A method and system for monitoring the communication quality of a communication network.

Abstract

This invention discloses a method and system for monitoring the communication quality of communication networks, relating to the field of communication analysis technology. It includes analyzing basic information of communication network scenarios, setting communication tolerance for each scenario, evaluating the communication tolerance of each scenario considering the basic topology and service requirements, and establishing standards for communication network scenarios to provide a reliable foundation for subsequent communication quality monitoring. The system categorizes communication network scenarios into stable and unstable scenarios. For stable scenarios, communication tolerance is compared to achieve communication quality monitoring. For unstable scenarios, multi-dimensional quality parameters and network adjustment response parameters are combined with communication tolerance comparisons to achieve communication quality monitoring, improving the accuracy and adaptability of communication quality monitoring and ensuring the stability and reliability of network communication.

Description

Technical Field

[0001] This invention relates to the field of communication analysis technology, and in particular to a method and system for monitoring the communication quality of a communication network. Background Technology

[0002] With the rapid development of emerging technologies such as 5G, IoT, and the Industrial Internet, communication networks are facing challenges of increasing complexity, dynamism, and high real-time requirements. Traditional communication quality monitoring solutions are mostly designed for stable network environments, relying on fixed thresholds and single parameters (such as latency and packet loss rate) for evaluation, making it difficult to adapt to network fluctuations in unstable scenarios (such as signal interference, frequent topology changes, and sudden traffic surges). Existing technologies lack quantitative assessment of the network's dynamic adjustment capabilities, such as real-time monitoring of the effects of handover strategies, routing optimization, or adaptive QoS adjustments.

[0003] In existing technologies, the methods for monitoring communication quality in stable and unstable network scenarios are relatively fixed, relying solely on a single parameter. This results in poor accuracy and adaptability of communication quality monitoring, failing to guarantee the stability and reliability of network communication.

[0004] Therefore, improving the accuracy and adaptability of communication quality monitoring is a technical problem that needs to be solved. Summary of the Invention

[0005] The purpose of this invention is to address the problems of poor accuracy and adaptability in existing communication quality monitoring technologies, and to propose a communication quality monitoring method for communication networks, comprising:

[0006] Collect all communication network scenarios and their basic information, analyze the basic information of the communication network scenarios, and set the communication tolerance for each communication network scenario;

[0007] Monitor the changing elements of the communication network scenario and divide the scenario into two categories: stable communication network scenario and unstable communication network scenario.

[0008] For stable communication network scenarios, multi-dimensional quality parameters are monitored, integrated, and compared with communication tolerance to achieve communication quality monitoring in stable communication network scenarios;

[0009] For unstable communication network scenarios, multi-dimensional quality parameters and network adjustment response parameters are monitored. By combining these parameters and comparing communication tolerance, communication quality monitoring in unstable communication network scenarios can be achieved.

[0010] In some embodiments of this application, basic information of the communication network scenario is analyzed to set the communication tolerance for each communication network scenario, including,

[0011] The basic information of a communication network scenario includes topology, equipment type, coverage area, and service requirements. Several core KPIs for the network communication scenario are determined, and the priority of each core KPI is determined by the service requirements.

[0012] Based on the topology, device type, and coverage area, the core KPI range for each scenario is determined. The upper and lower limits of the core KPI for the same scenario are determined by combining the topology, device type, and coverage area. Then, the core KPIs of all scenarios are combined a second time according to the priority of each scenario's core KPI, and the communication tolerance of each communication network scenario is set.

[0013] In some embodiments of this application, the upper and lower limits of the core KPIs for the same scenario are determined by comprehensively considering the topology, device type, and coverage area, including:

[0014] The intersection and union of the core KPI intervals for the same scenario are determined by statistical analysis of the topology, device type, and coverage area. The intersection of the core KPI intervals for the same scenario is taken as the lower limit of the core KPI for that scenario, and the union of the core KPI intervals for the same scenario is taken as the upper limit of the core KPI for that scenario.

[0015] In some embodiments of this application, the core KPIs of all scenarios are further synthesized based on the priority of each scenario's core KPIs, and the communication tolerance of each communication network scenario is set, including...

[0016] Tolerance evaluation is performed on the upper and lower limits of the core KPIs for each scenario to obtain the single core tolerance. Tolerance weights are assigned to different core KPIs based on their priority. The communication tolerance for each communication network scenario is then set in combination with the single core tolerance.

[0017] In some embodiments of this application, two types of scenarios are distinguished: stable communication network scenarios and unstable communication network scenarios, including:

[0018] Obtain the historical records of all communication network scenarios, extract the communication mode of each communication network scenario from the historical records, and expand the communication modes in chronological order to obtain the communication mode data axis.

[0019] Extract the data change segment of each variable element on the data axis of the communication mode, determine several variable parameters on the data change segment of the variable element, calculate the rate of change of each variable parameter, integrate the rate of change of all variable parameters under the same variable element, denote it as volatility, construct a volatility-time curve, divide the volatility curve into multiple curve segments, calculate the average volatility of each curve segment, set the sliding time window length under each curve segment time according to the average volatility, recalculate the standard rate of change of the variable parameters within the sliding time window, and combine the standard rate of change of the variable parameters under all curve segment time to classify the two scenarios into stable communication network scenarios and unstable communication network scenarios.

[0020] In some embodiments of this application, multi-dimensional quality parameters are integrated and communication tolerance is compared to achieve communication quality monitoring in stable communication network scenarios, including:

[0021] By integrating multi-dimensional quality parameters, the current tolerance of a stable communication network scenario is determined. Based on the current tolerance and communication tolerance of the stable communication network scenario, the first communication quality index of the stable communication network scenario is calculated, thereby realizing the monitoring of communication quality of the stable communication network scenario.

[0022] In some embodiments of this application, multi-dimensional quality parameters and network adjustment response parameters are combined to compare communication tolerance and achieve communication quality monitoring in unstable communication network scenarios, including:

[0023] Unstable communication network scenarios exist in two states: stable and unstable.

[0024] In the stable state of an unstable communication network scenario, the first communication quality index of the unstable communication network scenario is calculated based on multi-dimensional quality parameters to describe the communication quality in the stable state.

[0025] In an unstable communication network scenario, a second communication quality index is determined by combining the first communication quality index and network adjustment response parameters to describe the communication quality under unstable conditions.

[0026] In some embodiments of this application, a second communication quality index for unstable communication network scenarios is determined by combining a first communication quality index and network adjustment response parameters, including:

[0027] The second communication quality index for unstable communication network scenarios is determined based on the first communication quality index, the rate of change of multi-dimensional quality parameters, and network adjustment response parameters.

[0028] Correspondingly, this application also provides a communication quality monitoring system for a communication network, including,

[0029] The first module is used to collect all communication network scenarios and their basic information, analyze the basic information of the communication network scenarios, and set the communication tolerance for each communication network scenario.

[0030] The second module is used to monitor changing elements in the communication network scenario and divides the scenario into two categories: stable communication network scenario and unstable communication network scenario.

[0031] The third module is used to monitor multi-dimensional quality parameters for stable communication network scenarios, integrate multi-dimensional quality parameters, and compare communication tolerance to achieve communication quality monitoring for stable communication network scenarios.

[0032] The fourth module is used to monitor multi-dimensional quality parameters and network adjustment response parameters for unstable communication network scenarios. By combining the multi-dimensional quality parameters and network adjustment response parameters, and comparing the communication tolerance, the module achieves communication quality monitoring for unstable communication network scenarios.

[0033] Compared with the prior art, the beneficial effects of this invention are as follows:

[0034] 1. Analyze the basic information of communication network scenarios and set communication tolerance for each scenario. Consider the basic topology and service requirements of each scenario to evaluate its tolerance, establishing standards for the scenarios and providing a reliable foundation for subsequent communication quality monitoring. Classify communication network scenarios into stable and unstable categories, considering the changing elements within each scenario to determine the appropriate type. This allows for targeted communication quality monitoring solutions for each type of scenario.

[0035] 2. For stable communication network scenarios, communication tolerance is compared to monitor communication quality. For unstable communication network scenarios, multi-dimensional quality parameters and network adjustment response parameters are combined, and communication tolerance is compared to monitor communication quality. This improves the accuracy and adaptability of communication quality monitoring, ensures the stability and reliability of network communication, and enhances the user's communication experience. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating a communication quality monitoring method for a communication network proposed in this invention.

[0037] Figure 2 This is a schematic diagram of the structure of a communication quality monitoring system for a communication network proposed in this invention. Detailed Implementation

[0038] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0039] Reference Figure 1 A method for monitoring the communication quality of a communication network includes the following steps:

[0040] Step S101: Collect all communication network scenarios and their basic information, analyze the basic information of the communication network scenarios, and set the communication tolerance for each communication network scenario.

[0041] In this embodiment, the basic information includes basic communication network information such as topology, device type, coverage area and service requirements. Taking these basic communication network information into consideration, a reasonable communication tolerance is set (the maximum allowable performance fluctuation of the communication network in a specific scenario that can maintain normal business operation).

[0042] In some embodiments of this application, basic information of the communication network scenario is analyzed to set the communication tolerance for each communication network scenario, including,

[0043] The basic information of a communication network scenario includes topology, equipment type, coverage area, and service requirements. Several core KPIs for the network communication scenario are determined, and the priority of each core KPI is determined by the service requirements.

[0044] Based on the topology, device type, and coverage area, the core KPI range for each scenario is determined. The upper and lower limits of the core KPI for the same scenario are determined by combining the topology, device type, and coverage area. Then, the core KPIs of all scenarios are combined a second time according to the priority of each scenario's core KPI, and the communication tolerance of each communication network scenario is set.

[0045] In this embodiment, the topology can be star, ring, mesh, or hybrid.

[0046] Equipment types: base stations, routers, switches, terminal equipment, etc.

[0047] Coverage range: Local area network (LAN), metropolitan area network (MAN), wide area network (WAN), cellular network, etc. (or a specific physical distance).

[0048] Business requirements: voice calls, video streaming, IoT data transmission, industrial control, etc.

[0049] The core KPIs (performance metrics) of a scenario include latency, packet loss, and bandwidth. The required range of core KPIs varies depending on the topology, device type, and coverage area.

[0050] Different business needs have different priorities for core KPIs in a given scenario. All of this information will affect the setting of communication tolerance. Consider the topology, device type, coverage area and business needs when setting communication tolerance for different scenarios.

[0051] Prioritize core KPIs based on business needs. For example:

[0052] Video streaming services: latency (high priority), packet loss rate (high priority), bandwidth (medium priority).

[0053] IoT data transmission: packet loss rate (high priority), latency (medium priority), bandwidth (low priority).

[0054] In some embodiments of this application, the upper and lower limits of the core KPIs for the same scenario are determined by comprehensively considering the topology, device type, and coverage area, including:

[0055] The intersection and union of the core KPI intervals for the same scenario are determined by statistical analysis of the topology, device type, and coverage area. The intersection of the core KPI intervals for the same scenario is taken as the lower limit of the core KPI for that scenario, and the union of the core KPI intervals for the same scenario is taken as the upper limit of the core KPI for that scenario.

[0056] In this embodiment, examples include: star topology (e.g., home Wi-Fi), mesh topology (e.g., industrial IoT), and tree topology (e.g., enterprise campus network). Core KPIs: latency (ms), packet loss rate (%), throughput (Mbps). Star topology: low latency (<10ms), high throughput (>100Mbps). Mesh topology: relatively high latency (<50ms), low packet loss rate (<0.1%). Industrial sensors: low power consumption (<10mW), high stability (<1 time / 24 hours). Autonomous vehicles: high data update frequency (>100Hz), low latency (<1ms). Indoor coverage: high signal strength (>-60dBm), small coverage radius (<100m). Satellite communication: large coverage radius (>1000km), low signal strength (<-110dBm).

[0057] In the same scenario, the minimum common range of the core KPI intervals of topology, devices, and coverage is used as the lower bound (intersection), and the maximum coverage range of the core KPI intervals of topology, devices, and coverage is used as the upper bound (union).

[0058] In some embodiments of this application, the core KPIs of all scenarios are further synthesized based on the priority of each scenario's core KPIs, and the communication tolerance of each communication network scenario is set, including...

[0059] Tolerance evaluation is performed on the upper and lower limits of the core KPIs for each scenario to obtain the single core tolerance. Tolerance weights are assigned to different core KPIs based on their priority. The communication tolerance for each communication network scenario is then set in combination with the single core tolerance.

[0060] In this embodiment, the formula for the communication tolerance of the communication network scenario is as follows:

[0061] ;

[0062] in, For the first Communication tolerance in a specific communication network scenario. The number of core KPIs for the scenario. For the first Tolerance weights for core KPIs in each scenario For the first The first communication network scenario Single-core tolerance of core KPIs in each scenario This represents the maximum value among all core KPIs across all scenarios. For the first The first constant of a communication network scenario This represents the correction of the average value to the maximum value, with the first constant used to balance the magnitude of the correction function.

[0063] Step S102: Monitor the changing elements of the communication network scenario and divide the scenario into two categories: stable communication network scenario and unstable communication network scenario.

[0064] In this embodiment, the changing elements include changes in user behavior, network equipment, network environment, and service requirements (these elements that may change in the communication network scenario can cause fluctuations in communication monitoring), including but not limited to:

[0065] User behavior data: number of user connections, traffic distribution, session duration, and service type (such as video, voice, and data).

[0066] Network device data: device load, fault records, configuration changes, topology changes.

[0067] Network environment data: signal strength, interference level, packet loss rate, latency.

[0068] Business requirement data: QoS requirements, bandwidth requirements, priority changes.

[0069] In some embodiments of this application, two types of scenarios are distinguished: stable communication network scenarios and unstable communication network scenarios, including:

[0070] Obtain the historical records of all communication network scenarios, extract the communication mode of each communication network scenario from the historical records, and expand the communication modes in chronological order to obtain the communication mode data axis.

[0071] Extract the data change segment of each variable element on the data axis of the communication mode, determine several variable parameters on the data change segment of the variable element, calculate the rate of change of each variable parameter, integrate the rate of change of all variable parameters under the same variable element, denote it as volatility, construct a volatility-time curve, divide the volatility curve into multiple curve segments, calculate the average volatility of each curve segment, set the sliding time window length under each curve segment time according to the average volatility, recalculate the standard rate of change of the variable parameters within the sliding time window, and combine the standard rate of change of the variable parameters under all curve segment time to classify the two scenarios into stable communication network scenarios and unstable communication network scenarios.

[0072] In this embodiment, cluster analysis or pattern recognition is performed on historical data to extract typical communication patterns for each scenario.

[0073] Example:

[0074] Mode 1: Weekday morning rush hour (high traffic, low latency requirements).

[0075] Mode 2: Nighttime off-peak (low flow, high stability requirements).

[0076] The communication patterns are unfolded chronologically to form a time-series data axis. On this axis, data change segments for each variable element (user behavior, network devices, network environment, business requirements) are identified. For each variable element, several change rates for variable parameters are defined (e.g., traffic change rate, load growth rate, packet loss rate change rate; these rates are calculated at fixed periods, differing from the standard change rate timescale). The sliding time window length for each curve segment is set based on the average volatility. The larger the average volatility, the shorter the sliding time window, allowing for more accurate capture of changes. The standard change rate of the variable parameters within the sliding time window is recalculated. This is done to accurately analyze the changes in variable parameters across different time periods.

[0077] There are two types of communication network scenarios: stable and unstable. Stable communication network scenarios are those where fluctuations are minimal or changes can be predicted stably. Unstable communication network scenarios are those where fluctuations are significant or changes are difficult to predict stably.

[0078] Step S103: For stable communication network scenarios, monitor multi-dimensional quality parameters, integrate multi-dimensional quality parameters, and compare communication tolerance to achieve communication quality monitoring for stable communication network scenarios.

[0079] In some embodiments of this application, multi-dimensional quality parameters are integrated and communication tolerance is compared to achieve communication quality monitoring in stable communication network scenarios, including:

[0080] By integrating multi-dimensional quality parameters, the current tolerance of a stable communication network scenario is determined. Based on the current tolerance and communication tolerance of the stable communication network scenario, the first communication quality index of the stable communication network scenario is calculated, thereby realizing the monitoring of communication quality of the stable communication network scenario.

[0081] In this embodiment, the multi-dimensional quality parameters include performance quality parameters and user evaluation parameters. Performance parameters include: latency (ms), packet loss rate (%), throughput (Mbps), jitter (ms), and signal strength (dBm). Device load (%), failure rate (times / hour), and resource utilization rate (%) are also included. User evaluation parameters include: user satisfaction rating (1-5 points), complaint rate (times / thousand users), and number of perceived service interruptions. Service experience quality is also assessed (e.g., video stuttering rate, voice clarity rating).

[0082] Data is collected through network monitoring systems, user feedback platforms, and device logs.

[0083] Standardize parameters of different dimensions (e.g., normalize to the [0,1] interval) to eliminate the influence of dimensions.

[0084] Assign weights to parameters based on their importance (e.g., performance parameters have a weight of 0.6, user rating parameters have a weight of 0.4). Perform a weighted summation mapping to obtain the current tolerance level.

[0085] The formula for calculating the first communication quality index is as follows:

[0086] ;

[0087] in, For the first The primary communication quality indicator for a stable communication network scenario. For the first The current tolerance, determined by multi-dimensional quality parameters, in a stable communication network scenario. For the first Communication tolerance in a stable communication network scenario.

[0088] Step S104: For unstable communication network scenarios, monitor multi-dimensional quality parameters and network adjustment response parameters, and combine the multi-dimensional quality parameters and network adjustment response parameters to compare communication tolerance to achieve communication quality monitoring for unstable communication network scenarios.

[0089] In some embodiments of this application, multi-dimensional quality parameters and network adjustment response parameters are combined to compare communication tolerance and achieve communication quality monitoring in unstable communication network scenarios, including:

[0090] Unstable communication network scenarios exist in two states: stable and unstable.

[0091] In the stable state of an unstable communication network scenario, the first communication quality index of the unstable communication network scenario is calculated based on multi-dimensional quality parameters to describe the communication quality in the stable state.

[0092] In an unstable communication network scenario, a second communication quality index is determined by combining the first communication quality index and network adjustment response parameters to describe the communication quality under unstable conditions.

[0093] In some embodiments of this application, a second communication quality index for unstable communication network scenarios is determined by combining a first communication quality index and network adjustment response parameters, including:

[0094] The second communication quality index for unstable communication network scenarios is determined based on the first communication quality index, the rate of change of multi-dimensional quality parameters, and network adjustment response parameters.

[0095] In this embodiment, the stable state is:

[0096] Network quality parameters fluctuated within an acceptable range and did not trigger any major adjustments.

[0097] Example: Traffic fluctuates within a normal range, device load is stable, and user complaint rate is low.

[0098] Unstable state:

[0099] Network quality parameters exceeding the normal range may trigger network adjustments or pose potential risks.

[0100] Example: A sudden surge in traffic causes congestion, equipment failure leads to service interruption, and user complaint rates rise sharply.

[0101] Network adjustment parameters include: number of handovers, route adjustment frequency, retransmission rate, connection interruption time, load balancing efficiency, and adaptive adjustment latency.

[0102] The formula for calculating the second communication quality index is as follows:

[0103] ;

[0104] in, For the first The second communication quality indicator for an unstable communication network scenario under unstable conditions. For the first The transformation constant of an unstable communication network scenario under unstable conditions is used to transform the first communication quality index. For the first The first communication quality indicator for an unstable communication network scenario under unstable conditions. This is an adjustment coefficient obtained by mapping the rate of change of the quality parameters, used to adjust the first communication quality index. Adjust the number of response parameters for the network. For the first The network adjusts the combined weights of the response parameters. For the first The first unstable communication network scenario in an unstable state Each network adjusts the response parameters. For the first The second constant of an unstable communication network scenario in an unstable state. This indicates that the network adjustment response parameters correct the first communication quality index after conversion, and the second constant is used to balance the magnitude of the correction function.

[0105] Understandably, the first communication quality indicator is a ratio that needs to be converted and adjusted. The rate of change of the quality parameters will affect the reliability of the first communication quality indicator, thus requiring adjustment.

[0106] Correspondingly, this application also provides a communication quality monitoring system for communication networks, such as... Figure 2 As shown, including,

[0107] The first module is used to collect all communication network scenarios and their basic information, analyze the basic information of the communication network scenarios, and set the communication tolerance for each communication network scenario.

[0108] The second module is used to monitor changing elements in the communication network scenario and divides the scenario into two categories: stable communication network scenario and unstable communication network scenario.

[0109] The third module is used to monitor multi-dimensional quality parameters for stable communication network scenarios, integrate multi-dimensional quality parameters, and compare communication tolerance to achieve communication quality monitoring for stable communication network scenarios.

[0110] The fourth module is used to monitor multi-dimensional quality parameters and network adjustment response parameters for unstable communication network scenarios. By combining the multi-dimensional quality parameters and network adjustment response parameters, and comparing the communication tolerance, the module achieves communication quality monitoring for unstable communication network scenarios.

[0111] Compared with the prior art, the beneficial effects of this invention are as follows:

[0112] 1. Analyze the basic information of communication network scenarios and set communication tolerance for each scenario. Consider the basic topology and service requirements of each scenario to evaluate its tolerance, establishing standards for the scenarios and providing a reliable foundation for subsequent communication quality monitoring. Classify communication network scenarios into stable and unstable categories, considering the changing elements within each scenario to determine the appropriate type. This allows for targeted communication quality monitoring solutions for each type of scenario.

[0113] 2. For stable communication network scenarios, communication tolerance is compared to monitor communication quality. For unstable communication network scenarios, multi-dimensional quality parameters and network adjustment response parameters are combined, and communication tolerance is compared to monitor communication quality. This improves the accuracy and adaptability of communication quality monitoring, ensures the stability and reliability of network communication, and enhances the user's communication experience.

[0114] Through the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented in hardware or by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0115] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0116] Those skilled in the art will understand that the modules in the system of the implementation scenario can be distributed throughout the system of the implementation scenario as described, or they can be modified to reside in one or more systems different from this implementation scenario. The modules of the above-mentioned implementation scenario can be merged into one module, or they can be further divided into multiple sub-modules.

[0117] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for monitoring the communication quality of a communication network, characterized in that, include, Collect all communication network scenarios and their basic information, analyze the basic information of the communication network scenarios, and set the communication tolerance for each communication network scenario; The monitoring system identifies changing elements in communication network scenarios, categorizing them into stable and unstable scenarios. The system retrieves historical records for all communication network scenarios, extracts the communication modes for each scenario from the historical records, and expands the communication modes in chronological order to obtain a communication mode data axis. It then extracts the data change segments for each variable element on the communication mode data axis, identifies several variable parameters on these segments, calculates the rate of change for each parameter, integrates the rates of change of all variable parameters under the same variable element, denotes this as volatility, constructs a volatility-time curve, divides the volatility curve into multiple segments, calculates the average volatility of each segment, sets the sliding time window length for each segment based on the average volatility, recalculates the standard rate of change of the variable parameters within the sliding time window, and combines the standard rates of change of the variable parameters across all segments to classify the scenarios into stable and unstable communication network scenarios. For stable communication network scenarios, multi-dimensional quality parameters are monitored, integrated, and compared with communication tolerance to achieve communication quality monitoring in stable communication network scenarios; For unstable communication network scenarios, multi-dimensional quality parameters and network adjustment response parameters are monitored. By combining these parameters and comparing communication tolerance, communication quality monitoring in unstable communication network scenarios can be achieved.

2. The communication quality monitoring method for a communication network according to claim 1, characterized in that, Analyze the basic information of the communication network scenario and set the communication tolerance for each scenario. include, The basic information of a communication network scenario includes topology, equipment type, coverage area, and service requirements. Several core KPIs for the network communication scenario are determined, and the priority of each core KPI is determined by the service requirements. Based on the topology, device type, and coverage area, the core KPI range for each scenario is determined. The upper and lower limits of the core KPI for the same scenario are determined by combining the topology, device type, and coverage area. Then, the core KPIs of all scenarios are combined a second time according to the priority of each scenario's core KPI, and the communication tolerance of each communication network scenario is set.

3. The communication quality monitoring method for a communication network according to claim 2, characterized in that, The upper and lower limits of the core KPIs for the same scenario are determined by considering the topology, device type, and coverage area. include, The intersection and union of the core KPI intervals for the same scenario are determined by statistical analysis of the topology, device type, and coverage area. The intersection of the core KPI intervals for the same scenario is taken as the lower limit of the core KPI for that scenario, and the union of the core KPI intervals for the same scenario is taken as the upper limit of the core KPI for that scenario.

4. The communication quality monitoring method for a communication network according to claim 3, characterized in that, Furthermore, based on the priority of the core KPIs for each scenario, a secondary synthesis of all core KPIs is performed, and the communication tolerance for each communication network scenario is set, including... Tolerance evaluation is performed on the upper and lower limits of the core KPIs for each scenario to obtain the single core tolerance. Tolerance weights are assigned to different core KPIs based on their priority. The communication tolerance for each communication network scenario is then set in combination with the single core tolerance.

5. The communication quality monitoring method for a communication network according to claim 1, characterized in that, By integrating multi-dimensional quality parameters and comparing communication tolerance, communication quality monitoring of stable communication network scenarios can be achieved. This includes integrating multi-dimensional quality parameters to determine the current tolerance of the stable communication network scenario, and calculating the first communication quality index of the stable communication network scenario based on the current tolerance and communication tolerance, thereby achieving communication quality monitoring of stable communication network scenarios.

6. The communication quality monitoring method for a communication network according to claim 1, characterized in that, By combining multi-dimensional quality parameters and network adjustment response parameters, and comparing communication tolerance, communication quality monitoring in unstable communication network scenarios can be achieved, including: Unstable communication network scenarios exist in two states: stable and unstable. In the stable state of an unstable communication network scenario, the first communication quality index of the unstable communication network scenario is calculated based on multi-dimensional quality parameters to describe the communication quality in the stable state. In an unstable communication network scenario, a second communication quality index is determined by combining the first communication quality index and network adjustment response parameters to describe the communication quality under unstable conditions.

7. The communication quality monitoring method for a communication network according to claim 6, characterized in that, The second communication quality index for unstable communication network scenarios is determined by combining the first communication quality index and network adjustment response parameters, including: The second communication quality index for unstable communication network scenarios is determined based on the first communication quality index, the rate of change of multi-dimensional quality parameters, and network adjustment response parameters.

8. A communication quality monitoring system for a communication network, characterized in that, include, The first module is used to collect all communication network scenarios and their basic information, analyze the basic information of the communication network scenarios, and set the communication tolerance for each communication network scenario. The second module is used to monitor the changing elements of communication network scenarios, classifying them into stable and unstable communication network scenarios. This includes: acquiring historical records of all communication network scenarios; extracting the communication mode for each scenario from the historical records and expanding the communication mode in chronological order to obtain a communication mode data axis; extracting the data change segment for each changing element on the communication mode data axis; determining several changing parameters for each changing element's data change segment; calculating the rate of change for each changing parameter; integrating the rates of change of all changing parameters under the same changing element, denoted as volatility; constructing a volatility-time curve; splitting the volatility curve into multiple segments; calculating the average volatility of each segment; setting the sliding time window length for each segment based on the average volatility; recalculating the standard rate of change of the changing parameters within the sliding time window; and combining the standard rates of change of the changing parameters across all segments to classify the communication network scenarios into stable and unstable scenarios. The third module is used to monitor multi-dimensional quality parameters for stable communication network scenarios, integrate multi-dimensional quality parameters, and compare communication tolerance to achieve communication quality monitoring for stable communication network scenarios. This includes integrating multi-dimensional quality parameters to determine the current tolerance of stable communication network scenarios, and calculating the first communication quality index of stable communication network scenarios based on the current tolerance and communication tolerance, thereby achieving communication quality monitoring for stable communication network scenarios. The fourth module is used to monitor multi-dimensional quality parameters and network adjustment response parameters for unstable communication network scenarios. By combining the multi-dimensional quality parameters and network adjustment response parameters, and comparing the communication tolerance, the module achieves communication quality monitoring for unstable communication network scenarios.

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