Method, system, electronic device and storage medium for analyzing base station performance

Abstract

Embodiments of the present application relate to the technical field of communication, in particular to a base station performance analysis method and system, electronic equipment and storage medium. The base station performance analysis method comprises: configuring an IP flow collection task for a target bearing device, and issuing the IP flow collection task to the target bearing device; receiving an IP flow reported by the target bearing device; aggregating the received IP flow to obtain an aggregated flow; wherein the aggregated flow includes a plurality of IP flows; configuring an IOAM detection task for the aggregated flow, and issuing the IOAM detection task to the target bearing device, so that the target bearing device performs IOAM detection according to the IOAM detection task to obtain IOAM performance data; receiving the IOAM performance data reported by the target bearing device, and analyzing the quality performance of the base station associated with the IOAM performance data according to the IOAM performance data, so that the base station analysis is directly oriented to user perception from a single service, and the detection result based on IOAM can reflect the overall service capability of the system.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method, system, electronic device, and storage medium for analyzing base station performance. Background Technology

[0002] In the era of 5G (5th generation mobile networks), wireless services face higher demands in terms of bandwidth, latency, and connectivity flexibility, while also imposing more stringent requirements on the performance indicators of the bearer network. The layer-three to edge characteristics of 5G enable in-band operation administration and maintenance (IOAM) technology to provide more precise and reliable maintenance methods for 5G networks.

[0003] In current 5G engineering application scenarios, the bearer maintenance work mainly considers the service connectivity status from the service side perspective through IOAM technology. The detection results based on IOAM reflect the characteristics of a single service and cannot reflect the overall service capability of the system. Summary of the Invention

[0004] The main objective of this application is to propose a method, system, electronic device, and storage medium for analyzing base station performance, aiming to move from single-service-oriented base station analysis to analysis directly oriented towards user perception, so that the detection results based on IOAM can reflect the overall service capability of the system.

[0005] To achieve the above objectives, this application provides a method for analyzing base station performance, applied in a control center, comprising: configuring an IP flow collection task for a target bearer device and sending the IP flow collection task to the target bearer device, so that the target bearer device can collect IP flows according to the IP flow collection task; receiving IP flows reported by the target bearer device; aggregating the received IP flows to obtain an aggregated flow; wherein the aggregated flow includes several IP flows; configuring an IOAM detection task for the aggregated flow and sending the IOAM detection task to the target bearer device, so that the target bearer device can perform IOAM detection according to the IOAM detection task to obtain IOAM performance data; receiving the IOAM performance data reported by the target bearer device, and analyzing the quality performance of the base station associated with the IOAM performance data based on the IOAM performance data.

[0006] To achieve the above objectives, this application also provides a method for analyzing base station performance, applied to a target bearer device, comprising: receiving an IP flow collection task sent by a control center, and collecting IP flows according to the IP flow collection task to obtain IP flows; wherein the IP flow collection task is configured by the control center; reporting the collected IP flows to the control center so that the control center can aggregate the received IP flows to obtain aggregated flows, and configuring an IOAM detection task for the aggregated flows, wherein the aggregated flows include a plurality of IP flows; receiving an IOAM detection task for the aggregated flows issued by the control center, and performing IOAM detection according to the IOAM detection task to obtain IOAM performance data; and reporting the IOAM performance data to the control center so that the control center can analyze the quality performance of the base station associated with the IOAM performance data based on the IOAM performance data.

[0007] To achieve the above objectives, this application embodiment also provides a base station performance analysis system, including: a control center and a target bearer device. The control center is used to configure IP flow collection tasks for the target bearer device and distribute the IP flow collection tasks to the target bearer device. The target bearer device is used to receive the IP flow collection tasks, collect IP flows according to the IP flow collection tasks to obtain IP flows, and report the collected IP flows to the control center. The control center is used to aggregate the received IP flows to obtain aggregated flows, configure IOAM detection tasks for the aggregated flows, and distribute the IOAM detection tasks to the target bearer device. The aggregated flows include several IP flows. The target bearer device is used to receive the IOAM detection tasks, perform IOAM detection according to the IOAM detection tasks to obtain IOAM performance data, and report the IOAM performance data to the control center. The control center is used to receive the IOAM performance data and analyze the quality performance of the base station associated with the IOAM performance data.

[0008] To achieve the above objectives, embodiments of this application also provide an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor; if the electronic device is a control center, the instructions are executed by the at least one processor to enable the at least one processor to execute the aforementioned base station performance analysis method applied to the control center; if the electronic device is a target bearer device, the instructions are executed by the at least one processor to enable the at least one processor to execute the aforementioned base station performance analysis method applied to the target bearer device.

[0009] To achieve the above objectives, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for analyzing base station performance applied to a control center, or implements the above-described method for analyzing base station performance applied to a target bearer device.

[0010] In this embodiment, IOAM detection has been upgraded from the existing single IP flow IOAM detection based on the service dimension to aggregated flow IOAM detection based on the base station dimension. First, an IP flow collection task for the target bearer device is configured and issued. Then, the IP flows collected by the target bearer device based on the IP flow collection task are received, and the received IP flows are aggregated to obtain an aggregated flow. Next, an IOAM detection task for the aggregated flow is configured and issued. Then, IOAM performance data collected by the target bearer device based on the IOAM detection task is received, and the quality performance of the base station associated with the IOAM performance data is analyzed based on the IOAM performance data. By introducing aggregated flow detection technology, this embodiment can obtain the IOAM performance data of the aggregated flow, i.e., the IOAM detection result of the aggregated flow, greatly expanding the characterization capability of the IOAM detection results. It elevates the analysis from single-service-oriented to directly user-perceived base station performance, enabling the IOAM-based detection results to reflect the overall service capability of the system. Attached Figure Description

[0011] Figure 1 This is a flowchart of a base station performance analysis method mentioned in the embodiments of this application;

[0012] Figure 2 This is a schematic diagram of the application scenario of the control center mentioned in the embodiments of this application;

[0013] Figure 3 This is a flowchart illustrating the implementation of step 106 as described in the embodiments of this application;

[0014] Figure 4 This is a flowchart of another base station performance analysis method mentioned in the embodiments of this application;

[0015] Figure 5 This is a flowchart illustrating how the IOAM detection task of the aggregated stream is implemented according to the configuration mentioned in the embodiments of this application;

[0016] Figure 6 This is a flowchart of the base station performance analysis method applied to the target bearer device mentioned in the embodiments of this application;

[0017] Figure 7 This is a schematic diagram of the base station performance analysis system mentioned in the embodiments of this application;

[0018] Figure 8This is a schematic diagram of the structure of the electronic device mentioned in the embodiments of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this application to help readers better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments. The division of the various embodiments below is for the convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined with and referenced by each other without contradiction.

[0020] To facilitate understanding of the embodiments of this application, the relevant technologies involved in this application will be briefly described below:

[0021] In the 5G era, wireless services face higher demands in terms of bandwidth, latency, and connectivity flexibility, while also imposing more stringent requirements on the indicators of the bearer network. Based on the actual needs of operator network operations, network management is typically divided into three categories: Operation, Administration, and Maintenance (OAM). Operation primarily involves the analysis, prediction, planning, and configuration of daily network and service operations; Maintenance mainly involves routine operational activities such as testing and fault management of the network and its services. IOAM technology can quickly, sensitively, and in real-time detect changes in network quality indicators, significantly shortening the time for network fault location and handling. IOAM technology is a flow-based measurement technology based on real service flows. Based on the principle of flow-based detection, IOAM provides end-to-end and point-by-point detection capabilities for packet loss and latency in real service flows. It can quickly detect network performance-related faults and accurately delimit and troubleshoot them, making it an important tool for the operation and maintenance of 5G mobile bearer networks.

[0022] In current 5G engineering applications, bearer maintenance primarily relies on IOAM (Internet Protocol Stream) detection technology to assess service connectivity from a service-side perspective. However, IOAM-based detection results only reflect the characteristics of a single service and cannot represent the overall system service capabilities. This application's embodiment elevates IOAM detection from the existing service-dimensional single Internet Protocol Stream (IP stream) approach to a base station-dimensional aggregated stream approach. IOAM performance data based on aggregated streams significantly expands the analytical value of traditional single IP streams, moving from a single-service-oriented approach to a base station analysis directly impacting user perception. It allows for analysis of base station quality performance based on aggregated stream IOAM performance data, providing a more intuitive view of network access capabilities closer to the user. Existing IOAM detection is stream-oriented, and IOAM performance data is naturally stream-oriented as well. Introducing aggregated streams provides aggregated stream IOAM performance data, greatly expanding the characterization capabilities of IOAM detection results.

[0023] One embodiment of this application relates to a base station performance analysis method applied in a control center. This embodiment is applicable to, but not limited to, communication networks, particularly to 5G mobile communication bearer networks, including the quality and performance analysis of north-south services and east-west base stations. The implementation details of the base station performance analysis method of this embodiment are described below. These details are provided for ease of understanding and are not essential for implementing this solution.

[0024] Step 101: Configure the IP flow collection task for the target bearer device and send the IP flow collection task to the target bearer device so that the target bearer device can collect IP flow according to the IP flow collection task.

[0025] Step 102: Receive the IP stream reported by the target bearer device.

[0026] Step 103: Aggregate the received IP streams to obtain the aggregated stream.

[0027] Step 104: Configure the IOAM detection task for the aggregated stream and send the IOAM detection task to the target bearer device so that the target bearer device can perform IOAM detection according to the IOAM detection task to obtain IOAM performance data.

[0028] Step 105: Receive IOAM performance data of the IOAM detection task reported by the target bearer device.

[0029] Step 106: Analyze the quality performance of the base station associated with the IOAM performance data based on the IOAM performance data.

[0030] In one example, the control center may include multiple subsystems, and steps 101 to 106 above can be performed by different subsystems within the control center. For example, a schematic diagram of the control center can be found here. Figure 2 It includes: a strategy center subsystem, a unified data collection subsystem, a big data analysis subsystem, and an intelligent base station analysis subsystem.

[0031] In step 101, the target bearer device can be any device in the network that needs to have its IP flow collected, such as a core-side device or an access-side device. After receiving the IP flow collection task, the target bearer device can collect IP flows based on NetFlow. NetFlow is a network monitoring function that can collect IP flows entering and leaving the network interface. The target bearer device can report the collected IP flows to the management and control center.

[0032] In one example, the task parameters in the configured IP flow acquisition task may include one or a combination of the following: the scope and objects of IP flow acquisition, the NetFlow flow acquisition cycle, the execution start time, the execution duration (e.g., 60 minutes), the flow sampling rate, the flow aggregation duration, the monitoring port (e.g., using the U-side ports of all UPEs), the task type (scheduled task, real-time task), and the acquisition protocol. The task parameters in the IP flow acquisition task can be modified according to actual needs.

[0033] In its implementation, the intelligent base station analysis subsystem can configure IP flow collection tasks for the target bearer device and distribute these tasks to the unified collection subsystem. The unified collection subsystem then distributes these tasks to the target bearer device. The target bearer device collects IP flows according to the tasks and reports the collected IP flows to the unified collection subsystem. In other words, the IP flows collected by the target bearer device continuously pass through the unified collection subsystem and are then reported to the intelligent base station analysis subsystem. This allows the intelligent base station analysis subsystem to clearly identify which IP flows require further analysis. Specifically, when the unified collection subsystem sends IP flow collection tasks to the target bearer device, the target bearer device, upon receiving the task, activates NetFlow flow collection and statistics functions based on the task parameters, collecting the service flows (IP flows) generated by the target bearer device.

[0034] In step 102, the control center can receive IP flows reported by the target bearer device. Specifically, the unified acquisition subsystem in the control center can receive the raw data of the IP flows reported by the target bearer device. The unified acquisition subsystem then reports the raw data of the IP flows to the intelligent base station analysis subsystem. After parsing, the intelligent base station analysis subsystem obtains the first-flow characteristic information related to the service flow, i.e., the IP flow. The first-flow characteristic information includes: source IP address, source port number, destination IP address, destination port number, protocol number, priority (Differentiated Services Code Point, abbreviated as DSCP), and timestamp when the IP flow was reported. The unified acquisition subsystem can uniquely identify a service flow according to a five-tuple, which is: source IP address, source port number, destination IP address, destination port number, protocol number, etc.

[0035] In step 103, the control center can aggregate the received IP flows according to the aggregation strategy to obtain an aggregated flow. The aggregation strategy can include one or any combination of the following: service characteristic information of the IP flow, the base station to which the IP flow belongs, current alarm database information, and base station service quality level. The aggregation strategy can be generated in the strategy center subsystem, which can then send the generated aggregation strategy to the intelligent base station analysis subsystem, enabling the intelligent base station analysis subsystem to aggregate the received IP flows according to the aggregation strategy to obtain an aggregated flow.

[0036] In one example, if the aggregation strategy includes the service characteristic information of the IP streams, then the received IP streams are aggregated according to the aggregation strategy to obtain an aggregated stream, including: aggregating IP streams with the same service characteristic information according to the service characteristic information of the received IP streams to obtain an aggregated stream.

[0037] In one example, the aggregation strategy includes the base station to which the IP stream belongs. Based on the aggregation strategy, the received IP streams are aggregated to obtain an aggregated stream. This includes: aggregating IP streams belonging to the same base station based on their base station affiliation, thus forming an aggregated stream. This ensures that the characteristics of the aggregated stream effectively reflect the quality performance of the same base station. Alternatively, the aggregated stream can be obtained by aggregating IP streams belonging to the same type of base station based on their base station affiliation.

[0038] In one example, if the aggregation strategy includes current alarm database information, then the received IP streams are aggregated according to the aggregation strategy to obtain an aggregated stream. This includes aggregating the IP streams of base stations connected to access network elements with preset alarm levels based on the current alarm database information. The preset alarm levels can be set according to actual needs, such as Level 1 alarms, Level 2 alarms, Level 3 alarms, etc.

[0039] In one example, the aggregation strategy includes base station service quality levels. Based on the aggregation strategy, received IP flows are aggregated to obtain aggregated flows. This includes aggregating IP flows from base stations with the same service quality level, according to the base station to which the received IP flows belong. The base station service quality level can be categorized as: Normal, Important, Very Important, etc.

[0040] In one example, the aggregation strategy can also be a manually specified strategy, which refers to a strategy that is manually set by a person skilled in the art according to actual needs.

[0041] In step 104, the control center can configure IOAM detection tasks for the aggregated stream and distribute these tasks to the target bearer devices. Specifically, the intelligent base station analysis subsystem in the control center can configure IOAM detection tasks for the aggregated stream and distribute them to the unified acquisition subsystem. The unified acquisition subsystem then distributes the IOAM detection tasks to the target bearer devices, enabling the target bearer devices to collect quality performance data of the aggregated stream based on these IOAM detection tasks. In other words, the target bearer devices obtain IOAM performance data of the aggregated stream by performing IOAM detection according to the IOAM detection tasks.

[0042] In one example, an IOAM detection task may include detection parameters and control commands. The detection parameters include at least the interface information of the target bearer device, and the control commands can be commands to start or stop detection. The intelligent base station analysis subsystem in the control center can configure IOAM detection tasks for aggregated streams and distribute these tasks to the unified acquisition subsystem. The unified acquisition subsystem determines the interface of the target bearer device based on the detection parameters received in the IOAM detection task and sends the control commands for the IOAM detection task to the target bearer device. If the target bearer device receives a control command to start detection, it can collect quality performance data of the aggregated stream based on telemetry. The target bearer device then reports the collected quality performance data of the aggregated stream to the unified acquisition subsystem, which in turn reports the quality performance data to the intelligent base station analysis subsystem. Telemetry is a technology for remotely acquiring data at high speed from physical or virtual devices. In practical implementation, the detection parameters of the IOAM detection task can also include one or more of the five-tuples from each single IP flow in the aggregated flow, depending on actual needs. The five-tuples include: source IP address, source port number, destination IP address, destination port number, protocol number, and priority. In other words, in this embodiment, the specific IP five-tuple information can be disregarded when configuring the IOAM detection task.

[0043] In one example, configuring an IOAM detection task for an aggregated stream can include: configuring the IOAM detection task for the aggregated stream based on received detection parameters and control commands. The detection parameters and control commands can be input by technical personnel; for example, the control center may have a corresponding control interface where technical personnel can input the detection parameters and control commands, allowing the control center to configure the IOAM detection task for the aggregated stream based on the received detection parameters and control commands. In other words, in this example, the IOAM detection task for the aggregated stream can be created manually.

[0044] In step 105, the IOAM performance data can be understood as follows: An IOAM detection task is performed on the aggregated stream to obtain its IOAM performance data. This IOAM performance data can be understood as the quality performance data of the aggregated stream. The quality performance data of the aggregated stream can be the same quality performance data shared by multiple single IP streams within the aggregated stream. In other words, the quality performance data of the aggregated stream can characterize the commonalities in quality performance among multiple single IP streams within the aggregated stream.

[0045] In step 106, after receiving the IOAM performance data, the control center can process the data to obtain quality performance indicators such as packet loss rate, latency, and jitter. Based on these performance indicators, the control center can analyze the quality type of the base station associated with the IOAM performance data.

[0046] The control center can pre-store the correspondence between quality performance indicators and quality types, and can determine the quality type of the base station based on this correspondence. For example, the quality type of a base station can include: normal, degraded, and interrupted. A normal base station corresponds to normal quality performance indicators, a degraded base station corresponds to degraded quality performance indicators, and an interrupted base station corresponds to interrupted quality performance indicators.

[0047] In one example, the implementation of step 106 can be found by referring to... Figure 3 ,include:

[0048] Step 301: Obtain the quality performance indicators of the aggregated stream based on the IOAM performance data.

[0049] Step 302: Inject the path topology information corresponding to the aggregated flow into the quality performance index to obtain the quality performance index associated with the path topology information.

[0050] Step 303: Analyze the quality performance of base stations associated with IOAM performance data based on the quality performance indicators associated with path topology information.

[0051] The path topology information corresponding to the aggregated flow can be understood as the path that a single IP flow within the aggregated flow actually passes through multiple devices in the network physical topology. That is, the process of the aggregated flow transmitting from the base station to the target bearer device, passing through information from other devices in between. For example, the path topology information of the aggregated flow within the network physical topology can be represented as: Device A → Device B → Device C → Device D. Quality performance indicators associated with path topology information can include: quality performance indicators for the path between Device A and Device B, the path between Device B and Device C, and the path between Device C and Device D. Based on quality performance indicators associated with path topology information, the quality performance of the base station associated with IOAM performance data can be analyzed more accurately and reasonably.

[0052] In step 303, the quality performance indicators associated with path topology information can be further refined according to preset dimensions to obtain refined quality performance indicators. The preset dimensions include any one or a combination of the following: duration dimension, time period type dimension, indicator value type dimension, prediction trend dimension, and indicator combination form dimension. Based on the refined quality performance indicators, the quality performance of base stations associated with IOAM performance data is analyzed. Optional duration dimensions can be: minutes, seconds, hours, days, weeks, months, etc. It can be understood that IOAM performance data is typically IOAM performance data for a single collection period. Refining the quality performance indicators associated with path topology information based on the duration dimension can be understood as: aggregating the IOAM performance data of a single period into performance indicator data within a preset aggregation duration, so that the aggregated performance indicator data can reflect the network quality over a longer period, thereby enabling a more accurate analysis of the quality type of base stations associated with the IOAM performance data. The single collection period may be a short duration such as 1 second or 10 seconds, and the preset aggregation duration may be a longer duration such as 1 hour, 12 hours, or 24 hours. The selectable time period type dimension can be: busy time, off-peak time, holidays, morning peak, evening peak, etc.; the selectable indicator value type dimension can be: mean, peak, median, etc.; the selectable prediction trend dimension can be: the trend predicted from the past, present to the future; the selectable indicator combination dimension can be: the combination of different indicators such as jitter, latency, packet loss rate, flow rate, etc.

[0053] In practical implementation, the big data analysis subsystem in the control center can use big data processing technology and intelligent application algorithms to obtain refined quality performance indicators based on different time lengths (seconds, minutes, seconds, hours, days, weeks, months, etc.), different indicator value types (average, peak, etc.), different time periods (busy hours, idle hours, holidays, etc.), predictive trends from the past, present, to the future, and different indicator combinations (jitter, latency, packet loss rate, flow rate, etc.). Then, based on the association model between IP service flows and network element nodes, network element interfaces, and services, the quality performance of network element nodes, network element interfaces, and services can be extracted.

[0054] In one example, the control center can update the quality performance (e.g., normal, degraded, outage) of all base stations across the network based on IOAM performance data. It supports filtering for abnormal base stations of degraded or outage types based on the entire network and base station IP address segments, facilitating rapid group failure analysis. Optionally, the control center can also view the number of base stations belonging to the outage, degraded, and normal types for the current time period, view trend charts of various abnormal base stations at different time granularities such as month / week / day, and view real service flow performance diagnostic data for the signaling and data planes presented at the base station level, distinguishing between uplink and downlink directions.

[0055] In one example, after analyzing the quality performance of base stations associated with IOAM performance data, the process includes: if the base station's quality performance indicates service quality degradation, then based on the path topology information corresponding to the aggregated flow, path reconstruction is performed on each IP flow in the aggregated flow, and quality deviation delimitation is performed based on the path reconstruction results. Quality deviation delimitation can be understood as defining the locations of quality problems in the path of the aggregated flow. Quality deviation delimitation facilitates the identification of quality issues, making later maintenance easier. In another example, the control center can also view the quality characteristics of individual IP flows in the aggregated flow through drill-down and correlation operations, facilitating understanding the quality characteristics of individual IP flows according to actual needs. In other words, when a single IP flow experiences service quality degradation, or when a base station experiences service quality degradation, path reconstruction of all single IP flows in the aggregated flow can be analyzed and quality deviation delimitation can be performed.

[0056] In one example, the base station information associated with IOAM performance data can be obtained from the management and control center, and the base station information in the management and control center can be pre-imported by the user. Considering that the number of base stations in the entire network can reach tens of thousands, this embodiment can set a base station whitelist policy to perform quality control only on base stations accessed by preset services. The preset services can be set according to actual needs, such as services of concern to users or key services. In other words, the base stations associated with IOAM performance data in this embodiment belong to the base station whitelist.

[0057] In this embodiment, IOAM detection of aggregated flows is introduced. This allows for direct characterization of base station quality characteristics based on aggregated flows, enabling analysis of base station performance. This user-centric base station performance analysis better reflects the overall service capabilities of the system compared to traditional service-oriented state analysis. It elevates the analysis from existing service-based single-IP-flow IOAM detection to base station-centric aggregated-flow IOAM detection. The IOAM performance data based on aggregated flows significantly expands the analytical value of traditional single-IP-flow analysis, moving from single-service-oriented to user-perceived base station analysis. It allows for analysis of base station quality types based on aggregated-flow IOAM performance data, providing a more intuitive view of network access capabilities close to the user.

[0058] One embodiment of this application relates to a base station performance analysis method applied in a control center. The implementation details of the base station performance analysis method of this embodiment are described below. These details are provided for ease of understanding and are not essential for implementing this solution. A flowchart of the base station performance analysis method of this embodiment can be found here. Figure 4 ,include:

[0059] Step 401: Configure the IP flow collection task for the target bearer device and send the IP flow collection task to the target bearer device so that the target bearer device can collect IP flow according to the IP flow collection task.

[0060] Step 402: Receive the IP stream reported by the target bearer device.

[0061] Step 403: Aggregate the received IP streams to obtain the aggregated stream.

[0062] Step 404: Assign a globally unique tag value to the aggregated stream, and configure the IOAM detection task for the aggregated stream based on the tag value.

[0063] Step 405: Send the IOAM detection task to the target bearer device so that the target bearer device can perform IOAM detection and obtain IOAM performance data according to the IOAM detection task.

[0064] Step 406: Receive IOAM performance data of the IOAM detection task reported by the target bearer device.

[0065] Step 407: Analyze the quality performance of the base station associated with the IOAM performance data based on the IOAM performance data.

[0066] Steps 401 to 403 and steps 405 to 407 have been described in the above embodiments and will not be repeated here to avoid repetition.

[0067] In this embodiment, considering that in existing single-IP flow IOAM detection, each IP flow must have a unique tag value Key throughout the entire network, which is maintained throughout the IOAM detection process, see [link to relevant documentation]. Figure 2 For core-side devices, resource bottlenecks arise when the number of IOAM detection instances reaches a certain level. Therefore, in this embodiment, not every single IP flow needs to be configured with a globally unique tag value Key. Instead, as shown in step 404: when configuring an IOAM detection task, instead of assigning a globally unique tag value Key to each single IP flow, a globally unique tag value Key is assigned to a logically aggregated group of flows, along with the aggregation processing flag information. After the unified collection subsystem sends the IOAM detection task to the target bearer device, the target bearer device will process and report IOAM performance data for the aggregated group of flows, i.e., the aggregated flow, during internal processing. That is, in this embodiment, IOAM detection based on aggregated flows is based on aggregating multiple single IP flows to form an aggregated flow. This aggregated flow is not an aggregation of IPs in the network entity, but a logical aggregation concept, which facilitates the creation and maintenance of subsequent IOAM detection tasks and their distribution to the device side for identification.

[0068] This embodiment primarily addresses the resource bottleneck issue that arises when IOAM detection tasks are deployed across the entire network or over a large area, significantly increasing the pressure on the supporting equipment, especially the network core equipment. The inventors of this application discovered that the reason IOAM detection technology easily encounters resource bottlenecks on devices during large-scale deployment is that current IOAM detection technology is based on single-flow (IP 5-tuple) detection. Each flow corresponds to one analysis object, and to reflect the independence of the detection object, each flow must have a unique identifier key across the entire network. When the number of detection instances reaches tens of thousands or hundreds of thousands, resource bottlenecks occur in the allocation of key values ​​on the core equipment. In this embodiment, by introducing an aggregated flow detection method, multiple single IP flows are aggregated to obtain an aggregated flow. The aggregated flow obtained after aggregating multiple single IP flows is assigned a globally unique tag value key, effectively solving the resource bottleneck problem.

[0069] In one embodiment, the implementation of configuring the IOAM detection task for the aggregated flow mentioned in step 104 can be as follows: Configure the IOAM detection task for the aggregated flow based on the self-learning results of the IOAM detection task; wherein the self-learning results are learned based on a flow feature information database, which includes: first feature information and second feature information of the IP flow. The first feature information is feature information collected based on historical IP flow collection tasks, and the second feature information is feature information collected based on historical IOAM detection tasks. Alternatively, the first feature information of the IP flow can be understood as: flow feature information collected based on NetFlow, and the second feature information can be understood as: flow feature information collected based on Telemetry. The flow feature information collected based on NetFlow can include: source IP address, source port number, destination IP address, destination port number, protocol number, priority (DSCP), timestamp information when the IP flow is reported, etc. The flow feature information collected based on Telemetry can include: packet loss, latency, jitter, etc., as second feature information. That is to say, the flow feature information of the IP flow collected by the unified collection subsystem can also be used for the self-learning of the IOAM detection task. Self-learning is fundamental to the entire lifecycle of IOAM detection tasks, from creation to update to aging. Optionally, after the IP flow feature information is collected, it can be managed through deduplication, whitelist filtering, and aging maintenance to continuously maintain the integrity and timeliness of the IP flow feature information database without any residue. In this embodiment, configuring the IOAM detection task for aggregated flows through self-learning improves the ease of configuring IOAM detection tasks.

[0070] In one example, the IP flows in the flow feature information database conform to the flow feature whitelist strategy, which includes an activity level greater than a preset activity level. It is understandable that there is a large amount of IP flow data across the entire network; even with a certain sampling ratio, the collected IP flow data is still substantial. Therefore, in this embodiment, the collected IP flows can be filtered according to the preset flow feature whitelist strategy, and the feature information of the filtered IP flows can be added to the flow feature information database. The flow feature information database can serve as the basis for the automatic learning and maintenance of IOAM detection tasks. The flow feature whitelist strategy can be set according to actual needs; for example, it can be set to determine whether a flow is an active flow. The filtered IP flows are then considered active flows. The activity level of an active IP flow is greater than a preset activity level. The activity level of an IP flow can be determined by its idle time; a longer idle time indicates lower activity, and a shorter idle time indicates higher activity. The preset activity level can be set according to actual needs.

[0071] For newly added flows and flows with very low activity levels for a certain period of time, timely maintenance can be performed to create a flow feature information database. For example, the feature information of newly added IP flows can be added to the flow feature information database, while the feature information of IP flows with very low activity levels for a certain period of time can be deleted from the database. This ensures the integrity and timeliness of the IP flow feature information database without any remnants. In practical implementation, the control center can update the inactivity duration of each IP flow every cycle. If the inactivity duration of an IP flow exceeds a certain threshold, it can be considered that the IP flow is inactive, and it is regarded as an aging flow and removed from the flow feature information database.

[0072] In one example, the target bearer device can perform deduplication when reporting IP flow characteristic information, and only report IP flows with new characteristics to the control center to prevent duplicate reporting of flows already reported in the previous period from impacting system processing. Optionally, the target bearer device can report the full set of IP flow characteristic information at fixed intervals to prevent omissions of flow data reported in previous periods, thus ensuring the integrity of the flow characteristic information database.

[0073] In one embodiment, the implementation of the configuration for the IOAM detection task of the aggregated stream mentioned in step 104 can be referenced. Figure 5 ,include:

[0074] Step 501: Determine the target time period and / or target region based on the IP flow reported by the target bearer device.

[0075] The target time period and / or target region can be understood as: the time period and / or region where the traffic exceeds the preset traffic limit, determined based on the IP flow reported by the target bearer. The preset traffic range can be set according to actual needs. For example, if the traffic in region A is detected to be high between 6:00 and 8:00 based on the IP flow reported by the target bearer, then the target time period can be 6:00 to 8:00, and the target region can be region A.

[0076] Step 502: Determine the network element information associated with the target time period and / or target region.

[0077] The network element information can include network element nodes, network element interfaces, etc. In specific implementations, a correlation model between IP flows, time periods, and / or regions and network element information can be pre-created. Through this correlation model, the network element information associated with the target time period and / or target region can be obtained.

[0078] Step 503: Based on the network element information, configure an IOAM detection task to perform IOAM detection on the aggregated flow in the target time period and / or target region.

[0079] In other words, when configuring IOAM detection tasks for aggregated streams, you can associate them with target time periods and / or target regions, thereby enabling more targeted IOAM detection of aggregated streams within the target time periods and / or target regions.

[0080] In practical implementation, the big data analysis subsystem in the control center can perform refined indicator calculations on the IP flow data reported from the unified acquisition subsystem in real time to determine the target time period and / or target region. The big data analysis subsystem can also create and maintain a correlation model, determine the associated network element information for the target time period and / or target region based on the correlation model, and send the associated network element information, target time period, and / or target region to the intelligent base station analysis subsystem. This allows the intelligent base station analysis subsystem to configure IOAM detection tasks for the aggregated flow in the target time period and / or target region based on the network element information. By configuring IOAM detection tasks for the aggregated flow in the target time period and / or target region, targeted IOAM detection can be performed in specific target time periods and / or target regions, improving the targeting effectiveness of IOAM detection.

[0081] In one embodiment, after configuring the IOAM detection task for the aggregated stream, the method further includes:

[0082] After detecting the first triggering condition, the IOAM detection task of the aggregated stream is deleted. The first triggering condition is that the activity level of the aggregated stream is less than or equal to a preset activity level after a preset duration. The preset duration and preset activity level can be set by those skilled in the art according to actual needs. For example, the preset duration can be 24 hours, 48 ​​hours, etc. That is, after the IOAM detection task of the aggregated stream is created, if after 24 hours the activity level of the aggregated stream is detected to be very low, less than or equal to the preset activity level, then the aggregated stream can be considered inactive, and the IOAM detection task of the aggregated stream can be deleted, i.e., IOAM detection will no longer be performed on the aggregated stream. The activity level of the aggregated stream can be determined by the idle time of the aggregated stream, which can be determined by the idle time of each single IP stream in the aggregated stream. For example, the idle time of the aggregated stream can be the sum of the idle times of each single IP stream in the aggregated stream, or the average idle time of each single IP stream in the aggregated stream. This embodiment does not specifically limit this. The longer the idling time, the lower the activity level. If the idling time exceeds a certain threshold, the activity level of the aggregated flow can be considered to be less than or equal to the preset activity level, that is, the aggregated flow has basically no activity. The aggregated flow is then regarded as an aging flow, and the IOAM detection task for the aggregated flow is deleted.

[0083] Upon detecting the second triggering condition, the IOAM detection task of the aggregated stream is updated; wherein, the second triggering condition is a change in the feature information of the aggregated stream. It is understood that the feature information of the aggregated stream may be dynamically changing. If a change in the feature information of the aggregated stream is detected compared to when the IOAM detection task was configured, the detection parameters of the IOAM detection task can be updated based on the changed feature information, thereby completing the update of the IOAM detection task. This allows the updated IOAM detection task to adapt to the changed feature information of the aggregated stream, enabling more targeted IOAM detection of the aggregated stream.

[0084] Upon detecting the third triggering condition, the IOAM detection task of the aggregated stream is used as the IOAM detection task of the stream to be deduplicated. The third triggering condition is the requirement to configure an IOAM detection task for the stream to be deduplicated, which is identical to the aggregated stream. In other words, after configuring an IOAM detection task for the aggregated stream, if a new stream identical to the aggregated stream (the stream to be deduplicated) is detected, a new IOAM detection task will not be created for that stream. Instead, the already configured IOAM detection task of the aggregated stream will be used as the IOAM detection task for the stream to be deduplicated. This saves time configuring IOAM detection tasks and improves the efficiency of IOAM detection.

[0085] In practical implementation, the management interface of the control center can display created IOAM detection tasks, updated IOAM detection tasks, deduplicated IOAM detection tasks, deleted IOAM detection tasks, and distributed IOAM detection tasks. Among these, created IOAM detection tasks can also be referred to as configured IOAM detection tasks, and distributed IOAM detection tasks can be understood as IOAM detection tasks for aggregated flows having been distributed to the corresponding target bearer devices. By displaying the above information on the management interface, the different statuses of IOAM detection tasks for aggregated flows can be displayed more intuitively, facilitating the management of IOAM detection tasks.

[0086] In one embodiment, considering factors such as new base station entry, network exit, and configuration changes, the base station's information database cannot be updated in a timely manner. Therefore, this embodiment provides a function for automatically discovering base station information, eliminating the need for users to pre-import base station information. In this embodiment, the automatic discovery of base station information (also known as base station self-learning) is achieved primarily through Address Resolution Protocol (ARP) information and / or Neighbor Discovery (ND) information combined with a flow characteristic information database in the control center. In other words, the base station self-learning is based on ARP information and / or ND information, as well as the flow characteristic information database in the control center. The base stations associated with IOAM performance data mentioned in step 106 or step 407 can be determined in the following ways:

[0087] Based on Address Resolution Protocol (ARP) information and / or Neighbor Discovery Protocol (ND) information used for automatic base station discovery, base stations associated with IOAM performance data are automatically discovered; wherein, the ARP information and / or ND information is information collected from bearer devices in the network. The control center can collect and maintain ARP information and / or ND information from bearer devices in the network for base station self-learning and status maintenance. ARP information and / or ND information mainly includes: Access Control (AC) port information, base station IPv4 / IPv6 information, Virtual Local Area Network (VLAN) information, etc. The control center can collect ARP information and / or ND information periodically according to preset scheduling tasks, or manually trigger it to complete the collection of ARP information and / or ND information. It can also actively receive incremental ARP information and / or ND information reported by bearer devices to complete the collection of ARP information and / or ND information. Incremental ARP information and / or ND information can be ARP information and / or ND information reported by newly added bearer devices in the network. For example, the strategy center subsystem in the control center can periodically collect and maintain ARP and / or ND information from the bearer equipment according to preset scheduling tasks, and send it to the intelligent base station analysis subsystem for use by the intelligent base station analysis subsystem for base station self-learning and status maintenance. Optionally, after collecting ARP and / or ND information, the data can be maintained and updated in a timely manner to ensure that it is the most complete, up-to-date, and free of residual data.

[0088] In one example, considering that the AC port directly connected to the 5G base station is the access-side Ethernet interface of the L3VPN, the information of the 5G base station can be directly obtained from ARP / ND for the 5G network.

[0089] In one example, considering that the AC port directly connected to the 4G base station belongs to a Layer 2 Virtual Private Network (L2VPN) service entity, the automatic discovery method for 4G base stations can be as follows: First, through the bridging point between the L2VPN and the L3VPN, a virtual sub-interface facing the Layer 3 is used to query the ARP and / or ND information used for automatic base station discovery to find ARP and / or ND information that matches the virtual sub-interface facing the Layer 3; then, based on the matching ARP and / or ND information, the Layer 2 virtual sub-interface is determined; next, the L2VPN service entity corresponding to the Layer 2 virtual sub-interface is determined, and the access controller AC port belonging to the L2VPN service entity is determined; then, the User-facing-Provider Edge (UPE) device connected to the AC port is determined, and the base station connected to the UPE is used as the automatically discovered base station associated with the IOAM performance data.

[0090] In its implementation, the policy center subsystem is primarily responsible for: managing ARP and / or ND information, aggregation policy management, base station whitelist policy management, and flow feature whitelist policy management. Aggregation policies, base station whitelist policies, and flow feature whitelist policies can all be manually entered or imported into the policy center subsystem. ARP and / or ND information is used for base station self-learning and status maintenance. The policy center subsystem can query the ARP and / or ND information used for automatic base station discovery from the Layer 3 virtual sub-interface via the bridging point between L2VPN and L3VPN to find ARP and / or ND information matching the Layer 3 virtual sub-interface. Aggregation policies are used to aggregate IP flows; base station whitelist policies form the basis for the base station self-discovery process and base station analysis; and flow feature whitelist policies form the basis for flow feature library maintenance and automatic configuration of IOAM detection tasks.

[0091] In addition to automatic discovery, it also supports manual creation of base station information, which helps to avoid the inability to query base station information through ARP / ND information in some cases, such as when the base station and UPE are not directly connected (there is a router in the middle, that is, multiple base stations are connected to the UPE node through the router).

[0092] In one example, the base station information mainly includes: base station name, base station level, creation method, base station status, base station IPv4, base station IPv6, base station type, gateway network element, gateway network element port, gateway network element interface IPv4, gateway network element interface IPv6, associated UPE-side Virtual Routing Forwarding (VRF) name, and NPE outgoing interface list. For example, the base station information can be referenced in Table 1 below:

[0093] Table 1

[0094]

[0095] This embodiment introduces IOAM detection of aggregated flows, which directly characterizes the quality characteristics of base stations based on aggregated flows to analyze their quality performance. This user-oriented base station quality type analysis better reflects the overall service capabilities of the system compared to traditional service-oriented state analysis. It elevates analysis from single-flow analysis to aggregated flow analysis of base station services, and solves the resource bottleneck problem caused by single-flow (IP 5-tuple) IOAM detection. It improves upon the traditional analysis of "points" to analysis of "surfaces" and "clusters," providing greater guidance for network operation and maintenance and optimization. This embodiment enables automatic base station discovery and intelligent maintenance, rapid configuration and intelligent maintenance of in-band detection tasks (such as deletion, updating, and deduplication of IOAM detection tasks), and facilitates the simultaneous display of aggregated flow quality characteristics. It offers richer features, greater operational guidance, and facilitates network analysis from the base station perspective, as well as subsequent group fault analysis and quality defect delimitation.

[0096] It should be noted that the examples described above in the embodiments of this application are merely illustrative for ease of understanding and do not constitute a limitation on the technical solution of the present invention.

[0097] One embodiment of this application relates to a base station performance analysis method applied to a target bearer device. The implementation details of the base station performance analysis method of this embodiment are described below. These details are provided for ease of understanding and are not essential for implementing this solution. A flowchart of the base station performance analysis method of this embodiment can be found here. Figure 6 ,include:

[0098] Step 601: Receive the IP flow collection task sent by the control center, and collect IP flow according to the IP flow collection task to obtain IP flow; wherein, the IP flow collection task is configured by the control center.

[0099] Step 602: The collected IP streams are reported to the control center so that the control center can aggregate the received IP streams to obtain aggregated streams and configure IOAM detection tasks for the aggregated streams. The aggregated streams include several IP streams.

[0100] Step 603: Receive the IOAM detection task for the aggregated stream issued by the control center, and perform IOAM detection according to the IOAM detection task to obtain IOAM performance data.

[0101] Step 604: Report the IOAM performance data to the control center so that the control center can analyze the quality performance of the base stations associated with the IOAM performance data.

[0102] It is not difficult to see that this embodiment is a method embodiment applied to the target bearer device, corresponding to the method embodiment applied to the control center described above. This embodiment can be implemented in conjunction with the method embodiment applied to the control center described above. The relevant technical details and effects mentioned in the method embodiment applied to the control center described above are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the method embodiment applied to the control center described above.

[0103] The steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but without changing the core design of the algorithm and process, are also within the scope of protection of this patent.

[0104] One embodiment of this application relates to a base station performance analysis system, such as... Figure 7As shown, the system includes: a control center 701 and a target bearer device 702. The control center 701 is used to configure IP flow collection tasks for the target bearer device and distribute the IP flow collection tasks to the target bearer device. The target bearer device 702 is used to receive the IP flow collection tasks, collect IP flows according to the IP flow collection tasks, and report the collected IP flows to the control center 701. The control center 701 is used to aggregate the received IP flows to obtain aggregated flows, configure IOAM detection tasks for the aggregated flows, and distribute the IOAM detection tasks to the target bearer device 702. The aggregated flows include several IP flows. The target bearer device 702 is used to receive the IOAM detection tasks, perform IOAM detection according to the IOAM detection tasks to obtain IOAM performance data, and report the IOAM performance data to the control center 701. The control center 701 is used to receive the IOAM performance data and analyze the quality performance of the base stations associated with the IOAM performance data.

[0105] It is not difficult to see that this embodiment is a system embodiment corresponding to the above method embodiments, and this embodiment can be implemented in conjunction with the above method embodiments. The relevant technical details and technical effects mentioned in the above method embodiments are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above method embodiments.

[0106] One embodiment of this application relates to an electronic device, such as... Figure 8 As shown, it includes: at least one processor 801; and a memory 802 communicatively connected to the at least one processor 801; wherein the memory 802 stores instructions executable by the at least one processor 801; if the electronic device is a control center, the instructions are executed by the at least one processor 801 to enable the at least one processor to execute a base station performance analysis method applied to the control center; if the electronic device is a target bearer device, the instructions are executed by the at least one processor to enable the at least one processor to execute a base station performance analysis method applied to the target bearer device.

[0107] The memory 802 and processor 801 are connected via a bus, which can include any number of interconnecting buses and bridges. The bus connects various circuits of one or more processors 801 and memory 802 together. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 801 is transmitted over a wireless medium via an antenna, which further receives data and transmits it to processor 801.

[0108] The processor 801 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. The memory 802 can be used to store data used by the processor 801 during operation.

[0109] One embodiment of this application relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method embodiments described above.

[0110] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0111] Those skilled in the art will understand that the above embodiments are specific examples of implementing the present invention, and in practical applications, various changes in form and detail may be made without departing from the spirit and scope of the present invention.

Claims

1. A method for analyzing base station performance, characterized in that, Applications in control centers include: Configure an IP flow collection task for the target bearer device, and send the IP flow collection task to the target bearer device so that the target bearer device can collect IP flow according to the IP flow collection task; Receive the IP stream reported by the target bearer device; The received IP streams are aggregated to obtain an aggregated stream; wherein the aggregated stream includes several of the IP streams. Configure an IOAM detection task for the aggregated stream and send the IOAM detection task to the target bearer device so that the target bearer device can perform IOAM detection according to the IOAM detection task to obtain IOAM performance data; Receive the IOAM performance data reported by the target bearer device, and analyze the quality performance of the base station associated with the IOAM performance data based on the IOAM performance data; The step of analyzing the quality performance of base stations associated with the IOAM performance data based on the IOAM performance data includes: Based on the IOAM performance data, the quality performance indicators of the aggregated stream are obtained; By injecting the path topology information corresponding to the aggregated flow into the quality performance index, a quality performance index associated with the path topology information is obtained. Based on the quality performance indicators associated with the path topology information, analyze the quality performance of the base stations associated with the IOAM performance data.

2. The method for analyzing base station performance according to claim 1, characterized in that, The aggregation of the received IP streams to obtain the aggregated stream includes: According to the aggregation strategy, the received IP streams are aggregated to obtain aggregated streams; wherein, the aggregation strategy includes one or any combination of the following: the service characteristic information of the IP stream, the base station to which the IP stream belongs, the current alarm database information, and the service quality level of the base station.

3. The method for analyzing base station performance according to claim 2, characterized in that, The aggregation strategy includes the base station to which the IP stream belongs. The process of aggregating the received IP streams according to the aggregation strategy to obtain an aggregated stream includes: The received IP streams belonging to the same base station are aggregated to obtain the aggregated stream.

4. The method for analyzing base station performance according to claim 1, characterized in that, The configuration for the IOAM detection task of the aggregated stream includes: Assign a globally unique tag value to the aggregated stream; Configure an IOAM detection task for the aggregated stream based on the tag value.

5. The method for analyzing base station performance according to claim 1, characterized in that, The configuration for the IOAM detection task of the aggregated stream includes: Based on the self-learning results of the IOAM detection task, configure the IOAM detection task for the aggregated stream; wherein, the self-learning results are obtained based on the stream feature information database, which includes: first feature information and second feature information of the IP stream, the first feature information being feature information collected based on historical IP stream collection tasks, and the second feature information being feature information collected based on historical IOAM detection tasks.

6. The method for analyzing base station performance according to claim 5, characterized in that, The IP flows in the flow feature information database conform to the flow feature whitelist strategy, which includes: the activity level is greater than a preset activity level.

7. The method for analyzing base station performance according to claim 1, characterized in that, The configuration for the IOAM detection task of the aggregated stream includes: Based on the IP flow reported by the target bearer device, determine the target time period and / or target region; Determine the network element information associated with the target time period and / or target region; Based on the network element information, configure an IOAM detection task to perform IOAM detection on the aggregated flow in the target time period and / or target region.

8. The method for analyzing base station performance according to any one of claims 1 to 7, characterized in that, Following the configuration of the IOAM detection task for the aggregated stream, the following is also included: After detecting the first triggering condition, the IOAM detection task of the aggregated stream is deleted; wherein, the first triggering condition is that the activity level of the aggregated stream is less than or equal to a preset activity level after a preset duration; Upon detecting the second triggering condition, the IOAM detection task of the aggregated stream is updated; wherein, the second triggering condition is a change in the feature information of the aggregated stream; After the third triggering condition is detected, the IOAM detection task of the aggregated stream is used as the IOAM detection task of the deduplicated aggregated stream; wherein, the third triggering condition is that an IOAM detection task needs to be configured for the deduplicated aggregated stream that is the same as the aggregated stream.

9. The method for analyzing base station performance according to claim 1, characterized in that, The step of analyzing the quality performance of base stations associated with the IOAM performance data based on the quality performance indicators associated with the path topology information includes: According to preset dimensions, the quality performance indicators associated with the path topology information are refined to obtain refined quality performance indicators; wherein, the preset dimensions include any one or a combination of the following: duration dimension, time period type dimension, indicator value type dimension, prediction trend dimension, and indicator combination form dimension. Based on the refined quality performance indicators, analyze the quality performance of the base stations associated with the IOAM performance data.

10. The method for analyzing base station performance according to claim 1, characterized in that, After analyzing the quality performance of the base station associated with the IOAM performance data based on the IOAM performance data, the method further includes: If it is determined that the service quality of the base station has deteriorated based on the quality performance of the base station, the path of each IP flow in the aggregated flow is restored according to the path topology information corresponding to the aggregated flow, and the quality difference is delimited according to the result of the path restoration.

11. The method for analyzing base station performance according to claim 1, characterized in that, The base station associated with the IOAM performance data is determined in the following way: Based on Address Resolution Protocol (ARP) information and / or Neighbor Discovery Protocol (ND) information used for automatic base station discovery, base stations associated with the IOAM performance data are automatically discovered; wherein, the ARP information and / or ND information are information of bearer devices in the network collected.

12. The method for analyzing base station performance according to claim 11, characterized in that, The step of automatically discovering base stations associated with the IOAM performance data based on ARP information and / or ND information used for automatic base station discovery includes: The system uses a bridging point between a Layer 2 VPN and a Layer 3 VPN to query the ARP and / or ND information used for automatic base station discovery to find ARP and / or ND information that matches the Layer 3 virtual sub-interface. Based on the matched ARP information and / or ND information, determine the Layer 2 virtual sub-interface; Identify the L2VPN service entity corresponding to the Layer 2 virtual sub-interface, and determine the access controller AC port belonging to the L2VPN service entity; Identify the Service Provider Edge Equipment (UPE) connected to the AC port that is close to the user side, and designate the base station connected to the UPE as an automatically discovered base station associated with the IOAM performance data.

13. A method for analyzing base station performance, characterized in that, Applied to target bearer equipment, including: The system receives IP flow collection tasks sent by the control center and performs IP flow collection according to the IP flow collection tasks to obtain IP flows; wherein, the IP flow collection tasks are configured by the control center. The collected IP streams are reported to the control center, which then aggregates the received IP streams to obtain aggregated streams and configures IOAM detection tasks for the aggregated streams. The aggregated streams include several of the IP streams. Receive the IOAM detection task of the aggregated stream issued by the control center, and perform IOAM detection according to the IOAM detection task to obtain IOAM performance data; The IOAM performance data is reported to the control center so that the control center can analyze the quality performance of the base stations associated with the IOAM performance data. The step of analyzing the quality performance of base stations associated with the IOAM performance data based on the IOAM performance data includes: Based on the IOAM performance data, the quality performance indicators of the aggregated stream are obtained; By injecting the path topology information corresponding to the aggregated flow into the quality performance index, a quality performance index associated with the path topology information is obtained. Based on the quality performance indicators associated with the path topology information, analyze the quality performance of the base stations associated with the IOAM performance data.

14. A base station performance analysis system, characterized in that, include: Control center and target carrier equipment, The control center is used to configure IP flow collection tasks for the target bearer device and to send the IP flow collection tasks to the target bearer device. The target bearer device is used to receive the IP stream collection task, collect IP streams according to the IP stream collection task, and report the collected IP streams to the control center. The control center is used to aggregate the received IP streams to obtain aggregated streams, configure IOAM detection tasks for the aggregated streams, and send the IOAM detection tasks to the target bearer device; wherein, the aggregated stream includes several IP streams; The target bearer device is used to receive the IOAM detection task, perform IOAM detection according to the IOAM detection task to obtain IOAM performance data, and report the IOAM performance data to the control center; The control center is used to receive the IOAM performance data and analyze the quality performance of the base stations associated with the IOAM performance data based on the IOAM performance data. The control center, when analyzing the quality performance of base stations associated with the IOAM performance data based on the IOAM performance data, is used for: Based on the IOAM performance data, the quality performance indicators of the aggregated stream are obtained; By injecting the path topology information corresponding to the aggregated flow into the quality performance index, a quality performance index associated with the path topology information is obtained. Based on the quality performance indicators associated with the path topology information, analyze the quality performance of the base stations associated with the IOAM performance data.

15. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor; If the electronic device is a control center, the instructions are executed by the at least one processor to enable the at least one processor to execute the base station performance analysis method as described in any one of claims 1 to 12; If the electronic device is a target bearer device, the instructions are executed by the at least one processor to enable the at least one processor to execute the base station performance analysis method as described in claim 13.

16. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the base station performance analysis method according to any one of claims 1 to 12, or implements the base station performance analysis method according to claim 13.

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