An LTE terminal-based RRC link communication intelligent monitoring method and system

Abstract

The application discloses an LTE terminal-based RRC link communication intelligent monitoring method and system, relates to the technical field of communication, and comprises the following steps: acquiring network communication parameter information, acquiring network load data according to the network communication parameter information, sorting network load data of the same kind according to time sequences based on time stamps corresponding to the data according to the network load data, and acquiring data time sequence information corresponding to each kind of network load data. The application accurately analyzes the fluctuation condition of network load data through a time sliding window, is convenient for subsequent monitoring of the network load condition, selects a suitable network load monitoring frequency through a network load feature set, realizes accurate monitoring of the network load, provides a data basis for setting of RRC timing time, acquires an RRC link monitoring frequency through RRC timing time information, and ensures the stability and reliability of the RRC link.

Description

Technical Field

[0001] This invention relates to the field of communication technology, specifically to an intelligent monitoring method and system for RRC link communication based on an LTE terminal. Background Technology

[0002] In LTE mobile communication systems, RRC link communication is a core component ensuring signaling interaction, radio resource allocation, and stable service transmission between terminals and base stations. Its operational status directly determines network service quality and user experience. As a key protocol in the LTE protocol stack's Layer 3 (L3), the RRC link plays multiple crucial roles: firstly, it manages terminal states, achieving a dynamic balance between low-power standby and high-speed data transmission through state switching; secondly, it controls radio resource allocation, dynamically scheduling resources such as time-frequency blocks and power based on network load data such as the number of users accessing the base station and the utilization rate of time-frequency resources, avoiding network congestion and meeting the low-latency requirements of diverse services such as high-definition video and IoT sensing. Therefore, accurate monitoring of RRC link communication is a crucial prerequisite for ensuring efficient LTE network operation, optimizing resource scheduling, and improving user experience.

[0003] Currently, RRC link communication monitoring still suffers from several problems, including the inability to accurately analyze network load, the inability to determine an appropriate monitoring frequency based on network load data, and the inability to adjust RRC timing in a timely manner, resulting in network resource scheduling delays. Traditional solutions often use preset fixed monitoring frequencies, ignoring the impact of network load fluctuations on monitoring accuracy. They often directly analyze network load fluctuations using fixed thresholds, leading to the inability to accurately capture periods of sudden load changes and affecting the accuracy of RRC timing calculations. Summary of the Invention

[0004] To address the aforementioned technical problems, this paper provides a method and system for intelligent monitoring of RRC link communication based on LTE terminals. This technical solution solves the problems mentioned in the background, such as the inability to accurately analyze network load, the inability to determine a suitable monitoring frequency based on network load data, and the inability to adjust the RRC timing in a timely manner, which causes delays in network resource scheduling. Traditional solutions often use preset fixed monitoring frequencies, ignore the impact of network load fluctuations on monitoring accuracy, and often directly analyze the network load fluctuation status with fixed thresholds, resulting in the inability to accurately capture periods of sudden load changes and affecting the accuracy of RRC timing calculation.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for intelligent monitoring of RRC link communication based on LTE terminals, comprising:

[0007] Obtain network communication parameter information, which includes network load data and network transmission parameters;

[0008] Based on network communication parameter information, network load data is obtained, including information on the number of users accessing the base station, the base station time-frequency resource utilization rate, and the downlink data throughput rate.

[0009] Based on the network load data and the timestamps corresponding to the data, the same type of network load data is sorted by time series to obtain the data time series information corresponding to each type of network load data.

[0010] Based on network load data and data fluctuation analysis, time sliding window information is obtained;

[0011] Based on network load data and time sliding window information, and considering RRC link requirements, obtain RRC timing information.

[0012] Based on the RRC timing information, the reciprocal of the RRC timing time is used as the RRC link baseline monitoring frequency;

[0013] Based on the RRC timing information, obtain the data synchronization acquisition frequency;

[0014] Set a frequency coordination coefficient, and based on the data synchronization acquisition frequency, use the product of the frequency coordination coefficient and the RRC link benchmark monitoring frequency as the RRC link monitoring frequency;

[0015] The frequency coordination coefficient is an integer, and it is gradually increased until the RRC link monitoring frequency is greater than 1.2 times the data synchronization acquisition frequency.

[0016] RRC link communication is monitored based on the RRC link monitoring frequency.

[0017] Preferably, the step of obtaining RRC timing information based on network load data and time sliding window information, and based on RRC link requirements, specifically includes:

[0018] Based on the time series information of the data, the network load data corresponding to the same timestamp are aligned to obtain the network load data synchronization information;

[0019] Based on the network load data synchronization information and using the time sliding window information as a basis, the data is divided along the time series to obtain the network load dataset information, which includes network load data within the same time sliding window.

[0020] Based on the impact analysis of network load on RRC links, the weight corresponding to each type of network load data is determined.

[0021] Based on the network load dataset information, obtain the standard deviation of fluctuation for each type of network load data in each network load dataset;

[0022] The 90th percentile of the standard deviation of the fluctuation of the same type of network load data is used as the standard deviation of the fluctuation characteristics.

[0023] Based on the weight, standard deviation of volatility, and standard deviation of volatility characteristics corresponding to each type of network load data, obtain the comprehensive volatility index corresponding to each network load dataset;

[0024] The network load dataset corresponding to the maximum value of the comprehensive volatility index is used as the network load feature set;

[0025] Based on the network load characteristic set, obtain the RRC timing information;

[0026] Specifically, the comprehensive volatility index is as follows:

[0027]

[0028] In the formula, F is the comprehensive volatility index. The weight of the number of users accessing the base station. As the weight of base station time and frequency resource utilization, As a weight for data downlink throughput, The standard deviation of the number of users accessing the base station. This represents the standard deviation of the fluctuation in base station time-frequency resource utilization. The standard deviation of the data downlink throughput fluctuation. The standard deviation of the fluctuation characteristics of the number of users accessing the base station. The standard deviation of the fluctuation characteristics of base station time-frequency resource utilization. This represents the standard deviation of the volatility characteristics of the data downlink throughput.

[0029] Preferably, the step of obtaining time sliding window information based on network load data and data fluctuation analysis specifically includes:

[0030] Based on the time series information of the data, a coordinate system is constructed with the timestamp as the horizontal axis and the network load data value as the vertical axis to obtain the data time curve corresponding to each type of network load data.

[0031] Based on network load data, the mean of each type of network load data is used as the data benchmark value for that type of network load data;

[0032] Based on the data baseline value, draw the load baseline along the vertical axis;

[0033] Based on the data time curve, obtain information on extreme points;

[0034] The extreme points are divided according to the load baseline. The extreme points located above the load baseline are designated as the first extreme points, and the extreme points located below the load baseline are designated as the second extreme points, thereby obtaining the extreme point classification information.

[0035] Based on the extreme point classification information, the extreme point closest to the load baseline among the first extreme points is taken as the first reference extreme point, and the extreme point closest to the load baseline among the second extreme points is taken as the second reference extreme point.

[0036] Based on the extreme point information, the first benchmark extreme point, and the second benchmark extreme point, obtain the time sliding window information.

[0037] Preferably, the step of obtaining time sliding window information based on extreme point information, the first benchmark extreme point, and the second benchmark extreme point specifically includes:

[0038] The difference between the network load data at the first benchmark extreme point and the second benchmark extreme point is taken as the characteristic fluctuation amplitude of this type of network load data.

[0039] Based on the extreme point classification information, the ratio of the difference between the network load data corresponding to any two first extreme points to the network load data of the first benchmark extreme point is used as the fluctuation deviation coefficient of the two first extreme points, and the ratio of the difference between the network load data corresponding to any two second extreme points to the network load data of the second benchmark extreme point is used as the fluctuation deviation coefficient of the two second extreme points.

[0040] The ratio of the fluctuation deviation coefficient corresponding to any two first extreme points to the time difference between these two first extreme points is taken as the fluctuation time correlation coefficient between these two first extreme points.

[0041] The ratio of the fluctuation deviation coefficient corresponding to any two second extreme points to the time difference between these two second extreme points is taken as the fluctuation time correlation coefficient between these two second extreme points.

[0042] The two extreme points corresponding to the maximum value of the fluctuation time correlation coefficient are taken as the calibration extreme points, and the time interval between the calibration extreme points corresponding to each type of network load data is taken as the calibration time interval of that type of network load data.

[0043] Based on network load data, the maximum value of the calibration time interval is used as the length of the time sliding window to obtain the time sliding window information.

[0044] Preferably, obtaining RRC timing information based on the network load feature set specifically includes:

[0045] The data collection frequency is determined based on the network load characteristic set.

[0046] Based on the data collection frequency, network load data is monitored to obtain network load monitoring data;

[0047] The overall load value is obtained based on network load monitoring data and the weight corresponding to each type of network load monitoring data;

[0048] Based on the overall load value and network load capacity analysis, obtain the RRC timing information;

[0049] Among them, if the comprehensive load value The network load level is light, the RRC timing interval is 20 seconds, and the overall load value is... The network load level is medium, the RRC timing interval is 10 seconds, and the overall load value is... The network load level is heavy, and the RRC timing interval is 5 seconds.

[0050] The specific comprehensive load value is as follows:

[0051]

[0052] In the formula, This is the overall load value. Indicates the number of users connected to the base station. This indicates the maximum number of users that can access the base station. This indicates the utilization rate of base station time and frequency resources. This indicates the maximum utilization rate of time and frequency resources of the base station. This indicates the data downlink throughput. This indicates the maximum downlink throughput of the data.

[0053] Preferably, obtaining the data collection frequency based on the network load feature set specifically includes:

[0054] Based on the network load feature set, obtain the number of significant fluctuations in each type of network load data;

[0055] If the instantaneous fluctuation value of the network load data exceeds the standard deviation of the fluctuation characteristics, then the timestamp corresponding to the network load data is taken as the significant fluctuation point of the network load data.

[0056] The ratio of the number of significant fluctuations in each type of network load data to the length of the time sliding window is taken as the frequency of that significant fluctuation.

[0057] Based on the minimum frequency requirement of network measurement, a data acquisition frequency threshold is obtained, which includes the minimum data acquisition frequency and the maximum data acquisition frequency.

[0058] Based on the data collection frequency threshold and the significant fluctuation frequency, the data collection frequency for each type of network load data is obtained;

[0059] Specifically, if twice the significant fluctuation frequency of the network load data does not exceed the data collection frequency threshold, then twice the significant fluctuation frequency will be used as the data collection frequency for that type of network load data. If twice the significant fluctuation frequency is less than the minimum data collection frequency, then the minimum data collection frequency will be used as the data collection frequency for that type of network load data. If twice the significant fluctuation frequency is greater than the maximum data collection frequency, then the maximum data collection frequency will be used as the data collection frequency for that type of network load data.

[0060] The least common multiple of the data collection frequency for each type of network load data is used as the time synchronization reference frequency.

[0061] Based on the time synchronization reference frequency and the data acquisition frequency threshold, determine whether the time synchronization reference frequency meets the actual needs and obtain the data synchronization acquisition frequency;

[0062] If the time synchronization reference frequency does not exceed the data acquisition frequency threshold, the time synchronization reference frequency will be used as the data acquisition frequency. If the time synchronization reference frequency exceeds the data acquisition frequency threshold, the time synchronization reference frequency will be down-adapted until it meets the data acquisition frequency threshold.

[0063] Furthermore, an intelligent monitoring system for RRC link communication based on LTE terminals is proposed to implement the monitoring method described above, including:

[0064] The main control module is used to obtain network load dataset information by dividing the data along the time series based on network load data synchronization information and time sliding window information. Based on the network load dataset information, it obtains the standard deviation of fluctuation for each type of network load data in each dataset, and uses the 90th quantile of the standard deviation of fluctuation for the same type of network load data as the standard deviation of fluctuation characteristics. Based on the weight, standard deviation of fluctuation, and standard deviation of fluctuation characteristics corresponding to each type of network load data, it obtains the comprehensive fluctuation index corresponding to each network load dataset. The network load dataset corresponding to the maximum value of the comprehensive fluctuation index is used as the network load feature set. Based on the network load feature set, it obtains the RRC timing information, and based on the network load feature set, it obtains the number of significant fluctuations for each type of network load data. The ratio of the number of significant fluctuations for each type of network load data to the length of the time sliding window is used as the frequency of that significant fluctuation. The minimum frequency requirement for network measurement is determined, and a data acquisition frequency threshold is obtained. This threshold includes a minimum data acquisition frequency and a maximum data acquisition frequency. Based on the data acquisition frequency threshold and the significant fluctuation frequency, the data acquisition frequency for each type of network load data is obtained. The least common multiple of the data acquisition frequencies for each type of network load data is used as the time synchronization reference frequency. Based on the time synchronization reference frequency and the data acquisition frequency threshold, it is determined whether the time synchronization reference frequency meets the actual requirements. The data synchronization acquisition frequency is then obtained. Based on the RRC timing information, the reciprocal of the RRC timing time is used as the RRC link reference monitoring frequency. The data synchronization acquisition frequency is then obtained, and a frequency coordination coefficient is set. Based on the data synchronization acquisition frequency, the product of the frequency coordination coefficient and the RRC link reference monitoring frequency is used as the RRC link monitoring frequency. The RRC link communication is monitored based on the RRC link monitoring frequency.

[0065] The information acquisition module is used to acquire network communication parameter information, including network load data and network transmission parameters. Based on the network communication parameter information, the module acquires network load data, which includes information on the number of users accessing the base station, the base station time-frequency resource utilization rate, and the downlink throughput rate. Based on the network load data and the timestamps corresponding to the data, the module sorts the same type of network load data according to time series to acquire the data time series information corresponding to each type of network load data. Based on the data acquisition frequency, the module monitors the network load data to acquire network load monitoring data.

[0066] The evaluation module is used to construct a coordinate system based on the data time series information, with timestamps as the horizontal axis and network load data values ​​as the vertical axis. It obtains a data time curve for each type of network load data. Based on the network load data, it uses the mean of each type of network load data as the data baseline value. Using this baseline value as a basis, it draws a load baseline line along the vertical axis. Based on the data time curve, it obtains extreme point information and divides the extreme points using the load baseline line as a dividing standard, obtaining extreme point classification information. The difference between the network load data of the first and second baseline extreme points is used as the characteristic fluctuation amplitude of this type of network load data. Based on the extreme point classification information, it obtains a fluctuation deviation coefficient. Based on the fluctuation deviation coefficient and the fluctuation time correlation coefficient, it uses the two extreme points corresponding to the maximum value of the fluctuation time correlation coefficient as calibration extreme points. It uses the time interval between the calibration extreme points corresponding to each type of network load data as the calibration time interval for that type of network load data. Based on the network load data, it uses the maximum value of the calibration time interval as the length of the time sliding window, obtaining time sliding window information.

[0067] The display module interacts with the main control module and is used to output display time sliding window information, data acquisition frequency, network load monitoring data, RRC timing time and RRC link monitoring frequency.

[0068] Optionally, the main control module specifically includes:

[0069] The control unit is configured to: obtain the number of significant fluctuations for each type of network load data based on a network load feature set; use the ratio of the number of significant fluctuations for each type of network load data to the length of a time sliding window as the significant fluctuation frequency; obtain a data acquisition frequency threshold based on the minimum frequency requirement for network measurement, the data acquisition frequency threshold including a minimum data acquisition frequency and a maximum data acquisition frequency; obtain the data acquisition frequency for each type of network load data based on the data acquisition frequency threshold and the significant fluctuation frequency; use the least common multiple of the data acquisition frequencies for each type of network load data as the time synchronization reference frequency; determine whether the time synchronization reference frequency meets the actual requirements based on the time synchronization reference frequency and the data acquisition frequency threshold; obtain the data synchronization acquisition frequency; use the reciprocal of the RRC timing time as the RRC link reference monitoring frequency based on the RRC timing time information; obtain the data synchronization acquisition frequency based on the RRC timing time information; set a frequency coordination coefficient; use the product of the frequency coordination coefficient and the RRC link reference monitoring frequency as the RRC link monitoring frequency; and monitor RRC link communication based on the RRC link monitoring frequency.

[0070] An information receiving unit, which interacts with the information acquisition module and the evaluation module, is used to receive data and transmit it to the RRC timing unit.

[0071] The RRC timing unit is used to obtain network load dataset information by dividing the data along the time series based on network load data synchronization information and time sliding window information. Based on the network load dataset information, it obtains the standard deviation of fluctuation for each type of network load data in each network load dataset. The 90th percentile of the standard deviation of fluctuation for the same type of network load data is used as the standard deviation of fluctuation feature. Based on the weight, standard deviation of fluctuation, and standard deviation of fluctuation feature corresponding to each type of network load data, it obtains the comprehensive fluctuation index corresponding to each network load dataset. The network load dataset corresponding to the maximum value of the comprehensive fluctuation index is used as the network load feature set. Based on the network load feature set, it obtains the RRC timing information.

[0072] Optionally, the information acquisition module specifically includes:

[0073] The first acquisition unit is used to acquire network communication parameter information, which includes network load data and network transmission parameters. Based on the network communication parameter information, the first acquisition unit acquires network load data, which includes information on the number of users accessing the base station, the base station time-frequency resource utilization rate, and the downlink throughput rate.

[0074] The second acquisition unit is used to sort the same type of network load data according to the time series based on the timestamp corresponding to the data, acquire the data time series information corresponding to each type of network load data, monitor the network load data according to the data collection frequency, and acquire network load monitoring data.

[0075] Optionally, the first evaluation unit is used to construct a coordinate system based on the data time series information, with the timestamp as the horizontal axis and the network load data value as the vertical axis, to obtain the data time curve corresponding to each type of network load data, to use the mean of each type of network load data as the data benchmark value of that type of network load data, to draw a load benchmark line along the vertical axis based on the data benchmark value, to obtain extreme point information based on the data time curve, to divide the extreme points using the load benchmark line as the dividing standard, and to obtain extreme point classification information.

[0076] The second evaluation unit is used to take the difference between the network load data of the first benchmark extreme point and the second benchmark extreme point as the characteristic fluctuation amplitude of the network load data. Based on the extreme point classification information, it obtains the fluctuation deviation coefficient. According to the fluctuation deviation coefficient and the fluctuation time correlation coefficient, it takes the two extreme points corresponding to the maximum value of the fluctuation time correlation coefficient as the calibration extreme points. It takes the time interval of the calibration extreme points corresponding to each type of network load data as the calibration time interval of the network load data. According to the network load data, it takes the maximum value of the calibration time interval as the length of the time sliding window and obtains the time sliding window information.

[0077] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0078] This invention proposes an intelligent monitoring method and system for RRC link communication based on LTE terminals. By using a time sliding window, it accurately analyzes the fluctuation of network load data, facilitating subsequent monitoring of network load status. By selecting an appropriate network load monitoring frequency through a network load feature set, it achieves accurate monitoring of network load and provides a data foundation for setting RRC timing. By obtaining the RRC link monitoring frequency through RRC timing information, it ensures the stability and reliability of the RRC link. Attached Figure Description

[0079] Figure 1 This is a flowchart of an intelligent monitoring method for RRC link communication based on an LTE terminal proposed in this invention;

[0080] Figure 2 This is a flowchart of the RRC timing information acquisition process in this invention;

[0081] Figure 3 This is a flowchart of the time sliding window information acquisition process in this invention;

[0082] Figure 4 This is a flowchart illustrating the process of obtaining the data synchronization acquisition frequency in this invention.

[0083] Figure 5 This is a block diagram of an intelligent monitoring system for RRC link communication based on an LTE terminal proposed in this invention. Detailed Implementation

[0084] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0085] Reference Figure 1 - Figure 4 As shown in the figure, an embodiment of the present invention provides a method for intelligent monitoring of RRC link communication based on an LTE terminal, comprising:

[0086] Obtain network communication parameter information, which includes network load data and network transmission parameters;

[0087] Based on network communication parameter information, network load data is obtained, including information on the number of users accessing the base station, the base station time-frequency resource utilization rate, and the downlink data throughput rate.

[0088] Based on the network load data and the timestamps corresponding to the data, the same type of network load data is sorted by time series to obtain the data time series information corresponding to each type of network load data.

[0089] Based on network load data and data fluctuation analysis, time sliding window information is obtained;

[0090] Specifically, based on network load data and data fluctuation analysis, time-sliding window information is obtained, including:

[0091] Based on the time series information of the data, a coordinate system is constructed with the timestamp as the horizontal axis and the network load data value as the vertical axis to obtain the data time curve corresponding to each type of network load data.

[0092] Based on network load data, the mean of each type of network load data is used as the data benchmark value for that type of network load data;

[0093] Based on the data baseline value, draw the load baseline along the vertical axis;

[0094] Based on the data time curve, obtain information on extreme points;

[0095] The extreme points are divided according to the load baseline. The extreme points located above the load baseline are designated as the first extreme points, and the extreme points located below the load baseline are designated as the second extreme points, thereby obtaining the extreme point classification information.

[0096] Based on the extreme point classification information, the extreme point closest to the load baseline among the first extreme points is taken as the first reference extreme point, and the extreme point closest to the load baseline among the second extreme points is taken as the second reference extreme point.

[0097] Based on the extreme point information, the first benchmark extreme point, and the second benchmark extreme point, obtain the time sliding window information.

[0098] In this solution, a coordinate system is constructed with timestamps as the horizontal axis and network load data values ​​as the vertical axis to generate a data time curve. This visualizes the spatiotemporal distribution characteristics of network load data, providing an intuitive data carrier and analytical foundation for subsequent precise division of time sliding windows. By establishing a load baseline based on the average network load data, the direction of load data fluctuations is clearly defined, providing an objective standard for distinguishing between high load (first extreme point) and low load (second extreme point) states, avoiding the subjectivity of traditional experience-based divisions. By classifying extreme points according to the load baseline and selecting the nearest baseline extreme point, the "critical state" of load fluctuations is accurately captured, ensuring the relevance of the time sliding window. By determining the time sliding window information through extreme points and baseline extreme points, the sliding window size is dynamically adapted to the characteristics of network load fluctuations, providing a data foundation for subsequent RRC link monitoring. By using the aforementioned data fluctuation-driven windowing logic, the time-sliding window is efficiently adapted to the characteristics of load mutations. Compared with a fixed window, it significantly improves the accuracy and recall of RRC link status monitoring, while reducing the waste of signaling resources and the increase in terminal power consumption caused by invalid monitoring.

[0099] Specifically, based on the extreme point information, the first benchmark extreme point, and the second benchmark extreme point, time sliding window information is obtained, including:

[0100] The difference between the network load data at the first benchmark extreme point and the second benchmark extreme point is taken as the characteristic fluctuation amplitude of this type of network load data.

[0101] Based on the extreme point classification information, the ratio of the difference between the network load data corresponding to any two first extreme points to the network load data of the first benchmark extreme point is used as the fluctuation deviation coefficient of the two first extreme points, and the ratio of the difference between the network load data corresponding to any two second extreme points to the network load data of the second benchmark extreme point is used as the fluctuation deviation coefficient of the two second extreme points.

[0102] The ratio of the fluctuation deviation coefficient corresponding to any two first extreme points to the time difference between these two first extreme points is taken as the fluctuation time correlation coefficient between these two first extreme points.

[0103] The ratio of the fluctuation deviation coefficient corresponding to any two second extreme points to the time difference between these two second extreme points is taken as the fluctuation time correlation coefficient between these two second extreme points.

[0104] The two extreme points corresponding to the maximum value of the fluctuation time correlation coefficient are taken as the calibration extreme points, and the time interval between the calibration extreme points corresponding to each type of network load data is taken as the calibration time interval of that type of network load data.

[0105] Based on network load data, the maximum value of the calibration time interval is used as the length of the time sliding window to obtain the time sliding window information.

[0106] In this scheme, the characteristic fluctuation amplitude is obtained by calculating the difference between the network load data of the first and second benchmark extreme points. This achieves a quantitative definition of the "normal fluctuation range boundary" of a single network load data, avoiding the limitations of traditional methods that rely on fixed thresholds to judge whether load fluctuations are abnormal. It provides a core reference indicator that fits the actual fluctuation pattern of the data for subsequent window division. By comparing the load difference between any two similar extreme points with the corresponding benchmark extreme point data, a fluctuation deviation coefficient is obtained, which accurately portrays the "relative deviation degree" of load fluctuations. Compared with the absolute difference, this coefficient can more objectively reflect the actual impact of fluctuations under different load levels, laying a reliable data foundation for subsequent correlation time dimension analysis. By comparing the fluctuation deviation coefficient with the time difference of the extreme points, a fluctuation time correlation coefficient is obtained, achieving a deep coupling analysis of "load fluctuation intensity" and "time change". This can accurately locate the key period of "severe fluctuation in a short period of time", solving the problem of missing key fluctuation nodes caused by traditional methods that only focus on the single dimension of load value or time. By selecting the extreme point corresponding to the maximum value of the fluctuation time correlation coefficient as the calibration extreme point and determining the calibration time interval, the time sliding window is accurately adapted to the "period of most severe load fluctuation"—the window length is anchored to the time span of the most significant fluctuation, ensuring that the window can fully cover the key fluctuation process without including irrelevant data due to excessive window length, thus improving the accuracy of subsequent network load dataset analysis. By using the maximum calibration time interval of various network load data as the final window length, the time sliding window achieves "maximum compatibility coverage" for multi-dimensional load data—avoiding the problem that a single load data window cannot adapt to other load fluctuation characteristics. This ensures that when conducting collaborative analysis of multiple data such as the number of users accessing the base station, time-frequency resource utilization, and downlink throughput, the window can take into account the key fluctuations of all loads, providing comprehensive and accurate load data support for subsequent RRC timing calculations.

[0107] It is understandable that the fluctuations in network load data vary significantly. The fluctuations in network load data are related to the usage of base stations. As network data interacts, network load data itself fluctuates, but this does not necessarily mean that the overall network load has changed significantly. If fixed data (such as mean, median, etc.) is used as the basis to evaluate the fluctuations, the fluctuations in network load are often amplified, making it impossible to accurately analyze the network load. Therefore, it is necessary to classify network load fluctuations to achieve further data fluctuation analysis.

[0108] Based on network load data and time sliding window information, and considering RRC link requirements, obtain RRC timing information.

[0109] Specifically, based on network load data and time sliding window information, and considering RRC link requirements, RRC timing information is obtained, including:

[0110] Based on the time series information of the data, the network load data corresponding to the same timestamp are aligned to obtain the network load data synchronization information;

[0111] Based on the network load data synchronization information and using the time sliding window information as a basis, the data is divided along the time series to obtain the network load dataset information, which includes network load data within the same time sliding window.

[0112] Based on the impact analysis of network load on RRC links, the weight corresponding to each type of network load data is determined.

[0113] Based on the network load dataset information, obtain the standard deviation of fluctuation for each type of network load data in each network load dataset;

[0114] The 90th percentile of the standard deviation of the fluctuation of the same type of network load data is used as the standard deviation of the fluctuation characteristics.

[0115] Based on the weight, standard deviation of volatility, and standard deviation of volatility characteristics corresponding to each type of network load data, obtain the comprehensive volatility index corresponding to each network load dataset;

[0116] The network load dataset corresponding to the maximum value of the comprehensive volatility index is used as the network load feature set;

[0117] Based on the network load characteristic set, obtain the RRC timing information;

[0118] Specifically, the comprehensive volatility index is as follows:

[0119]

[0120] In the formula, F is the comprehensive volatility index. The weight of the number of users accessing the base station. As the weight of base station time and frequency resource utilization, As a weight for data downlink throughput, The standard deviation of the number of users accessing the base station. This represents the standard deviation of the fluctuation in base station time-frequency resource utilization. The standard deviation of the data downlink throughput fluctuation. The standard deviation of the fluctuation characteristics of the number of users accessing the base station. The standard deviation of the fluctuation characteristics of base station time-frequency resource utilization. This represents the standard deviation of the volatility characteristics of the data downlink throughput.

[0121] This solution aligns multi-dimensional network load data using the same timestamp, achieving spatiotemporal synchronization of data such as the number of users accessing the base station, time-frequency resource utilization, and downlink throughput. This avoids misjudgments of RRC link status caused by traditional asynchronous data (e.g., a surge in user numbers but lagging time-frequency resource data leading to distorted load assessment), providing a unified time benchmark for subsequent multi-load collaborative analysis. By dividing the network load dataset along the time series using a time sliding window, "time-segmented focused analysis" of the load data is achieved—each dataset corresponds to a continuous load state within a window. This not only fully preserves the load fluctuation characteristics within that time period (e.g., sudden increases and decreases in load during morning peak hours) but also avoids the ambiguity of fluctuation patterns caused by mixing data across time periods, laying the foundation for accurately extracting core load characteristics. By calculating the standard deviation of fluctuation and the standard deviation of the 90th percentile fluctuation characteristics, a scientific definition of the "normal range" and "abnormal threshold" of load fluctuations is achieved—the standard deviation of fluctuation characteristics eliminates interference from extreme outliers, providing an objective standard for judging whether a single type of load is in a "significantly fluctuating state," avoiding the bias of subjective experience thresholds. A comprehensive volatility index is calculated based on weights, volatility standard deviation, and feature standard deviation, achieving a "normalized quantitative assessment" of multi-dimensional load volatility. This transforms discrete volatility data such as user numbers, time-frequency resources, and throughput into a unified index, enabling rapid identification of the dataset with the "most unstable overall load," thus solving the problem that single load volatility cannot reflect the overall network's impact on RRC links. The dataset with the largest comprehensive volatility index is selected as the network load feature set, achieving precise matching between RRC timing and the "worst load scenario"—the load feature set represents the period of greatest network pressure on RRC links (e.g., the highest comprehensive volatility index during evening peak hours). Based on this, the determined RRC timing (e.g., 5 seconds under heavy load) ensures that RRC links remain stable under high load, avoiding link disconnections or resource waste that occur during sudden load changes with traditional fixed timing.

[0122] Specifically, based on the network load characteristic set, the RRC timing information is obtained, including:

[0123] The data collection frequency is determined based on the network load characteristic set.

[0124] Based on the data collection frequency, network load data is monitored to obtain network load monitoring data;

[0125] The overall load value is obtained based on network load monitoring data and the weight corresponding to each type of network load monitoring data;

[0126] Based on the overall load value and network load capacity analysis, obtain the RRC timing information;

[0127] Among them, if the comprehensive load value The network load level is light, the RRC timing interval is 20 seconds, and the overall load value is... The network load level is medium, the RRC timing interval is 10 seconds, and the overall load value is... The network load level is heavy, and the RRC timing interval is 5 seconds.

[0128] The specific comprehensive load value is as follows:

[0129]

[0130] In the formula, This is the overall load value. Indicates the number of users connected to the base station. This indicates the maximum number of users that can access the base station. This indicates the utilization rate of base station time and frequency resources. This indicates the maximum utilization rate of time and frequency resources of the base station. This indicates the data downlink throughput. This indicates the maximum downlink throughput of the data.

[0131] In this solution, a comprehensive load value formula is used to integrate network load data from multiple dimensions, such as the number of users accessing the base station, the utilization rate of time and frequency resources, and the downlink throughput rate. The comprehensive load value is calculated accurately by combining the ratio of each value to the maximum threshold and the corresponding weight. This allows the network load to be divided into different levels, namely light load, medium load, and heavy load. This solves the problem that the traditional single load indicator is not comprehensive in assessing the network status and can more objectively reflect the actual network load. Different RRC timing times are matched according to different load levels (20s for light load, 10s for medium load, and 5s for heavy load). Under light load, a longer timing interval can reduce terminal power consumption and save network resources; under heavy load, a shorter timing interval can improve the timeliness and stability of RRC links, avoiding link anomalies caused by excessive load. This enables dynamic adjustment of the RRC timing interval according to network load, improving the reliability and resource utilization efficiency of RRC link communication. The data acquisition frequency is determined based on the network load feature set, and the network load data is monitored. Then, the RRC timing interval is obtained through load comprehensive value analysis. This ensures that data acquisition and RRC timing interval settings are coordinated to match the network load status, ensuring that effective data support for determining the RRC timing interval can be provided in a timely and accurate manner at an appropriate acquisition frequency. This further improves the accuracy and practicality of intelligent monitoring of RRC link communication.

[0132] It is important to note that in this solution, the overall load value is used to compare each type of network load data with its own maximum threshold to reflect the baseline load operation status. A threshold of 0.3 corresponds to a low load, with sufficient network resources still available and no significant impact on service experience. A threshold of 0.7 corresponds to network resources approaching saturation, with service experience starting to experience stuttering and packet loss, indicating a critical heavy load. The intermediate range (0.3~0.7) represents a moderate load, reflecting the transition from "controllable" to "overloaded" load. According to "Modern Cellular Network Optimization Technology" (Electronic Industry Press), network resources in the 30%-70% resource utilization range are in a state of "reasonable consumption and not reaching the bottleneck".

[0133] Specifically, the data collection frequency is obtained based on the network load characteristic set, including:

[0134] Based on the network load feature set, obtain the number of significant fluctuations in each type of network load data;

[0135] If the instantaneous fluctuation value of the network load data exceeds the standard deviation of the fluctuation characteristics, then the timestamp corresponding to the network load data is taken as the significant fluctuation point of the network load data.

[0136] The ratio of the number of significant fluctuations in each type of network load data to the length of the time sliding window is taken as the frequency of that significant fluctuation.

[0137] Based on the minimum frequency requirement of network measurement, a data acquisition frequency threshold is obtained, which includes the minimum data acquisition frequency and the maximum data acquisition frequency.

[0138] Based on the data collection frequency threshold and the significant fluctuation frequency, the data collection frequency for each type of network load data is obtained;

[0139] Specifically, if twice the significant fluctuation frequency of the network load data does not exceed the data collection frequency threshold, then twice the significant fluctuation frequency will be used as the data collection frequency for that type of network load data. If twice the significant fluctuation frequency is less than the minimum data collection frequency, then the minimum data collection frequency will be used as the data collection frequency for that type of network load data. If twice the significant fluctuation frequency is greater than the maximum data collection frequency, then the maximum data collection frequency will be used as the data collection frequency for that type of network load data.

[0140] The least common multiple of the data collection frequency for each type of network load data is used as the time synchronization reference frequency.

[0141] Based on the time synchronization reference frequency and the data acquisition frequency threshold, determine whether the time synchronization reference frequency meets the actual needs and obtain the data synchronization acquisition frequency;

[0142] If the time synchronization reference frequency does not exceed the data acquisition frequency threshold, the time synchronization reference frequency will be used as the data acquisition frequency. If the time synchronization reference frequency exceeds the data acquisition frequency threshold, the time synchronization reference frequency will be down-adapted until it meets the data acquisition frequency threshold.

[0143] In this scheme, significant fluctuation points are defined by "instantaneous fluctuation value exceeding the standard deviation of fluctuation characteristics", which accurately screens out load changes that have a real impact on RRC links (such as a sudden increase in the number of users accessing the base station or a sudden drop in the utilization rate of time and frequency resources) and eliminates interference from minor random fluctuations. The significant fluctuation frequency is then calculated using the ratio of "number of significant fluctuations / length of the time sliding window," directly linking the data acquisition frequency to the intensity of load fluctuations—the more frequent the fluctuations, the higher the acquisition frequency. This avoids the problem of "missed data collection during severe fluctuations and redundant data collection during mild fluctuations" associated with traditional fixed-frequency methods, improving the targeting and effectiveness of data acquisition. Based on the minimum requirements of network measurement, a threshold for "minimum / maximum data acquisition frequency" is set. The single-load data acquisition frequency is dynamically determined by comparing "twice the significant fluctuation frequency" with the threshold: ensuring that the acquisition frequency is not lower than the minimum requirements (avoiding the loss of key fluctuation data due to excessively low frequency) and does not exceed the maximum threshold (preventing high-frequency acquisition from causing a surge in terminal power consumption and wasting signaling resources). The least common multiple of the acquisition frequencies of multi-dimensional load data such as the number of users accessing the base station, time-frequency resource utilization, and downlink throughput is taken as the "time synchronization reference frequency," ensuring that different load data are acquired synchronously at the same timestamp (e.g., 5 times / minute for user acquisition, 6 times / minute for time-frequency resource acquisition, with the least common multiple of 30 times / minute as the synchronization reference). It solves the problem of "data time misalignment" caused by traditional multi-load asynchronous acquisition (such as the number of users has been updated but the time-frequency resource data is lagging behind), and provides spatiotemporally unified multi-load data support for subsequent RRC link status assessment.

[0144] Understandably, based on the Nyquist theorem, for distortion-free signal recovery, the sampling frequency must be no less than twice the highest frequency of the signal. In this scheme, the frequency of significant network load fluctuations is the highest frequency component of the signal; therefore, using twice that frequency as the data acquisition frequency threshold ensures that the acquired data accurately reconstructs the load fluctuation characteristics, which is a common application in this field.

[0145] It should be noted that in this embodiment, the time synchronization reference frequency is down-adapted, specifically by calculating the down-frequency coefficient:

[0146]

[0147] In the formula, This is the frequency reduction factor. It is a rounding function. For time synchronization reference frequency, This represents the maximum frequency of data collection.

[0148] By using a frequency reduction factor to adapt the time synchronization reference frequency, the synchronization of multiple load data is ensured, and the acquisition frequency is made in line with the actual carrying capacity of the network. This avoids problems such as data transmission delay and terminal lag caused by excessively high frequency, thereby improving the stability of the monitoring system.

[0149] For example , , , , ,but , (10=30 / 3, 20=30 / 1.5, 30=30 / 1), which meets the timestamp alignment requirements; if If the condition is not met, increase the value of k until... , This indicates the frequency of data collection for the number of users connected to the base station. The data acquisition frequency represents the base station's time-frequency resource utilization rate. The data collection frequency represents the downlink throughput. This indicates the time synchronization reference frequency after frequency downsampling and adaptation.

[0150] Based on the RRC timing information, the reciprocal of the RRC timing time is used as the RRC link baseline monitoring frequency;

[0151] Based on the RRC timing information, obtain the data synchronization acquisition frequency;

[0152] Set a frequency coordination coefficient, and based on the data synchronization acquisition frequency, use the product of the frequency coordination coefficient and the RRC link benchmark monitoring frequency as the RRC link monitoring frequency;

[0153] The frequency coordination coefficient is an integer, and it is gradually increased until the RRC link monitoring frequency is greater than 1.2 times the data synchronization acquisition frequency.

[0154] RRC link communication is monitored based on the RRC link monitoring frequency.

[0155] In this scheme, the minimum frequency coordination coefficient is 5, ensuring that at least 5 effective monitoring operations are completed within one RRC time period. The frequency coordination coefficient is gradually increased from 5 until the product of the frequency coordination coefficient and the RRC link reference monitoring frequency is greater than 1.2 times the data synchronization acquisition frequency, ensuring that the RRC monitoring frequency is not lower than 1.2 times the load acquisition frequency (to avoid load changes detecting anomalies before RRC monitoring).

[0156] Reference Figure 5As shown, further, combining the above-mentioned intelligent monitoring method for RRC link communication based on LTE terminals, an intelligent monitoring system for RRC link communication based on LTE terminals is proposed, including:

[0157] The main control module is used to obtain network load dataset information by dividing the data along the time series based on network load data synchronization information and time sliding window information. Based on the network load dataset information, it obtains the standard deviation of fluctuation for each type of network load data in each dataset, and uses the 90th quantile of the standard deviation of fluctuation for the same type of network load data as the standard deviation of fluctuation characteristics. Based on the weight, standard deviation of fluctuation, and standard deviation of fluctuation characteristics corresponding to each type of network load data, it obtains the comprehensive fluctuation index corresponding to each network load dataset. The network load dataset corresponding to the maximum value of the comprehensive fluctuation index is used as the network load feature set. Based on the network load feature set, it obtains the RRC timing information, and based on the network load feature set, it obtains the number of significant fluctuations for each type of network load data. The ratio of the number of significant fluctuations for each type of network load data to the length of the time sliding window is used as the frequency of that significant fluctuation. The minimum frequency requirement for network measurement is determined, and a data acquisition frequency threshold is obtained. This threshold includes a minimum data acquisition frequency and a maximum data acquisition frequency. Based on the data acquisition frequency threshold and the significant fluctuation frequency, the data acquisition frequency for each type of network load data is obtained. The least common multiple of the data acquisition frequencies for each type of network load data is used as the time synchronization reference frequency. Based on the time synchronization reference frequency and the data acquisition frequency threshold, it is determined whether the time synchronization reference frequency meets the actual requirements. The data synchronization acquisition frequency is then obtained. Based on the RRC timing information, the reciprocal of the RRC timing time is used as the RRC link reference monitoring frequency. The data synchronization acquisition frequency is then obtained, and a frequency coordination coefficient is set. Based on the data synchronization acquisition frequency, the product of the frequency coordination coefficient and the RRC link reference monitoring frequency is used as the RRC link monitoring frequency. The RRC link communication is monitored based on the RRC link monitoring frequency.

[0158] The information acquisition module is used to acquire network communication parameter information, including network load data and network transmission parameters. Based on the network communication parameter information, the module acquires network load data, which includes information on the number of users accessing the base station, the base station time-frequency resource utilization rate, and the downlink throughput rate. Based on the network load data and the timestamps corresponding to the data, the module sorts the same type of network load data according to time series to acquire the data time series information corresponding to each type of network load data. Based on the data acquisition frequency, the module monitors the network load data to acquire network load monitoring data.

[0159] The evaluation module is used to construct a coordinate system based on the data time series information, with timestamps as the horizontal axis and network load data values ​​as the vertical axis. It obtains a data time curve corresponding to each type of network load data. Based on the network load data, it uses the mean of each type of network load data as the data baseline value. Using the data baseline value as a basis, it draws a load baseline line along the vertical axis. Based on the data time curve, it obtains extreme point information, divides the extreme points using the load baseline line as a dividing standard, and obtains extreme point classification information. The difference between the network load data of the first and second baseline extreme points is used as the characteristic fluctuation amplitude of this type of network load data. Based on the extreme point classification information, it obtains a fluctuation deviation coefficient. Based on the fluctuation deviation coefficient and the fluctuation time correlation coefficient, it uses the two extreme points corresponding to the maximum value of the fluctuation time correlation coefficient as calibration extreme points. It uses the time interval between the calibration extreme points corresponding to each type of network load data as the calibration time interval for this type of network load data. Based on the network load data, it uses the maximum value of the calibration time interval as the length of the time sliding window and obtains time sliding window information.

[0160] The display module interacts with the main control module and is used to output display time sliding window information, data acquisition frequency, network load monitoring data, RRC timing time and RRC link monitoring frequency.

[0161] The main control module specifically includes:

[0162] The control unit is configured to: obtain the number of significant fluctuations for each type of network load data based on a network load feature set; use the ratio of the number of significant fluctuations for each type of network load data to the length of a time sliding window as the significant fluctuation frequency; obtain a data acquisition frequency threshold based on the minimum frequency requirement for network measurement, the data acquisition frequency threshold including a minimum data acquisition frequency and a maximum data acquisition frequency; obtain the data acquisition frequency for each type of network load data based on the data acquisition frequency threshold and the significant fluctuation frequency; use the least common multiple of the data acquisition frequencies for each type of network load data as the time synchronization reference frequency; determine whether the time synchronization reference frequency meets the actual requirements based on the time synchronization reference frequency and the data acquisition frequency threshold; obtain the data synchronization acquisition frequency; use the reciprocal of the RRC timing time as the RRC link reference monitoring frequency based on the RRC timing time information; obtain the data synchronization acquisition frequency based on the RRC timing time information; set a frequency coordination coefficient; use the product of the frequency coordination coefficient and the RRC link reference monitoring frequency as the RRC link monitoring frequency; and monitor RRC link communication based on the RRC link monitoring frequency.

[0163] An information receiving unit, which interacts with the information acquisition module and the evaluation module, is used to receive data and transmit it to the RRC timing unit.

[0164] The RRC timing unit is used to obtain network load dataset information by dividing the data along the time series based on network load data synchronization information and time sliding window information. Based on the network load dataset information, it obtains the standard deviation of fluctuation for each type of network load data in each network load dataset. The 90th percentile of the standard deviation of fluctuation for the same type of network load data is used as the standard deviation of fluctuation feature. Based on the weight, standard deviation of fluctuation, and standard deviation of fluctuation feature corresponding to each type of network load data, it obtains the comprehensive fluctuation index corresponding to each network load dataset. The network load dataset corresponding to the maximum value of the comprehensive fluctuation index is used as the network load feature set. Based on the network load feature set, it obtains the RRC timing information.

[0165] The information acquisition module specifically includes:

[0166] The first acquisition unit is used to acquire network communication parameter information, which includes network load data and network transmission parameters. Based on the network communication parameter information, the first acquisition unit acquires network load data, which includes information on the number of users accessing the base station, the base station time-frequency resource utilization rate, and the downlink throughput rate.

[0167] The second acquisition unit is used to sort the same type of network load data according to the time series based on the timestamp corresponding to the data, acquire the data time series information corresponding to each type of network load data, monitor the network load data according to the data collection frequency, and acquire network load monitoring data.

[0168] The evaluation module specifically includes:

[0169] The first evaluation unit is used to construct a coordinate system based on the data time series information, with the timestamp as the horizontal axis and the network load data value as the vertical axis, to obtain the data time curve corresponding to each type of network load data. Based on the network load data, the mean of each type of network load data is used as the data benchmark value of that type of network load data. Based on the data benchmark value, a load benchmark line is drawn along the vertical axis. Based on the data time curve, extreme point information is obtained. The extreme points are divided according to the load benchmark line to obtain extreme point classification information.

[0170] The second evaluation unit is used to take the difference between the network load data of the first benchmark extreme point and the second benchmark extreme point as the characteristic fluctuation amplitude of the network load data. Based on the extreme point classification information, it obtains the fluctuation deviation coefficient. According to the fluctuation deviation coefficient and the fluctuation time correlation coefficient, it takes the two extreme points corresponding to the maximum value of the fluctuation time correlation coefficient as the calibration extreme points. It takes the time interval of the calibration extreme points corresponding to each type of network load data as the calibration time interval of the network load data. According to the network load data, it takes the maximum value of the calibration time interval as the length of the time sliding window and obtains the time sliding window information.

[0171] In summary, the advantages of this invention are as follows: by analyzing network load data and data fluctuations, time-sliding window information is obtained; through the time-sliding window, the fluctuation of network load data is accurately analyzed, facilitating subsequent monitoring of network load status; by using the network load feature set, an appropriate network load monitoring frequency is selected, achieving accurate monitoring of network load; this provides a data foundation for setting RRC timing; and by using RRC timing information, the RRC link monitoring frequency is obtained, ensuring the stability and reliability of the RRC link.

[0172] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for intelligent monitoring of RRC link communication based on an LTE terminal, characterized in that, include: Obtain network communication parameter information, which includes network load data and network transmission parameters; Based on network communication parameter information, network load data is obtained, including information on the number of users accessing the base station, the base station time-frequency resource utilization rate, and the downlink data throughput rate. Based on the network load data and the timestamps corresponding to the data, the same type of network load data is sorted by time series to obtain the data time series information corresponding to each type of network load data. Based on network load data and data fluctuation analysis, time sliding window information is obtained; Based on network load data and time sliding window information, and considering RRC link requirements, obtain RRC timing information. Based on the RRC timing information, the reciprocal of the RRC timing time is used as the RRC link baseline monitoring frequency; Based on the RRC timing information, obtain the data synchronization acquisition frequency; Set a frequency coordination coefficient, and based on the data synchronization acquisition frequency, use the product of the frequency coordination coefficient and the RRC link benchmark monitoring frequency as the RRC link monitoring frequency; The frequency coordination coefficient is an integer, and it is gradually increased until the RRC link monitoring frequency is greater than 1.2 times the data synchronization acquisition frequency. RRC link communication is monitored based on the RRC link monitoring frequency. The step of obtaining RRC timing information based on network load data and time sliding window information, and based on RRC link requirements, specifically includes: Based on the time series information of the data, the network load data corresponding to the same timestamp are aligned to obtain the network load data synchronization information; Based on the network load data synchronization information and using the time sliding window information as a basis, the data is divided along the time series to obtain the network load dataset information, which includes network load data within the same time sliding window. Based on the impact analysis of network load on RRC links, the weight corresponding to each type of network load data is determined. Based on the network load dataset information, obtain the standard deviation of fluctuation for each type of network load data in each network load dataset; The 90th percentile of the standard deviation of the fluctuation of the same type of network load data is used as the standard deviation of the fluctuation characteristics. Based on the weight, standard deviation of volatility, and standard deviation of volatility characteristics corresponding to each type of network load data, obtain the comprehensive volatility index corresponding to each network load dataset; The network load dataset corresponding to the maximum value of the comprehensive volatility index is used as the network load feature set; Based on the network load characteristic set, obtain the RRC timing information; Specifically, the comprehensive volatility index is as follows: In the formula, F is the comprehensive volatility index. The weight of the number of users accessing the base station. As the weight of base station time and frequency resource utilization, As a weight for data downlink throughput, The standard deviation of the number of users accessing the base station. This represents the standard deviation of the fluctuation in base station time-frequency resource utilization. The standard deviation of the data downlink throughput fluctuation. The standard deviation of the fluctuation characteristics of the number of users accessing the base station. The standard deviation of the fluctuation characteristics of base station time-frequency resource utilization. The standard deviation of the volatility characteristics of the data downlink throughput; The step of obtaining time-sliding window information based on network load data and data fluctuation analysis specifically includes: Based on the time series information of the data, a coordinate system is constructed with the timestamp as the horizontal axis and the network load data value as the vertical axis to obtain the data time curve corresponding to each type of network load data. Based on network load data, the mean of each type of network load data is used as the data benchmark value for that type of network load data; Based on the data baseline value, draw the load baseline along the vertical axis; Based on the data time curve, obtain information on extreme points; The extreme points are divided according to the load baseline. The extreme points located above the load baseline are designated as the first extreme points, and the extreme points located below the load baseline are designated as the second extreme points, thereby obtaining the extreme point classification information. Based on the extreme point classification information, the extreme point closest to the load baseline among the first extreme points is taken as the first reference extreme point, and the extreme point closest to the load baseline among the second extreme points is taken as the second reference extreme point. Based on the extreme point information, the first benchmark extreme point, and the second benchmark extreme point, obtain the time sliding window information; The step of obtaining RRC timing information based on the network load feature set specifically includes: The data collection frequency is determined based on the network load characteristic set. Based on the data collection frequency, network load data is monitored to obtain network load monitoring data; The overall load value is obtained based on network load monitoring data and the weight corresponding to each type of network load monitoring data; Based on the overall load value and network load capacity analysis, obtain the RRC timing information; Among them, if the comprehensive load value The network load level is light, the RRC timing interval is 20 seconds, and the overall load value is... The network load level is medium, the RRC timing interval is 10 seconds, and the overall load value is... The network load level is heavy, and the RRC timing interval is 5 seconds. The specific comprehensive load value is as follows: In the formula, This is the overall load value. Indicates the number of users connected to the base station. This indicates the maximum number of users that can access the base station. This indicates the utilization rate of base station time and frequency resources. This indicates the maximum utilization rate of time and frequency resources of the base station. This indicates the data downlink throughput. This indicates the maximum downlink throughput of the data.

2. The intelligent monitoring method for RRC link communication based on an LTE terminal according to claim 1, characterized in that, The step of obtaining time sliding window information based on extreme point information, the first benchmark extreme point, and the second benchmark extreme point specifically includes: The difference between the network load data at the first benchmark extreme point and the second benchmark extreme point is taken as the characteristic fluctuation amplitude of this type of network load data. Based on the extreme point classification information, the ratio of the difference between the network load data corresponding to any two first extreme points to the network load data of the first benchmark extreme point is used as the fluctuation deviation coefficient of the two first extreme points, and the ratio of the difference between the network load data corresponding to any two second extreme points to the network load data of the second benchmark extreme point is used as the fluctuation deviation coefficient of the two second extreme points. The ratio of the fluctuation deviation coefficient corresponding to any two first extreme points to the time difference between these two first extreme points is taken as the fluctuation time correlation coefficient between these two first extreme points. The ratio of the fluctuation deviation coefficient corresponding to any two second extreme points to the time difference between these two second extreme points is taken as the fluctuation time correlation coefficient between these two second extreme points. The two extreme points corresponding to the maximum value of the fluctuation time correlation coefficient are taken as the calibration extreme points, and the time interval between the calibration extreme points corresponding to each type of network load data is taken as the calibration time interval of that type of network load data. Based on network load data, the maximum value of the calibration time interval is used as the length of the time sliding window to obtain the time sliding window information.

3. The intelligent monitoring method for RRC link communication based on an LTE terminal according to claim 2, characterized in that, The step of obtaining the data collection frequency based on the network load characteristic set specifically includes: Based on the network load feature set, obtain the number of significant fluctuations in each type of network load data; If the instantaneous fluctuation value of the network load data exceeds the standard deviation of the fluctuation characteristics, then the timestamp corresponding to the network load data is taken as the significant fluctuation point of the network load data. The ratio of the number of significant fluctuations in each type of network load data to the length of the time sliding window is taken as the frequency of that significant fluctuation. Based on the minimum frequency requirement of network measurement, a data acquisition frequency threshold is obtained, which includes the minimum data acquisition frequency and the maximum data acquisition frequency. Based on the data collection frequency threshold and the significant fluctuation frequency, the data collection frequency for each type of network load data is obtained; Specifically, if twice the significant fluctuation frequency of the network load data does not exceed the data collection frequency threshold, then twice the significant fluctuation frequency will be used as the data collection frequency for that type of network load data. If twice the significant fluctuation frequency is less than the minimum data collection frequency, then the minimum data collection frequency will be used as the data collection frequency for that type of network load data. If twice the significant fluctuation frequency is greater than the maximum data collection frequency, then the maximum data collection frequency will be used as the data collection frequency for that type of network load data. The least common multiple of the data collection frequency for each type of network load data is used as the time synchronization reference frequency. Based on the time synchronization reference frequency and the data acquisition frequency threshold, determine whether the time synchronization reference frequency meets the actual needs and obtain the data synchronization acquisition frequency; If the time synchronization reference frequency does not exceed the data acquisition frequency threshold, the time synchronization reference frequency will be used as the data acquisition frequency. If the time synchronization reference frequency exceeds the data acquisition frequency threshold, the time synchronization reference frequency will be down-adapted until it meets the data acquisition frequency threshold.

4. A smart monitoring system for RRC link communication based on an LTE terminal, used to implement the monitoring method as described in any one of claims 1-3, characterized in that, include: The main control module is used to obtain network load dataset information by dividing the data along the time series based on network load data synchronization information and time sliding window information. Based on the network load dataset information, it obtains the standard deviation of fluctuation for each type of network load data in each dataset, and uses the 90th quantile of the standard deviation of fluctuation for the same type of network load data as the fluctuation characteristic standard deviation. Based on the weight, standard deviation of fluctuation, and standard deviation of fluctuation characteristic for each type of network load data, it obtains the comprehensive fluctuation index corresponding to each network load dataset. The network load dataset corresponding to the maximum value of the comprehensive fluctuation index is used as the network load feature set. Based on the network load feature set, it obtains the RRC timing information and the number of significant fluctuations for each type of network load data. The ratio of the number of significant fluctuations for each type of network load data to the length of the time sliding window is used as the frequency of that significant fluctuation. The minimum frequency requirement for network measurement is determined, and a data acquisition frequency threshold is obtained. The data acquisition frequency threshold includes a minimum data acquisition frequency and a maximum data acquisition frequency. Based on the data acquisition frequency threshold and the significant fluctuation frequency, the data acquisition frequency for each type of network load data is obtained. The least common multiple of the data acquisition frequencies for each type of network load data is used as the time synchronization reference frequency. Based on the time synchronization reference frequency and the data acquisition frequency threshold, it is determined whether the time synchronization reference frequency meets the actual requirements, and the data synchronization acquisition frequency is obtained. Based on the RRC timing information, the reciprocal of the RRC timing time is used as the RRC link reference monitoring frequency. Based on the RRC timing information, the data synchronization acquisition frequency is obtained, and a frequency coordination coefficient is set. Based on the data synchronization acquisition frequency, the product of the frequency coordination coefficient and the RRC link reference monitoring frequency is used as the RRC link monitoring frequency. The RRC link communication is monitored based on the RRC link monitoring frequency. The information acquisition module is used to acquire network communication parameter information, including network load data and network transmission parameters. Based on the network communication parameter information, the module acquires network load data, which includes information on the number of users accessing the base station, the base station time-frequency resource utilization rate, and the downlink throughput rate. Based on the network load data and the timestamps corresponding to the data, the module sorts the same type of network load data according to time series to acquire the data time series information corresponding to each type of network load data. Based on the data acquisition frequency, the module monitors the network load data to acquire network load monitoring data. The evaluation module is used to construct a coordinate system based on the data time series information, with timestamps as the horizontal axis and network load data values ​​as the vertical axis. It obtains a data time curve for each type of network load data. Based on the network load data, it uses the mean of each type of network load data as the data baseline value. Using this baseline value as a basis, it draws a load baseline line along the vertical axis. Based on the data time curve, it obtains extreme point information and divides the extreme points using the load baseline line as a dividing standard, obtaining extreme point classification information. The difference between the network load data of the first and second baseline extreme points is used as the characteristic fluctuation amplitude of this type of network load data. Based on the extreme point classification information, it obtains a fluctuation deviation coefficient. Based on the fluctuation deviation coefficient and the fluctuation time correlation coefficient, it uses the two extreme points corresponding to the maximum value of the fluctuation time correlation coefficient as calibration extreme points. It uses the time interval between the calibration extreme points corresponding to each type of network load data as the calibration time interval for that type of network load data. Based on the network load data, it uses the maximum value of the calibration time interval as the length of the time sliding window, obtaining time sliding window information. The display module interacts with the main control module and is used to output display time sliding window information, data acquisition frequency, network load monitoring data, RRC timing time and RRC link monitoring frequency.

5. The intelligent monitoring system for RRC link communication based on an LTE terminal according to claim 4, characterized in that, The main control module specifically includes: The control unit is configured to: obtain the number of significant fluctuations for each type of network load data based on a network load feature set; use the ratio of the number of significant fluctuations for each type of network load data to the length of a time sliding window as the significant fluctuation frequency; obtain a data acquisition frequency threshold based on the minimum frequency requirement for network measurement, the data acquisition frequency threshold including a minimum data acquisition frequency and a maximum data acquisition frequency; obtain the data acquisition frequency for each type of network load data based on the data acquisition frequency threshold and the significant fluctuation frequency; use the least common multiple of the data acquisition frequencies for each type of network load data as the time synchronization reference frequency; determine whether the time synchronization reference frequency meets the actual requirements based on the time synchronization reference frequency and the data acquisition frequency threshold; obtain the data synchronization acquisition frequency; use the reciprocal of the RRC timing time as the RRC link reference monitoring frequency based on the RRC timing time information; obtain the data synchronization acquisition frequency based on the RRC timing time information; set a frequency coordination coefficient; use the product of the frequency coordination coefficient and the RRC link reference monitoring frequency as the RRC link monitoring frequency; and monitor RRC link communication based on the RRC link monitoring frequency. An information receiving unit, which interacts with the information acquisition module and the evaluation module, is used to receive data and transmit it to the RRC timing unit. The RRC timing unit is used to obtain network load dataset information by dividing the data along the time series based on network load data synchronization information and time sliding window information. Based on the network load dataset information, it obtains the standard deviation of fluctuation for each type of network load data in each network load dataset. The 90th percentile of the standard deviation of fluctuation for the same type of network load data is used as the standard deviation of fluctuation feature. Based on the weight, standard deviation of fluctuation, and standard deviation of fluctuation feature corresponding to each type of network load data, it obtains the comprehensive fluctuation index corresponding to each network load dataset. The network load dataset corresponding to the maximum value of the comprehensive fluctuation index is used as the network load feature set. Based on the network load feature set, it obtains the RRC timing information.

6. The intelligent monitoring system for RRC link communication based on an LTE terminal according to claim 4, characterized in that, The information acquisition module specifically includes: The first acquisition unit is used to acquire network communication parameter information, which includes network load data and network transmission parameters. Based on the network communication parameter information, the first acquisition unit acquires network load data, which includes information on the number of users accessing the base station, the base station time-frequency resource utilization rate, and the downlink throughput rate. The second acquisition unit is used to sort the same type of network load data according to the time series based on the timestamp corresponding to the data, acquire the data time series information corresponding to each type of network load data, monitor the network load data according to the data collection frequency, and acquire network load monitoring data.

7. The intelligent monitoring system for RRC link communication based on an LTE terminal according to claim 4, characterized in that, The evaluation module specifically includes: The first evaluation unit is used to construct a coordinate system based on the data time series information, with the timestamp as the horizontal axis and the network load data value as the vertical axis, to obtain the data time curve corresponding to each type of network load data. Based on the network load data, the mean of each type of network load data is used as the data benchmark value of that type of network load data. Based on the data benchmark value, a load benchmark line is drawn along the vertical axis. Based on the data time curve, extreme point information is obtained. The extreme points are divided according to the load benchmark line to obtain extreme point classification information. The second evaluation unit is used to take the difference between the network load data of the first benchmark extreme point and the second benchmark extreme point as the characteristic fluctuation amplitude of the network load data. Based on the extreme point classification information, it obtains the fluctuation deviation coefficient. According to the fluctuation deviation coefficient and the fluctuation time correlation coefficient, it takes the two extreme points corresponding to the maximum value of the fluctuation time correlation coefficient as the calibration extreme points. It takes the time interval of the calibration extreme points corresponding to each type of network load data as the calibration time interval of the network load data. According to the network load data, it takes the maximum value of the calibration time interval as the length of the time sliding window and obtains the time sliding window information.

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