A wireless transmit and receive test system and method

Abstract

The present application relates to the technical field of wireless transmission and reception test, and particularly relates to a wireless transmission and reception test system and method. The method comprises the following steps: setting a spectrum range for a standard RX to obtain spectrum range configuration data; sampling environmental noise according to the spectrum range configuration data, extracting noise characteristics, and obtaining an environmental noise characteristic vector; generating an environmental noise spectrum for the environmental noise characteristic vector and the spectrum range configuration data to obtain an environmental noise characteristic spectrum; calculating a dynamic threshold value according to the environmental noise characteristic spectrum and a preset signal-to-noise ratio margin parameter to obtain an adaptive TX power threshold value; and collecting TX signal strength of a to-be-tested TX to obtain actual TX signal strength data. The present application reduces the cost of test equipment and manpower, speeds up the test, and improves the test efficiency through the wireless transmission and reception test technology.

Description

Technical Field

[0001] This invention relates to the field of wireless transmission and reception testing technology, and in particular to a wireless transmission and reception testing system and method. Background Technology

[0002] During the production of UHF microphones, it is necessary to ensure that the transmit power of the TX and the receive sensitivity of the RX meet the design requirements. However, traditional wireless testing equipment (such as spectrum analyzers and signal generators) is expensive and complex to operate, usually requiring professional operators and complicated calibration procedures, which is time-consuming and labor-intensive, and represents a significant burden for cost-sensitive PCBA manufacturers and OEMs.

[0003] Traditional fixed-threshold-based testing methods are easily affected by environmental noise and individual device differences, and the test results are not intuitive, leading to inaccurate results or even misjudgments. Summary of the Invention

[0004] Therefore, it is necessary to provide a wireless transmission and reception testing system and method to solve at least one of the above-mentioned technical problems.

[0005] To achieve the above objectives, a wireless transmission and reception testing method includes the following steps:

[0006] Step S1: Set the spectrum range for the standard RX to obtain spectrum range configuration data; sample environmental noise based on the spectrum range configuration data, extract noise features, and obtain environmental noise feature vectors; generate environmental noise spectrum based on the environmental noise feature vectors and the spectrum range configuration data to obtain environmental noise feature spectrum.

[0007] Step S2: Calculate the adaptive TX power threshold based on the environmental noise characteristic spectrum and the preset signal-to-noise ratio margin parameters; collect the TX signal strength of the TX under test to obtain the measured TX signal strength data; dynamically determine the measured TX signal strength data and the adaptive TX power threshold, and store the test data to obtain the TX power test data record.

[0008] Step S3: Set the starting power and step size through the upper unit to obtain the gradient scan configuration parameters; monitor the receiving status of the RX under test to obtain the receiving status monitoring command; perform RX sensitivity test on the RX under test according to the receiving status monitoring command to obtain the raw RX sensitivity data; generate the sensitivity gradient curve according to the raw RX sensitivity data to obtain the RX sensitivity gradient curve; store the test data according to the RX sensitivity gradient curve to obtain the RX sensitivity test data record.

[0009] Step S4: Perform performance parameter statistical analysis on the TX power test data records and RX sensitivity test data records, and conduct quality trend evaluation to obtain performance parameter statistics and quality trend indicators; detect abnormal test results based on performance parameter statistics and quality trend indicators to obtain abnormal alarm information; perform dynamic threshold self-optimization adjustment on the adaptive TX power threshold to obtain the optimized dynamic TX power threshold; perform gradient step self-optimization adjustment on the gradient scan configuration parameters to obtain the optimized gradient scan step value; generate a self-optimized parameter set based on the optimized dynamic TX power threshold and the optimized gradient scan step value to obtain a self-optimized test parameter set; update and apply the parameters of the self-optimized test parameter set to obtain parameter update instructions; encapsulate the test data of the self-optimized test parameter set in a structured manner to obtain a structured test data packet; use the abnormal alarm information, parameter update instructions, and structured test data packet to realize the wireless transmission and reception test task.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: The host computer sets the spectrum range for the standard RX to obtain spectrum range configuration data;

[0012] Step S12: The standard RX performs environmental noise sampling based on the spectral range configuration data to obtain raw noise sampling data; the raw noise sampling data is preprocessed to obtain preprocessed noise data.

[0013] Step S13: The host computer receives preprocessed noise data from the standard RX, extracts noise features, and obtains the environmental noise feature vector; the host computer sends self-calibration commands to the standard TX and standard RX respectively, and obtains self-calibration configuration commands.

[0014] Step S14: The standard TX performs transmitter loopback calibration according to the self-calibration configuration command to obtain the transmit calibration parameters; the standard RX performs receiver loopback calibration according to the self-calibration configuration command to obtain the receive calibration parameters.

[0015] Step S15: The host computer generates an environmental noise spectrum based on the environmental noise feature vector and the spectral range configuration data, thus obtaining the environmental noise feature spectrum.

[0016] Preferably, step S2 includes the following steps:

[0017] Step S21: The upper unit extracts the frequency band noise floor from the ambient noise feature spectrum to obtain the frequency band noise floor data; the minimum received power is calculated based on the frequency band noise floor data and the preset signal-to-noise ratio margin parameter to obtain the minimum received power threshold.

[0018] Step S22: Obtain standard RX specification parameters; perform standard receiver sensitivity compensation based on standard RX specification parameters to obtain receiver sensitivity compensation value;

[0019] Step S23: Based on the receiver sensitivity compensation value and the minimum received power threshold, perform dynamic transmit power threshold back-calculation to obtain the adaptive TX power threshold;

[0020] Step S24: The host unit starts the transmitter under test (TX) and obtains the transmission command for the TX under test; the standard RX collects the signal strength according to the transmission command for the TX under test and obtains the measured TX signal strength data.

[0021] Step S25: The upper control unit dynamically determines the measured TX signal strength data and the adaptive TX power threshold to obtain the TX power test results;

[0022] Step S26: Store the adaptive TX power threshold, measured TX signal strength data, and TX power test results in test data to obtain TX power test data records.

[0023] Preferably, step S3 includes the following steps:

[0024] Step S31: Set the starting power and step size through the upper camera to obtain the gradient scan configuration parameters;

[0025] Step S32: Set the standard transmitter power of the standard TX according to the spectrum range configuration data to obtain the current power value of the standard TX; monitor the reception status of the RX under test to obtain the reception status monitoring command;

[0026] Step S33: Perform RX sensitivity testing on the RX under test according to the current power value of the standard TX and the receiving status monitoring command to obtain the raw RX sensitivity data;

[0027] Step S34: Generate the sensitivity gradient curve based on the original RX sensitivity data to obtain the RX sensitivity gradient curve;

[0028] Step S35: Store the gradient scan configuration parameters, raw RX sensitivity data, and RX sensitivity gradient curve as test data to obtain RX sensitivity test data records.

[0029] Preferably, step S33 includes the following steps:

[0030] Step S331: Based on the current power value of the standard TX and the receiving status monitoring command, perform gradient point data acquisition on the RX under test to obtain single-point gradient test data;

[0031] Step S332: Perform power step decay control based on gradient scan configuration parameters and the current power value of standard TX to obtain the next power value of standard TX;

[0032] Step S333: Determine the scan termination condition based on the gradient scan configuration parameters, the next power value of the standard TX, and the single-point gradient test data to obtain the scan termination flag;

[0033] Step S334: Based on the scan termination flag, integrate the gradient data of the single-point gradient test data to obtain the raw RX sensitivity data.

[0034] Preferably, step S34 includes the following steps:

[0035] Step S341: Extract power-RSSI data pairs from the raw RX sensitivity data and generate power-RSSI curve data to obtain the power RSSI curve data point set;

[0036] Step S342: Extract power-PER / BER data pairs from the raw RX sensitivity data and generate power-PER / BER curve data to obtain the power error rate curve data point set;

[0037] Step S343: Determine the sensitivity threshold value for the power error rate curve data point set to obtain the sensitivity threshold power value;

[0038] Step S344: Generate a sensitivity gradient curve based on the power RSSI curve data point set and the power error rate curve data point set according to the sensitivity threshold power value, and mark the threshold to obtain the RX sensitivity gradient curve.

[0039] Preferably, step S4 includes the following steps:

[0040] Step S41: The host computer loads historical test data from the environmental noise characteristic spectrum, TX power test data records, and RX sensitivity test data records to create a historical test dataset.

[0041] Step S42: Perform performance parameter statistical analysis on the historical test dataset to obtain performance parameter statistics; evaluate the quality trend based on the performance parameter statistics to obtain quality trend indicators;

[0042] Step S43: Based on the performance parameter statistics, quality trend indicators and environmental noise characteristic spectrum, the adaptive TX power threshold is dynamically self-optimized to obtain the optimized dynamic TX power threshold.

[0043] Step S44: Adjust the gradient scan configuration parameters by gradient stepping self-optimization based on performance parameter statistics and quality trend indicators to obtain the optimized gradient scan stepping value;

[0044] Step S45: Generate a self-optimized parameter set by optimizing the dynamic TX power threshold and the gradient scan step value after optimization, and obtain the self-optimized test parameter set;

[0045] Step S46: The host computer performs structured encapsulation of the environmental noise characteristic spectrum, transmission calibration parameters, reception calibration parameters, TX power test data records, RX sensitivity test data records, and self-optimization test parameter set to obtain a structured test data package.

[0046] Preferably, step S43 includes the following steps:

[0047] Step S431: Read the current dynamic TX power threshold of the adaptive TX power threshold to obtain the current dynamic threshold;

[0048] Step S432: Evaluate the recent TX power test yield of the performance parameter statistics to obtain the recent TX yield;

[0049] Step S433: Calculate the threshold adjustment amount based on the yield deviation for the recent TX yield and the preset yield target value to obtain the yield deviation adjustment amount;

[0050] Step S434: Obtain the recent environmental noise level changes from the quality trend indicators and the environmental noise characteristic spectrum to obtain the recent noise change amount;

[0051] Step S435: Calculate the threshold adjustment amount based on the recent noise change amount to obtain the noise change adjustment amount;

[0052] Step S436: Calculate the total adjustment amount by comprehensively adjusting the yield deviation and noise change.

[0053] Step S438: Adjust the dynamic TX power threshold according to the total adjustment amount and the current dynamic threshold to obtain the optimized dynamic TX power threshold.

[0054] Preferably, step S44 includes the following steps:

[0055] Step S441: Evaluate the recent RX sensitivity test data volatility of the performance parameter statistics to obtain the recent sensitivity volatility;

[0056] Step S442: Evaluate the stability of the product's RX sensitivity performance based on the quality trend index to obtain the RX performance stability;

[0057] Step S443: Calculate the volatility-based step adjustment amount based on the recent sensitivity volatility to obtain the volatility adjustment amount;

[0058] Step S444: Calculate the step value adjustment based on performance stability according to the RX performance stability to obtain the stability adjustment amount;

[0059] Step S445: Calculate the combined step value adjustment for volatility adjustment and stability adjustment to obtain the combined step value adjustment;

[0060] Step S446: Obtain the current step value; adjust the gradient scan step value according to the comprehensive step value adjustment amount and the current step value to obtain the optimized gradient scan step value.

[0061] Preferably, the present invention also provides a wireless transmission and reception test system for performing the wireless transmission and reception test method described above, the wireless transmission and reception test system comprising:

[0062] The system initialization module is used to set the spectrum range of the standard RX to obtain spectrum range configuration data; to sample environmental noise based on the spectrum range configuration data, and to extract noise features to obtain an environmental noise feature vector; and to generate an environmental noise spectrum based on the environmental noise feature vector and the spectrum range configuration data to obtain an environmental noise feature spectrum.

[0063] The adaptive transmit power test module is used to perform dynamic threshold calculation based on the environmental noise characteristic spectrum and preset signal-to-noise ratio margin parameters to obtain the adaptive TX power threshold; to acquire the TX signal strength of the TX under test to obtain the measured TX signal strength data; to dynamically determine the measured TX signal strength data and the adaptive TX power threshold, and to store the test data to obtain the TX power test data record.

[0064] The receiver sensitivity gradient scanning module is used to set the starting power and step size through the upper unit to obtain gradient scanning configuration parameters; monitor the receiver status of the RX under test to obtain receiver status monitoring instructions; perform RX sensitivity testing on the RX under test according to the receiver status monitoring instructions to obtain raw RX sensitivity data; generate a sensitivity gradient curve based on the raw RX sensitivity data to obtain the RX sensitivity gradient curve; and store the test data based on the RX sensitivity gradient curve to obtain the RX sensitivity test data record.

[0065] The threshold self-optimization module performs statistical analysis of performance parameters and quality trend evaluation on TX power test data records and RX sensitivity test data records, obtaining performance parameter statistics and quality trend indicators. It also detects abnormal test results based on these statistics and indicators, generating abnormal alarm information. Furthermore, it dynamically optimizes the adaptive TX power threshold to obtain an optimized dynamic TX power threshold. It further optimizes the gradient scan configuration parameters by adjusting the gradient step, obtaining an optimized gradient scan step value. Finally, it generates a self-optimized test parameter set from the optimized dynamic TX power threshold and the optimized gradient scan step value. This self-optimized test parameter set is then updated and applied, generating parameter update instructions. Finally, the self-optimized test parameter set is structurally encapsulated into a structured test data packet. Finally, the module utilizes abnormal alarm information, parameter update instructions, and the structured test data packet to perform wireless transmission and reception test tasks.

[0066] This invention achieves effective characterization of test environment noise by setting the spectrum range of a standard RX and sampling environmental noise, followed by noise feature extraction and environmental noise spectrum generation. Its advantages lie in accurately acquiring the noise spectrum distribution of the test environment, providing an important environmental noise benchmark for subsequent adaptive threshold calculation and dynamic test judgment. The generation of the environmental noise feature spectrum enables the test system to perceive and adapt to different test environment noise levels, avoiding misjudgments caused by environmental noise fluctuations in traditional fixed threshold testing methods, thus improving the accuracy and environmental adaptability of wireless device testing. Simultaneously, the environmental noise spectrum data also provides a necessary data foundation for subsequent quality trend analysis and test parameter self-optimization. Based on the environmental noise feature spectrum generated in step S1 and combined with preset signal-to-noise ratio margin parameters, a dynamic threshold calculation is performed to obtain the adaptive TX power threshold. This adaptive threshold can be dynamically adjusted according to the actual environmental noise level, making the TX power test more closely resemble real-world application scenarios. By acquiring the TX signal strength of the TX under test and dynamically judging it against the adaptive TX power threshold, accurate evaluation of the transmission power is achieved. Its beneficial effects include effectively reducing the impact of environmental noise on TX power test results, avoiding misjudging qualified transmitters as unqualified in high-noise environments or unqualified transmitters as qualified in low-noise environments, thus improving the reliability and accuracy of TX power testing and providing reliable TX power test data records for subsequent quality control. Gradient scanning is configured at the upper station, and RX sensitivity testing is performed on the RX under test to generate RX sensitivity gradient curves. Its beneficial effects include the ability to comprehensively and meticulously evaluate the receiving sensitivity performance of the RX under test. The gradient scanning method can depict the performance of the RX at different receiving powers in detail, and intuitively show the receiving sensitivity characteristics and data packet reception reliability of the RX through power-RSSI curves and power-PER curves. The generation of sensitivity gradient curves makes the test results easier to analyze and understand, and can accurately determine the sensitivity threshold of the RX. The storage of RX sensitivity test data records provides detailed RX sensitivity performance data support for subsequent quality analysis, performance evaluation, and self-optimization parameter adjustment. By performing statistical analysis and quality trend evaluation on TX power test data and RX sensitivity test data, performance parameter statistics and quality trend indicators were obtained. The beneficial effects are that it enables comprehensive monitoring of product wireless performance and effective control of quality trends. Anomaly detection allows for timely identification and alerting of abnormal situations during the testing process, ensuring test quality. Dynamic threshold self-optimization and gradient step self-optimization enable the test system to automatically optimize test parameters based on historical test data and quality trends, improving test efficiency and accuracy while reducing manual intervention. The generation, updating, and application of self-optimizing parameter sets realize the intelligence and adaptability of the test system.The generation of structured test data packets facilitates the storage, transmission, and reuse of test data, providing strong support for the automation and intelligence of wireless transmission and reception testing tasks, ultimately improving the overall efficiency and quality assurance level of wireless product testing. Therefore, this invention provides a wireless transmission and reception testing method that reduces testing equipment and labor costs by using standard TX / RX and host computer software, as well as a simplified testing process. Dynamic threshold calculation, real-time RSSI monitoring, and color-coded display functions accelerate testing speed and improve testing efficiency. Dynamic threshold calculation and environmental noise compensation mechanisms effectively reduce the impact of environmental noise and individual device differences on test results, improving test accuracy and reliability. This invention provides a low-cost, high-efficiency, high-precision, and intelligent wireless transmission and reception testing solution, solving the drawbacks of existing testing methods, such as expensive testing equipment, cumbersome processes, and the inaccuracy and lack of intuitiveness of fixed threshold testing methods. Attached Figure Description

[0067] Figure 1 This is a flowchart illustrating the steps of a wireless transmission and reception testing method.

[0068] Figure 2 This is a detailed flowchart illustrating the implementation steps of step S4 in this invention.

[0069] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0070] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0071] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0072] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0073] To achieve the above objectives, please refer to Figures 1 to 2 A wireless transmission and reception test method includes the following steps:

[0074] Step S1: Set the spectrum range for the standard RX to obtain spectrum range configuration data; sample environmental noise based on the spectrum range configuration data, extract noise features, and obtain environmental noise feature vectors; generate environmental noise spectrum based on the environmental noise feature vectors and the spectrum range configuration data to obtain environmental noise feature spectrum.

[0075] Step S2: Calculate the adaptive TX power threshold based on the environmental noise characteristic spectrum and the preset signal-to-noise ratio margin parameters; collect the TX signal strength of the TX under test to obtain the measured TX signal strength data; dynamically determine the measured TX signal strength data and the adaptive TX power threshold, and store the test data to obtain the TX power test data record.

[0076] Step S3: Set the starting power and step size through the upper unit to obtain the gradient scan configuration parameters; monitor the receiving status of the RX under test to obtain the receiving status monitoring command; perform RX sensitivity test on the RX under test according to the receiving status monitoring command to obtain the raw RX sensitivity data; generate the sensitivity gradient curve according to the raw RX sensitivity data to obtain the RX sensitivity gradient curve; store the test data according to the RX sensitivity gradient curve to obtain the RX sensitivity test data record.

[0077] Step S4: Perform performance parameter statistical analysis on the TX power test data records and RX sensitivity test data records, and conduct quality trend evaluation to obtain performance parameter statistics and quality trend indicators; detect abnormal test results based on performance parameter statistics and quality trend indicators to obtain abnormal alarm information; perform dynamic threshold self-optimization adjustment on the adaptive TX power threshold to obtain the optimized dynamic TX power threshold; perform gradient step self-optimization adjustment on the gradient scan configuration parameters to obtain the optimized gradient scan step value; generate a self-optimized parameter set based on the optimized dynamic TX power threshold and the optimized gradient scan step value to obtain a self-optimized test parameter set; update and apply the parameters of the self-optimized test parameter set to obtain parameter update instructions; encapsulate the test data of the self-optimized test parameter set in a structured manner to obtain a structured test data packet; use the abnormal alarm information, parameter update instructions, and structured test data packet to realize the wireless transmission and reception test task.

[0078] In this embodiment of the invention, reference is made to Figure 1 The diagram shown is a flowchart illustrating the steps of the wireless transmission and reception testing method of the present invention. In this example, the wireless transmission and reception testing method includes the following steps:

[0079] Step S1: Set the spectrum range for the standard RX to obtain spectrum range configuration data; sample environmental noise based on the spectrum range configuration data, extract noise features, and obtain environmental noise feature vectors; generate environmental noise spectrum based on the environmental noise feature vectors and the spectrum range configuration data to obtain environmental noise feature spectrum.

[0080] In this embodiment of the invention, the host computer first receives the spectrum range configuration through the user interface and sends parameters such as the start frequency, end frequency, and step value to the standard RX via serial port commands to complete the spectrum range setting. Based on the received spectrum range configuration, the standard RX performs environmental noise sampling, performing multiple RSSI samples at each frequency point and averaging them to form raw noise sampling data, which is then transmitted to the host computer via serial port. The host computer preprocesses the received raw noise sampling data, including outlier removal and moving average filtering, to obtain preprocessed noise data. The host computer performs frequency domain analysis on the preprocessed noise data, such as FFT transformation, to extract noise feature vectors, including statistical features and frequency domain information such as average noise power, noise power standard deviation, and average power values ​​of the main noise frequency bands. Simultaneously, the host computer sends self-calibration commands to the standard TX and standard RX, which respectively perform transmitter loopback calibration and receiver loopback calibration to obtain their respective calibration parameters. Finally, the host computer integrates the environmental noise feature vectors and spectrum range configuration data to generate an environmental noise feature spectrum, which is displayed on the user interface in the form of a spectrum graph and a data table.

[0081] Step S2: Calculate the adaptive TX power threshold based on the environmental noise characteristic spectrum and the preset signal-to-noise ratio margin parameters; collect the TX signal strength of the TX under test to obtain the measured TX signal strength data; dynamically determine the measured TX signal strength data and the adaptive TX power threshold, and store the test data to obtain the TX power test data record.

[0082] In this embodiment of the invention, the host computer first loads the environmental noise feature spectrum generated in step S1, extracts the noise power spectral density data within the operating frequency band of the U-band microphone under test, and calculates the average noise power of this frequency band as the frequency band noise floor data. The system reads the preset signal-to-noise ratio margin parameter and calculates the minimum received power threshold in combination with the frequency band noise floor data. The system obtains the specification parameters of the standard RX and, based on the sensitivity compensation value and the minimum received power threshold of the standard RX, deduces the adaptive TX power threshold, which takes into account environmental noise and receiver characteristics. The host computer sends a transmit command to the TX under test, initiating the transmission signal of the TX under test. The standard RX collects the measured TX signal strength data of the TX under test and transmits the data to the host computer. The host computer performs statistical analysis on the measured TX signal strength data, calculates the average measured TX signal strength, and dynamically judges it against the adaptive TX power threshold to obtain the TX power test result (PASS / NG). Finally, the system stores the adaptive TX power threshold, the measured TX signal strength data, and the TX power test result in the local test database, generating a TX power test data record.

[0083] Step S3: Set the starting power and step size through the upper unit to obtain the gradient scan configuration parameters; monitor the receiving status of the RX under test to obtain the receiving status monitoring command; perform RX sensitivity test on the RX under test according to the receiving status monitoring command to obtain the raw RX sensitivity data; generate the sensitivity gradient curve according to the raw RX sensitivity data to obtain the RX sensitivity gradient curve; store the test data according to the RX sensitivity gradient curve to obtain the RX sensitivity test data record.

[0084] In this embodiment of the invention, the tester sets the starting power, power step value, and scan termination condition for gradient scanning through the host computer user interface, forming gradient scanning configuration parameters. The host computer sets the operating frequency of the standard TX according to the spectrum range configuration data and the starting transmit power of the standard TX according to the gradient scanning configuration parameters, while simultaneously sending a receive status monitoring command to the RX under test. At each power gradient point, the standard TX maintains its current transmit power, and the RX under test collects receive status data, including RSSI and PER values, and transmits the data to the host computer, which records it as single-point gradient test data. The host computer attenuates the transmit power of the standard TX according to the power step value and repeats the data acquisition process until the scan termination condition is met. After the scan is completed, the host computer integrates all single-point gradient test data to form raw RX sensitivity data. Based on the raw RX sensitivity data, the host computer generates a power-RSSI curve and a power-PER curve, and marks the sensitivity threshold power value on the power-PER curve, forming the RX sensitivity gradient curve. Finally, the system stores the gradient scanning configuration parameters, raw RX sensitivity data, and RX sensitivity gradient curve in the local test database, generating RX sensitivity test data records.

[0085] Step S4: Perform performance parameter statistical analysis on the TX power test data records and RX sensitivity test data records, and conduct quality trend evaluation to obtain performance parameter statistics and quality trend indicators; detect abnormal test results based on performance parameter statistics and quality trend indicators to obtain abnormal alarm information; perform dynamic threshold self-optimization adjustment on the adaptive TX power threshold to obtain the optimized dynamic TX power threshold; perform gradient step self-optimization adjustment on the gradient scan configuration parameters to obtain the optimized gradient scan step value; generate a self-optimized parameter set based on the optimized dynamic TX power threshold and the optimized gradient scan step value to obtain a self-optimized test parameter set; update and apply the parameters of the self-optimized test parameter set to obtain parameter update instructions; encapsulate the test data of the self-optimized test parameter set in a structured manner to obtain a structured test data packet; use the abnormal alarm information, parameter update instructions, and structured test data packet to realize the wireless transmission and reception test task.

[0086] In this embodiment of the invention, the host computer loads historical test data, including environmental noise characteristic spectrum, TX power test data records, and RX sensitivity test data records, forming a historical test dataset. The system performs performance parameter statistical analysis on the historical test dataset, calculating statistics such as TX power PASS rate, RX sensitivity threshold, and environmental noise power. Based on the performance parameter statistics, the system performs quality trend evaluation, obtaining quality trend indicators, such as PASS rate trend and sensitivity threshold control chart. The system detects abnormal test results based on the performance parameter statistics and quality trend indicators, generating abnormal alarm information. The system dynamically adjusts the adaptive TX power threshold based on recent TX yield deviation and recent changes in environmental noise level, obtaining an optimized dynamic TX power threshold. The system dynamically adjusts the gradient scan step value based on recent RX sensitivity test data volatility and RX performance stability, obtaining an optimized gradient scan step value. The system integrates the optimized dynamic TX power threshold and the optimized gradient scan step value into a self-optimized test parameter set. The system updates and applies the self-optimizing test parameter set, and encapsulates all test data, including environmental noise characteristic spectrum, calibration parameters, test data records, and the self-optimizing test parameter set, into a structured test data package for subsequent wireless transmission and reception test tasks.

[0087] Preferably, step S1 includes the following steps:

[0088] Step S11: The host computer sets the spectrum range for the standard RX to obtain spectrum range configuration data;

[0089] Step S12: The standard RX performs environmental noise sampling based on the spectral range configuration data to obtain raw noise sampling data; the raw noise sampling data is preprocessed to obtain preprocessed noise data.

[0090] Step S13: The host computer receives preprocessed noise data from the standard RX, extracts noise features, and obtains the environmental noise feature vector; the host computer sends self-calibration commands to the standard TX and standard RX respectively, and obtains self-calibration configuration commands.

[0091] Step S14: The standard TX performs transmitter loopback calibration according to the self-calibration configuration command to obtain the transmit calibration parameters; the standard RX performs receiver loopback calibration according to the self-calibration configuration command to obtain the receive calibration parameters.

[0092] Step S15: The host computer generates an environmental noise spectrum based on the environmental noise feature vector and the spectral range configuration data, thus obtaining the environmental noise feature spectrum.

[0093] In this embodiment of the invention, the host computer first starts the test control program, whose user interface presents spectrum range configuration options. The tester inputs the operating frequency range of the UHF microphone under test through the user interface; for example, the starting frequency is set to 470MHz and the ending frequency to 960MHz. The frequency step value is set to 1MHz, which determines the resolution of the spectrum scan. The host computer encodes these configuration parameters, including the starting frequency, ending frequency, and frequency step value, into control commands conforming to the standard RX communication protocol. For example, a predefined binary data packet format is used, with the packet header containing a command type identifier and the packet body containing the encoded spectrum range parameters. The host computer sends this control command to the MCU of the standard RX via a USB-to-serial module using serial communication. After receiving and parsing the command, the MCU extracts the spectrum range parameters and stores them in the standard RX's memory for subsequent environmental noise spectrum scanning operations. At this point, the spectrum range configuration data is generated and stored in both the standard RX and the host computer system, providing parameter basis for subsequent environmental scanning.

[0094] After receiving the spectrum range configuration data, the MCU of the standard RX controls its internal RF front-end to enter spectrum scanning mode. The frequency synthesizer of the standard RX begins scanning at the set starting frequency, for example, 470MHz. At each scanning frequency point, such as 470MHz, 471MHz, 472MHz up to 960MHz, the RF receiving circuit of the standard RX performs multiple rapid RSSI (Received Signal Strength Indication) samples, for example, 10 samples at each frequency point. The MCU performs an arithmetic average of these 10 sample values ​​to reduce random noise interference, obtaining the average RSSI value for that frequency point. The standard RX sends the average RSSI value of each frequency point and the corresponding frequency information to the host computer via serial port according to a predefined serial data frame format, such as CSV or JSON. After receiving the raw noise sampling data, the host computer first performs an outlier removal operation, for example, using the 3σ principle, removing data points that deviate significantly from the average RSSI value of that frequency point by more than 3 standard deviations. These data points are caused by transient strong interference. Subsequently, the host computer performs moving average filtering on the RSSI data after removing outliers. For example, a 5-point moving average filter is used to further smooth the noisy data, reduce the impact of random noise, and obtain more stable preprocessed noise data, which is then stored in the host computer's memory in preparation for subsequent noise feature extraction.

[0095] After receiving the preprocessed noise data, the host computer initiates the noise feature extraction module. This module first performs frequency domain analysis on the preprocessed noise data. For example, it uses a Fast Fourier Transform (FFT) algorithm to convert the time-domain RSSI data to the frequency domain, obtaining the noise power spectral density distribution. By analyzing the power spectral density, the main frequency bands and components of the environmental noise are identified. Furthermore, the noise feature extraction module calculates the statistical characteristics of the preprocessed noise data. For example, within the set UHF microphone operating frequency range, it calculates statistics such as average noise power, standard deviation of noise power, and maximum noise power value. These statistics and frequency domain analysis results are combined into an environmental noise feature vector, such as a data vector containing average noise power, standard deviation of noise power, and average power values ​​for several main noise frequency bands. The host computer then generates a self-calibration configuration command, which includes the target transmit power value of the standard TX (e.g., 10 dBm) and the target receive sensitivity value of the standard RX (e.g., -95 dBm). This self-calibration configuration command is then sent via serial port to the MCUs of the standard TX and standard RX devices, respectively, initiating the self-calibration process for the standard devices.

[0096] After receiving the self-calibration configuration command, the MCU of the standard TX controls the internal RF switch to switch to the loopback calibration path, coupling the output of the power amplifier (PA) to the input of the low-noise amplifier (LNA) through an attenuator, forming an internal closed loop. The MCU controls the digitally controlled attenuator to adjust the transmit power and uses an internal power detector to measure the received power on the loopback path. Through a PID closed-loop control algorithm, the MCU continuously adjusts the PA gain and the attenuation value of the digitally controlled attenuator, gradually bringing the actual transmit power closer to the target transmit power value set in the self-calibration configuration command, such as 10 dBm. When the transmit power stabilizes near the target value and the error is within the allowable range, such as ±0.1 dBm, the transmitter loopback calibration is complete. The MCU stores the PA gain calibration parameters and the digitally controlled attenuator calibration parameters at this time in the non-volatile memory of the standard TX as transmit calibration parameters.

[0097] After receiving the self-calibration configuration command, the MCU of the standard RX controls the internal signal generator to produce a calibration signal of known power, such as -60dBm, and injects this signal into the input of the LNA, forming a receiver loopback calibration path. The MCU controls the digitally controlled attenuator to adjust the received signal strength and monitors the RSSI value and bit error rate (BER, if applicable) of the received signal. By adjusting the LNA gain and the attenuation value of the digitally controlled attenuator, the receiver sensitivity of the standard RX gradually approaches the target sensitivity value set in the self-calibration configuration command, such as -95dBm. When the receiver sensitivity reaches the target value and the BER is lower than a preset threshold, the receiver loopback calibration is complete. The MCU stores the LNA gain calibration parameters and the digitally controlled attenuator calibration parameters at this time in the non-volatile memory of the standard RX as receiver calibration parameters.

[0098] After receiving the environmental noise feature vector sent by the standard RX and the spectrum range configuration data set in step S11, the host computer starts the environmental noise spectrum generation module. The environmental noise spectrum generation module integrates the various feature parameters in the environmental noise feature vector, such as average noise power, noise power standard deviation, and average power values ​​of the main noise frequency bands, with the frequency range information in the spectrum range configuration data. Based on the integrated data, the host computer program generates an environmental noise spectrum graph, which is displayed intuitively on the user interface with frequency as the horizontal axis and noise power spectral density as the vertical axis. Different colors or line styles can be used to distinguish the noise intensity levels of different frequency bands. Simultaneously, the host computer program also displays the numerical feature parameters in the environmental noise feature vector, such as the average noise power value, in a data table format on the user interface for easy viewing by testers.

[0099] Preferably, step S2 includes the following steps:

[0100] Step S21: The upper unit extracts the frequency band noise floor from the ambient noise feature spectrum to obtain the frequency band noise floor data; the minimum received power is calculated based on the frequency band noise floor data and the preset signal-to-noise ratio margin parameter to obtain the minimum received power threshold.

[0101] Step S22: Obtain standard RX specification parameters; perform standard receiver sensitivity compensation based on standard RX specification parameters to obtain receiver sensitivity compensation value;

[0102] Step S23: Based on the receiver sensitivity compensation value and the minimum received power threshold, perform dynamic transmit power threshold back-calculation to obtain the adaptive TX power threshold;

[0103] Step S24: The host unit starts the transmitter under test (TX) and obtains the transmission command for the TX under test; the standard RX collects the signal strength according to the transmission command for the TX under test and obtains the measured TX signal strength data.

[0104] Step S25: The upper control unit dynamically determines the measured TX signal strength data and the adaptive TX power threshold to obtain the TX power test results;

[0105] Step S26: Store the adaptive TX power threshold, measured TX signal strength data, and TX power test results in test data to obtain TX power test data records.

[0106] In this embodiment of the invention, the host computer program first loads the environmental noise characteristic spectrum data generated in step S15. The program analyzes this spectrum data and, based on the pre-set operating frequency band of the U-band microphone under test, for example, a frequency band with a center frequency of 500MHz and a bandwidth of 20MHz, extracts the noise power spectral density data within the 20MHz bandwidth range from the environmental noise characteristic spectrum. To obtain the frequency band noise floor data, the program calculates the average value of the noise power spectral density at all frequency points within the frequency band and converts the average value to dBm units as the noise floor power level of the frequency band. Subsequently, the program reads the preset signal-to-noise ratio (SNR) margin parameter, for example, setting the SNR margin to 20dB. This parameter represents the degree to which the expected received signal power exceeds the noise floor. The minimum received power threshold is calculated by adding the frequency band noise floor data to the SNR margin parameter. For example, if the frequency band noise floor is -90dBm and the SNR margin is 20dB, then the minimum received power threshold is calculated to be -70dBm. The calculated minimum received power threshold is stored in the host computer's memory as the basis for subsequent dynamic transmit power threshold calculations.

[0107] The host computer program retrieves the specifications of the standard RX from a pre-configured database or configuration file. These specifications include the nominal receiver sensitivity of the standard RX, such as the minimum signal power that the standard RX can reliably receive at a specific bit error rate (BER), assuming a nominal sensitivity of -95dBm. To compensate for sensitivity deviations in practical applications, a receiver sensitivity compensation mechanism is introduced. The compensation value is determined based on the standard RX's calibration report or historical test data. For example, if calibration data indicates that the actual sensitivity of the standard RX degrades by 2dB compared to the nominal value, the receiver sensitivity compensation value is set to +2dB. If the actual sensitivity of the standard RX is better than the nominal value, the compensation value can be set to a negative value or zero. The receiver sensitivity compensation value is used for subsequent dynamic transmit power threshold back-calculation to ensure the accuracy of the test results. The compensation value is stored in the host computer's memory for later retrieval.

[0108] The host computer program receives the minimum received power threshold calculated in step S21, for example -70dBm, and the receiver sensitivity compensation value determined in step S22, for example +2dB. The reverse calculation process for the dynamic transmit power threshold is as follows: First, the minimum received power threshold is added to the receiver sensitivity compensation value to obtain the compensated minimum received power threshold, for example -70dBm + 2dB = -68dBm. Considering the fluctuations in the actual test environment and to ensure test margin, a safety margin, for example 3dB, is added to the compensated minimum received power threshold. Therefore, the adaptive TX power threshold is finally calculated as the compensated minimum received power threshold plus the safety margin, for example -68dBm + 3dB = -65dBm. This adaptive TX power threshold represents the minimum transmit power level that the TX under test needs to achieve under the current environmental noise conditions to ensure that the standard RX can reliably receive signals and meet the preset SNR requirements. The calculated adaptive TX power threshold is stored in the host computer memory for subsequent dynamic TX power determination.

[0109] The host computer program generates a transmission command for the target transistor (TX), which includes parameters such as the TX's operating frequency and modulation scheme. For example, the operating frequency is set to 500MHz and the modulation scheme to GFSK. The host computer sends this transmission command to the TX's MCU via a USB-to-serial module using serial communication. After receiving and parsing the transmission command, the TX's MCU controls its RF transmission circuit to start the transmission function, continuously transmitting RF signals according to the operating frequency and modulation scheme set in the command. Simultaneously, the standard RX is in receive mode, continuously monitoring the RF signal transmitted by the TX. After receiving the signal from the TX, the standard RX's RF receiver front-end circuit demodulates the signal and measures the RSSI (Received Signal Strength Indication) value. The standard RX's MCU periodically reads the RSSI register value and converts it into an actual received power value (in dBm). The conversion relationship is determined according to the standard RX's calibration data or specifications. The standard RX then sends the converted received power value to the host computer in real time via serial port. The host computer program receives and buffers the received power data sent by the standard RX, and continuously collects received power data for a period of time, such as 5 seconds, to provide a data basis for subsequent signal strength assessment.

[0110] After receiving the measured TX signal strength data collected and transmitted by the standard RX, the host computer program first performs statistical analysis on the collected multi-point received power data. For example, it calculates the average value of the received power data over this period to eliminate the influence of instantaneous fluctuations, obtaining the average measured TX signal strength. Then, the host computer program compares this average measured TX signal strength with the adaptive TX power threshold calculated in step S23. The dynamic judgment logic is as follows: if the average measured TX signal strength is greater than or equal to the adaptive TX power threshold, the transmit power of the TX under test is judged to be qualified (PASS). If the average measured TX signal strength is less than the adaptive TX power threshold, the transmit power of the TX under test is judged to be unqualified (NG). To improve the robustness of the judgment, a small judgment margin can be set; for example, only when the average measured TX signal strength is significantly higher than the threshold, such as higher than 3dB, is it judged as PASS. The final TX power test result (PASS or NG) is recorded in the host computer memory and used for user interface display and test report generation.

[0111] After completing the dynamic determination of TX power, the host computer program starts the test data storage module. This module integrates the key data items of this TX power test, including the adaptive TX power threshold calculated in step S23, the measured TX signal strength data collected in step S24 (e.g., average, maximum, minimum, etc.), the TX power test result (PASS or NG) obtained in step S25, and information such as the test timestamp and the unique identifier of the TX under test (e.g., serial number SN), into a complete TX power test data record. The host computer program stores this data record in the local test database according to a predefined database schema or data format, such as structured JSON or CSV format. The database can be a relational database such as MySQL, or a non-relational database such as MongoDB, choosing the appropriate database type based on the data volume and query requirements. The storage operation includes establishing a database connection, constructing SQL insert statements (for relational databases) or calling the database API (for non-relational databases), and writing the data into a pre-created data table or data set. To ensure data integrity, the data storage operation should have a transaction processing mechanism to ensure the atomicity of data writing. The stored TX power test data records will be used for subsequent quality traceability, data analysis, and report generation, providing data support for product quality management. After data storage is completed, the host computer program can update the user interface, display the PASS / NG result of this TX power test, and provide feedback on the storage status of the test data records to the testers, for example, by displaying a "Data has been successfully saved" message, thus completing the operation process of step S26.

[0112] Preferably, step S3 includes the following steps:

[0113] Step S31: Set the starting power and step size through the upper camera to obtain the gradient scan configuration parameters;

[0114] Step S32: Set the standard transmitter power of the standard TX according to the spectrum range configuration data to obtain the current power value of the standard TX; monitor the reception status of the RX under test to obtain the reception status monitoring command;

[0115] Step S33: Perform RX sensitivity testing on the RX under test according to the current power value of the standard TX and the receiving status monitoring command to obtain the raw RX sensitivity data;

[0116] Step S34: Generate the sensitivity gradient curve based on the original RX sensitivity data to obtain the RX sensitivity gradient curve;

[0117] Step S35: Store the gradient scan configuration parameters, raw RX sensitivity data, and RX sensitivity gradient curve as test data to obtain RX sensitivity test data records.

[0118] The host computer program initiates the RX sensitivity gradient scan test procedure. The tester configures the gradient scan parameters through the host computer user interface. The initial transmit power is set to a relatively high value, such as 0dBm, to ensure that the RX under test can stably receive signals in the initial stage of the scan. The power step value is set to a small value, such as 1dBm, to achieve fine sensitivity threshold detection. The scan termination condition can be set to the minimum transmit power, such as -100dBm, or to the receive packet error rate (PER) reaching a preset threshold, such as 10%. These configuration parameters, including the initial transmit power, power step value, and scan termination condition, are integrated by the host computer program to form a gradient scan configuration parameter set. This parameter set is stored in the host computer's memory in the form of a data structure and is called during subsequent gradient scans to control the transmit power adjustment and scan procedure of the standard TX. The proper setting of the gradient scan configuration parameters directly affects the accuracy and test time of the RX sensitivity test.

[0119] The host computer program first extracts the operating frequency of the standard TX (e.g., 500MHz) from the spectrum range configuration data generated in step S11. Then, the host computer encodes the initial transmit power value (e.g., 0dBm) from the gradient scan configuration parameters, along with the operating frequency information, into a standard TX power setting command. This command is sent to the MCU of the standard TX via a USB-to-serial module using serial communication. Upon receiving the power setting command, the MCU of the standard TX controls its RF transmission circuit, setting the transmit frequency to 500MHz and adjusting the transmit power to 0dBm. The power control loop of the standard TX ensures accurate and stable transmit power. Simultaneously, the host computer program generates a receive status monitoring command and sends it to the MCU of the RX under test (DUT) via a USB-to-serial module using serial communication. Upon receiving the receive status monitoring command, the MCU of the DUT activates the receive status monitoring function, continuously monitoring the RF signal from the standard TX and preparing to feed back the receive status data to the host computer. The current standard TX power value, i.e. the set 0dBm, is recorded in the host computer's memory as the starting power point for gradient scanning.

[0120] After the standard TX transmits a signal at the set initial power and the RX under test (DUT) enters the receive status monitoring mode, the host computer program begins executing the RX sensitivity test loop. At each power gradient point, the host computer first instructs the standard TX to maintain the current power transmission state and instructs the DUT to continuously receive signals and collect receive status data. While receiving signals, the MCU of the DUT periodically measures the Received Signal Strength Indication (RSSI) value and Packet Error Rate (PER). The RSSI value reflects the power level of the received signal, and the PER reflects the reliability of packet reception. The DUT sends the collected RSSI and PER data to the host computer in real time, according to a predefined serial port data frame format, for example, sending a data frame containing the average RSSI and PER values ​​once per second. The host computer program receives and records this data, forming single-point gradient test data. This data contains the receive performance indicators of the DUT under the current standard TX transmit power and is part of the raw RX sensitivity data. After completing single-point data acquisition, the host computer program enters the power step attenuation control loop, preparing for the next power point test.

[0121] After completing the entire power gradient scan process, the host computer program terminates the scan when the standard TX transmit power drops to the minimum value set by the scan termination condition, or when the receiving performance of the RX under test deteriorates to the preset PER threshold. At this point, the host computer's memory has accumulated raw RX sensitivity data, which includes the RSSI and PER values ​​of the RX under test at different standard TX transmit powers. The host computer program then initiates the sensitivity gradient curve generation module. This module first processes and analyzes the raw RX sensitivity data, for example, calculating the average RSSI and average PER values ​​at each power point. Then, the program plots the RX sensitivity gradient curve with the standard TX transmit power as the horizontal axis and the average RSSI or average PER value of the RX under test as the vertical axis. Power-RSSI and power-PER curves can be generated simultaneously to evaluate RX sensitivity characteristics from different dimensions. To more intuitively display the sensitivity threshold, the program can mark the standard TX transmit power value corresponding to when the PER value reaches a preset threshold (e.g., 10%) on the power-PER curve, serving as a reference point for the sensitivity threshold. The generated RX sensitivity gradient curve is displayed graphically on the host computer user interface and stored in the host computer memory in the form of a data table, serving as an important component of the RX sensitivity test results.

[0122] After the RX sensitivity gradient curve is generated, the host computer program starts the RX sensitivity test data storage module. This module integrates the key data items of this RX sensitivity gradient scan test, including the gradient scan configuration parameters set in step S31, the raw RX sensitivity data collected in step S33 (including RSSI and PER data for all power points), the RX sensitivity gradient curve data (curve data point set or curve image file) generated in step S34, as well as the test timestamp, the unique identifier of the RX under test (e.g., serial number SN code), etc., to form a complete RX sensitivity test data record. The host computer program stores this data record in the local test database according to a predefined database schema or data format, such as structured JSON or CSV format. The storage operation includes establishing a database connection, constructing SQL insert statements (for relational databases) or calling the database API (for non-relational databases), to write the data into a pre-created data table or data set. The stored RX sensitivity test data record will be used for subsequent quality traceability, data analysis, and report generation, providing data support for product acceptance performance evaluation. After data storage is completed, the host computer program can update the user interface, display the RX sensitivity gradient curve, and provide feedback on the storage status of the test data record to the tester, for example, displaying the prompt message "RX sensitivity test data has been successfully saved" to complete the operation process of step S35.

[0123] Preferably, step S33 includes the following steps:

[0124] Step S331: Based on the current power value of the standard TX and the receiving status monitoring command, perform gradient point data acquisition on the RX under test to obtain single-point gradient test data;

[0125] Step S332: Perform power step decay control based on gradient scan configuration parameters and the current power value of standard TX to obtain the next power value of standard TX;

[0126] Step S333: Determine the scan termination condition based on the gradient scan configuration parameters, the next power value of the standard TX, and the single-point gradient test data to obtain the scan termination flag;

[0127] Step S334: Based on the scan termination flag, integrate the gradient data of the single-point gradient test data to obtain the raw RX sensitivity data.

[0128] In this embodiment of the invention, after the standard TX stabilizes its transmission signal according to the current power value set in step S32, the host computer program controls the data acquisition module to start. The data acquisition module instructs the RX under test (DUT) to continuously receive the radio frequency signal from the standard TX and perform data sampling for a predetermined time period, for example, the continuous acquisition time is set to 2 seconds. During the data sampling period, the MCU of the DUT reads the value of the Received Signal Strength Indicator (RSSI) register at a fixed sampling frequency, for example, 10 times per second, and counts the number of erroneous packets received to calculate the Packet Error Rate (PER). During the 2-second sampling time, the DUT accumulates multiple RSSI sample values ​​and packet error statistics. After the data acquisition is completed, the MCU of the DUT performs an arithmetic average of all RSSI sample values ​​acquired during this period to obtain the average RSSI value, and divides the counted number of erroneous packets by the total number of transmitted packets to calculate the average PER value. The average RSSI value and average PER value are combined into single-point gradient test data, which reflects the receiving performance level of the RX under test under the current standard TX transmit power. This data is stored in the host computer memory in the form of a data structure to provide a basis for subsequent sensitivity threshold determination.

[0129] After completing the single-point gradient test data acquisition, the host computer program executes a power step-attenuation control operation. The program first reads the preset power step value, for example, 1dBm, from the gradient scan configuration parameters. Then, the program reads the current transmit power value of the standard TX from memory, for example, -30dBm. The next power value of the standard TX is calculated by subtracting the power step value from the current power value; for example, -30dBm - 1dBm = -31dBm. The calculated next power value, -31dBm, is determined as the transmit power level that the standard TX needs to be set at the next gradient scan point. The host computer program encodes this next power value, along with the standard TX's operating frequency information, into a new standard TX power setting command. This command is sent to the standard TX's MCU via a USB-to-serial module using serial communication, controlling the standard TX to adjust its transmit power to the new value, preparing for the test data acquisition at the next gradient point.

[0130] After the standard TX transmit power is adjusted to the next power value, the host computer program executes a scan termination condition judgment. The judgment is based on the termination condition set in the gradient scan configuration parameters and the single-point gradient test data collected in step S331. The termination condition can be set as a minimum transmit power threshold, such as -90dBm, or a receive packet error rate (PER) threshold, such as 10%. The host computer program first checks whether the next power value of the standard TX is already lower than the minimum transmit power threshold. If the next power value is already lower than the minimum transmit power threshold, the scan termination condition is met. Alternatively, the program analyzes the PER value in the single-point gradient test data collected in step S331. If the PER value exceeds a preset PER threshold, such as exceeding 10%, the scan termination condition is also met, indicating that the receiving performance of the RX under test has deteriorated to an unacceptable level. If any of the above termination conditions are met, the host computer program generates a scan termination flag, for example, by setting a Boolean variable to TRUE, indicating that the gradient scan process should stop. The scan termination flag is used to control the end of the gradient scan cycle.

[0131] After completing the acquisition of single-point gradient test data and the determination of scan termination conditions at each gradient point, the host computer program associates the acquired single-point gradient test data with the corresponding standard TX transmit power value and integrates these data according to the power gradient scan order to form the raw RX sensitivity data. The raw RX sensitivity data can be stored using data structures such as data lists or data dictionaries. For example, in a data list, each element can be a data structure containing the standard TX transmit power value, average RSSI value, and average PER value. In a data dictionary, the standard TX transmit power value can be used as the key, and the corresponding average RSSI value and average PER value can be used as the value. In this way, all single-point gradient test data acquired throughout the gradient scan process are effectively organized and stored, forming a complete raw RX sensitivity dataset. This raw data will be used in subsequent data analysis and processing stages such as sensitivity gradient curve generation and sensitivity threshold determination, and also provides detailed data records for the traceability and analysis of test results.

[0132] Preferably, step S34 includes the following steps:

[0133] Step S341: Extract power-RSSI data pairs from the raw RX sensitivity data and generate power-RSSI curve data to obtain the power RSSI curve data point set;

[0134] Step S342: Extract power-PER / BER data pairs from the raw RX sensitivity data and generate power-PER / BER curve data to obtain the power error rate curve data point set;

[0135] Step S343: Determine the sensitivity threshold value for the power error rate curve data point set to obtain the sensitivity threshold power value;

[0136] Step S344: Generate a sensitivity gradient curve based on the power RSSI curve data point set and the power error rate curve data point set according to the sensitivity threshold power value, and mark the threshold to obtain the RX sensitivity gradient curve.

[0137] In this embodiment of the invention, the host computer program receives the raw RX sensitivity data integrated in step S334. This data is stored in a structured form, containing multiple data points. Each data point corresponds to a standard TX transmit power value and the average RSSI value and average PER value measured by the RX under test at that power. To generate a power-RSSI curve, the data processing module first extracts the "standard TX transmit power value" and the corresponding "average RSSI value" of all data points from the raw RX sensitivity data, forming a series of power-RSSI data pairs. For example, if the raw data contains 10 power gradient points, 10 power-RSSI data pairs are extracted. These data pairs are organized into a power-RSSI curve data point set, for example, using a list data structure, where each element in the list is a coordinate point object, containing an X coordinate (standard TX transmit power value) and a Y coordinate (average RSSI value). The power-RSSI curve data point set is a discrete set of data points used for subsequent curve plotting and data analysis, and is stored in the host computer's memory.

[0138] The host computer program processes the raw RX sensitivity data obtained in step S334 again. Similar to step S341, in order to generate the power-PER / BER curve, the data processing module extracts the "standard TX transmit power value" and the corresponding "average PER value" for all data points from the raw RX sensitivity data, forming a series of power-PER / BER data pairs. If the RX under test outputs the bit error rate (BER) instead of the packet error rate (PER) in the sensitivity test, then the power-BER data pairs are extracted. The power-PER / BER data pairs are used to describe the data reception error rate of the RX under test at different transmit powers. These data pairs are organized into a power error rate curve data point set. The data structure can also be a list, where each element is a coordinate point object containing the X coordinate (standard TX transmit power value) and the Y coordinate (average PER or BER value). The power error rate curve data point set is the basic data for generating the power error rate curve and is stored in the host computer's memory for curve plotting and sensitivity threshold determination.

[0139] The host computer program receives the power error rate curve data point set generated in step S342 and starts the sensitivity threshold determination module. The sensitivity threshold determination module has a preset threshold for PER or BER; for example, the PER threshold is set to 10%. The module traverses the power error rate curve data point set, searching for data points where the PER value first exceeds the preset threshold. The standard TX transmit power value corresponding to this data point is determined as the sensitivity threshold power value. For example, if in the power-PER curve data point set, the PER value is 8% when the transmit power is -75dBm, and becomes 12% when the transmit power decreases to -76dBm, then the sensitivity threshold power value is determined to be -76dBm. If there are no data points in the power error rate curve data point set where the PER value exceeds the threshold, the sensitivity threshold determination fails, or the minimum transmit power value is used as an approximate sensitivity threshold value. The sensitivity threshold power value represents the minimum receive power level at which the RX under test can reliably receive signals and is a key indicator of RX sensitivity performance. The determination result is stored in the host computer's memory for subsequent threshold marking and test report generation.

[0140] The host computer program receives the power RSSI curve data point set generated in step S341, the power error rate curve data point set generated in step S342, and the sensitivity threshold power value determined in step S343. The curve generation module uses a graphics library, such as Matplotlib or Chart.js, to plot curves based on the power RSSI curve data point set and the power error rate curve data point set, respectively. The horizontal axis of the curve represents the standard TX transmit power value, and the vertical axes represent the average RSSI value and the average PER / BER value, respectively. To visually display the sensitivity threshold in the curve, the curve generation module specially marks the points corresponding to the sensitivity threshold power values ​​on the power-PER / BER curve, for example, using red dots or vertical dashed lines. The marked positions correspond to the power points where the PER / BER value reaches the preset threshold. The final generated RX sensitivity gradient curve, including the power-RSSI curve and the power-PER / BER curve, as well as the sensitivity threshold marker, is displayed graphically on the host computer user interface and can be exported as an image file or a vector graphic file. At the same time, the curve data point set and threshold value data are also stored in the host computer memory, forming a complete RX sensitivity gradient curve test result.

[0141] As an example of the present invention, reference is made to... Figure 2 As shown, step S4 in this example includes:

[0142] Step S41: The host computer loads historical test data from the environmental noise characteristic spectrum, TX power test data records, and RX sensitivity test data records to create a historical test dataset.

[0143] In this embodiment of the invention, the host computer program initiates the historical test data loading module. This module first connects to a local test database, which can be a relational database such as PostgreSQL or a non-relational database such as MongoDB. The data loading module retrieves historical test data from the database according to preset data filtering conditions. These filtering conditions can include product model, production batch, and test time range. For example, it can be set to load TX power test data records and RX sensitivity test data records for all "UMic Pro" UHF microphone products within the past month, as well as the environmental noise characteristic spectrum collected during these tests. The retrieved historical test data includes the environmental noise characteristic spectrum data generated in step S1, the TX power test data records generated in step S2, and the RX sensitivity test data records generated in step S3. The data loading module stores these retrieved data according to their data type into different data structures in the host computer's memory. For example, a list is used to store the TX power test results, a dictionary is used to store the RX sensitivity gradient curve data, and a multidimensional array is used to store the environmental noise characteristic spectrum data, ultimately forming a historical test dataset for subsequent performance parameter statistical analysis and self-optimization adjustment modules.

[0144] Step S42: Perform performance parameter statistical analysis on the historical test dataset to obtain performance parameter statistics; evaluate the quality trend based on the performance parameter statistics to obtain quality trend indicators;

[0145] In this embodiment of the invention, the host computer program receives the historical test dataset loaded in step S41 and starts the performance parameter statistical analysis module. This module first performs statistical analysis on the historical TX power test data records, for example, calculating the average, standard deviation, minimum, and maximum values ​​of the TX power pass rate for historical test batches, as well as the average and standard deviation of the measured TX signal strength. For RX sensitivity test data records, the statistical analysis module analyzes the average and standard deviation of the RX sensitivity threshold values ​​for historical test batches, and the histogram of the sensitivity threshold value distribution. For environmental noise characteristic spectrum data, the statistical analysis module calculates the average and standard deviation of the average noise power in the historical test environment, and the statistical characteristics of noise power in a specific frequency band. The calculated performance parameter statistics, such as the average pass rate, the average sensitivity threshold, and the average noise power, are stored in the host computer's memory as basic data for quality trend assessment.

[0146] The quality trend assessment module performs product quality trend analysis based on performance parameter statistics. For example, for the TX power pass rate, the module uses time series analysis methods, such as calculating the moving average pass rate over the past week and month, observing the trend of the pass rate over time, and determining whether the quality is stable or declining. For the RX sensitivity threshold, the module plots control charts, such as X-bar control charts, to monitor whether the fluctuation range of the sensitivity threshold exceeds the control limit, and determines whether the RX sensitivity performance is stable. For the environmental noise level, the module analyzes the trend of the average power of environmental noise over time to determine whether the noise level of the test environment has changed significantly. The results of the quality trend assessment, such as the downward trend of the pass rate, the control chart alarm of the sensitivity threshold, and the upward trend of the environmental noise level, are quantified into quality trend indicators and stored in the host computer memory for subsequent self-optimization adjustments and anomaly alarms.

[0147] Step S43: Based on the performance parameter statistics, quality trend indicators and environmental noise characteristic spectrum, the adaptive TX power threshold is dynamically self-optimized to obtain the optimized dynamic TX power threshold.

[0148] In this embodiment of the invention, the host computer program receives the performance parameter statistics and quality trend indicators generated in step S42, as well as the historical environmental noise characteristic spectrum data loaded in step S41, and starts the dynamic threshold self-optimization adjustment module. This module first analyzes the TX power PASS rate trend in the quality trend indicator. If the PASS rate is consistently lower than the preset target yield, for example, below 95%, and the quality trend indicator shows a downward trend in the PASS rate, it indicates that the current adaptive TX power threshold may be too high, leading to an increased false positive rate. At this time, the self-optimization adjustment module calculates the threshold adjustment amount that needs to be reduced based on the degree of PASS rate deviation; for example, if the PASS rate deviation is large, it is reduced by 0.5 dBm. Simultaneously, the module analyzes the environmental noise level change trend in the quality trend indicator. If the environmental noise level increases consistently, the adaptive TX power threshold needs to be increased to compensate for the impact of increased noise and ensure the rigor of the test. The adjustment amount is proportional to the noise increase; for example, if the noise increases by 1 dBm, the threshold increases by 1 dBm. The final optimized dynamic TX power threshold is obtained by summing the current adaptive TX power threshold with a comprehensive adjustment calculated based on the pass rate deviation and environmental noise changes. The optimized threshold is stored in the host computer's memory for subsequent dynamic TX power determination and is output as part of the self-optimization parameter set.

[0149] Step S44: Adjust the gradient scan configuration parameters by gradient stepping self-optimization based on performance parameter statistics and quality trend indicators to obtain the optimized gradient scan stepping value;

[0150] In this embodiment of the invention, the host computer program receives the performance parameter statistics and quality trend indicators generated in step S42 and initiates the gradient step self-optimization adjustment module. This module first analyzes the standard deviation of the RX sensitivity threshold value in the performance parameter statistics. If the standard deviation is large, it indicates high volatility in the RX sensitivity test results, requiring a reduction in the gradient scan power step value to improve the accuracy of the sensitivity threshold detection. The magnitude of the step value reduction is proportional to the standard deviation of the sensitivity threshold value, and a minimum step value limit is set, for example, 0.5 dBm. Simultaneously, the module analyzes the RX sensitivity performance stability index in the quality trend indicators. If the RX sensitivity performance is stable over a long period with low volatility, the gradient scan power step value can be appropriately increased to reduce the number of scan points, shorten the test time, and improve test efficiency. The magnitude of the step value increase is inversely proportional to the RX sensitivity performance stability index, and a maximum step value limit is set, for example, 2 dBm. The final optimized gradient scan step value is obtained by comprehensively adjusting the sensitivity threshold volatility and RX performance stability. The optimized step value is stored in the host computer's memory for subsequent RX sensitivity gradient scan tests and is output as part of the self-optimizing parameter set.

[0151] Step S45: Generate a self-optimized parameter set by optimizing the dynamic TX power threshold and the gradient scan step value after optimization, and obtain the self-optimized test parameter set;

[0152] In this embodiment of the invention, the host computer program receives the optimized dynamic TX power threshold output in step S43 and the optimized gradient scan step value output in step S44. The self-optimizing parameter set generation module integrates these two optimized parameters, along with other potentially self-optimized test parameters, such as SNR margin, receiver sensitivity compensation value, and scan termination condition, into a data structure to form a self-optimized test parameter set. This parameter set can be structured using JSON or XML format, containing the name and value of each optimized parameter. For example, a JSON-formatted self-optimized parameter set can be represented as: `{"dynamicTxPowerThreshold":-66.5,"gradientStepValue":0.8,"snrMargin":22,"rxSensitivityCompensation":1}`. The self-optimized test parameter set represents the result of the test system's self-learning and self-optimization, containing the current optimal test parameter configuration. This parameter set is stored in the host computer's memory and used to update the test system's operating parameters, guiding subsequent TX power and RX sensitivity test procedures. The self-optimizing test parameter set is also stored in the local test database as part of the test data management, serving as a version record and traceability basis for the test parameters.

[0153] Step S46: The host computer performs structured encapsulation of the environmental noise characteristic spectrum, transmission calibration parameters, reception calibration parameters, TX power test data records, RX sensitivity test data records, and self-optimization test parameter set to obtain a structured test data package;

[0154] In this embodiment of the invention, after completing all testing and self-optimization processes, the host computer program initiates the test data structured encapsulation module. This module integrates and encapsulates various key data generated during the test to form a structured test data package. The encapsulated data includes: environmental noise characteristic spectrum data generated in step S1, standard TX transmission calibration parameters and standard RX reception calibration parameters generated in step S14, TX power test data records generated in step S2, RX sensitivity test data records generated in step S3, and a self-optimized test parameter set generated in step S45. The structured encapsulation uses a predefined data format, such as JSON or XML, to organize different types of data according to a hierarchical structure to form a complete data package. For example, a JSON-formatted structured test data package can contain top-level fields such as "environmentNoiseProfile", "txCalibrationParameters", "rxCalibrationParameters", "txPowerTestRecord", "rxSensitivityTestRecord", and "selfOptimizedParameterSet". Each field corresponds to a type of data, and its value is the specific content of that type of data. The purpose of structured test data packages is to facilitate the storage, transmission, parsing, and reuse of test data. For example, structured test data packages can be stored on a local file system, uploaded to a cloud database, or used to generate test reports. Structured test data packages are the final output of test data management and the foundation for achieving cloud integration and quality traceability of test data. After the data is structurally encapsulated, the testing process ends, and the host computer program can display a test completion message and provide feedback to the testers regarding the storage path or upload status of the structured test data package.

[0155] Preferably, step S43 includes the following steps:

[0156] Step S431: Read the current dynamic TX power threshold of the adaptive TX power threshold to obtain the current dynamic threshold;

[0157] Step S432: Evaluate the recent TX power test yield of the performance parameter statistics to obtain the recent TX yield;

[0158] Step S433: Calculate the threshold adjustment amount based on the yield deviation for the recent TX yield and the preset yield target value to obtain the yield deviation adjustment amount;

[0159] Step S434: Obtain the recent environmental noise level changes from the quality trend indicators and the environmental noise characteristic spectrum to obtain the recent noise change amount;

[0160] Step S435: Calculate the threshold adjustment amount based on the recent noise change amount to obtain the noise change adjustment amount;

[0161] Step S436: Calculate the total adjustment amount by comprehensively adjusting the yield deviation and noise change.

[0162] Step S438: Adjust the dynamic TX power threshold according to the total adjustment amount and the current dynamic threshold to obtain the optimized dynamic TX power threshold.

[0163] In this embodiment of the invention, after the host computer program initiates the dynamic threshold self-optimization adjustment process, it first performs a current dynamic TX power threshold reading operation. The program accesses the memory, which can be a local configuration file, memory variable, or database record on the host computer. The memory stores the adaptive TX power threshold currently being used by the test system, which was set in previous step S43 or during system initialization. The reading operation is implemented by calling the corresponding memory access function, file reading function, or database query statement. The read current dynamic TX power threshold, for example, a value of -67.0 dBm, is loaded into the host computer program's memory variable. This value serves as the starting reference value for dynamic threshold self-optimization adjustment, and subsequent threshold adjustment calculations will be based on this reference value for increment and decrement operations. Successfully reading the current dynamic threshold is a prerequisite step for the subsequent threshold self-optimization adjustment process, ensuring the continuity and traceability of the adjustment process.

[0164] The host computer program receives the performance parameter statistics data generated in step S42. This data includes PASS / NG results from historical TX power tests. The program extracts recent TX power test yield data from the performance parameter statistics data. The "recent" time window can be preset, for example, set to the last 24 hours or the last 1000 completed TX power tests. The yield evaluation operation is achieved by calculating the proportion of TX power tests that passed within the set time window. For example, if 960 out of the last 1000 TX power tests passed, the recent TX yield is calculated as 960 / 1000 = 96%. The calculated recent TX yield, expressed as a percentage or proportion, reflects the overall pass rate of recent TX power tests, providing a quantitative basis for subsequent threshold adjustments based on yield deviation. The evaluated recent TX yield data is stored in the host computer's memory for subsequent steps.

[0165] The host computer program obtains the recent TX yield obtained from step S432, for example, 96%, and reads the preset yield target value. The yield target value is the expected TX power test pass rate, for example, set to 98%. The threshold adjustment calculation based on yield deviation first calculates the deviation between the recent TX yield and the yield target value. The yield deviation is calculated by subtracting the recent TX yield from the yield target value, for example, 98% - 96% = 2%. If the yield deviation is positive, it indicates that the recent yield is lower than the target value, and the dynamic TX power threshold needs to be appropriately reduced to improve the yield. The magnitude of the threshold adjustment is related to the degree of yield deviation, and a proportional coefficient can be set, for example, every 1% yield deviation corresponds to a threshold adjustment of -0.2dBm. Therefore, in this example, the yield deviation adjustment is calculated as 2% * (-0.2dBm / %) = -0.4dBm. If the yield deviation is negative or close to zero, the yield deviation adjustment amount is zero or a small positive value, indicating that no adjustment is needed or the threshold is slightly increased. The calculated yield deviation adjustment amount, in dBm, reflects the threshold adjustment magnitude based on yield feedback and is stored in the host computer's memory.

[0166] The host computer program receives the quality trend index data generated in step S42. This data includes long-term trend information on environmental noise levels and historical environmental noise characteristic spectrum data loaded in step S41. The program then performs a recent environmental noise level change acquisition operation, analyzing the time-series data on average environmental noise power within the quality trend index. The program can calculate the average environmental noise power over a recent period, such as the most recent week or day. Simultaneously, it also reads the average environmental noise power over an earlier period, such as the previous week or day. The recent change in environmental noise level is obtained by calculating the difference between the average environmental noise power values ​​of these two periods. For example, if the average noise power for the most recent week is -92 dBm and the average noise power for the previous week is -95 dBm, the recent noise change is calculated as -92 dBm - (-95 dBm) = +3 dBm, indicating a recent increase of 3 dBm in environmental noise level. The calculated recent noise change, expressed in dBm, reflects the fluctuation range of the recent environmental noise level and is stored in the host computer's memory, providing a basis for threshold adjustments based on noise changes.

[0167] The host computer program obtains the recent noise change amount obtained in step S434, for example, +3dBm. Based on the noise change, the threshold adjustment calculation operation determines the adjustment range of the dynamic TX power threshold according to the magnitude and direction of the recent noise change. The basic principle of adjustment is: when the ambient noise level increases, the dynamic TX power threshold should be increased accordingly to maintain the rigor and reliability of the test; when the ambient noise level decreases, the dynamic TX power threshold can be appropriately decreased, but it is generally not recommended to decrease it to maintain the stability of quality control. The magnitude of the noise change adjustment can be proportional to the recent noise change amount. For example, a proportionality coefficient of 1:1 can be set, meaning that for every 1dBm increase in ambient noise, the threshold increases by 1dBm. Therefore, in this example, the noise change adjustment is calculated as +3dBm*1 = +3dBm. The calculated noise change adjustment, in dBm, reflects the threshold adjustment range based on the ambient noise change and is stored in the host computer's memory.

[0168] The host computer program receives the yield deviation adjustment amount calculated in step S433, for example, -0.4dBm, and the noise change adjustment amount calculated in step S435, for example, +3dBm. It then performs a combined adjustment calculation, merging the yield deviation adjustment amount and the noise change adjustment amount to obtain the final total adjustment amount, which is used to update the dynamic TX power threshold. A simple addition method can be used, where the two adjustment amounts are directly added together. For example, the total adjustment amount is calculated as yield deviation adjustment amount + noise change adjustment amount = -0.4dBm + 3dBm = +2.6dBm. A more complex method could consider setting different weighting coefficients for the two adjustment amounts; for example, giving a greater weight to the noise change adjustment amount, since environmental noise is a direct and primary factor affecting the dynamic threshold. The final calculated total adjustment amount, in dBm, reflects the overall adjustment magnitude of the dynamic TX power threshold after comprehensively considering yield feedback and environmental noise changes, and is stored in the host computer's memory.

[0169] The host computer program obtains the total adjustment amount calculated in step S436, for example, +2.6dBm, and the current dynamic TX power threshold read in step S431, for example, -67.0dBm. The dynamic TX power threshold adjustment operation adds the current dynamic TX power threshold to the total adjustment amount to obtain the optimized dynamic TX power threshold. For example, the optimized dynamic TX power threshold is calculated as: current dynamic TX power threshold + total adjustment amount = -67.0dBm + 2.6dBm = -64.4dBm. To prevent the threshold adjustment from exceeding a reasonable range, upper and lower limits for the dynamic TX power threshold can be preset, for example, an upper limit of -60dBm and a lower limit of -75dBm. If the calculated optimized threshold exceeds the preset range, it is clipped to the range. For example, if the calculation result is -59dBm, the final optimized threshold is -60dBm; if the calculation result is -76dBm, the final optimized threshold is -75dBm.

[0170] Preferably, step S44 includes the following steps:

[0171] Step S441: Evaluate the recent RX sensitivity test data volatility of the performance parameter statistics to obtain the recent sensitivity volatility;

[0172] Step S442: Evaluate the stability of the product's RX sensitivity performance based on the quality trend index to obtain the RX performance stability;

[0173] Step S443: Calculate the volatility-based step adjustment amount based on the recent sensitivity volatility to obtain the volatility adjustment amount;

[0174] Step S444: Calculate the step value adjustment based on performance stability according to the RX performance stability to obtain the stability adjustment amount;

[0175] Step S445: Calculate the combined step value adjustment for volatility adjustment and stability adjustment to obtain the combined step value adjustment;

[0176] Step S446: Obtain the current step value; adjust the gradient scan step value according to the comprehensive step value adjustment amount and the current step value to obtain the optimized gradient scan step value.

[0177] In this embodiment of the invention, the sensitivity threshold power values ​​from multiple recent RX sensitivity tests are extracted from performance parameter statistics. The "recent" time range is set to the N most recently completed RX sensitivity tests, where N is determined based on testing requirements and data volume, for example, 20 tests. For these N sensitivity threshold power values, the sample standard deviation is calculated. The formula for calculating the sample standard deviation is: σ = sqrt[Σ(Xi-μ)^2 / (N-1)], where Xi represents the sensitivity threshold power value of the i-th test, μ represents the average of these N sensitivity threshold power values, and N is the number of tests. The calculated sample standard deviation σ, in dBm, quantifies the degree of fluctuation in recent RX sensitivity test results. The larger the sample standard deviation value, the higher the fluctuation of recent RX sensitivity test results. This sample standard deviation value is recorded as the recent sensitivity fluctuation assessment result and used for subsequent gradient scan step value self-optimization adjustment. For example, if the calculated sample standard deviation is 0.8 dBm, this value is the recent sensitivity fluctuation obtained in this assessment.

[0178] The RX sensitivity threshold control chart data from the quality trend index is used. The control chart is an X-bar control chart, with its center line representing the long-term average of historical RX sensitivity threshold power values. The upper and lower control limits are set based on the fluctuation range of historical data; for example, the control limit width is 3 times the standard deviation. The evaluation operation monitors the distribution of recent RX sensitivity threshold power values ​​on the control chart. If M consecutive sensitivity threshold power value points (M is preset, e.g., 5) fall within the control limits and no obvious trend or periodic fluctuations are observed, the RX sensitivity performance is considered stable. The stability evaluation results are described using qualitative indicators, such as "stable" or "unstable." When judged as "stable," it is advisable to appropriately increase the gradient scan step value to improve testing efficiency; when judged as "unstable," the current step value needs to be maintained or decreased to ensure testing accuracy. For example, if the threshold values ​​of the past 5 RX sensitivity tests all fall within the control limits of the control chart, and no abnormal patterns are displayed on the control chart, the evaluation result is "stable," indicating that the RX sensitivity performance exhibits good stability.

[0179] Based on the recent sensitivity variability value obtained in step S441, the power step value of the gradient scan is dynamically adjusted. The variability adjustment amount is calculated through a linear mapping relationship. A variability threshold Th_volatility is set, for example, to 0.5 dBm. If the recent sensitivity variability σ is less than Th_volatility, the variability adjustment amount Δstep_volatility is calculated as a positive value, with the formula: Δstep_volatility = K_volatility_increase * (Th_volatility - σ), where K_volatility_increase is the variability amplification coefficient, for example, set to 0.2 dBm / dBm. In this case, it indicates that the variability is low, and the step value can be appropriately increased. If the recent sensitivity volatility σ is greater than or equal to Th_volatility, the volatility adjustment Δstep_volatility is calculated as a negative or zero value, using the formula: Δstep_volatility = K_volatility_decrease * (Th_volatility - σ), where K_volatility_decrease is the volatility reduction coefficient, for example, set to -0.3dBm / dBm. In this case, it indicates high volatility, requiring a reduction or maintenance of the step value. The unit of the volatility adjustment Δstep_volatility is dBm, and its magnitude and sign indicate the magnitude and direction of the step value adjustment. The calculated volatility adjustment is used in subsequent comprehensive step value adjustment calculations. For example, if the recent sensitivity volatility σ is 0.7dBm, the volatility threshold Th_volatility is set to 0.5dBm, and the volatility reduction coefficient K_volatility_decrease is set to -0.3dBm / dBm. Since σ (0.7dBm) is greater than Th_volatility (0.5dBm), it indicates high volatility, requiring a reduction in the step size. The volatility adjustment Δstep_volatility is calculated as: Δstep_volatility = K_volatility_decrease * (σ - Th_volatility) = -0.3dBm / dBm * (0.7dBm - 0.5dBm) = -0.06dBm. This result of -0.06dBm indicates that, based on recent sensitivity volatility assessment, the gradient scan step size should be reduced by 0.06dBm. Alternatively, if the recent sensitivity volatility σ is 0.3dBm, which is less than the volatility threshold Th_volatility (0.5dBm), the volatility increase factor K_volatility_increase is set to 0.2dBm / dBm.At this point, the volatility adjustment Δstep_volatility is calculated as follows: Δstep_volatility = K_volatility_increase * (Th_volatility - σ) = 0.2dBm / dBm * (0.5dBm - 0.3dBm) = 0.04dBm. This result of 0.04dBm indicates that, based on the recent sensitivity volatility assessment, the gradient scan step value can be increased by 0.04dBm. The volatility adjustment result is passed to subsequent steps for comprehensive adjustment of the gradient scan step value.

[0180] The step value adjustment operation based on performance stability involves adjusting the gradient scan step value according to the RX performance stability evaluation result obtained in step S442. The stability evaluation result is either "stable" or "unstable". When the RX performance stability evaluation result is "stable", it indicates that the RX sensitivity performance has shown good stability in recent tests. In this case, to improve testing efficiency, the gradient scan step value can be appropriately increased. The stability adjustment amount Δstep_stability is calculated as a positive value, with the formula: Δstep_stability = Step_increase_value, where Step_increase_value is a preset fixed increase in the step value, for example, set to 0.1dBm. This fixed value represents the allowable increase in step value under stable performance conditions. When the RX performance stability evaluation result is "unstable", it indicates that the RX sensitivity performance is fluctuating or abnormal. To ensure the reliability of the test results, it is necessary to maintain or decrease the gradient scan step value, and it is not advisable to increase the step value. In this case, the stability adjustment amount Δstep_stability is set to zero, i.e., Δstep_stability = 0dBm. The performance stability adjustment Δstep_stability is measured in dBm. A positive value indicates an increase in the step value, while a zero value indicates no adjustment. The calculated stability adjustment is used in subsequent comprehensive step value adjustments. For example, if the RX performance stability assessment result is "stable," and the preset fixed step value increase Step_increase_value is 0.1 dBm, then the stability adjustment Δstep_stability = 0.1 dBm. If the RX performance stability assessment result is "unstable," then the stability adjustment Δstep_stability = 0 dBm.

[0181] The comprehensive step value adjustment calculation combines the volatility adjustment Δstep_volatility obtained in step S443 with the stability adjustment Δstep_stability obtained in step S444 to obtain the final comprehensive step value adjustment Δstep_comprehensive. The synthesis method uses simple addition, directly adding the two adjustments: Δstep_comprehensive = Δstep_volatility + Δstep_stability. This method considers both the volatility of recent sensitivity test data and the stability of RX performance to comprehensively adjust the gradient scan step value. For example, if the volatility adjustment Δstep_volatility is calculated to be -0.06dBm (indicating a need to decrease the step value by 0.06dBm), and the stability adjustment Δstep_stability is calculated to be 0.1dBm (indicating a need to increase the step value by 0.1dBm), then the comprehensive step value adjustment Δstep_comprehensive = -0.06dBm + 0.1dBm = 0.04dBm. The final comprehensive step adjustment, Δstep_comprehensive, is positive at 0.04 dBm, indicating that the gradient scan step value needs to be increased by 0.04 dBm after comprehensively considering volatility and stability. This comprehensive step adjustment is used in the next step to update the current gradient scan step value.

[0182] The gradient scan step value adjustment operation first reads the currently used gradient scan step value, Step_current, from the system configuration parameters. Then, based on the comprehensive step value adjustment amount Δstep_comprehensive calculated in step S445, the current step value is adjusted to obtain the optimized gradient scan step value, Step_optimized. The adjustment method is to add the current step value to the comprehensive step value adjustment amount, using the formula: Step_optimized = Step_current + Δstep_comprehensive. To prevent the step value adjustment from exceeding a reasonable range, a minimum value Step_min and a maximum value Step_max limit are set for the gradient scan step value. For example, Step_min is set to 0.5dBm, and Step_max is set to 2.0dBm. If the calculated optimized step value Step_optimized is less than Step_min, the final optimized gradient scan step value is Step_min. If Step_optimized is greater than Step_max, the final optimized gradient scan step value is Step_max. If Step_optimized falls between Step_min and Step_max, the final optimized gradient scan step value is the calculated value Step_optimized. The final optimized gradient scan step value Step_optimized is updated in the system configuration parameters, replacing the original current step value Step_current. In subsequent RX sensitivity gradient scan tests, the system will use this optimized step value for power step decay control to achieve self-optimization of the gradient scan step value. For example, if the current step value Step_current is 1.0dBm and the comprehensive step value adjustment Δstep_comprehensive is 0.04dBm, the calculated Step_optimized = 1.04dBm. Assuming the minimum step value Step_min is 0.5dBm and the maximum step value Step_max is 2.0dBm, since the calculated value of 1.04dBm falls within the preset range of 0.5dBm and 2.0dBm, the final optimized gradient scan step value Step_optimized is 1.04dBm. The host computer program updates this 1.04dBm value to the gradient scan configuration parameter set. Subsequently, when performing subsequent RX sensitivity gradient scan test steps, the system will directly call this updated gradient scan step value of 1.04dBm to control the step magnitude of power reduction during gradient scan of the standard transmitter.Through this self-optimizing step value adjustment mechanism, the gradient scan testing process can dynamically adjust the scan step based on historical test data and product performance trends. This maximizes testing efficiency while ensuring the accuracy of RX sensitivity testing, or automatically reduces the step value to maintain the reliability of test results when product performance volatility increases. The optimized gradient scan step value is recorded as part of the self-optimizing test parameter set and used in subsequent testing processes.

[0183] Preferably, the present invention also provides a wireless transmission and reception test system for performing the wireless transmission and reception test method described above, the wireless transmission and reception test system comprising:

[0184] The system initialization module is used to set the spectrum range of the standard RX to obtain spectrum range configuration data; to sample environmental noise based on the spectrum range configuration data, and to extract noise features to obtain an environmental noise feature vector; and to generate an environmental noise spectrum based on the environmental noise feature vector and the spectrum range configuration data to obtain an environmental noise feature spectrum.

[0185] The adaptive transmit power test module is used to perform dynamic threshold calculation based on the environmental noise characteristic spectrum and preset signal-to-noise ratio margin parameters to obtain the adaptive TX power threshold; to acquire the TX signal strength of the TX under test to obtain the measured TX signal strength data; to dynamically determine the measured TX signal strength data and the adaptive TX power threshold, and to store the test data to obtain the TX power test data record.

[0186] The receiver sensitivity gradient scanning module is used to set the starting power and step size through the upper unit to obtain gradient scanning configuration parameters; monitor the receiver status of the RX under test to obtain receiver status monitoring instructions; perform RX sensitivity testing on the RX under test according to the receiver status monitoring instructions to obtain raw RX sensitivity data; generate a sensitivity gradient curve based on the raw RX sensitivity data to obtain the RX sensitivity gradient curve; and store the test data based on the RX sensitivity gradient curve to obtain the RX sensitivity test data record.

[0187] The threshold self-optimization module performs statistical analysis of performance parameters and quality trend evaluation on TX power test data records and RX sensitivity test data records, obtaining performance parameter statistics and quality trend indicators. It also detects abnormal test results based on these statistics and indicators, generating abnormal alarm information. Furthermore, it dynamically optimizes the adaptive TX power threshold to obtain an optimized dynamic TX power threshold. It further optimizes the gradient scan configuration parameters by adjusting the gradient step, obtaining an optimized gradient scan step value. Finally, it generates a self-optimized test parameter set from the optimized dynamic TX power threshold and the optimized gradient scan step value. This self-optimized test parameter set is then updated and applied, generating parameter update instructions. Finally, the self-optimized test parameter set is structurally encapsulated into a structured test data packet. Finally, the module utilizes abnormal alarm information, parameter update instructions, and the structured test data packet to perform wireless transmission and reception test tasks.

[0188] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0189] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A wireless transmission and reception testing method, characterized in that, Includes the following steps: Step S1: Set the spectrum range for the standard RX to obtain spectrum range configuration data; sample environmental noise based on the spectrum range configuration data, extract noise features, and obtain environmental noise feature vectors; An environmental noise spectrum is generated by analyzing the environmental noise feature vector and the spectral range configuration data; Step S2: Calculate the dynamic threshold based on the environmental noise characteristic spectrum and the preset signal-to-noise ratio margin parameters to obtain the adaptive TX power threshold; The TX signal strength of the TX signal to be tested is acquired to obtain the measured TX signal strength data; The measured TX signal strength data and the adaptive TX power threshold are dynamically determined and the test data is stored to obtain the TX power test data record. Step S2 includes the following steps: Step S21: The upper unit extracts the frequency band noise floor from the ambient noise feature spectrum to obtain the frequency band noise floor data; the minimum received power is calculated based on the frequency band noise floor data and the preset signal-to-noise ratio margin parameter to obtain the minimum received power threshold. Step S22: Obtain standard RX specification parameters; perform standard receiver sensitivity compensation based on standard RX specification parameters to obtain receiver sensitivity compensation value; Step S23: Based on the receiver sensitivity compensation value and the minimum received power threshold, perform dynamic transmit power threshold back-calculation to obtain the adaptive TX power threshold; Step S24: The host unit starts the transmitter under test (TX) and obtains the transmission command for the TX under test; the standard RX collects the signal strength according to the transmission command for the TX under test and obtains the measured TX signal strength data. Step S25: The upper control unit dynamically determines the measured TX signal strength data and the adaptive TX power threshold to obtain the TX power test results; Step S26: Store the adaptive TX power threshold, measured TX signal strength data, and TX power test results to obtain the TX power test data record; Step S3: Set the starting power and step size through the upper unit to obtain the gradient scan configuration parameters; monitor the receiving status of the RX under test to obtain the receiving status monitoring command; perform RX sensitivity test on the RX under test according to the receiving status monitoring command to obtain the raw RX sensitivity data; generate the sensitivity gradient curve according to the raw RX sensitivity data to obtain the RX sensitivity gradient curve; store the test data according to the RX sensitivity gradient curve to obtain the RX sensitivity test data record. Step S4: Perform performance parameter statistical analysis on the TX power test data records and RX sensitivity test data records, and conduct quality trend evaluation to obtain performance parameter statistics and quality trend indicators; detect abnormal test results based on performance parameter statistics and quality trend indicators to obtain abnormal alarm information; perform dynamic threshold self-optimization adjustment on the adaptive TX power threshold to obtain the optimized dynamic TX power threshold; perform gradient step self-optimization adjustment on the gradient scan configuration parameters to obtain the optimized gradient scan step value; generate a self-optimized parameter set based on the optimized dynamic TX power threshold and the optimized gradient scan step value to obtain a self-optimized test parameter set; update and apply the parameters of the self-optimized test parameter set to obtain parameter update instructions; encapsulate the test data of the self-optimized test parameter set in a structured manner to obtain a structured test data packet; use the abnormal alarm information, parameter update instructions, and structured test data packet to realize the wireless transmission and reception test task.

2. The wireless transmission and reception test method according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: The host computer sets the spectrum range for the standard RX to obtain spectrum range configuration data; Step S12: The standard RX performs environmental noise sampling based on the spectral range configuration data to obtain raw noise sampling data; the raw noise sampling data is preprocessed to obtain preprocessed noise data. Step S13: The host computer receives preprocessed noise data from the standard RX, extracts noise features, and obtains the environmental noise feature vector; the host computer sends self-calibration commands to the standard TX and standard RX respectively, and obtains self-calibration configuration commands. Step S14: The standard TX performs transmitter loopback calibration according to the self-calibration configuration command to obtain the transmit calibration parameters; the standard RX performs receiver loopback calibration according to the self-calibration configuration command to obtain the receive calibration parameters. Step S15: The host computer generates an environmental noise spectrum based on the environmental noise feature vector and the spectral range configuration data, thus obtaining the environmental noise feature spectrum.

3. The wireless transmission and reception test method according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Set the starting power and step size through the upper camera to obtain the gradient scan configuration parameters; Step S32: Set the standard transmitter power of the standard TX according to the spectrum range configuration data to obtain the current power value of the standard TX; monitor the reception status of the RX under test to obtain the reception status monitoring command; Step S33: Perform RX sensitivity testing on the RX under test according to the current power value of the standard TX and the receiving status monitoring command to obtain the raw RX sensitivity data; Step S34: Generate the sensitivity gradient curve based on the original RX sensitivity data to obtain the RX sensitivity gradient curve; Step S35: Store the gradient scan configuration parameters, raw RX sensitivity data, and RX sensitivity gradient curve as test data to obtain RX sensitivity test data records.

4. The wireless transmission and reception test method according to claim 3, characterized in that, Step S33 includes the following steps: Step S331: Based on the current power value of the standard TX and the receiving status monitoring command, perform gradient point data acquisition on the RX under test to obtain single-point gradient test data; Step S332: Perform power step decay control based on gradient scan configuration parameters and the current power value of standard TX to obtain the next power value of standard TX; Step S333: Determine the scan termination condition based on the gradient scan configuration parameters, the next power value of the standard TX, and the single-point gradient test data to obtain the scan termination flag; Step S334: Based on the scan termination flag, integrate the gradient data of the single-point gradient test data to obtain the raw RX sensitivity data.

5. The wireless transmission and reception test method according to claim 3, characterized in that, Step S34 includes the following steps: Step S341: Extract power-RSSI data pairs from the raw RX sensitivity data and generate power-RSSI curve data to obtain the power RSSI curve data point set; Step S342: Extract power-PER / BER data pairs from the raw RX sensitivity data and generate power-PER / BER curve data to obtain the power error rate curve data point set; Step S343: Determine the sensitivity threshold value for the power error rate curve data point set to obtain the sensitivity threshold power value; Step S344: Generate a sensitivity gradient curve based on the power RSSI curve data point set and the power error rate curve data point set according to the sensitivity threshold power value, and mark the threshold to obtain the RX sensitivity gradient curve.

6. The wireless transmission and reception test method according to claim 2, characterized in that, Step S4 includes the following steps: Step S41: The host computer loads historical test data from the environmental noise characteristic spectrum, TX power test data records, and RX sensitivity test data records to create a historical test dataset. Step S42: Perform performance parameter statistical analysis on the historical test dataset to obtain performance parameter statistics; evaluate the quality trend based on the performance parameter statistics to obtain quality trend indicators; Step S43: Based on the performance parameter statistics, quality trend indicators and environmental noise characteristic spectrum, the adaptive TX power threshold is dynamically self-optimized to obtain the optimized dynamic TX power threshold. Step S44: Adjust the gradient scan configuration parameters by gradient stepping self-optimization based on performance parameter statistics and quality trend indicators to obtain the optimized gradient scan stepping value; Step S45: Generate a self-optimized parameter set by optimizing the dynamic TX power threshold and the gradient scan step value after optimization, and obtain the self-optimized test parameter set; Step S46: The host computer performs structured encapsulation of the environmental noise characteristic spectrum, transmission calibration parameters, reception calibration parameters, TX power test data records, RX sensitivity test data records, and self-optimization test parameter set to obtain a structured test data package.

7. The wireless transmission and reception test method according to claim 6, characterized in that, Step S43 includes the following steps: Step S431: Read the current dynamic TX power threshold of the adaptive TX power threshold to obtain the current dynamic threshold; Step S432: Evaluate the recent TX power test yield of the performance parameter statistics to obtain the recent TX yield; Step S433: Calculate the threshold adjustment amount based on the yield deviation for the recent TX yield and the preset yield target value to obtain the yield deviation adjustment amount; Step S434: Obtain the recent environmental noise level changes from the quality trend indicators and the environmental noise characteristic spectrum to obtain the recent noise change amount; Step S435: Calculate the threshold adjustment amount based on the recent noise change amount to obtain the noise change adjustment amount; Step S436: Calculate the total adjustment amount by comprehensively adjusting the yield deviation and noise change. Step S438: Adjust the dynamic TX power threshold according to the total adjustment amount and the current dynamic threshold to obtain the optimized dynamic TX power threshold.

8. The wireless transmission and reception test method according to claim 6, characterized in that, Step S44 includes the following steps: Step S441: Evaluate the recent RX sensitivity test data volatility of the performance parameter statistics to obtain the recent sensitivity volatility; Step S442: Evaluate the stability of the product's RX sensitivity performance based on the quality trend index to obtain the RX performance stability; Step S443: Calculate the volatility-based step adjustment amount based on the recent sensitivity volatility to obtain the volatility adjustment amount; Step S444: Calculate the step value adjustment based on performance stability according to the RX performance stability to obtain the stability adjustment amount; Step S445: Calculate the combined step value adjustment for volatility adjustment and stability adjustment to obtain the combined step value adjustment; Step S446: Obtain the current step value; adjust the gradient scan step value according to the comprehensive step value adjustment amount and the current step value to obtain the optimized gradient scan step value.

9. A wireless transmission and reception testing system, characterized in that, For performing the wireless transmission and reception test method as described in claim 1, the wireless transmission and reception test system includes: The system initialization module is used to set the spectrum range of the standard RX to obtain spectrum range configuration data; to sample environmental noise based on the spectrum range configuration data, and to extract noise features to obtain an environmental noise feature vector; and to generate an environmental noise spectrum based on the environmental noise feature vector and the spectrum range configuration data to obtain an environmental noise feature spectrum. The adaptive transmit power test module is used to perform dynamic threshold calculation based on the environmental noise characteristic spectrum and preset signal-to-noise ratio margin parameters to obtain the adaptive TX power threshold; to acquire the TX signal strength of the TX under test to obtain the measured TX signal strength data; to dynamically determine the measured TX signal strength data and the adaptive TX power threshold, and to store the test data to obtain the TX power test data record. The receiver sensitivity gradient scanning module is used to set the starting power and step size through the upper unit to obtain gradient scanning configuration parameters; monitor the receiver status of the RX under test to obtain receiver status monitoring instructions; perform RX sensitivity testing on the RX under test according to the receiver status monitoring instructions to obtain raw RX sensitivity data; generate a sensitivity gradient curve based on the raw RX sensitivity data to obtain the RX sensitivity gradient curve; and store the test data based on the RX sensitivity gradient curve to obtain the RX sensitivity test data record. The threshold self-optimization module performs statistical analysis of performance parameters and quality trend evaluation on TX power test data records and RX sensitivity test data records, obtaining performance parameter statistics and quality trend indicators. It also detects abnormal test results based on these statistics and indicators, generating abnormal alarm information. Furthermore, it dynamically optimizes the adaptive TX power threshold to obtain an optimized dynamic TX power threshold. It further optimizes the gradient scan configuration parameters by adjusting the gradient step, obtaining an optimized gradient scan step value. Finally, it generates a self-optimized test parameter set from the optimized dynamic TX power threshold and the optimized gradient scan step value. This self-optimized test parameter set is then updated and applied, generating parameter update instructions. Finally, the self-optimized test parameter set is structurally encapsulated into a structured test data packet. Finally, the module utilizes abnormal alarm information, parameter update instructions, and the structured test data packet to perform wireless transmission and reception test tasks.

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