Dynamic segmented scanning optimization method of network analyzer and self-adaptive frequency stepping algorithm

Abstract

The invention discloses a dynamic segmented scanning optimization method of a network analyzer and an adaptive frequency stepping algorithm, and relates to the technical field. The dynamic segmented scanning optimization method for the network analyzer comprises the following steps: acquiring set parameters and current state data of the network analyzer, executing low-density pre-scanning, analyzing frequency point response to construct a frequency band structure, evaluating the risk degree of each segment, allocating scanning priority levels, generating a corresponding scanning task plan, and finally executing segmented scanning according to the plan. According to the method, a low-density pre-scanning mechanism is introduced, reflection amplitude, phase and interference data are acquired, frequency bands are dynamically divided, response risks are evaluated, a differential strategy of high-density scanning of key frequency bands and low-density scanning of stable frequency bands is realized, and high-efficiency and accurate frequency response acquisition is realized. Jumping scanning leakage and redundant sampling are avoided, the scanning precision and efficiency are improved, the method adapts to complex test scenes, and the system resource utilization rate and the engineering practicability are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field, specifically to a method for optimizing dynamic segmented scanning of a network analyzer and an adaptive frequency stepping algorithm. Background Art

[0002] A network analyzer is an instrument used to measure the transmission characteristics and reflection characteristics of radio frequency and microwave devices at different frequencies, and is widely used in the S-parameter measurement of measured devices such as antennas, filters, amplifiers, and network performance evaluation. In the actual test process, the network analyzer usually needs to perform multi-frequency point scanning on the measured device within a set frequency range to obtain response information such as amplitude and phase at each frequency point, and judge the electrical performance of the device within the target frequency band based on this.

[0003] The scanning strategies of existing network analyzers are mostly based on the fixed step size method, that is, the frequency points are divided at equal intervals between the starting frequency and the ending frequency and measured in sequence. This equal-interval frequency point scanning mode has the advantages of simple implementation and fixed structure, but there are also certain limitations in actual applications. For example, the response characteristic complexities of devices in different frequency bands may be significantly different. There are obvious response jumps or interference enhancements in some frequency intervals, while other frequency bands may be relatively stable. If the same density is always used for scanning, it is easy to miss key information or waste measurement resources.

[0004] In addition, in the actual test environment, the test port status of the network analyzer will also have a significant impact on the measurement results. If these status factors are not dynamically sensed and processed, it may lead to the lack of flexibility in the frequency band scanning strategy, thereby affecting the accuracy and efficiency of the final test.

[0005] The limitations of the existing technology at least include the following problems. In the frequency domain scanning process of the network analyzer, the fixed frequency step size and equal-interval sampling strategy are generally adopted, which is difficult to finely adjust according to the response complexity differences of the measured device in different frequency intervals, resulting in significant structural deficiencies in the scanning process. On the one hand, in the interval where the frequency response changes violently, if the sparse step size strategy is still used, it is extremely easy to miss the jump points and fail to capture the amplitude mutations, directly affecting the subsequent model fitting and mismatch determination accuracy. On the other hand, in the section where the response is stable or the noise is low, if the high-density scanning is still used, it not only wastes resources, but also significantly increases the overall scanning time and computational load, restricting the real-time processing ability of the analyzer in high-frequency bands or multi-port continuous test scenarios. In addition, the existing methods lack a comprehensive evaluation of the real-time status of the test ports when performing the scanning task, and it is difficult to effectively sense and correct scanning anomalies. This makes it easy to accumulate data deviations and misjudge the true electrical characteristics when the test environment is unstable or the access conditions fluctuate, further weakening the reliability of the measurement results. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides a method for optimizing dynamic segmented scanning of a network analyzer and an adaptive frequency stepping algorithm, which solves the problems in the prior art that it is difficult to dynamically adjust the scanning strategy according to the complexity of the frequency band response and the disturbance of the test state, resulting in incomplete scanning results, low measurement efficiency and insufficient data credibility.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for optimizing dynamic segmented scanning of a network analyzer, comprising the following steps: obtaining the set test frequency range and current state data of the network analyzer; based on the set step size, performing a low-density frequency point pre-scan on the device under test within the frequency range set by the network tester, and collecting the frequency point response data of the device under test at each frequency point; establishing a frequency band division structure based on the frequency point response data of the device under test at each frequency point, and evaluating the response risk degree of the device under test in each frequency band interval; based on the response risk degree of the device under test in each frequency band interval, determining the scanning priority level of the device under test in each frequency band interval, and generating a scanning task plan for each frequency band interval; based on the scanning task plan for each frequency band interval, performing segmented scanning on the device under test, and triggering a dynamic correction strategy based on the execution result.

[0008] Further, the test frequency range includes a start frequency and an end frequency, the current state data includes the current temperature value of the port, the current contact resistance value of the port, the reflection amplitude value of the current connection state, the reflection voltage value of the current connection state, the reflection amplitude value of the standard load state, and the reflection voltage value of the standard load state, and the frequency point response data includes the reflection amplitude value, the reflection phase value, and the background electromagnetic interference intensity value.

[0009] Further, the specific steps for evaluating the response risk degree of the device under test in each frequency band interval are as follows: dividing the frequency point response data of the device under test at each frequency point into frequency band intervals according to the set width, obtaining several frequency band intervals of the device under test, and each frequency band interval includes several frequency points; for each frequency band interval of the device under test, analyzing the response state set respectively, and the response state set includes the maximum amplitude jump amount, the maximum phase jump amount, and the average background interference intensity; obtaining the cable shielding attenuation value of the network analyzer, and comprehensively analyzing it respectively in combination with the response state set of each frequency band interval of the device under test, as well as the current contact resistance value of the port of the network analyzer, the reflection voltage value of the current connection state, and the reflection voltage value of the standard load state, to obtain the response risk degree of the device under test in each frequency band interval.

[0010] Further, for each frequency band interval of the device under test, the specific steps for analyzing the response state set are as follows: For several frequency points within each frequency band interval of the device under test, group them according to adjacent frequency points respectively, and obtain several groups of adjacent frequency points for each frequency band interval of the device under test; For each group of adjacent frequency points in each frequency band interval of the device under test, perform reflection amplitude difference analysis, reflection phase difference analysis, and comparative analysis respectively, and obtain the maximum amplitude jump value and the maximum phase jump value for each frequency band interval of the device under test; Analyze the mean value of the background electromagnetic interference intensity values of several frequency points within each frequency band interval of the device under test respectively, and obtain the average background interference intensity for each frequency band interval of the device under test.

[0011] Further, the specific formula for calculating the response risk degree of the device under test at a certain frequency band interval is as follows: ; where is the response risk degree of the device under test at a certain frequency band interval, is the maximum amplitude jump value of the device under test at a certain frequency band interval, is the maximum amplitude jump value of the device under test at a certain frequency band interval, is the response sharp adjustment coefficient stored in the database, is the current contact resistance value of the port of the network analyzer, is the cable shielding attenuation value of the network analyzer, is the contact sensitivity adjustment coefficient stored in the database, is the average background interference intensity of the device under test at a certain frequency band interval, is the reflected voltage value of the current connection state of the network analyzer, is the reflected voltage value of the standard load state of the network analyzer, is the interference suppression factor stored in the database, is the environmental interference enhancement coefficient stored in the database.

[0012] Further, the specific steps for determining the scanning priority level of the device under test at each frequency band interval are as follows: Analyze the mean value and standard deviation of the reflection amplitude values of several frequency points within each frequency band interval of the device under test respectively, and obtain the mean reflection amplitude and the standard deviation of the reflection amplitude for each frequency band interval of the device under test; Obtain the historical calibration port temperature value of the network analyzer, and perform comprehensive analysis respectively in combination with the mean reflection amplitude, the standard deviation of the reflection amplitude, the response risk degree of each frequency band interval of the device under test, as well as the current port temperature value, the current connection state reflection amplitude value, and the standard load state reflection amplitude value of the network analyzer, and obtain the scanning priority level of the device under test at each frequency band interval.

[0013] Further, the specific formula for calculating the scanning priority level of the device under test at a certain frequency band interval is as follows: ; where, is the scanning priority level of the device under test at a certain frequency band interval, is the response risk level of the device under test at a certain frequency band interval, is the standard deviation of the reflection amplitude of the device under test at a certain frequency band interval, is the average value of the reflection amplitude of the device under test at a certain frequency band interval, is the fluctuation adjustment coefficient stored in the database, is the current temperature value of the port of the network analyzer, is the historical calibration port temperature value of the network analyzer, is the temperature drift sensitivity adjustment coefficient stored in the database, is the reflection amplitude value of the current connection state of the network analyzer, is the reflection amplitude value of the standard load state of the network analyzer, is the amplitude adjustment factor stored in the database, is the connection deviation enhancement coefficient stored in the database.

[0014] Furthermore, the specific steps to generate the scanning task plan for each frequency band interval are as follows: Obtain a preset number of scanning level evaluation intervals, and each scanning level evaluation interval corresponds to a scanning strategy, and each scanning strategy includes a number of target sampling points; Judge and analyze the scanning priority levels of the device under test at each frequency band interval respectively with the preset number of scanning level evaluation intervals; Take the scanning strategy corresponding to the scanning priority level of the device under test at each frequency band interval falling within the preset scanning level evaluation interval as the scanning task plan for each frequency band interval.

[0015] The adaptive frequency stepping algorithm includes the following steps: Read the number of target sampling points in the scanning strategy of the device under test at each frequency band interval, and take the starting frequency of the device under test at each frequency band interval as the first scanning frequency point, and perform frequency jumps based on the set step size to obtain the frequency position of the second scanning frequency point of the device under test at each frequency band interval; For the second scanning frequency point of the device under test at each frequency band interval, read the adjacent frequency points in the corresponding frequency band interval from the low-density frequency point pre-scanning and perform position analysis to obtain the frequency position of the third scanning frequency point of the device under test at each frequency band interval; Repeat the position analysis step until the expected scanning requirements are met, that is, the number of sampling points meets the task plan requirements or the scanning frequency reaches the termination boundary of the current frequency band interval.

[0016] Further, the specific steps to obtain the frequency position of the third scanning frequency point of the device under test in each frequency band interval are as follows: Read the reflection amplitude value and reflection phase value of the adjacent frequency point to the second scanning frequency point in the corresponding frequency band interval from the low-density frequency point pre-scan, and analyze the reflection amplitude difference and reflection phase difference of the second scanning frequency point of the device under test in each frequency band interval; Obtain the historical calibration port contact resistance value of the network analyzer, and comprehensively analyze it in combination with the current port contact resistance value, current port temperature value, historical calibration port temperature value, reflection voltage value under the current connection state, and reflection voltage value under the standard load state of the network analyzer to obtain the state disturbance factor of the network analyzer; Combine the reflection amplitude difference and reflection phase difference of the second scanning frequency point of the device under test in each frequency band interval with the state disturbance factor of the network analyzer to obtain the frequency position of the third scanning frequency point of the device under test in each frequency band interval.

[0017] The present invention has the following beneficial effects:

[0018] (1). In the dynamic segmented scanning optimization method of this network analyzer, a low-density frequency point pre-scan mechanism is introduced before the formal scan. By combining information such as the reflection amplitude value, phase value, and background interference intensity of the device under test at each frequency point, a frequency band division structure is constructed and the response risk degree is evaluated. This enables the network analyzer to reasonably allocate scanning task resources according to the response complexity and fluctuation characteristics of each frequency band interval. Compared with the traditional strategy of using a fixed step size and equal-interval scanning in the prior art, in this method, high-density sampling points can be preferentially allocated in the interval with a drastic response change, while in the frequency band interval with a stable response or low interference, a low-density sampling or boundary sparse strategy is executed to achieve a differential configuration of spectrum resources. This differential scanning mechanism not only avoids missing the scanning of key jump points but also reduces the consumption of redundant scanning points on the system scanning time and storage resources, effectively improving the overall processing efficiency of the network analyzer in multi-band continuous scanning or large-scale device testing tasks, and enhancing the practicality and engineering scalability of the method.

[0019] (2). In the dynamic segmented scanning optimization method of this network analyzer, by obtaining the current state data before each scanning task, including port temperature, contact resistance, reflection amplitude and voltage values under the current connection state, etc., and combining historical calibration data, such as historical calibration temperature and standard load response values, a multi-dimensional state disturbance factor is established. This enables the scanning decision to have state awareness ability and provides an adaptive adjustment basis for the sampling point density allocation at the frequency band level, effectively improving the system's recognition and response ability to non-ideal factors such as poor connection and environmental interference, and enhancing the robustness and data credibility of the network analyzer under dynamically changing test conditions.

[0020] (3) The dynamic segmented scanning optimization method of this network analyzer sets a dynamic correction strategy during the execution phase. Immediately after each frequency band scanning task is completed, a local quality assessment is performed on the sampled data to analyze whether there are phenomena such as drastic fluctuations, boundary jumps, abnormal drops in signal-to-noise ratio, or serious deviations from the expected model in the actually collected reflection amplitude and phase values. If the above situations are identified, the system will automatically trigger a correction mechanism, recalculate the scanning priority level of this segment, and generate a new scanning task plan according to the corrected risk level, realizing precise supplementary scanning and enhanced sampling of abnormal segments. This closed-loop correction mechanism greatly improves the response flexibility and measurement accuracy of the scanning strategy, avoids mis-scanning and missed scanning of high-risk sections, and ultimately ensures the integrity, continuity, and interpretability of the entire spectrum response result, meeting the engineering requirements in high-reliability measurement scenarios.

[0021] (4) The adaptive frequency stepping algorithm realizes the point-by-point optimization of the frequency sampling position by dynamically adjusting the stepping interval between frequency points. This algorithm first reads the number of target sampling points within each frequency band interval and generates an initial frequency point sequence with a fixed step size. Subsequently, the system uses the pre-scanned data to extract the reflection amplitude and phase change trends of the current frequency point, combines the current temperature, contact resistance, voltage deviation of the test port with the historical calibration reference to construct a state perturbation factor as the basis for stepping correction, and then jointly models the jump degree between frequency points and the change of the connection environment. Through this mechanism, the frequency stepping value automatically shrinks in intervals with strong response or strong environmental perturbation to increase the sampling density of key jump frequency points, while in areas with stable response and small perturbation, the frequency stepping value automatically enlarges to avoid excessive redundant sampling and shorten the scanning duration. Compared with the traditional equal-spacing sampling strategy, the present invention realizes the adaptive adjustment ability of frequency positions, significantly improves the scanning efficiency and the feature capture ability of high-risk sections while ensuring data integrity.

[0022] Of course, it is not necessary for any product implementing the present invention to simultaneously achieve all the above-mentioned advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flowchart of the dynamic segmented scanning optimization method of the network analyzer of the present invention.

[0024] Figure 2 It is a flowchart of the specific steps for evaluating the response risk degree of the device under test in each frequency band interval in the dynamic segmented scanning optimization method of the network analyzer of the present invention.

[0025] Figure 3 It is a flowchart of the adaptive frequency stepping algorithm of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0026] Please refer to Figure 1, an embodiment of the present invention provides a technical solution: a method for optimizing dynamic segmented scanning of a network analyzer, including the following steps: obtaining the set test frequency range and current state data of the network analyzer; based on a set step size (such as 5 MHz), within the frequency range set by the network tester, performing a low-density frequency point pre-scan on the device under test, and collecting the frequency point response data of the device under test at each frequency point; establishing a frequency band division structure based on the frequency point response data of the device under test at each frequency point, and evaluating the response risk degree of the device under test in each frequency band interval; based on the response risk degree of the device under test in each frequency band interval, determining the scanning priority level of the device under test in each frequency band interval, and generating a scanning task plan for each frequency band interval; based on the scanning task plan for each frequency band interval, performing segmented scanning on the device under test, and triggering a dynamic correction strategy based on the execution result, that is, after completing the segmented scanning of the device under test based on the scanning task plan for each frequency band interval, the network tester will perform real-time analysis and quality evaluation on the scanning result to determine whether there are any abnormalities or potential error risks in the scanning process. Specifically:

[0027] Verifying each segment of the scanning result of each frequency band interval one by one to determine whether its sampling result meets the preset quality determination rules, including but not limited to the following situations:

[0028] First, if the reflected amplitude value or phase value actually collected in a certain frequency band fluctuates violently and exceeds the response tolerance threshold set by the system;

[0029] Second, if it is detected during the scanning process that there are phenomena such as abnormal signal attenuation, sudden increase in background noise, or significant decrease in signal-to-noise ratio within this frequency band;

[0030] Third, if this frequency band shows discontinuous boundaries or abnormal breakpoints between the front and rear frequency bands;

[0031] Fourth, if there are situations such as data loss, sampling failure, or obvious inconsistency with the historical model during the scanning process;

[0032] If any of the above abnormal situations occur, the system will automatically mark this frequency band as a segment to be corrected, and trigger a dynamic correction mechanism to re-evaluate the scanning priority level and sampling strategy. According to the corrected risk level and connection status, the system can generate a new local scanning task plan, which will adjust the sampling parameters of this frequency band, including measures such as increasing the sampling point density, enabling a dual sampling mode, adding transition buffer frequency points at the beginning and end of the segment, extending the sampling time, or introducing filtering enhancement.

[0033] The test frequency range includes the starting frequency and the ending frequency. The current status data includes the current temperature value of the port, the current contact resistance value of the port, the reflection amplitude value of the current connection status, the reflection voltage value of the current connection status, the reflection amplitude value of the standard load status, and the reflection voltage value of the standard load status. The frequency point response data includes the reflection amplitude value, the reflection phase value, and the background electromagnetic interference intensity value.

[0034] Among them, the current temperature value of the port refers to the real-time temperature measurement value of the test port of the network tester in the current operating state, which reflects the thermal state of the test channel under the current environment and working conditions. The unit is degree Celsius (°C), and it can be measured and obtained through a thermistor or a digital temperature sensor (such as NTC / PTC or digital IC).

[0035] The current contact resistance value of the port refers to the estimated contact resistance value between the test port of the network tester and the connected cable / device. The unit is ohm (Ω). This value reflects the quality of the connection and is closely related to reflection fluctuations and signal loss. It is obtained by the network tester after performing standard calibration operations such as open circuit, short circuit, and load, by comparing the deviation of the reflection coefficient (S parameter) in these states and combining a preset model to inversely calculate the contact resistance value at the current port connection.

[0036] The reflection amplitude value of the current connection status refers to the reflection voltage amplitude value measured by the network tester at a certain frequency point or within a frequency range when it is in the connected state with the device under test. The unit is volt (V) or converted to dB (logarithmic value). It is obtained by the network tester sending an excitation signal to the device under test through the internal signal source, and at the same time, the receiving module (reflection channel) collects the RF signal reflected by the device under test, and through detection and amplitude demodulation, the reflection amplitude value at this frequency point is obtained.

[0037] The reflection voltage value of the current connection status refers to the amplitude value of the RF reflection voltage signal collected by the network tester through its reflection port at a specific frequency point or within a frequency range when it is in the connected state with the device under test. This value reflects the actual performance of the reflection characteristics of the device under test in the current connection state. The unit is volt (V) or the dB value after conversion. It is obtained when the network tester performs a scanning operation. The network tester applies an excitation signal to the device under test through its internal RF excitation source and collects the reflection signal through the reflection receiving channel of the test port. After this signal is processed by the power detector and the amplitude demodulation module, it is converted into a DC voltage value, which is the reflection voltage amplitude value at the current frequency point.

[0038] The reflection amplitude value in the standard load state refers to the reflection amplitude value measured by a network tester under the condition of loading a known standard load (such as a 50Ω matching load) at the same frequency point, which is used as an ideal reference value for the reflection response. Before the test, the network tester will guide the user to perform standard calibration operations, including connecting standard open-circuit, short-circuit, load and other devices. When the network tester connects the standard load, it emits an excitation signal and records the reflection amplitude value in this standard state as the ideal response reference.

[0039] The reflection voltage value in the standard load state refers to the ideal reflection voltage amplitude value measured by a network tester under the condition of loading a standard load (such as a 50Ω matching load) at the same test frequency point. This value is used as a reference benchmark under ideal electrical connection conditions, with the unit of volts (V) or dB. During the standard calibration procedure of the network tester, according to the guidance, standard devices such as open-circuit, short-circuit, and load are sequentially connected to the test port. When loading the standard load (such as a 50Ω matching load), the network tester sends an excitation signal to it and collects the returned signal through the reflection channel. After power detection and amplitude demodulation of this signal, the obtained reflection voltage amplitude is the reflection value in the standard state.

[0040] Specifically, as Figure 2 shown, the specific steps to evaluate the response risk degree of the device under test in each frequency band interval are as follows: Divide the frequency point response data of the device under test at each frequency point into frequency band intervals according to a set width, obtaining several frequency band intervals of the device under test, and each frequency band interval includes several frequency points; for each frequency band interval of the device under test, analyze the response state set respectively, and the response state set includes the maximum amplitude jump amount, the maximum phase jump amount, and the average background interference intensity; obtain the cable shielding attenuation value of the network analyzer, and comprehensively analyze it respectively in combination with the response state set of each frequency band interval of the device under test, as well as the current contact resistance value of the port of the network analyzer, the current connection state reflection voltage value, and the standard load state reflection voltage value, to obtain the response risk degree of the device under test in each frequency band interval.

[0041] Among them, the cable shielding attenuation value refers to the suppression ability of the RF cable connecting the network analyzer to external electromagnetic interference signals, which is usually quantified in dB. This value can be calculated by the network analyzer by connecting the reference standard cable and the cable under test respectively under the condition of no signal input and measuring the difference in background noise levels within the same frequency range; it can also be provided by the equipment manufacturer and written into the parameter database during the production calibration or calibration stage, or obtained by analyzing the cable structure and shielding layer integrity through the built-in shielding performance identification module, and is used to characterize the anti-interference ability in the frequency band risk assessment.

[0042] For each frequency band interval of the device under test, the specific steps for analyzing the response state set are as follows: For several frequency points within each frequency band interval of the device under test, group them according to adjacent frequency points respectively, and obtain several groups of adjacent frequency points for each frequency band interval of the device under test; For each group of adjacent frequency points in each frequency band interval of the device under test, perform reflection amplitude difference analysis (i.e., the reflection amplitude value of the latter frequency point minus the reflection amplitude value of the former frequency point), reflection phase difference analysis (i.e., the reflection phase value of the latter frequency point minus the reflection phase value of the former frequency point), and perform comparative analysis to obtain the maximum amplitude jump amount and the maximum phase jump amount for each frequency band interval of the device under test; Analyze the background electromagnetic interference intensity values of several frequency points within each frequency band interval of the device under test respectively, and obtain the average background interference intensity for each frequency band interval of the device under test.

[0043] The specific formula for calculating the response risk degree of the device under test at a certain frequency band interval is as follows: ; where is the response risk degree of the device under test at a certain frequency band interval, is the maximum amplitude jump amount of the device under test at a certain frequency band interval, is the maximum amplitude jump amount of the device under test at a certain frequency band interval, is the response sharp adjustment coefficient stored in the database, is the current contact resistance value of the port of the network analyzer, is the cable shielding attenuation value of the network analyzer, is the contact sensitivity adjustment coefficient stored in the database, is the average background interference intensity of the device under test at a certain frequency band interval, is the reflected voltage value of the current connection state of the network analyzer, is the reflected voltage value of the standard load state of the network analyzer, is the interference suppression factor stored in the database, is the environmental interference enhancement coefficient stored in the database.

[0044] It should be noted that the response sharp adjustment coefficient , the contact sensitivity adjustment coefficient , the interference suppression factor , the environmental interference enhancement coefficient The specific acquisition steps are as follows: During the long-term historical testing process of the network analyzer, it is obtained through the joint modeling and analysis of a large amount of frequency response data of the DUTs that have been scanned and the test environment state parameters. Specifically, after each frequency band scan is completed, the network analyzer records key physical parameters such as the reflection amplitude jump amount, phase fluctuation characteristics, test port contact state, cable shielding characteristics, and electromagnetic interference intensity at each frequency point of the DUT, and synchronously collects state information such as the environmental background noise, electrical connection resistance, and system temperature at the time of testing. Subsequently, the system uses clustering analysis and multi-factor regression algorithms to model the response intensity and test stability characteristics of different device types in the typical frequency range, extracts the response intensity adjustment coefficient that can characterize the complexity of the device response, the contact sensitivity adjustment coefficient that is sensitive to changes in the contact state, the interference suppression factor for the background noise shielding correction ability, and the environmental interference enhancement coefficient for the response ability to the external interference change trend. These parameters are written into the parameter database after verification and are automatically matched and retrieved by the network analyzer control system according to the DUT type before the actual scan task is generated, so as to be used as the weight correction basis in the scan task adjustment mechanism.

[0045] In this implementation plan, by constructing a frequency band interval division mechanism based on frequency point response data and introducing multi-dimensional response state characteristics such as the maximum amplitude jump amount, maximum phase jump amount, and average background interference intensity, the fineness and interpretability of frequency band risk identification are effectively improved. At the same time, combined with the current contact resistance value, connection state reflection voltage value, and cable shielding attenuation value collected by the network analyzer in real time, as well as historical calibration reference data, a dynamic state perception model is established, so that the calculation of the frequency band risk level not only depends on the response behavior of the device itself, but also can accurately reflect the comprehensive influence of the current measurement environment and connection state. On this basis, the response intensity adjustment coefficient, contact sensitivity adjustment coefficient, interference suppression factor, and environmental interference enhancement coefficient obtained by training and extraction from the historical large sample data can be dynamically adapted to different device types and working scenarios, making the risk calculation have stronger generalization ability and task adaptability. The above mechanism significantly enhances the network analyzer's ability to identify key frequency bands in the high-frequency dynamic environment, provides reliable quantitative support for the subsequent priority level evaluation and scan task formulation, and comprehensively improves the accuracy, stability, and intelligence level of the scan strategy.

[0046] Specifically, the specific steps to determine the scanning priority level of the device under test in each frequency band interval are as follows: Analyze the mean and standard deviation of the reflection amplitude values of several frequency points within each frequency band interval of the device under test to obtain the mean reflection amplitude and the standard deviation of the mean reflection amplitude of each frequency band interval of the device under test; Obtain the historical calibration port temperature value of the network analyzer (i.e., the port temperature value during the last calibration), and comprehensively analyze it in combination with the mean reflection amplitude, the standard deviation of the mean reflection amplitude, the response risk degree of each frequency band interval of the device under test, as well as the current port temperature value of the network analyzer, the reflection amplitude value in the current connection state, and the reflection amplitude value in the standard load state to obtain the scanning priority level of the device under test in each frequency band interval.

[0047] The specific formula for calculating the scanning priority level of the device under test in a certain frequency band interval is as follows: ; where is the scanning priority level of the device under test in a certain frequency band interval, is the response risk degree of the device under test in a certain frequency band interval, is the standard deviation of the mean reflection amplitude of the device under test in a certain frequency band interval, is the mean reflection amplitude of the device under test in a certain frequency band interval, is the fluctuation adjustment coefficient stored in the database, is the current port temperature value of the network analyzer, is the historical calibration port temperature value of the network analyzer, is the temperature drift sensitivity adjustment coefficient stored in the database, is the reflection amplitude value in the current connection state of the network analyzer, is the reflection amplitude value in the standard load state of the network analyzer, is the amplitude adjustment factor stored in the database, used to prevent the denominator from being zero, is the connection deviation enhancement coefficient stored in the database.

[0048] It should be noted that the fluctuation adjustment coefficient , the temperature drift sensitivity adjustment coefficient , the amplitude adjustment factor , the connection deviation enhancement coefficient The specific acquisition steps are as follows: During the long-term historical testing process, the network analyzer automatically extracts from the system database after statistically analyzing the response behaviors of various devices under test in different frequency ranges and training the error model. Specifically, in each successive segmented scanning task, the network analyzer records the reflection amplitude jump frequency and amplitude change amplitude within each frequency band, and based on the response consistency of the same device under multiple scans, evaluates the influence weight of frequency band fluctuations on scanning stability, and then trains and solidifies the fluctuation adjustment coefficient corresponding to the frequency band response fluctuation characteristics. For the temperature drift sensitivity adjustment coefficient, the system collects the difference change curve between the current temperature of the acquisition port and the historical calibration temperature, and combines the measurement error trend under the temperature drift condition to construct the mapping relationship between temperature drift and response deviation, thereby deriving the sensitivity degree of temperature drift to the score impact, which is the temperature drift sensitivity adjustment coefficient. The amplitude adjustment factor is calculated from the deviation ratio between the ideal reflection amplitude value collected under the standard load connection state and the reflection amplitude value under the actual connection state, representing the reference benchmark of the connection state for amplitude accuracy. The connection deviation enhancement coefficient is obtained by fitting the weight according to the amplification trend of the scoring error after identifying the deviation amplitude response characteristics in the case of connection anomalies or miscontacts in a large amount of test data, and is used to adjust the response sensitivity of the scoring model to connection anomalies.

[0049] In this implementation plan, by introducing the scanning priority level calculation mechanism, it is possible to quantitatively evaluate the urgency of scanning for each frequency band based on the reflection amplitude statistical characteristics (including the mean and standard deviation of the reflection amplitude) of the device under test in different frequency band ranges and its response risk level. On this basis, the system further introduces various state data such as the current temperature value of the network analyzer, the historical calibration temperature value, the current connection state, and the reflection amplitude value under the standard load state, and performs normalization calculation on the above parameters through a set of database coefficients obtained from historical training (such as the fluctuation adjustment coefficient, the temperature drift sensitivity adjustment coefficient, the amplitude adjustment factor, and the connection deviation enhancement coefficient) to establish a dynamic scoring model, so that the scanning priority level not only reflects the volatility of the frequency band response itself, but also comprehensively considers the actual impacts of test environment changes and connection state deviations. This priority level model based on multi-factor fusion significantly enhances the flexibility and accuracy of scanning task scheduling, effectively improves the identification accuracy of key frequency bands, and at the same time avoids over-sampling in low-risk areas, ensuring the spectrum integrity and data reliability while improving the overall measurement efficiency, demonstrating a high degree of intelligence and engineering adaptability.

[0050] Specifically, the specific steps for generating the scanning task plan at each frequency band interval are as follows: Obtain a preset number of scanning level evaluation intervals, and each scanning level evaluation interval corresponds to a scanning strategy, and each scanning strategy includes a number of target sampling points; Judge and analyze the scanning priority levels of the device under test at each frequency band interval with the preset number of scanning level evaluation intervals; Take the scanning strategy corresponding to the scanning priority level of the device under test at each frequency band interval falling within the preset scanning level evaluation interval as the scanning task plan at each frequency band interval.

[0051] Among them, the scanning task plan at each frequency band interval includes but is not limited to the following examples:

[0052] Scanning priority level < 1.0: Sparse scanning plan, that is, sample 3 points per segment, no buffering, and dual sampling disabled;

[0053] 1.0 ≤ scanning priority level < 2.0: Standard scanning plan, that is, sample 5 - 7 points per segment, allowing boundary buffer points;

[0054] Scanning priority level ≥ 2.0: Dense scanning plan, that is, sample ≥ 10 points per segment, enable dual sampling and buffer, and give priority to processing and scheduling.

[0055] In this implementation scheme, by constructing the mapping relationship between the scanning priority level and the preset scanning strategy, the intelligent hierarchical scheduling of the frequency band scanning task is realized. The system matches each frequency band's scanning priority level to the corresponding scanning strategy interval according to it, thereby dynamically determining the number of sampling points required for each segment and whether to enable functions such as dual sampling and boundary buffering. Compared with the traditional fixed scanning scheme, this method can automatically perform dense sampling in high-risk sections, simplify the processing in low-risk sections, ensure the priority of accuracy in key frequency bands, and greatly improve the overall scanning efficiency at the same time. This strategy takes into account both measurement integrity and resource optimization, and has good engineering adaptability and algorithm expansion ability.

[0056] Please refer to Figure 3 , the embodiment of the present invention provides a technical solution: an adaptive frequency stepping algorithm, including the following steps: Read the number of target sampling points in the scanning strategy of the device under test at each frequency band interval, and use the starting frequency of the device under test at each frequency band interval as the first scanning frequency point, and perform frequency jumps based on the set step size to obtain the frequency position of the second scanning frequency point of the device under test at each frequency band interval; For the second scanning frequency point of the device under test at each frequency band interval, read the adjacent frequency points in the corresponding frequency band interval from the low-density frequency point pre-scanning and perform position analysis to obtain the frequency position of the third scanning frequency point of the device under test at each frequency band interval; Repeat the position analysis step until the expected scanning requirements are met, that is, the number of sampling points meets the task plan requirements or the scanning frequency reaches the termination boundary of the current frequency band interval.

[0057] Specifically, the specific steps to obtain the frequency position of the third scanning frequency point of the device under test in each frequency band interval are as follows: Read the reflection amplitude value and reflection phase value of the adjacent frequency point to the second scanning frequency point in the corresponding frequency band interval from the low-density frequency point pre-scan, and analyze the reflection amplitude difference and reflection phase difference of the second scanning frequency point of the device under test in each frequency band interval; Obtain the historical calibration port contact resistance value of the network analyzer (i.e., the port contact resistance value during the last calibration), and comprehensively analyze it in combination with the current port contact resistance value of the network analyzer, the current port temperature value, the historical calibration port temperature value, the reflection voltage value of the current connection state, and the reflection voltage value of the standard load state to obtain the state disturbance factor of the network analyzer; Combine the reflection amplitude difference and reflection phase difference of the second scanning frequency point of the device under test in each frequency band interval with the state disturbance factor of the network analyzer to obtain the frequency position of the third scanning frequency point of the device under test in each frequency band interval.

[0058] Among them, the specific formulas for calculating the state disturbance factor of the network analyzer and the frequency position of the third scanning frequency point of the device under test in each frequency band interval are as follows: ; where is the frequency position of the third scanning frequency point of the device under test in each frequency band interval, is the set step size, is the reflection amplitude difference of the second scanning frequency point of the device under test in each frequency band interval, is the reflection amplitude difference adjustment coefficient stored in the database, is the reflection phase difference of the second scanning frequency point of the device under test in each frequency band interval, is the reflection phase difference adjustment coefficient stored in the database, is the state disturbance factor of the network analyzer, is the state disturbance adjustment coefficient stored in the database, is the current port temperature value of the network analyzer, is the historical calibration port temperature value of the network analyzer, is the temperature adjustment factor stored in the database to prevent the denominator from being zero, is the temperature response adjustment coefficient stored in the database, is the temperature drift weight coefficient stored in the database, is the current port contact resistance value of the network analyzer, is the historical calibration port contact resistance value of the network analyzer, is the contact resistance adjustment factor stored in the database to prevent the denominator from being zero, is the resistance response adjustment coefficient stored in the database, is the contact resistance weight coefficient stored in the database, is the reflection amplitude value of the current connection state of the network analyzer, is the reflection amplitude value of the standard load state of the network analyzer, is the amplitude adjustment factor stored in the database, is the voltage deviation adjustment coefficient stored in the database, is the connection voltage deviation weight coefficient stored in the database.

[0059] It should be explained that the reflection amplitude difference adjustment coefficient stored in the database , the reflection phase difference adjustment coefficient , and the state perturbation adjustment coefficient are obtained through the following specific steps: During the historical frequency band scanning task of the network analyzer, statistical modeling is performed on the response change patterns under different factors such as the types of devices under test, frequency band intervals, and access structures. The system performs clustering analysis on the reflection amplitude differences, phase jump trends, and jump frequencies between adjacent frequency points within each frequency band interval, extracts the typical amplitude difference change distribution and the phase response slope change curve, and sets the corresponding adjustment weight coefficients according to their sensitivity performances under different physical states. At the same time, in the state perturbation feature modeling, factors such as port temperature, cable shielding, and reflection voltage deviation are introduced to statistically analyze the amplification or suppression trends of response differences, forming the state perturbation response adjustment coefficient. The above parameters are written into the database after being trained with multiple batches of data and corrected by error feedback, and are used for dynamic adjustment of the difference weights during the subsequent scanning point calculation process.

[0060] The temperature adjustment factor stored in the database , and the contact resistance adjustment factor are obtained through the following specific steps: During each standard calibration process of the network analyzer, the reflection stability index of the port under different temperature conditions is recorded, and the port temperature drift curve and the corresponding amplitude fluctuation performance are continuously monitored during subsequent test tasks. The system constructs a temperature response deviation mapping model based on the fitting relationship between temperature change and response deviation, and extracts the correction slope therein as the temperature adjustment factor; similarly, the system analyzes the functional relationship between the actually measured contact resistance and the reflection error under standard connection states such as open circuit, short circuit, and load, and extracts the reflection drift sensitivity caused by the resistance change as the contact resistance adjustment factor. Both are embedded in the database through the fitting statistical model as device individual adjustment parameters.

[0061] The temperature response adjustment coefficient stored in the database , the temperature drift weight coefficient , the resistance response adjustment coefficient , and the contact resistance weight coefficient , Voltage deviation adjustment coefficient , Connection voltage deviation weight coefficient The specific acquisition steps are as follows: During long-term historical tests and multi-device statistical analyses by the network analyzer, they are obtained through training with the large-sample error tracking and sensitivity extraction method. The system respectively performs clustering on the response error change rates under factors such as temperature changes, resistance perturbations, and connection state fluctuations, extracts their influence weights on scanning accuracy, reflection consistency, and interference stability, and combines device types, frequency band characteristics, and connection structures to automatically optimize the weight adjustment factors. Among them, the temperature coefficients ( , ) focus on the correlation modeling of response delay and drift rate, the resistance coefficients ( , ) emphasize the abnormal offset trend of reflections caused by electrical contact fluctuations, and the voltage coefficients ( , ) are used to correct the perturbation effect of abnormal deviation of reflected voltage on the positioning strategy.

[0062] In this implementation plan, by introducing the combined regulation mechanism of difference driving and state perception, the dynamic optimization of the scanning frequency point position is achieved. The system calculates the reflection amplitude difference and phase difference based on the low-density pre-scanning results, combines the temperature, resistance, and reflected voltage deviation conditions of the network analyzer in the current test state, constructs a state perturbation factor and introduces it into the step calculation, thereby dynamically adjusting the frequency position of the next hop frequency point. This mechanism fully considers the combined influence of the response change trend of the device under test itself and the external environment on the measurement accuracy, ensuring the flexibility and physical adaptability of the scanning frequency point distribution. At the same time, the reflection difference adjustment coefficient, temperature and resistance adjustment factors, perturbation response weights, etc. used in the algorithm are all extracted through historical data training and model regression, and stored in the database for task adaptive matching, making the scanning strategy highly scalable and device adaptable. Compared with the traditional fixed-step scanning method, this mechanism significantly improves the accuracy of jump frequency point identification and the sampling accuracy of important frequency bands, and further enhances the measurement stability and data reliability of the network analyzer under non-ideal test conditions.

[0063] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concepts. Therefore, the appended claims are intended to be interpreted to include the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0064] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. Method for optimizing dynamic segmented scanning of network analyzer, characterized in that It includes the following steps: Obtain the test frequency range and current status data set by the network analyzer; Based on the set step size, perform a low-density frequency point pre-scan on the device under test within the frequency range set by the network tester, and collect the frequency point response data of the device under test at each frequency point; Establish a frequency band division structure based on the frequency point response data of the device under test at each frequency point, and evaluate the response risk degree of the device under test in each frequency band interval; Based on the response risk degree of the device under test in each frequency band interval, determine the scanning priority level of the device under test in each frequency band interval, and generate a scanning task plan for each frequency band interval; Based on the scanning task plan for each frequency band interval, perform segmented scanning on the device under test, and trigger a dynamic correction strategy based on the execution result.

2. The dynamic segmented scanning optimization method of a network analyzer according to claim 1, characterized in that, The test frequency range includes a start frequency and an end frequency, and the current status data includes the current port temperature value, the current port contact resistance value, the reflection amplitude value of the current connection state, the reflection voltage value of the current connection state, the reflection amplitude value of the standard load state, and the reflection voltage value of the standard load state. The frequency point response data includes the reflection amplitude value, the reflection phase value, and the background electromagnetic interference intensity value.

3. The network analyzer dynamic segmentation scanning optimization method according to claim 2, wherein The specific steps for evaluating the response risk degree of the device under test in each frequency band interval are as follows: Divide the frequency point response data of the device under test at each frequency point into frequency bands according to the set width, obtain several frequency band intervals of the device under test, and each frequency band interval includes several frequency points; For each frequency band interval of the device under test, analyze the response state set respectively, and the response state set includes the maximum amplitude jump amount, the maximum phase jump amount, and the average background interference intensity; Obtain the cable shielding attenuation value of the network analyzer, and perform comprehensive analysis by combining the response state set of each frequency band interval of the device under test, the current port contact resistance value of the network analyzer, the reflection voltage value of the current connection state, and the reflection voltage value of the standard load state to obtain the response risk degree of the device under test in each frequency band interval.

4. The network analyzer dynamic segmented scanning optimization method according to claim 3, wherein The specific steps for analyzing the response state set for each frequency band interval of the device under test are as follows: For several frequency points within each frequency band interval of the device under test, divide them into groups according to adjacent frequency points respectively, and obtain several groups of adjacent frequency points for each frequency band interval of the device under test; For each group of adjacent frequency points in each frequency band interval of the device under test, perform reflection amplitude difference analysis and reflection phase difference analysis respectively, and perform comparative analysis to obtain the maximum amplitude jump amount and the maximum phase jump amount for each frequency band interval of the device under test; Perform mean analysis on the background electromagnetic interference intensity values of several frequency points within each frequency band interval of the device under test to obtain the average background interference intensity for each frequency band interval of the device under test.

5. The method for optimizing dynamic segmented scanning of a network analyzer according to claim 3, wherein The specific formula for calculating the response risk degree of the device under test in a certain frequency band interval is as follows: ； Among them, , , , are, in sequence, the response risk degree, the maximum amplitude jump value, the maximum amplitude jump value, and the average background interference intensity of the device under test at a certain frequency band interval, , , , are, in sequence, the current contact resistance value of the port of the network analyzer, the cable shielding attenuation value, the reflected voltage value of the current connection state, and the reflected voltage value of the standard load state, , , , are, in sequence, the response sharp adjustment coefficient, the contact sensitivity adjustment coefficient, the interference suppression factor, and the environmental interference enhancement coefficient stored in the database.

6. The method for optimizing dynamic segmented scanning of a network analyzer according to claim 3, wherein The specific steps for determining the scanning priority level of the device under test in each frequency band interval are as follows: Perform mean and standard deviation analysis on the reflection amplitude values of several frequency points within each frequency band interval of the device under test to obtain the reflection amplitude mean and the reflection amplitude standard deviation for each frequency band interval of the device under test; Obtain the historical calibration port temperature value of the network analyzer, and comprehensively analyze it in combination with the mean reflection amplitude, mean standard deviation of reflection amplitude, response risk level of each frequency band interval of the device under test, as well as the current port temperature value of the network analyzer, the reflection amplitude value in the current connection state, and the reflection amplitude value in the standard load state, to obtain the scanning priority level of the device under test in each frequency band interval.

7. The dynamic segmented scanning optimization method of a network analyzer according to claim 1, characterized in that The specific formula for calculating the scanning priority level of the device under test in a certain frequency band interval is as follows: ； Among them, , , , are, in sequence, the scan priority level, response risk degree, mean standard deviation of reflection amplitude, and mean value of reflection amplitude of the device under test at a certain frequency band interval, , , , are, in sequence, the current temperature value of the port of the network analyzer, the historical calibration port temperature value, the reflection amplitude value of the current connection state, and the reflection amplitude value of the standard load state, , , , are, in sequence, the fluctuation adjustment coefficient, temperature drift sensitivity adjustment coefficient, amplitude adjustment factor, and connection deviation enhancement coefficient stored in the database.

8. The dynamic segmented scanning optimization method of a network analyzer according to claim 1, wherein The specific steps for generating the scanning task plan for each frequency band interval are as follows: Obtain a preset number of scanning level evaluation intervals, and each scanning level evaluation interval corresponds to a scanning strategy respectively. Each scanning strategy includes a number of target sampling points; Respectively judge and analyze the scanning priority levels of the device under test in each frequency band interval with a preset number of scanning level evaluation intervals; Take the scanning strategy corresponding to the scanning priority level of the device under test in each frequency band interval falling within the preset scanning level evaluation interval as the scanning task plan for each frequency band interval.

9. Adaptive frequency stepping algorithm, applying the network analyzer dynamic segmented scanning optimization method according to any one of claims 1-8, characterized in that Include the following steps: Read the number of target sampling points in the scanning strategy of the device under test in each frequency band interval, take the starting frequency of the device under test in each frequency band interval as the first scanning frequency point, and perform frequency jump based on the set step size to obtain the frequency position of the second scanning frequency point of the device under test in each frequency band interval; For the second scanning frequency point of the device under test in each frequency band interval, read the adjacent frequency points in the corresponding frequency band interval from the low-density frequency point pre-scan, and perform position analysis to obtain the frequency position of the third scanning frequency point of the device under test in each frequency band interval; Repeat the position analysis step until the expected scanning requirements are met.

10. The adaptive frequency stepped algorithm according to claim 8, characterized in that, The specific steps for obtaining the frequency position of the third scanning frequency point of the device under test in each frequency band interval are as follows: Read the reflection amplitude value and reflection phase value of the adjacent frequency points in the corresponding frequency band interval to the second scanning frequency point from the low-density frequency point pre-scan, and analyze the reflection amplitude difference and reflection phase difference of the second scanning frequency point of the device under test in each frequency band interval; Obtain the historical calibration port contact resistance value of the network analyzer, and comprehensively analyze it in combination with the current port contact resistance value of the network analyzer, the current port temperature value, the historical calibration port temperature value, the current connection state reflection voltage value, and the standard load state reflection voltage value to obtain the state disturbance factor of the network analyzer; Combine the reflection amplitude difference and reflection phase difference of the second scanning frequency point of the device under test in each frequency band interval with the state disturbance factor of the network analyzer to obtain the frequency position of the third scanning frequency point of the device under test in each frequency band interval.

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