frequency step value and excitation energy level set for each block in the partitioned signal excitation configuration group. During the frequency point scanning process, the input impedance value and output impedance value of the corresponding frequency point are collected in real time at the set time interval to obtain the impedance monitoring data set;

[0094] Signal injection is implemented for each frequency band of the microwave component. During the execution process, the step value and energy level corresponding to each frequency block in the signal configuration group need to be mapped one by one to the scanning control module. The frequency starting value is set to 8.000GHz, the step value is 2MHz, and a total of 50 frequency points are injected. Each injection lasts for 0.2 seconds. At the same time, the power module is controlled to output the corresponding amplitude according to the excitation level in the signal configuration. For example, the output at 9.000GHz is 0.5mW, and at 9.002GHz is 0.6mW. During the scanning process, the impedance sampling unit is synchronously connected to collect the input impedance and output impedance corresponding to each frequency point. The bridge measurement principle is used. After signal injection, the voltage and current components are extracted. If the input voltage is 2.5 volts and the input current is 0.025 amperes at a certain frequency point, the input impedance is 100 ohms. The output impedance is obtained by measuring the value at the output terminal at the same frequency point. For example, if the voltage measured at the output terminal is 1.8 volts and the current is 0.018 amperes, the output impedance is 100 ohms. The input and output impedance values of all frequency points in the full frequency band are collected to form a frequency-impedance matching matrix array. Each set of data uses the frequency point as the index, and the impedance value pair is stored as the content in a two-dimensional array structure for subsequent calculation of the matching coefficient to finally obtain the impedance monitoring data set.

[0095] The matching coefficient calculation submodule is based on the impedance monitoring data set and calls the input impedance value and output impedance value of each frequency point in sequence according to the frequency point sequence, using the formula:

[0096] ;

[0097] Calculate frequency points The corresponding matching coefficient , integration obtains the matching coefficient sequence;

[0098] in, Indicates frequency point The input impedance value, Indicates frequency point The output impedance value, Indicates the input properties. Indicates output terminal attributes.

[0099] Set frequency point GHz, its input impedance Ohms, output impedance Ohms, the matching coefficient at this frequency point is calculated as follows:

[0100] ;

[0101] In this way, all frequency points in the frequency scanning process are calculated one by one to obtain the matching coefficient sequence , where each item represents the mismatch ratio of the input and output impedance at that frequency point. The value range is between 0 and 1. The closer it is to 0, the closer the impedance is to matching. If the matching coefficient exceeds 0.3, it means that there is an impedance deviation at that point and needs to be marked for subsequent attention. The sequence will be passed to the next module for frequency difference calculation, and finally the matching coefficient sequence is obtained.

[0102] The tuning annotation generation submodule extracts the matching coefficient differences between adjacent frequency points in order of frequency points as matching gradient values based on the matching coefficient sequence. The matching gradient values are classified and organized by block to form a gradient sequence set. The matching gradient value corresponding to each frequency point is numerically compared with the set tuning trigger threshold, and the frequency points that meet the tuning trigger threshold conditions are marked to generate a frequency point tuning response annotation table.

[0103] The matching coefficient difference between adjacent frequency points is extracted in frequency order as the matching gradient value, and a matching gradient sequence array is constructed. For example, if the frequency point sequence is [9.000, 9.002, 9.004, 9.006] GHz and its matching coefficient is [0.085, 0.111, 0.134, 0.102], then the corresponding gradient is |0.111-0.085|=0.026, |0.134-0.111|=0.023, |0.102-0.134|=0.032, and the matching gradient sequence is [0.026, 0.023, 0.032]. The gradient values of all frequency points are sorted and archived in units of frequency blocks to form a gradient distribution set. The gradient value of each frequency point is compared with the The set tuning trigger threshold is used for numerical comparison and judgment. The tuning trigger threshold is set to 0.03, which comes from the matching fluctuation mean of 0.028 obtained by scanning the historical component in a stable state. In order to distinguish the boundary value, it is rounded up and set to 0.03. If the frequency point matching gradient value is greater than the threshold, it is marked as a tunable point. For example, in the previous example, the corresponding gradient value of 9.006GHz is 0.032, which is greater than 0.03 and is determined to be a tuning frequency point. All frequency points that meet the conditions are marked accordingly, and they are indexed and matched with the signal configuration group number used and the state distribution coordinates corresponding to the frequency point to form a three-dimensional annotation structure, which contains the frequency point, matching gradient value, signal excitation number, and state coordinate point set identifier. Finally, a frequency point tuning response annotation table is established.

[0104] See also Figure 5 , the dynamic test execution module includes:

[0105] The signal excitation configuration extraction submodule extracts the signal excitation configuration parameters of the corresponding frequency points based on the frequency points marked in the frequency point tuning response annotation table, loads the signal source configuration, including information such as signal energy level and frequency granularity, and loads the current settings of the corresponding inductor and capacitor tuning modules based on the direction of the matching gradient. It reads the set values of the inductor and capacitor modules, detects whether the tuning modules need to be adjusted, and obtains the inductor and capacitor tuning settings;

[0106] First, extract the corresponding signal excitation configuration parameters item by item according to the target frequency points marked in the frequency marking table. The parameter group usually includes the frequency granularity, excitation signal level and adaptive power range applicable to the corresponding frequency point. In the actual test scenario, assuming that the frequency point is 9.012GHz, its excitation parameter configuration is 0.002GHz granularity and 15dBm signal energy. After reading the parameters, enter the tuning device call process, call the tuning unit module configured in the current system and load its current setting value. The tuning units are distributed in two physical structures: series inductance and parallel capacitance. The current tuning step value of each type of device is output by the memory or register. For example, the inductance is currently set to 8.5nH and the capacitance is set to 10. The default setting is 1.4pF. The matching direction required for the current frequency point is detected, that is, the gradient change direction of the parameter corresponding to the current response curve is determined. If the reflection parameter curve at the target frequency shows an upward trend, it is determined to be a positive gradient, and the inductor series direction needs to be adjusted. If the curve decreases, it is determined to be a negative gradient, and the response adjustment in the capacitor parallel direction needs to be performed. The basis for gradient matching is to compare the change direction of the reflection coefficient corresponding to the two test frequencies before and after the current frequency point. For example, the reflection coefficient at the frequency point of 9.010GHz is 0.31, and the reflection coefficient at the frequency point of 9.012GHz is 0.34. The calculated gradient is positive and the direction is determined to be positive. Prepare to load the inductor tuning configuration to continue the adjustment operation and obtain the inductor and capacitor tuning settings.

[0107] The response recording and adjustment submodule loads the inductor and capacitor tuning settings and selects the adjustment method by judging the direction of the matching gradient. If the matching gradient direction is positive, the microstep value of the series inductor is increased. If the matching gradient direction is negative, the microstep value of the parallel capacitor is decreased. The reflection coefficient, amplitude response, and phase response data of the corresponding frequency point after each adjustment are recorded to obtain the frequency point response matching data set.

[0108] According to the signal excitation configuration, the inductor and capacitor tuning settings loaded by the submodule are extracted and the actual tuning adjustment operation is performed. If the direction is positive, the micro-step value increment adjustment is performed for the series inductor module. For example, the current inductance value is 8.5nH, and the fine-tuning step size is 0.1nH each time. After updating to 8.6nH, the excitation signal is reloaded and the reflection coefficient, amplitude response and phase response values in the new state are recorded. On the contrary, if the direction is negative, the micro-step value decrement operation is performed for the parallel capacitor module. For example, the current capacitance value is 1.4pF, and it is adjusted to 1.3pF in 0.1pF steps before injecting the excitation signal and collecting Results: During the above-mentioned fine-tuning process, a reflection coefficient meter is required to collect the S11 parameter. The example record is 0.29. The amplitude response is obtained by a power meter, with a value of -22.1dBm. The phase response is synchronously measured by a phase sensor, with a result of 34.2°. All collected parameters are bound and saved corresponding to the adjusted frequency points, and a response data group structure is constructed with frequency as the index. The data are arranged in sequence according to the test frequency to form a complete set. The set contains four data columns: frequency point, reflection coefficient, amplitude response, and phase response, forming a two-dimensional extended result record matrix corresponding to the frequency response, and obtaining a frequency point response matching data group.

[0109] See also Figure 6 , the calibration path switching module includes:

[0110] The fluctuation detection marking submodule obtains the phase values of three consecutive frequency points in the frequency response matching data group, calculates the change slope, and the difference between two adjacent slopes as the fluctuation rate, and determines whether the fluctuation rate exceeds the phase slope mutation threshold. If it meets the threshold, the frequency point is marked as a fluctuation boundary point, and the test path information and available path candidate group to which the fluctuation boundary point belongs are retrieved to obtain the fluctuation boundary point path distribution information set;

[0111] To obtain the phase values of three consecutive frequency points in the frequency response matching data group and calculate the change slope, it is necessary to extract the phase values corresponding to each group of three adjacent frequency points in the frequency order. Assume that the frequency point is The phase values of the three points are , then with equal frequency intervals Calculate the slopes before and after the reference, namely: , .

[0112] Compute the difference between two consecutive slopes: .

[0113] As the current frequency point The fluctuation rate at the frequency point is traversed in this way through the entire frequency response matching data group to generate a complete frequency point fluctuation rate sequence. On this basis, it is judged whether the fluctuation rate of each frequency point exceeds the set phase slope mutation threshold. The threshold is set to 400° / GHz. This value is derived from the statistical results of the peak distribution of fluctuation rate changes in the continuous scanning of microwave components in the experiment. The corresponding value of the 90% quantile is rounded up and set to 400° / GHz as the definition benchmark. If the current frequency point fluctuation rate exceeds the set phase slope mutation threshold, the threshold is set to 400° / GHz. If the frequency is greater than the threshold, it is marked as a fluctuation boundary point, and the test path number corresponding to the fluctuation boundary point is retrieved backward. The mapping relationship from the frequency point to the test path can be obtained from the frequency block index table. Then, all the configured information under the current path is called through the path scheduling index table, and the available path alternative group is read at the same time. The alternative path is a set of paths that are not occupied by the current frequency point in the same block, and finally the fluctuation boundary point path distribution information set is obtained.

[0114] The path switching optimization submodule compares the frequency granularity and response resolution parameters of each path in the available path alternative group based on the fluctuation boundary point path distribution information set, selects the configuration path with finer granularity or higher resolution as an alternative option for the current path, establishes a corresponding alternative relationship, and generates a frequency point path optimization adjustment list;

[0115] According to the fluctuation boundary point path distribution information set obtained in the fluctuation detection marking submodule, the frequency granularity and response resolution parameters of the original test path and all available alternative paths corresponding to the current fluctuation boundary point are extracted. The frequency granularity refers to the spacing value between frequency points in the current path, and the response resolution parameter refers to the minimum measurable change value of the amplitude and phase response of the path within the current block. Assume that the frequency granularity of the current path is 2MHz, the granularities of the alternative paths are 1MHz, 3MHz, and 2MHz respectively, the response resolution capability is 0.1dBm for amplitude and 0.2° for phase, and the alternative paths are path 1: 0.08dBm / 0.15°, path 2: 0.15dBm / 0.25°, and path 3: 0.1dBm / 0.18°. Two comparisons are required: the frequency granularity and response resolution parameters of each path. The first step is to determine whether the granularity of the alternative path is smaller than that of the current path. The second step is to determine whether its minimum measurable range in the amplitude and phase dimensions is better than the current path settings. That is, at least one of the two indicators is better than the current configuration. The priority is set using a weighting method: if the amplitude resolution is better than the current setting value by 0.1dBm, 1 point is awarded; if the phase resolution is better than the current setting value by 0.2°, another point is awarded; if the frequency granularity is better than 2MHz of the current path, another point is awarded.

[0116] According to this logic, the first alternative path scores , the second alternative path score is , the third alternative path score is ,Therefore, the first path is selected as the alternative, a one-to-one association relationship between the frequency point and the alternative path is constructed, and the path number, frequency point position, alternative path granularity and resolution parameters are recorded, and finally a frequency point path optimization adjustment list is generated.

[0117] The microwave component automated testing method is based on the above-mentioned microwave component automated testing system and includes the following steps:

[0118] S1: Obtain data of microwave components on the test platform, construct a coordinate map and divide it into equally spaced blocks, calculate the distribution density of state nodes within the blocks, and obtain the frequency band state distribution coordinate set;

[0119] S2: According to the state node distribution density of the frequency band state distribution coordinate set, the signal parameter combination that meets the differentiation conditions is screened to generate a partition signal excitation configuration group;

[0120] S3: Based on the frequency step value and excitation energy level set for each block in the partitioned signal excitation configuration group, signal injection is performed on each frequency band of the microwave component, frequency points that meet the conditions are marked, and a frequency point tuning response annotation table is generated;

[0121] S4: Based on the frequency points marked in the frequency point tuning response annotation table, perform micro-step value adjustment of the series inductor or the parallel capacitor to generate a frequency point response matching data group;

[0122] S5: Obtain a frequency response matching data group, select a configuration that is better than the current path as a new path, and generate a frequency path optimization adjustment list.

[0123] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. Microwave component automated testing system, characterized in that: The system comprises: The node density identification module obtains data from the microwave component on the test platform, constructs a coordinate map and divides it into equally spaced blocks, calculates the state node distribution density within the block, and obtains a frequency band state distribution coordinate set. The energy level control module selects signal parameter combinations that meet the differentiation conditions based on the state node distribution density of the frequency band state distribution coordinate set and generates a partitioned signal excitation configuration group. The matching gradient extraction module implements signal injection into each frequency band of the microwave component according to the frequency step value and excitation energy level set for each block in the partitioned signal excitation configuration group, marks the frequency points that meet the conditions, and generates a frequency point tuning response annotation table; The dynamic test execution module performs micro-step value adjustment of the series inductor or the parallel capacitor based on the frequency points marked in the frequency point tuning response annotation table to generate a frequency point response matching data group; The frequency band state distribution coordinate set includes a frequency interval identifier, a corresponding block density level label, and a three-dimensional coordinate set of the state point. The partitioned signal excitation configuration group specifically includes the signal frequency granularity, the signal energy level, and the block corresponding excitation parameter index. The frequency point tuning response annotation table includes a frequency point position label, a matching gradient direction identifier, and a signal parameter association index. The frequency point response matching data group specifically refers to the reflection coefficient record value, the amplitude response data column, and the phase response data column.

2. The microwave component automated testing system according to claim 1, characterized in that: The node density identification module includes: The state parameter construction submodule obtains the three types of parameter data of the microwave component under each frequency injection in the test platform: response amplitude, phase offset, and temperature rise. It divides the frequency into multiple continuous frequency blocks according to the frequency step. The three types of response parameters corresponding to each frequency block are used as input variables of the amplitude axis, phase axis, and temperature rise axis respectively. The coordinate representation of the state point in the three-dimensional response space is constructed, and each state point is marked in the frequency block to which it belongs to generate a state response coordinate set. The density calculation submodule extracts the amplitude response sequence and phase offset sequence of all state points in each frequency block based on the number of state points in the state response coordinate set, using the formula: ； Calculation frequency block Normalized node density value of , sort the density values by frequency blocks to obtain the normalized density sequence of frequency band nodes; in, is a frequency block The number of state nodes in is a frequency block The standard deviation of the internal amplitude response sequence, is the maximum value of the standard deviation of the amplitude response in all frequency blocks, is a frequency block Middle The phase response value of the state point, is a frequency block Middle The phase response value of the state point, is a frequency block The total number of state nodes in is the maximum value among the phase change average values in all frequency blocks, is the index of the state point in the sequence, is the total number of state nodes in the frequency block; The density classification submodule extracts the density values of various positions in the sequence as the demarcation criteria to divide the corresponding density areas according to the normalized density value of each frequency block in the normalized density sequence of the frequency band node, summarizes the frequency range, attribution level, and three-dimensional coordinates of the state point of each type of block, and generates a frequency band state distribution coordinate set.

3. The microwave component automated testing system according to claim 1, wherein: The energy level control module includes: The amplitude difference calculation submodule extracts the amplitude response values of all state points within the frequency range covered by each density block based on the frequency range and coordinate point distribution of the density block in the frequency band state distribution coordinate set, obtains the amplitude difference of each pair of state points, and identifies the minimum amplitude response difference in the amplitude difference set; The phase difference extraction submodule reads the phase response value of the state point in each frequency block from the state coordinate point, arranges them in ascending order of frequency, and then compares the phase values of adjacent state points in turn to determine the maximum value, thereby obtaining the maximum adjacent phase offset change value; The signal excitation screening submodule calls all signal energy level and frequency granularity combination items set in the signal library, compares the frequency granularity with the minimum amplitude response difference, and screens out combination items with granularity values greater than the minimum amplitude difference. The signal energy level and phase difference are compared with the maximum adjacent phase offset change value to perform excitation threshold judgment, and the combination items with excitation capabilities lower than the current maximum phase offset are screened out. The remaining combination items are normalized and matched by frequency and energy dimensions, using the formula: ； Calculate the Stimulus adaptation score of signal combination , sort the incentive adaptation scores and establish a partition signal incentive configuration group; in, Indicates the The signal combination The difference between the frequency granularity item and the median frequency of the excitation frequency interval, Indicates the The signal combination The energy level value of the signal, Indicates the The amplitude response difference of the state points, Indicates the The phase difference of the state points, Indicates the number of signal parameter items in the combination to be filtered.

4. The microwave component automated testing system according to claim 1, wherein: The matching gradient extraction module includes: The injection scanning submodule performs a signal injection operation on the frequency band of the microwave component according to the frequency step value and excitation energy level set for each block in the partitioned signal excitation configuration group. During the frequency point scanning process, the input impedance value and output impedance value of the corresponding frequency point are collected in real time at set time intervals to obtain an impedance monitoring data set; The matching coefficient calculation submodule is based on the impedance monitoring data set and calls the input impedance value and output impedance value of each frequency point in order of frequency points, using the formula: ； Calculate frequency points The corresponding matching coefficient , integration obtains the matching coefficient sequence; in, Indicates frequency point The input impedance value, Indicates frequency point The output impedance value, Indicates the input properties. Indicates output terminal attributes; The tuning annotation generation submodule extracts the matching coefficient differences between adjacent frequency points as matching gradient values based on the matching coefficient sequence, classifies the matching gradient values by block to form a gradient sequence set, compares the matching gradient value corresponding to each frequency point with the set tuning trigger threshold, marks the frequency points that meet the tuning trigger threshold conditions, and generates a frequency point tuning response annotation table.

5. The microwave component automatic testing system according to claim 1, characterized in that: The dynamic test execution module includes: The signal excitation configuration extraction submodule extracts the signal excitation configuration parameters of the corresponding frequency points based on the frequency points marked in the frequency point tuning response annotation table, loads the signal source configuration, including the signal energy level and frequency granularity information, and loads the corresponding current settings of the inductor and capacitor tuning modules based on the direction of the matching gradient, reads the set values of the inductor and capacitor modules, detects whether the tuning modules need to be adjusted, and obtains the inductor and capacitor tuning settings; The response recording and adjustment submodule loads the inductor and capacitor tuning settings, selects an adjustment mode by determining the direction of the matching gradient, and executes a microstep value increment operation for the series inductor if the matching gradient direction is positive; and executes a microstep value decrement operation for the parallel capacitor if the matching gradient direction is negative. The reflection coefficient, amplitude response, and phase response data of the corresponding frequency point after each adjustment are recorded to obtain a frequency point response matching data set.

6. The microwave component automated testing system according to claim 1, characterized in that: The method further includes a calibration path switching module, which obtains the frequency response matching data group, selects a configuration that is better than the current path as a new path, and generates a frequency path optimization adjustment list.

7. The microwave component automatic testing system according to claim 6, characterized in that: The frequency point path optimization adjustment list includes a fluctuation boundary point index, a path optimization parameter group, and a post-replacement path setting label.

8. The microwave component automatic testing system according to claim 6, characterized in that: The calibration path switching module includes: The fluctuation detection marking submodule obtains the phase values of three consecutive frequency points in the frequency point response matching data group, calculates the change slope, and the difference between two adjacent slopes as the fluctuation rate, determines whether the fluctuation rate exceeds the phase slope mutation threshold, and if so, marks the frequency point as a fluctuation boundary point, retrieves the test path information and the available path candidate group to which the fluctuation boundary point belongs, and obtains the fluctuation boundary point path distribution information set; The path switching optimization submodule compares the frequency granularity and response resolution parameters of the paths in the available path alternative group based on the fluctuation boundary point path distribution information set, selects configuration paths with finer granularity or higher resolution as alternative options for the current path, establishes corresponding alternative relationships, and generates a frequency point path optimization adjustment list.

9. A microwave component automated testing method, characterized in that: The microwave component automated testing system according to any one of claims 1 to 8 comprises the following steps: S1: Acquire data of the microwave component on the test platform, construct a coordinate map and divide it into equally spaced blocks, calculate the distribution density of state nodes in the blocks, and obtain a frequency band state distribution coordinate set, wherein the frequency band state distribution coordinate set includes a frequency interval identifier, a corresponding block density level label, and a state point three-dimensional coordinate set. The partition signal excitation configuration group specifically includes a signal frequency granularity, a signal energy level, and a block corresponding excitation parameter index. The frequency point tuning response annotation table includes a frequency point position label, a matching gradient direction identifier, and a signal parameter association index. The frequency point response matching data group specifically includes a reflection coefficient record value, an amplitude response data column, and a phase response data column. S2: Screening signal parameter combinations that meet the differentiation conditions based on the state node distribution density of the frequency band state distribution coordinate set to generate a partitioned signal excitation configuration group; S3: According to the frequency step value and excitation energy level set for each block in the partitioned signal excitation configuration group, perform signal injection on each frequency band of the microwave component, mark the frequency points that meet the conditions, and generate a frequency point tuning response annotation table; S4: Based on the frequency points marked in the frequency point tuning response annotation table, performing micro-step value adjustment of the series inductor or the parallel capacitor to generate a frequency point response matching data set; S5: Acquire the frequency response matching data group, select a configuration that is better than the current path as a new path, and generate a frequency path optimization adjustment list.

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