An antenna near-field plane attribute evaluation detection method, electronic equipment and storage medium

Abstract

This application provides a method, electronic device, and storage medium for evaluating and detecting the near-field planar properties of an antenna. The method includes using an electromagnetic field probe to move in a grid pattern within a two-dimensional plane at λ / 2 sampling step intervals, simultaneously measuring the amplitude and phase values ​​of the electric field vector at each sampling point; analyzing and identifying sub-regions of feed phase faults based on phase distribution characteristics; analyzing and outputting the boundaries of adjacent regions of continuous anomalies based on the sub-regions of feed phase faults and near-field amplitude anomalies, performing far-field transformation on these adjacent regions, and outputting far-field radiation characteristic data for verifying the anomaly results of these regions. This processing method, through anomaly region identification and association, secondary detection verification, combined with near-field fine scanning positioning and far-field back-calculation verification, forms a closed-loop diagnostic logic. This approach improves the reliability of the final output anomaly results and reduces quality misjudgments.

Description

Technical Field

[0001] This application relates to the field of antenna quality testing and evaluation technology, and in particular to an antenna near-field planar attribute evaluation and testing method, electronic device and storage medium. Background Technology

[0002] With the continuous development of communication technology, antennas, as a core component of wireless communication systems, directly affect the signal transmission quality of the entire system. The electromagnetic field around an antenna is divided into the near field (a mixed region of induced and radiated fields) and the far field (a pure radiated field). Far-field measurements require a large space (typically requiring the antenna size D and wavelength λ to satisfy R > 2D² / λ) and are greatly affected by the environment. This has become a key issue in current antenna research and development and production.

[0003] In antenna design and manufacturing, it is often necessary to test and analyze the near-field region of the antenna. The near-field region refers to the area near the antenna's radiating aperture, and the electromagnetic field distribution in this region has a significant impact on the antenna's radiation characteristics. The high-density near-field attribute information collected is then processed and analyzed. Traditional antenna testing methods often rely on far-field radiation testing, while near-field region evaluation is more complex and difficult to accurately measure through simple experiments. This process is of significant technical importance for obtaining feedback on the properties of the near-field plane and for diagnosing antenna problems using near-field information.

[0004] Currently, common methods for antenna near-field measurement include probe measurement, optical measurement, and computational simulation. While these methods can provide some near-field information, they generally suffer from insufficient measurement accuracy, often neglecting the analysis of the properties of the near-field plane and its hidden features for further analysis and identification. The sampling accuracy of traditional methods is insufficient to meet requirements, making it difficult to reveal minute changes in the electromagnetic field in the antenna's near field, thus failing to achieve a comprehensive and accurate quality analysis and evaluation of the near-field plane. Summary of the Invention

[0005] This application provides a method, electronic device, and storage medium for evaluating and detecting near-field planar properties of an antenna, in order to solve the problem of unreliable accuracy in the detection of near-field planar properties in the prior art.

[0006] In a first aspect, this application provides a method for evaluating and detecting the near-field planar properties of an antenna, including:

[0007] In the near-field region 1-5 wavelengths from the antenna radiating aperture, establish a two-dimensional plane scanning coordinate system parallel to the antenna aperture surface;

[0008] An electromagnetic field probe is used to move in a grid pattern within the two-dimensional plane at sampling step intervals of λ / 2, and the amplitude and phase values ​​of the electric field vector are measured synchronously at each sampling point.

[0009] Construct a three-dimensional dataset containing the position coordinates of the electromagnetic field probe and the amplitude and phase values ​​of the electric field vector at the sampling point;

[0010] Based on the three-dimensional dataset, the two-dimensional plane of the near-field region is divided into sub-regions, and the amplitude and phase values ​​of the electric field vector distribution in each sub-region are calculated. Anomalous sub-regions are extracted using the amplitude and phase values ​​of the electric field vector distribution. The anomalous sub-regions are analyzed to obtain deducible near-field attribute information.

[0011] Preferably, the electromagnetic field probe moves in a grid pattern within the two-dimensional plane at sampling step intervals of λ / 2, simultaneously measuring the amplitude and phase values ​​of the electric field vector at each sampling point. Specifically, this includes the following steps:

[0012] The two-dimensional plane is used as the area to be scanned, and the area to be scanned is divided into N×M sub-regions;

[0013] An electromagnetic field probe is used to move in a grid pattern within the two-dimensional plane at sampling step intervals of λ / 2, and to sample the amplitude and phase values ​​of the electric field vector at the corresponding sampling points.

[0014] Preferably, the two-dimensional plane of the near-field region is divided into sub-regions based on the three-dimensional dataset, and the amplitude and phase values ​​of the electric field vector distribution in each sub-region are calculated; abnormal sub-regions are extracted using the amplitude and phase values ​​of the electric field vector distribution; and the abnormal sub-regions are analyzed to obtain deducible near-field attribute information. The specific operation steps are as follows:

[0015] Based on the aforementioned 3D dataset, near-field distribution analysis is performed, and the 2D plane of the near-field region is divided into several sub-regions; the amplitude and phase values ​​of the electric field vector distribution in each sub-region within the 2D plane of the near-field region are extracted.

[0016] The amplitude of the electric field vector is evaluated based on the amplitude value of the electric field vector as input, and sub-regions with abnormal near-field amplitude are identified and screened in the near-field region.

[0017] Based on the phase distribution characteristics analysis, sub-regions of feed phase faults in the near-field region are identified and screened; based on the analysis of the sub-regions of feed phase faults and sub-regions of near-field amplitude anomalies, the boundaries of continuously anomalous adjacent regions are obtained.

[0018] The boundaries of the continuous abnormal adjacent regions are locked, the far-field radiation characteristic data corresponding to the continuous abnormal adjacent regions are obtained, and the far-field radiation characteristic data of the continuous abnormal adjacent regions are calculated and output through the far-near field transformation algorithm.

[0019] Preferably, the deducible near-field attribute information includes multiple attribute information covering various sub-regions within the analyzed near-field region.

[0020] Preferably, near-field distribution analysis is performed based on the three-dimensional dataset to divide the two-dimensional plane of the near-field region into several sub-regions; the amplitude and phase values ​​of the electric field vector distribution in each sub-region within the two-dimensional plane of the near-field region are extracted; the amplitude of the electric field vector is evaluated based on the amplitude value of the electric field vector as input, and sub-regions with abnormal near-field amplitude are identified and filtered. Specifically, the following steps are included:

[0021] Obtain the amplitude of the electric field vector corresponding to multiple sampling points in each sub-region of the two-dimensional plane, and calculate the electric field vector of the j-th sampling point in the i-th sub-region. ;

[0022] The first sampling point is calculated based on the amplitude of the electric field vector corresponding to the plurality of sampling points. The standard deviation of the magnitude of the electric field vector in each sub-region and the The average amplitude of the electric field vector in each sub-region ;

[0023] The first Each sub-region and its surroundings The sub-regions are aggregated as follows

[0024] Using the electric field vector of the j-th sampling point in the i-th sub-region With the Subregional amplitude standard deviation and the Sub-region amplitude average Calculate the first local amplitude deviation rate for each sampling point;

[0025] Using the first Each sub-region and its surroundings The set of subregions Calculate the first The second local amplitude deviation rate corresponding to each sub-region;

[0026] The third local amplitude deviation rate is calculated using the first local amplitude deviation rate and the second local amplitude deviation rate.

[0027] Preset a first standard threshold, a second standard threshold, a reference deviation threshold, and an abnormal reference density threshold;

[0028] Determine whether the first local amplitude deviation rate is greater than or equal to the first standard threshold;

[0029] If so, then the deviation between the amplitude of the electric field vector corresponding to the current single sampling point and the average amplitude of the sub-region is determined to be abnormal, and the sampling point is abnormal;

[0030] Calculate the abnormal density of the sampling point in the sub-region corresponding to the sampling point; use the abnormal density of the sampling point to determine whether it is greater than the abnormal reference density threshold;

[0031] If so, the current sub-region corresponding to the sampling point is determined to be a sub-region with abnormal near-field amplitude;

[0032] Determine whether the second local amplitude deviation rate is greater than or equal to the second standard threshold;

[0033] If so, then determine that the current sub-region has an abnormal amplitude deviation from the K surrounding sub-regions, and filter the current sub-region as a sub-region with near-field amplitude abnormality;

[0034] If the first local amplitude deviation rate is less than a preset first standard threshold and the second local amplitude deviation rate is less than a preset second standard threshold, then it is determined whether the third local amplitude deviation rate is greater than a reference deviation threshold.

[0035] If so, the current sub-region is directly determined to be a sub-region with abnormal near-field amplitude.

[0036] Preferably, the sub-regions of feed phase faults in the near-field region are identified and screened based on phase distribution characteristics; the boundaries of continuously anomalous adjacent regions are obtained by analyzing the sub-regions of feed phase faults and sub-regions of near-field amplitude anomalies, specifically including the following operation steps:

[0037] The residual phase field distribution Φres(x,y) is extracted from the two-dimensional plane of the near-field region, and the characteristics of the phase distribution are further analyzed to obtain the features of the residual phase field distribution Φres(x,y).

[0038] Based on the characteristics of the phase distribution, calculations are performed in the spatial domain or frequency domain to obtain the phase abrupt change gradient or phase gradient field; the sampling points of each sub-region are identified according to the phase abrupt change gradient or phase gradient field to obtain the sub-region of the power supply phase fault.

[0039] Connectivity analysis is performed on the sub-regions of near-field amplitude anomalies and feed phase faults to obtain continuous anomalous adjacent regions; edge analysis is performed on the continuous anomalous adjacent regions to obtain the boundaries of the continuous anomalous adjacent regions.

[0040] Preferably, the residual phase field distribution Φres(x,y) is extracted from the two-dimensional plane of the near-field region, and the residual phase field distribution Φres(x,y) is further analyzed to obtain the characteristics of the phase distribution; the phase abrupt change gradient is calculated in the spatial domain based on the characteristics of the phase distribution; the sampling points of each sub-region are identified according to the phase abrupt change gradient to obtain the sub-region of the power supply phase fault, including;

[0041] The residual phase field distribution Φres(x,y) of each sub-region is extracted based on the distribution characteristics of the phase value of the electric field vector in the two-dimensional plane of the near-field region.

[0042] The phase distribution characteristics are obtained by analyzing each sub-region using the residual phase field distribution Φres(x,y);

[0043] Based on the phase distribution characteristics, the phase abrupt gradient in the spatial domain is used to identify and aggregate abnormal sampling points, thereby obtaining the sub-region of the feed phase fault.

[0044] Based on the phase distribution characteristics, the phase gradient field of the frequency domain field is used to identify and aggregate abnormal sampling points to obtain the sub-region of the feed phase fault.

[0045] Preferably, based on the phase distribution characteristics, the phase gradient field of the frequency domain field is used to identify and aggregate abnormal sampling points to obtain the sub-region of the feed phase fault, specifically including the following steps:

[0046] Based on the distribution characteristics of the phase values ​​of the electric field vector in the two-dimensional plane, the residual phase field distribution Φres(x,y) is calculated for each sub-region; the phase residual distribution of each sub-region is analyzed using the calculated residual phase field distribution Φres(x,y) to obtain the characteristics of the phase distribution; a two-dimensional discrete Fourier transform is performed on the residual phase field distribution Φres(x,y) to obtain the spatial spectrum Φ(kx,ky).

[0047] A Kaiser window function W(k) is applied to the spatial spectrum Φ(kx,ky) to suppress high-frequency noise;

[0048] The phase distribution Φsmooth(x,y) of the spatial spectrum Φ(kx,ky) after reconstruction and filtering is obtained by inverse Fourier transform;

[0049] Calculate the phase gradient field ▽Φsmooth and mark the gradient abrupt change points;

[0050] The global "anomaly intensity field" is obtained by calculating the maximum neighborhood phase difference, combining it with distance weighting, and calculating abrupt changes in phase curvature in sub-regions. ;

[0051] Get Calculate the global "anomaly intensity field", and then based on the obtained global "anomaly intensity field"... Initially marked as a phase metric anomaly at the sampling point level, if the current number There is at least one anomalous sampling point in the sub-region Then further filter the current abnormal sampling points. The current number The sub-region is a sub-region of the feed phase fault region.

[0052] Accordingly, this application also provides an electronic device, characterized in that it includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are to be invoked and executed by the processing component to implement an antenna near-field plane attribute evaluation and detection method as described in any of the above steps.

[0053] Accordingly, this application also provides a computer storage medium, characterized in that it stores a computer program, which, when executed by a computer, implements an antenna near-field plane attribute evaluation and detection method as described in any of the above steps.

[0054] The technical solution of this application has the following beneficial effects:

[0055] This application provides a method for evaluating and detecting near-field planar properties of an antenna. It obtains high-resolution near-field distribution data by establishing a fine grid (λ / 2 step size) in the near-field region (1-5 wavelengths) and performing synchronous amplitude / phase measurements. Then, by combining amplitude evaluation and phase distribution characteristic analysis, it can accurately locate specific sub-regions in the near-field plane where amplitude anomalies (such as element failure, obstruction, or deformation) and feed phase faults (such as phase shifter faults or feed network problems) occur. This technical solution can not only identify discrete anomalies but also, by analyzing the boundaries of adjacent areas of continuous anomalies, identify related fault regions with physical significance (potentially corresponding to a faulty component or region), rather than isolated points.

[0056] The core technical advantage of this processing method lies in the introduction of a far-field verification mechanism to confirm the authenticity and validity of anomalies identified in the near field. Through anomaly region identification and correlation, as well as secondary detection verification, combined with near-field fine-scan localization and far-field reverse verification, a closed-loop diagnostic logic is formed. Near-field localization provides the suspected region, while far-field reverse verification provides independent evidence for validation. This significantly improves the reliability and accuracy of the final anomaly output, avoiding misjudgments that might result from relying solely on a single near-field or far-field data source. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 A flowchart of an antenna near-field planar property evaluation and detection method provided in this application is shown;

[0059] Figure 2 This paper presents a schematic diagram of the near-field plane of an antenna near-field plane attribute evaluation and detection method provided in this application.

[0060] Figure 3 This paper presents a flowchart illustrating the identification of the boundaries of continuous anomalous adjacent regions using an antenna near-field planar property evaluation and detection method provided in this application.

[0061] Figure 4 This paper illustrates a two-dimensional planar analysis of the near-field region of an antenna near-field planar attribute evaluation and detection method provided in this application.

[0062] Figure 5 A schematic diagram of the structure of an electronic device provided in this application is shown.

[0063] Labels: Storage component 31; Processing component 32. Detailed Implementation

[0064] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0065] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The sequence numbers of the operations, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0066] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0067] Example 1

[0068] like Figure 1 As shown, Figure 2 As shown in the figure, this application provides a method for evaluating and detecting the near-field planar properties of an antenna, the method comprising:

[0069] S1: In the near-field region 1-5 wavelengths away from the antenna radiating aperture, establish a two-dimensional plane scanning coordinate system parallel to the antenna aperture surface;

[0070] S2: An electromagnetic field probe is used to move in a grid pattern within the two-dimensional plane at sampling step intervals of λ / 2, and the amplitude and phase values ​​of the electric field vector are measured synchronously at each sampling point;

[0071] S3: Construct a three-dimensional dataset containing the position coordinates of the electromagnetic field probe and the amplitude and phase values ​​of the electric field vector at the sampling point;

[0072] S4: Based on the three-dimensional dataset, the two-dimensional plane of the near-field region is divided into sub-regions, and the amplitude and phase values ​​of the electric field vector distribution in each sub-region are calculated; abnormal sub-regions are extracted using the amplitude and phase values ​​of the electric field vector distribution; the abnormal sub-regions are analyzed to obtain deductive near-field attribute information.

[0073] It should be noted that in step S1 above, a two-dimensional plane scanning coordinate system is established in the near-field region 1-5 wavelengths away from the antenna radiating port. A two-dimensional coordinate system is determined for scanning in the near-field region to ensure that the range and direction of data acquisition are adapted to the distribution characteristics of the electromagnetic field. This region is 1-5 wavelengths away from the antenna radiating port and is within the near-field range of the antenna, which is crucial for observing the electric and magnetic field distribution of the antenna. At the same time, the near-field region of the antenna is a key area for analyzing its radiation characteristics and optimizing its design. Scanning in this region can obtain more accurate electromagnetic field data, which helps to understand the performance of the antenna in actual operation.

[0074] In step S2 above, the electromagnetic field probe moves in a grid pattern at sampling step intervals of λ / 2 to measure the electric field amplitude and phase values. This step, which moves in a grid pattern in a two-dimensional plane at a step size of λ / 2 (half a wavelength), can measure the electric field amplitude and phase at each sampling point with high precision. The fine sampling interval helps to capture the detailed characteristics of the electric field. The half-wavelength sampling step ensures the resolution and accuracy of the data, can capture minute changes in the electromagnetic field, and avoids information loss due to insufficient sampling.

[0075] In step S3 above, a three-dimensional dataset containing the electromagnetic field probe position and electric field amplitude and phase values ​​is constructed. By recording the probe position and the electric field amplitude and phase of each sampling point, a three-dimensional dataset is constructed, which can provide comprehensive spatial and electromagnetic field information for subsequent data processing. The three-dimensional dataset provides spatial structured information for subsequent analysis, which facilitates the visualization of electromagnetic field distribution in practice and comprehensive analysis, and can more clearly see the changes of electromagnetic field in different spatial locations.

[0076] In step S4 above, the near-field region is segmented into sub-regions, the amplitude and phase values ​​of the electric field vector are calculated, and abnormal sub-regions are extracted and analyzed. Dividing the near-field region into sub-regions allows for the local calculation of the electric field distribution characteristics of each region. By calculating the electric field amplitude and phase of each sub-region, abnormal electric field distributions can be accurately located. Further analysis of these abnormal sub-regions yields more detailed derivational near-field attribute information. The above steps of segmenting the electric field into sub-regions can precisely identify potential local problems in the antenna (such as non-uniform radiation distribution, directional deviation, etc.) and enable more targeted optimization. Furthermore, the extraction and analysis of abnormal regions helps diagnose potential antenna faults or design defects.

[0077] Preferably, step S2 can be considered as a preferred implementation scheme; the step of using an electromagnetic field probe to move in a gridded manner in the two-dimensional plane at sampling step intervals of λ / 2, and simultaneously measuring the amplitude and phase values ​​of the electric field vector at each sampling point, specifically includes the following steps:

[0078] S21: Take the two-dimensional plane as the area to be scanned, and divide the area to be scanned into N×M sub-regions;

[0079] It should be noted that the two-dimensional plane to be scanned is divided into several sub-regions (such as a 10cm×10cm rectangular grid), and each sub-region contains multiple sampling points; the divided sub-regions can provide spatial analysis units for subsequent local anomaly detection (amplitude deviation rate calculation in S411, phase change analysis in S412); and this step is directly related to continuous anomaly adjacent area locking (S414), and large-area fault boundaries are determined by sub-region aggregation;

[0080] S22: An electromagnetic field probe is used to move in a grid pattern within the two-dimensional plane at sampling step intervals of λ / 2 and to sample the amplitude and phase values ​​of the electric field vector at the corresponding sampling points.

[0081] It should be noted that the probe moves in steps of half wavelength (λ / 2) to record the electric field vector (|E|, Φ) at each sampling point. The amplitude and phase values ​​of the electric field vector in this step can support the construction of the three-dimensional dataset (S3), ensuring that the spatial changes of the electric field amplitude / phase are fully captured and avoiding the loss of high-frequency components (such as abrupt edge changes and interference fringes). It can also provide the original input for near-field analysis (S41). At the same time, λ / 2 is the embodiment of the Nyquist sampling theorem in the spatial domain, ensuring that the highest spatial frequency component of the electromagnetic field can be captured (avoiding aliasing). Furthermore, λ / 2 is the optimal step size that balances resolution and scanning efficiency (a denser step size will greatly increase the time consumption, while a sparser step size will result in missed anomalies).

[0082] Specifically, in step S4, the two-dimensional plane of the near-field region is divided into sub-regions based on the three-dimensional dataset, and the amplitude and phase values ​​of the electric field vector distribution in each sub-region are calculated; abnormal sub-regions are extracted using the amplitude and phase values ​​of the electric field vector distribution; and the abnormal sub-regions are analyzed to obtain deducible near-field attribute information. The specific operation steps are as follows:

[0083] S41: Perform near-field distribution analysis based on the three-dimensional dataset, and divide the two-dimensional plane of the near-field region into several sub-regions; extract the amplitude and phase values ​​of the electric field vector distribution in each sub-region of the two-dimensional plane of the near-field region (that is, the distribution of amplitude and phase in the two-dimensional plane).

[0084] The amplitude of the electric field vector is evaluated based on the amplitude value of the electric field vector as input, and sub-regions with abnormal near-field amplitude are identified and screened in the near-field region.

[0085] Based on the phase distribution characteristics analysis, sub-regions of feed phase faults in the near-field region are identified and screened; based on the analysis of the sub-regions of feed phase faults and sub-regions of near-field amplitude anomalies, the boundaries of continuously anomalous adjacent regions are obtained (i.e., by comparing the calculated Gaussian taper of the near-field plane with the measured Gaussian taper, it is determined whether there is a near-field amplitude anomaly in the current sub-region (i.e., the near-field amplitude (i.e., the amplitude of the electric field vector) of the phased array antenna shows a "Gaussian taper" with a high center and low edge (as designed), but if a local depression is found in the actual measurement, it will indicate that the unit has failed)).

[0086] S42: Lock the boundary of the continuous abnormal adjacent area, obtain the far-field radiation characteristic data corresponding to the continuous abnormal adjacent area, and calculate and output the deducible near-field attribute information for the continuous abnormal adjacent area by using the far-near field transformation algorithm on the far-field radiation characteristic data of the continuous abnormal adjacent area.

[0087] The deduced near-field attribute information includes multiple attribute information covering various sub-regions in the near-field region (i.e., sub-regions with feed phase faults and sub-regions with near-field amplitude anomalies in the near-field region).

[0088] The explanation is as follows: Step S41 above only involves accurately measuring the electromagnetic field (amplitude and phase) distribution radiated by the antenna in the near-field region (usually within a few wavelengths), which is only a preliminary judgment of the anomalous sub-region. Then, the technical solution involved in step S42 acquires far-field radiation characteristic data and derives the deducible near-field attribute information of the continuous anomalous adjacent region through a transformation algorithm (far-near-field transformation algorithm). During this process, a secondary analysis is performed to verify whether the deduced near-field attribute information of the continuous anomalous adjacent region is true. This further verifies the verification of the sub-region anomaly judgment and its regional anomaly type. The final anomaly result (the secondary verification result of the anomalous sub-region) is output to evaluate the near-field planar attribute quality of the antenna.

[0089] The quantization parameters corresponding to the abnormal sub-regions may include the following parameters, such as the local amplitude deviation rate (used to detect the relative deviation between the abnormal point of the unit failure and the average amplitude of the sub-region), the phase change gradient (used to detect the phase jump between the abnormal point and the adjacent region), and the polarization distortion degree (used to detect the cross-polarization ratio of the abnormal point). Other quantization parameters will not be elaborated here.

[0090] The above-mentioned near-field plane attribute evaluation and detection method for antennas focuses on data detection and analysis. The core idea of ​​the back-field measurement technology is to accurately measure the electromagnetic field (amplitude and phase) distribution radiated by the antenna in the near-field region (usually within a few wavelengths) to conduct preliminary data analysis and preliminary anomaly judgment. Then, through strict transformation processing of far-field radiation characteristics (far-near field transformation), the deducible near-field attribute information is derived. In this process, the attributes (for secondary verification) and quality of the near-field plane are analyzed and fed back.

[0091] In the above steps S41 and S42, the electromagnetic field around the antenna is divided into a near field (a mixed region of induced and radiated fields) and a far field (a pure radiated field). Far-field measurements require a large space and are greatly affected by the environment. Near-field measurements can be performed in a relatively small, anechoic chamber, with high accuracy and good controllability.

[0092] During planar scanning, a precisely moving probe (such as a small open waveguide or dipole probe) is used to measure the amplitude and phase of the electromagnetic field at discrete grid points on a plane close to the antenna aperture (parallel to the antenna aperture plane). This plane is called the "near-field plane." Core data analysis is then performed to acquire complex electromagnetic field data for each sampling point on the near-field plane, such as the amplitude and phase distribution characteristics of the electric field component ExEy or magnetic field component, as well as other electric field vectors. Subsequently, the acquired high-density near-field data is processed and analyzed for preliminary judgment, providing initial and direct analysis of the near-field plane data (i.e., amplitude, phase, and polarization) to reflect the properties of the near-field plane.

[0093] The application of transformation methods transforms far-field data to calculate deductive near-field attribute information of near-field data, enabling secondary sub-region verification and identification processing.

[0094] In summary, the core technical effect of this approach is that it locates suspected fault areas through high-precision near-field scanning and innovatively uses far-field data from these areas for inversion verification. This achieves accurate location, reliable diagnosis, and effective verification of near-field planar attribute anomalies of the antenna, significantly improving the accuracy, efficiency, and engineering practicality of antenna performance evaluation and fault diagnosis.

[0095] Better, such as Figure 3 As shown, Figure 4 As shown, step S41 can be considered a preferred implementation scheme; based on the three-dimensional dataset, near-field distribution analysis is performed to divide the two-dimensional plane of the near-field region into several sub-regions; the amplitude and phase values ​​of the electric field vector distribution in each sub-region within the two-dimensional plane of the near-field region are extracted; the amplitude value of the electric field vector is used as input to evaluate the amplitude of the electric field vector, and sub-regions with near-field amplitude anomalies in the near-field region are identified and screened; based on the phase distribution characteristics, sub-regions with feed phase faults in the near-field region are identified and screened; based on the analysis of the sub-regions with feed phase faults and the sub-regions with near-field amplitude anomalies, the boundaries of continuously anomalous adjacent regions are obtained, specifically including the following operation steps:

[0096] S411: Obtain the amplitude of the electric field vector corresponding to multiple sampling points in each sub-region of the two-dimensional plane, and calculate the electric field vector of the j-th sampling point in the i-th sub-region. ;

[0097] The first sampling point is calculated based on the amplitude of the electric field vector corresponding to the plurality of sampling points. The standard deviation of the magnitude of the electric field vector in each sub-region and the The average amplitude of the electric field vector in each sub-region ;

[0098] The first Each sub-region and its surroundings The sub-regions are aggregated as follows ;

[0099] Using the electric field vector of the j-th sampling point in the i-th sub-region With the Subregional amplitude standard deviation and the Sub-region amplitude average Calculate the first local amplitude deviation rate for each sampling point (i.e., the sampling point to be detected; and the sampling point to be detected may be identified as an abnormal point or a normal point in the future);

[0100] Using the first Each sub-region and its surroundings The set of subregions Calculate the first The second local amplitude deviation rate corresponding to each sub-region;

[0101] The third local amplitude deviation rate is calculated using the first local amplitude deviation rate and the second local amplitude deviation rate, and the expression is as follows:

[0102] ;

[0103] In the formula, The representative sub-region (i.e., the 'i' in this technical solution represents a sub-region, which can only be identified as an anomaly in subsequent processes) is considered a sub-region. (Representing sub-regions with near-field amplitude anomalies) numbered;

[0104] This represents the position of the j-th sampling point in the i-th sub-region (i.e., the index of the sampling point's position in this sub-region).

[0105] Represented as the first The average amplitude in the sub-region;

[0106] Represented as the first The standard deviation of amplitude in the sub-region;

[0107] Represents the weighting factor for adjacent regions (default 0.5);

[0108] Represented as the first Each sub-region and its surroundings A set of subregions;

[0109] It is expressed as the first local amplitude deviation rate, which reflects the degree of deviation of the amplitude value of the current sampling point j relative to the entire i-th sub-region;

[0110] This represents the electric field vector at the j-th sampling point in the i-th sub-region; This represents the amplitude of the electric field vector, used to quantify the local electric field intensity at the sampling point;

[0111] This is expressed as the second local amplitude deviation rate, which is the... The overall deviation of a sub-region compared to its neighboring sub-region K;

[0112] In the above formula, the first Average amplitude in sub-region Where N represents the total number of sub-regions in the two-dimensional plane; n represents the nth sampling point in the i-th sub-region;

[0113] Preset a first standard threshold, a second standard threshold, a reference deviation threshold, and an abnormal reference density threshold;

[0114] Determine whether the first local amplitude deviation rate is greater than or equal to the first standard threshold;

[0115] If so, then the deviation between the amplitude of the electric field vector corresponding to the current single sampling point and the average amplitude of the sub-region is determined to be abnormal, and the sampling point is abnormal;

[0116] The abnormal density of the sampling point is calculated for the sub-region corresponding to the sampling point (i.e., the abnormal density of the sampling point in the corresponding sub-region is calculated by the number of all sampling points in the sub-region); the abnormal density of the sampling point is used to determine whether it is greater than the abnormal reference density threshold.

[0117] If so, the current sub-region corresponding to the sampling point is determined to be a sub-region with abnormal near-field amplitude (i.e., the current sub-region is the i-th sub-region).

[0118] Determine whether the second local amplitude deviation rate is greater than or equal to the second standard threshold;

[0119] If so, it is determined that there is an amplitude deviation anomaly between the current sub-region and the K surrounding sub-regions, and the current sub-region is selected as the sub-region with near-field amplitude anomaly (i.e., the current sub-region is the i-th sub-region).

[0120] If the first local amplitude deviation rate is less than a preset first standard threshold and the second local amplitude deviation rate is less than a preset second standard threshold, then it is determined whether the third local amplitude deviation rate is greater than a reference deviation threshold.

[0121] If so, the current sub-region is directly determined to be a sub-region with abnormal near-field amplitude (i.e., the current sub-region is the i-th sub-region).

[0122] It should be noted that the first local amplitude deviation rate, the second local amplitude deviation rate, and the third local amplitude deviation rate are calculated by presetting the first standard threshold, the second standard threshold, the reference deviation threshold, and the abnormal reference density threshold.

[0123] The first standard threshold primarily determines the significant anomaly of a single sampling point, capturing the significant deviation within a sub-region, thereby reflecting unit-level fault anomalies in the corresponding sub-region. Simultaneously, the second standard threshold determines the amplitude consistency between the sub-region as a whole and its adjacent regions, thus reflecting system-level fault anomalies in the corresponding sub-region. Furthermore, the reference deviation threshold identifies potential anomalies, requiring further verification of the sub-region's deviation rate to improve detection sensitivity and prevent missed detection of minor anomalies. Finally, the anomaly reference density threshold determines whether the sub-region corresponding to an anomaly at a single sampling point is abnormal overall, distinguishing between localized and regional faults (i.e., the relationship between this anomaly reference density threshold and the first and second standard thresholds clearly distinguishes between further verification of sampling points and verification of the overall sub-region).

[0124] When detection If the amplitude of a single sampling point is greater than or equal to the preset first standard threshold, it is determined that the deviation between the amplitude of the current single sampling point and the average amplitude of the sub-region is abnormal. The abnormal sampling point is indicated by the presence of a unit failure causing a sudden drop in amplitude (cold spot) or coupling resonance causing an amplitude spike (hot spot).

[0125] Determine whether the abnormal density (i.e. the number) of sampling points in the current sub-region is greater than the abnormal reference density. If so, determine that the current sub-region is an abnormal sub-region.

[0126] When detection If the amplitude deviation is greater than or equal to the preset second standard threshold, it is determined that the current sub-region and surrounding sub-regions have an abnormal amplitude deviation, and a warning is issued indicating that the antenna radiating aperture structure has deformed, resulting in a large-area amplitude collapse; the current sub-region is then filtered. The sub-region is an abnormal sub-region;

[0127] When detection The local amplitude deviation rate is less than a preset first standard threshold and the second local amplitude deviation rate is less than a preset second standard threshold; however, the local amplitude deviation rate... If the value exceeds the preset reference deviation value, the current i-th sub-region is determined to be an abnormal sub-region.

[0128] The above formula In the middle, the first item " "Detecting local anomalies within a sub-region, i.e., measuring the deviation between the amplitude of a single sampling point and the average amplitude of the sub-region, the second item..." "Compare the overall deviation of adjacent sub-regions to avoid misjudging the design taper as a fault;

[0129] Analysis of the formulas in the above steps shows that The larger the value, the greater the risk of anomalies in the sub-region; the above This reflects the situation where the local electric field deviates significantly from the sub-region; in the first factor, when When element failure causes a sudden drop in amplitude (cold spot) or coupling resonance causes an amplitude spike (hot spot), it is used to assess the overall deviation of a sub-region from its adjacent regions; when Neighborhood mean When the deformation is large, the structural deformation leads to large-area collapse;

[0130] S412: Extract the residual phase field distribution Φres(x,y) from the two-dimensional plane of the near-field region, and further analyze the residual phase field distribution Φres(x,y) to obtain the characteristics of the phase distribution.

[0131] Based on the characteristics of the phase distribution, calculations are performed in the spatial domain or frequency domain to obtain the phase abrupt change gradient or phase gradient field; the sampling points of each sub-region are identified according to the phase abrupt change gradient or phase gradient field to obtain the sub-region of the power supply phase fault.

[0132] It should be noted that, based on the distribution characteristics of the phase values ​​of the electric field vector in the two-dimensional plane of the near-field region described above, the residual phase field distribution Φres(x,y) of each sub-region is extracted. Phase analysis may not be accompanied by amplitude changes (e.g., constant amplitude feeding but phase error). Relying solely on amplitude detection (S411) will miss these errors. Gradient calculations amplify minute phase distortions and independently analyze the residual phase field. The residual phase field can remove residual phase after the antenna design's expected phase distribution (e.g., plane wavefront), highlighting non-ideal perturbations. Based on the characteristics of the phase distribution, spatial domain phase analysis is performed. The abrupt gradient (i.e., phase anomaly metric) and / or the phase gradient field of the frequency domain are used to identify and aggregate anomalous sampling points to obtain the sub-region of the feed phase fault. The sub-region of the feed phase fault is further obtained. The local abrupt change of the phase field is directly captured by spatial domain differentiation (such as the first-order gradient ▽Φ or the second-order Laplace operator ∇²Φ), while the phase field is transformed to the wavenumber domain using Fourier transform. After noise suppression by filtering, the smooth phase field is reconstructed and the gradient is calculated. Based on the complementarity of spatial domain and frequency domain analysis in the above steps, the multimodal characteristics of phase faults in complex electromagnetic environments can be addressed.

[0133] S413: Perform connected component analysis on the sub-region of near-field amplitude anomaly and the sub-region of feed phase fault to obtain continuous abnormal adjacent regions; perform edge analysis on the continuous abnormal adjacent regions to obtain the boundaries of the continuous abnormal adjacent regions.

[0134] It should be noted that the consecutive abnormal adjacent regions of the sub-regions with near-field amplitude anomalies and the sub-regions with feed phase faults are used as the initial set of sub-regions.

[0135] Boundaries are extracted from the sub-regions with near-field amplitude anomalies and the sub-regions with feed phase faults in the initial sub-region set. The boundaries and neighborhoods of the sub-regions with near-field amplitude anomalies and the sub-regions with feed phase faults are then screened to see if there are multiple boundary connections between the sub-regions with near-field amplitude anomalies or the sub-regions with feed phase faults.

[0136] If so, then the boundary of a continuous abnormal adjacent region is formed;

[0137] Multiple sub-regions with amplitude anomalies and multiple sub-regions with phase anomalies in the electric field vector are identified and filtered out, and then merged to form an initial set of sub-regions.

[0138] In the initial set of sub-regions, multiple sub-regions that identify amplitude and / or phase anomalies of the electric field vector within the current sub-region and the eight-neighbor area of ​​the current sub-region are identified to form continuous sub-region boundaries (i.e., the boundaries of continuous anomalous adjacent regions are obtained from the analysis output of the sub-regions of feed phase faults and near-field amplitude anomalies (i.e., since the near-field region is divided into sub-regions by a two-dimensional plane, the boundaries of each sub-region are the same size, and the boundaries of continuous anomalous adjacent regions are formed by continuous sub-region boundaries)): When at least three sub-regions are detected as anomalous sub-regions, the boundaries of the three continuously distributed (meaning that the sub-regions adjacent to the eight-neighbor area are continuous) anomalous sub-regions are set as the boundaries of continuous anomalous adjacent regions.

[0139] Preferably, step S412 above can be implemented as follows: Extract the residual phase field distribution Φres(x,y) from the two-dimensional plane of the near-field region, and further analyze the residual phase field distribution Φres(x,y) to obtain the characteristics of the phase distribution; calculate the phase abrupt change gradient in the spatial domain based on the characteristics of the phase distribution; identify the sampling points of each sub-region according to the phase abrupt change gradient to obtain the sub-region of the power supply phase fault, including;

[0140] S4121: Extract the residual phase field distribution Φres(x,y) of each sub-region based on the distribution characteristics of the phase values ​​of the electric field vector in the two-dimensional plane of the near-field region;

[0141] The phase distribution characteristics are obtained by analyzing each sub-region using the residual phase field distribution Φres(x,y);

[0142] Based on the phase distribution characteristics, the phase change gradient (phase anomaly measurement) in the spatial domain is used to identify and aggregate abnormal sampling points, thereby obtaining the sub-region of the power supply phase fault.

[0143] S4122: Based on the phase distribution characteristics, the phase gradient field of the frequency domain field is used to identify and aggregate abnormal sampling points to obtain the sub-region of the feed phase fault.

[0144] It should be noted that in the above steps, the phase gradient represents the rate of change of the signal phase in space or time, describing the "tilt" of the phase change, which can help us understand the propagation speed and direction of the signal; and the phase abrupt gradient refers to the place where the signal phase suddenly changes significantly, which means that the signal has been interfered with or has malfunctioned, such as a sudden interruption or jump in the signal.

[0145] Simultaneously, in the above steps, the identification and aggregation of abnormal sampling points are performed using phase change gradients and phase gradient fields to obtain sub-regions of feeder phase faults, identifying certain features or anomalies in the signal. For example, when analyzing phase gradients, "identification" involves calculating the phase changes of the signal to find important change points in the signal (such as phase change points, places with abnormal change rates, etc.). In signal processing, the goal of identification is usually to find certain features or faults in the signal, which may be a specific phase fluctuation or irregularity. Aggregation, on the other hand, integrates or summarizes multiple identified signal features or data points. This is usually done to simplify analysis and extract higher-level information. For example, multiple phase change points can be aggregated into an overall signal change trend, providing a clearer understanding of the overall signal behavior. In many applications, the purpose of aggregation is to summarize a large amount of scattered information into concise and meaningful results, thereby aiding in decision-making or further processing.

[0146] Preferably, step S4122 above can be considered as a preferred implementation scheme; based on the phase distribution characteristics, the phase gradient field of the frequency domain field is used to identify and aggregate abnormal sampling points to obtain the sub-region of the feed phase fault, specifically including the following steps:

[0147] S41221: Calculate the residual phase field distribution Φres(x,y) for each sub-region based on the distribution characteristics of the phase values ​​of the electric field vector in the two-dimensional plane; analyze the phase residual distribution of each sub-region using the calculated residual phase field distribution Φres(x,y) to obtain the characteristics of the phase distribution; perform a two-dimensional discrete Fourier transform on the residual phase field distribution Φres(x,y) to obtain the spatial spectrum Φ(kx,ky);

[0148] It should be noted that the above steps extract the residual phase field distribution Φres(x, y) to obtain the phase characteristics of the signal, revealing the distribution of different frequency components in the signal; the two-dimensional discrete Fourier transform (FFT) converts the spatial domain signal to the frequency domain, helping to analyze the spectral distribution of the signal and facilitating subsequent frequency domain processing; the Fourier transform provides the foundation for frequency domain analysis, enabling the observation of different frequency components in the signal, which is helpful for subsequent denoising and extraction of important features;

[0149] S41222: Apply the Kaiser window function W(k) to the spatial spectrum Φ(kx,ky) to suppress high-frequency noise;

[0150] It should be noted that the Kaiser window is a window function used to suppress high-frequency noise and attenuate high-frequency components in the frequency domain. By applying this window function, noise components in the signal are filtered out, making the signal smoother. Noise is usually contained in the high-frequency part of the signal. Using a window function can effectively remove unnecessary high-frequency noise, improve signal quality, and make subsequent analysis more accurate.

[0151] S41223: The phase distribution Φsmooth(x,y) of the spatial spectrum Φ(kx,ky) after reconstruction and filtering by inverse Fourier transform;

[0152] It should be noted that the inverse Fourier transform converts the frequency domain signal back to the spatial domain, restoring the filtered phase distribution Φsmooth(x, y), which is the recovery of the smoothed signal after noise suppression. By recovering the signal through the inverse transform, the signal can be further analyzed in the spatial domain while ensuring the denoising effect.

[0153] S41224: Calculate the phase gradient field ▽Φ_smooth and mark the gradient abrupt change points;

[0154] It should be noted that calculating the phase gradient field (i.e., the rate of phase change) and marking gradient abrupt changes is crucial. The phase gradient typically indicates regions in the signal where significant changes occur (such as edges or abrupt changes). Marking gradient abrupt changes helps detect regions of anomalous change in the signal, especially in image processing or signal detection, where these abrupt changes are often important features that can help identify problems.

[0155] S41225: The global "abnormal intensity field" is obtained by calculating the maximum neighborhood phase difference, combining it with distance weighting, and calculating the abrupt changes in phase curvature in sub-regions. ;

[0156] ;

[0157] In the formula, Representing abnormal sampling points The set of 8 neighboring points; Representing abnormal sampling points to abnormal sampling point The Euclidean distance; Represents the distance attenuation coefficient (default) ); : No. Laplace operator (second-order differential) for phase distribution in subregions; Curvature weighting factor (default 0.3);

[0158] In the above formula, the first term calculates the maximum neighborhood phase difference (distance-weighted), and the second term captures abrupt changes in phase curvature in the sub-region;

[0159] Get Calculate the global "anomaly intensity field", and then based on the obtained global "anomaly intensity field"... Initially marked as a phase metric anomaly at the sampling point level, if the current number There is at least one anomalous sampling point in the sub-region Then further filter the current abnormal sampling points. The current number The sub-region is a sub-region of the feed phase fault region;

[0160] ;

[0161] In the formula, Indicated as an abnormal sampling point The set of 8 neighboring points; Represented as sampling points to sampling point The Euclidean distance; Represented as distance attenuation coefficient (default) ); Represented as the first Laplace operator (second-order differential) for phase distribution in subregions; Represented as curvature weighting factor (default 0.3);

[0162] In the above formula, the first term calculates the maximum neighborhood phase difference (distance-weighted), and the second term captures abrupt changes in the phase curvature of the capture region;

[0163] Based on all abnormal sampling points A set of sub-regions with phase anomalies is formed.

[0164] It should be noted that by calculating the maximum neighborhood phase difference and combining it with distance weighting and phase curvature abrupt change, a global "abnormal intensity field" is obtained, which helps to more accurately locate and measure the intensity of abnormal regions. At the same time, the above steps of calculating phase difference, distance weighting, and curvature can more comprehensively capture the degree of abnormal changes in the signal, forming an abnormal intensity field. This provides a quantitative basis for further anomaly detection and signal processing, which can help the system effectively identify abnormal points in different regions and improve the overall detection effect and accuracy.

[0165] Specifically, in step S4121, the residual phase field distribution Φres(x,y) of each sub-region is extracted based on the distribution characteristics of the phase values ​​of the electric field vector in the two-dimensional plane of the near-field region; the residual phase field distribution Φres(x,y) is used to analyze each sub-region to obtain the characteristics of the phase distribution characteristics; based on the characteristics of the phase distribution characteristics, the spatial domain phase abrupt change gradient (phase anomaly measurement) is performed to identify and aggregate the abnormal sampling points, thereby obtaining the sub-region of the power supply phase fault. The specific operation steps are as follows:

[0166] S41211: Calculate the residual phase field distribution Φres(x,y) for each sub-region based on the distribution characteristics of the phase values ​​of the electric field vector in the two-dimensional plane; analyze the phase residual distribution of each sub-region using the calculated residual phase field distribution Φres(x,y) to obtain the characteristics of the phase distribution; perform a two-dimensional discrete Fourier transform on the residual phase field distribution Φres(x,y) to obtain the spatial spectrum Φ(kx,ky);

[0167] S41212: Apply the Kaiser window function W(k) to the spatial spectrum Φ(kx,ky) to suppress high-frequency noise;

[0168] S41213: The phase distribution Φ_smooth(x,y) of the spatial spectrum Φ(kx,ky) after reconstruction and filtering by inverse Fourier transform;

[0169] S41214: Performs calculation of the global "anomaly intensity field" of the spatial spectrum Φ(kx,ky). Indicators (refer to step S41225 above);

[0170] S41215: Calculate the phase gradient field ▽Φ_smooth and mark the gradient abrupt change points;

[0171] Global "abnormal intensity field" The index is essentially a phase anomaly measurement at the sampling point level. By aggregating the sampling points, a sub-region level anomaly evaluation can be obtained.

[0172] Global "abnormal intensity field" The indicator execution steps can be added after S41213 and before S41214 to calculate the global "abnormal intensity field" and identify suspicious sampling points based on thresholds or sorting. The advantage of doing this is the global "abnormal intensity field". The indicators belong to spatial domain analysis, while Fourier filtering belongs to frequency domain smoothing; the two are complementary. The above processing procedure prioritizes filtering, which improves the robustness of anomaly detection and reduces false positives. This step is an extension of step S4122 above and will not be elaborated further.

[0173] Preferably, as a preferred implementation scheme: the boundaries of the continuous anomalous adjacent regions are locked, the far-field radiation characteristic data corresponding to the continuous anomalous adjacent regions are obtained, and the far-field radiation characteristic data of the continuous anomalous adjacent regions are calculated and output using a far-near field transformation algorithm to output inferred near-field attribute information for the continuous anomalous adjacent regions; the existence of a real anomaly in the continuous anomalous adjacent regions is analyzed and verified based on the inferred near-field attribute information to obtain the final anomaly result; the final anomaly result is output; the inferred near-field attribute information includes multiple attribute information covering the analysis of near-field sub-regions; that is, to explain that the above step S41 is only in the near-field region (usually in several...) Precisely measuring the electromagnetic field (amplitude and phase) distribution radiated by the antenna within the wavelength range only provides a preliminary assessment of the anomalous sub-region. Then, in step S42, this technical solution acquires far-field radiation characteristic data and derives the deducible near-field attribute information of the continuous anomalous adjacent regions using a transformation algorithm (far-near-field transformation algorithm). During this process, a secondary analysis verifies whether the deduced near-field attribute information of the continuous anomalous adjacent regions contains genuine anomalies, thereby further verifying the sub-region anomaly assessment and its anomaly type. The final anomaly result is output, specifically including the following operational steps (i.e., performing far-field verification and secondary diagnostic processing of the anomalous region):

[0174] The coordinates of the locked continuous anomalous adjacent region boundaries are used to acquire far-field radiation characteristic data; the azimuth angle of the main beam corresponding to the anomalous region is then determined. to Encrypted sampling within a certain range;

[0175] Record the far-field radiation characteristics in three dimensions: ;

[0176] Perform far-field to near-field inversion calculations; output the inverted near-field distribution using a Huygens-based transformation algorithm. (amplitude) phase ):

[0177] ;

[0178] Execute dual criteria for exception verification;

[0179] Criterion 1: The inversion consistency error analysis results obtained after inversion. numerical sum Numerical value;

[0180] ;

[0181] like or The existence of the sub-anomaly secondary verification corresponding to the deducible near-field attribute information was confirmed; the deducible near-field attribute information includes... numerical sum Numerical value;

[0182] Based on the inversion field characteristics and verification indicators, fault feature matching is achieved for the sub-anomalies corresponding to the current near-field attribute information.

[0183] Example 2

[0184] In one possible design of this invention, Figure 1 The antenna near-field plane attribute evaluation and detection method of the embodiment shown can be implemented using a computing device, such as... Figure 5 As shown, the computing device may include a storage component 31 and a processing component 32;

[0185] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0186] The processing component 32 is used for the above Figure 1 The embodiment describes a method for evaluating and detecting the near-field planar properties of an antenna.

[0187] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0188] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0189] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0190] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0191] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0192] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0193] Example 3

[0194] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown is a method for evaluating and detecting the near-field planar properties of an antenna.

[0195] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0196] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0197] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0198] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for evaluating and detecting the near-field planar properties of an antenna, characterized in that, include: In the near-field region 1-5 wavelengths from the antenna radiating aperture, establish a two-dimensional plane scanning coordinate system parallel to the antenna aperture surface; An electromagnetic field probe is used to move in a grid pattern within the two-dimensional plane at sampling step intervals of λ / 2, and the amplitude and phase values ​​of the electric field vector are measured synchronously at each sampling point. Construct a three-dimensional dataset containing the position coordinates of the electromagnetic field probe and the amplitude and phase values ​​of the electric field vector at the sampling point; Based on the three-dimensional dataset, the two-dimensional plane of the near-field region is divided into sub-regions, and the amplitude and phase values ​​of the electric field vector distribution in each sub-region are calculated; abnormal sub-regions are extracted using the amplitude and phase values ​​of the electric field vector distribution; the abnormal sub-regions are analyzed to obtain deductive near-field attribute information; The electromagnetic field probe moves in a grid pattern within the two-dimensional plane at sampling step intervals of λ / 2, synchronously measuring the amplitude and phase values ​​of the electric field vector at each sampling point. Specifically, this includes the following steps: The two-dimensional plane is used as the area to be scanned, and the area to be scanned is divided into N×M sub-regions; An electromagnetic field probe is used to move in a grid pattern within the two-dimensional plane at sampling step intervals of λ / 2 and to sample the amplitude and phase values ​​of the electric field vector at the corresponding sampling points. Based on the three-dimensional dataset, the two-dimensional plane of the near-field region is divided into sub-regions, and the amplitude and phase values ​​of the electric field vector distribution in each sub-region are calculated. Abnormal sub-regions are extracted using the amplitude and phase values ​​of the electric field vector distribution. The abnormal sub-regions are analyzed to obtain deducible near-field attribute information. The specific operation steps are as follows: Based on the aforementioned 3D dataset, near-field distribution analysis is performed, and the 2D plane of the near-field region is divided into several sub-regions; the amplitude and phase values ​​of the electric field vector distribution in each sub-region within the 2D plane of the near-field region are extracted. The amplitude of the electric field vector is evaluated based on the amplitude value of the electric field vector as input, and sub-regions with abnormal near-field amplitude are identified and screened in the near-field region. Based on the phase distribution characteristics analysis, sub-regions of feed phase faults in the near-field region are identified and screened; based on the analysis of the sub-regions of feed phase faults and sub-regions of near-field amplitude anomalies, the boundaries of continuously anomalous adjacent regions are obtained. The boundaries of the continuous abnormal adjacent regions are locked, the far-field radiation characteristic data corresponding to the continuous abnormal adjacent regions are obtained, and the far-field and near-field transformation algorithm is applied to the far-field radiation characteristic data of the continuous abnormal adjacent regions to calculate and output the deduced near-field attribute information for the continuous abnormal adjacent regions.

2. The antenna near-field planar attribute evaluation and detection method according to claim 1, characterized in that, The deduced near-field attribute information includes multiple attribute information covering various sub-regions within the analyzed near-field region.

3. The antenna near-field planar attribute evaluation and detection method according to claim 2, characterized in that, Based on the aforementioned 3D dataset, near-field distribution analysis is performed, dividing the 2D plane of the near-field region into several sub-regions; the amplitude and phase values ​​of the electric field vector distribution in each sub-region within the 2D plane of the near-field region are extracted; the amplitude value of the electric field vector is used as input to evaluate the amplitude of the electric field vector, identifying and filtering sub-regions with abnormal near-field amplitudes in the near-field region. Specifically, the following steps are included: Obtain the amplitude of the electric field vector corresponding to multiple sampling points in each sub-region of the two-dimensional plane, and calculate the electric field vector of the j-th sampling point in the i-th sub-region. ; The first sampling point is calculated based on the amplitude of the electric field vector corresponding to the plurality of sampling points. The standard deviation of the magnitude of the electric field vector in each sub-region and the The average amplitude of the electric field vector in each sub-region ; The first Each sub-region and its surroundings The subregions are aggregated and denoted as follows: ; Using the electric field vector of the j-th sampling point in the i-th sub-region With the Standard deviation of amplitude in individual regions and the Sub-region amplitude average Calculate the first local amplitude deviation rate for each sampling point; Using the first Each sub-region and its surroundings The set of subregions Calculate the first The second local amplitude deviation rate corresponding to each sub-region; The third local amplitude deviation rate is calculated using the first local amplitude deviation rate and the second local amplitude deviation rate; Preset a first standard threshold, a second standard threshold, a reference deviation threshold, and an abnormal reference density threshold; Determine whether the first local amplitude deviation rate is greater than or equal to the first standard threshold; If so, then the deviation between the amplitude of the electric field vector corresponding to the current single sampling point and the average amplitude of the sub-region is determined to be abnormal, and the sampling point is abnormal; Calculate the sampling point anomaly density for the sub-region corresponding to the sampling point; The abnormal density of the sampling points is used to determine whether it is greater than the abnormal reference density threshold; If so, the current sub-region corresponding to the sampling point is determined to be a sub-region with abnormal near-field amplitude; Determine whether the second local amplitude deviation rate is greater than or equal to the second standard threshold; If so, then determine that the current sub-region has an abnormal amplitude deviation from the K surrounding sub-regions, and filter the current sub-region as a sub-region with near-field amplitude abnormality; If the first local amplitude deviation rate is less than a preset first standard threshold and the second local amplitude deviation rate is less than a preset second standard threshold, then it is determined whether the third local amplitude deviation rate is greater than a reference deviation threshold. If so, the current sub-region is directly determined to be a sub-region with abnormal near-field amplitude.

4. The antenna near-field planar attribute evaluation and detection method according to claim 3, characterized in that, Based on phase distribution characteristics analysis, sub-regions of feed phase faults in the near-field region are identified and screened; based on the analysis of the sub-regions of feed phase faults and sub-regions of near-field amplitude anomalies, the boundaries of continuously anomalous adjacent regions are obtained, specifically including the following operation steps: The residual phase field distribution Φres(x,y) is extracted from the two-dimensional plane of the near-field region, and the characteristics of the phase distribution are further analyzed to obtain the features of the residual phase field distribution Φres(x,y). Based on the characteristics of the phase distribution, spatial domain or frequency domain fields are calculated to obtain phase change gradient or phase gradient field; sampling points of each sub-region are identified according to the phase change gradient or phase gradient field to obtain the sub-region of power supply phase fault. Connectivity analysis is performed on the sub-regions of near-field amplitude anomalies and feed phase faults to obtain continuous anomalous adjacent regions; edge analysis is performed on the continuous anomalous adjacent regions to obtain the boundaries of the continuous anomalous adjacent regions.

5. The antenna near-field planar attribute evaluation and detection method according to claim 4, characterized in that, The residual phase field distribution Φres(x,y) is extracted from the two-dimensional plane of the near-field region, and the residual phase field distribution Φres(x,y) is further analyzed to obtain the characteristics of the phase distribution. Based on the characteristics of the phase distribution, the phase abrupt change gradient is calculated in the spatial domain. The sampling points of each sub-region are identified according to the phase abrupt change gradient to obtain the sub-region of the power supply phase fault, including: The residual phase field distribution Φres(x,y) of each sub-region is extracted based on the distribution characteristics of the phase value of the electric field vector in the two-dimensional plane of the near-field region. The phase distribution characteristics are obtained by analyzing each sub-region using the residual phase field distribution Φres(x,y); Based on the phase distribution characteristics, the phase abrupt gradient in the spatial domain is used to identify and aggregate abnormal sampling points, thereby obtaining the sub-region of the feed phase fault. Based on the phase distribution characteristics, the phase gradient field of the frequency domain field is used to identify and aggregate abnormal sampling points to obtain the sub-region of the feed phase fault.

6. The antenna near-field planar attribute evaluation and detection method according to claim 5, characterized in that, Based on the phase distribution characteristics, the phase gradient field of the frequency domain field is used to identify and aggregate abnormal sampling points to obtain the sub-region of the feed phase fault. The specific steps include the following: Based on the distribution characteristics of the phase values ​​of the electric field vector in the two-dimensional plane, the residual phase field distribution Φres(x,y) is calculated for each sub-region; the residual phase field distribution Φres(x,y) calculated for each sub-region is used to analyze the phase residual distribution of each sub-region to obtain the characteristics of the phase distribution; the spatial spectrum Φ(kx,ky) is obtained by performing a two-dimensional discrete Fourier transform on the residual phase field distribution Φres(x,y). A Kaiser window function W(k) is applied to the spatial spectrum Φ(kx,ky) to suppress high-frequency noise; The phase distribution Φsmooth(x,y) of the spatial spectrum Φ(kx,ky) after reconstruction and filtering is obtained by inverse Fourier transform; Calculate the phase gradient field ▽Φsmooth and mark the gradient abrupt change points; The global "anomaly intensity field" is obtained by calculating the maximum neighborhood phase difference, combining it with distance weighting, and calculating abrupt changes in phase curvature in sub-regions. ; Get Calculate the global "anomaly intensity field", and then based on the obtained global "anomaly intensity field"... Initially marked as a phase metric anomaly at the sampling point level, if the current number There is at least one anomalous sampling point in the sub-region Then further filter the current abnormal sampling points. The current number The sub-region is a sub-region of the feed phase fault region.

7. An electronic device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the antenna near-field plane attribute evaluation and detection method as described in any one of claims 1 to 6.

8. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements an antenna near-field planar attribute evaluation and detection method as described in any one of claims 1 to 6.

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