An automated testing method and system for multi-channel antenna line sources

Abstract

This invention belongs to the field of antenna testing technology. It discloses an automated testing method and system for multi-channel antenna line sources. The method includes: before starting the multi-channel line source test, constructing a phase closed-loop matrix between channels using a full-channel mutual excitation sequence; during acquisition, using a real-time phase deviation drift prediction algorithm to compensate for phase deviations in real time, outputting phase-locked multi-channel amplitude and phase data; performing dual-time-scale decomposition on the multi-channel amplitude and phase data to separate short-period structural difference components and long-period environmental drift components; using an online environmental drift tracking factor to remove drift, retaining only the structural difference components as multi-dimensional difference vectors; mapping the multi-dimensional difference vectors to a channel-difference parameter bipartite graph, analyzing the connected component distribution pattern of abnormal parameters, and automatically inferring the location of abnormal sources by combining the channel physical topology mapping table, outputting a set of abnormal channels with spatial pattern labels; this facilitates reliable verification of test results and improves testing efficiency.

Description

Technical Field

[0001] This invention relates to the field of antenna testing technology, and more specifically, to an automated testing method and system for multi-channel antenna line sources. Background Technology

[0002] Patent publication number CN117572097A discloses an automated testing system and method for near-field phased array active phased array antennas. The system includes a spectrum analyzer, a vector network analyzer, a main control computer, and a phased array antenna system, all connected to a switch. The phased array antenna system contains a beam controller and a frequency synthesizer connected to the antenna array. The spectrum analyzer is connected to a directional coupler, and the vector network analyzer is connected to a waveguide probe via the directional coupler, and simultaneously connected to the antenna array and frequency synthesizer of the phased array antenna system. The waveguide probe is mounted on a three-dimensional scanning mechanism and connected to the main control computer. This invention improves the accuracy, stability, and efficiency of test results. It can be used for channel amplitude and phase calibration of phased array antennas, as well as for measuring the synthesized field strength pattern of phased array antennas. It can be used for phased array antenna debugging in an anechoic chamber and for field strength calibration during field tests.

[0003] Existing automated testing methods and systems for multi-channel antenna line sources mainly suffer from the following problems:

[0004] Most existing methods rely solely on a single environmental parameter for drift compensation, neglecting the electromagnetic-thermal coupling effects of antenna arrays during operation. The impact of strong electromagnetic interference on the drift of antenna channel amplitude and phase data is not effectively identified and modeled, leading to insufficient compensation accuracy. Existing compensation strategies cannot effectively distinguish between long-period drift caused by slow temperature changes and short-period changes caused by electromagnetic interference or rapid disturbances, easily causing signal aliasing and masking true structural differences. The lack of repeatable coupling coefficient calibration experiments, coupled with inconsistent sources of drift compensation coefficients under different test conditions, results in poor consistency across batches and scenarios, and low recalculation capability. In complex outdoor or production line environments, with multiple factors such as electromagnetic interference and temperature fluctuations, existing algorithms cannot simultaneously adapt to various interference factors, resulting in insufficient stability of test results.

[0005] Existing technologies typically perform sequential testing of anomalous channels or connected components one by one, resulting in lengthy testing times, complex paths, and numerous repetitive operations. The testing sequence in existing technologies is usually fixed based on initial anomaly identification results and cannot be adjusted according to real-time changes in the amplitude of anomalous channel anomalies. In multi-channel antenna array testing, the spatial distribution of channels is complex; if spatial distance is not considered, the testing sequence may lead to lengthy testing paths. Existing technologies cannot ensure that testing covers all anomalous channels and their spatial correlation characteristics, and also lack refined testing strategies for anomalous connected components.

[0006] In view of this, the present invention proposes an automated testing method for multi-channel antenna line sources to solve the above problems. Summary of the Invention

[0007] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution:

[0008] An automated testing method for multi-channel antenna line sources includes:

[0009] S1. Before the multi-channel line source test is started, a phase closed-loop matrix between channels is constructed through the full-channel mutual excitation sequence. During the acquisition process, the phase deviation is compensated in real time using the real-time phase deviation drift prediction algorithm, and the phase-locked multi-channel amplitude and phase data is output.

[0010] S2. Perform dual-timescale decomposition on the multi-channel amplitude and phase data to separate it into short-period structural difference components and long-period environmental drift components; use the online environmental drift tracking factor to remove drift and retain only the structural difference components as multidimensional difference vectors.

[0011] S3. Map the multidimensional difference vector to a channel-difference parameter bipartite graph, analyze the connected component distribution pattern of the abnormal parameters, and automatically infer the location of the anomaly source by combining the channel physical topology mapping table, and output a set of abnormal channels with spatial pattern labels.

[0012] S4. Based on the set of abnormal channels, calculate the minimum test coverage set, and use the mode priority dynamic adjustment mechanism to prioritize the execution of tests on high-impact abnormal domains; introduce a cross-channel task merging strategy to generate an adaptive secondary test task sequence for secondary testing.

[0013] S5. After the secondary test is completed, a test report is generated that binds the original acquisition frame ID, phase closed-loop matrix version number and drift elimination parameters. The recalculated results are compared with the original results through the consistency scoring mechanism to realize the recalculation and reliable verification of the test results.

[0014] Specifically, the method for constructing the inter-channel phase closed-loop matrix includes:

[0015] All antenna channels to be tested are counted. Before the test starts, all antenna channels are clocked and calibrated. A mutual excitation sequence is designed for each antenna channel. One antenna channel is selected as the excitation source channel in a preset order and a known reference excitation signal is transmitted to the other antenna channels.

[0016] During each excitation process, the signal amplitude and phase information of the excitation signal received by each receiving channel are collected, and the measurement results are recorded as the phase difference between the receiving channel and the excitation source channel. Mutual excitation operation is performed on all antenna channels in sequence to complete the phase measurement between each pair of all channels, forming a phase closed-loop matrix between channels. Each element of the matrix represents the measured phase difference between the corresponding receiving channel and the excitation source channel.

[0017] Specifically, the method for outputting phase-locked multi-channel amplitude and phase data includes:

[0018] The generated inter-antenna channel phase closed-loop matrix is ​​used as the reference phase to collect real-time amplitude and phase data for each antenna channel; the instantaneous phase currently collected for each antenna channel is compared with the corresponding reference phase in the inter-antenna channel phase closed-loop matrix to calculate the phase deviation between antenna channels;

[0019] The phase deviation between antenna channels is input into a real-time phase deviation drift prediction algorithm to predict the phase change trend caused by environmental drift, temperature change and electromagnetic interference, and generate corresponding compensation amounts.

[0020] Compensation is applied to the real-time amplitude and phase data of each antenna channel. The compensation amount is superimposed or corrected with the acquired real-time amplitude and phase data to keep the real-time amplitude and phase data of each antenna channel in a phase-locked state, and the phase-locked multi-channel amplitude and phase data is output.

[0021] Specifically, the method for separating short-period structural difference components and long-period environmental drift components includes:

[0022] A dual-timescale decomposition algorithm is used to decompose multi-channel amplitude-phase data into short-period structural difference components and long-period environmental drift components. The short-period structural difference components are those with periods less than a preset period threshold in the multi-channel amplitude-phase data, which are extracted through high-pass filtering to reflect the amplitude-phase changes and structural differences in each channel. The long-period environmental drift components are those with periods greater than or equal to the preset period threshold in the multi-channel amplitude-phase data, which are extracted through low-pass filtering to reflect the impact of environmental drift and temperature changes on the multi-channel amplitude-phase data.

[0023] Specifically, the method of retaining only the structural difference components as a multidimensional difference vector includes:

[0024] During the drift removal process, a drift separation model based on the physical field coupling relationship is established to decompose the long-period environmental drift component into thermal drift component and electromagnetic interference drift component: the thermal drift component is obtained by collecting the temperature field data of the antenna, and the temperature field data is obtained by the infrared thermal imager deployed around the antenna.

[0025] The electromagnetic interference drift component is obtained by real-time acquisition of the magnetic flux density change around the antenna, which is measured by an array of electromagnetic compatibility probes deployed around the antenna; the coupling relationship between the thermal drift component and the electromagnetic interference drift component is determined by a pre-conducted calibration experiment.

[0026] The calibration experiment includes heating the antenna with a controllable heat source to measure the thermal coupling coefficient, and measuring the electromagnetic coupling coefficient under different electromagnetic excitation conditions using a near-field scanning device, forming a coupling matrix that can characterize the effects of thermal effects and electromagnetic interference effects on phase and amplitude drift.

[0027] In the actual test, the coupling matrix is ​​used to calculate the collected temperature field data and magnetic flux density changes to obtain the current thermal drift component and electromagnetic interference drift component. These components are then subtracted from the multi-channel amplitude and phase data, leaving only the short-period structural difference component. After vectorization, a multi-dimensional difference vector is formed.

[0028] Specifically, the method for outputting a set of abnormal channels with spatial pattern markers includes:

[0029] The multidimensional difference vector is mapped to a channel-difference parameter bipartite graph, where the antenna channel is regarded as one set of nodes in the graph and the structural difference component is regarded as another set of nodes. If the amplitude of the structural difference component of any antenna channel exceeds the preset structural difference component amplitude threshold, an edge connection is established between the antenna channel node and the corresponding structural difference component node to form a weighted bipartite graph, and the edge weight represents the degree of amplitude deviation.

[0030] In the bipartite graph of channel-difference parameters, identify all abnormal connected regions with edge weights greater than preset edge weights, and analyze the association patterns between antenna channel nodes and structural difference component nodes in the abnormal connected regions. The association patterns include the number, distribution and morphological characteristics of nodes in the connected regions.

[0031] Based on the preset antenna channel physical topology mapping table, the abnormal connected regions identified in the channel-difference parameter bipartite graph are mapped back to the physical antenna array. Combining edge weight size, connected region density and spatial proximity relationship, the most likely abnormal channel location is automatically inferred, and a set of abnormal channels with spatial pattern labels is output.

[0032] Specifically, the method for prioritizing the execution of high-impact anomaly domain tests includes:

[0033] Based on the set of anomalous channels with spatial pattern labels, the spatial distribution and connectivity characteristics of the anomalous channels are analyzed. The set coverage algorithm is used to calculate the minimum test coverage set so that each channel in the coverage set can represent other channels in its anomalous connectivity domain.

[0034] For the abnormal connected components in the minimum test coverage set, the abnormal amplitude, abnormal connected component size, abnormal connected component density, and historical abnormal frequency are statistically analyzed and weighted to obtain a comprehensive score for each abnormal connected component, which serves as a priority indicator. The abnormal connected components are then sorted according to their comprehensive scores, and the abnormal connected components with higher scores are given priority for testing.

[0035] Specifically, the method for conducting the secondary test includes:

[0036] If the difference between two abnormal connected components is less than the preset priority difference threshold, they are determined to have similar priorities. A cross-channel task merging strategy is introduced to divide abnormal channels with similar priorities and spatial adjacency into the same secondary test task, forming an adaptive secondary test task sequence.

[0037] For each secondary test task, a weighted delay function is defined, and an optimized sequence of secondary test tasks is generated by minimizing the weighted delay function of all secondary test tasks. During the secondary test, the priority of the abnormal connected components is adaptively adjusted based on the real-time amplitude change rate of the abnormal channels within each abnormal connected component. The secondary tests are then executed sequentially according to the optimized and adjusted adaptive sequence of secondary test tasks.

[0038] Specifically, the method for achieving recalculation and reliable verification of test results includes:

[0039] After the secondary test is completed, a test report is generated, which binds the original acquisition frame ID, the version number of the phase closed-loop matrix used, and the drift removal parameters corresponding to each data point. Using the verification consistency scoring mechanism, the amplitude and phase data of the secondary test are compared with the multi-channel amplitude and phase data to calculate the amplitude deviation, phase deviation, and structural difference variation of each channel. The consistency between the test results and the original data is evaluated according to the preset deviation template to verify the recalculation and reliability of the secondary test data, and the verification results are included in the test report.

[0040] An automated testing system for multi-channel antenna line sources includes:

[0041] Before the multi-channel line source test is started, the phase interlock acquisition module constructs a phase closed-loop matrix between channels through a full-channel mutual excitation sequence. During the acquisition process, the phase deviation drift prediction algorithm is used to compensate for the phase deviation in real time and output phase-locked multi-channel amplitude and phase data.

[0042] The drift difference extraction module performs dual-timescale decomposition on multi-channel amplitude and phase data to separate short-period structural difference components and long-period environmental drift components; it uses an online environmental drift tracking factor to remove drift and retains only the structural difference components as a multidimensional difference vector.

[0043] The anomaly analysis module maps multidimensional difference vectors to a channel-difference parameter bipartite graph, analyzes the connected component distribution pattern of anomaly parameters, and automatically infers the location of anomaly sources by combining the channel physical topology mapping table, outputting a set of anomaly channels with spatial pattern labels.

[0044] The pattern-driven compression module calculates the minimum test coverage set based on the set of abnormal channels, uses a pattern priority dynamic adjustment mechanism to prioritize the execution of tests in high-impact abnormal domains, and introduces a cross-channel task merging strategy to generate an adaptive secondary test task sequence for secondary testing.

[0045] The end-to-end reversible verification module generates a test report after the secondary test is completed, which is bound to the original acquisition frame ID, phase closed-loop matrix version number and drift elimination parameters. The recalculation results are compared with the original results through the verification consistency scoring mechanism, so as to realize the recalculation and reliable verification of the test results.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] This invention achieves comprehensive separation and compensation for major environmental drift factors by modeling the thermal drift effect of temperature field on antenna amplitude and phase data, and the electromagnetic interference drift effect of magnetic flux density changes on amplitude and phase data. By calculating and subtracting thermal drift and electromagnetic interference drift through the coupling matrix, only short-period structural difference components are retained, reducing drift residuals and more clearly preserving amplitude and phase change characteristics. The coupling coefficient is obtained through controlled physical field experiments, and the compensation calculation process can be reproduced under arbitrary conditions, ensuring the consistency and verifiability of test results across batches and scenarios. It maintains high stability and high compensation accuracy in complex field environments with significant temperature gradients, electromagnetic interference, and mechanical disturbances, improving the reliability of automated testing systems in production lines and field tests. The multidimensional difference vector obtained after drift removal more realistically reflects structural difference characteristics, reducing false alarms and false negatives due to environmental factors and improving the accuracy of anomaly source location.

[0048] By employing cross-channel task merging strategies and optimizing secondary test tasks, the number of tasks and redundant operations are reduced, enabling rapid coverage of abnormal channels and improving overall testing efficiency. Real-time amplitude change rate is used to adjust the priority of abnormal connected components, achieving closed-loop adaptive testing. This allows the system to prioritize detecting abnormal regions with significant amplitude changes, improving anomaly identification accuracy and real-time response capabilities. A weighted delay function comprehensively considers priority, test time, and spatial distance to generate an optimal secondary test task sequence, reducing test path length and system resource consumption, achieving efficient and scientific test scheduling. The task order is dynamically adjusted based on real-time amplitude changes during task execution, achieving intelligent adaptive scheduling to meet the testing requirements of complex multi-channel antenna arrays. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of an automated testing method for a multi-channel antenna line source according to the present invention.

[0050] Figure 2 This is a schematic diagram of the structure of an automated testing system for a multi-channel antenna line source according to the present invention.

[0051] Figure 3 This is a schematic flowchart of the method for outputting an abnormal channel set with spatial pattern markings provided by the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Example 1

[0054] Please see Figure 1 and Figure 3 As shown, this embodiment further illustrates the automated testing method for multi-channel antenna line sources proposed in this invention, including:

[0055] With the widespread application of multi-channel antenna arrays in fields such as communications, radar, and electronic measurement and control, the demand for automated testing of multi-channel antenna line sources is increasing. However, existing automated testing methods and systems for multi-channel antenna line sources still have several technical problems in practical applications and urgently need improvement.

[0056] Existing drift compensation methods primarily rely on single environmental parameters, such as temperature or humidity, to correct antenna channel amplitude and phase data. These methods fail to consider the electromagnetic-thermal coupling effects of the antenna array during operation, resulting in the ineffective identification and modeling of the impact of strong electromagnetic interference on the amplitude and phase data, leading to insufficient compensation accuracy. Furthermore, existing compensation strategies struggle to distinguish between long-period drift caused by slow temperature changes and short-period changes caused by electromagnetic interference or rapid disturbances, easily causing signal aliasing and masking true structural differences. The lack of systematic coupling coefficient calibration experiments in existing technologies, coupled with inconsistent sources of drift compensation coefficients, results in poor consistency of test results under different test conditions, batches, or scenarios, and insufficient recalculation. Moreover, in complex outdoor or production line environments, influenced by multiple factors such as electromagnetic interference and temperature fluctuations, existing algorithms cannot simultaneously adapt to various interference factors, resulting in insufficient stability of test results.

[0057] Existing multi-channel antenna line source testing methods have significant shortcomings in abnormal channel detection and secondary testing. Typically, abnormal channels or abnormal connected regions require sequential single-channel testing, resulting in lengthy testing times, complex paths, and numerous repetitive operations. The testing order is usually fixed based on initial anomaly identification results and cannot be dynamically adjusted according to real-time changes in channel abnormal amplitude, making rapid response to abnormal channels difficult. Furthermore, the spatial distribution of channels in multi-channel antenna arrays is complex; without considering the spatial location and adjacency relationships of channels, the testing path can easily become lengthy, impacting efficiency. Simultaneously, existing technologies cannot ensure test coverage of all abnormal channels and their spatial correlation characteristics, and lack refined testing strategies for abnormal connected regions, failing to meet the systematic testing requirements of high-precision, multi-channel arrays.

[0058] In view of this, the present invention proposes an automated testing method for multi-channel antenna line sources, comprising:

[0059] S1. Before the multi-channel line source test is started, a phase closed-loop matrix between channels is constructed through the full-channel mutual excitation sequence. During the acquisition process, the phase deviation is compensated in real time using the real-time phase deviation drift prediction algorithm, and the phase-locked multi-channel amplitude and phase data is output.

[0060] S2. Perform dual-timescale decomposition on the multi-channel amplitude and phase data to separate it into short-period structural difference components and long-period environmental drift components; use the online environmental drift tracking factor to remove drift and retain only the structural difference components as multidimensional difference vectors.

[0061] S3. Map the multidimensional difference vector to a channel-difference parameter bipartite graph, analyze the connected component distribution pattern of the abnormal parameters, and automatically infer the location of the anomaly source by combining the channel physical topology mapping table, and output a set of abnormal channels with spatial pattern labels.

[0062] S4. Based on the set of abnormal channels, calculate the minimum test coverage set, and use the mode priority dynamic adjustment mechanism to prioritize the execution of tests on high-impact abnormal domains; introduce a cross-channel task merging strategy to generate an adaptive secondary test task sequence for secondary testing.

[0063] S5. After the secondary test is completed, a test report is generated that binds the original acquisition frame ID, phase closed-loop matrix version number and drift elimination parameters. The recalculated results are compared with the original results through the consistency scoring mechanism to realize the recalculation and reliable verification of the test results.

[0064] Methods for constructing inter-channel phase closed-loop matrices include:

[0065] All antenna channels to be tested are counted. Before the test starts, all antenna channels are clocked and calibrated. A mutual excitation sequence is designed for each antenna channel. One antenna channel is selected as the excitation source channel in a preset order and a known reference excitation signal is transmitted to the other antenna channels.

[0066] During each excitation process, the signal amplitude and phase information of the excitation signal received by each receiving channel are collected, and the measurement results are recorded as the phase difference between the receiving channel and the excitation source channel. Mutual excitation operation is performed on all antenna channels in sequence to complete the phase measurement between each pair of all channels, forming a phase closed-loop matrix between channels. Each element of the matrix represents the measured phase difference between the corresponding receiving channel and the excitation source channel.

[0067] Methods for outputting phase-locked multi-channel amplitude and phase data include:

[0068] The generated inter-antenna channel phase closed-loop matrix is ​​used as the reference phase to collect real-time amplitude and phase data for each antenna channel; the instantaneous phase currently collected for each antenna channel is compared with the corresponding reference phase in the inter-antenna channel phase closed-loop matrix to calculate the phase deviation between antenna channels;

[0069] The phase deviation between antenna channels is input into a real-time phase deviation drift prediction algorithm to predict the phase change trend caused by environmental drift, temperature change and electromagnetic interference, and generate corresponding compensation amounts.

[0070] Compensation is applied to the real-time amplitude and phase data of each antenna channel. The compensation amount is superimposed or corrected with the acquired real-time amplitude and phase data to keep the real-time amplitude and phase data of each antenna channel in a phase-locked state, and the phase-locked multi-channel amplitude and phase data is output.

[0071] Methods for separating short-period structural difference components and long-period environmental drift components include:

[0072] A dual-timescale decomposition algorithm is used to decompose multi-channel amplitude-phase data into short-period structural difference components and long-period environmental drift components. The short-period structural difference components are those with periods less than a preset period threshold in the multi-channel amplitude-phase data, which are extracted through high-pass filtering to reflect the amplitude-phase changes and structural differences in each channel. The long-period environmental drift components are those with periods greater than or equal to the preset period threshold in the multi-channel amplitude-phase data, which are extracted through low-pass filtering to reflect the impact of environmental drift and temperature changes on the multi-channel amplitude-phase data.

[0073] Methods that retain only structural difference components as multidimensional difference vectors include:

[0074] During the drift removal process, a drift separation model based on the physical field coupling relationship is established to decompose the long-period environmental drift component into thermal drift component and electromagnetic interference drift component: the thermal drift component is obtained by collecting the temperature field data of the antenna, and the temperature field data is obtained by the infrared thermal imager deployed around the antenna.

[0075] The long-period environmental drift component is: ;in, Represents the thermal drift coupling matrix; Indicates a point in time Temperature field data; This represents the electromagnetic interference drift coupling matrix; Indicates the rate of change of magnetic flux density; Indicates a point in time , the magnetic flux density vector; This represents random noise, i.e., random errors other than temperature and electromagnetic factors, such as measurement noise, quantization error, and transient small disturbances.

[0076] The electromagnetic interference drift component is obtained by real-time acquisition of the magnetic flux density change around the antenna, which is measured by an array of electromagnetic compatibility probes deployed around the antenna; the coupling relationship between the thermal drift component and the electromagnetic interference drift component is determined by a pre-conducted calibration experiment.

[0077] The calibration experiment includes heating the antenna with a controllable heat source to measure the thermal coupling coefficient, and measuring the electromagnetic coupling coefficient under different electromagnetic excitation conditions using a near-field scanning device, forming a coupling matrix that can characterize the effects of thermal effects and electromagnetic interference effects on phase and amplitude drift.

[0078] In the actual test, the coupling matrix is ​​used to calculate the collected temperature field data and magnetic flux density changes to obtain the current thermal drift component and electromagnetic interference drift component. These components are then subtracted from the multi-channel amplitude and phase data, leaving only the short-period structural difference component. After vectorization, a multi-dimensional difference vector is formed.

[0079] This paper addresses the following issues in existing technologies: Most existing methods rely solely on a single environmental parameter for drift compensation, neglecting the electromagnetic-thermal coupling effects of the antenna array during operation; the impact of strong electromagnetic interference (such as near-field coupling and adjacent channel interference) on the drift of antenna channel amplitude and phase data is not effectively identified and modeled, leading to insufficient compensation accuracy. Existing compensation strategies cannot effectively distinguish between long-period drift caused by slow temperature changes and short-period changes caused by electromagnetic interference or rapid disturbances, easily causing signal aliasing and masking true structural differences. There is a lack of repeatable coupling coefficient calibration experiments; drift compensation coefficients come from different sources under different test conditions, resulting in poor consistency across batches and scenarios, and low recalculation capability. In complex outdoor or production line environments, electromagnetic interference, temperature fluctuations, and other factors are intertwined; existing algorithms cannot simultaneously adapt to multiple interference factors, resulting in insufficient stability of test results.

[0080] Compared to existing technologies, the advantages are as follows: Simultaneously modeling the thermal drift effect of temperature field on antenna amplitude and phase data, and the electromagnetic interference drift effect of magnetic flux density changes on amplitude and phase data, achieves full-coverage separation and compensation for major environmental drift factors. By calculating and subtracting thermal drift and electromagnetic interference drift through the coupling matrix, only short-period structural difference components are retained, reducing drift residuals and more clearly preserving amplitude and phase change characteristics. The coupling coefficient is obtained through controlled physical field experiments, and the compensation calculation process can be reproduced under arbitrary conditions, ensuring the consistency and verifiability of test results across batches and scenarios. It maintains high stability and high compensation accuracy in complex field environments with significant temperature gradients, electromagnetic interference, and mechanical disturbances, improving the reliability of automated testing systems in production lines and field tests. The multi-dimensional difference vector obtained after drift removal more realistically reflects structural difference characteristics, reducing false alarms and false negatives due to environmental factors and improving the accuracy of anomaly source location.

[0081] Methods for outputting a set of anomalous channels with spatial pattern markers include:

[0082] The multidimensional difference vector is mapped to a channel-difference parameter bipartite graph, where the antenna channel is regarded as one set of nodes in the graph and the structural difference component is regarded as another set of nodes. If the amplitude of the structural difference component of any antenna channel exceeds the preset structural difference component amplitude threshold, an edge connection is established between the antenna channel node and the corresponding structural difference component node to form a weighted bipartite graph, and the edge weight represents the degree of amplitude deviation.

[0083] In the bipartite graph of channel-difference parameters, identify all abnormal connected regions with edge weights greater than preset edge weights, and analyze the association patterns between antenna channel nodes and structural difference component nodes in the abnormal connected regions. The association patterns include the number, distribution and morphological characteristics of nodes in the connected regions.

[0084] Based on a pre-defined antenna channel physical topology mapping table, abnormal connected components identified in the channel-difference parameter bipartite graph are mapped back to the physical antenna array. Combining edge weights, connected component density, and spatial proximity, the most likely abnormal channel locations are automatically inferred, outputting a set of abnormal channels with spatial pattern labels. This set includes the physical location, abnormal amplitude information, and connected component characteristics of the abnormal channels, providing a basis for subsequent high-priority testing or system diagnostics.

[0085] Methods for prioritizing high-impact outlier domain testing include:

[0086] Based on the set of anomalous channels with spatial pattern labels, the spatial distribution and connectivity characteristics of the anomalous channels are analyzed. The set coverage algorithm is used to calculate the minimum test coverage set so that each channel in the coverage set can represent other channels in its anomalous connectivity domain.

[0087] For the abnormal connected components in the minimum test coverage set, the abnormal amplitude, abnormal connected component size, abnormal connected component density, and historical abnormal frequency are statistically analyzed and weighted to obtain a comprehensive score for each abnormal connected component, which serves as a priority indicator. The abnormal connected components are then sorted according to their comprehensive scores, and the abnormal connected components with higher scores are given priority for testing.

[0088] Methods for conducting secondary testing include:

[0089] If the difference between two abnormal connected components is less than the preset priority difference threshold, they are determined to have similar priorities. A cross-channel task merging strategy is introduced to divide abnormal channels with similar priorities and spatial adjacency into the same secondary test task, forming an adaptive secondary test task sequence.

[0090] For each quadratic test task, a weighted delay function is defined, and an optimized sequence of quadratic test tasks is generated by minimizing the weighted delay function of all quadratic test tasks.

[0091] The weighted delay function is: ;in, Indicates the first One secondary test task; Indicates the index of the secondary test task; Indicates task A single abnormal channel in the system; Indicates an abnormal channel The overall score of the abnormal connected component; Indicates an abnormal channel The time required for a single-channel test; This represents the space cost weighting coefficient, used to balance the weights of testing time and spatial distance optimization; This indicates two abnormal channels within the secondary test task. and Spatial distance between them;

[0092] During the secondary testing process, the priority of each abnormal connected component was adaptively adjusted based on the real-time amplitude change rate of the abnormal channel within that component; the adaptive adjustment was then performed as follows: ;in, Indicates the priority of the updated abnormal connected components; Indicates the priority of the abnormal connected components before the update; This represents the adjustment coefficient, used to control the impact of amplitude changes on priority updates; Indicates an abnormal channel At the point of time The real-time amplitude change rate; This represents the reference amplitude, i.e., the amplitude under normal channel conditions; the secondary tests are executed sequentially according to the optimized and adjusted adaptive secondary test task sequence.

[0093] This solution addresses the following problems with existing technologies: Existing technologies typically perform sequential testing of anomalous channels or connected components one channel at a time, resulting in lengthy testing times, complex paths, and numerous repetitive operations. The testing sequence in existing technologies is usually fixed based on initial anomaly identification results and cannot be adjusted according to real-time changes in the amplitude of anomalous channel anomalies. In multi-channel antenna array testing, the spatial distribution of channels is complex; without considering spatial distance, the testing sequence may lead to lengthy testing paths. Existing technologies cannot ensure that testing covers all anomalous channels and their spatial correlation characteristics, and also lack refined testing strategies for anomalous connected components.

[0094] Compared to existing technologies, the advantages are as follows: By employing a cross-channel task merging strategy and optimizing secondary test tasks, the number of tasks and redundant operations are reduced, enabling rapid coverage of abnormal channels and improving overall testing efficiency. Real-time amplitude change rate is used to adjust the priority of abnormal connected components, achieving closed-loop adaptive testing. This allows the system to prioritize detecting abnormal regions with significant amplitude changes, improving anomaly identification accuracy and real-time response capabilities. A weighted delay function comprehensively considers priority, test time, and spatial distance to generate the optimal secondary test task sequence, reducing test path length and system resource consumption, achieving efficient and scientific test scheduling. The task order is dynamically adjusted based on real-time amplitude changes during task execution, achieving intelligent adaptive scheduling to meet the testing requirements of complex multi-channel antenna arrays.

[0095] Methods for achieving reproducible and reliable verification of test results include:

[0096] After the secondary test is completed, a test report is generated, which binds the original acquisition frame ID, the version number of the phase closed-loop matrix used, and the drift removal parameters corresponding to each data point. Using the verification consistency scoring mechanism, the amplitude and phase data of the secondary test are compared with the multi-channel amplitude and phase data to calculate the amplitude deviation, phase deviation, and structural difference variation of each channel. The consistency between the test results and the original data is evaluated according to the preset deviation template to verify the recalculation and reliability of the secondary test data, and the verification results are included in the test report.

[0097] The preset cycle threshold is set by staff based on historical data analysis results. This historical analysis process includes the system collecting data from multiple cycles and calculating their average value as a reference to obtain the preset cycle threshold. Similarly, the preset structural difference component amplitude threshold and the preset priority difference threshold are also set and adjusted by staff according to the system's historical operating data and the specific application scenario requirements.

[0098] This embodiment achieves full-coverage separation and compensation of major environmental drift factors by modeling the thermal drift effect of temperature field on antenna amplitude and phase data, and the electromagnetic interference drift effect of magnetic flux density changes on amplitude and phase data. By calculating and subtracting thermal drift and electromagnetic interference drift using the coupling matrix, only short-period structural difference components are retained, reducing drift residuals and more clearly preserving amplitude and phase change characteristics. The coupling coefficient is obtained through controlled physical field experiments, and the compensation calculation process can be reproduced under arbitrary conditions, ensuring the consistency and verifiability of test results across batches and scenarios. It maintains high stability and high compensation accuracy in complex field environments with significant temperature gradients, electromagnetic interference, and mechanical disturbances, improving the reliability of automated testing systems in production lines and field tests. The multi-dimensional difference vector obtained after drift removal more realistically reflects structural difference characteristics, reducing false alarms and false negatives of environmental factors and improving the accuracy of anomaly source location.

[0099] By employing cross-channel task merging strategies and optimizing secondary test tasks, the number of tasks and redundant operations are reduced, enabling rapid coverage of abnormal channels and improving overall testing efficiency. Real-time amplitude change rate is used to adjust the priority of abnormal connected components, achieving closed-loop adaptive testing. This allows the system to prioritize detecting abnormal regions with significant amplitude changes, improving anomaly identification accuracy and real-time response capabilities. A weighted delay function comprehensively considers priority, test time, and spatial distance to generate an optimal secondary test task sequence, reducing test path length and system resource consumption, achieving efficient and scientific test scheduling. The task order is dynamically adjusted based on real-time amplitude changes during task execution, achieving intelligent adaptive scheduling to meet the testing requirements of complex multi-channel antenna arrays.

[0100] Example 2

[0101] Please see Figure 2As shown, for parts not described in detail in this embodiment, please refer to the description in Embodiment 1. A multi-channel antenna line source automated testing system is provided, including:

[0102] Before the multi-channel line source test is started, the phase interlock acquisition module constructs a phase closed-loop matrix between channels through a full-channel mutual excitation sequence. During the acquisition process, the phase deviation drift prediction algorithm is used to compensate for the phase deviation in real time and output phase-locked multi-channel amplitude and phase data.

[0103] The drift difference extraction module performs dual-timescale decomposition on multi-channel amplitude and phase data to separate short-period structural difference components and long-period environmental drift components; it uses an online environmental drift tracking factor to remove drift and retains only the structural difference components as a multidimensional difference vector.

[0104] The anomaly analysis module maps multidimensional difference vectors to a channel-difference parameter bipartite graph, analyzes the connected component distribution pattern of anomaly parameters, and automatically infers the location of anomaly sources by combining the channel physical topology mapping table, outputting a set of anomaly channels with spatial pattern labels.

[0105] The pattern-driven compression module calculates the minimum test coverage set based on the set of abnormal channels, uses a pattern priority dynamic adjustment mechanism to prioritize the execution of tests in high-impact abnormal domains, and introduces a cross-channel task merging strategy to generate an adaptive secondary test task sequence for secondary testing.

[0106] The end-to-end reversible verification module generates a test report after the secondary test is completed, which is bound to the original acquisition frame ID, phase closed-loop matrix version number and drift elimination parameters. The recalculation results are compared with the original results through the verification consistency scoring mechanism, so as to realize the recalculation and reliable verification of the test results.

[0107] Since the electronic device described in this embodiment is the one used in implementing the automated testing method and system for multi-channel antenna line sources described in this application, those skilled in the art can understand the specific implementation methods and various variations of the electronic device in this embodiment based on the automated testing method and system for multi-channel antenna line sources described in this application. Therefore, how the electronic device implements the method in this application will not be described in detail here. Any electronic device used by those skilled in the art in implementing the automated testing method and system for multi-channel antenna line sources described in this application falls within the scope of protection of this application.

[0108] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0109] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. An automated testing method for multi-channel antenna line sources, characterized in that, include: S1. Before the multi-channel line source test is started, a phase closed-loop matrix between channels is constructed through the full-channel mutual excitation sequence. During the acquisition process, the phase deviation is compensated in real time using the real-time phase deviation drift prediction algorithm, and the phase-locked multi-channel amplitude and phase data is output. S2. Perform dual-timescale decomposition on the multi-channel amplitude and phase data to separate it into short-period structural difference components and long-period environmental drift components; use the online environmental drift tracking factor to remove drift and retain only the structural difference components as multidimensional difference vectors. S3. Map the multidimensional difference vector to a channel-difference parameter bipartite graph, analyze the connected component distribution pattern of the abnormal parameters, and automatically infer the location of the anomaly source by combining the channel physical topology mapping table, and output a set of abnormal channels with spatial pattern labels. S4. Based on the set of abnormal channels, calculate the minimum test coverage set, and use the mode priority dynamic adjustment mechanism to prioritize the execution of tests on high-impact abnormal domains; introduce a cross-channel task merging strategy to generate an adaptive secondary test task sequence for secondary testing. S5. After the secondary test is completed, a test report is generated that binds the original acquisition frame ID, phase closed-loop matrix version number and drift elimination parameters. The recalculated results are compared with the original results through the consistency scoring mechanism to realize the recalculation and reliable verification of the test results.

2. The automated testing method for a multi-channel antenna line source according to claim 1, characterized in that, The method for constructing the inter-channel phase closed-loop matrix includes: All antenna channels to be tested are counted. Before the test starts, all antenna channels are clocked and calibrated. A mutual excitation sequence is designed for each antenna channel. One antenna channel is selected as the excitation source channel in a preset order and a known reference excitation signal is transmitted to the other antenna channels. During each excitation process, the signal amplitude and phase information of the excitation signal received by each receiving channel are collected, and the measurement results are recorded as the phase difference between the receiving channel and the excitation source channel. Mutual excitation operation is performed on all antenna channels in sequence to complete the phase measurement between each pair of all channels, forming a phase closed-loop matrix between channels. Each element of the matrix represents the measured phase difference between the corresponding receiving channel and the excitation source channel.

3. The automated testing method for a multi-channel antenna line source according to claim 2, characterized in that, The method for outputting phase-locked multi-channel amplitude and phase data includes: The generated inter-antenna channel phase closed-loop matrix is ​​used as the reference phase to collect real-time amplitude and phase data for each antenna channel; the instantaneous phase currently collected for each antenna channel is compared with the corresponding reference phase in the inter-antenna channel phase closed-loop matrix to calculate the phase deviation between antenna channels; The phase deviation between antenna channels is input into the real-time phase deviation drift prediction algorithm to predict the phase change trend caused by environmental drift, temperature change and electromagnetic interference, and generate the corresponding compensation amount; compensation is applied to the real-time amplitude and phase data of each antenna channel, and the compensation amount is superimposed or corrected with the acquired real-time amplitude and phase data to keep the real-time amplitude and phase data of each antenna channel in a phase-locked state, and output the phase-locked multi-channel amplitude and phase data.

4. The automated testing method for a multi-channel antenna line source according to claim 3, characterized in that, The method for separating short-period structural difference components and long-period environmental drift components includes: A dual-timescale decomposition algorithm is used to decompose multi-channel amplitude-phase data into short-period structural difference components and long-period environmental drift components. The short-period structural difference components are those with periods less than a preset period threshold in the multi-channel amplitude-phase data, which are extracted through high-pass filtering to reflect the amplitude-phase changes and structural differences in each channel. The long-period environmental drift components are those with periods greater than or equal to the preset period threshold in the multi-channel amplitude-phase data, which are extracted through low-pass filtering to reflect the impact of environmental drift and temperature changes on the multi-channel amplitude-phase data.

5. The automated testing method for a multi-channel antenna line source according to claim 4, characterized in that, The method of retaining only structural difference components as multidimensional difference vectors includes: During the drift removal process, a drift separation model based on the physical field coupling relationship is established to decompose the long-period environmental drift component into thermal drift component and electromagnetic interference drift component: the thermal drift component is obtained by collecting the temperature field data of the antenna, and the temperature field data is obtained by the infrared thermal imager deployed around the antenna. The electromagnetic interference drift component is obtained by real-time acquisition of the magnetic flux density change around the antenna, which is measured by an array of electromagnetic compatibility probes deployed around the antenna; the coupling relationship between the thermal drift component and the electromagnetic interference drift component is determined by a pre-conducted calibration experiment. The calibration experiment includes heating the antenna with a controllable heat source to measure the thermal coupling coefficient, and measuring the electromagnetic coupling coefficient under different electromagnetic excitation conditions using a near-field scanning device, forming a coupling matrix that can characterize the effects of thermal effects and electromagnetic interference effects on phase and amplitude drift. During the test, the coupling matrix is ​​used to calculate the collected temperature field data and magnetic flux density changes to obtain the current thermal drift component and electromagnetic interference drift component. These components are then subtracted from the multi-channel amplitude and phase data, leaving only the short-period structural difference component. After vectorization, a multi-dimensional difference vector is formed.

6. The automated testing method for a multi-channel antenna line source according to claim 5, characterized in that, The method for outputting anomaly channel sets with spatial pattern markers includes: The multidimensional difference vector is mapped to a channel-difference parameter bipartite graph, where the antenna channel is regarded as one set of nodes in the graph and the structural difference component is regarded as another set of nodes. If the amplitude of the structural difference component of any antenna channel exceeds the preset structural difference component amplitude threshold, an edge connection is established between the antenna channel node and the corresponding structural difference component node to form a weighted bipartite graph, and the edge weight represents the degree of amplitude deviation. In the bipartite graph of channel-difference parameters, identify all abnormal connected regions with edge weights greater than preset edge weights, and analyze the association patterns between antenna channel nodes and structural difference component nodes in the abnormal connected regions. The association patterns include the number, distribution and morphological characteristics of nodes in the connected regions. Based on the preset antenna channel physical topology mapping table, the abnormal connected regions identified in the channel-difference parameter bipartite graph are mapped back to the physical antenna array. Combining edge weight size, connected region density and spatial proximity relationship, the most likely abnormal channel location is automatically inferred, and a set of abnormal channels with spatial pattern labels is output.

7. The automated testing method for a multi-channel antenna line source according to claim 6, characterized in that, The method for prioritizing the execution of high-impact anomaly domain tests includes: Based on the set of anomalous channels with spatial pattern labels, the spatial distribution and connectivity characteristics of the anomalous channels are analyzed. The set coverage algorithm is used to calculate the minimum test coverage set so that each channel in the coverage set can represent other channels in its anomalous connectivity domain. For the abnormal connected components in the minimum test coverage set, the abnormal amplitude, abnormal connected component size, abnormal connected component density, and historical abnormal frequency are statistically analyzed and weighted to obtain a comprehensive score for each abnormal connected component, which serves as a priority indicator. The abnormal connected components are then sorted according to their comprehensive scores, and the abnormal connected components with higher scores are given priority for testing.

8. The automated testing method for a multi-channel antenna line source according to claim 7, characterized in that, The method for conducting secondary testing includes: If the difference between two abnormal connected components is less than the preset priority difference threshold, they are determined to have similar priorities. A cross-channel task merging strategy is introduced to divide abnormal channels with similar priorities and spatial adjacency into the same secondary test task, forming an adaptive secondary test task sequence. For each secondary test task, a weighted delay function is defined, and an optimized sequence of secondary test tasks is generated by minimizing the weighted delay function of all secondary test tasks. During the secondary test, the priority of the abnormal connected components is adaptively adjusted based on the real-time amplitude change rate of the abnormal channels within each abnormal connected component. The secondary tests are then executed sequentially according to the optimized and adjusted adaptive sequence of secondary test tasks.

9. The automated testing method for a multi-channel antenna line source according to claim 8, characterized in that, The methods for achieving reproducible and reliable verification of test results include: After the secondary test is completed, a test report is generated, which binds the original acquisition frame ID, the version number of the phase closed-loop matrix used, and the drift removal parameters corresponding to each data point. Using the verification consistency scoring mechanism, the amplitude and phase data of the secondary test are compared with the multi-channel amplitude and phase data to calculate the amplitude deviation, phase deviation, and structural difference variation of each channel. The consistency between the test results and the original data is evaluated according to the preset deviation template to verify the recalculation and reliability of the secondary test data, and the verification results are included in the test report.

10. An automated testing system for multi-channel antenna line sources, used to implement the automated testing method for multi-channel antenna line sources according to any one of claims 1 to 9, characterized in that, include: Before the multi-channel line source test is started, the phase interlock acquisition module constructs a phase closed-loop matrix between channels through a full-channel mutual excitation sequence. During the acquisition process, the phase deviation drift prediction algorithm is used to compensate for the phase deviation in real time and output phase-locked multi-channel amplitude and phase data. The drift difference extraction module performs dual-timescale decomposition on multi-channel amplitude and phase data to separate short-period structural difference components and long-period environmental drift components; it uses an online environmental drift tracking factor to remove drift and retains only the structural difference components as a multidimensional difference vector. The anomaly analysis module maps multidimensional difference vectors to a channel-difference parameter bipartite graph, analyzes the connected component distribution pattern of anomaly parameters, and automatically infers the location of anomaly sources by combining the channel physical topology mapping table, outputting a set of anomaly channels with spatial pattern labels. The pattern-driven compression module calculates the minimum test coverage set based on the set of abnormal channels, uses a pattern priority dynamic adjustment mechanism to prioritize the execution of tests in high-impact abnormal domains, and introduces a cross-channel task merging strategy to generate an adaptive secondary test task sequence for secondary testing. The end-to-end reversible verification module generates a test report after the secondary test is completed, which is bound to the original acquisition frame ID, phase closed-loop matrix version number and drift elimination parameters. The recalculation results are compared with the original results through the verification consistency scoring mechanism, so as to realize the recalculation and reliable verification of the test results.

Images