Wireless signal quality monitoring method, system and device in mobile communication scenario

Abstract

This invention discloses a method, system, and device for monitoring wireless signal quality in mobile communication scenarios, relating to the field of computer signal monitoring technology. The method includes: collecting data and constructing a signal quality generation state vector; performing time-series correlation modeling on the signal quality generation state vector to generate a signal quality generation trajectory; performing quality inversion analysis to back-map the wireless signal quality change results to the combination of generation state components, identifying the dominant generation path type; determining a signal quality generation reference system; comparing and analyzing the signal quality generation trajectory with the signal quality generation reference system to establish a comparative analysis result; and outputting wireless signal quality monitoring results based on the comparative analysis result and the dominant generation path type. This invention solves the technical problem in existing technologies where it is difficult to conduct targeted monitoring of wireless signal quality based on differences in mobile communication scenarios, leading to insufficient reliability of monitoring results, and achieves the technical effect of improving the reliability of monitoring results.

Description

Technical Field

[0001] This invention relates to the field of computer signal monitoring technology, specifically to methods, systems, and devices for monitoring wireless signal quality in mobile communication scenarios. Background Technology

[0002] With the rapid development of mobile communication technology, the communication needs of mobile terminals in high-speed movement, cross-regional handover, and various complex environments are constantly increasing. Wireless signal quality has become one of the key factors affecting communication service experience and network operation efficiency. Existing wireless signal quality monitoring technologies are usually based on physical layer measurement indicators on the terminal side or simple statistical analysis methods to independently or statically evaluate parameters such as signal strength, signal-to-noise ratio, and bit error rate. In practical applications, these methods often ignore the correlation between terminal-side wireless measurement data, link layer transmission behavior data, and network-side scheduling feedback data, making it difficult to reflect the dynamic formation mechanism of wireless signal quality evolution over time. In addition, during mobile communication of computer devices, the dominant factors causing changes in wireless signal quality vary significantly under different communication scenarios, and a single indicator or empirical threshold is insufficient to accurately characterize the causes of signal quality changes. When the terminal is in a complex or rapidly changing mobile communication scenario, existing technologies struggle to distinguish signal quality fluctuations caused by terminal movement, link state changes, or network scheduling strategies, easily leading to distorted or misjudged monitoring results. Summary of the Invention

[0003] This application provides a method, system, and device for monitoring wireless signal quality in mobile communication scenarios, which solves the technical problem in the prior art that it is difficult to conduct targeted monitoring of wireless signal quality based on the differences in mobile communication scenarios, resulting in insufficient reliability of monitoring results.

[0004] The first aspect of this application provides a method for monitoring wireless signal quality in a mobile communication scenario, the method comprising:

[0005] During mobile communication in computer devices, wireless measurement data, link layer transmission behavior data, and network-side scheduling feedback data are collected from the terminal side. Based on the collected data, a signal quality generation state vector is constructed to characterize the formation mechanism of wireless signal quality. The signal quality generation state vector is then used for time-series correlation modeling to generate a signal quality generation trajectory describing the evolution of wireless signal quality over time. This trajectory is then used to perform quality inversion analysis, mapping the observed changes in wireless signal quality back to the corresponding combinations of generation state components to pinpoint the dominant generation path type causing the current signal quality change. The current mobile communication scenario of the terminal is obtained, and a signal quality generation reference system corresponding to this scenario is determined. The signal quality generation trajectory is compared and analyzed with the signal quality generation reference system to establish a comparison analysis result. Finally, the wireless signal quality monitoring result is output based on the comparison analysis result and the dominant generation path type.

[0006] A second aspect of this application provides a wireless signal quality monitoring system for mobile communication scenarios, the system comprising:

[0007] Data Acquisition Module: During mobile communication of computer devices, this module collects wireless measurement data from the terminal side, link layer transmission behavior data, and network-side scheduling feedback data. Based on the collected data, it constructs a signal quality generation state vector to characterize the formation mechanism of wireless signal quality. Temporal Modeling Module: This module performs temporal correlation modeling on the signal quality generation state vector to generate a signal quality generation trajectory describing the evolution of wireless signal quality over time. Quality Inversion Module: This module performs quality inversion analysis using the signal quality generation trajectory, mapping the observed wireless signal quality changes back to the corresponding generation state component combinations to identify the dominant generation path type causing the current signal quality change. Scene Determination Module: This module acquires the current mobile communication scene of the terminal and determines the signal quality generation reference system corresponding to that scene. Comparison Analysis Module: This module compares the signal quality generation trajectory with the signal quality generation reference system to establish comparison analysis results. Result Output Module: This module outputs the wireless signal quality monitoring results based on the comparison analysis results and the dominant generation path type.

[0008] A third aspect of this application provides an electronic device, comprising: a memory for storing executable instructions; and a processor for executing the executable instructions stored in the memory to implement the wireless signal quality monitoring method for mobile communication scenarios provided in this application.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] In mobile communication using computer devices, wireless measurement data from the terminal side, link-layer transmission behavior data, and network-side scheduling feedback data are collected. Based on the collected data, a signal quality generation state vector is constructed to characterize the formation mechanism of wireless signal quality. Next, time-series correlation modeling is performed on the signal quality generation state vector to generate a signal quality generation trajectory describing the evolution of wireless signal quality over time. Furthermore, quality inversion analysis is performed using the signal quality generation trajectory, mapping the observed wireless signal quality changes back to the corresponding generation state component combinations to identify the dominant generation path type causing the current signal quality change. Then, the current mobile communication scenario of the terminal is obtained, and the corresponding signal quality generation reference system is determined. The signal quality generation trajectory is compared and analyzed with the signal quality generation reference system to establish a comparative analysis result. Finally, the wireless signal quality monitoring result is output based on the comparative analysis result and the dominant generation path type. This solves the technical problem in existing technologies where it is difficult to conduct targeted monitoring of wireless signal quality based on differences in mobile communication scenarios, leading to insufficient reliability of monitoring results, and achieves the technical effect of improving the reliability of monitoring results. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic flowchart of a wireless signal quality monitoring method in a mobile communication scenario provided in an embodiment of this application.

[0013] Figure 2 This is a schematic diagram of the structure of a wireless signal quality monitoring system in a mobile communication scenario provided in an embodiment of this application;

[0014] Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of this application.

[0015] Figure labeling: Data acquisition module 11, time series modeling module 12, quality inversion module 13, scene determination module 14, comparative analysis module 15, result output module 16, processor 21, memory 22, input device 23, output device 24. Detailed Implementation

[0016] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0017] Example 1, as Figure 1 As shown in the figure, this application provides a method for monitoring wireless signal quality in a mobile communication scenario, wherein the method includes:

[0018] During mobile communication of computer devices, wireless measurement data, link layer transmission behavior data, and network-side scheduling feedback data are collected from the terminal side. Based on the collected data, a signal quality generation state vector is constructed to characterize the formation mechanism of wireless signal quality.

[0019] During mobile communication in computer devices, the terminal collects real-time wireless measurement data via a wireless interface. This terminal-side wireless measurement data includes at least reference signal received power, reference signal received quality, signal-to-noise ratio, channel quality indicator, and cell handover related measurement results. Simultaneously, it acquires link-layer transmission behavior data from the link layer, which includes at least packet retransmission counts, modulation and coding schemes, scheduling intervals, transmission delays, and packet loss. It also acquires scheduling feedback data corresponding to the terminal from the network side, which includes at least scheduling resource allocation ratios, time-frequency resource block allocation results, power control commands, and scheduling priority information. The acquired terminal-side wireless measurement data, link-layer transmission behavior data, and network-side scheduling feedback data are timestamped according to a unified time base and synchronized based on a preset sampling period to form a multi-source data set within the same time slice. The preset sampling period is set to 100ms~1000ms, preferably 200ms or 500ms. The maximum allowable time deviation threshold for timestamp alignment is set to no more than 20% of the sampling period. For data from different sources, outlier removal, missing value imputation, and normalization are performed respectively. Outlier removal adopts a three-standard-deviation rule based on a sliding time window. The length of the sliding time window is set to 5 to 10 sampling periods to eliminate the impact of dimensional differences on subsequent modeling.

[0020] After data preprocessing, based on the sequential relationships between environmental state, link state, and network scheduling state during the formation of wireless signal quality, the multi-source data set is systematically combined. Terminal-side wireless measurement data reflecting environmental and channel states, link-layer transmission behavior data reflecting link bearer states, and network-side scheduling feedback data reflecting resource control states are sequentially mapped into multiple generative state components. These components are then concatenated according to a preset component order and dimensions to construct a signal quality generative state vector characterizing the wireless signal quality formation mechanism. This signal quality generative state vector has a fixed dimension and defined component meaning at each sampling time, serving as the foundational input for subsequent temporal correlation modeling and signal quality generation trajectory construction.

[0021] Furthermore, in the process of mobile communication of computer devices, the collection of terminal-side wireless measurement data, link layer transmission behavior data and network-side scheduling feedback data adopts a strategy that combines event triggering and periodic collection.

[0022] Periodic acquisition is used to obtain basic data reflecting the normal evolution trend of wireless signal quality, while event-triggered acquisition is used to obtain timely key data when significant changes occur in the wireless communication state. Specifically, for terminal-side wireless measurement data, measurement results such as signal strength, signal-to-noise ratio, and channel quality indication are periodically acquired according to a preset periodic sampling interval, preferably 100ms to 1s. When preset events such as cell handover, reference signal received power decreasing by more than 3dB within two consecutive sampling periods, signal-to-noise ratio suddenly dropping by more than 5dB, or wireless link interruption recovery are detected, encrypted acquisition of terminal-side wireless measurement data is immediately triggered. For link layer transmission behavior data, the number of packet retransmissions, delay, and packet loss are periodically summarized according to a preset statistical window (500ms to 2s). When the link layer detects that the number of retransmissions exceeds a preset retransmission threshold (e.g., 3 times) within the statistical window, the average delay increases by more than 30% compared to the historical average, or the packet loss rate exceeds 5%, event acquisition of link layer transmission behavior data is triggered to capture transient changes in the link state. For network-side scheduling feedback data, resource allocation and power control related information are acquired according to the scheduling cycle (1ms~10ms); when the network-side scheduling policy is adjusted, the number of resource blocks allocated to the terminal changes by more than a preset proportion (e.g., 20%) within a continuous scheduling cycle, or the terminal priority changes, the event collection of network-side scheduling feedback data is triggered.

[0023] For example, in a mobile communication scenario where the terminal is moving on an urban road, the system sets the periodic acquisition period for terminal-side wireless measurement data, link layer transmission behavior data, and network-side scheduling feedback data to 100 milliseconds. When data is acquired at 10:00:00:000 according to the periodic acquisition strategy, the terminal-side wireless measurement data shows that the reference signal received power is approximately -85dBm, the reference signal received quality is approximately -9dB, the signal-to-noise ratio is approximately 18dB, and the channel quality indication value is 11. The link layer transmission behavior data shows that the number of packet retransmissions within the statistical window is 1, the average transmission delay is approximately 25 milliseconds, and the packet loss rate is approximately 0.2%. The network-side scheduling feedback data shows that the terminal's time-frequency resource block allocation ratio is approximately 20%, the modulation and coding scheme used is 64QAM, and the transmit power control command is at a normal level. Subsequently, during the terminal's movement, a cell handover event occurred at 10:00:00.350, triggering the event acquisition mechanism. The system immediately collected corresponding multi-source data. Terminal-side wireless measurement data showed a decrease in reference signal received power to approximately -97dBm, a decrease in reference signal received quality to approximately -14dB, a decrease in signal-to-noise ratio to approximately 6dB, and a decrease in channel quality indicator to 5. Link-layer transmission behavior data showed an increase in packet retransmission count to 6, an increase in average transmission delay to approximately 78 milliseconds, and an increase in packet loss rate to approximately 3.5%. Network-side scheduling feedback data showed a decrease in the time-frequency resource block allocation ratio to approximately 12%, an adjustment in modulation and coding scheme to 16QAM, and a power increase command in the transmit power control. After aligning the aforementioned periodically collected data and event-triggered data with timestamps, handling outliers, and normalizing the mapping, the system combined the terminal-side wireless measurement data, link-layer transmission behavior data, and network-side scheduling feedback data in a predetermined order to construct the signal quality generation state vector for the corresponding time moment. This vector is used for subsequent time-series correlation modeling and signal quality generation trajectory analysis, thereby achieving continuous characterization of the wireless signal quality change process.

[0024] Furthermore, a signal quality generator state vector is constructed based on the collected data to characterize the formation mechanism of wireless signal quality, including:

[0025] The terminal-side wireless measurement data, link-layer transmission behavior data, and network-side scheduling feedback data collected are processed hierarchically according to time resolution and data update cycle to generate multiple time-scale data subsets. Within each time-scale data subset, data decorrelation processing and normalization mapping are performed to eliminate dimensional differences and statistical coupling between different data sources. The evolutionary order of environment-link-scheduling in the wireless signal quality formation process is obtained. Based on this evolutionary order, the processed data subsets are combined with sequential constraints to construct a generator state component sequence. A time-consistent weight allocation rule is applied to the generator state component sequence to form a signal quality generator state vector with fixed dimensions and sequential constraints.

[0026] Preferably, based on the sampling frequency, temporal resolution, and update cycle of each type of data, the data is divided into multiple time-scale subsets. Each time-scale includes at least a short time-scale (sampling cycle of 50-200ms) high-frequency data subset reflecting transient changes, and a longer time-scale (sampling cycle of 1-5s) low-frequency data subset reflecting trend changes. Within each time-scale subset, decorrelation and normalization mapping are performed on data from different sources. The decorrelation process uses a feature selection method based on the Pearson correlation coefficient. When the absolute value of the correlation coefficient between any two data features within the same time-scale is greater than 0.8, the feature with greater information content is retained, and the weight of the other feature is reduced. The normalization mapping process uses a minimum-maximum normalization method to uniformly map data with different physical meanings and dimensional ranges to the [0, 1] numerical range, thereby eliminating dimensional differences and numerical scale inconsistencies between terminal-side, link-layer, and network-side data.

[0027] After completing the hierarchical processing and data preprocessing, the evolution order of environmental state, link state, and network scheduling state during the formation of wireless signal quality is obtained. The environmental state corresponds to terminal-side wireless measurement data, the link state corresponds to link-layer transmission behavior data, and the network scheduling state corresponds to network-side scheduling feedback data. The evolution order is set as environmental state - link state - network scheduling state. Based on this evolution order, processed subsets of data at different time scales are combined according to a preset sequential order, mapping various data types into sequentially arranged generative components to construct a generative component sequence. In this generative component sequence, a weighting rule is applied to each generative component based on its temporal consistency within a continuous time window. The continuous time window length is set to 5-10 sampling periods. When the variance of a generative component within the continuous time window is less than a preset stability threshold of 0.05, a higher weight of 0.6-0.8 is assigned; when the variance of a generative component is greater than or equal to the stability threshold, a lower weight of 0.2-0.4 is assigned. By weighted integration of the generated state component sequences, a signal quality generated state vector with fixed dimensions, clear component order, and weight constraints is finally formed. This vector is used to characterize the formation mechanism of wireless signal quality and serves as input for subsequent time-series correlation modeling.

[0028] Furthermore, constructing a signal quality generator state vector based on the collected data to characterize the formation mechanism of wireless signal quality also includes:

[0029] A credibility assessment is performed based on the numerical stability, consistency of change, and matching degree with the constraints of the mobile communication scenario of the corresponding generator components within a continuous time window; the corresponding signal quality generator vector is then labeled based on the credibility assessment results.

[0030] Preferably, within a continuous time window, the numerical stability, consistency of change, and matching degree with the constraints of the mobile communication scenario are evaluated for each generator component in the signal quality generator vector. The length of the continuous time window can be preset to 3-10 sampling periods, preferably 5 sampling periods. Numerical stability characterizes whether the fluctuation amplitude of the generator component within the continuous time window is within a preset stability range. Specifically, it is obtained by calculating the ratio of the standard deviation of the generator component to its mean within the time window. When the stability index is less than a preset stability threshold, the generator component is considered to have high numerical stability. The stability threshold can be preset to 0.2-0.5. Consistency of change characterizes whether the change trend of the generator component maintains a consistent direction or a continuous rate of change within the continuous time window. Specifically, it is obtained by judging the consistency ratio of the change directions of adjacent sampling points within the time window. When the consistency ratio of the change directions is higher than a preset consistency threshold, the consistency requirement is met. The consistency threshold can be preset to 70%-90%. The matching degree with the constraints of the mobile communication scenario is used to characterize whether the change characteristics of the generated state components conform to the reasonable change range under the current mobile communication scenario of the terminal. Specifically, it can be achieved by comparing the change amplitude and change rate of the generated state components with the reference interval corresponding to the mobile communication scenario. When the proportion of time that the generated state components fall within the reference interval is higher than the preset matching degree threshold, the matching degree is determined to meet the requirements. The matching degree threshold can be preset to 80%~95%. Based on the above numerical stability, change consistency and matching degree evaluation results, a corresponding credibility evaluation value is calculated for each generated state component. The credibility evaluation value can be obtained by weighted summation and compared with a preset credibility threshold. The credibility threshold can be preset to 0.6~0.8. If the credibility evaluation value of a certain generated state component is lower than the credibility threshold, it is determined that the generated state component has an anomaly or uncertainty within the corresponding time window. Based on the reliability assessment results, the corresponding signal quality generator vectors are labeled. This labeling process includes at least attaching a reliability flag or anomaly flag to the generator vector, indicating whether the vector requires weight reduction, result notification, or anomaly handling during subsequent time-series correlation modeling or quality inversion analysis. This labeling process improves the reliability and robustness of subsequent signal quality monitoring results without affecting the overall structure of the signal quality generator vectors.

[0031] The numerical stability, consistency of change, and matching degree with the constraints of the mobile communication scenario are evaluated for each generator component in the signal quality generator vector. Specifically, this includes: First, within a continuous time window, extracting the numerical sequence of each generator component and calculating its mean, fluctuation amplitude, or range of change within the time window to characterize its numerical stability. When the fluctuation amplitude is within a preset stability range, the generator component is considered to have high numerical stability within the current time window; when the fluctuation amplitude exceeds the preset stability range, its numerical stability is considered low. Second, considering the change trend of the generator component within the continuous time window, calculating the direction and rate of change of the numerical values ​​at adjacent moments to characterize the consistency of change. When the change direction of the generator component remains consistent within the time window, or the rate of change changes smoothly and continuously between adjacent moments, the generator component is considered to have high consistency of change; when the change direction frequently reverses or the rate of change abruptly changes, its consistency of change is considered low. Further, considering the current mobile communication scenario of the terminal, the matching degree of the generator component with the constraints of the mobile communication scenario is evaluated. Specifically, based on different mobile communication scenarios, corresponding reasonable change ranges or constraint thresholds are pre-set. The numerical level, change amplitude, and change rate of the generated state component within the current time window are compared with the constraint thresholds. When the change characteristics of the generated state component meet the constraint conditions of the mobile communication scenario, its matching degree is determined to be high; when it does not meet the constraint conditions, its matching degree is determined to be low.

[0032] After evaluating the numerical stability, consistency of change, and matching degree with the constraints of the mobile communication scenario for each generator component in the signal quality generator vector, the evaluation results are comprehensively processed to generate a reliability evaluation value for the corresponding generator component. Specifically, for each generator component, within a continuous time window, based on the numerical stability evaluation results, consistency of change evaluation results, and scenario matching degree evaluation results, corresponding stability evaluation values, consistency evaluation values, and matching degree evaluation values ​​are obtained. Each evaluation value is mapped to a unified evaluation interval to represent the reliability of the generator component under the corresponding evaluation dimension. Based on this, the stability evaluation value, consistency evaluation value, and matching degree evaluation value are weighted and combined to obtain the reliability evaluation value of the generator component. The weights of each evaluation dimension can be set according to the mobile communication scenario type or monitoring requirements to reflect the relative importance of different evaluation dimensions to the reliability of the generator component. When the reliability evaluation value is higher than a preset reliability threshold, the corresponding generator component is determined to be a high-reliability generator component; when the reliability evaluation value is lower than the reliability threshold, the corresponding generator component is determined to be a low-reliability generator component.

[0033] For example, taking wireless signal quality monitoring in a mobile communication scenario on urban roads as an example, wireless measurement data, link layer transmission behavior data, and network-side scheduling feedback data are collected from the terminal side within a continuous 5-second time window with a sampling period of 1 second. Within this time window, the sampling sequence of the reference signal received power in the terminal side wireless measurement data is -88dBm, -87dBm, -89dBm, -88dBm, -87dBm, and the sampling sequence of the signal-to-noise ratio is 15dB, 16dB, 15dB, 16dB, 15dB. The fluctuation range of the reference signal received power within the time window is 2dB, which does not exceed the preset stable threshold of 3dB. The direction of change remains consistent and is within the reasonable signal range for a mobile communication scenario on urban roads. Based on the aforementioned numerical stability, consistency of change, and scenario constraint matching, according to the preset evaluation mapping rules, the numerical stability evaluation value, consistency of change evaluation value, and scenario matching degree evaluation value corresponding to the generated state component are calculated respectively. All evaluation values ​​are within the high range of the preset evaluation interval, indicating that the generated state component has a high degree of reliability within the current time window. Within the same time window, the number of packet retransmissions in the link layer transmission behavior data are 1, 1, 2, 1, and 1 respectively, with corresponding transmission delays of 28ms, 30ms, 35ms, 29ms, and 30ms. The overall fluctuation of the retransmission count is small, but it increases at individual sampling moments. Its numerical stability and consistency of change evaluation results are at a moderate level, and it still meets the link load constraint conditions under the urban road scenario. The corresponding scenario matching degree evaluation result is at a moderately high level. Network-side scheduling feedback data shows that the proportion of time-frequency resources allocated to the terminal remains between 15% and 18% within the time window, the modulation and coding level is basically stable, and no scheduling strategy switching events occur. Its numerical stability, consistency of change, and matching degree with the urban road mobile communication scenario are all high. Based on the above evaluation results, the numerical stability evaluation value, change consistency evaluation value, and scene matching degree evaluation value of each generator component are mapped to a unified evaluation interval and weighted according to preset weights. The resulting reliability evaluation values ​​are approximately 0.90 for the terminal-side wireless measurement generator component, approximately 0.75 for the link-layer generator component, and approximately 0.92 for the network-side scheduling generator component. Further, the reliability evaluation values ​​are compared with a preset reliability threshold of 0.80. The terminal-side wireless measurement generator component and the network-side scheduling generator component are marked as high-reliability generator components, and the link-layer generator component is marked as a generally reliable generator component. These marking results are then appended to the corresponding signal quality generator vector. This allows for adjustments to the generator component's participation weight or triggering of anomaly alerts based on its reliability during subsequent time-series correlation modeling and quality inversion analysis, thereby improving the reliability and stability of the wireless signal quality monitoring results.

[0034] The signal quality generation state vector is executed to perform temporal correlation modeling, generating a signal quality generation trajectory that describes the evolution of wireless signal quality over time.

[0035] Furthermore, the signal quality generation state vector is used for temporal correlation modeling to generate a signal quality generation trajectory that describes the evolution of wireless signal quality over time, including:

[0036] The process involves: acquiring the mobile communication scenario identifier corresponding to the signal quality generator state vector during data acquisition; determining the time-series correlation parameter set corresponding to the scenario based on the mobile communication scenario identifier; performing time alignment and correlation window division on the signal quality generator state vectors at consecutive time points according to the time-series correlation parameter set to form a generator state vector sequence under scenario constraints; calculating the evolution increment between adjacent signal quality generator state vectors in the generator state vector sequence based on the change rate threshold and correlation window length corresponding to the mobile communication scenario; applying the evolution increment and the evolution discrimination rule corresponding to the mobile communication scenario to the generator state vector sequence for continuous evolution constraints and scenario-triggered mutation marking processing; and constructing a signal quality generation trajectory based on the processed generator state vector sequence.

[0037] Preferably, the mobile communication scenario identifier corresponding to each signal quality generation state vector during the data acquisition period is obtained. The mobile communication scenario identifier characterizes the current communication state type of the terminal, and includes at least one of the following: stationary or low-speed mobile scenario, high-speed mobile scenario, dense cell coverage scenario, or frequent handover scenario. Based on the mobile communication scenario identifier, a set of time-series association parameters pre-associated with that scenario is invoked. The time-series association parameter set includes at least parameters such as time alignment tolerance, association window length, and change rate threshold. The time alignment tolerance in the time-series association parameter set is preset based on the timestamp accuracy of the acquired data, preferably 5ms to 50ms; the association window length is set according to the changing characteristics of the mobile communication scenario, preferably 1s to 3s in low-speed or stationary scenarios, and preferably 100ms to 500ms in high-speed mobile or frequent handover scenarios. After determining the set of time-series correlation parameters, time alignment processing is performed on the signal quality generator vectors acquired at consecutive time points according to the time-series correlation parameter set to eliminate time offsets caused by different data sources and sampling periods. The time-aligned generator vectors are then divided into windows based on a preset correlation window length to form a generator vector sequence that satisfies the constraints of the mobile communication scenario. In this generator vector sequence, based on the rate of change threshold and correlation window length corresponding to the mobile communication scenario, the evolution increment between signal quality generator vectors at adjacent time points is calculated one by one. This evolution increment characterizes the magnitude and direction of change of the wireless signal quality formation state over continuous time. The rate of change threshold limits the maximum allowable change magnitude between adjacent signal quality generator vectors. Its value is determined based on historical statistical results or empirical rules, preferably the mean of the rate of change of the Euclidean distance between generator vectors plus one to two standard deviations. When the evolution increment between adjacent generator vectors exceeds the rate of change threshold, it is determined that the continuous evolution condition is not met. Furthermore, using the evolutionary increment and combining it with the evolutionary discrimination rules corresponding to the mobile communication scenario, continuous evolution constraint processing is performed on the generated state vector sequence to determine whether the changes in the generated state vector over time satisfy the reasonable evolution conditions under the scenario. When the evolutionary increment exceeds the change rate threshold of the corresponding scenario, a scenario-triggered mutation marker is applied to the corresponding time point to indicate abnormal changes or state abrupt changes in wireless signal quality. The evolutionary discrimination rules include at least a continuity discrimination rule and a mutation discrimination rule. The continuity discrimination rule is used to determine whether the evolutionary increment maintains the same direction or slow change characteristics within a continuous association window. The mutation discrimination rule is used to determine whether the evolutionary increment experiences a sudden increase or decrease within a single association window. When the mutation discrimination rule is satisfied, a scenario-triggered mutation marker is applied to the corresponding time point.After completing the continuous evolution constraint and mutation labeling processing, the generated state vector sequence and its corresponding evolution increment and mutation label are correlated and integrated in chronological order to construct a signal quality generation trajectory describing the evolution of wireless signal quality over time. This signal quality generation trajectory serves as the foundational data for subsequent quality inversion analysis and scene comparison analysis.

[0038] By performing quality inversion analysis using the signal quality generation trajectory, the observed wireless signal quality changes are mapped inversely to the corresponding generator state component combinations, thus identifying the dominant generation path type that causes the current signal quality change.

[0039] Furthermore, performing quality inversion analysis using the signal quality generation trajectory includes:

[0040] In the signal quality generation trajectory, trajectory change features characterizing the amplitude, duration, and recovery characteristics of wireless signal quality changes are extracted. These trajectory change features are then correlated and matched with the evolution features of each generator component in the signal quality generation vector within the corresponding time period to construct a correspondence between the trajectory change features and the generator components. Based on this correspondence, the contribution index of each generator component to the current wireless signal quality change is calculated. The distribution results of the contribution index are used to locate the dominant generation path type that causes the current signal quality change.

[0041] Preferably, a target time interval for changes in wireless signal quality is determined within the signal quality generation trajectory, and trajectory change features characterizing the changes in wireless signal quality are extracted within this target time interval. The trajectory change features include at least the amplitude of the wireless signal quality change, reflecting the degree to which the signal quality deviates from the normal level; the duration of the change, reflecting the length of time the abnormal or degraded signal quality state is maintained; and the change recovery characteristics, reflecting the speed and trend of the signal quality recovering from the abnormal state to a stable state. The amplitude of the wireless signal quality change can be quantified by the change in Reference Signal Received Quality (RSRQ) or Channel Quality Indicator (CQI), the duration of the change can be set to the length of time during which the stability threshold is continuously not met, and the change recovery characteristics can be characterized by the time required for the signal quality index to recover to the stable interval or the change slope. After obtaining the trajectory change features, a signal quality generation state vector sequence corresponding to the target time interval is extracted, and the evolution characteristics of each generation state component in the generation state vector within the corresponding time period are further obtained. These evolution characteristics include at least the amplitude, rate, and duration of change of the generation state components. The trajectory change features are correlated and matched one by one with the evolution features of each generator component to construct a correspondence between the trajectory change features and the generator components, which is used to describe the degree of correlation between the changes of different generator components and the overall signal quality change. The change rate can be defined as the difference between the generator component values ​​in adjacent sampling periods, and the change persistence can be defined as the number of time windows in which the generator component continuously exceeds a preset change threshold. Based on the correspondence, the contribution index of each generator component to the current wireless signal quality change is calculated. The contribution index is used to quantify the extent to which the evolution features of the generator components participate in and influence the wireless signal quality change process. A contribution distribution result is formed based on the contribution index corresponding to each generator component. The contribution index can be calculated based on the correlation between the generator component change amplitude and the trajectory change amplitude, the overlap ratio of change time sequence, or the normalized weighted result, and the contribution index is normalized to the [0, 1] interval. Furthermore, based on the contribution distribution results, generator components with contribution values ​​exceeding a preset threshold are selected. Combining the temporal order and continuity of these generator components, a combined analysis is performed to identify the dominant generation path type causing the current change in wireless signal quality. The preset threshold can be set between 0.6 and 0.8. When the contribution value of a single generator component or a combination of generator components exceeds the preset threshold, it is determined to be the dominant generation path. If multiple generator components simultaneously meet the threshold condition, the dominant generation path type is determined according to their order of appearance and duration priority.

[0042] Based on the correspondence between the trajectory change characteristics and the generator components, the contribution index of each generator component to the current wireless signal quality change is calculated. Specifically, firstly, a target time interval for the change in wireless signal quality is determined, and within this target time interval, the amplitude, duration, and recovery characteristics of the wireless signal quality change are obtained as a baseline description of the trajectory change characteristics. Secondly, for each generator component, within the same target time interval, the amplitude, duration, and trend characteristics of the numerical change of the generator component are calculated. The amplitude of the generator component's change is obtained by comparing the maximum and minimum values ​​of the generator component within the target time interval; the duration of the generator component's change is obtained by statistically analyzing the length of time the generator component continuously deviates from its stable interval; and the trend of the generator component's change is determined by judging whether the generator component continuously increases, continuously decreases, or changes first and then stabilizes within the target time interval. After obtaining the evolutionary characteristics of the generator components, amplitude matching calculations are performed on the amplitude of the generator component changes and the amplitude of the wireless signal quality changes. Temporal overlap calculations are also performed on the duration of the generator component changes and the duration of the wireless signal quality changes. Finally, trend consistency judgments are made on the trend of the generator component changes and the trend of the wireless signal quality changes. Amplitude matching measures the closeness between the intensity of the generator component changes and the intensity of the signal quality changes; temporal overlap measures the degree of temporal overlap between abnormal changes in the generator component and abnormal changes in the signal quality; and trend consistency determines whether the direction of the generator component changes is consistent with the direction of the signal quality changes. Based on the calculation results of these three dimensions, corresponding amplitude matching scores, temporal overlap scores, and trend consistency scores are generated for each generator component. These three scores are then combined according to a preset weighting rule to obtain a comprehensive contribution evaluation value for the generator component to the current wireless signal quality change. Finally, the comprehensive contribution evaluation values ​​of all generator components are normalized to ensure that the contribution indicators of each generator component are within a uniform numerical range, thus forming a set of contribution indicators for each generator component to the current wireless signal quality change. The contribution index is used to characterize the relative influence of different generator state components on the current changes in wireless signal quality, providing a quantitative basis for determining the dominant generation path type in the future.

[0043] Obtain the current mobile communication scenario of the terminal and determine the signal quality generation reference system corresponding to the mobile communication scenario.

[0044] Based on terminal-side wireless measurement data, link-layer transmission behavior data, and network-side scheduling feedback data, the current communication state of the terminal is determined. This scenario determination includes at least a comprehensive analysis of the terminal's movement state, cell handover frequency, channel stability, and network load characteristics to identify the current mobile communication scenario type. The mobile communication scenario type includes at least one of the following: stationary or low-speed movement scenario, high-speed movement scenario, dense user access scenario, and frequent cell handover scenario. Specifically, based on a comprehensive analysis of the terminal's displacement speed, number of cell handovers, reference signal received power fluctuation amplitude, and link-layer retransmission rate within a continuous 5-second time window, a stationary or low-speed movement scenario is determined when the terminal's average movement speed is less than 3 km / h and the number of cell handovers is less than or equal to 1; a high-speed movement or frequent handover scenario is determined when the terminal's average movement speed is greater than 60 km / h or at least 3 cell handovers occur within 10 seconds; and a dense user access scenario is determined when the downlink resource block occupancy rate is greater than 80% within the scheduling period and the terminal's average scheduling interval is greater than a preset threshold.

[0045] After determining the mobile communication scenario type, a pre-associated signal quality generation reference system is invoked. This reference system characterizes the typical generation state and normal evolution range of wireless signal quality under the corresponding mobile communication scenario. It includes at least the distribution characteristics of the generation state vector, the evolution characteristics of the generation trajectory, and the rate of change constraints obtained from historical monitoring data or long-term statistical analysis under that scenario. Specifically, for stationary or low-speed mobile scenarios, the rate of change threshold for each component in the generation state vector is set to no more than 1.5 times the corresponding standard deviation; for high-speed mobile or frequent handover scenarios, the rate of change threshold is set to no more than 3 times the corresponding standard deviation; for dense user access scenarios, the rate of change threshold is dynamically relaxed to a range of 2 to 4 times the standard deviation based on the scheduling load. Specifically, based on the mobile communication scenario type, a reference system matching the current scenario is selected from a pre-stored set of scenario reference systems, and this reference system is mapped to a set of reference parameters consistent with the dimension of the signal quality generation trajectory for subsequent comparative analysis. This set of reference parameters is used at least to define the normal fluctuation range and reasonable evolution path of wireless signal quality under the corresponding mobile communication scenario.

[0046] The signal quality generation trajectory is compared and analyzed with the signal quality generation reference system to establish the comparison and analysis results.

[0047] Specifically, a dimensionality consistency check is performed on the signal quality generation trajectory and the signal quality generation reference system to ensure that the generated state vectors at each time point in the generation trajectory are consistent with the reference parameters in the generation reference system in terms of component dimensions, order, and weight meaning. After completing the dimensionality consistency check, the signal quality generation trajectory is mapped to the time window corresponding to the signal quality generation reference system in chronological order. The time window is a sliding window of a preset time length, which can be set to 1s to 5s, preferably 2s. Within each time window, the generated state vector at the corresponding time point in the generation trajectory is compared and analyzed with the corresponding baseline generated state vector or baseline distribution interval in the generation reference system. Specifically, the difference between the numerical amplitude of each component of the generated state vector and the baseline mean of the corresponding component in the generation reference system is calculated, and the difference is normalized to obtain the amplitude deviation. The rate of change of the generated state vector at adjacent time points is compared with a preset rate of change threshold in the generation reference system. The rate of change threshold is preset according to different mobile communication scenarios. For example, in low-speed or stationary scenarios, the rate of change threshold is set to no more than 1.2 times the baseline rate of change, and in high-speed mobile scenarios, the rate of change threshold is set to no more than 1.5 times the baseline rate of change. The evolution trend of the generated state vector within a continuous time window is judged to be consistent with the typical evolution trend in the generation reference system. When the evolution direction is inconsistent for more than three consecutive time windows, it is determined to be a trend deviation. When the amplitude deviation of the generated state vector within the time window is less than the preset deviation threshold, the rate of change does not exceed the rate of change threshold of the corresponding scenario, and the evolution trend is consistent with the generation reference system, it is determined to be a scenario-consistent evolution. The amplitude deviation threshold can be set to ±10% of the baseline mean. When the generated state vector meets any deviation condition, it is determined to be a scenario-deviation evolution. Based on the above comparison process, the deviation of the signal quality generation trajectory in different time periods is summarized and analyzed to form a comparative analysis result that characterizes the difference between the signal quality generation state and the scene reference benchmark. The comparative analysis result includes at least the deviation position, deviation degree, and deviation duration of the generation trajectory in the time dimension, which serves as the basis for subsequent wireless signal quality monitoring result output.

[0048] Based on the comparative analysis results and the dominant generated path type, output the wireless signal quality monitoring results.

[0049] Based on the comparative analysis results, the deviation characteristics of the signal quality generation trajectory relative to the signal quality generation reference system are obtained, including the time and location of the deviation, the magnitude of the deviation, and the duration of the deviation. The magnitude of the deviation is characterized by the normalized deviation value of the generated state vector relative to the mean of the reference system within the corresponding time window. A normalized deviation value greater than 0.15 is considered a slight deviation, and greater than 0.30 is considered a significant deviation. The duration of the deviation is counted by the number of consecutive deviation windows. When the number of consecutive deviation windows is not less than 3 (corresponding to a time length of not less than 1.5 seconds), the wireless signal quality is determined to be in an abnormal state. Furthermore, the type of wireless signal quality anomaly is determined by combining the dominant generation path type. Specifically, an anomaly is classified as follows: when the contribution of environment-related generative components in the dominant generation path is not less than 50%, it is determined to be an environment-dominated anomaly; when the contribution of link-layer generative components is not less than 50%, it is determined to be a link-dominated anomaly; when the contribution of network scheduling-related generative components is not less than 50%, it is determined to be a scheduling-dominated anomaly; and when the contribution of any two types of generative components is not less than 30%, it is determined to be a multi-factor coupled anomaly. After determining the anomaly type, the trajectory deviation characteristics reflected in the comparative analysis results are correlated and integrated with the dominant generation path type to generate a monitoring result parameter set for characterizing the type of wireless signal quality anomaly and its generation mechanism attribution results. The monitoring result parameter set includes at least the deviation degree index of the signal quality anomaly, the anomaly duration index, the dominant generation path identifier, and the corresponding mobile communication scenario information. Finally, according to the preset result organization rules, the monitoring result parameter set is structured and encapsulated to output the wireless signal quality monitoring results. This enables the monitoring results to simultaneously reflect the abnormal performance of wireless signal quality and its corresponding generation mechanism attribution results, thereby providing a basis for subsequent network optimization, fault location, or strategy adjustment.

[0050] Furthermore, based on the comparative analysis results and the dominant generated path type, the wireless signal quality monitoring results are output, including:

[0051] Based on the comparative analysis results, a trajectory offset index reflecting the degree to which the current wireless signal quality deviates from the signal quality generation reference system is calculated; the trajectory offset index is associated and combined with the dominant generation path type to form a monitoring result parameter set characterizing the degree and cause of wireless signal quality anomalies; the monitoring result parameter set is structured and encapsulated according to preset result organization rules to generate wireless signal quality monitoring results containing the degree of quality offset, the dominant generation path identifier, and corresponding scene information.

[0052] Preferably, based on the comparative analysis results, a trajectory offset index is calculated to reflect the degree of deviation of the current wireless signal quality from the signal quality generation reference system. The trajectory offset index quantifies the deviation of the signal quality generation trajectory from the corresponding scenario reference in terms of numerical amplitude, rate of change, or evolution trend. The trajectory offset index is calculated as follows: within the same association window, the Euclidean distance or weighted absolute difference between the signal quality generation state vector and the corresponding reference generation state vector in the signal quality generation reference system is calculated, and the deviation results within a continuous time window are accumulated or averaged to obtain the trajectory offset value. When the trajectory offset value is greater than or equal to a preset offset judgment threshold, it is determined that the current wireless signal quality has an abnormal deviation. The offset judgment threshold is determined based on historical normal communication data statistics, preferably a numerical range corresponding to the average historical trajectory offset value plus one to two standard deviations. After obtaining the trajectory offset index, it is associated and combined with the dominant generation path type to simultaneously reflect the severity of the wireless signal quality anomaly and its causes. Specifically, when the trajectory offset index is within a preset first offset interval, it is determined to be a slight anomaly; when the trajectory offset index is within a preset second offset interval, it is determined to be a moderate anomaly; and when the trajectory offset index exceeds a preset third offset interval, it is determined to be a severe anomaly. The boundary values ​​of each offset interval are set based on the statistical distribution of historical monitoring data in the corresponding mobile communication scenario. Finally, the monitoring result parameter set is structured and encapsulated according to preset result organization rules to generate wireless signal quality monitoring results. The result organization rules include at least arranging the monitoring result parameters in the order of "timestamp-trajectory offset index-anomaly level-dominant generation path identifier-mobile communication scenario identifier," and outputting them in a structured data format. This ensures that the generated wireless signal quality monitoring results clearly include information on the degree of wireless signal quality offset, the dominant generation path identifier, and the corresponding mobile communication scenario information, facilitating subsequent analysis, display, or decision-making applications of the wireless communication status. Furthermore, based on the stability of the trajectory offset degree and the dominant generation path type, instantaneous monitoring results and time-cumulative monitoring results are generated respectively, and wireless signal quality monitoring results are generated based on the instantaneous monitoring results and the time-cumulative monitoring results.

[0053] The degree of trajectory deviation can be quantified by a trajectory deviation index, which can be represented, for example, by the normalized deviation between the current generated state vector and the center value of the corresponding signal quality generation reference system, with a value range of 0 to 1. When the trajectory deviation index is greater than or equal to 0.3 and less than 0.6, it is judged as a slight deviation; when the trajectory deviation index is greater than or equal to 0.6 and less than 0.8, it is judged as a moderate deviation; and when the trajectory deviation index is greater than or equal to 0.8, it is judged as a severe deviation.

[0054] The time window corresponding to the instantaneous monitoring result is set to, for example, 1 to 3 sampling periods. When the trajectory offset index exceeds the corresponding deviation threshold within the time window, the instantaneous monitoring result is generated. The time window corresponding to the cumulative monitoring result is set to, for example, 30 seconds to 5 minutes. It is obtained by performing cumulative statistics or weighted average calculation on the trajectory offset index within the time window.

[0055] The stability of the dominant generation path type can be evaluated by the number of times or the proportion of its consecutive occurrence within the time cumulative monitoring window. For example, when the proportion of the same dominant generation path type occurring within the time window is greater than or equal to 70%, the dominant generation path type is determined to be a stable path; when the proportion is less than 70%, it is determined to be an unstable path.

[0056] When generating wireless signal quality monitoring results, if the instantaneous monitoring result indicates a moderate or severe deviation and the corresponding dominant generation path type is a stable path, the time-cumulative monitoring result is used as the main output; if the instantaneous monitoring result indicates a slight deviation or the dominant generation path type is unstable, the instantaneous monitoring result is used as the main output, or both the instantaneous monitoring result and the time-cumulative monitoring result are output simultaneously.

[0057] In summary, the embodiments of this application have at least the following technical effects:

[0058] In mobile communication using computer devices, wireless measurement data from the terminal side, link-layer transmission behavior data, and network-side scheduling feedback data are collected. Based on the collected data, a signal quality generation state vector is constructed to characterize the formation mechanism of wireless signal quality. Next, time-series correlation modeling is performed on the signal quality generation state vector to generate a signal quality generation trajectory describing the evolution of wireless signal quality over time. Furthermore, quality inversion analysis is performed using the signal quality generation trajectory, mapping the observed wireless signal quality changes back to the corresponding generation state component combinations to identify the dominant generation path type causing the current signal quality change. Then, the current mobile communication scenario of the terminal is obtained, and the corresponding signal quality generation reference system is determined. The signal quality generation trajectory is compared and analyzed with the signal quality generation reference system to establish a comparative analysis result. Finally, the wireless signal quality monitoring result is output based on the comparative analysis result and the dominant generation path type. This solves the technical problem in existing technologies where it is difficult to conduct targeted monitoring of wireless signal quality based on differences in mobile communication scenarios, leading to insufficient reliability of monitoring results, and achieves the technical effect of improving the reliability of monitoring results.

[0059] Example 2, based on the same inventive concept as the wireless signal quality monitoring method in the mobile communication scenario in the foregoing examples, such as... Figure 2As shown, this application provides a wireless signal quality monitoring system for mobile communication scenarios, wherein the system includes:

[0060] Data Acquisition Module 11: During mobile communication of computer equipment, it collects wireless measurement data, link layer transmission behavior data, and network-side scheduling feedback data from the terminal side, and constructs a signal quality generation state vector to characterize the formation mechanism of wireless signal quality based on the collected data; Temporal Modeling Module 12: It performs temporal correlation modeling on the signal quality generation state vector to generate a signal quality generation trajectory describing the evolution of wireless signal quality over time; Quality Inversion Module 13: It performs quality inversion analysis using the signal quality generation trajectory, and maps the observed wireless signal quality change results back to the corresponding generation state component combination to locate the dominant generation path type that causes the current signal quality change; Scene Determination Module 14: It obtains the mobile communication scene where the terminal is currently located and determines the signal quality generation reference system corresponding to the mobile communication scene; Comparison Analysis Module 15: It compares and analyzes the signal quality generation trajectory with the signal quality generation reference system to establish a comparison analysis result; Result Output Module 16: It outputs the wireless signal quality monitoring result based on the comparison analysis result and the dominant generation path type.

[0061] Furthermore, the data acquisition module 11 is used to perform the following methods:

[0062] The terminal-side wireless measurement data, link-layer transmission behavior data, and network-side scheduling feedback data collected are processed hierarchically according to time resolution and data update cycle to generate multiple time-scale data subsets. Within each time-scale data subset, data decorrelation processing and normalization mapping are performed to eliminate dimensional differences and statistical coupling between different data sources. The evolutionary order of environment-link-scheduling in the wireless signal quality formation process is obtained. Based on this evolutionary order, the processed data subsets are combined with sequential constraints to construct a generator state component sequence. A time-consistent weight allocation rule is applied to the generator state component sequence to form a signal quality generator state vector with fixed dimensions and sequential constraints.

[0063] Furthermore, the timing modeling module 12 is used to perform the following methods:

[0064] The process involves: acquiring the mobile communication scenario identifier corresponding to the signal quality generator state vector during data acquisition; determining the time-series correlation parameter set corresponding to the scenario based on the mobile communication scenario identifier; performing time alignment and correlation window division on the signal quality generator state vectors at consecutive time points according to the time-series correlation parameter set to form a generator state vector sequence under scenario constraints; calculating the evolution increment between adjacent signal quality generator state vectors in the generator state vector sequence based on the change rate threshold and correlation window length corresponding to the mobile communication scenario; applying the evolution increment and the evolution discrimination rule corresponding to the mobile communication scenario to the generator state vector sequence for continuous evolution constraints and scenario-triggered mutation marking processing; and constructing a signal quality generation trajectory based on the processed generator state vector sequence.

[0065] Furthermore, the quality inversion module 13 is used to perform the following method:

[0066] In the signal quality generation trajectory, trajectory change features characterizing the amplitude, duration, and recovery characteristics of wireless signal quality changes are extracted. These trajectory change features are then correlated and matched with the evolution features of each generator component in the signal quality generation vector within the corresponding time period to construct a correspondence between the trajectory change features and the generator components. Based on this correspondence, the contribution index of each generator component to the current wireless signal quality change is calculated. The distribution results of the contribution index are used to locate the dominant generation path type that causes the current signal quality change.

[0067] Furthermore, the result output module 16 is used to perform the following method:

[0068] Based on the comparative analysis results, a trajectory offset index reflecting the degree to which the current wireless signal quality deviates from the signal quality generation reference system is calculated; the trajectory offset index is associated and combined with the dominant generation path type to form a monitoring result parameter set characterizing the degree and cause of wireless signal quality anomalies; the monitoring result parameter set is structured and encapsulated according to preset result organization rules to generate wireless signal quality monitoring results containing the degree of quality offset, the dominant generation path identifier, and corresponding scene information.

[0069] Furthermore, the result output module 16 is used to perform the following method:

[0070] Based on the degree of trajectory deviation and the stability of the dominant generated path type, instantaneous monitoring results and time-accumulated monitoring results are generated respectively, and wireless signal quality monitoring results are generated based on the instantaneous monitoring results and the time-accumulated monitoring results.

[0071] Furthermore, the data acquisition module 11 is used to perform the following methods:

[0072] In the process of mobile communication of computer equipment, the collection of terminal-side wireless measurement data, link layer transmission behavior data and network-side scheduling feedback data adopts a strategy of combining event triggering and periodic collection.

[0073] Furthermore, the data acquisition module 11 is used to perform the following methods:

[0074] A credibility assessment is performed based on the numerical stability, consistency of change, and matching degree with the constraints of the mobile communication scenario of the corresponding generator components within a continuous time window; the corresponding signal quality generator vector is then labeled based on the credibility assessment results.

[0075] Example 3, Figure 3 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality or scope of the embodiments of the present invention. Figure 3 As shown, the electronic device includes a processor 21, a memory 22, an input device 23, and an output device 24; the number of processors 21 in the electronic device can be one or more. Figure 3 Taking a processor 21 as an example, the processor 21, memory 22, input device 23, and output device 24 in an electronic device can be connected via a bus or other means. Figure 3 Taking the example of a connection between China and Israel via a bus.

[0076] The memory 22, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the wireless signal quality monitoring method in the mobile communication scenario in this embodiment of the invention. The processor 21 executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory 22, thereby realizing the wireless signal quality monitoring method in the aforementioned mobile communication scenario.

[0077] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for monitoring wireless signal quality in mobile communication scenarios, characterized in that, The method includes: During mobile communication of computer equipment, wireless measurement data, link layer transmission behavior data and network-side scheduling feedback data are collected from the terminal side. Based on the collected data, a signal quality generation state vector is constructed to characterize the formation mechanism of wireless signal quality. The signal quality generation state vector is executed to perform time-series correlation modeling, generating a signal quality generation trajectory that describes the evolution of wireless signal quality over time. The signal quality generation trajectory is used to perform quality inversion analysis, and the observed wireless signal quality change results are mapped inversely to the corresponding generator state component combination to locate the dominant generation path type that causes the current signal quality change; Obtain the current mobile communication scenario of the terminal and determine the signal quality generation reference system corresponding to the mobile communication scenario; The signal quality generation trajectory is compared and analyzed with the signal quality generation reference system to establish the comparison and analysis results; Based on the comparative analysis results and the dominant generated path type, output the wireless signal quality monitoring results.

2. The wireless signal quality monitoring method in a mobile communication scenario as described in claim 1, characterized in that, Based on the collected data, a signal quality generator state vector is constructed to characterize the formation mechanism of wireless signal quality, including: The terminal-side wireless measurement data, link-layer transmission behavior data, and network-side scheduling feedback data in the collected data are processed in layers according to time resolution and data update cycle to generate multiple time-scale data subsets. In the data subsets at each time scale, data decorrelation processing and normalization mapping are performed separately to eliminate dimensional differences and statistical coupling between different data sources; The evolution order of environment-link-scheduling in the formation process of wireless signal quality is obtained, and the sequential constraint combination of the processed data subset is performed according to the evolution order to construct the generator component sequence; A time-consistent weighting rule is applied to the generated state component sequence to form a signal quality generated state vector with fixed dimensions and order constraints.

3. The wireless signal quality monitoring method in a mobile communication scenario as described in claim 1, characterized in that, Perform time-series correlation modeling on the signal quality generation state vector to generate a signal quality generation trajectory describing the evolution of wireless signal quality over time, including: Obtain the mobile communication scenario identifier corresponding to the signal quality generation state vector during the data acquisition period, and determine the time-series correlation parameter set corresponding to the scenario based on the mobile communication scenario identifier; According to the set of time-series correlation parameters, time alignment and correlation window division are performed on the signal quality generator state vectors at consecutive time points to form a generator state vector sequence under scene constraints. In the generated state vector sequence, based on the change rate threshold and associated window length corresponding to the mobile communication scenario, the evolution increment between adjacent signal quality generated state vectors is calculated; Using the evolutionary increment and the evolutionary discrimination rules corresponding to the mobile communication scenario, the generated state vector sequence is subjected to continuous evolution constraints and scenario-triggered mutation marking processing; Construct a signal quality generation trajectory based on the processed generator state vector sequence.

4. The wireless signal quality monitoring method in a mobile communication scenario as described in claim 1, characterized in that, Performing quality inversion analysis using the signal quality generation trajectory includes: In the signal quality generation trajectory, trajectory change features characterizing the amplitude, duration, and recovery characteristics of wireless signal quality changes are extracted; The trajectory change features are correlated and matched with the evolution features of each generator state component in the signal quality generator state vector within the corresponding time period to construct the correspondence between the trajectory change features and the generator state components; Based on the aforementioned correspondence, the contribution index of each generated state component to the current wireless signal quality change is calculated; The distribution results of the contribution index are used to locate the dominant generation path type that causes the current signal quality change.

5. The wireless signal quality monitoring method in a mobile communication scenario as described in claim 1, characterized in that, Based on the comparative analysis results and the dominant generated path type, the wireless signal quality monitoring results are output, including: Based on the comparative analysis results, a trajectory offset index is calculated to reflect the degree to which the current wireless signal quality deviates from the signal quality generation reference system; The trajectory offset index is associated and combined with the dominant generated path type to form a set of monitoring result parameters characterizing the degree and cause of wireless signal quality anomalies. According to the preset result organization rules, the monitoring result parameter set is structured and encapsulated to generate wireless signal quality monitoring results that include the quality offset degree, the dominant generation path identifier, and the corresponding scene information.

6. The wireless signal quality monitoring method in a mobile communication scenario as described in claim 5, characterized in that, Based on the degree of trajectory deviation and the stability of the dominant generated path type, instantaneous monitoring results and time-accumulated monitoring results are generated respectively, and wireless signal quality monitoring results are generated based on the instantaneous monitoring results and the time-accumulated monitoring results.

7. The wireless signal quality monitoring method in a mobile communication scenario as described in claim 1, characterized in that, In the process of mobile communication of computer equipment, the collection of terminal-side wireless measurement data, link layer transmission behavior data and network-side scheduling feedback data adopts a strategy of combining event triggering and periodic collection.

8. The wireless signal quality monitoring method in a mobile communication scenario as described in claim 1, characterized in that, Based on the collected data, a signal quality generator state vector is constructed to characterize the formation mechanism of wireless signal quality, including: Credibility assessment is performed based on the numerical stability, consistency of change, and matching degree with the constraints of the mobile communication scenario of the corresponding generated state components within a continuous time window. The corresponding signal quality generator state vector is labeled based on the credibility assessment results.

9. A wireless signal quality monitoring system for mobile communication scenarios, characterized in that, The system is used to implement the wireless signal quality monitoring method in a mobile communication scenario according to any one of claims 1-8, the system comprising: Data acquisition module: During the mobile communication process of computer equipment, it collects wireless measurement data from the terminal side, link layer transmission behavior data, and network side scheduling feedback data, and constructs a signal quality generation state vector based on the collected data to characterize the formation mechanism of wireless signal quality. Temporal modeling module: Executes the signal quality generation state vector to perform temporal correlation modeling, generating a signal quality generation trajectory to describe the evolution of wireless signal quality over time; Quality Inversion Module: Performs quality inversion analysis using the signal quality generation trajectory, maps the observed wireless signal quality change results back to the corresponding generator state component combination, and locates the dominant generation path type that causes the current signal quality change; Scene determination module: acquires the current mobile communication scene of the terminal and determines the signal quality generation reference system corresponding to the mobile communication scene; Comparative analysis module: Compares and analyzes the signal quality generation trajectory with the signal quality generation reference system to establish comparative analysis results; Results output module: Outputs wireless signal quality monitoring results based on the comparative analysis results and the dominant generated path type.

10. An electronic device, characterized in that, The electronic device includes: Memory, used to store executable instructions; The processor, when executing executable instructions stored in the memory, implements the wireless signal quality monitoring method in any one of claims 1-8 in a mobile communication scenario.

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