Test regulation and control system and method for 5G terminal

Abstract

The invention relates to the technical field of test regulation and control for 5G terminals, and discloses a test regulation and control system and method for 5G terminals. And connecting the service plane data after the UE-base station is established. Firstly, test parameters and cyclic variables are uniformly initialized, throughput rate is acquired point by point according to a current sampling interval, and a network state is accurately captured. Calculating a mean value and a standard deviation of the window sample, adaptively determining a fractional order derivative order according to the mean value and the standard deviation, and calculating a fractional order derivative sequence by using a fractional order binomial coefficient; and counting the local fluctuation quantity mapped by the sequence standard deviation, taking the local fluctuation quantity as a closed smooth reconstruction weight, and quickly outputting the smooth throughput rate. Based on the smoothing result and the fluctuation quantity, adaptively adjusting the sampling interval and the starting moment of the next window: prolonging the interval to save resources when the network is stable, and shortening the interval to improve the sampling density when the fluctuation is violent; and finally, automatically terminating the test according to an end condition and outputting a smooth throughput rate and a self-adaptive sampling interval sequence.

Description

Technical Field

[0001] The present invention relates to the field of test and control technologies for 5G terminals, and specifically to a test and control system and method for 5G terminals. Background Art

[0002] The high-speed data transmission capabilities of 5G networks are fundamental to enabling a variety of innovative applications, including high-definition video streaming, virtual reality (VR), augmented reality (AR), and cloud gaming. During 5G network construction and optimization, throughput testing helps operators, equipment manufacturers, and testing organizations evaluate the actual network performance and ensure that the network can provide stable and efficient services. 5G terminal service plane data throughput testing methods often use fixed-interval sampling and process the sampled results using a simple sliding average or exponential smoothing algorithm. These solutions require pre-setting the sampling interval and window length, and the smoothing coefficient is often fixed and cannot automatically adjust to fluctuations in network status.

[0003] Traditional test systems typically uniformly sample terminal throughput at fixed, empirically defined intervals (e.g., 1 second, 500 milliseconds), ignoring the non-stationary nature of throughput during peaks and valleys. Fixed intervals result in insufficient resolution during periods of significant fluctuations, while wasting test resources during periods of relative stability. Sampled data is often processed using an N-point sliding average or exponentially weighted average. The sliding average cannot distinguish the severity of fluctuations at different stages, while exponential smoothing requires a pre-defined smoothing factor, which is difficult to adapt to sudden network changes and can easily lead to information lag or over-suppression of significant fluctuations. Some test solutions calculate the difference between adjacent sampling points to estimate the rate of throughput change, but these methods are limited to first-order or second-order differences, ignoring the long-memory nature of network fluctuations. Integer-order differences can only reflect instantaneous changes within a limited number of recent sampling points and are unable to capture persistent and cumulative small fluctuations. Existing technologies often rely on domain experience or trial and error when setting parameters such as the sliding window length, smoothing factor, and threshold determination, lacking an adaptive mechanism for automatically generating parameters based on real-time statistical characteristics. This requires frequent adjustments to the test system in different network environments, reducing deployment efficiency. Fixed sampling and processing logic can easily lead to oversampling during steady-state network conditions, wasting test bandwidth and terminal computing resources. During periods of sudden network jitter, insufficient sampling and processing rates prevent timely capture of critical fluctuations.

[0004] To this end, this case aims to propose a test and control system and method for 5G terminals. First, window-level parameters are automatically generated during the initialization phase. Then, the throughput is dynamically acquired within each test window according to the current sampling rhythm. Next, the fractional derivative depth is determined through real-time statistical analysis to capture historical memory characteristics and transient fluctuations. A closed-form smoothing model is then used to perform weighted reconstruction of the original data. Finally, the sampling rhythm of the next window is adaptively adjusted based on the smoothing results and local fluctuation feedback until the termination condition is met. Summary of the Invention

[0005] The present invention provides a test and control system and method for 5G terminals, which promote the solution of the problems mentioned in the above background technology.

[0006] The present invention provides the following technical solution: a test and control method for a 5G terminal, comprising: S1. Adaptively adjust the sampling interval and starting time of the next window based on the smooth reconstruction result and local fluctuation; S2. Based on the sampling interval of the current window, calculate each sampling time point in sequence and synchronously obtain the throughput rate at the corresponding time; S3. Calculate the sample mean and sample standard deviation for the sampled samples, and adaptively determine the order of the fractional derivative according to the coefficient of variation; S4. performing a derivative operation on the sampled data using the fractional binomial coefficients according to the adaptively determined fractional order to obtain a fractional derivative sequence; S5. Perform statistical analysis on the fractional derivative sequence, calculate its sample mean and sample standard deviation, and calculate the local throughput rate fluctuation based on the standard deviation; S6. Constructing a smoothing coefficient according to a predetermined closed-form smoothing algorithm, performing weighted reconstruction on the sampled throughput sequence, and outputting a smoothed throughput sequence; S7. Update the sampling interval based on the smooth reconstruction result and the local fluctuation amount, and set the start time and sampling interval of the next test window; S8. Determine whether a preset end condition is met. If so, output the smoothed throughput sequence and the adaptive sampling interval sequence and terminate the test.

[0007] Optionally, the adaptively adjusting the sampling interval and starting time of the next window based on the smooth reconstruction result and the local fluctuation amount specifically includes: Set the total test duration to , the first window sampling interval is ; Record the test start time , the test ends at ; Set the throughput acquisition function to , ;in, is the set of real numbers greater than 0; Calculate the number of sampling points per window ; Initialize the loop variable: , , ;in, Number the window; For the Window start time; For the Window sampling interval.

[0008] Optionally, the step of sequentially calculating each sampling time point based on the sampling interval of the current window and synchronously obtaining the throughput rate at the corresponding time point specifically includes: when When established, execute this window Otherwise, go to step S8 and end; right Perform the following steps: S201, calculate the Sampling time: ; S202, synchronous acquisition throughput: ;in, is the sampling point index within the window; For the Window Second sampling moment; No. Window The throughput rate obtained by sampling.

[0009] Optionally, calculating the sample mean and sample standard deviation of the samples, and adaptively determining the order of the fractional derivative according to the coefficient of variation, specifically includes: Calculate the original mean and standard deviation of this window: ; ; in, For the The average sample throughput of the window; For the The sample standard deviation of the window; Calculate the coefficient of variation and fractional order of this window: , , ;in, For the coefficient of variation of the window; For the The order of the fractional derivatives of the window.

[0010] Optionally, performing a derivative operation on the sampled data using fractional binomial coefficients according to the adaptively determined fractional order to obtain a fractional derivative sequence specifically includes: For each calculate: ; in, , is the fractional binomial coefficient; For the Window The fractional derivative of samples; To sum the index.

[0011] Optionally, performing statistical analysis on the fractional derivative sequence, calculating its sample mean and sample standard deviation, and calculating the local throughput fluctuation amount based on the standard deviation specifically includes: Calculate the Windowed fractional derivative mean ; Calculate the Standard deviation of windowed fractional derivatives ; Set the The local throughput fluctuation of the window is .

[0012] Optionally, constructing a smoothing coefficient according to a predetermined closed-form smoothing algorithm, performing weighted reconstruction on the sampled throughput sequence, and outputting a smoothed throughput sequence specifically includes: Construction Closed-form smoothing coefficient of the window ; To this window , smooth construction Window Smoothed throughput of subsamples : .

[0013] Optionally, updating the sampling interval based on the smooth reconstruction result and the local fluctuation amount, and setting the start time and sampling interval of the next test window, specifically includes: like , let the global benchmark average throughput ; Set up the first The sampling interval of the window is ; Set up the first Window The sampling time is , ; Return to step S2.

[0014] Optionally, the determining whether a preset termination condition is satisfied, and if so, outputting the smoothed throughput sequence and the adaptive sampling interval sequence and terminating the test, specifically includes: when When it is no longer satisfied, the total number of effective processing windows ; Output smoothed throughput sequence , where the index mapping , ; Adaptive sampling interval sequence ;in, For the smoothed throughput samples; is the global sample number of the smoothed throughput linear sequence; For the The actual sampling interval used by the window; The window index.

[0015] A system for implementing the test and control method for a 5G terminal, comprising: Initialization module: used to build the test environment and define variables; Sampling module: used to collect data on 5G terminal throughput according to the current sampling interval; Statistics and adaptive module: used to calculate the original statistics within the window and adaptively determine the order of fractional derivatives; Fractional derivative calculation module: used to accurately calculate the fractional derivative sequence based on the determined order; Fluctuation module: used to count fractional derivative data and determine local throughput fluctuation; Smoothing reconstruction module: used to reconstruct the throughput based on the closed smoothing model; Interval update module: used to adaptively update the sampling interval and set the next sampling window based on the reconstruction results and fluctuation; Termination and output module: used to determine whether the test is completed and output the smoothed throughput sequence and sampling interval sequence.

[0016] The present invention has the following beneficial effects: 1. During the test initialization phase, the solution uniformly sets the total test duration and first-window sampling interval, establishes the measurement environment, and defines all loop control variables. By including time boundaries and sampling functions as part of the overall environment configuration, the test process has a clear time plan and calling framework from the outset. This design avoids the common drawbacks of traditional testing, where environmental parameters are scattered and difficult to reproduce, and provides a reliable foundation for subsequent adaptive control. This phase also introduces a systematic management strategy for resource allocation and environmental consistency, addressing the issue of inconsistent test environment configurations that make comparison and analysis of results difficult.

[0017] 2. During the throughput data acquisition phase, the solution sequentially calculates each sampling moment based on the sampling interval of the current window and synchronously acquires the corresponding throughput value. The sampling moments are automatically mapped to the test timeline, and precise synchronization is achieved through a built-in clock and acquisition function, eliminating the need for manual intervention. This design improves the timing accuracy of data acquisition and avoids measurement errors caused by inaccurate trigger mechanisms in traditional methods. This phase does not rely on fixed triggers or external signals, maintaining high timing consistency even when network status fluctuates dramatically, eliminating resource waste and measurement blind spots caused by invalid or repeated sampling.

[0018] 3. During the statistical analysis and adaptive order determination phase, the solution calculates the average level and fluctuation amplitude of the throughput samples collected in the current window and adaptively selects the fractional differential order based on the relative fluctuation index. Using the coefficient of variation as the basis for adaptive adjustment, the fractional differential order can be dynamically adjusted based on network fluctuation characteristics. This design helps reduce model complexity when the network is stable and improves sensitivity when the network fluctuates violently, balancing computational overhead and response accuracy. Traditional methods often use fixed differential orders or empirical parameters. This phase achieves intelligent, unsupervised parameter updates through real-time fluctuation assessment, resolving the bottleneck of highly subjective parameter selection and difficulty adapting to changing network environments.

[0019] 4. During the fractional-order differential sequence calculation phase, the solution performs fractional-order differential operations on the sampled data based on the adaptively determined order to extract the historical long-memory characteristics and subtle dynamic changes of the throughput sequence. Fractional-order binomial weights are applied to the data sequence to achieve comprehensive capture of multi-time domain information without the need for additional historical window management. This design is beneficial in enhancing the dual perception capabilities of network emergencies and slow trend changes, and improving the test's response sensitivity to transient disturbances. Traditional integer-order differentials only focus on short-term changes. In this phase, fractional-order operations are used to take into account both short-term fluctuations and long-term trends, solving the problem of measurement distortion caused by ignoring network memory effects.

[0020] 5. During the local throughput fluctuation assessment phase, the solution further statistically calculates the average level and dispersion of the fractional differential series and maps the results into a local fluctuation index with the same dimension as the throughput. A secondary statistical analysis structure is introduced to transform the fractional differential results into intuitive indicators that can be used for smoothing and sampling adjustments. This design facilitates the presentation of complex differential fluctuation information in a quantitative form, facilitating direct invocation by subsequent algorithm modules. Traditional methods often use empirical thresholds or a single fluctuation metric. This phase enhances the objectivity and robustness of fluctuation assessment through two-level statistics, addressing the previous indicators' vulnerability to outliers and poor stability.

[0021] 6. During the closed-form smoothing reconstruction phase, the solution constructs closed-form smoothing coefficients based on local volatility indicators and performs a weighted reconstruction of the original throughput sequence to obtain the smoothed data output. The optimal smoothing weights are directly calculated using closed-form analytical expressions, eliminating the need for iterative optimization or filter design. This design reduces computational complexity and avoids filtering delays, making the smoothing process both efficient and real-time. Traditional smoothing often relies on sliding window filtering or iterative least squares. This closed-form solution completes the reconstruction in one go, resolving the issues of long data delay and heavy computational burden after filtering, while also improving the timeliness and accuracy of the smoothed data.

[0022] 7. During the adaptive sampling interval update phase, the solution dynamically adjusts the sampling interval and start time of the next window based on the smoothed reconstruction results and local fluctuation indicators. This dual decision-making process combines smoothing and fluctuation results, introducing an adjustment strategy that combines global benchmarks with local indicators. This design automatically extends the sampling interval when the network is stable to save resources, and shortens the interval to capture critical information when network fluctuations increase. Traditional methods use fixed or single-factor fluctuations as a basis for judgment. This phase improves the adaptability and reliability of the control strategy through multi-faceted fusion decision-making, addressing the issues of slowness or overreaction caused by single-factor decisions.

[0023] 8. During the termination condition judgment and result output phase, the solution automatically determines the test termination based on the total test duration or the preset threshold of the number of valid processing windows, and outputs a smoothed throughput sequence and a corresponding sampling interval sequence. Taking both the test progress and the processing quality into consideration, the termination decision meets both time constraints and ensures the integrity of the accumulated data. This design helps avoid insufficient data due to premature termination, and avoids meaningless overtime operations that waste resources. Unlike traditional methods that are often based solely on time or experience, this phase achieves intelligent termination through comprehensive progress and data validity, solving the problems of uneven data fragmentation and ineffective system burden caused by tests that are too short or too long. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] Example, see Figure 1 , a test and control method for a 5G terminal, comprising: End-to-end testing (base station and user) prerequisites: 1. Connection establishment: Connect the test device to the 5G network.

[0027] 2. Data transmission: Perform large-scale data transmission to test the speed of file upload and download.

[0028] 3. Start the test equipment and begin the throughput test. Typically, the test tool will display the data transfer rate in real time during file uploads and downloads. During the test, pay attention to the test duration, which typically lasts from several minutes to several hours, as well as the network environment. Specifically, the 5G network coverage and signal quality may affect throughput.

[0029] 4. Data monitoring: Use network monitoring tools to track throughput in real time and record transmission rates.

[0030] S1. Adaptively adjust the sampling interval and starting time of the next window based on the smooth reconstruction result and local fluctuation; S2. Based on the sampling interval of the current window, calculate each sampling time point in sequence and synchronously obtain the throughput rate at the corresponding time; S3. Calculate the sample mean and sample standard deviation for the sampled samples, and adaptively determine the order of the fractional derivative according to the coefficient of variation; S4. performing a derivative operation on the sampled data using the fractional binomial coefficients according to the adaptively determined fractional order to obtain a fractional derivative sequence; S5. Perform statistical analysis on the fractional derivative sequence, calculate its sample mean and sample standard deviation, and calculate the local throughput rate fluctuation based on the standard deviation; S6. Constructing a smoothing coefficient according to a predetermined closed-form smoothing algorithm, performing weighted reconstruction on the sampled throughput sequence, and outputting a smoothed throughput sequence; S7. Update the sampling interval based on the smooth reconstruction result and the local fluctuation amount, and set the start time and sampling interval of the next test window; S8. Determine whether a preset end condition is met. If so, output the smoothed throughput sequence and the adaptive sampling interval sequence and terminate the test.

[0031] By uniformly initializing the test environment and loop variables, test parameter consistency is ensured, providing clear time boundaries for subsequent window divisions. Throughput data is collected and recorded sequentially at set intervals, achieving high-precision capture of network status. The statistical analysis phase calculates the average level and fluctuation amplitude of the current window data and adaptively selects the differential order, addressing the issues of insufficient sensitivity and wasted computational resources caused by fixed parameters in traditional methods. During the fractional-order differential calculation phase, this method comprehensively considers throughput information from multiple past time periods, focusing on sudden fluctuations while preserving long-term trends. This makes the test more sensitive to sudden network changes. The fluctuation evaluation step converts the differential results into intuitive and quantifiable indicators, providing accurate input for smooth reconstruction. The closed-form smooth reconstruction phase utilizes analytical expressions for weighted processing, responsive to network jitter in real time while avoiding time-consuming iterative filtering. Subsequently, adaptive sampling interval updating leverages the smoothing results and fluctuation indicators, appropriately extending the sampling interval to conserve resources when the network is stable and shortening it to increase sampling density when the network fluctuates significantly. Finally, by determining the termination of the test based on the total test duration or the number of windows, automated control of the test process is achieved, avoiding premature termination or meaningless continuation. Through these continuous steps, this method resolves the conflict between accuracy and efficiency between fixed sampling and hysteresis smoothing, improving the dynamic adaptability and resource utilization of the test process while ensuring the integrity and reliability of the test results.

[0032] The adaptive adjustment of the sampling interval and the starting time of the next window based on the smooth reconstruction result and the local fluctuation amount specifically includes: Set the total test duration to , clarify the duration range of the entire test process and provide time boundaries for subsequent window division; the first window sampling interval is , determine the temporal resolution of samples in the first window as the initial benchmark for adaptive adjustment; Record the test start time , marking the starting point of the test process, all sampling moments are referenced to this; the test ends at , clearly define the test deadline so that out-of-bounds judgment can be performed in the loop; Set the throughput acquisition function to , ;in, is a set of real numbers greater than 0; the encapsulation terminal is at any time The throughput read operation on ,facilitates subsequent consistent calls; Calculate the number of sampling points per window ;Balance the number of samples in each window and the computational complexity to ensure sufficient data for subsequent analysis; Initialize the loop variable: , , ;in, Number the window; For the Window start time; For the Window sampling interval; set the window number, start time and initial value of the sampling interval to prepare for the first round of sampling.

[0033] By presetting the total test duration and the sampling interval for the first window, a strict timeframe is established for the entire test, ensuring that each window runs within this range and preventing data collection from being overrun or missed. Recording the test start and end times provides an accurate reference time stamp, ensuring a consistent timing baseline for all subsequent sampling points, thus resolving data incomparability issues caused by inconsistent timing baselines across multiple runs. Configuring throughput acquisition is a packaged operation, simplifying subsequent calls and ensuring a consistent acquisition function interface, reducing implementation complexity. By calculating the number of sampling points per window, the method balances varying test durations with sampling requirements, ensuring sufficient data within each window for statistical analysis while limiting the computational workload within the window and avoiding resource burdens caused by excessive samples. Finally, loop variables such as the window number, start time, and sampling interval are initialized, ensuring the smooth start of the first round of sampling and providing an accurate starting point for subsequent adaptive adjustments. This series of steps, through systematic pre-test configuration, addresses the issues of fragmented environmental parameter distribution, susceptibility to human error, and inefficient reconfiguration in traditional test solutions, thereby improving test reproducibility and project deployment efficiency.

[0034] The sampling interval based on the current window is used to calculate each sampling time point in sequence, and the throughput rate at the corresponding time point is obtained synchronously, specifically including: when When established, execute this window Otherwise, go to step S8 and end; ensure that all the current window Each sample can be collected within the test time to avoid crossing the limit; right Perform the following steps: S201, calculate the Sampling time: ; get the first Window The exact time point of subsampling; S202, synchronous acquisition throughput: ;in, is the sampling point index within the window; For the Window Second sampling moment; No. Window The throughput rate obtained by sampling is read and recorded at that moment for subsequent statistics and analysis.

[0035] By determining whether the current window is still within the test duration, this method avoids the out-of-bounds problem of continuing sampling after the test ends, ensuring the consistency of data acquisition with the overall time constraint. When it is confirmed that acquisition is possible within the window, data is acquired sequentially according to the preset sampling rhythm, so that the sampling sequence strictly follows the time plan while being flexible and can be flexibly changed according to subsequent adaptive adjustments. The synchronous acquisition throughput operation binds data reading to the time stamp, eliminating the risk of data misalignment caused by timing deviation and asynchronous triggering. This design is beneficial to improving the sampling accuracy of network testing in high-load or network switching scenarios, while reducing the system burden caused by uncoordinated calls and repeated measurements. Compared with existing timed triggering or external event-driven methods, this method pays more attention to the close connection with the test duration and window structure, making the sampling process both stable and controllable, and able to quickly respond to the dynamic adjustment requirements of the superior control strategy, thereby improving data integrity and system operation efficiency.

[0036] The calculation of the sample mean and sample standard deviation of the samples, and the adaptive determination of the fractional derivative order according to the coefficient of variation, specifically includes: Calculate the original mean and standard deviation of this window: ; Obtain the overall throughput level benchmark for this window; ; Quantify the fluctuation range of throughput rate within this window; in, For the The average sample throughput of the window; For the The sample standard deviation of the window; Calculate the coefficient of variation and fractional order of this window: , , ;in, For the The coefficient of variation of the window provides a relative volatility indicator for adaptive order generation; For the The order of the fractional derivative of the window is automatically determined according to the degree of fluctuation, which improves the sensitivity of the model to transient changes.

[0037] By calculating two statistical indicators, the average level and dispersion of the sampled data, the method first obtains a baseline value for the current network throughput and a quantitative description of its fluctuations, providing a reliable basis for subsequent fractional order selection. By using the ratio of fluctuation to average level as an adaptive parameter, the fractional order selection automatically adjusts to the stability or drastic fluctuations of the network state, resolving the issues of insufficient sensitivity or excessive computation in traditional fixed-order differentials under different network conditions. When the network state is stable, a lower fractional order reduces model complexity and computational overhead; when the network fluctuates drastically, a higher fractional order enhances the sensitivity of the differential operation to transient changes. This adaptive mechanism, by providing real-time feedback on data fluctuations, beneficially improves the intelligence level and resource utilization efficiency of the overall testing process, avoids performance instability and test result deviations caused by subjective experience-based parameter settings, and brings higher accuracy and reliability to throughput capture in complex network environments.

[0038] The method of performing derivative operations on the sampled data using fractional binomial coefficients according to the adaptively determined fractional order to obtain a fractional derivative sequence specifically includes: For each calculate: ; in, , is the fractional binomial coefficient; For the Window The fractional derivative of samples; To sum the index; based on Define and calculate fractional derivatives to capture the historical long memory characteristics and subtle fluctuations of throughput.

[0039] By utilizing fractional-order binomial coefficients to perform weighted accumulation operations on each piece of sampled data within the current window, this method pays dual attention to recent changes and historical trends. It can capture short-term information brought about by sudden fluctuations while also reflecting the cumulative trend of long-term throughput changes. Unlike ordinary integer-order differentials that only focus on instantaneous changes, this method aggregates contributions from multiple time periods through fractional-order weighting, allowing the calculation results to reflect the "long memory" effect of network status, thereby improving the ability to predict slow trend changes and the speed of response to sudden events. This step achieves a more comprehensive perception of network dynamics by integrating multi-time period information into a single-point operation, which is beneficial for providing more stable and reliable data support in scenarios with severe network fluctuations or sudden failures. At the same time, it does not rely on additional historical data window management, simplifying system implementation and improving operational efficiency.

[0040] The statistical analysis of the fractional derivative sequence is performed to calculate its sample mean and sample standard deviation, and the local throughput rate fluctuation is calculated based on the standard deviation, specifically including: Calculate the Windowed fractional derivative mean ;Measure the central tendency of the fractional derivative of this window; Calculate the Standard deviation of windowed fractional derivatives ;Measure the discrete degree of fractional derivative and reflect the intensity of fluctuation; Set the The local throughput fluctuation of the window is ; Convert the fractional-order fluctuation back to the same dimension as the throughput to facilitate subsequent smoothing.

[0041] After obtaining the fractional derivative sequence, the method performs a second quantification of the high-level differential information by calculating its central tendency and dispersion, transforming the complex fractional-order fluctuation characteristics into a directly usable local fluctuation index. This index preserves the network dynamic details revealed by the fractional-order operation while converting it into a form consistent with the throughput dimension, facilitating direct invocation by subsequent smoothing and sampling adjustment modules. This step overcomes the difficulty of intuitively assessing the degree of fluctuation using only differential results. The overall algorithm automatically extracts and quantifies network fluctuation characteristics without manually setting thresholds, providing a clearer decision-making basis for smooth reconstruction and balanced resource allocation, thereby improving the method's robustness and automation.

[0042] The method of constructing a smoothing coefficient according to a predetermined closed-form smoothing algorithm, weightedly reconstructing the sampled throughput sequence, and outputting a smoothed throughput sequence specifically includes: Construction Closed-form smoothing coefficient of the window ;Determine the weight ratio of the original value and the mean during smoothing, closed-form adaptive; To this window , smooth construction Window Smoothed throughput of subsamples : ; Output smoothed throughput sequence to reduce the impact of extreme jitter.

[0043] By constructing an analytical closed-form expression for the smoothing coefficient, the method can determine the optimal weighting ratio in a single operation, eliminating the need for iterative calculations or complex filter design required by traditional filtering algorithms. This design effectively reduces computational latency, enabling the smoothing process to closely follow real-time data input, thereby reducing the signal lag introduced by filtering. The weighted reconstruction step balances the original data with the local average, suppressing sudden jitter and noise while retaining the true throughput trend. This mechanism addresses the problem of conventional sliding average or iterative least squares smoothing being prone to distortion or delay when processing sudden fluctuations, improving the timeliness and accuracy of obtaining stable, high-quality throughput curves and providing more reliable basic data for subsequent adaptive sampling.

[0044] The updating of the sampling interval based on the smooth reconstruction result and the local fluctuation amount, and setting the start time and sampling interval of the next test window, specifically includes: like , let the global benchmark average throughput ; The window average is used as the standard for adjusting the sampling interval of all subsequent windows; Set up the first The sampling interval of the window is Automatically speed up or slow down the sampling frequency based on the comparison between the current window and the global benchmark; Set up the first Window The sampling time is , ; Calculate the start time of the next window and increment the window number to enter the next round of collection; Return to step S2.

[0045] By combining smoothed data curves with fluctuation indicators for decision-making, this method automatically extends the sampling interval during stable network phases to reduce invalid data collection and system resource consumption; it automatically shortens the interval when network fluctuations intensify, increasing the sampling frequency to capture key information. This dual-decision strategy balances test efficiency and information integrity, resolving the issues of response lag and resource waste caused by traditional fixed sampling or single fluctuation judgment. Furthermore, by cyclically incrementing the window number and calculating the start time of the next window, the method ensures the continuity and timing correctness of the test process, facilitating long-term, highly reliable network performance monitoring and enhancing the intelligent scheduling capabilities and operational stability of the test system.

[0046] The determining whether a preset termination condition is satisfied, and if so, outputting a smoothed throughput sequence and an adaptive sampling interval sequence and terminating the test, specifically includes: when When it is no longer satisfied, the total number of effective processing windows ;Determine how many complete windows of processing were actually completed; Output smoothed throughput sequence , where the index mapping , ; Adaptive sampling interval sequence ;in, For the smoothed throughput samples; is the global sample number of the smoothed throughput linear sequence; For the The actual sampling interval used by the window; It is the window index; it provides the smoothed throughput rate and sampling interval data series required for subsequent analysis and visualization.

[0047] By simultaneously considering the two dimensions of total test duration and the number of effective processing windows, the method ensures that the test can cover a sufficiently long monitoring time and process a sufficient amount of complete window data, thereby avoiding insufficient data due to premature termination or waste of resources due to excessive continuation. After the termination conditions are met, the method will output the smoothed throughput sequence and the corresponding sampling interval records in full, providing a continuous and high-quality data stream for subsequent performance evaluation and visual analysis. This step solves the problem that it is difficult to balance test integrity and efficiency with a single time or single window number judgment through dual condition judgment, making the test termination both in line with engineering needs and flexible, which beneficially improves the controllability of the test process and the availability of the results.

[0048] This embodiment further provides a system for a 5G terminal test and control method, including: Initialization module: used to build the test environment and define variables; Sampling module: used to collect data on 5G terminal throughput according to the current sampling interval; Statistics and adaptive module: used to calculate the original statistics within the window and adaptively determine the order of fractional derivatives; Fractional derivative calculation module: used to accurately calculate the fractional derivative sequence based on the determined order; Fluctuation module: used to count fractional derivative data and determine local throughput fluctuation; Smoothing reconstruction module: used to reconstruct the throughput based on the closed smoothing model; Interval update module: used to adaptively update the sampling interval and set the next sampling window based on the reconstruction results and fluctuation; Termination and output module: used to determine whether the test is completed and output the smoothed throughput sequence and sampling interval sequence.

[0049] By decomposing the various functions of the method into modular system units, the method improves the scalability and maintainability of the system design. The environment initialization module uniformly configures test parameters and variables, laying a consistent operating foundation for the system; the sampling module is responsible for acquiring data with high time series precision, ensuring the real-time nature of information input; the statistics and adaptation module automatically processes window data and generates differential orders, avoiding manual intervention and empirical parameter setting errors; the fractional order calculation and fluctuation evaluation modules work together to convert data into advanced indicators for measuring network dynamics; the smooth reconstruction and interval update module continuously optimizes data quality and sampling strategies based on real-time feedback; and the termination output module implements closed-loop control and results provision for the entire test process. This modular design solves the problems of traditional systems with high coupling and difficulty in flexible upgrades or reuse, facilitating rapid integration and customization for different application scenarios and hardware platforms, and is beneficial for achieving efficient and reliable network testing and monitoring in industrial deployments.

[0050] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0051] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A test and control method for a 5G terminal, characterized in that: include: S1. Adaptively adjust the sampling interval and starting time of the next window based on the smooth reconstruction result and local fluctuation; S2. Based on the sampling interval of the current window, calculate each sampling time point in sequence and synchronously obtain the throughput rate at the corresponding time; S3. Calculate the sample mean and sample standard deviation for the sampled samples, and adaptively determine the order of the fractional derivative according to the coefficient of variation; S4. performing a derivative operation on the sampled data using the fractional binomial coefficients according to the adaptively determined fractional order to obtain a fractional derivative sequence; S5. Perform statistical analysis on the fractional derivative sequence, calculate its sample mean and sample standard deviation, and calculate the local throughput rate fluctuation based on the standard deviation; S6. Constructing a smoothing coefficient according to a predetermined closed-form smoothing algorithm, performing weighted reconstruction on the sampled throughput sequence, and outputting a smoothed throughput sequence; S7. Update the sampling interval based on the smooth reconstruction result and the local fluctuation amount, and set the start time and sampling interval of the next test window; S8. Determine whether a preset end condition is met. If so, output the smoothed throughput sequence and the adaptive sampling interval sequence and terminate the test.

2. A test and control method for a 5G terminal according to claim 1, characterized in that: The adaptive adjustment of the sampling interval and the starting time of the next window based on the smooth reconstruction result and the local fluctuation amount specifically includes: Set the total test duration to , the first window sampling interval is ; Record the test start time , the test ends at ; Set the throughput acquisition function to , ;in, is the set of real numbers greater than 0; Calculate the number of sampling points per window ; Initialize the loop variable: , , ;in, Number the window; For the Window start time; For the Window sampling interval.

3. A test and control method for a 5G terminal according to claim 2, characterized in that: The sampling interval based on the current window is used to calculate each sampling time point in sequence, and the throughput rate at the corresponding time point is obtained synchronously, specifically including: when When established, execute this window Otherwise, go to step S8 and end; right Perform the following steps: S201, calculate the Sampling time: ; S202, Synchronous Collection Throughput: ;in, is the sampling point index within the window; For the Window Second sampling moment; No. Window The throughput rate obtained by sampling.

4. A test and control method for a 5G terminal according to claim 3, characterized in that: The calculation of the sample mean and sample standard deviation of the samples, and adaptively determining the order of the fractional derivative according to the coefficient of variation, specifically includes: Calculate the original mean and standard deviation of this window: ； ； in, For the The average sample throughput of the window; For the The sample standard deviation of the window; Calculate the coefficient of variation and fractional order of this window: , , ;in, For the coefficient of variation of the window; For the The order of the fractional derivatives of the window.

5. A test and control method for a 5G terminal according to claim 4, characterized in that: The method of performing derivative operations on the sampled data using fractional binomial coefficients according to the adaptively determined fractional order to obtain a fractional derivative sequence specifically includes: For each calculate: ; in, , is the fractional binomial coefficient; For the Window The fractional derivative of samples; To sum the index.

6. A test and control method for a 5G terminal according to claim 5, characterized in that: The statistical analysis of the fractional derivative sequence is performed to calculate its sample mean and sample standard deviation, and the local throughput rate fluctuation is calculated based on the standard deviation, specifically including: Calculate the Windowed fractional derivative mean ; Calculate the Standard deviation of windowed fractional derivatives ; Set the The local throughput fluctuation of the window is .

7. A test and control method for a 5G terminal according to claim 6, characterized in that: The method of constructing a smoothing coefficient according to a predetermined closed-form smoothing algorithm, weightedly reconstructing the sampled throughput sequence, and outputting a smoothed throughput sequence specifically includes: Construction Closed-form smoothing coefficient of the window ; To this window , smooth construction Window Smoothed throughput of subsamples : 。 8. A test and control method for a 5G terminal according to claim 7, characterized in that: The updating of the sampling interval based on the smooth reconstruction result and the local fluctuation amount, and setting the start time and sampling interval of the next test window, specifically includes: like , let the global benchmark average throughput ; Set up the first The sampling interval of the window is ; Set up the first Window The sampling time is , ; Return to step S2.

9. A test and control method for a 5G terminal according to claim 8, characterized in that: The determining whether a preset termination condition is satisfied, and if so, outputting a smoothed throughput sequence and an adaptive sampling interval sequence and terminating the test, specifically includes: when When it is no longer satisfied, the total number of effective processing windows ; Output smoothed throughput sequence , where the index mapping , ; Adaptive sampling interval sequence ;in, For the smoothed throughput samples; is the global sample number of the smoothed throughput linear sequence; For the The actual sampling interval used by the window; The window index.

10. A system using the test and control method for 5G terminals according to claim 9, characterized in that: include: Initialization module: used to build the test environment and define variables; Sampling module: used to collect data on 5G terminal throughput according to the current sampling interval; Statistics and adaptive module: used to calculate the original statistics within the window and adaptively determine the order of fractional derivatives; Fractional derivative calculation module: used to accurately calculate the fractional derivative sequence based on the determined order; Fluctuation module: used to count fractional derivative data and determine local throughput fluctuation; Smoothing reconstruction module: used to reconstruct the throughput based on the closed smoothing model; Interval update module: used to adaptively update the sampling interval and set the next sampling window based on the reconstruction results and fluctuation; Termination and output module: used to determine whether the test is completed and output the smoothed throughput sequence and sampling interval sequence.

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