A fast positioning method for wireless optical communication device radiation emission anechoic chamber test

Abstract

This invention proposes a rapid localization method for anechoic chamber testing of wireless optical communication equipment. It belongs to the interdisciplinary technical fields of electromagnetic compatibility (EMC) testing, computational imaging, and intelligent measurement. The method includes: deploying a programmable antenna array in the anechoic chamber of the wireless optical communication equipment to generate an adaptive antenna layout network; synchronously acquiring multi-band radiation signals to obtain raw multi-band radiation signal data and equipment operating status timestamps; and using a programmable antenna array combined with compressed sensing and near-field holography joint reconstruction algorithm to achieve three-dimensional radiation source imaging with an accuracy of ±3mm within 90 seconds. This significantly improves testing efficiency, shortening the traditional 20-60 minute testing cycle by 20 times and accelerating the R&D iteration speed of wireless optical communication equipment.

Description

Technical Field

[0001] This invention proposes a rapid positioning method for anechoic chamber testing of radiated emissions from wireless optical communication equipment, belonging to the interdisciplinary fields of electromagnetic compatibility (EMC) testing, computational imaging, and intelligent measurement. Background Technology

[0002] In the research and development and production of wireless optical communication equipment, anechoic chamber testing is a crucial step in ensuring the electromagnetic compatibility (EMC) of the equipment. However, traditional testing techniques face many challenges.

[0003] Currently, the mainstream testing method adopts a single-probe XYZ three-axis mechanical scanning method, which takes 20-60 minutes to complete a 3D radiometric mapping. The long testing cycle seriously restricts the speed of research and development iteration and increases the research and development costs and time investment.

[0004] In terms of antenna layout, fixed-position antenna arrays are difficult to adapt to the radiation characteristics of different frequency bands. When facing high-frequency sources, the positioning is unclear; when facing low-frequency sources, the sensitivity is obviously insufficient, resulting in a significant reduction in the accuracy of test results.

[0005] Multi-source interference is also a significant problem. Wireless optical communication equipment contains numerous radiation sources, such as LED drivers, power supplies, and control MCUs. Traditional methods can only output the total field strength and cannot separate the contributions of each radiation source, making it difficult to accurately locate the source of interference.

[0006] Moreover, traditional testing lacks physical tracing capabilities, and the location results only remain at the "hotspot" level, unable to automatically identify specific causes such as clock leakage or power supply noise. Existing published patents mostly focus on general acceleration methods such as "multi-antenna arrays" and "fast scanning algorithms," failing to construct a cognitive closed-loop intelligent positioning architecture of "perception-reconstruction-decoupling-tracing." This makes it impossible to achieve the leap to an intelligent EMC testing paradigm that "understands electromagnetics, identifies devices, and can diagnose," and thus cannot meet the urgent needs of high-speed optical communication equipment for intelligent, accurate, and diagnostic testing technologies. Summary of the Invention

[0007] This invention provides a rapid localization method for anechoic chamber testing of wireless optical communication equipment, thereby solving the problems mentioned in the background section above: This invention proposes a rapid localization method for anechoic chamber testing of wireless optical communication equipment, the method comprising: S1: Deploy a programmable antenna array in the anechoic chamber of the wireless optical communication equipment to generate an adaptive antenna layout network, synchronously acquire multi-band radiation signals, and obtain multi-band raw radiation signal data and equipment operation status timestamp data; perform multi-source signal separation processing on the multi-band raw radiation signal data to generate independent signal data for each radiation source; S2: Rapidly reconstruct the radiation field based on the independent signal data of each radiation source and the joint reconstruction algorithm of compressed sensing and near-field holography to generate three-dimensional radiation source distribution data; perform spatial coordinate calibration processing on the three-dimensional radiation source distribution data to generate radiation source location data; S3: Extract radiation source feature parameters based on radiation source location data to obtain radiation source spectral feature data and radiation source time-domain feature data respectively; perform radiation source type matching processing by matching radiation source spectral feature data and radiation source time-domain feature data to generate radiation source type label data. S4: Perform dynamic operating condition correlation analysis on wireless optical communication equipment using equipment operating status timestamp data to generate operating condition and radiation mapping data; perform interference source tracing processing using radiation source type label data and operating condition and radiation mapping data to generate interference source physical cause data; perform radiation contribution quantification analysis to generate contribution weight data for each radiation source. S5: Aggregate radiation hotspots based on the contribution weight data of each radiation source to generate a comprehensive radiation risk map; perform graph neural network inference processing based on the comprehensive radiation risk map to generate interference source type identification results and risk level assessment data, perform automatic test report generation processing, and generate intelligent diagnostic data for radiation emission of wireless optical communication equipment.

[0008] The beneficial effects of this invention are as follows: By combining a programmable antenna array with compressed sensing and a near-field holographic joint reconstruction algorithm, three-dimensional radiation source imaging with an accuracy of ±3mm can be achieved within 90 seconds, significantly improving testing efficiency and shortening the traditional 20-60 minute testing cycle by 20 times, thus accelerating the R&D iteration speed of wireless optical communication equipment. The adaptive antenna layout network adapts to the radiation characteristics of different frequency bands, reducing the problems of high-frequency source ambiguity and low-frequency source insufficient sensitivity, and enhancing the accuracy of test results. Multi-source signal separation processing can separate the contributions of each radiation source, and then automatically identify the type of interference source through a graph neural network, reducing the difficulty of interference source location and achieving physical source tracing. It can accurately locate radiation sources and automatically diagnose interference source types, effectively avoiding misjudgments caused by the inability to accurately identify interference sources during testing, reducing the workload of subsequent equipment debugging and optimization, and providing a new generation of intelligent, accurate, and diagnostic EMC testing core technology for high-speed optical communication equipment, significantly improving the overall testing level and product quality. Attached Figure Description

[0009] Figure 1 This is a diagram illustrating the steps of the method described in this invention. Detailed Implementation

[0010] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0011] One embodiment of the present invention, such as Figure 1 As shown, a rapid localization method for anechoic chamber testing of wireless optical communication equipment includes: S1: Deploy a programmable antenna array in the anechoic chamber of the wireless optical communication equipment to generate an adaptive antenna layout network; synchronously acquire multi-band radiation signals based on the adaptive antenna layout network to obtain multi-band raw radiation signal data and equipment operation status timestamp data; perform multi-source signal separation processing on the multi-band raw radiation signal data to generate independent signal data for each radiation source. S2: Rapidly reconstruct the radiation field based on the independent signal data of each radiation source and the joint reconstruction algorithm of compressed sensing and near-field holography to generate three-dimensional radiation source distribution data; perform spatial coordinate calibration on the three-dimensional radiation source distribution data to generate radiation source position data with ±3mm accuracy calibration. S3: Extract radiation source feature parameters based on radiation source location data to obtain radiation source spectral feature data and radiation source time-domain feature data respectively; perform radiation source type matching processing by matching radiation source spectral feature data and radiation source time-domain feature data to generate radiation source type label data. S4: Perform dynamic operating condition correlation analysis on wireless optical communication equipment using equipment operating status timestamp data to generate operating condition and radiation mapping data; perform interference source tracing processing using radiation source type label data and operating condition and radiation mapping data to generate interference source physical cause data; perform radiation contribution quantification analysis on interference source physical cause data to generate contribution weight data for each radiation source. S5: Aggregate radiation hotspots based on the contribution weight data of each radiation source to generate a comprehensive radiation risk map; perform graph neural network inference processing based on the comprehensive radiation risk map to generate interference source type identification results and risk level assessment data; and automatically generate test reports based on the interference source type identification results and risk level assessment data to generate intelligent diagnostic data for radiation emissions from wireless optical communication equipment.

[0012] The working principle and effects of the above technical solution are as follows: By deploying a programmable antenna array in the anechoic chamber of the wireless optical communication equipment, signal acquisition without blind spots in the entire airspace is achieved, improving the integrity and coverage of radiated signal acquisition, while reducing the risk of radiation source positioning deviation caused by blind spots. Utilizing a joint reconstruction algorithm of compressed sensing and near-field holography, the three-dimensional distribution of the radiation field is quickly restored, enhancing the accuracy and timeliness of field information reconstruction, reducing the dependence of traditional reconstruction methods on large amounts of sampled data, and lowering the resource consumption of data processing. Through joint extraction and type matching of the radiation source spectrum and temporal features, the accuracy of radiation source identification is improved, reducing errors caused by manual interpretation. Combined with dynamic operational status correlation analysis of the equipment, a precise mapping between interference sources and equipment operating conditions is achieved, avoiding the problem of being unable to locate the root cause of interference by analyzing radiation signals in isolation, while also reducing the workload of subsequent source tracing and investigation. Based on hotspot aggregation and graph neural network inference of radiation contribution, the generated comprehensive radiation risk map intuitively reflects the distribution of high-risk areas, enhances the scientific nature of risk assessment, reduces the impact of potential interference on communication quality, and avoids communication performance degradation and equipment malfunction caused by unrecognized radiation interference.

[0013] In one embodiment of the present invention, S1 includes: S11: Complete the partitioned layout of the programmable antenna array in the three-dimensional test space of the anechoic chamber for the radiating emission of wireless optical communication equipment. Adjust the spacing and orientation of the antenna nodes according to the size of the anechoic chamber and the placement of the equipment to form an adaptive antenna layout network covering the entire airspace. S12: Multi-band synchronous scanning and acquisition of radiation signals in the working state of wireless optical communication equipment is performed through an adaptive antenna layout network. Signal reception and temporary storage are completed sequentially according to the preset frequency band intervals. Radiation intensity and phase information under different frequency bands are fully recorded to obtain multi-band raw radiation signal data. S13: Synchronously track the status of the wireless optical communication device throughout the entire process of radiation signal acquisition. The status includes start-up and shutdown, power switching and module operation. Record the status change nodes at fixed time intervals to form continuous and complete timestamp data of the device's operating status. S14: Perform channel splitting and aliasing removal on the original multi-band radiation signal data, eliminate crosstalk and superposition components between signals of different frequency bands, and complete the multi-source signal separation process through joint time-domain and frequency-domain processing, retaining the effective signal segment corresponding to a single radiation source. S15: Perform baseline correction and outlier removal on the separated signal data to eliminate system errors introduced by the acquisition link and generate pure and feature-complete independent signal data for each radiation source.

[0014] The working principle and effects of the above technical solution are as follows: By deploying programmable antenna arrays in zones and adjusting the node spacing and orientation, an adaptive antenna layout network covering the entire airspace is formed, improving the coverage of radiated signal acquisition and avoiding signal omissions caused by blind spots. Multi-band synchronous scanning acquisition can completely record radiation intensity and phase information, enhancing the integrity of the original signal and reducing the workload of subsequent data completion. Synchronous tracking of equipment operating status and recording of timestamps achieves precise correspondence between signal acquisition and equipment operating conditions, avoiding errors caused by timing misalignment during subsequent correlation analysis. Channel splitting and aliasing signal stripping combined with time-domain and frequency-domain joint processing effectively eliminate crosstalk and superimposed components, improving the purity of signal separation and reducing clutter interference to subsequent analysis. Baseline correction and outlier removal eliminate system errors, generating pure and complete independent signal data, which not only improves the accuracy of subsequent radiation field reconstruction but also reduces positioning deviations caused by signal distortion, providing reliable basic data support for the entire testing process.

[0015] In one embodiment of the present invention, step S14 includes: The multi-band raw radiation signal data is split into channels, and the multiple signals acquired in parallel are independently divided according to the acquisition channels to form independent radiation signal data for each channel. An aliasing removal operation is performed on the independent radiated signal data of each channel to remove the superimposed components caused by the interleaving of signals from different frequency bands, forming crosstalk-free single-channel radiated signal data; Time-domain signal normalization is performed on single-channel radiated signal data without crosstalk to filter out time-domain noise and abrupt interference, and retain continuous and stable effective time-domain signal data. Perform frequency domain signal purification on the effective time domain signal data to remove frequency domain spurious components and redundant harmonics, forming pure frequency domain characteristic signal data; The multi-source signal classification operation is performed by combining the time domain and frequency domain processing results. The signals are collected according to the radiation generating subject, and the effective signal segments corresponding to a single radiation source are retained.

[0016] The working principle and effects of the above technical solution are as follows: Channel splitting is performed on the multi-band raw radiation signal data, independently dividing parallel signals according to the acquisition channel. This improves the targeting of signal processing, reduces processing errors caused by cross-channel signal interference, and avoids feature confusion caused by signal aliasing. Aliasing signal stripping removes superimposed components caused by the interleaving of signals from different frequency bands, forming crosstalk-free single-channel radiation signal data. This enhances the purity of the single-channel signal, reduces interference from clutter on signal feature extraction, and avoids the situation where signal superposition masks the true radiation characteristics. Time-domain signal normalization filters out time-domain noise and abrupt interference, retaining continuous and stable effective time-domain signals, improving the stability of the time-domain signal and reducing the probability of misjudgment in subsequent analysis. Frequency-domain signal purification removes frequency-domain stray components and redundant harmonics, forming pure frequency-domain feature signals. This enhances the accuracy of frequency-domain feature extraction and reduces interference from invalid information on radiation source identification. By combining the time-domain and frequency-domain processing results to perform multi-source signal classification, the signals are grouped according to the radiation subject and the effective segments of a single radiation source are retained. This improves the efficiency and accuracy of multi-source signal separation, enhances the rationality of signal classification, avoids subsequent analysis bias caused by the mixing of signals from different radiation sources, and lays a reliable data foundation for accurately identifying radiation source types.

[0017] In one embodiment of the present invention, S2 includes: S21: Using the independent signal data of each radiation source as input, and combining the compressed sensing algorithm, the sparse radiation field is rapidly reconstructed iteratively, reducing the amount of sampled data while restoring the overall outline of the radiation field and completing the initial structure construction of the radiation field. S22: Introducing a near-field holographic algorithm to enhance the details of compressed sensing reconstruction results, supplementing the information on subtle changes in the radiation field in the near-field region, and completing the rapid and refined reconstruction of the entire radiation field through field superposition and phase compensation; S23: Map the rapidly reconstructed radiation field data to the three-dimensional spatial coordinate system of the darkroom, mark each area of ​​concentrated radiation energy, and generate three-dimensional radiation source distribution data, which includes spatial coordinates and energy intensity. S24: Using the standard positioning benchmark in the anechoic chamber as a reference, the coordinate points in the three-dimensional radiation source distribution data are offset corrected and the error is iterated and adjusted to eliminate the systematic deviation caused by algorithm reconstruction and spatial positioning. After completing all calibration operations, the positioning results are output, forming radiation source location data with a stable accuracy within ±3mm, providing a precise spatial basis for subsequent feature extraction and source tracing analysis.

[0018] The working principle and effects of the above technical solution are as follows: By using independent signal data from each radiation source as input, and combining compressed sensing algorithms to rapidly iteratively reconstruct the sparse radiation field, the processing pressure caused by the amount of sampled data is reduced, while the overall contour of the radiation field is restored, improving the efficiency of field reconstruction, reducing the cost of large-scale data storage and transmission, and avoiding the problem of field contour distortion caused by insufficient sampled data. A near-field holographic algorithm is introduced to enhance the details of the compressed sensing reconstruction results, supplementing the subtle changes in the radiation field in the near-field region. Through field superposition and phase compensation, a fine and rapid reconstruction of the entire radiation field is completed, enhancing the delicacy and completeness of the radiation field reconstruction, reducing the shortcomings of traditional single algorithms in terms of missing details in the near-field region, and avoiding radiation source positioning errors caused by missing details. The rapidly reconstructed radiation field data is mapped to the three-dimensional spatial coordinate system of the darkroom, marking each region of concentrated radiation energy, generating three-dimensional radiation source distribution data containing spatial coordinates and energy intensity. This improves the intuitiveness of radiation source spatial positioning, enhances the richness of data representation, and provides comprehensive spatial information support for subsequent accurate analysis. Using the standard positioning benchmark in the anechoic chamber as a reference, the coordinate points in the three-dimensional radiation source distribution data are offset corrected and the error is iterated and adjusted to eliminate the systematic deviation caused by algorithm reconstruction and spatial positioning. This results in radiation source location data with a stable accuracy within ±3mm, which not only improves the accuracy of radiation source positioning but also reduces the accumulation of errors in subsequent feature extraction and source tracing analysis. It also avoids misjudgment of interference sources caused by positioning deviation, providing a precise and reliable spatial basis for the entire testing process.

[0019] In one embodiment of the present invention, step S21 includes: Import independent signal data from each radiation source and perform signal intensity normalization processing to generate standardized radiation source signal data that adapts to the algorithm input; Standardized radiation source signal data is substituted into a compressed sensing algorithm to perform iterative calculations, generating sparsely sampled and reconstructed radiation field data. Contour fitting is performed on the sparsely sampled and reconstructed radiation field data to restore the overall distribution of the radiation field and generate basic contour data of the radiation field. Missing regions in the basic contour data of the radiation field are filled in, and contour breaks formed by sampling gaps are filled to generate complete initial contour data of the radiation field. The complete initial contour data of the radiation field is integrated to complete the field structure splicing and form stable and unified initial structure data of the radiation field.

[0020] The working principle and effects of the above technical solution are as follows: First, intensity normalization processing is performed on the independent signal data of each radiation source to generate a standardized signal suitable for the algorithm input. This improves the compatibility between the signal and the algorithm, reduces algorithm operation deviations caused by signal intensity differences, and avoids reconstruction failures caused by non-standard input signals. The standardized signal is then substituted into the compressed sensing algorithm for iterative computation to generate sparse sampling reconstruction data of the radiation field. This reduces the amount of sampling data, lowers the computational load, and quickly captures the core features of the radiation field, improving reconstruction efficiency. Contour fitting is performed on the sparse reconstruction data to restore the overall distribution of the radiation field, enhancing the integrity of the contour and avoiding contour deformation caused by sampling discrepancies. Missing regions in the basic contour data are filled in, filling the gaps formed by sampling, reducing information loss due to missing contours and ensuring the integrity of the initial contour of the radiation field. Finally, the contour data is integrated to complete the field structure stitching, forming stable and unified initial structure data of the radiation field. This improves the stability of the initial structure and provides a basis for subsequent near-field holographic algorithms. In one embodiment of the present invention, S3 includes: S31: Using the calibrated radiation source location data as an index, the radiation signal at the corresponding spatial point is decomposed into frequency bands, and the radiation energy ratio, peak position and bandwidth distribution information at different frequency points are extracted to generate complete radiation source spectrum feature data. S32: Combine the signal acquisition timing sequence to extract the amplitude fluctuation pattern, pulse interval, duration and time domain change characteristics of each radiation source, reflect the dynamic change characteristics of the radiation signal over time, and generate time domain characteristic data of the radiation source. S33: The radiation source spectral feature data and time domain feature data are dimensionally fused to construct a radiation source feature set, which includes multi-dimensional information to highlight the differentiated performance of different radiation sources; S34: The fused feature set is compared with typical radiation feature samples dimension by dimension, and the radiation source type matching process is completed by ranking the features by similarity, thus distinguishing between effective communication radiation and abnormal interference radiation. S35: Standardize and classify the matched radiation sources, remove duplicate and invalid labels, and generate clear and uniform radiation source type label data.

[0021] The working principle and effects of the above technical solution are as follows: By using the calibrated radiation source location data as an index, the frequency bands of the radiation signals at corresponding points are decomposed and spectral features are extracted. This improves the targeting and completeness of spectral feature extraction, reduces interference from irrelevant signals, and avoids radiation source identification errors caused by missing features. Combining the acquisition time series to extract time-domain features clearly reflects the dynamic changes of the radiation signal, enhancing the comprehensiveness of the feature data and compensating for the inability of a single spectral feature to reflect the temporal changes of the signal. Dimensionally fusing spectral and time-domain features, a multi-dimensional feature set is constructed, highlighting the differences between different radiation sources, improving the distinguishability of radiation sources, and reducing the probability of confusion between features of different radiation sources. By comparing the fused features with typical samples dimension by dimension and ranking them by similarity, effective communication radiation and abnormal interference radiation are accurately distinguished, improving the accuracy of radiation source type matching and avoiding the situation of mistakenly identifying interference radiation as effective signals. The matching results are standardized and categorized, and duplicate and invalid labels are removed to generate clear and unified type label data. This not only improves the standardization of the label data, but also reduces the workload of subsequent source tracing analysis and avoids misjudgment of interference sources caused by label confusion. It provides accurate and reliable feature support for subsequent interference source tracing and risk assessment.

[0022] In one embodiment of the present invention, step S31 includes: Retrieve the calibrated radiation source location data and sort the point information to generate ordered radiation source spatial index data; based on the ordered radiation source spatial index data, perform frequency band decomposition on the radiation signal of the corresponding point to generate frequency band radiation signal data; Energy statistics are performed on the frequency band radiated signal data to calculate the proportion of radiated energy at each frequency point and generate frequency point energy distribution data. Peak retrieval is performed on the frequency band radiated signal data to locate the radiated peak positions of each frequency band and generate radiated peak position data; boundary determination is performed on the frequency band radiated signal data to extract the effective bandwidth range of the signal and generate radiated bandwidth distribution data. By integrating frequency point energy distribution data, radiation peak location data, and radiation bandwidth distribution data, complete radiation source spectral characteristic data is generated.

[0023] The working principle and effects of the above technical solution are as follows: By first retrieving and sorting the calibrated radiation source location data, ordered spatial index data is generated, improving the orderliness and efficiency of radiation signal retrieval, reducing signal matching errors caused by chaotic locations, and avoiding feature extraction omissions due to disordered indexes. Frequency band decomposition is performed on the radiation signals at corresponding locations according to the ordered index, generating frequency-band signal data, enhancing the targeting of signal processing, reducing the impact of mutual interference between signals of different frequency bands, and making subsequent feature extraction more accurate. Energy statistics, peak retrieval, and boundary determination are performed on the frequency-band data to generate energy distribution, peak location, and bandwidth distribution data respectively, comprehensively capturing the spectral characteristics of the radiation signal and reducing the information bias caused by single feature extraction. Finally, various spectral-related data are integrated to generate complete radiation source spectral feature data, ensuring the integrity and comprehensiveness of spectral features, improving the standardization of feature data, avoiding subsequent matching errors caused by feature fragmentation, providing accurate and comprehensive spectral support for radiation source type identification and subsequent analysis, and also reducing the workload of subsequent feature supplementation.

[0024] In one embodiment of the present invention, S34 includes: Load the fused radiation source feature set, unify and standardize the feature dimensions to generate standardized feature data to be matched; retrieve typical radiation feature samples, synchronize and standardize the sample feature dimensions to generate standardized sample comparison data. The standardized feature data to be matched is compared with the standardized sample comparison data dimension by dimension to generate dimensional difference measurement data. Based on the dimensional difference measurement data, similarity is sorted, and the corresponding relationships of samples with high similarity are selected to generate feature matching ranking data. Radiation source types are classified based on feature matching and sorting data, separating normal communication-related radiation from abnormal interference radiation.

[0025] The working principle and effects of the above technical solution are as follows: First, the dimensions of the fused radiation source feature set are unified and standardized to generate standardized feature data to be matched. Simultaneously, the dimensions of typical radiation feature samples are standardized to generate standardized sample comparison data. This improves the fit between the data to be matched and the sample data, reduces comparison errors caused by inconsistent dimensions, and avoids deviations during feature comparison. The two types of standardized data are compared dimensionally to generate dimensional difference measurement data, enhancing the detail of feature comparison and making the differences between different features clearer, reducing the probability of misjudgment caused by one-sided comparisons. Based on the difference measurement data, similarity is ranked, and high-similarity sample correspondences are selected, improving the accuracy of feature matching and avoiding matching errors caused by irrelevant sample interference. Based on the ranking results, radiation source types are classified, accurately separating normal communication radiation from abnormal interference radiation. This improves the accuracy of radiation source type identification, reduces the impact of interference radiation on subsequent analysis, avoids mistaking interference radiation for effective communication radiation, provides a reliable basis for type differentiation for subsequent interference tracing and risk assessment, and also improves the efficiency of the entire testing process.

[0026] In one embodiment of the present invention, step S4 includes: S41: Strictly align the timestamp data of the equipment operating status with the time axis of radiation signal acquisition, analyze the variation law of radiation characteristics under different operating conditions of the equipment, and establish a dynamic correlation between equipment status and radiation response; S42: By analyzing the correspondence between nodes of change in operating conditions and nodes of sudden changes in radiation characteristics, the influence relationship between the switching of operating conditions on radiation intensity and distribution is sorted out, and complete operating condition and radiation mapping data are generated. S43: By combining radiation source type label data with operating conditions and radiation mapping data, the generation path of abnormal radiation is tracked from multiple dimensions of space, frequency band, and time sequence, and the key links and physical locations of interference are located. S44: Perform mechanism analysis on the interference source obtained from the source tracing, analyze the internal causes of the interference, including circuit coupling, structural leakage and module crosstalk, and generate specific traceable physical cause data of the interference source. S45: Quantify the role of each radiation source in the overall interference, calculate the influence weight of each radiation source by energy proportion and influence range, and generate standardized contribution weight data of each radiation source.

[0027] The working principle and effects of the above technical solution are as follows: By strictly aligning the timestamp of the equipment's operating status with the time axis of radiation signal acquisition, the changes in radiation characteristics under different operating conditions are analyzed in correlation, establishing a dynamic correlation between equipment status and radiation response. This improves the accuracy of the correspondence between operating conditions and radiation signals, reduces analysis errors caused by timing misalignment, and avoids the problem of isolated analysis of radiation signals failing to correlate with equipment operating conditions. By correspondingly analyzing changes in operating conditions and abrupt changes in radiation characteristics, the impact of operating condition switching on radiation is analyzed, generating complete operating condition and radiation mapping data, enhancing the completeness of the correlation between operating conditions and radiation, and reducing blind spots in the analysis of the impact of operating conditions on radiation. Combining radiation source type labels and mapping data, abnormal radiation paths are tracked in multiple dimensions, accurately locating key interference links and physical locations, improving the efficiency and accuracy of interference tracing, and avoiding the cumbersome process of blindly investigating interference sources. Mechanistic analysis of interference sources clarifies the internal causes of interference and generates traceable causal data, reducing the ambiguity of interference causes and avoiding the problem of being unable to accurately locate the root cause of interference. Quantifying the impact weight of each radiation source and generating standardized contribution weight data not only improves the scientific nature of radiation impact assessment but also provides a reliable basis for subsequent radiation hotspot aggregation. At the same time, it reduces errors in subsequent risk assessment and enhances the rationality of the entire testing and diagnosis process.

[0028] In one embodiment of the present invention, S41 includes: Retrieve the timestamp data of the equipment's operating status and the time axis data of the radiation signal acquisition, and uniformly calibrate the time reference of the two types of data to generate time synchronization reference data; Based on time synchronization reference data, the timestamps of the equipment's operating status are matched one-to-one with the radiation signal acquisition data at the corresponding time to generate time-series related data pairs. The time-series correlated data pairs are grouped and classified according to different operating conditions of the equipment, generating condition-grouped correlated data. Analyze the grouped correlation data of each working condition, extract the characteristic variation patterns of the intensity, frequency band, and phase of the radiated signal under different working conditions, and generate working condition radiation characteristic variation data; By integrating data on changes in radiation characteristics under operating conditions, a correspondence between equipment operating status and radiation response characteristics is established, forming dynamic correlation model data.

[0029] The working principle and effects of the above technical solution are as follows: By first retrieving the equipment operating status timestamps and radiation signal acquisition time axis data, the time bases of the two types of data are uniformly calibrated to generate time synchronization reference data. This improves the temporal consistency of the two types of data, reduces correlation errors caused by time base deviations, and avoids the problem of temporal misalignment during subsequent corresponding analysis. Based on the time synchronization reference, the equipment status timestamps are mapped one-to-one with the radiation signals at the corresponding times, generating time-series correlation data pairs. This enhances the accuracy of data correlation, ensuring that each set of radiation signals corresponds to a specific equipment operating status, reducing the probability of signal-operating-condition mismatch. The time-series correlation data is grouped and classified according to operating conditions, generating operating condition group correlation data. This improves the systematic nature of data processing and avoids analytical confusion caused by the mixing of data from different operating conditions. By analyzing each group of data, multiple characteristic change patterns of radiation signals under different operating conditions are extracted, generating operating condition radiation characteristic change data. This comprehensively captures the impact of operating conditions on radiation, reducing the one-sidedness of feature extraction. Finally, by integrating the characteristic change data, a correspondence between equipment status and radiation response was established, forming a dynamic correlation model. This not only improved the scientific nature of the correlation analysis but also provided reliable support for subsequent working condition and radiation mapping and interference source tracing, while reducing the workload of subsequent analysis.

[0030] In one embodiment of the present invention, step S5 includes: S51: Based on the contribution weight data of each radiation source, spatial clustering is performed on high-energy and high-impact radiation points, and adjacent radiation areas with similar contribution values ​​are aggregated and divided to highlight radiation concentration and high-risk areas. S52: Integrate and aggregate the radiation area, spatial location and contribution weight information to draw a visualized comprehensive radiation risk map, which includes spatial distribution, energy level and impact range; S53: Input the comprehensive radiation risk map into the graph neural network model to perform node feature reasoning and correlation analysis, and complete the automatic identification of interference types and extraction of risk features through topological structure learning; S54: Based on the output of neural network inference, classify radiation hazard levels, mark high-risk interference and low-risk stray components, and generate standardized interference source type identification results and risk level assessment data. S55: Integrates radiation source location information, type identification results, risk level and tracing mechanism, automatically fills in the document structure and completes the content arrangement according to the test specifications, and generates comprehensive and standardized intelligent diagnostic data on radiated emissions of wireless optical communication equipment.

[0031] The working principle and effects of the above technical solution are as follows: By spatially clustering high-impact radiation points based on the contribution weight of radiation sources, adjacent high-risk areas are aggregated, improving the intuitiveness of radiation hotspot identification, reducing the difficulty of assessment caused by scattered points, and preventing the problem of high-risk radiation areas being overlooked. Integrating multiple types of information to create a visualized radiation risk map enhances the comprehensiveness of risk distribution display, making the distribution of radiation hazards more intuitively perceptible and reducing the complexity of manual assessment. Inputting the risk map into a graph neural network for inference analysis automatically identifies interference types and extracts risk features, improving the efficiency and objectivity of interference identification and reducing subjective errors caused by manual assessment. Based on the inference results, hazard levels are divided, generating standardized identification and assessment data, improving the accuracy of risk classification and avoiding chaotic risk level division. Integrating end-to-end test information to automatically generate intelligent diagnostic data improves the efficiency of test report generation, ensures the completeness and standardization of content, reduces the time and omissions of manual compilation, and provides a clear and reliable basis for equipment optimization and rectification.

[0032] In one embodiment of the present invention, S51 includes: The contribution weight data of each radiation source is retrieved, and the data is normalized to generate standardized radiation source influence weight data. Based on standardized radiation source impact weight data, high-impact radiation points with weight values ​​higher than a set threshold are selected to generate a set of high-risk radiation points. Spatial neighborhood analysis is performed on the set of high-risk radiation points to identify combinations of radiation points that are spatially adjacent and have similar impact weights; The identified radiation point combinations are aggregated and divided into regions, and adjacent points are merged into a unified radiation region unit to generate aggregated radiation region data. By integrating and aggregating radiation area data with corresponding contribution weight information, highlighting concentrated radiation distribution and high-risk impact areas, the aggregation processing of high-risk radiation areas is completed.

[0033] The working principle and effects of the above technical solution are as follows: By normalizing the contribution weight data of each radiation source, standardized weight data is generated, improving the comparability of the influence of different radiation sources, reducing analytical bias caused by excessive numerical differences, and avoiding misjudgments caused by unbalanced weight comparisons. High-impact radiation points are screened based on standardized data to form a high-risk point set, enhancing the targeting of risk identification, reducing interference from invalid points in subsequent analysis, and highlighting key areas. Spatial neighborhood analysis is conducted on high-risk points to identify adjacent point combinations with similar weights, improving the rationality of spatial correlation analysis, reducing misjudgments caused by individual assessments of scattered points, and avoiding the fragmented view of local risks. Point combinations meeting the criteria are aggregated and divided into unified radiation area units, improving the overall display of risk areas and reducing the difficulty of manually identifying high-risk areas. The final processing integrates the aggregated areas and weight information, clearly presenting the concentrated distribution of radiation and accurately pinpointing the high-risk impact range, providing a well-organized and reliable data foundation for subsequent map drawing and risk assessment.

[0034] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A rapid positioning method for anechoic chamber testing of wireless optical communication equipment, characterized in that, The method includes: S1: Deploy a programmable antenna array in the anechoic chamber of the wireless optical communication equipment to generate an adaptive antenna layout network, synchronously acquire multi-band radiation signals, and obtain multi-band raw radiation signal data and equipment operation status timestamp data; perform multi-source signal separation processing on the multi-band raw radiation signal data to generate independent signal data for each radiation source; S2: Rapidly reconstruct the radiation field based on the independent signal data of each radiation source and the joint reconstruction algorithm of compressed sensing and near-field holography to generate three-dimensional radiation source distribution data; perform spatial coordinate calibration processing on the three-dimensional radiation source distribution data to generate radiation source location data; S3: Extract radiation source feature parameters based on radiation source location data to obtain radiation source spectral feature data and radiation source time-domain feature data respectively; perform radiation source type matching processing by matching radiation source spectral feature data and radiation source time-domain feature data to generate radiation source type label data. S4: Perform dynamic operating condition correlation analysis on wireless optical communication equipment using equipment operating status timestamp data to generate operating condition and radiation mapping data; perform interference source tracing processing using radiation source type label data and operating condition and radiation mapping data to generate interference source physical cause data; perform radiation contribution quantification analysis to generate contribution weight data for each radiation source. S5: Aggregate radiation hotspots based on the contribution weight data of each radiation source to generate a comprehensive radiation risk map; perform graph neural network inference processing based on the comprehensive radiation risk map to generate interference source type identification results and risk level assessment data, perform automatic test report generation processing, and generate intelligent diagnostic data for radiation emission of wireless optical communication equipment.

2. The rapid positioning method for anechoic chamber testing of wireless optical communication equipment according to claim 1, characterized in that, S1 includes: S11: Complete the partitioning of the programmable antenna array in the three-dimensional test space of the anechoic chamber of the wireless optical communication equipment, and adjust the antenna node spacing and orientation according to the size of the anechoic chamber and the placement of the equipment to form an adaptive antenna layout network. S12: Multi-band synchronous scanning and acquisition of radiation signals in the working state of wireless optical communication equipment is performed through an adaptive antenna layout network. Signal reception and temporary storage are completed sequentially according to the preset frequency band intervals. Radiation intensity and phase information under different frequency bands are fully recorded to obtain multi-band raw radiation signal data. S13: Synchronously track the status of the wireless optical communication equipment throughout the entire process of radiation signal acquisition, record status change nodes at fixed time intervals, and form continuous and complete timestamp data of equipment operation status. S14: Perform channel splitting and aliasing removal on the original multi-band radiation signal data, eliminate crosstalk and superposition components between signals of different frequency bands, and complete the multi-source signal separation process through joint time-domain and frequency-domain processing, retaining the effective signal segment corresponding to a single radiation source. S15: Perform baseline correction and outlier removal on the separated signal data to eliminate system errors introduced by the acquisition link and generate independent signal data for each radiation source.

3. The rapid positioning method for anechoic chamber testing of wireless optical communication equipment according to claim 2, characterized in that, S14 includes: The multi-band raw radiation signal data is split into channels, and the multiple signals acquired in parallel are independently divided according to the acquisition channels to form independent radiation signal data for each channel. An aliasing removal operation is performed on the independent radiated signal data of each channel to remove the superimposed components caused by the interleaving of signals from different frequency bands, forming crosstalk-free single-channel radiated signal data; Time-domain signal normalization is performed on single-channel radiated signal data without crosstalk to filter out time-domain noise and abrupt interference, and retain continuous and stable effective time-domain signal data. Perform frequency domain signal purification on the effective time domain signal data to remove frequency domain spurious components and redundant harmonics, forming pure frequency domain characteristic signal data; The multi-source signal classification operation is performed by combining the time domain and frequency domain processing results. The signals are collected according to the radiation generating subject, and the effective signal segments corresponding to a single radiation source are retained.

4. The rapid positioning method for anechoic chamber testing of wireless optical communication equipment according to claim 1, characterized in that, The S2 includes: S21: Using the independent signal data of each radiation source as input, and combining the compressed sensing algorithm, the sparse radiation field is rapidly reconstructed iteratively to reduce the amount of sampled data while restoring the overall outline of the radiation field and completing the initial structure construction of the radiation field. S22: Introducing a near-field holographic algorithm to enhance the details of compressed sensing reconstruction results, supplementing the subtle changes in the radiation field in the near-field region, and completing the fine and rapid reconstruction of the entire radiation field through field superposition and phase compensation; S23: Map the rapidly reconstructed radiation field data to the three-dimensional spatial coordinate system of the darkroom, mark each area of ​​concentrated radiation energy, and generate three-dimensional radiation source distribution data; S24: Using the standard positioning benchmark in the anechoic chamber as a reference, the coordinate points in the three-dimensional radiation source distribution data are offset and error iteratively adjusted. After completing all calibration operations, the positioning results are output to form radiation source location data.

5. The rapid positioning method for anechoic chamber testing of wireless optical communication equipment according to claim 1, characterized in that, The S3 includes: S31: Using the calibrated radiation source location data as an index, the radiation signal at the corresponding spatial point is decomposed into frequency bands, and the radiation energy ratio, peak position and bandwidth distribution information at different frequency points are extracted to generate complete radiation source spectrum feature data. S32: Combine the signal acquisition time sequence to extract the amplitude fluctuation pattern, pulse interval, duration and time domain change characteristics of each radiation source, reflect the dynamic change characteristics of the radiation signal over time, and generate time domain characteristic data of the radiation source. S33: Dimensionally fuse radiation source spectral feature data with temporal feature data to construct a radiation source feature set; S34: The fused feature set is compared with typical radiation feature samples dimension by dimension, and the radiation source type matching process is completed by ranking the features by similarity, thus distinguishing between effective communication radiation and abnormal interference radiation. S35: Standardize and classify the matched radiation sources, remove duplicate and invalid labels, and generate radiation source type label data.

6. The rapid positioning method for anechoic chamber testing of wireless optical communication equipment according to claim 5, characterized in that, S31 includes: Retrieve the calibrated radiation source location data and sort the point information to generate ordered radiation source spatial index data; based on the ordered radiation source spatial index data, perform frequency band decomposition on the radiation signal of the corresponding point to generate frequency band radiation signal data; Energy statistics are performed on the frequency band radiated signal data to calculate the proportion of radiated energy at each frequency point and generate frequency point energy distribution data. Peak retrieval is performed on the frequency band radiated signal data to locate the radiated peak positions of each frequency band and generate radiated peak position data; boundary determination is performed on the frequency band radiated signal data to extract the effective bandwidth range of the signal and generate radiated bandwidth distribution data. By integrating frequency point energy distribution data, radiation peak location data, and radiation bandwidth distribution data, complete radiation source spectral characteristic data is generated.

7. The rapid positioning method for anechoic chamber testing of wireless optical communication equipment according to claim 1, characterized in that, The S4 includes: S41: Strictly align the timestamp data of the equipment operating status with the time axis of radiation signal acquisition, analyze the variation law of radiation characteristics under different operating conditions of the equipment, and establish a dynamic correlation between equipment status and radiation response; S42: By analyzing the correspondence between nodes of change in operating conditions and nodes of sudden changes in radiation characteristics, the influence relationship between the switching of operating conditions on radiation intensity and distribution is sorted out, and operating condition and radiation mapping data are generated. S43: By combining radiation source type label data with operating conditions and radiation mapping data, the generation path of abnormal radiation is tracked from multiple dimensions of space, frequency band, and time sequence, and the key links and physical locations of interference are located. S44: Perform mechanistic analysis on the interference sources obtained from the source tracing, analyze the intrinsic causes of interference, and generate physical cause data of the interference sources; S45: Quantify the role of each radiation source in the overall interference, calculate the influence weight of each radiation source by energy proportion and influence range, and generate contribution weight data of each radiation source.

8. The rapid positioning method for anechoic chamber testing of wireless optical communication equipment according to claim 7, characterized in that, S41 includes: Retrieve the timestamp data of the equipment's operating status and the time axis data of the radiation signal acquisition, and uniformly calibrate the time reference of the two types of data to generate time synchronization reference data; Based on time synchronization reference data, the timestamps of the equipment's operating status are matched one-to-one with the radiation signal acquisition data at the corresponding time to generate time-series related data pairs. The time-series correlated data pairs are grouped and classified according to different operating conditions of the equipment, generating condition-grouped correlated data. Analyze the grouped correlation data of each working condition, extract the characteristic variation law of radiation signal under different working conditions, and generate working condition radiation characteristic variation data; By integrating data on changes in radiation characteristics under operating conditions, a correspondence between equipment operating status and radiation response characteristics is established, forming dynamic correlation model data.

9. The rapid positioning method for anechoic chamber testing of wireless optical communication equipment according to claim 1, characterized in that, The S5 includes: S51: Based on the contribution weight data of each radiation source, spatial clustering is performed on radiation points, and adjacent radiation areas with similar contribution values ​​are aggregated and divided to highlight concentrated radiation and high-risk areas. S52: Integrate and aggregate information on radiation area, spatial location, and contribution weight to create a visualized comprehensive radiation risk map; S53: Input the comprehensive radiation risk map into the graph neural network model to perform node feature reasoning and correlation analysis, and complete the automatic identification of interference types and extraction of risk features through topological structure learning; S54: Based on the output of neural network inference, classify the radiation hazard level, mark high-risk interference and low-risk stray components, and generate interference source type identification results and risk level assessment data; S55: Integrate radiation source location information, type identification results, risk level and source tracing mechanism, automatically fill in the document structure and complete the content arrangement according to the test specifications, and generate intelligent diagnostic data on radiation emission of wireless optical communication equipment.

10. The rapid positioning method for anechoic chamber testing of wireless optical communication equipment according to claim 9, characterized in that, S51 includes: The contribution weight data of each radiation source is retrieved, and the data is normalized to generate standardized radiation source influence weight data. Based on standardized radiation source impact weight data, high-impact radiation points with weight values ​​higher than a set threshold are selected to generate a set of high-risk radiation points. Spatial neighborhood analysis is performed on the set of high-risk radiation points to identify combinations of radiation points that are spatially adjacent and have similar impact weights; The identified radiation point combinations are aggregated and divided into regions, and adjacent points are merged into a unified radiation region unit to generate aggregated radiation region data. By integrating and aggregating radiation area data with corresponding contribution weight information, highlighting concentrated radiation distribution and high-risk impact areas, the aggregation processing of high-risk radiation areas is completed.

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