A multi-source antenna three-dimensional radiation pattern data processing method and system

Abstract

The application discloses a kind of multi-source antenna three-dimensional radiation pattern data processing method and system, the method includes: reading original data, unit data is obtained by parsing;Unit data is executed coordinate transformation, and standard data is obtained by extracting;Standard template is output based on standard data;Wherein, the data format of original data at least includes SetEnv test field format, HFSS simulation CSV format and 168 near-field instrument TXT format.Thereby, cover test field, electromagnetic simulation, near-field test three big data sources, eliminate the inconsistent risk generated by the version of tool dispersion, without code modification because of data format change.Permit different format multi-port input, also support multi-version data output, conversion processing is convenient.Data cleaning, identification, analysis, conversion and the like process do not involve manual, processing process is the same specification and can be traced back, support batch processing, job efficiency is significantly improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for processing three-dimensional radiation pattern data of multi-source antennas. Background Technology

[0002] In the research and testing of 5G base station antennas, the antenna's three-dimensional radiation pattern data originates from three different measurement / simulation methods, generating proprietary data files with vastly different formats. These include test field data in SetEnv's internal format, electromagnetic simulation data in HFSS simulation CSV format, and near-field measurement data in 168 near-field instrument TXT format. These three types of data differ fundamentally in data arrangement, coordinate system reference, row / column correspondence, polarization storage strategy, and file organization structure. This necessitates manually writing and maintaining independent conversion scripts for each format, making version consistency difficult to guarantee. Furthermore, not only does the data conversion process involve manual work, but the calculation of antenna performance parameters also requires the use of applications such as MATLAB or Excel to manually calculate key electrical performance parameters such as HPBW, main lobe peak position, and front-to-back ratio required in the BASTA format file header. This process is inconsistent in methodology, lacks traceability, does not support batch processing, and consumes significant time. Summary of the Invention

[0003] Embodiment 1 of the present invention discloses a method for processing three-dimensional radiation pattern data of a multi-source antenna, specifically including: Read the raw data and parse it to obtain the unit data; Perform coordinate transformation on the unit data to extract standardized data; Output the specified data based on the standard template; The original data formats include at least SetEnv test field format, HFSS simulation CSV format, and 168 near-field instrument TXT format.

[0004] As an optional implementation, when the original data is in SetEnv test field format, the step of reading the original data and parsing it to obtain unit data includes: Initialize configuration parameters; Iterate through and filter out invalid data; The row correspondence is determined by comparing the valid original data, the frequency block boundary is detected and a structure object is generated, a 180×361 matrix is ​​constructed, dual verification is performed on port parity and data position, and the unit data is generated based on the structure object.

[0005] As an optional implementation, performing coordinate transformation on the unit data to extract standardized data includes: Perform a three-step coordinate transformation on the structural objects contained in the unit data to obtain an 181×360 matrix; The 181×360 matrix is ​​used to locate the main lobe peak position; Calculate the horizontal HPBW and vertical HPBW based on the main lobe peak position; The maximum gain value within the range of ±180°±30° is calculated based on the peak position of the main lobe as stated in the budget.

[0006] As an optional implementation, the method further includes: Based on the main lobe peak position, the first adjacent angle pair that crosses the 3dB drop threshold is searched angle by angle to the left and right. Linear interpolation is performed on the intersection interval to measure the measurement angle corresponding to 3dB.

[0007] As an optional implementation, performing a three-step coordinate transformation on the structural objects contained in the unit data to obtain an 181×360 matrix includes: The 180×361 matrix is ​​split into three queues: M180 reverse (rows 90-180), P180 forward (rows 90-180), and M180 reverse (rows 0-90). The matrix is ​​then shifted 180 rows in a row-by-row manner until Phi=0° aligns with the first row. The matrix is ​​then transposed to obtain the 181×360 matrix.

[0008] As an optional implementation, when the original data is in HFSS simulation CSV format, the method further includes: Read the four columns of data in the original data line by line, construct a 180×361 matrix based on the Phi / Theta value, store P45 in Matrix[0] matrix and M45 in Matrix[1] matrix; Based on the path of the original data, the port number is extracted, and polarization determination is performed; Path inclination angle extracted based on the original data; Filename extraction frequency based on the original data.

[0009] As an optional implementation, when the original data is in 168 Near Field Instrument TXT format, the method further includes: Based on the E+45mag.txt and E-45mag.txt data in the original data, construct 360×181 matrices respectively, calculate the global maximum value based on the two, and perform polarization determination; Reverse the order from Phi=270° to Phi=0°, and then reverse the order from Phi=359° to Phi=271°, and then concatenate and transpose to obtain an 181×360 matrix.

[0010] As an optional implementation, the output of the specification data based on the standard template includes: When the version configuration value is V2.0_3drp, the specification data is output based on the BASTA V2.0 3DRP text format containing 18 header fields; When the version is configured as V3.0_JSON, the specification data is output based on the BASTA V3.0 JSO text format containing 24 header fields; When the version is configured as V13.0_JSON, the specification data is output based on the BASTA V13.0 JSON text format containing 32 header fields.

[0011] Embodiment 2 of the present invention discloses a three-dimensional radiation pattern data processing system for multi-source antennas, characterized in that it includes: The parsing module is used to read the raw data and parse it to obtain unit data; A coordinate transformation module is used to perform coordinate transformations on the unit data; The parameter extraction module is used to extract standardized data based on the unit data. The specification output module is used to output the specification data based on the standard template; The original data formats include at least SetEnv test field format, HFSS simulation CSV format, and 168 near-field instrument TXT format.

[0012] Compared with the prior art, this embodiment has the following beneficial effects: 1. It covers three major data sources: test field, electromagnetic simulation, and near-field testing, eliminating the risk of version inconsistency caused by scattered tools and eliminating the need to modify code due to changes in data format.

[0013] 2. It allows input from multiple ports in different formats and also supports output of multiple versions of data, making conversion and processing convenient.

[0014] 3. Data cleaning, identification, analysis, and transformation processes do not involve manual labor. The processing procedures are standardized and traceable, support batch processing, and significantly improve work efficiency. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this embodiment, the accompanying drawings used in the embodiment will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1This is a schematic diagram of the workflow of a three-dimensional radiation pattern data processing method for a multi-source antenna disclosed in Embodiment 1; Figure 2 This is a schematic diagram of the system structure of a three-dimensional radiation pattern data processing system for a multi-source antenna disclosed in Embodiment 2. Detailed Implementation

[0017] The technical solutions in this embodiment 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.

[0018] Example 1 Please see Figure 1 This embodiment discloses a method for processing three-dimensional radiation pattern data of a multi-source antenna, including: S1. Read the raw data and parse it to obtain the unit data.

[0019] In this embodiment, the raw data formats include at least SetEnv test field format, HFSS simulation CSV format, and 168 near-field instrument TXT format.

[0020] As an optional implementation, when the original data is in SetEnv test field format, the original data is read and parsed to obtain unit data, including: Initialize configuration parameters; Iterate through and filter out invalid data; The system compares valid raw data to determine row correspondence, detects frequency block boundaries and generates structure objects, constructs a 180×361 matrix, performs dual verification on port parity and data location, and generates unit data based on the structure objects.

[0021] For example, for a 32-port 4×8 antenna array, the measurement frequencies include 1710MHz, 2360MHz, and 2690MHz, and the downtilt angle ranges include 0°, 3°, 6°, 9°, and 12°, a total of 5 ranges. At this time, it is necessary to process 32 ports × 5 downtilt angle ranges = 160 sets of raw data.

[0022] Here, we first initialize the configuration parameters, including the format type, file root directory, output directory, target version, tilt correction requirements, etc.

[0023] Perform batch validation on the raw data. For each .txt file in the root directory, validate the file extension. If the file name is not a pure numeric format referring to the port number after removing the extension, it is filtered out. Also, abnormal empty files with a file size of less than 1000KB are filtered out. Raw data that passes the validation is added to the processing queue to wait for further processing, while raw data that fails the validation is recorded in the error log.

[0024] Then, the Tab-separated data is read row by row, and a two-dimensional 180×361 matrix is ​​reconstructed based on the Phi value of the first row and first column (the comparison value between the radian value and the initial Phi value) and the fixed number of Theta columns (361 columns).

[0025] When the original data contains multi-frequency data, it is automatically divided into independent frequency blocks according to the frequency value change boundary, generating the corresponding intermediate data structure, and the matrix size can be verified based on the following rules: total number of rows = effective Phi sampling points × 2 (polarization components), total number of columns = Theta sampling points × 2 (main / cross-polarization side by side).

[0026] For the SetEnv test field format, polarization determination is performed based on a double check mechanism.

[0027] First, a pre-judgment is made based on the parity of the port number. If the port number is even, it is determined to be M45 polarization; if the port number is odd, it is determined to be P45 polarization. Then, the column index of the global maximum value is located in the 180×361 matrix, and it is verified whether it is located in the left or right half of the matrix. This is cross-validated with the parity prediction result. If the verification results are inconsistent, it is recorded as an anomaly, and the parity prediction result of the port number takes precedence, ensuring that the polarization determination result is stable and traceable.

[0028] As an alternative implementation method, when the original data is in HFSS simulation CSV format, the four columns of data in the original data are read line by line, and a 180×361 matrix is ​​constructed based on the Phi / Theta value. P45 is stored in Matrix[0] matrix and M45 is stored in Matrix[1] matrix. The port number is extracted based on the path of the original data, and polarization determination is performed. Path inclination angle extracted from raw data; Filename extraction frequency based on raw data.

[0029] Here, the four columns of data in the CSV (Phi angle, Theta angle, P45 gain value, M45 gain value) are read line by line. The number of Phi sampling points is detected based on the change of Phi value. It is verified whether the total number of rows is equal to the number of Phi points × the number of Theta points. Each row of data is written into the corresponding matrix according to the Phi index and Theta index. The dual polarization components can be obtained in a single parsing.

[0030] At this point, the polarization determination is based solely on the port number for parity assessment. If the port number is even, it is determined to be M45 polarization; if the port number is odd, it is determined to be P45 polarization. This does not depend on the data content and avoids the determination process being affected by abnormal data.

[0031] S2. Perform coordinate transformation on the unit data to extract the standardized data.

[0032] In this embodiment, the coordinate system of the original data in different formats is normalized, and standardized data extraction is performed.

[0033] As an optional implementation, a coordinate transformation is performed on the unit data to extract standardized data, including: Perform a three-step coordinate transformation on the structural objects contained in the unit data to obtain an 181×360 matrix; The peak position of the main lobe was located using a 181×360 matrix. Calculate the horizontal HPBW and vertical HPBW based on the main lobe peak position; And the maximum gain value within the range of ±180°±30° is calculated based on the peak position of the main lobe.

[0034] Furthermore, a three-step coordinate transformation is performed on the structural objects contained in the unit data to obtain an 181×360 matrix, including: The 180×361 matrix is ​​split into three queues: M180 reverse (rows 90-180), P180 forward (rows 90-180), and M180 reverse (rows 0-90). The matrix is ​​then shifted 180 rows in a row-by-row manner until Phi=0° aligns with the first row. The matrix is ​​then transposed to obtain a 181×360 matrix.

[0035] Here, a three-step method can be used to perform coordinate system normalization on the raw data in SetEnv test field format and HFSS simulation CSV format.

[0036] As an optional implementation, the tilt angle is also extracted from the penultimate layer of the path and the frequency range is extracted from the penultimate layer based on regular expressions.

[0037] As an optional implementation, when the original data is in the 168 near-field instrument TXT format, a 360×181 matrix is ​​constructed based on the E+45mag.txt data and E-45mag.txt data in the original data, the global maximum value is calculated based on the two, and polarization determination is performed. Reverse the order from Phi=270° to Phi=0°, and then reverse the order from Phi=359° to Phi=271°, and then concatenate and transpose to obtain an 181×360 matrix.

[0038] Here, since the 168 near-field instrument TXT format has a 270° phase shift with the BASTA standard, it is first extracted in reverse order in blocks, and then the two are spliced ​​together in sequence so that 270° becomes the first line, which meets the alignment requirement of Phi=0° in the BASTA standard.

[0039] As an optional implementation, based on the main lobe peak position, the first adjacent angle pair that crosses the 3dB drop threshold is searched angle by angle to the left and right, and linear interpolation is performed on the cross interval to measure the measurement angle corresponding to 3dB.

[0040] Therefore, based on the bidirectional search strategy and linear interpolation method, the accurate calculation of the antenna's half-power beamwidth can be achieved, and the compliance with design specifications can be verified.

[0041] In summary, based on the three-step method or the 270° folding scheme, targeted transformations can be achieved for different coordinate systems. Compared with the existing point-by-point loop scheme, the computational efficiency is improved by hundreds of times. The transformation time for a 180×361 matrix is ​​reduced from milliseconds to microseconds, and the entire floating-point operation does not require type conversion, thereby eliminating the precision loss in the transformation process. It can support input data with arbitrary Theta / Phi step resolution and has strong adaptability.

[0042] Furthermore, based on the dual verification mechanism of port parity and data location, the path port directory encoding and the comparison of the maximum value of the two files can achieve highly reliable automatic polarization identification, completely eliminating the error risk of manual polarization labeling.

[0043] In particular, for the polarization determination process of the 168 near-field instrument in TXT format, since it does not depend on the numbering rules of the hardware ports, it can still achieve accurate and efficient polarization determination in special scenarios where the main polarization is not fixed.

[0044] S3. Output standardized data based on standard templates.

[0045] In this embodiment, after the normalization process is performed in steps S1 and S2 for raw data of different formats, the data can also be output as normalized data of different formats according to the output requirements.

[0046] As an optional implementation, when the version configuration value is V2.0_3drp, the specification data is output based on the BASTA V2.0 3DRP text format containing 18 header fields; When the version is configured as V3.0_JSON, the specification data is output based on the BASTA V3.0 JSO text format containing 24 header fields; When the version is configured as V13.0_JSON, the output specification data is based on the BASTA V13.0 JSON text format containing 32 header fields.

[0047] That is, different input formats and different output formats can be flexibly matched and combined to meet the full-chain requirements of simulation verification (HFSS simulation CSV format → BASTA), near-field acceptance (168 near-field instrument TXT format → BASTA) and product delivery (SetEnv test field format → BASTA).

[0048] Understandably, after configuration, when faced with raw data in various formats, one only needs to select the required version configuration, without the need for manual identification of raw data in different formats, selection of specific conversion scripts, or adjustment and modification of conversion scripts, thus significantly improving work efficiency.

[0049] Compared with the prior art, this embodiment has the following beneficial effects: 1. It covers three major data sources: test field, electromagnetic simulation, and near-field testing, eliminating the risk of version inconsistency caused by scattered tools and eliminating the need to modify code due to changes in data format.

[0050] 2. It allows input from multiple ports in different formats and also supports output of multiple versions of data, making conversion and processing convenient.

[0051] 3. Data cleaning, identification, analysis, and transformation processes do not involve manual labor. The processing procedures are standardized and traceable, support batch processing, and significantly improve work efficiency.

[0052] Example 2 Please see Figure 2 The system disclosed in this embodiment includes: The parsing module is used to read the raw data and parse it to obtain unit data; The coordinate transformation module is used to perform coordinate transformations on unit data. The parameter extraction module is used to extract standardized data based on unit data; The specification output module is used to output specification data based on standard templates; The raw data formats include at least SetEnv test field format, HFSS simulation CSV format, and 168 near-field instrument TXT format.

Claims

1. A method for processing three-dimensional radiation pattern data of a multi-source antenna, characterized in that, include: Read the raw data and parse it to obtain the unit data; Perform coordinate transformation on the unit data to extract standardized data; Output the specified data based on the standard template; The original data formats include at least SetEnv test field format, HFSS simulation CSV format, and 168 near-field instrument TXT format.

2. The method for processing three-dimensional radiation pattern data of a multi-source antenna according to claim 1, characterized in that, When the original data is in SetEnv test field format, the process of reading the original data and parsing it to obtain unit data includes: Initialize configuration parameters; Iterate through and filter out invalid data; The row correspondence is determined by comparing the valid original data, the frequency block boundary is detected and a structure object is generated, a 180×361 matrix is ​​constructed, dual verification is performed on port parity and data position, and the unit data is generated based on the structure object.

3. The method for processing three-dimensional radiation pattern data of a multi-source antenna according to claim 2, characterized in that, The step of performing coordinate transformation on the unit data to extract standardized data includes: Perform a three-step coordinate transformation on the structural objects contained in the unit data to obtain an 181×360 matrix; The 181×360 matrix is ​​used to locate the main lobe peak position; Calculate the horizontal HPBW and vertical HPBW based on the main lobe peak position; The maximum gain value within the range of ±180°±30° is calculated based on the peak position of the main lobe as stated in the budget.

4. The method for processing three-dimensional radiation pattern data of a multi-source antenna according to claim 3, characterized in that, The method further includes: Based on the main lobe peak position, the first adjacent angle pair that crosses the 3dB drop threshold is searched angle by angle to the left and right. Linear interpolation is performed on the intersection interval to measure the measurement angle corresponding to 3dB.

5. The method for processing three-dimensional radiation pattern data of a multi-source antenna according to claim 3, characterized in that, The three-step coordinate transformation performed on the structural objects contained in the unit data to obtain an 181×360 matrix includes: The 180×361 matrix is ​​split into three queues: M180 reverse (rows 90-180), P180 forward (rows 90-180), and M180 reverse (rows 0-90). The matrix is ​​then shifted 180 rows in a row-by-row manner until Phi=0° aligns with the first row. The matrix is ​​then transposed to obtain the 181×360 matrix.

6. The method for processing three-dimensional radiation pattern data of a multi-source antenna according to claim 2, characterized in that, When the original data is in HFSS simulation CSV format, the method further includes: Read the four columns of data in the original data line by line, construct a 180×361 matrix based on the Phi / Theta value, store P45 in Matrix[0] matrix and M45 in Matrix[1] matrix; Based on the path of the original data, the port number is extracted, and polarization determination is performed; Path inclination angle extracted based on the original data; Filename extraction frequency based on the original data.

7. The method for processing three-dimensional radiation pattern data of a multi-source antenna according to claim 2, characterized in that, When the original data is in 168 near-field instrument TXT format, the method further includes: Based on the E+45mag.txt and E-45mag.txt data in the original data, construct 360×181 matrices respectively, calculate the global maximum value based on the two, and perform polarization determination; Reverse the order from Phi=270° to Phi=0°, and then reverse the order from Phi=359° to Phi=271°, and then concatenate and transpose to obtain an 181×360 matrix.

8. The method for processing three-dimensional radiation pattern data of a multi-source antenna according to claim 1, characterized in that, The output of the standardized data based on the standard template includes: When the version configuration value is V2.0_3drp, the specification data is output based on the BASTA V2.0 3DRP text format containing 18 header fields; When the version is configured as V3.0_JSON, the specification data is output based on the BASTA V3.0 JSO text format containing 24 header fields; When the version is configured as V13.0_JSON, the specification data is output based on the BASTA V13.0 JSON text format containing 32 header fields.

9. A three-dimensional radiation pattern data processing system for multi-source antennas, characterized in that, include: The parsing module is used to read the raw data and parse it to obtain unit data; A coordinate transformation module is used to perform coordinate transformations on the unit data; The parameter extraction module is used to extract standardized data based on the unit data. The specification output module is used to output the specification data based on the standard template; The original data formats include at least SetEnv test field format, HFSS simulation CSV format, and 168 near-field instrument TXT format.

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