A bluetooth earphone anti-interference performance test system

Abstract

The present application relates to a kind of bluetooth earphone anti-interference performance test system, it is related to data processing technical field, system includes: environmental parameter module, for obtaining initial interference environmental parameter;Scene parameter module, generates composite interference scene parameter;Feature extraction module, for extracting human dynamic interference characteristics according to composite interference scene parameter;Link evaluation module, for obtaining link quality evaluation data based on human dynamic interference characteristics;Audio evaluation module, for calling audio quality evaluation model based on deep learning based on link quality evaluation data, calculate dynamic audio quality score, obtain audio performance evaluation result;Test optimization module, for calculating interference adaptability index based on audio performance evaluation result, output optimization suggestion data.The present application can improve the effect of bluetooth earphone anti-interference test.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Bluetooth earphones, and particularly relates to a Bluetooth earphone anti-interference performance test system. BACKGROUND

[0002] With the widespread application of wireless communication technology, Bluetooth earphones have become an important part of portable audio devices and are widely used in scenarios such as calls, music playback, and voice interaction. The existing test methods for the anti-interference performance of Bluetooth earphones mainly include introducing fixed-frequency and power wireless interference sources (such as Wi-Fi signals, other Bluetooth devices, microwave ovens, etc.) in a shielded room or an open space, and measuring the signal connection stability, audio quality, and data packet loss rate of the earphones under different interference environments. These methods usually rely on traditional channel simulators, transmit power control devices, and standardized audio quality evaluation tools such as PESQ (Perceptual Evaluation of Speech Quality) or MOS (Mean Opinion Score) indicators.

[0003] However, the existing test scenarios are relatively single, and it is difficult to truly simulate the interference characteristics in multi-source interference, dynamic scenarios, and complex environments (such as subways, shopping malls, and office buildings). Moreover, most test methods lack dynamic control over the position, mobility, and interference strength of the interference sources, making it difficult to reflect the robustness performance of Bluetooth earphones in the face of sudden, multi-band cross-interference in actual use. In addition, the existing test methods usually do not fully consider the performance impact of built-in sensors (such as acceleration sensors and gyroscopes) in Bluetooth earphones in dynamic environments, such as signal stability and anti-interference ability in motion state, which further limits the comprehensiveness and authenticity of the test results. SUMMARY

[0004] The present application provides a Bluetooth earphone anti-interference performance test system, method, electronic device and non-transitory computer readable storage medium that can improve the anti-interference test effect of Bluetooth earphones.

[0005] The technical solution of the present application to solve the above technical problems is as follows:

[0006] The present application provides a Bluetooth earphone anti-interference performance test system, which comprises:

[0007] An environmental parameter module is configured to construct a multi-dimensional interference source matrix, control multiple interference sources to be dynamically arranged in a three-dimensional space through a programmable mechanical arm, form an electromagnetic interference environment with adjustable interference strength and spatial distribution, and obtain initial interference environment parameters.

[0008] a scene parameter module, configured to construct an environmental acoustic characteristic simulation system based on the initial interference environment parameter, simulate acoustic noise characteristics of a use environment by adjusting a loudspeaker array and a variable acoustic material, and generate composite interference scene parameters;

[0009] a feature extraction module, configured to control a bionic human body model with human body electromagnetic characteristics to perform a preset dynamic action according to the composite interference scene parameters, and collect absorption and reflection data of the bionic model to a Bluetooth signal in real time, and extract human body dynamic interference characteristics;

[0010] a link evaluation module, configured to collect link parameters in a Bluetooth communication process in real time by using a non-intrusive Bluetooth link quality monitoring system based on the human body dynamic interference characteristics, compare the link parameters with preset reference performance indicators, and obtain link quality evaluation data;

[0011] an audio evaluation module, configured to call a deep learning-based audio quality evaluation model based on the link quality evaluation data, calculate a dynamic audio quality score, and obtain an audio performance evaluation result;

[0012] a test optimization module, configured to calculate an interference adaptability index based on the audio performance evaluation result, and output optimization suggestion data.

[0013] Optionally, the environment parameter module is further configured to:

[0014] generate corresponding spatial distribution parameters according to the three-dimensional layout positions and emission parameters of the interference sources;

[0015] calculate corresponding electromagnetic interference intensity parameters based on the working frequency band characteristics and emission power of the interference sources;

[0016] input the spatial distribution parameters and the electromagnetic interference intensity parameters into a preset interference superposition model, and output the initial interference environment parameter.

[0017] Optionally, the scene parameter module is further configured to:

[0018] control the sound pressure level and frequency distribution of the loudspeaker array according to the initial interference environment parameter, and generate traffic noise characteristic parameters;

[0019] generate acoustic environment parameters by adjusting the acoustic reflection coefficient of the variable acoustic material based on the traffic noise characteristic parameters;

[0020] input the traffic noise characteristic parameters and the acoustic environment parameters into an environmental noise superposition algorithm, and output the composite interference scene parameters.

[0021] Optionally, the feature extraction module is further configured to:

[0022] According to the preset dynamic action sequence, motion data of the bionic human body model is collected, and corresponding motion characteristic parameters are generated;

[0023] The motion characteristic parameters are input into a conductive characteristic model, and absorption and reflectivity of the human body to a Bluetooth signal are calculated;

[0024] Based on the absorption and reflectivity, the human body dynamic interference characteristics are output through a signal interference model.

[0025] Optionally, the link evaluation module is further used for:

[0026] Real-time collection of the link parameters;

[0027] According to the link parameters, a data packet loss rate and a retransmission rate of the Bluetooth link are calculated;

[0028] The data packet loss rate and the retransmission rate are input into a link quality score model, and the link quality evaluation data are output.

[0029] Optionally, the audio evaluation module is further used for:

[0030] Obtaining a training data set containing normal environment reference audio samples and interference environment test audio samples

[0031] According to the training data set, a deep learning model is trained;

[0032] In the training process, a preset loss function containing an audio quality index error term and a jitter penalty term is used to optimize the model parameters, and the dynamic audio quality score is obtained.

[0033] Optionally, the test optimization module is further used for:

[0034] Obtaining performance indicators under each test scenario;

[0035] The performance indicators are normalized to generate standardized performance data;

[0036] The standardized performance data are input into a time response model for calculation, and the interference adaptability index is obtained.

[0037] Optionally, the test optimization module is further used for:

[0038] According to the performance bottleneck identification report, abnormal parameters in the link quality evaluation data are extracted;

[0039] Based on the abnormal parameters, a corresponding improvement scheme is matched in a preset optimization strategy library;

[0040] The improvement scheme is combined with the interference adaptability index to generate the optimization suggestion data.

[0041] Optionally, the system further comprises:

[0042] A full-band signal acquisition module is configured to acquire Bluetooth communication signals in a 2.4 GHz and 5 GHz frequency band range.

[0043] A real-time decoding module is configured to extract a data packet loss rate, a frequency hopping behavior and signal strength change information from the Bluetooth communication signals.

[0044] A comparative analysis module is configured to compare the decoding result with a reference performance index and output the link quality evaluation data.

[0045] Optionally, the bionic human body model is further configured to:

[0046] A conductive framework structure is configured to simulate electromagnetic characteristics of human body tissues.

[0047] A human head micro-motion simulation, hand touch simulation and whole body motion simulation are configured.

[0048] A sensor array is configured to collect motion data and signal absorption and reflection information in real time.

[0049] The application further provides a Bluetooth earphone anti-interference performance test method, and the method comprises the following steps:

[0050] A multi-dimensional interference source matrix is constructed, a plurality of interference sources are dynamically arranged in a three-dimensional space through a programmable mechanical arm, an electromagnetic interference environment with adjustable interference intensity and spatial distribution is formed, and initial interference environment parameters are obtained.

[0051] Based on the initial interference environment parameters, an environment acoustic characteristic simulation system is constructed, acoustic noise characteristics of a use environment are simulated through adjustment of a loudspeaker array and variable acoustic materials, and composite interference scene parameters are generated.

[0052] According to the composite interference scene parameters, a bionic human body model with human electromagnetic characteristics is controlled to perform a preset dynamic action, absorption and reflection data of the bionic model to Bluetooth signals are collected in real time, and human dynamic interference characteristics are extracted.

[0053] Based on the human dynamic interference characteristics, a non-invasive Bluetooth link quality monitoring system is used to collect link parameters in a Bluetooth communication process in real time, the link parameters are compared with preset reference performance indexes, and link quality evaluation data are obtained.

[0054] Based on the link quality evaluation data, a deep learning-based audio quality evaluation model is called, a dynamic audio quality score is calculated, and an audio performance evaluation result is obtained.

[0055] Based on the audio performance evaluation result, an interference adaptability index is calculated, and optimization suggestion data is output.

[0056] In addition, to achieve the above object, the application further provides an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby realizing the Bluetooth earphone anti-interference performance test method as described above.

[0057] In addition, to achieve the above object, the application further provides a non-transitory computer readable storage medium, wherein the storage medium stores a computer software program, and the computer software program is executed by a processor to realize the Bluetooth earphone anti-interference performance test method as described above.

[0058] The application has the following advantages:

[0059] (1) The application can highly restore typical high-interference environments such as subways, offices, and shopping malls by constructing a three-dimensional interference matrix containing Wi-Fi, Bluetooth, microwave, electromagnetic, and other multi-band interference sources, and introducing an acoustic simulation system. Compared with the existing test scheme mainly using a single static interference source, the application can cover a wider range of actual use scenarios, and the test results are more representative and have application value.

[0060] (2) All interference sources in the application are dynamically scheduled by a programmable mechanical arm, which can accurately control the position, direction, and intensity of the interference sources, meet the test requirements of dynamic interference switching, simulate the dynamic process of user movement between different scenarios and rapid changes of interference sources, and effectively evaluate the link stability and adaptive ability of the earphone.

[0061] (3) The application introduces a bionic human model with conductive properties, and simulates the influence of the human body on signal propagation during actual use through head and hand movements, improving the realism of channel environment modeling, measuring and quantifying absorption and reflectivity, and supplementing the link performance fluctuation problem caused by "human shielding and interference" that cannot be evaluated by traditional empty load testing.

[0062] In summary, the application can effectively solve the problems of single test scene, lack of dynamic control, inability to reflect real use scenarios, and subjective evaluation mechanism in the anti-interference performance test of the built-in sensor of the existing Bluetooth earphone, and provides an efficient, reliable, and intelligent test method for the evaluation and optimization of the anti-interference ability of wireless audio devices, which has significant technical value and application prospect. BRIEF DESCRIPTION OF DRAWINGS

[0063] Figure 1 A scene diagram of the Bluetooth earphone anti-interference performance test method provided by the application;

[0064] Figure 2 A structural schematic diagram of a Bluetooth earphone anti-interference performance test system provided by the present application is shown in the figure.

[0065] Figure 3 A flowchart of a Bluetooth earphone anti-interference performance test method provided by the present application is shown in the figure.

[0066] Figure 4 A hardware structural schematic diagram of a possible electronic device provided by the present application is shown in the figure.

[0067] Figure 5 A hardware structural schematic diagram of a possible computer readable storage medium provided by the present application is shown in the figure. DETAILED DESCRIPTION

[0068] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0069] In the description of the present application, the terms "first", "second" are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0070] In the description of the present application, the term "for example" is used to indicate "as an example, illustration or explanation". Any embodiment described as "for example" in the present application is not necessarily interpreted as more preferred or more advantageous than other embodiments. The following description is given in order to enable any person skilled in the art to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that those skilled in the art can realize the present application without using these specific details. In other examples, well-known structures and processes will not be described in detail in order to avoid unnecessary details making the description of the present application obscure. Therefore, the present application is not intended to be limited to the shown embodiments, but is consistent with the broadest scope of principles and features disclosed.

[0071] Please refer to Figure 1 , Figure 1 A scene diagram of a Bluetooth earphone anti-interference performance test method provided by the present application is shown in the figure. As Figure 1As shown, the terminal and the server are connected through a network, such as a wired or wireless network connection. The terminal can include, but is not limited to, a mobile phone, a tablet, and other portable terminals installed with various network platform applications, as well as computers, kiosks, and advertising machines. The server provides various services for users, including service push servers and user recommendation servers.

[0072] It should be noted that Figure 1 The scenario diagram of the anti-interference performance test method of the Bluetooth headset shown is only an example. The terminal, server, and application scenarios described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not limit the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, as the system evolves and new service scenarios appear, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0073] The terminal can be used for:

[0074] A multi-dimensional interference source matrix is constructed, a plurality of interference sources are controlled by a programmable mechanical arm to be dynamically arranged in a three-dimensional space, an electromagnetic interference environment with adjustable interference intensity and spatial distribution is formed, and initial interference environment parameters are obtained;

[0075] Based on the initial interference environment parameters, an environmental acoustic characteristic simulation system is constructed, the acoustic noise characteristics of the use environment are simulated by adjusting the loudspeaker array and the variable acoustic material, and composite interference scene parameters are generated;

[0076] According to the composite interference scene parameters, a bionic human body model with human body electromagnetic characteristics is controlled to perform a preset dynamic action, and absorption and reflection data of the bionic model to the Bluetooth signal are collected in real time, and human body dynamic interference characteristics are extracted;

[0077] Based on the human body dynamic interference characteristics, a non-invasive Bluetooth link quality monitoring system is used to collect link parameters in the Bluetooth communication process in real time, and the link parameters are compared with preset reference performance indicators to obtain link quality evaluation data;

[0078] Based on the link quality evaluation data, a deep learning-based audio quality evaluation model is called to calculate a dynamic audio quality score, and an audio performance evaluation result is obtained;

[0079] Based on the audio performance evaluation result, an interference adaptability index is calculated, and optimization suggestion data is output.

[0080] Please refer to Figure 2 , Figure 2 A structural schematic diagram of a Bluetooth headset anti-interference performance test system provided by the present application.

[0081] As Figure 2 shown, the anti-interference performance test system of the Bluetooth headset proposed by the embodiment of the application comprises:

[0082] The environmental parameter module 201 is configured to construct a multi-dimensional interference source matrix, control multiple interference sources to be dynamically arranged in a three-dimensional space through a programmable mechanical arm, form an electromagnetic interference environment with adjustable interference intensity and spatial distribution, and obtain initial interference environment parameters.

[0083] In the embodiment, first, multiple electromagnetic interference sources are arranged in the shielding test room, including a 2.4 GHz / 5 GHz dual-band Wi-Fi signal generator, a multi-band Bluetooth interference simulator, a microwave oven electromagnetic radiation simulator, and a wireless video transmission device simulator. Each interference source is installed at the end of a programmable three-dimensional mechanical arm, and the relative position, height, and orientation of each interference source in space are set through a motion control program to realize dynamic adjustment of the spatial distribution of the interference sources. At the same time, the transmission power, channel configuration, and working mode of each interference source are independently adjusted through a radio frequency signal controller. The system automatically configures the interference source combination and spatial layout according to the preset test scenario, and preliminarily measures the electromagnetic field distribution in the test area through a field strength scanner to generate initial interference environment parameters including interference type, spatial coordinates, transmission intensity, and frequency band information, which serve as input basis for subsequent environment simulation.

[0084] The scene parameter module 202 is configured to construct an environmental acoustic characteristic simulation system based on the initial interference environment parameters, simulate the acoustic noise characteristics of the use environment by adjusting the loudspeaker array and the variable acoustic material, and generate composite interference scene parameters.

[0085] After obtaining the initial interference environment parameters, the environmental acoustic characteristic simulation system is started. The system includes a multi-channel loudspeaker array arranged around and a wall-adjustable acoustic absorption / reflection panel. According to the test scenario set in step 301 (such as a subway, a shopping mall, or an office environment), the system calls the corresponding noise sample library to control the loudspeaker to play background noise with specific spectral characteristics and sound pressure level. For example, when simulating a subway environment, a composite sound field mainly composed of low-frequency vibration noise is played; when simulating a shopping mall environment, mixed noise such as human voice conversation and advertisement broadcast is superimposed. At the same time, the opening angle and position of the variable acoustic material are changed through the electric adjustment mechanism to adjust the reverberation time and sound reflection characteristics of the test space. The system fuses the electromagnetic interference parameters and the acoustic parameters to generate composite interference scene parameters containing the dual interference characteristics of electromagnetism and acoustics, which are used to drive the subsequent test process.

[0086] The feature extraction module 203 is configured to control a bionic human body model with human body electromagnetic characteristics to perform a preset dynamic action according to the composite interference scene parameters, and to collect the absorption and reflection data of the bionic model to the Bluetooth signal in real time to extract dynamic interference characteristics of the human body.

[0087] After obtaining the initial interference environment parameters, the environmental acoustic characteristic simulation system is started. The system includes a multi-channel loudspeaker array arranged around and a wall-adjustable acoustic absorption / reflection panel. According to the test scene set in step 301 (such as a subway, a shopping mall or an office environment), the system calls the corresponding noise sample library, controls the loudspeaker to play background noise with specific spectral characteristics and sound pressure level. For example, when simulating a subway environment, a composite sound field dominated by low-frequency vibration noise is played; when simulating a shopping mall environment, mixed noise such as human voice conversation and advertisement broadcast is superimposed. At the same time, the opening angle and position of the variable acoustic material are changed through the electric adjustment mechanism to adjust the reverberation time and sound reflection characteristics of the test space. The system fuses the electromagnetic interference parameters and the acoustic parameters to generate composite interference scene parameters containing electromagnetic and acoustic double interference characteristics, which are used to drive the subsequent test process.

[0088] The link evaluation module 204 is configured to collect link parameters in a Bluetooth communication process in real time based on the human body dynamic interference characteristics by using a non-invasive Bluetooth link quality monitoring system, and compare the link parameters with preset reference performance indicators to obtain link quality evaluation data.

[0089] Under the action of human body dynamic interference, the non-invasive Bluetooth link quality monitoring system is started. The system captures the communication signals between the Bluetooth headset and the sound source device under test through a high-sensitivity wideband receiving antenna, and analyzes the link layer data packet in real time by using a protocol analyzer to obtain key link parameters such as data packet loss rate, retransmission times, received signal strength indication (RSSI), channel hopping sequence and link control command. All data are recorded in time series and compared with the reference performance indicators measured in the non-interference environment item by item. The system calculates derived indicators such as link stability score and anti-interference recovery time according to the difference degree, and finally generates link quality evaluation data to comprehensively reflect the running state of the Bluetooth link under dynamic composite interference.

[0090] The audio evaluation module 205 is configured to call a deep learning-based audio quality evaluation model based on the link quality evaluation data, calculate a dynamic audio quality score, and obtain an audio performance evaluation result.

[0091] On the basis of obtaining the link quality evaluation data, a pre-trained deep learning audio quality evaluation model is called. The model takes the link parameters, the received audio signal waveform and the reference pure audio as inputs, extracts perception-related features such as spectral distortion, speech intelligibility decline, playback delay and jitter, and sound discontinuity through a convolutional neural network and a time series modeling module. The model output is a dynamic audio quality score (DAQS), which quantitatively represents the degree of decline in the user's actual listening experience. The system determines the audio quality level according to the score interval, and generates an audio performance evaluation result for subsequent comprehensive evaluation.

[0092] The test optimization module 206 is configured to calculate an interference adaptability index based on the audio performance evaluation result, and output optimization suggestion data.

[0093] Finally, the system calculates an interference adaptability index (IAI) based on the audio performance evaluation result and the performance degradation degree and recovery response time in each test scenario, to measure the ability of the Bluetooth earphone to maintain available audio quality in different interference environments. The system further calls a built-in performance bottleneck analysis module, combines link quality, human body interference, and audio degradation data, identifies the key factors causing performance decline (such as specific frequency band interference sensitivity, slow response to human body shielding, etc.), and generates structured optimization suggestion data from the aspects of antenna design, protocol configuration, audio codec strategy, etc., to provide technical basis for product improvement.

[0094] In some embodiments, the environmental parameter module 201 is further configured to:

[0095] generate corresponding spatial distribution parameters according to the three-dimensional arrangement positions and transmission parameters of each interference source;

[0096] calculate corresponding electromagnetic interference intensity parameters based on the working frequency band characteristics and transmission power of the interference source;

[0097] input the spatial distribution parameters and the electromagnetic interference intensity parameters into a preset interference superposition model, and output initial interference environment parameters.

[0098] First, a plurality of electromagnetic interference sources (such as 2.4 GHz Wi-Fi signal generators, 5 GHz Wi-Fi signal generators, multi-band Bluetooth interference simulators, microwave oven electromagnetic radiation simulators, ZigBee interference simulators, wireless video transmission devices, etc.) are installed on a programmable robot arm with six degrees of freedom. Through a preset spatial distribution strategy, the arrangement positions (X, Y, Z coordinates) and attitude parameters such as orientations (pitch angle, yaw angle, roll angle) of each interference source in three-dimensional space are controlled to form a structured spatial interference distribution pattern.

[0099] Subsequently, according to the transmission frequency bands (such as 2.4 GHz, 5 GHz, 915 MHz, etc.), modulation modes (such as Wi-Fi OFDM, Bluetooth BR / EDR, BLE GFSK, etc.), transmission power (unit dBm), and transmission period (Duty Cycle) of each interference source, and combining the spatial arrangement information, a spatial distribution parameter model of the interference source in the test area is established. This model records the theoretical radiation influence range of each interference source on any sampling point in the test area.

[0100] Next, based on the frequency band characteristics and the transmit power, electromagnetic interference intensity parameters generated by each interference source in the target test area are calculated, and modeling is performed using the following steps: the signal attenuation from each interference source to the position of the earphone under test is calculated using a free space propagation loss model or a higher-order three-dimensional multipath fading model; considering the interference coupling coefficients and antenna gain correction factors under different frequency bands, the unit frequency band interference power value (unit dBm / Hz) of each interference source at the target point is obtained; the interference intensity distribution map of each interference source is constructed, and is uniformly mapped to three-dimensional grid data under a uniform spatial resolution.

[0101] Finally, the above spatial distribution parameters and corresponding electromagnetic interference intensity parameters are input into a preset interference superposition model. The model can comprehensively calculate the equivalent interference energy density, spectral overlap degree, and instantaneous interference index of each spatial unit or measured point based on the linear superposition principle, interference power weighting model, or empirical data-driven nonlinear fusion network, and output initial interference environment parameters A in a unified format.

[0102] The initial interference environment parameters A are used as input basis for acoustic environment construction, human body interference modeling, and link quality evaluation in subsequent test processes, have the characteristics of high spatial accuracy, rich parameter dimension, and dynamic update, and significantly improve the authenticity and controllability of the interference environment simulation.

[0103] In some embodiments, the scene parameter module 202 is further configured to:

[0104] According to the initial interference environment parameters, the sound pressure level and frequency distribution of the loudspeaker array are controlled to generate traffic noise feature parameters;

[0105] Based on the traffic noise feature parameters, the acoustic reflection coefficient of the variable acoustic material is adjusted to generate acoustic environment parameters;

[0106] The traffic noise feature parameters and the acoustic environment parameters are input into an environmental noise superposition algorithm to output composite interference scene parameters.

[0107] In an embodiment, to simulate a dynamic noise interference environment in a real use scenario, the system first controls the sound pressure level and frequency distribution of multiple loudspeaker arrays according to the aforementioned initial interference environment parameters. Specifically, the system calculates the sound field coverage condition of the earphone wearing area that may be simultaneously exposed to multi-source interference at different time points according to the relative positions of the interference sources in space, the radiation directions, and the corresponding electromagnetic interference intensities.

[0108] Next, the system sets the target output frequency range of the loudspeaker array according to the characteristics of common noise sources in urban traffic scenes (including motor vehicle acceleration, horn honking, road friction, and reverberation characteristics, etc.), such as low-frequency <500Hz engine noise and high-frequency >2kHz brake sound, and dynamically adjusts the sound pressure level to match the intensity changes of typical traffic noise at different times to generate traffic noise characteristic parameters representing a specific traffic sound environment.

[0109] Further, based on the traffic noise characteristic parameters, the material state of the adjustable acoustic interface (such as variable structure foam wall, sound response canvas, etc.) in the experimental cabin is controlled. The system adjusts the acoustic reflection coefficient of these variable acoustic materials to simulate the reverberation characteristics and absorption characteristics of different sound fields such as closed carriages, open roads, tunnels, etc., to generate corresponding acoustic environment parameters.

[0110] Finally, the above traffic noise characteristic parameters and acoustic environment parameters are input into a preset environmental noise superposition algorithm. Based on finite element acoustic simulation and multi-channel reverberation model, the algorithm calculates the interference superposition effect of multi-source noise in a complex environment, outputs composite interference scene parameters describing the overall sound field distribution and time-frequency dynamic characteristics of the current test environment, as input reference for subsequent anti-interference performance test and sound quality analysis.

[0111] In some embodiments, the feature extraction module 203 is further configured to:

[0112] According to a preset dynamic action sequence, motion data of the bionic human body model is collected to generate corresponding motion characteristic parameters;

[0113] The motion characteristic parameters are input into a conductive characteristic model to calculate the absorption rate and reflectivity of the human body to Bluetooth signals;

[0114] Based on the absorption rate and reflectivity, the human body dynamic interference characteristics are output through a signal interference model.

[0115] In a specific embodiment, to simulate the interference effect of human body motion on Bluetooth signals in real use scenarios, the system presets a set of dynamic action sequences, which cover head rotation, shoulder swing, arm swing, running jitter, and other typical user action modes when wearing earphones. The action sequence is executed by driving a bionic human body model with multiple degrees of freedom joints. During execution, the inertial measurement unit (IMU) and flexible displacement sensor integrated in each key part of the bionic human body model are used to collect continuous three-dimensional space motion data, and based on the data, corresponding motion characteristic parameters are generated, including but not limited to: limb swing amplitude, velocity vector, relative displacement, acceleration change rate, etc.

[0116] Subsequently, the motion characteristic parameters are input into a preset conductivity model. This model comprehensively considers factors such as the surface conductivity of human skin, tissue composition, water content, wearing position, and posture angle to calculate the absorption rate (α) and reflectivity (β) of the bionic model for Bluetooth radio frequency signals under specific posture and dynamic conditions. The absorption rate represents the proportion of signal energy absorbed by human tissue, and the reflectivity represents the proportion of signal bounce caused by surface irregularities or structural abrupt changes; both dynamically change with time and movement rhythm.

[0117] Furthermore, the system inputs the absorption rate and reflectivity parameters into the constructed signal interference model, and performs interference superposition analysis by combining each physical channel in the Bluetooth communication protocol (such as the frequency offset tolerance after GFSK modulation, transmission window, etc.), and outputs the corresponding human dynamic interference characteristic parameters to characterize the degree of influence of the bionic human body on the Bluetooth signal quality under multiple actions and postures.

[0118] This process enables accurate modeling and real-time feedback of human dynamic characteristics on wireless communication interference, providing key interference source inputs for subsequent interference robustness assessment.

[0119] In some embodiments, the link evaluation module 204 is further configured to:

[0120] Real-time acquisition of link parameters;

[0121] Calculate the packet loss rate and retransmission rate of the Bluetooth link based on the link parameters;

[0122] Input the packet loss rate and retransmission rate into the link quality scoring model, and output link quality assessment data.

[0123] In this embodiment, the system first collects the link parameters of the target headphone device in real time during the test through the Bluetooth protocol stack interface. The link parameters include, but are not limited to, Received Signal Strength Indicator (RSSI), link latency, link jitter, channel utilization, and connection stability indicators.

[0124] Subsequently, based on the collected link parameters, the system uses statistical analysis methods to calculate the data packet loss rate and data packet retransmission rate of the target earphone within a specified test time window. The data packet loss rate refers to the proportion of data packets sent but not successfully received per unit time to the total number of sent packets; the data packet retransmission rate refers to the proportion of data packets retransmitted due to reception failure to the total number of sent packets.

[0125] The system further uses the packet loss rate and retransmission rate as input variables and inputs them into a preset link quality scoring model. The scoring model is constructed based on a weighted modulation function or a multi-factor regression algorithm, which can integrate multi-dimensional link degradation indicators and output quantitative link quality assessment data.

[0126] Finally, the link quality assessment data can be used to determine the anti-interference performance level of the Bluetooth headset in different dynamic interference scenarios, providing quantitative support for subsequent comprehensive scoring.

[0127] In some embodiments, the audio evaluation module 205 is also used to:

[0128] Obtain a training data set containing normal environment reference audio samples and interference environment test audio samples

[0129] According to the training data set, the deep learning model is trained;

[0130] During the training process, a preset loss function containing an audio quality index error term and a jitter penalty term is used to optimize the model parameters, obtaining a dynamic audio quality score.

[0131] In this embodiment, the system first obtains an audio sample data set for training, which includes normal environment reference audio samples and test audio samples in an interference environment. Among them, the normal environment audio sample is a standard audio signal collected under the condition of no external interference source, and the interference environment audio sample is a real audio signal collected in a simulated dynamic multi-source interference (such as human body shielding, environmental reflection, device cross interference, etc.) scene.

[0132] Subsequently, the system performs supervised training on the constructed deep learning audio quality evaluation model based on the training data set. The model uses a combined architecture of a multi-layer convolutional neural network and a time series processing structure (such as LSTM or Transformer) to enhance the sensitivity to spectral changes and time sequence disturbances.

[0133] During the model training process, the system introduces a joint loss function containing an audio quality index error term and a jitter penalty term for iterative optimization of model parameters. Among them:

[0134] The audio quality index error term is constructed based on the difference between the target audio quality score (such as MOS subjective score or PESQ objective index, etc.) and the model output;

[0135] The jitter penalty term is used to constrain the output score change amplitude of the model for consecutive audio frames, so as to reduce the hypersensitive response of the model to short-time fluctuations and improve the stability and robustness of the score.

[0136] In some embodiments, the dynamic audio quality score can be represented as:

[0137]

[0138] Among them, is the dynamic audio quality score, is the i-th basic audio quality index, It is the weight of the i-th sound quality index. It is the scene adaptability coefficient. It is a time-varying jitter function. It is the jitter penalty coefficient. It is the evaluation time window.

[0139] Specifically, The final output dynamic audio quality score is used to comprehensively evaluate the listening experience quality of Bluetooth headphones in specific interference scenarios. The higher the value, the better the sound quality performance. Indicates the first i Basic audio quality metrics include signal-to-noise ratio (SNR), distortion rate (THD), speech clarity (PESQ), and frequency response consistency. In order to be with the first i The weight parameters corresponding to the basic sound quality indicators are determined based on the correlation between subjective sound quality scores and objective measurement results in the training samples, and are used to highlight the contribution of important indicators to the overall score. The scene adaptability coefficient is adjusted based on the audio complexity of the current test scene (such as subway, bus, street, indoor, etc.) to enhance the model's generalization ability to different usage environments. This is a time-varying jitter function used to describe the test audio within the evaluation time window. T The degree of audio playback jitter caused by factors such as unstable link and insufficient anti-interference. The jitter penalty coefficient is used to control the impact of jitter on the overall sound quality score. Its value can be optimized through cross-validation to achieve optimal perceptual consistency. The evaluation time window refers to the effective time interval selected during the audio quality evaluation process, which is usually a few seconds to a dozen seconds, depending on the length of the test task and the user's perception sensitivity.

[0140] This scoring function integrates a static sound quality metric aggregation term with a dynamic jitter penalty term. By introducing integral quantization of temporal perturbations, it significantly improves the accuracy of sound quality evaluation in dynamic scenarios with multiple disturbances. This structure is particularly suitable for objectively evaluating the performance of Bluetooth headphones in high-interference environments such as subway car traffic, high-speed trains, or crowded places, overcoming the limitation of existing methods that rely solely on static metrics and ignore dynamic fluctuations.

[0141] Through the above training strategy, the system can obtain a set of optimized model parameters, thereby realizing the dynamic generation of audio quality scores for the audio output quality of Bluetooth headphones under different interference environments. These scores can be used for subsequent quantitative evaluation and comparative analysis of anti-interference performance.

[0142] In some embodiments, the test optimization module 206 is further configured to:

[0143] Obtain performance metrics in various test scenarios;

[0144] The performance metrics are normalized to generate standardized performance data;

[0145] Standardized performance data is input into a time-response model for calculation to obtain the interference adaptability index.

[0146] In this embodiment, the Bluetooth headset system under test is first run in various preset test scenarios. These test scenarios include, but are not limited to, representative interference environments such as subway cars, highways, shopping malls, offices, and open streets. For each test scenario, multiple performance indicators related to anti-interference performance are collected using a testing device. These performance indicators include, but are not limited to, link stability, audio clarity, latency, packet loss rate, and user subjective listening experience ratings.

[0147] Subsequently, the collected performance indicators are normalized using linear normalization or Z-score standardization methods to convert performance data of different dimensions and scales into dimensionless standardized performance data within a unified range, facilitating subsequent unified processing and analysis.

[0148] The standardized performance data is input into the constructed time response model for calculation. This model comprehensively evaluates the Bluetooth headset's response speed and stability to external interference during changes in the interference environment, based on the changing trends of each performance indicator across different time segments. The model models the dynamic changes of performance indicators before, during, and after interference occurs, outputting a numerical result as the interference adaptability index for this test scenario.

[0149] Ultimately, by combining the interference adaptability index under various test scenarios, we can construct a comprehensive anti-interference capability profile of headphones under different interference environments, providing a basis for product performance optimization, scenario matching, and quality assessment.

[0150] In some embodiments, the interference adaptability index can be expressed as:

[0151]

[0152] in, It is the interference adaptability index. The headphones are in the first Performance metrics under various interference environments It is a performance indicator under a non-interference benchmark environment. It is the headphones that adapt to interference environments. Time required is the baseline adaptation time, k is the time sensitivity coefficient, and M is the total number of interference test scenarios.

[0153] Specifically, IAI (Interference Adaptability Index) represents the overall adaptability index of the Bluetooth headset in an interference environment. The higher the value, the stronger the adaptability. M represents the total number of interference environment scenarios covered by the test. For example, subway, high-speed rail, airport, and densely populated outdoor areas are each considered an independent scenario. is the standardized performance index of the headset in the jth interference environment scenario. This index is obtained by weighting and fusing multiple raw performance values (such as signal stability, audio clarity, packet loss rate, etc.), representing the performance level of the headset in this scenario. is the standardized performance index value of the headset in an ideal non-interference environment, serving as a unified reference. Its value can be obtained by measuring under external interference-free conditions. represents the response time required for the headset to reach a stable state of performance in the jth interference environment. This value can be obtained by detecting the stable inflection point in the performance curve, reflecting the adaptability speed of the headset to environmental disturbances. represents the response time under the reference condition, serving as a reference point for time adaptability. k is a time sensitivity coefficient that adjusts the steepness of the exponential function. The larger the k value, the more sensitive the response time difference. This parameter can be determined through model training or empirical setting.

[0154] In some embodiments, the test optimization module 206 is also used to:

[0155] According to the performance bottleneck identification report, extract abnormal parameters in the link quality evaluation data;

[0156] Based on the abnormal parameters, match the corresponding improvement scheme in the preset optimization strategy library;

[0157] Jointly analyze the improvement scheme and the interference adaptability index to generate optimization suggestion data.

[0158] In this embodiment, to optimize the performance of the Bluetooth headset in a multi-interference dynamic scenario, the system first analyzes the link quality evaluation data collected in each test scenario according to the performance bottleneck identification report, identifies and extracts abnormal parameters. The abnormal parameters can include but are not limited to: data packet loss rate exceeding the preset threshold, retransmission rate continuously rising, RSSI (Received Signal Strength) fluctuation anomaly, SNR (Signal-to-Noise Ratio) below the critical value, etc. The system automatically determines the key indicators causing performance degradation by comparing the set abnormal identification rules with the normal reference range.

[0159] Next, the system matches the extracted abnormal parameters in a preset optimization strategy library based on the extracted abnormal parameters. The optimization strategy library contains a plurality of predefined intervention strategies and their adaptation conditions, specifically including: adjusting the Bluetooth channel, switching the audio coding protocol (such as SBC to AAC or aptX), modifying the buffer queue length, dynamically reducing the bit rate to reduce the link load, etc. The system selects a plurality of candidate improvement schemes that best match the current abnormal mode through a parameter feature matching algorithm.

[0160] Further, to improve the adaptability and accuracy of the optimization scheme, the system jointly analyzes the above-mentioned candidate improvement schemes and the previously calculated interference adaptability index (IAI). The analysis process includes: taking the historical optimization effect of each improvement scheme under different IAI intervals as a reference weight, combining the current IAI value to dynamically adjust the priority and application conditions of the optimization suggestion, and thus generating a set of optimization suggestion data, which specifically points to the strategy combination most suitable for the current earphone in this scenario.

[0161] Finally, the optimization suggestion data can be used for subsequent device-side control strategy updating, firmware upgrade suggestion or user layer prompting, thereby realizing the active improvement of the anti-interference ability and the dynamic enhancement of the link performance.

[0162] In some embodiments, the system further comprises:

[0163] A full-band signal capture module for capturing Bluetooth communication signals in the 2.4 GHz and 5 GHz frequency band range;

[0164] A real-time decoding module for extracting packet loss rate, frequency hopping behavior and signal strength change information from the Bluetooth communication signal;

[0165] A comparative analysis module for comparing the decoding result with the benchmark performance index and outputting link quality evaluation data.

[0166] The full-band signal capture module is configured to scan and capture the communication signals between the Bluetooth earphone and the paired device in real time in the 2.4 GHz and 5 GHz frequency band range. Through the cooperation of the wideband radio frequency front end and the spectrum sensing unit, the module can: accurately track the frequency hopping spread spectrum characteristics; support parallel capture of communication signals under different Bluetooth protocols (such as BLE, BR / EDR); continuously record the channel usage distribution and conflict events in a dynamic interference environment.

[0167] Through the deployment of this module, the spectrum occupation of the earphone under multiple scene conditions can be comprehensively reflected, providing signal basic data for subsequent link evaluation.

[0168] Real-time decoding module is used to perform high-speed demodulation and protocol analysis based on capturing the original Bluetooth communication signals, extracting key parameters reflecting communication link stability, including:

[0169] 1. Packet Loss Rate: By counting the ratio of packet loss times to total transmission times during transmission, the communication integrity is evaluated;

[0170] 2. Frequency Hopping Dynamics: Track the change pattern of frequency hopping sequence, analyze whether there are frequency congestion, conflict frequency and other abnormalities;

[0171] 3. RSSI Trace: Real-time extraction of received signal strength (RSSI) and analysis of its fluctuation range in unit time to identify physical layer interference.

[0172] The module adopts a hardware and software cooperative structure to ensure real-time and low-delay decoding analysis without affecting the link communication.

[0173] The comparative analysis module evaluates the current communication quality based on the link performance data output by the real-time decoding module and the system's preset benchmark performance index model. Specifically, it includes:

[0174] Comparing the extracted packet loss rate, frequency hopping behavior and signal strength fluctuation with the benchmark values measured in a standard interference-free environment;

[0175] Using a weighted scoring mechanism to calculate the quality change of the link;

[0176] Output comprehensive link quality evaluation data, including instantaneous stability score, frequency hopping behavior offset index, RSSI fluctuation coefficient, etc.

[0177] The output of this module will be an important input for interference awareness analysis and optimization suggestion generation, providing decision support for adaptive anti-interference performance optimization and loop adjustment.

[0178] Through the cooperative operation of the above modules, this embodiment can restore the actual performance of the Bluetooth earphone communication link with high precision in various typical interference scenarios, providing reliable basis for subsequent interference adaptability modeling and system optimization.

[0179] In some embodiments, the bionic human model is also used for:

[0180] Simulate the conductive skeleton structure of human tissue electromagnetic characteristics;

[0181] Simulate human head micro-motion, hand touch and whole body movement;

[0182] A sensor array for real-time acquisition of motion data and signal absorption and reflection information.

[0183] In the present embodiment, the bionic human body model is configured with the following key structures and functional modules to achieve high-precision simulation and real-time acquisition of human dynamic interference characteristics:

[0184] The bionic human body model is internally provided with a skeleton structure made of high-conductivity material to simulate the electromagnetic characteristics of human tissues. The skeleton is covered with multiple layers of composite material, which has similar conductivity and dielectric constant to human skin, muscles and bones, achieving real absorption and reflection effect simulation of Bluetooth radio frequency signals. This structure helps to accurately reflect the shielding and scattering characteristics of human body to wireless signals, improving the authenticity of anti-interference testing.

[0185] The model is equipped with a multi-degree-of-freedom driving system, which can simulate various typical dynamic actions of the human body, including but not limited to: head micro-motions such as shaking, nodding, turning, etc.; hand touch actions such as touching the earphone, operating the mobile phone, etc.; whole body movement patterns such as walking, running, sitting and standing conversion, etc. The motion execution mechanism is controlled by a pre-set action sequence to achieve high-precision and highly repeatable dynamic motion simulation, facilitating the system to stably reproduce the influence of human motion on Bluetooth signals in multiple scenarios.

[0186] The bionic model is equipped with multiple types of sensor arrays laid at key positions, including inertial measurement units (IMU), electromagnetic field sensors and displacement sensors, which can acquire motion state data and Bluetooth signal absorption and reflection information in real time. The collected data is uploaded to the central control system through a high-speed data acquisition and transmission module, providing basic data support for subsequent interference characteristic modeling and performance analysis.

[0187] Through the integration of the above structures and functions, the bionic human body model achieves accurate simulation of the interaction between the human body and Bluetooth signals in a dynamic multi-dimensional interference environment, greatly improving the reliability and practical value of Bluetooth earphone anti-interference performance testing.

[0188] Please refer to Figure 3 , which provides a flowchart of a Bluetooth earphone anti-interference performance testing method of the present application, comprising the following steps:

[0189] Step 301, construct a multi-dimensional interference source matrix, control multiple interference sources in three-dimensional space through a programmable robot arm to form an electromagnetic interference environment with adjustable interference intensity and spatial distribution, and obtain initial interference environment parameters.

[0190] Step 302, based on the initial interference environment parameters, construct an environmental acoustic characteristic simulation system, simulate the acoustic noise characteristics of the use environment by adjusting the loudspeaker array and variable acoustic material, and generate composite interference scene parameters.

[0191] Step 303, according to the composite interference scene parameters, control the bionic human model with human electromagnetic characteristics to perform the preset dynamic action, and collect the absorption and reflection data of the bionic model to the Bluetooth signal in real time, and extract the human dynamic interference characteristics.

[0192] Step 304, based on the human dynamic interference characteristics, using a non-invasive Bluetooth link quality monitoring system to collect link parameters in the Bluetooth communication process in real time, and comparing the link parameters with the preset reference performance index, obtaining link quality evaluation data.

[0193] Step 305, based on the link quality evaluation data, calling a deep learning-based audio quality evaluation model, calculating a dynamic audio quality score, and obtaining an audio performance evaluation result.

[0194] Step 306, based on the audio performance evaluation result, calculating an interference adaptability index, and outputting optimization suggestion data.

[0195] Please refer to Figure 4 , Figure 4 The embodiment of the electronic device provided by the embodiment of the application is shown in the figure. As shown in Figure 4 The embodiment of the application provides an electronic device 400, which includes a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0196] Construct a multi-dimensional interference source matrix, control multiple interference sources to perform dynamic layout in a three-dimensional space through a programmable mechanical arm, form an electromagnetic interference environment with adjustable interference intensity and spatial distribution, and obtain initial interference environment parameters;

[0197] Based on the initial interference environment parameters, an environmental acoustic characteristic simulation system is constructed, the acoustic noise characteristics of the use environment are simulated through adjusting the loudspeaker array and the variable acoustic material, and the composite interference scene parameters are generated;

[0198] According to the composite interference scene parameters, control the bionic human model with human electromagnetic characteristics to perform the preset dynamic action, and collect the absorption and reflection data of the bionic model to the Bluetooth signal in real time, and extract the human dynamic interference characteristics;

[0199] Based on the human dynamic interference characteristics, using a non-invasive Bluetooth link quality monitoring system to collect link parameters in the Bluetooth communication process in real time, and comparing the link parameters with the preset reference performance index, obtaining link quality evaluation data;

[0200] Based on the link quality evaluation data, a deep learning-based audio quality evaluation model is called to calculate a dynamic audio quality score, and an audio performance evaluation result is obtained.

[0201] Based on the audio performance evaluation result, an interference adaptability index is calculated, and optimization suggestion data is output.

[0202] Please refer to Figure 5 , Figure 5 An embodiment of a computer-readable storage medium provided by the embodiment of the application is shown in the figure. Figure 5 As shown in the figure, the embodiment provides a computer-readable storage medium 500, which stores a computer program 411, and the computer program 411 is executed by a processor to implement the following steps:

[0203] A multi-dimensional interference source matrix is constructed, a plurality of interference sources are controlled by a programmable mechanical arm to be dynamically arranged in a three-dimensional space, an electromagnetic interference environment with adjustable interference intensity and spatial distribution is formed, and initial interference environment parameters are obtained;

[0204] Based on the initial interference environment parameters, an environmental acoustic characteristic simulation system is constructed, the acoustic noise characteristics of the use environment are simulated by adjusting the loudspeaker array and the variable acoustic material, and composite interference scene parameters are generated;

[0205] According to the composite interference scene parameters, a bionic human body model with human body electromagnetic characteristics is controlled to perform a preset dynamic action, and absorption and reflection data of the bionic model to the Bluetooth signal are collected in real time, and human body dynamic interference characteristics are extracted;

[0206] Based on the human body dynamic interference characteristics, a non-invasive Bluetooth link quality monitoring system is used to collect link parameters in the Bluetooth communication process in real time, and the link parameters are compared with the preset reference performance index, and link quality evaluation data are obtained;

[0207] Based on the link quality evaluation data, a deep learning-based audio quality evaluation model is called to calculate a dynamic audio quality score, and an audio performance evaluation result is obtained.

[0208] Based on the audio performance evaluation result, an interference adaptability index is calculated, and optimization suggestion data is output.

[0209] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0210] Those skilled in the art will appreciate that embodiments of the present application can be devised for a system, method, or computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer readable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code embodied thereon.

[0211] The present application is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to embodiments of the present application. It will be understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0212] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0213] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0214] While preferred embodiments of the application have been described, modifications and variations can be effected to such embodiments by those of ordinary skill in the art once the necessary conceptual underpinnings are appreciated. Accordingly, it is to be understood that the appended claims are intended to cover all such modifications and variations as falling within the scope of the application.

[0215] Obviously, many modifications and variations of the present application are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A Bluetooth headset anti-interference performance testing system, characterized in that, The system includes: The environmental parameter module is used to construct a multi-dimensional interference source matrix. It controls multiple interference sources to be dynamically deployed in three-dimensional space through a programmable robotic arm, forming an electromagnetic interference environment with adjustable interference intensity and spatial distribution, and obtaining initial interference environment parameters. The scene parameter module is used to construct an environmental acoustic characteristic simulation system based on the initial interference environment parameters. By adjusting the loudspeaker array and variable acoustic materials, it simulates the acoustic noise characteristics of the usage environment and generates composite interference scene parameters. The feature extraction module is used to control a bionic human model with human electromagnetic properties to perform preset dynamic actions based on the parameters of the composite interference scene, and to collect data on the absorption and reflection of Bluetooth signals by the bionic model in real time to extract human dynamic interference features. The link evaluation module is used to collect link parameters in real time during Bluetooth communication based on the human body dynamic interference characteristics using a non-invasive Bluetooth link quality monitoring system, and compare the link parameters with preset benchmark performance indicators to obtain link quality evaluation data. The audio evaluation module is used to call a deep learning-based audio quality evaluation model based on the link quality evaluation data to calculate a dynamic audio quality score and obtain audio performance evaluation results. The test optimization module is used to calculate the interference adaptability index based on the audio performance evaluation results and output optimization suggestion data.

2. The Bluetooth headset anti-interference performance testing system according to claim 1, characterized in that, The environmental parameter module is also used for: Based on the three-dimensional deployment location and emission parameters of each interference source, corresponding spatial distribution parameters are generated; Based on the operating frequency band characteristics and transmission power of the interference source, calculate the corresponding electromagnetic interference intensity parameters; The spatial distribution parameters and the electromagnetic interference intensity parameters are input into a preset interference superposition model, and the initial interference environment parameters are output.

3. The Bluetooth headset anti-interference performance testing system according to claim 2, characterized in that, The scene parameter module is also used for: Based on the initial interference environment parameters, the sound pressure level and frequency distribution of the loudspeaker array are controlled to generate traffic noise characteristic parameters; Based on the traffic noise characteristic parameters, acoustic environment parameters are generated by adjusting the acoustic reflection coefficient of the variable acoustic material. The traffic noise characteristic parameters and the acoustic environment parameters are input into the environmental noise superposition algorithm to output the composite interference scene parameters.

4. The Bluetooth headset anti-interference performance testing system according to claim 3, characterized in that, The feature extraction module is also used for: Based on a preset dynamic action sequence, motion data of a bionic human model is collected, and corresponding motion feature parameters are generated. The motion characteristic parameters are input into the conductivity model to calculate the absorption and reflection rates of Bluetooth signals by the human body. Based on the absorption rate and the reflectivity, the dynamic interference characteristics of the human body are output through a signal interference model.

5. The Bluetooth headset anti-interference performance testing system according to claim 4, characterized in that, The link evaluation module is also used for: The link parameters are collected in real time; Based on the link parameters, calculate the data packet loss rate and retransmission rate of the Bluetooth link; The packet loss rate and the retransmission rate are input into the link quality scoring model, and the link quality assessment data is output.

6. The Bluetooth headset anti-interference performance testing system according to claim 5, characterized in that, The audio evaluation module is also used for: Obtain a training dataset containing reference audio samples from normal environments and test audio samples from interfering environments; The deep learning model is trained based on the training dataset. During training, a preset loss function, which includes an error term for audio quality indicators and a jitter penalty term, is used to optimize the model parameters to obtain the dynamic audio quality score.

7. The Bluetooth headset anti-interference performance testing system according to claim 6, characterized in that, The test optimization module is also used for: Obtain performance metrics in various test scenarios; The performance indicators are normalized to generate standardized performance data; The standardized performance data is input into a time response model for calculation to obtain the interference adaptability index.

8. The Bluetooth headset anti-interference performance testing system according to claim 7, characterized in that, The test optimization module is also used for: Based on the performance bottleneck identification report, extract the abnormal parameters from the link quality assessment data; Based on the abnormal parameters, a corresponding improvement scheme is matched in the preset optimization strategy library; The improved scheme is combined with the interference adaptability index to generate the optimization suggestion data.

9. The Bluetooth headset anti-interference performance testing system according to claim 8, characterized in that, The system also includes: A full-band signal acquisition module for capturing Bluetooth communication signals in the 2.4GHz and 5GHz frequency bands; The real-time decoding module is used to extract data packet loss rate, frequency hopping behavior and signal strength change information from the Bluetooth communication signal; The comparative analysis module is used to compare the decoding results with benchmark performance indicators and output the link quality assessment data.

10. The Bluetooth headset anti-interference performance testing system according to claim 9, characterized in that, The bionic human model is also used for: A conductive framework structure that simulates the electromagnetic properties of human tissue; Simulates micro-movements of the human head, hand touch control, and full-body movement; A sensor array that collects motion data and signal absorption and reflection information in real time.

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