Method for detecting communication performance of small antenna, medium and equipment

Abstract

The application provides a communication performance detection method, medium and equipment of a small antenna, relates to the technical field of antenna performance testing, and matches communication requirements based on a signal source setting library, connects to the antenna to be tested after setting the transmission frequency and power of the signal source; installs the antenna to be tested on a rotating table and rotates according to a preset rotating angle scale; performs multiple signal parameter analyses, obtains a signal parameter set sequence, performs centralized analysis, and determines a representative signal parameter sequence; iteratively analyzes the sequence to determine an effective radiation area; further completes communication directivity detection analysis, and obtains a detection result. The application solves the technical problems in the prior art that signal source parameter configuration relies on manual setting, different communication requirements are difficult to accurately adapt, and the test preparation efficiency is low, the adaptability is poor, and the subsequent detection is insufficient, and achieves the technical effects of enhancing test flexibility and accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of antenna performance testing, in particular to a communication performance detection method, medium and equipment of a small antenna. BACKGROUND

[0002] In the field of wireless communication, small antennas are widely used in mobile terminals, portable devices and other scenarios due to their compact structure and flexible use. In order to ensure their communication performance in actual application, their radiation directivity, gain characteristics and other indicators need to be tested and evaluated. In the existing communication performance detection method of small antennas, the transmission frequency and transmission power of the signal source are usually manually set by the test personnel according to experience or test manual. However, due to the significant differences in communication frequency bands and signal strength requirements of different test scenarios, manual setting is prone to parameter matching errors or improper configuration, which affects the effectiveness and comparability of the test results. At the same time, frequent manual adjustment not only increases the complexity of the test process, but also reduces the overall test efficiency. In addition, in complex or batch test tasks, the lack of systematic management and automated matching mechanism for signal source parameter setting makes it difficult to quickly adapt to changing communication needs, which becomes an important bottleneck restricting the reliability and automation level of antenna performance testing. SUMMARY

[0003] The present application provides a communication performance detection method, medium and equipment of a small antenna, which solves the technical problems that the existing technology relies on manual setting of signal source parameter configuration, making it difficult to quickly and accurately set test conditions according to different communication needs, resulting in low test preparation efficiency, poor adaptability and insufficient subsequent detection representativeness, and achieves the technical effects of improving the automation level of performance detection and enhancing the flexibility and accuracy of testing.

[0004] In view of the above problems, on the one hand, the present application provides a communication performance detection method of a small antenna, which comprises: matching based on communication needs and a signal source setting library, setting the transmission frequency and transmission power of the signal source according to the matching result, and connecting the signal source with the set transmission frequency and transmission power to the antenna to be tested; installing the antenna to be tested on a rotating table, rotating according to a pre-set rotating angle scale to obtain a rotating angle sequence, wherein the rotating table can rotate circumferentially; using a spectrum analyzer to perform multiple signal parameter analyses on the antenna to be tested at each rotating angle in the rotating angle sequence to obtain a signal parameter set sequence; performing intra-set signal parameter analysis on the signal parameter set sequence to determine a representative signal parameter sequence; performing circumferential trend iterative analysis on the representative signal parameter sequence to determine an effective radiation area; and performing communication directivity detection analysis based on the representative signal parameter sequence and the effective radiation area to obtain a communication directivity detection result.

[0005] Preferably, the in-set signal parameter analysis of the signal parameter set sequence is performed to determine the representative signal parameter sequence, including: performing fluctuation analysis on the signal parameter set sequence to determine a signal parameter fluctuation factor sequence; determining a signal parameter set analysis scale based on the signal parameter fluctuation factor in the signal parameter fluctuation factor sequence, and obtaining a signal parameter set analysis scale sequence; performing in-set signal parameter analysis on the signal parameter set sequence based on the signal parameter set analysis scale sequence to determine the representative signal parameter sequence.

[0006] Preferably, the in-set signal parameter analysis of the signal parameter set sequence based on the signal parameter set analysis scale sequence is performed to determine the representative signal parameter sequence, including: extracting a first signal parameter set analysis scale and a corresponding first signal parameter set from the signal parameter set analysis scale sequence and the signal parameter set sequence; randomly extracting a first first signal parameter from the first signal parameter set; constructing a first first signal parameter neighborhood corresponding to the first first signal parameter according to the first signal parameter set analysis scale in the first signal parameter set; extracting a second first signal parameter from the first signal parameter set again; constructing a second first signal parameter neighborhood corresponding to the second first signal parameter according to the first signal parameter set analysis scale in the first signal parameter set; comparing the neighborhood quantity of the first first signal parameter neighborhood and the second first signal parameter neighborhood to determine a set analysis direction; performing iteration on the first first signal parameter or the second first signal parameter according to the first signal parameter set analysis scale based on the set analysis direction until a preset iteration number is met to obtain a first representative signal parameter; and mapping and analyzing according to the signal parameter set analysis scale sequence and the signal parameter set sequence to determine the representative signal parameter sequence.

[0007] Preferably, the comparison of the neighborhood quantity of the first first signal parameter neighborhood and the second first signal parameter neighborhood to determine the set analysis direction includes: when the neighborhood quantity of the first first signal parameter neighborhood is greater than or equal to the neighborhood quantity of the second first signal parameter neighborhood, the direction from the second first signal parameter to the first first signal parameter is taken as the set analysis direction; and when the neighborhood quantity of the first first signal parameter neighborhood is less than the neighborhood quantity of the second first signal parameter neighborhood, the direction from the first first signal parameter to the second first signal parameter is taken as the set analysis direction.

[0008] Preferably, the circumferential trend iterative analysis is performed on the representative signal parameter sequence to determine the effective radiation region, including: extracting a maximum value from the representative signal parameter sequence, and taking a rotation angle corresponding to the maximum value as a region center angle; performing circumferential effective radiation region identification with the region center angle as a starting point according to a preset circumferential trend iterative constraint to determine an iterative analysis region left side angle and an iterative analysis region right side angle; and taking a region surrounded by the iterative analysis region left side angle and the iterative analysis region right side angle as the effective radiation region.

[0009] 6Preferably, the transmission frequency and the transmission power of the signal source are set based on matching of the communication demand and a signal source setting library, and the signal source with the set transmission frequency and the transmission power is connected to the antenna to be tested, including: pre-constructing the signal source setting library; matching the signal source setting library with the communication demand to obtain a matching result, wherein the matching result includes Q representative aggregate sample transmission frequencies-transmission powers, and Q is a positive integer; performing reliability identification on the Q representative aggregate sample transmission frequencies-transmission powers to determine Q reliability weights; and performing weighted calculation on the Q representative aggregate sample transmission frequencies-transmission powers according to the Q reliability weights to determine the transmission frequency and the transmission power.

[0010] Preferably, the reliability identification on the Q representative aggregate sample transmission frequencies-transmission powers to determine Q reliability weights includes: respectively counting usage frequencies of the Q representative aggregate sample transmission frequencies-transmission powers to obtain Q usage frequencies; and iteratively calculating ratios of the Q usage frequencies to a sum of the Q usage frequencies to obtain the Q reliability weights.

[0011] Preferably, the pre-constructed signal source setting library includes: obtaining a plurality of sample communication demands and a plurality of sample transmission frequencies-transmission powers; performing same-type aggregation on the plurality of sample communication demands to obtain K aggregate sample communication demand sets, wherein K is a positive integer greater than or equal to Q; performing mapping aggregation on the plurality of sample transmission frequencies-transmission powers according to the K aggregate sample communication demand sets to obtain K aggregate sample transmission frequency-transmission power sets; performing set union calculation on the K aggregate sample communication demand sets to obtain K representative aggregate sample communication demands; performing mean value processing on the K aggregate sample transmission frequency-transmission power sets to obtain K representative aggregate sample transmission frequencies-transmission powers; and performing relationship mapping based on the K representative aggregate sample communication demands and the K representative aggregate sample transmission frequencies-transmission powers to construct the signal source setting library.

[0012] In a second aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the communication performance detection method of the small antenna. In a second aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the steps of the communication performance detection method of the small antenna.

[0013] In a third aspect, the present application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the communication performance detection method of the small antenna.

[0014] The one or more technical solutions provided in the present application have at least the following beneficial effects:

[0015] By pre-establishing the communication demand and signal source setting library, the corresponding signal source parameters are accurately matched according to the specific test requirements, the frequency and power of the signal source are automatically set, on the one hand, the errors caused by manual parameter setting are avoided, on the other hand, the signal source parameters can be quickly and accurately adjusted according to different communication scenarios, and the efficiency and flexibility of test preparation are improved. By installing the antenna to be tested on the rotating table and rotating at a predetermined angle to obtain a rotation angle sequence, different placement directions and working scenarios of the small antenna in actual use can be simulated, the communication performance of the antenna at each angle in the circumferential direction is covered, and it is ensured that the detection result can fully reflect the communication ability of the antenna in each direction, avoiding the performance evaluation deviation caused by single or incomplete detection angle. The signal parameter set sequence is obtained by using the spectrum analyzer to analyze the signal parameters of the antenna to be tested at each rotation angle multiple times, which not only increases the data sampling amount, but also reduces the influence of accidental errors on the results through multiple measurements, so that the obtained signal parameters are more representative and reliable, and provide detailed data basis for subsequent in-depth analysis of the antenna performance. By centrally analyzing the signal parameter set sequence, the representative signal parameter sequence is determined, the redundant and interference data are removed, the deviation caused by manual subjective judgment and selection of parameters is avoided, the data used for subsequent evaluation is ensured to be simple, accurate and typical, and the objectivity and efficiency of the small antenna communication performance detection analysis are improved. By iterating the circumferential trend analysis through the traversal of the representative signal parameter sequence, the change trend of the signal strength, quality and other parameters of the antenna at different angles in the circumferential direction can be clearly presented, and according to the change trend, the effective radiation area of the antenna is determined, which clearly defines the key area for subsequent communication directivity detection analysis, so that the detection result is more targeted and has practical application value. Based on the representative signal parameter sequence and the effective radiation area, the communication directivity detection analysis is performed, the communication directivity performance of the small antenna is comprehensively and systematically evaluated, the communication directivity detection result is obtained, so that the communication effect of the antenna in different directions is intuitively and accurately reflected, and the complete closed loop of the small antenna communication performance detection is realized.

[0016] In summary, the application significantly improves the flexibility, accuracy, reliability and automation of small antenna communication performance detection, provides strong technical support and guarantee for the design optimization, quality detection and actual application deployment of small antennas, and can better meet the growing communication technology needs and diverse application scenario requirements.

[0017] The above description is only a summary of the technical solutions of the application. In order to better understand the technical means of the application, the application can be implemented according to the content of the specification, and in order to make the above and other purposes, characteristics and advantages of the application more obvious and easy to understand, the following specific embodiments of the application are described. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The flowchart of the communication performance detection method of the small antenna provided by the embodiment of the application is shown.

[0019] Figure 2 The structure diagram of determining the representative signal parameter sequence in the communication performance detection method of the small antenna provided by the embodiment of the application is shown.

[0020] Figure 3 The structure diagram of the electronic device provided by the embodiment of the application is shown.

[0021] Explanation of reference numerals: bus 300, receiver 301, processor 302, transmitter 303, memory 304, bus interface 305. DETAILED DESCRIPTION

[0022] The embodiment of the application provides a communication performance detection method, medium and device of a small antenna, solves the technical problems that the existing technology is difficult to quickly and accurately set test conditions according to different communication needs due to the dependence of signal source parameter configuration on manual setting, leads to low test preparation efficiency, poor adaptability and insufficient subsequent detection representation, and achieves the technical effects of improving the automation degree of performance detection and enhancing the test flexibility and accuracy.

[0023] Embodiment one, as shown in the figure, the embodiment of the application provides a communication performance detection method of a small antenna, the method comprises: Figure 1

[0024] Step S100: matching based on communication demand and signal source setting library, setting the transmission frequency and transmission power of the signal source according to the matching result, and connecting the signal source with the set completion to the antenna to be tested.

[0025] ​Specifically, the communication requirement refers to the specific requirements of the small antenna corresponding to the test task in terms of communication distance, data transmission rate, signal bandwidth, etc. The signal source setting library is a pre-constructed data set containing a plurality of recommended combinations of transmission frequency and transmission power parameters under multiple communication scenarios, which is used to provide reference setting values for testing. Transmission frequency and transmission power respectively represent the frequency (unit: Hz) and power (unit: dBm or W) of the electromagnetic wave output by the signal source, which are important parameters affecting the test effect of the antenna performance. The antenna under test refers to the small antenna currently in need of communication performance detection. The signal source is a device for generating electromagnetic signals of specific frequency and power, such as an arbitrary waveform generator, a radio frequency signal generator, etc.

[0026] By receiving user input, system preset, etc. to determine the specific communication requirement, and then using this communication requirement as an index to perform a matching query in the pre-constructed signal source setting library. The signal source setting library stores a plurality of signal source parameter combinations corresponding to different communication requirements, including transmission frequency and transmission power information. After matching, one or more signal source parameter results that meet the current communication requirement are obtained. Next, according to the parameter results obtained by matching, the transmission frequency and transmission power of the signal source are accurately set. After the setting is completed, the configured signal source is connected with the antenna under test to ensure that the signal can be stably output from the signal source and transmitted through the antenna under test, providing an accurate and reliable signal basis for subsequent detection steps.

[0027] This step realizes the automatic setting of the signal source transmission parameters by introducing the signal source setting library and the communication requirement automatic matching mechanism, avoids the errors and limitations brought by the traditional reliance on manual experience setting, improves the flexibility and setting accuracy of the test adaptability, provides a standard consistent input signal environment for subsequent testing, and significantly enhances the standardization level of the antenna communication performance test.

[0028] Step S200: Install the antenna under test on the rotating table and rotate according to the pre-set rotation angle scale to obtain a rotation angle sequence, wherein the rotating table can rotate circumferentially.

[0029] Specifically, the rotating table is a platform device that can adjust the angle of the antenna under test, supports 360° circumferential rotation, and is used to simulate the receiving or transmitting characteristics of the antenna in different directions. The pre-set rotation angle scale is the angle step size of each rotation, for example, 5° or 10° per rotation. The rotation angle sequence is a set of test angles generated according to the angle step size, for example, a plurality of angle points from 0° to 360°.

[0030] Securely mount the antenna under test onto the rotating stage, ensuring stable positioning and reliable connection during rotation. Pre-set a suitable rotation angle scale according to test requirements, for example, performing tests every 5°, thus constructing a complete rotation angle sequence (e.g., 0°, 5°, 10°… up to 360°). The rotating stage uses an electronically controlled stepper drive system or a servo control system for precise positioning control. During rotation, the antenna maintains its connection to the signal source and spectrum analyzer, ensuring test continuity and accuracy. This rotation angle sequence controls the rotating stage to stop at specific angles, providing a foundation for subsequent data acquisition with angular dimensions.

[0031] This step, by mounting the antenna onto a precision rotary table and controlling its rotation based on a preset angular scale, enables spatial scanning of the antenna's communication performance in different directions. This provides a systematic foundation for subsequent multi-angle signal analysis and enhances the spatial coverage and data diversity of the test.

[0032] Step S300: Use a spectrum analyzer to perform multiple signal parameter analyses on the antenna under test at each rotation angle in the rotation angle sequence to obtain a set sequence of signal parameters.

[0033] Specifically, after each angular rotation, a spectrum analyzer is used to sample the signal parameters of the antenna facing the current direction multiple times (more than 10 samples per angle can be set) to obtain stable and repeatable signal parameters. In this embodiment, the signal parameter specifically refers to the signal power. The signal parameters are archived into a signal parameter set, forming a complete sequence of signal parameter sets according to angular order. This process can be controlled by an automated testing system to manage the acquisition and archiving process, avoiding errors from human intervention.

[0034] By sampling signal parameters multiple times and constructing an angle sequence set, the impact of measurement fluctuations on the results was effectively reduced, the stability and accuracy of the test data were improved, and a high-quality data foundation was provided for subsequent extraction of representative parameters and trend identification.

[0035] Step S400: Perform a set-based analysis of the signal parameters within the set to determine a representative signal parameter sequence.

[0036] Specifically, the representative signal parameter sequence is a sequence of representative signal parameters selected by analysis from the signal parameter set at each angle. In the signal parameter set collected at each angle, first, fluctuation analysis is performed to calculate the fluctuation factor, which quantifies the dispersion of parameters in the set, and then the analysis scale is determined according to the size of the fluctuation factor. Subsequently, under the guidance of the analysis scale, representative data points are gradually selected from the set, such as using the neighborhood density method to select the most concentrated and characteristic data points as representative parameters. This process is repeated for all angles to obtain a complete representative signal parameter sequence, which reflects the subjective response characteristics of the antenna in each direction.

[0037] This step realizes automatic reduction and feature extraction of signal parameters through centralized analysis of signal parameters, avoids the subjectivity and consistency problems caused by manual intervention, and enhances the reliability of signal expression.

[0038] Step S500: Iterative analysis of circumferential trend is performed on the representative signal parameter sequence to determine the effective radiation area.

[0039] Specifically, the iterative analysis of circumferential trend refers to identifying trend changes along the angle direction based on the representative parameter sequence starting from the maximum value, and extracting the high-efficiency radiation area. The effective radiation area refers to the area where the antenna has the best communication performance within a certain angle range, corresponding to the main beam direction. First, identify the angle corresponding to the maximum value in the representative parameter sequence, set as the central angle of the area. Then, starting from this angle, expand step by step to the left and right sides by angle steps, and judge whether the parameters meet the set trend constraints (such as not more than 3dB, continuous decline angle not more than 30°, etc.). Once the trend condition is not met, the radiation boundary in that direction is determined, thereby defining the effective radiation area. This process can be completed using an interval iteration algorithm.

[0040] Through trend iteration analysis, the main radiation direction and coverage range of the antenna are accurately identified, which not only improves the accuracy of directionality determination, but also provides more valuable direction configuration suggestions for actual deployment.

[0041] Step S600: Based on the representative signal parameter sequence and the effective radiation area, communication directionality detection analysis is performed to obtain the communication directionality detection result.

[0042] Specifically, the communication directivity detection analysis is to integrate the representative signal parameter sequence and the effective radiation area to determine the directional distribution characteristics of the antenna communication capability. The communication directivity detection result is the conclusive data for describing the directional performance of the antenna, such as the main direction, the half-power angle, the front-to-back ratio, etc. After obtaining the representative signal parameter sequence and the effective radiation area, the directivity indexes of the antenna are further calculated, for example, the maximum gain angle, the main beam width (half-power angle), the front-to-back ratio, the sidelobe suppression ratio, etc. These parameters can be obtained by numerical fitting or morphological analysis of the representative parameter sequence. Finally, a detection result report containing the directivity atlas and the numerical indexes is output to guide the antenna optimization design or the field deployment positioning.

[0043] This step realizes the quantitative evaluation and visual expression of the communication directivity of the small antenna by comprehensively calculating the extracted parameters and the area, thereby providing scientific and systematic technical support for subsequent product debugging, engineering deployment and performance evaluation.

[0044] Further, the step S100 comprises:

[0045] Step S110: Pre-constructing a signal source setting library.

[0046] Step S120: Indexing the signal source setting library according to the communication demand to obtain a matching result, wherein the matching result comprises Q representative aggregate sample transmission frequencies-transmission powers, and Q is a positive integer.

[0047] Step S130: Reliability identification of the Q representative aggregate sample transmission frequencies-transmission powers to determine Q reliability weights.

[0048] Step S140: Weighted calculation of the Q representative aggregate sample transmission frequencies-transmission powers according to the Q reliability weights to determine the transmission frequency and the transmission power.

[0049] Further, the step S130 comprises:

[0050] Step S131: Respectively counting the use frequencies of the Q representative aggregate sample transmission frequencies-transmission powers to obtain Q use frequencies.

[0051] Step S132: Iterative calculation of the ratios of the Q use frequencies to the sum of the Q use frequencies to obtain Q reliability weights.

[0052] Specifically, a large number of sample communication requirements and corresponding sample transmit frequency-transmit power data are obtained from historical detection records, industry standard communication scenarios, and actual application cases, a signal source setting library is pre-constructed, which covers typical communication scenarios (such as short distance low power transmission, medium distance high interference suppression, long range strong signal penetration, etc.), each combination sample in the library is bound with historical usage records, usage frequency and other metadata, which is convenient for subsequent retrieval and credibility analysis.

[0053] Based on the user input or system recognized communication requirement, it is decomposed into specific parameters such as communication distance, data transmission rate, signal bandwidth, and standardized processing is performed according to the communication requirement format in the signal source setting library. The standardized communication requirement is used as an index to perform a matching query operation in the signal source setting library. The nearest neighbor algorithm based on Euclidean distance, cosine similarity algorithm and other methods are used to measure the similarity between the input communication requirement and each representative aggregated sample communication requirement stored in the library. According to the set matching threshold or the top Q most similar results, the Q representative aggregated sample transmit frequency-transmit power combinations that best match the communication requirement are extracted from the library as the matching results. Wherein, Q is a positive integer, representing the number of matched representative aggregated sample transmit frequency-transmit power.

[0054] After obtaining the matching results, the Q representative aggregated sample transmit frequency-transmit power combinations need to be further evaluated and analyzed to determine their reliability and credibility in actual application, and Q reliability weights of the corresponding Q representative aggregated sample transmit frequency-transmit power are obtained. The reliability weight reflects the proportion of the corresponding representative aggregated sample in the final calculation of transmit frequency and transmit power. The greater the weight, the higher the reliability of the representative aggregated sample in the matching result, and the greater the role it plays in subsequent calculations.

[0055] The reliability weight calculation process is as follows: the historical usage frequency (i.e. the number of times selected in historical communication configuration) of the Q representative aggregated sample transmit frequency-transmit power selected from the signal source setting library is counted to obtain Q usage frequencies. The higher the usage frequency, the more the representative aggregated sample transmit frequency-transmit power has been applied in actual communication scenarios in the past, and its reliability and applicability have been verified more. Then, the ratio of Q usage frequencies to the sum of Q usage frequencies is calculated one by one. For example, Q=3, the usage frequencies of the three combinations are F1, F2, and F3, and the sum of the Q usage frequencies is Sum=F1+F2+F3. Then, the ratio of each combination's usage frequency to the sum is calculated, i.e. the weight ω1 of combination 1 is F1 / Sum, the weight ω2 of combination 2 is F2 / Sum, and the weight ω3 of combination 3 is F3 / Sum. By traversing all Q combinations, the reliability weight corresponding to each combination is obtained, i.e. Q reliability weights.

[0056] Each of the representative aggregated sample transmission frequency-transmission power is multiplied by its corresponding reliability weight, respectively, to obtain Q weighted transmission frequency values and Q weighted transmission power values. Then, the Q weighted transmission frequency values and the Q weighted transmission power values are summed, respectively, to obtain a total weighted transmission frequency and a total weighted transmission power as the final transmission frequency and transmission power for setting the test signal source.

[0057] For example, Q=3, and the matching results obtained from the signal source setting library are shown in Table 1:

[0058] Table 1 - Example of matching results

[0059] Number Transmit frequency (MHz) Transmit power (dBm) Usage frequency Sample 1 2.2 12 25 Sample 2 2.1 10 18 Sample 3 2.25 13 7

[0060] According to Table 1, the reliability weights of each sample are as follows: the weight ω1 of sample 1 is 25 / (25+18+7)=0.5, the weight ω2 of sample 2 is 18 / (25+18+7)=0.36, and the weight ω3 of sample 3 is 7 / (25+18+7)=0.14. The transmission frequency is 2.2*0.5+2.1*0.36+2.25*0.14=2.171 MHz, and the transmission power is 12*0.5+10*0.36+13*0.14=11.42 dBm.

[0061] Further, step S110 comprises:

[0062] Step S111: obtaining a plurality of sample communication requirements and a plurality of sample transmission frequency-transmission power.

[0063] Step S112: performing same-type aggregation on the plurality of sample communication requirements to obtain K aggregated sample communication requirement sets, wherein K is a positive integer greater than or equal to Q.

[0064] Step S113: performing mapping aggregation on the plurality of sample transmission frequency-transmission power according to the K aggregated sample communication requirement sets to obtain K aggregated sample transmission frequency-transmission power sets.

[0065] Step S114: performing set union on the K aggregated sample communication requirement sets to obtain K representative aggregated sample communication requirements.

[0066] Step S115: performing mean processing on the K aggregated sample transmission frequency-transmission power sets to obtain K representative aggregated sample transmission frequency-transmission power.

[0067] Step S116: performing relationship mapping based on the K representative aggregated sample communication requirements and the K representative aggregated sample transmission frequency-transmission power to construct the signal source setting library.

[0068] Specifically, the sample communication requirement refers to representative communication requirements collected from various practical application scenarios or historical data, including communication distance, data transmission rate, signal bandwidth, and other parameters. The sample transmission frequency-transmission power refers to specific values of the signal source transmission frequency and transmission power corresponding to the sample communication requirement, used to meet the sample communication requirement. A large number of sample communication requirements and corresponding sample transmission frequency-transmission power data are obtained from historical detection records, industry standard communication scenarios, and actual application cases.

[0069] The similarity of multiple sample communication requirements is analyzed by using K-means clustering, clustering algorithm based on Euclidean distance, fuzzy C-means, and other methods, and multiple sample communication requirements with similar characteristics are classified together to obtain K aggregated sample communication requirement sets, where K is a positive integer greater than or equal to Q. Taking the K-Means algorithm as an example, first determine the number of clusters K (estimated according to experience or data distribution), then represent the sample communication requirement as a feature vector, and input these feature vectors into the K-Means clustering algorithm. The algorithm automatically divides the samples into K clusters according to their similarity, and each cluster corresponds to an aggregated sample communication requirement set. After multiple iterations and optimization, the clustering result is stable, and K aggregated sample communication requirement sets are obtained. Then, according to the K aggregated sample communication requirement sets, the sample transmission frequency-transmission power corresponding to the communication requirement in the same cluster is classified into the same set to form K aggregated sample transmission frequency-transmission power sets.

[0070] For each aggregated sample communication requirement set, analyze the various feature parameters of all sample communication requirements contained therein, such as communication distance, data transmission rate, signal bandwidth, etc. For each feature parameter, determine its maximum value and minimum value to construct a comprehensive representative aggregated sample communication requirement. For example, for the communication distance parameter in the aggregated sample communication requirement set, take the maximum and minimum values of all sample communication distances as the range; for the data transmission rate parameter, the signal bandwidth also takes its range value. These range values together constitute the representative aggregated sample communication requirement.

[0071] For each aggregated sample transmission frequency-transmission power set, extract all transmission frequency values and all transmission power values therein. For the transmission frequency values, calculate their arithmetic mean as the representative transmission frequency of the set; for the transmission power values, also calculate their arithmetic mean as the representative transmission power of the set, to obtain K representative aggregated sample transmission frequency-transmission power.

[0072] Each representative aggregate sample's communication requirement is associated with its corresponding representative aggregate sample's transmission frequency and transmission power, creating a signal source setting library. For example, in a database management system, a table named "Signal Source Setting Library" is created, containing three fields: "Communication Requirement ID" (to uniquely identify each representative aggregate sample's communication requirement), "Transmission Frequency," and "Transmission Power." Then, the communication requirements of K representative aggregate samples and their corresponding transmission frequencies and transmission powers are sequentially inserted into this table, with each communication requirement ID corresponding to one record containing the corresponding transmission frequency and transmission power values.

[0073] Furthermore, such as Figure 2 As shown, step S400 includes:

[0074] Step S410: Traverse the sequence of signal parameters to perform fluctuation analysis and determine the sequence of signal parameter fluctuation factors.

[0075] Step S420: Based on the magnitude of the signal parameter fluctuation factor in the signal parameter fluctuation factor sequence, determine the signal parameter set analysis scale and obtain the signal parameter set analysis scale sequence.

[0076] Step S430: Based on the scale sequence of the signal parameter set analysis, perform a set-based signal parameter set analysis on the signal parameter set sequence to determine the representative signal parameter sequence.

[0077] Specifically, the volatility factor is used to quantify the degree of volatility of signal parameters within a set, and is represented by variance. By traversing the signal parameter set corresponding to each angle in the rotation angle sequence, the volatility of the signal parameters within the set is analyzed based on the volatility variance, resulting in a signal parameter volatility factor sequence. As an indicator of the dispersion within a set, volatility variance can effectively measure the stability of the signal at that angle. The specific calculation process is as follows: For each signal parameter set, the average value of each signal parameter is calculated. Then, the difference between each signal parameter and the average value is calculated, and the difference is squared to obtain a set of squared differences. Finally, the average of these squared differences is calculated, which yields the volatility variance.

[0078] Based on the magnitude of the aforementioned signal parameter fluctuation factor, an adaptive setting of the signal parameter ensemble analysis scale is established, forming a sequence of signal parameter ensemble analysis scales. This signal parameter ensemble analysis scale is the convergence strength or tolerance fluctuation threshold used when summarizing representative values. Specifically, a function can be constructed. Where i is the angle index. Let i be the scale for the concentrated analysis of signal parameters corresponding to angle i. The scaling factor set for the system. is the variance of the wave for the angle i. Through the above function, the size of the analysis scale in the signal parameter set is calculated, so that the smaller the wave is, the larger the analysis scale is, and the analysis precision is enhanced; the larger the wave is, the smaller the analysis scale is, and the fault tolerance to abnormal points is improved.

[0079] Based on the analysis scale sequence in the signal parameter set, the centralized analysis operation is performed on the signal parameter set under each angle, the representative signal parameter which best represents the characteristics of the signal parameter set is extracted, and then the representative signal parameter sequence is formed, so as to significantly reduce the interference of signal sampling error on the analysis result, improve the stability and accuracy of the representative signal parameter sequence, and provide reliable basis for subsequent effective radiation area identification and communication directionality evaluation.

[0080] Further, the step S430 comprises:

[0081] Step S431: extracting a first signal parameter centralized analysis scale and a corresponding first signal parameter set from the signal parameter centralized analysis scale sequence and the signal parameter set sequence.

[0082] Step S432: randomly extracting a first first signal parameter from the first signal parameter set.

[0083] Step S433: constructing a first first signal parameter neighborhood corresponding to the first first signal parameter in the first signal parameter set according to the first signal parameter centralized analysis scale.

[0084] Step S434: extracting a second first signal parameter from the first signal parameter set again.

[0085] Step S435: constructing a second first signal parameter neighborhood corresponding to the second first signal parameter in the first signal parameter set according to the first signal parameter centralized analysis scale.

[0086] Step S436: comparing the neighborhood size of the first first signal parameter neighborhood and the second first signal parameter neighborhood to determine the centralized analysis direction.

[0087] Step S437: based on the centralized analysis direction, iterating the first first signal parameter or the second first signal parameter according to the first signal parameter centralized analysis scale until a preset iteration number is met, and obtaining a first representative signal parameter.

[0088] Step S438: by analogy, the mapping analysis is performed according to the signal parameter centralized analysis scale sequence and the signal parameter set sequence, and the representative signal parameter sequence is determined.

[0089] Further, the step S436 comprises:

[0090] Step S436-1: When the neighborhood quantity of the first first signal parameter neighborhood is greater than or equal to the neighborhood quantity of the second first signal parameter neighborhood, the direction from the second first signal parameter to the first first signal parameter is taken as the direction of the concentration analysis.

[0091] Step S436-2: When the neighborhood quantity of the first first signal parameter neighborhood is less than the neighborhood quantity of the second first signal parameter neighborhood, the direction from the first first signal parameter to the second first signal parameter is taken as the direction of the concentration analysis.

[0092] Specifically, the first signal parameter set refers to any one signal parameter set in the signal parameter set sequence to be subjected to concentration analysis. The first signal parameter concentration analysis scale is a concentration analysis scale value corresponding to the first signal parameter set in the signal parameter concentration analysis scale sequence, which provides a specific scale basis for the concentration analysis of the current signal parameter set, and is used to determine the range of the screened signal parameters. Any one signal parameter set in the signal parameter set sequence is extracted, denoted as the first signal parameter set. According to the rotation angle label of the first signal parameter set, the first signal parameter concentration analysis scale corresponding to the first signal parameter set is extracted from the signal parameter concentration analysis scale sequence.

[0093] A random number generation algorithm is used to randomly select a signal parameter in the first signal parameter set as the first first signal parameter, which serves as the initial reference point for subsequent neighborhood construction and direction determination. After obtaining the first first signal parameter, the first signal parameter concentration analysis scale is expanded on both sides of the data centering on the first first signal parameter, to determine the first first signal parameter neighborhood. For example, the first signal parameter concentration analysis scale is 0.45 (the unit is consistent with the signal parameter), and the first first signal parameter is -20.2 dBm. Then, the neighborhood range can be defined as [(-20.2-0.45) dBm, (-20.2+0.45) dBm]=-20.65 dBm, -19.75 dBm]. Then, all signal parameters in the first signal parameter set are traversed, and the signal parameters falling within this range are screened out to form the first first signal parameter neighborhood.

[0094] Again, a random number generation algorithm is used to randomly select a signal parameter from the first signal parameter set as the second first signal parameter, which is different from the first first signal parameter. After obtaining the second first signal parameter, the neighborhood range is determined according to the first signal parameter concentration analysis scale.

[0095] The neighborhood quantity sizes in the first first-signal-parameter neighborhood and the second first-signal-parameter neighborhood are calculated respectively, i.e., the number of signal parameters contained in the neighborhood. According to the comparison result, the centralized analysis direction is determined. Specifically, when the neighborhood quantity of the first first-signal-parameter neighborhood is greater than or equal to the neighborhood quantity of the second first-signal-parameter neighborhood, it indicates that the signal parameters have a higher degree of concentration around the first first-signal-parameter, and the centralized analysis direction is determined to be from the second first-signal-parameter to the first first-signal-parameter. The subsequent iterative analysis will be performed along this direction, so as to better capture the concentration trend of the signal parameters. Conversely, if the neighborhood quantity of the first first-signal-parameter neighborhood is less than the neighborhood quantity of the second first-signal-parameter neighborhood, it indicates that the signal parameters have a higher degree of concentration around the second first-signal-parameter, and the centralized analysis direction is determined to be from the first first-signal-parameter to the second first-signal-parameter. The subsequent iterative analysis will be performed along this direction.

[0096] With the determined centralized analysis direction as a guide, the selection range of the signal parameters is adjusted according to the first-signal-parameter centralized analysis scale in each iteration. For example, if the centralized analysis direction is from the second signal parameter to the first signal parameter, then in each iteration, the signal parameters are moved in the direction of the first signal parameter by a certain step (the first-signal-parameter centralized analysis scale), the neighborhood is reconstructed, the signal parameters in the neighborhood are screened, and a new first signal parameter is calculated. This process is repeated until a preset number of iterations is reached. Finally, the first signal parameter obtained in the last iteration is taken as the first representative signal parameter. For example, in each iteration, the average value of the signal parameters in the neighborhood can be calculated as a new reference point, and the neighborhood is continuously reconstructed and iterated until the preset number of iterations is completed, and finally the average value of the signal parameters in the neighborhood is obtained as the first representative signal parameter.

[0097] The above processing is sequentially performed on other elements in the signal parameter centralized analysis scale sequence and the signal parameter set sequence, and a complete representative signal parameter sequence is mapped. The representative signal parameter sequence can better reflect the concentration trend of the signal parameters under each rotation angle, and effectively improve the reliability of subsequent radiation region identification and communication directionality evaluation.

[0098] Further, the step S500 includes:

[0099] Step S510: Extracting a maximum value from the representative signal parameter sequence, and taking the rotation angle corresponding to the maximum value as a region center angle.

[0100] Step S520: Performing circumferential effective radiation region identification with the region center angle as a starting point according to a preset circumferential trend iteration constraint, and determining an iterative analysis region left side angle and an iterative analysis region right side angle.

[0101] Step S530: taking the region surrounded by the left side angle of the iterative analysis region and the right side angle of the iterative analysis region as the effective radiation region.

[0102] Specifically, the entire representative signal parameter sequence is traversed to find the maximum value therein. The maximum value represents the point at which the signal parameter (signal power) reaches the strongest under all rotation angles. Then, the rotation angle corresponding to the maximum value is obtained, which is the region center angle, and the angle is the position of the main radiation direction of the antenna.

[0103] The preset circumferential trend iteration constraint refers to a threshold criterion for limiting the signal parameter strength when determining the effective radiation region, and is usually set to half of the maximum value, that is, when the signal parameter strength drops to half of the maximum value, it is considered that the boundary of the radiation region is reached. After the region center angle is determined, iterative analysis is performed in the left and right directions respectively with the region center angle as the starting point. Taking the left direction as an example, starting from the region center angle, the signal parameter value under the angle is checked whether it is greater than or equal to half of the maximum value by moving left by a preset rotation angle scale each time. If the condition is met, continue to move left; if the condition is not met, determine the position as the left side angle of the iterative analysis region. In the same way, iterative analysis is performed in the right direction to determine the right side angle.

[0104] After the left side angle and the right side angle of the iterative analysis region are determined, the entire region between the left side angle and the right side angle is defined as the effective radiation region. The signal parameters corresponding to all rotation angles in this region satisfy the preset circumferential trend iteration constraint condition (that is, the signal strength is above half of the maximum value), which represents the effective radiation range of the antenna in the circumferential direction.

[0105] In summary, the communication performance detection method of the small antenna provided by the embodiment of the application has the following beneficial effects:

[0106] Firstly, the communication demand is matched with the pre-built signal source setting library, and the reliability weight calculated by the aggregation characteristics and usage frequency of historical samples is combined to realize the accurate adaptive setting of the transmission frequency and power, and improve the rationality and efficiency of the signal source configuration. Subsequently, the antenna to be tested is installed on the rotating table for multi-angle rotation sampling, and the signal parameter set of each angle is collected by the spectrum analyzer; then, the scale adaptive analysis strategy driven by the fluctuation factor is used to analyze the signal parameter set, and the representative signal parameter sequence is extracted by combining the neighborhood construction and directionality iteration, so as to suppress the measurement noise interference while keeping the intrinsic characteristics of the data. Further, taking the maximum radiation intensity angle as the center, the effective radiation area is dynamically identified by means of the beam width theory and trend iteration constraint, and the main radiation range of the antenna is accurately limited. Finally, the quantitative detection and evaluation of the communication directionality are completed by combining the representative signal parameters and the radiation area, the global identification and directionality evaluation of the communication performance are realized, and the flexibility, accuracy and reliability of the small antenna communication performance test are improved, which provides technical support for efficient evaluation and optimization in multiple scenarios.

[0107] Embodiment two, based on the same inventive concept of the communication performance detection method of the small antenna in the foregoing embodiment one, the present application also provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to realize the processes of the communication performance detection method of the small antenna in the foregoing embodiment one, and the same technical effects can be achieved. To avoid repetition, this will not be repeated here.

[0108] Embodiment three, based on the same inventive concept of the communication performance detection method of the small antenna in the foregoing embodiment one, the present application also provides an electronic device, comprising: at least one processor, and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the steps of the method in the foregoing embodiment one.

[0109] As Figure 3As shown, the bus architecture is represented with a bus 300, which can include any number of interconnected buses and bridges, the bus 300 connects various circuits including one or more processors represented by processor 302 and memory represented by memory 304. The bus 300 can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and thus, will not be further described herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and transmitter 303 can be the same device, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium. The processor 302 is responsible for managing the bus 300 and general processing, while the memory 304 can be used for storing data used by the processor 302 in executing operations.

[0110] The foregoing description of the disclosed embodiments enables a person skilled in the art to make or use the application. Numerous modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for testing the communication performance of a small antenna, characterized in that, The method includes: Based on communication requirements, the signal source setting library is matched, and the transmission frequency and transmission power of the signal source are set according to the matching results. The set signal source is then connected to the antenna under test. The antenna under test is mounted on a rotating platform and rotated according to a preset rotation angle scale to obtain a rotation angle sequence. The rotating platform can rotate circumferentially. The signal parameters of the antenna under test at each rotation angle in the rotation angle sequence are analyzed multiple times using a spectrum analyzer to obtain a sequence of signal parameters. Perform a set-based analysis of the signal parameters within the set to determine a representative signal parameter sequence; The effective radiation area is determined by iterative analysis of the circumferential trend through the representative signal parameter sequence. Communication directionality detection analysis is performed based on the representative signal parameter sequence and the effective radiation area to obtain communication directionality detection results; Specifically, the process of performing a set-based analysis of the signal parameter sequence to determine a representative signal parameter sequence includes: The signal parameter set sequence is traversed to perform fluctuation analysis and determine the signal parameter fluctuation factor sequence. Based on the magnitude of the signal parameter fluctuation factor in the signal parameter fluctuation factor sequence, the signal parameter set analysis scale is determined, and the signal parameter set analysis scale sequence is obtained. Based on the scale sequence of the signal parameter set analysis, perform a set-based signal parameter set analysis on the signal parameter set sequence to determine the representative signal parameter sequence; Specifically, the process of performing a set-based signal parameter aggregation analysis on the signal parameter set sequence based on the aggregated analysis scale sequence of the signal parameters to determine the representative signal parameter sequence includes: Extract the first signal parameter set analysis scale and the corresponding first signal parameter set from the signal parameter set analysis scale sequence and the signal parameter set sequence; Randomly extract the first first signal parameter from the first set of signal parameters; Based on the analysis scale of the first signal parameter set, construct the first neighborhood of the first signal parameter corresponding to the first signal parameter in the first signal parameter set; Extract the second first signal parameter from the first signal parameter set again; Based on the analysis scale of the first signal parameter set, construct the neighborhood of the second first signal parameter corresponding to the second first signal parameter in the first signal parameter set; By comparing the neighborhood values ​​of the first and second first signal parameter neighborhoods, the direction of focused analysis is determined. Based on the centralized analysis direction, the first first signal parameter or the second first signal parameter is iterated according to the first signal parameter centralized analysis scale until the preset number of iterations is met, and the first representative signal parameter is obtained. Similarly, a mapping analysis is performed based on the scale sequence of the signal parameter set and the sequence of the signal parameter set to determine the representative signal parameter sequence.

2. The communication performance testing method for a small antenna as described in claim 1, characterized in that, Comparing the neighborhood values ​​of the first and second first signal parameter neighborhoods to determine the direction of focused analysis includes: When the neighborhood size of the first first signal parameter neighborhood is greater than or equal to the neighborhood size of the second first signal parameter neighborhood, the direction from the second first signal parameter to the first first signal parameter is taken as the direction of concentrated analysis. When the neighborhood size of the first first signal parameter neighborhood is less than the neighborhood size of the second first signal parameter neighborhood, the direction from the first first signal parameter to the second first signal parameter is taken as the direction of concentrated analysis.

3. The communication performance testing method for a small antenna as described in claim 1, characterized in that, Iterative analysis of the circumferential trend is performed by traversing the representative signal parameter sequence to determine the effective radiation area, including: Extract the maximum value from the representative signal parameter sequence, and use the rotation angle corresponding to the maximum value as the center angle of the region; According to the preset circumferential trend iteration constraint, the effective circumferential radiation area is identified starting from the center angle of the region, and the left angle and right angle of the iterative analysis area are determined. The region enclosed by the left and right angles of the iterative analysis region is defined as the effective radiation region.

4. The communication performance testing method for a small antenna as described in claim 1, characterized in that, Based on communication requirements and a signal source setting library, the system matches the signal source with its transmission frequency and power according to the matching results. The set signal source is then connected to the antenna under test, including: Pre-built signal source configuration library; Using the communication requirements as an index, the signal source setting library is matched to obtain matching results, wherein the matching results include Q representative aggregated sample transmission frequency-transmission power, where Q is a positive integer; Reliability identification is performed on the transmission frequency-transmission power of the Q representative aggregated samples to determine Q reliability weights; The transmission frequency and transmission power are determined by weighting the transmission frequency and transmission power of the Q representative aggregated samples according to the Q reliability weights.

5. The communication performance testing method for a small antenna as described in claim 4, characterized in that, For the Q representative aggregated samples, reliability identification is performed based on their transmission frequency-transmission power, and Q reliability weights are determined, including: The usage frequency of each of the Q representative aggregated samples is counted based on its transmission frequency and transmission power, thus obtaining the Q usage frequencies. The ratio of the Q usage frequencies to the sum of the Q usage frequencies is calculated to obtain Q reliability weights.

6. The communication performance testing method for a small antenna as described in claim 4, characterized in that, Pre-built signal source configuration library, including: Obtain the communication requirements of multiple samples and the transmission frequency-transmission power of multiple samples; The multiple sample communication requests are aggregated into similar categories to obtain K aggregated sample communication request sets, where K is a positive integer greater than or equal to Q; Based on the K aggregated sample communication demand sets, the multiple sample transmission frequencies and transmission powers are mapped and aggregated to obtain K aggregated sample transmission frequency and transmission power sets; The union of the K aggregated sample communication requirement sets is performed to obtain K representative aggregated sample communication requirements. The K aggregated sample transmission frequency-transmission power sets are averaged to obtain K representative aggregated sample transmission frequencies-transmission powers; The signal source setting library is constructed by mapping the relationship between the communication requirements of the K representative aggregated samples and the transmission frequency-transmission power of the K representative aggregated samples.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the communication performance testing method for a small antenna as described in any one of claims 1-6.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the communication performance testing method for a small antenna according to any one of claims 1-6.

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