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CN-121231889-B - Self-adaptive vector network scanning method, vector network analyzer, medium and product

CN121231889BCN 121231889 BCN121231889 BCN 121231889BCN-121231889-B

Abstract

The invention discloses a self-adaptive vector network scanning method, a vector network analyzer, a medium and a product, and relates to the field of measuring electric variables. The method comprises the steps of generating a sparse frequency point sequence through chaotic mapping to preliminarily detect S parameters, calculating time domain reflection peaks, frequency domain curvature, information entropy, wavelet transformation and other characteristics of data, intelligently dividing a frequency band into a high sensitive area and a flat area based on the characteristics, mapping a time domain key area into a frequency domain ripple area, distributing optimized frequency step length and point number for each area by using a quantum genetic algorithm to form a scanning strategy, executing sectional scanning according to the strategy, carrying out real-time verification based on local fitting residual errors, dynamically inserting supplementary frequency points and executing supplementary scanning if the residual errors exceed a threshold value, and integrating all the data to generate a complete and accurate S parameter curve. The method can relieve the contradiction between the scanning speed and the precision of the vector network in the test of the new energy automobile parts.

Inventors

  • ZHAO YU
  • YOU DI

Assignees

  • 西安磐维防务科技有限公司

Dates

Publication Date
20260512
Application Date
20250915

Claims (9)

  1. 1. An adaptive vector network scanning method, comprising: generating a non-uniformly distributed sparse frequency point sequence through chaotic mapping in a set test frequency band; detecting S parameter measurement data of a vector network of the new energy automobile component based on the sparse frequency point sequence; calculating time domain features and frequency domain features according to the S parameter measurement data, wherein the time domain features comprise reflection peak features in time domain reflection contours, and the frequency domain features comprise curvature features, information entropy features and wavelet transformation features; Dividing a frequency domain hypersensitive region and a frequency domain flat region on a frequency domain according to a preset frequency domain feature mutation threshold and the frequency domain feature, and determining a time domain key region on a time domain according to a reflection peak feature in the time domain feature; Mapping the time domain key region back to a frequency domain to define a frequency domain ripple region, wherein the frequency domain ripple region represents a frequency band of which the S parameter shows periodical weak fluctuation in a broadband range; the method specifically comprises the steps of extracting a characteristic time delay amount corresponding to each reflection peak from the time domain key region, determining a theoretical ripple period corresponding to a frequency domain according to the characteristic time delay amount, carrying out frequency dependency correction on the theoretical ripple period based on the dispersion characteristic of a signal transmission path to obtain an actual ripple period, and expanding a symmetrical frequency band interval on a frequency domain by taking the actual ripple period as a reference to define the frequency domain ripple region; Respectively distributing corresponding optimized frequency step length and scanning sampling point number for the frequency domain hypersensitive region, the frequency domain flat region and the frequency domain ripple region through a quantum genetic algorithm so as to obtain an optimized scanning strategy; performing sectional scanning on the frequency domain hypersensitive region, the frequency domain flat region and the frequency domain ripple region according to the optimized scanning strategy to obtain real-time scanning data; calculating local fitting residual errors of all areas based on the real-time scanning data; If any local fitting residual exceeds a confidence threshold, dynamically inserting a supplementary frequency point in a corresponding frequency domain interval and executing supplementary scanning to obtain supplementary scanning data; And generating an S parameter curve of the new energy automobile part in the test frequency band based on the real-time scanning data and the supplementary scanning data.
  2. 2. The method according to claim 1, wherein the performing frequency-dependent correction on the theoretical ripple period based on the dispersion characteristic of the signal transmission path to obtain an actual ripple period specifically includes: Acquiring medium parameters of a vector network transmission path in the new energy automobile part from a preset material characteristic database; Constructing a phase velocity frequency dependent characteristic model of electromagnetic wave propagation in the vector network transmission path based on the medium parameters; and performing frequency-dependent compensation on the theoretical ripple period according to the phase velocity frequency-dependent characteristic model to obtain an actual ripple period.
  3. 3. The method of claim 1, wherein determining a theoretical ripple period corresponding in the frequency domain from the characteristic delay amount comprises: determining the double-pass propagation delay amount of the electromagnetic wave in the signal transmission path based on the characteristic delay amount and the reflection physical mechanism; And converting the two-way propagation delay amount into a period parameter corresponding to the frequency domain ripple wave to obtain a theoretical ripple wave period.
  4. 4. A method according to any one of claims 1-3, wherein the dynamically inserting supplemental frequency points and performing supplemental scanning within the corresponding frequency domain interval comprises: Determining the frequency domain interval of which the local fitting residual exceeds a confidence threshold as a target subinterval needing to increase scanning density; determining the insertion density of the supplementary frequency points based on the number of scanned frequency points in the target subinterval and the fitting curvature change rate; and inserting the supplementary frequency points into the target subinterval according to the insertion density of the supplementary frequency points, and executing supplementary scanning based on the currently allocated frequency step.
  5. 5. The method of claim 4, wherein determining the insertion density of supplemental bins based on the number of scanned bins and the rate of change of the fitted curvature within the target subinterval comprises: Counting the total number of scanned frequency points in the target subinterval; Calculating the average fitting curvature absolute value of the scanned frequency points according to the total number; determining a density grade based on the total number and the average fit curvature absolute value; And determining the corresponding complementary frequency point insertion density according to the density grade.
  6. 6. The method of claim 1, wherein the generating the unevenly distributed sparse sequence of frequency points by chaotic mapping comprises: Setting an initial value and a nonlinear control parameter of chaotic mapping; performing repeated iterative operation based on the nonlinear control parameters to obtain a chaotic variable sequence; mapping the chaotic variable sequence into the test frequency band to form a sparse frequency point sequence with non-uniform distribution.
  7. 7. A vector network analyzer comprising one or more processors and memory; The memory is coupled to the one or more processors, the memory for storing computer program code comprising computer instructions that the one or more processors invoke to cause the vector network analyzer to perform the method of any of claims 1-6.
  8. 8. A computer readable storage medium storing computer instructions which, when run on a vector network analyzer, cause the vector network analyzer to perform the method of any of claims 1-6.
  9. 9. A computer program product, characterized in that the computer program product, when run on a vector network analyzer, causes the vector network analyzer to perform the method according to any of claims 1-6.

Description

Self-adaptive vector network scanning method, vector network analyzer, medium and product Technical Field The invention relates to the technical field of measuring electric variables, in particular to a self-adaptive vector network scanning method, a vector network analyzer, a medium and a product. Background Vector network analyzers (Vector Network Analyzer, abbreviated VNA) are indispensable core test instruments in the field of modern radio frequency, microwave and high-speed digital circuits, which accurately characterize the electrical performance of a device under test (Device Under Test, abbreviated DUT) by scanning and measuring its scattering parameters (S-parameters) over a wide frequency range. As a core instrument for accurately characterizing electrical characteristics, the application of VNA has also penetrated the full chain of new energy automobiles in research, development and production. From the quality control of 800V high-voltage wire harnesses, connectors and power battery packs, which are related to the safety of the whole vehicle, to the signal integrity verification of a vehicle-mounted high-speed data link, a millimeter wave radar antenna and a V2X communication module, which ensure the stability of an intelligent driving system, the VNA plays an indispensable role and is a key testing tool for ensuring the high performance and the high reliability of a new energy automobile. However, in industrial scale applications, existing VNA testing techniques face a challenging technical contradiction, namely, a conflict of speed and accuracy of vector network scanning. In order to accurately capture key electrical characteristics of a tested part of a new energy automobile, such as weak potential safety hazard signals caused by small manufacturing defects of a high-voltage wire harness or sharp resonance peaks of a millimeter wave radar antenna, the VNA needs to scan by adopting extremely small frequency steps. However, this results in a single test that is too long to meet the beat requirements of mass production in the automotive industry. On the contrary, if a large step scan is used to pursue the test speed, the critical detail features are very likely to be missed or distorted, so that the test result is not accurate enough, and reliable evaluation on the performance and safety of the product cannot be performed. The contradiction between the speed and the precision becomes a technical bottleneck for restricting the quality improvement and the efficiency improvement of the new energy automobile industry. Disclosure of Invention Aiming at the technical problems and defects, the invention aims to provide a self-adaptive vector network scanning method, a vector network analyzer, a medium and a product, which can relieve contradiction between the vector network scanning speed and the vector network scanning precision in the test of new energy automobile parts. In order to achieve the above object, according to a first aspect, the present invention provides a self-adaptive vector network scanning method, which includes generating a non-uniformly distributed sparse frequency point sequence through chaotic mapping in a set test frequency band, detecting S parameter measurement data of a vector network of a new energy automobile component based on the sparse frequency point sequence, calculating time domain features and frequency domain features according to the S parameter measurement data, wherein the time domain features include reflection peak features in a time domain reflection contour, the frequency domain features include curvature features, information entropy features and wavelet transform features, dividing a frequency domain high-sensitivity region and a frequency domain flat region on a frequency domain according to preset frequency domain feature mutation threshold values and frequency domain features, determining a time domain key region on a time domain according to reflection peak features in the time domain features, mapping the time domain key region back to a frequency domain to define a frequency domain ripple region, wherein the frequency domain ripple region represents a frequency band in which the S parameter exhibits periodic weak fluctuation in a wideband range, respectively distributing corresponding optimized frequency steps and scanning sampling strategies for the frequency domain high-sensitivity region, the frequency domain flat region and the frequency domain ripple region through quantum genetic algorithm, respectively, and the frequency domain ripple region according to the optimized scanning strategies, dividing the frequency domain high-sensitivity region, the frequency domain flat region and the frequency domain ripple region, and the frequency domain flat region, and performing a real-time domain complementary scanning algorithm if the frequency domain complementary scanning data is more than the real-time complementary frequency domain complementary to th