Business Flow Detection Method, Device, System, Terminal and Storage Medium

Abstract

An embodiment of the present invention relates to the field of communication technologies, and discloses a service flow detection method, apparatus, system, terminal, and storage medium. The service flow detection method in this embodiment includes: determining a service flow to be detected in a target network according to relevant information of the service flow; searching for IP flow information of the service flow to be detected in a pre-generated association model, and configuring an in-band detection task for the service flow to be detected according to the IP flow information; where the association model is used to describe the correspondence between relevant information of service flows in the target network and IP flow information; executing the in-band detection task, and acquiring performance data collected according to the in-band detection task. By the above technical means, the configuration process of the in-band detection task for service flows in the network is simplified, the automatic configuration of the in-band detection task is realized, and the efficiency of performance and quality detection of service flows is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of communication technologies, and in particular, to a service flow detection method, apparatus, system, terminal, and storage medium. Background Art

[0002] With the development of communication technologies, wireless mobile communication has entered the 5G (5th Generation mobile networks) era. The new communication technology brings higher bandwidth, lower latency, and more flexible connection methods. In a 5G network, the in-band OAM (Operations, Administration, and Maintenance) detection technology can provide more accurate and reliable network maintenance means. When the network quality indicators change, it can be sensed in a timely manner, thereby improving the response speed of network fault location and reducing the time for fault handling. In-band OAM is a flow measurement technology based on real service flows. Based on the flow detection principle, in-band OAM provides the ability to detect packet loss and latency of real service flows end-to-end and point-by-point, can quickly sense network performance-related faults, and perform accurate demarcation and troubleshooting, which is an important means for future 5G mobile bearer network operation and maintenance.

[0003] However, currently, the detection object of the in-band OAM detection task is the service flow, and the configuration method is cumbersome when establishing an in-band detection task. When it is necessary to batch deploy in-band OAM detection sessions for service flows, a large amount of manpower is required to complete the configuration of the detection sessions. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to propose a service flow detection method, system, terminal, and storage medium, which can automatically configure in-band detection tasks for service flows, improve task deployment efficiency, and reduce labor costs.

[0005] To achieve the above object, the embodiments of the present application provide a service flow detection method, including: determining a service flow to be detected according to relevant information of the service flow; searching for IP flow information of the service flow to be detected in a pre-generated association model, and configuring an in-band detection task for the service flow to be detected according to the IP flow information; where the association model is used to describe the correspondence between relevant information of service flows and IP flow information in a target network; performing the in-band detection task, and obtaining performance data collected according to the in-band detection task.

[0006] To achieve the above object, an embodiment of the present application further provides a service flow detection device, including: a determination module, configured to determine a service flow to be detected according to relevant information of the service flow; a query module, configured to find IP flow information of the service flow to be detected in a pre-generated association model; wherein, the association model is used to describe the correspondence between relevant information of service flows in the target network and IP flow information; a configuration module, configured to configure an in-band detection task for the service flow to be detected according to the IP flow information; an execution module, configured to execute the in-band detection task and obtain performance data collected according to the in-band detection task.

[0007] To achieve the above object, an embodiment of the present application further provides a terminal, 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, and the instructions are executed by the at least one processor so that the at least one processor can execute the service flow detection method as described above.

[0008] To achieve the above object, an embodiment of the present application further provides a computer-readable storage medium, storing a computer program, and when the computer program is executed by a processor, the service flow detection method as described above is implemented.

[0009] The service flow detection method proposed by the present application determines a service flow to be detected from all service flows in the target network according to relevant information of the service flow, then finds the IP flow information of the service flow to be detected in a pre-generated association model used to describe the correspondence between relevant information of service flows in the target network and IP flow information, and completes the configuration of the in-band detection task for the service flow to be detected according to the IP flow information, thereby realizing the automatic configuration of the in-band detection task, being able to automatically complete the configuration of batch tasks, and improving the deployment efficiency of the in-band detection task. Description of the Drawings

[0010] Figure 1 is a flowchart of the service flow detection method in the first embodiment of the present invention;

[0011] Figure 2 is a flowchart of the association model generation method in the first embodiment of the present invention;

[0012] Figure 3 is a flowchart of the service flow detection method in the second embodiment of the present invention;

[0013] Figure 4 is a schematic structural diagram of the service flow detection system in the third embodiment of the present invention;

[0014] Figure 5 is a schematic structural diagram of the service flow detection device in the fourth embodiment of the present invention;

[0015] Figure 6 It is a schematic structural diagram of a terminal in the fifth embodiment of the present invention. Specific embodiments

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the embodiments of the present application will be elaborated in detail below with reference to the accompanying drawings. However, those of ordinary skill in the art can understand that in the embodiments of the present application, many technical details are provided to help readers better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in the present application can still be implemented. The following division of each embodiment is for convenience of description and should not constitute any limitation on the specific implementation manner of the present application. Each embodiment can be combined and cross-referenced with each other on the premise of no contradiction.

[0017] The first embodiment of the present invention relates to a service flow detection method, including: determining a service flow to be detected according to relevant information of the service flow; searching for IP flow information of the service flow to be detected in a pre-generated association model, and configuring an in-band detection task for the service flow to be detected according to the IP flow information; wherein, the association model is used to describe the correspondence between relevant information of service flows in a target network and IP flow information; executing the in-band detection task, and obtaining performance data collected by the in-band detection task. The execution subject of this embodiment is a terminal for deploying the in-band detection task.

[0018] The following further elaborates this embodiment with reference to the accompanying drawings. The service flow detection method in this embodiment is as Figure 1 shown, including:

[0019] Step 101, determining a service flow to be detected in a target network according to relevant information of the service flow.

[0020] Specifically, this embodiment is applied to a network that needs to monitor service quality, that is, the target network mentioned in this embodiment. In the target network, the relevant information of the service flow includes: the service name to which the service flow belongs, the identifier of the service flow, the identifier of the access device on the bearer side of the service flow, the access interface identifier, network element nodes related to the service flow, and network element interfaces, etc. This relevant information of the service flow can be queried through the network management system in the target network.

[0021] In an example, when the terminal for deploying the in-band detection task receives the input relevant information of the service flow, it queries the corresponding service flow in the network management system according to the relevant information of the service flow as the service flow to be detected. In addition, the terminal for deploying the in-band detection task can also automatically select from the queried service flows based on preset rules.

[0022] Further, when the terminal where the in-band detection task is deployed receives the relevant information of the input service flow, it searches for the service flow corresponding to the relevant information of the service flow in the association model according to the relevant information of the service flow, and then determines the selected service flow in the corresponding service flow as the service flow to be detected according to the received selection instruction for the service flow. That is, after the terminal queries the corresponding service flow, it can display all the queried service flows to the user, and the user can specifically select the service flow to be detected.

[0023] In practical applications, when the user specifies a relevant service, or network element, or network element sub-interface on the terminal where the in-band detection task is deployed, the terminal queries and generates all the service flows under the service, or all the services and service flows corresponding to the network element, or all the services and service flows corresponding to the network element sub-interface through the pre-generated association model. Among them, the pre-generated association model is used to describe the correspondence between the relevant information of the service flow in the target network and the IP flow information. After the user determines the service flow to be detected through the terminal, it searches for the IP flow information corresponding to the service flow to be detected through the association model.

[0024] Step 102, search for the IP flow information of the service flow to be detected in the pre-generated association model.

[0025] Specifically, in the related technology, when configuring the in-band detection task for the service flow, it is necessary to specify parameters such as the network element node, source interface, source IP address, destination interface, destination IP address, and priority corresponding to the service flow. All these information need to be queried through the network management system and then manually fill in the relevant parameters to complete the configuration of the task.

[0026] In an example, the association model is generated by the data collection end in the target network and the terminal for data analysis according to the IP flow information collection task and the task-related parameters, and the IP flow information collection task and the task-related parameters are sent by the terminal where the in-band detection task is deployed. The generation method of the association model is as Figure 2 shown, including:

[0027] Step 201, the data collection end obtains the IP flow information collection task.

[0028] Specifically, the generation of the association model is jointly completed by the data collection end in the target network and the terminal for data analysis. When a user needs to establish an association model, the terminal for task deployment issues an IP flow information collection task and task-related parameters to the data collection end. Among them, the issuance, start, and stop of the task are all controlled by the terminal for task deployment. The collection of IP flow information can be carried out through the NetFlow function. The collected data is mainly IP flow information related to the service flow. The task-related parameters determine the collection object, collection protocol, sampling rate, and collection period of the IP flow information, etc.

[0029] When collecting IP flow information through the NetFlow function, the set parameters include the collection period, start time, collection duration, flow sampling rate, flow aggregation duration, monitoring port, and task type, etc. In actual applications, the collection period is generally calculated in days or weeks. At the same time, the attributes of instant or timed tasks can also be added to the task according to the start time of the task.

[0030] Step 202, the data collection end obtains the IP flow information of all service flows in the target network from the bearer device of the target network.

[0031] Specifically, the bearer device of the target network includes the access-side device and the core-side device in the target network. After receiving the service flow collection task and the instruction to start executing the task, the data collection end starts to collect the IP flow information of all service flows in the target network according to the parameters carried in the collection task. Since the relevant data of the collected IP flow information is raw data, it is also necessary to analyze and pre-statistic these data to obtain the IP flow information related to the service flow, and identify the uniquely corresponding service flow through the five-tuple. The five-tuple includes: source IP address, source port number, destination IP address, destination port number, protocol number, priority (Differentiated Services Code Point). Pre-statistic refers to analyzing the collected service flow to obtain preliminary service characteristics.

[0032] Step 203, the data analysis end queries all the relevant information of the service flows in the network management system of the target network according to the IP flow information.

[0033] Specifically, after the data collection end completes the IP flow information collection task and conducts the analysis and pre-statistic of the raw data, it sends the pre-statistic IP flow information to the terminal for data analysis. The relevant information of the service flow includes: the first access device on the bearer side, the first access interface, the second access device on the bearer side, the second access interface, the L3VPN service identifier, the creation time, the update time, etc.

[0034] In actual applications, the terminal for data analysis can obtain the source IP addresses corresponding to each service flow through the network management system, query the L3VPN service routing table, match the target network segment of the virtual routing interface, and obtain the first access device and the first access interface on the bearer side corresponding to the source IP address of the service flow; at the same time, obtain the L3VPN service information to which the service flow belongs. Similarly, the destination IP addresses corresponding to each service flow can also be obtained through the network management system, query the L3VPN service routing table, match the target network segment of the virtual routing interface, and obtain the second access device and the second access interface on the bearer side corresponding to the destination IP address of the service flow.

[0035] Step 204, the data analysis end generates an association model according to the relevant information of all service flows.

[0036] Specifically, establish the association information between each service flow and the service flow-related information and IP flow information, that is, the association information of the access device, access interface, and service on the bearer side, including: source IP address, the first access device on the bearer side, the first access interface, source port number, destination IP address, the second access device on the bearer side, the second access interface, destination port number, protocol number, priority (DSCP), L3VPN service identifier, creation time, update time, etc. Then, according to these association information and IP flow information, generate the final association model.

[0037] Furthermore, the data analysis terminal can also obtain the data related to the performance of each service flow through the network management system of the target network. Taking parameters such as the jitter, latency, packet loss rate, and flow rate of the service flow as indicators, from time dimensions such as seconds, minutes, quarters, hours, days, weeks, and months, indicator types such as average value or peak value, and different time periods such as busy hours, idle hours, and holidays, extract the performance and quality characteristics of the service flow according to the historical data related to performance. It can be used as the current service characteristics of the service flow and can also predict the future change trend of the service characteristics. Thus, through the collection and analysis of all service IP flow information and service characteristics in the target network, the IP information and service characteristics of the service are associated with the service flow, realizing the binding of the detection object and the service characteristics.

[0038] In one example, based on the established association model, the service characteristics of network element nodes, network element sub-interfaces, services, etc. in the target network, including performance and quality indicators, are all knowable to users. The system can automatically display the matching service flow according to the relevant information of the service flow input by the user. Therefore, when deploying an in-band detection task, the user can purposefully input the characteristics of services within a specific area range, that is, for a certain range of services. The terminal automatically filters out the corresponding service flows within the specific range for the user to deploy the in-band detection task, so as to realize the deployment of in-band detection tasks for target areas of interest, such as high-activity areas, areas with obvious tidal effects, areas where traffic bottlenecks may occur, etc., and can more intelligently monitor the performance and quality data of service flows.

[0039] Step 103, configure an in-band detection task for the service flow to be detected according to the IP flow information.

[0040] Specifically, in the related art, in the actual networking structure, when configuring an in-band detection task for a service flow, parameters such as the network element node, source interface, source IP address, destination interface, destination IP address, and priority corresponding to the service flow need to be specified. The IP address of the core-side device is allocated through an IP address pool. At the beginning, it is impossible or very difficult to obtain the specific IP address of the core-side device because the IP information is necessary information. At this time, the parameters of the downstream in-band detection task cannot be configured. By using the technical means in this embodiment, there is no need to pay attention to which specific IP address is configured. After selecting the network element node, the detectable IP flow information can be automatically recognized. This process of automatically completing the parameter configuration of the in-band detection task is performed without manual operation. When configuring a batch of in-band detection tasks, the terminal used for in-band detection task deployment can automatically fill in all the parameters of the in-band detection tasks for all service flows to be detected, thus realizing the automatic configuration of in-band detection tasks.

[0041] Based on the service flow detection method in this embodiment, in actual applications, when a user inputs relevant information of a service flow through a terminal where an in-band detection task is deployed, such as a network element name or a network element name and specific network element interfaces, if 15 matching service flows are found in the association model through the network element name or specific network element interfaces, then these 15 service flows will be displayed on the interaction interface through the terminal where the in-band detection task is deployed for the user's reference; meanwhile, the five-tuple of IP flow information corresponding to the network element name or specific network element interfaces is synchronously obtained in the background. At this time, if the user has pre-set the rules for in-band detection task deployment, the service flows to be detected are automatically selected, and the in-band detection tasks for the service flows to be detected are configured according to the five-tuple of IP flow information, and the parameters required for configuration are filled in. If the user has not pre-set the rules for in-band detection task deployment, after waiting for the user to input an instruction to select an in-band detection task, the in-band detection tasks for the service flows to be detected are configured according to the IP flow information, and the parameters required for configuration are filled in.

[0042] Step 104, execute the in-band detection task and obtain the performance data collected by the in-band detection task.

[0043] Specifically, the terminal for in-band detection task deployment executes the configured in-band detection task, monitors and records the performance data of the corresponding service flow, and when the performance data of the service flow is found to be abnormal, identifies faults related to network performance and locates and troubleshoots them.

[0044] Compared with the related technologies in this field, the service flow detection method in this embodiment pre-establishes an association model for describing the correspondence between the relevant information of service flows in the target network and the IP flow information, realizing the association of the detection object with service characteristics and IP flow information. When establishing an in-band detection task for a service flow, the IP information corresponding to the service flow is queried according to the association model, and the configuration of the in-band detection task is automatically completed. In the scenario of batch in-band detection task deployment, the configuration of the in-band detection task can be quickly completed without manual intervention. This enables the in-band detection technology to be applied in more scenarios, with higher deployment efficiency, and at the same time, it can also reduce the costs of task deployment and maintenance.

[0045] It should be noted that the above examples in this embodiment are all illustrative examples for easy understanding and do not limit the technical solutions of the present invention.

[0046] The second embodiment of the present invention relates to a service flow detection method. The second embodiment is substantially the same as the first embodiment, and the main difference is that: after the association model is saved in the database in this embodiment, when the IP flow information changes, that is, when the newly collected IP flow information is different from the IP flow information existing in the current association model, the IP flow information is modified and the association model is updated according to the modified IP flow information. In addition, after performing the in-band detection task, if it is queried that there is currently an in-band detection task established for the IP flow information before modification, the in-band detection task established for the IP flow information after modification is modified or deleted.

[0047] The following further elaborates on this embodiment with reference to the accompanying drawings. The service flow detection method in this embodiment is as Figure 3 shown and includes:

[0048] Step 301, determine the service flow to be detected according to the relevant information of the service flow.

[0049] Step 302, search for the IP flow information of the service flow to be detected in the pre-generated association model.

[0050] Step 303, configure an in-band detection task for the service flow to be detected according to the IP flow information.

[0051] Step 304, execute the in-band detection task and obtain the performance data collected by the in-band detection task.

[0052] Steps 301 to 304 are the same as steps 101 to 104 in the first embodiment of the present invention. The relevant implementation details have been specifically described in the first embodiment of the present invention and will not be elaborated here.

[0053] Step 305, if it is queried that there is currently an in-band detection task established for the IP flow information before modification, modify or delete the in-band detection task established for the IP flow information after modification.

[0054] Specifically, in this embodiment, after the association model is generated, if a new IP flow information collection task is received and it is found during the collection process that the newly collected IP flow information is inconsistent with the existing IP flow information, the IP flow information is modified to the newly collected IP flow information, and then the data in the association model is modified according to the new IP flow information to generate a new association model. The maintenance of the association model is completed.

[0055] In addition, the terminals deploying in-band detection tasks can also obtain the relevant information of the service flow through the network management system, so as to monitor the relevant information of the service flow in the target network in real time. When the bearer-side access device, access interface, and service information are modified or deleted, and the system synchronously modifies or deletes the relevant information in the corresponding service flow and the service association model of the bearer-side access device, access interface, and service, it also checks whether there is an in-band OAM detection session constructed for this information in the system. If so, it modifies or deletes the corresponding in-band detection task. At the same time, when the service flow ages and the association information between the service flow and the bearer-side access device, access interface, and service is deleted, it also checks whether there is an in-band detection task constructed for this service flow in the system. If so, it deletes them together, so as to realize the intelligent update and maintenance of the in-band detection tasks.

[0056] In the related technology, the maintenance of in-band detection tasks is static. When the service changes, the service flow to be detected also changes accordingly. If the service flow being detected no longer exists, the user can only first analyze whether the service flow really does not exist and then decide whether to update the original in-band detection task. When the number of in-band detection tasks reaches a certain scale, the related technology obviously cannot meet the basic operation and maintenance requirements. Therefore, the technical means for maintaining in-band detection tasks mentioned in this embodiment can more intelligently identify dead service flows and update them in a timely manner.

[0057] Furthermore, when the IP flow information or the relevant information of the service flow in the association model changes, it queries the currently running in-band detection tasks in real time to find out whether there is an in-band detection task established for the service flow corresponding to the IP flow information or the relevant information of the service flow before the modification. If there is currently an in-band detection task established for the service flow corresponding to the IP flow information or the relevant information of the service flow before the modification, it deletes the corresponding in-band detection task, or modifies the configuration parameters of the corresponding in-band detection task and then redeploys the modified in-band detection task. It further realizes the real-time monitoring of in-band detection tasks, enables the timely discovery and reporting to the maintaining user when the in-band detection tasks become dead, reduces the degree of manual participation, and realizes a certain degree of intelligent update and maintenance of in-band detection tasks.

[0058] In a specific implementation, when the bearer-side access device, access interface, and service information are modified or deleted, the terminal deploying the in-band detection task synchronously modifies or deletes the corresponding in-band detection task and the relevant information of the service flow in the service association model such as the bearer-side access device and access interface.

[0059] In addition, when there are increases in the bearer-side access device, access interface, and service information, the maintenance personnel can manually trigger the real-time collection task of the service flow for the specified monitoring port, and generate the association information of the service flow with the bearer-side access device, access interface, and service flow based on the collected service flow; or wait for the scheduled collection of the service flow and then generate the association information of the newly added service flow with the bearer-side access device, access interface, and service.

[0060] After the data collection end collects the IP flow information according to the newly received IP flow information collection task, it uniquely identifies a service flow according to the five-tuple (source IP address, source port number, destination IP address, destination port number, protocol number). For the newly added service flow, it establishes an association model of the service flow with the bearer-side access device, access interface, and service; for the existing service flow, it modifies the update time in the association information of the service flow with the bearer-side access device, access interface, and service to the time when the relationship is maintained. When the time difference between the update time in the association information and the current time exceeds the set relationship aging time, the association information of the service flow with the bearer-side access device, access interface, and service is deleted.

[0061] Compared with the related technologies in this field, after the in-band detection task is deployed in this embodiment, the status of the in-band detection task is continuously monitored. When the IP flow information is updated, it actively queries whether there are zombie tasks; at the same time, when new IP flow information is collected, the association of the newly added service flow with the service flow-related information and the IP flow information is added. It further realizes the real-time monitoring of the in-band detection task, enables the in-band detection task to be discovered and reported to the maintenance user in time when a zombie occurs, reduces the degree of manual participation, and realizes a certain degree of intelligent update and maintenance of the in-band detection task.

[0062] It should be noted that the above examples in this embodiment are all illustrative examples for easy understanding and do not limit the technical solution of the present invention.

[0063] The step division of the above various methods is only for clear description. When implemented, 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 protection scope of this patent; adding insignificant modifications to the algorithm or process or introducing insignificant designs, but not changing the core design of the algorithm and process are all within the protection scope of this patent.

[0064] The third embodiment of the present invention relates to a service flow detection device, as Figure 3 shown, including:

[0065] A determination module 401, configured to determine the service flow to be detected according to the relevant information of the service flow;

[0066] A query module 402 is configured to find the IP flow information of the service flow to be detected in a pre-generated association model, where the association model is used to describe the correspondence between the relevant information of the service flow in the target network and the IP flow information.

[0067] A configuration module 403 is configured to configure an in-band detection task for the service flow to be detected according to the IP flow information.

[0068] An execution module 404 is configured to execute the in-band detection task and obtain performance data collected according to the in-band detection task.

[0069] It should be noted that each module involved in this embodiment is a logical module. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or implemented as a combination of multiple physical units. In addition, to highlight the innovative part of the present invention, units that are not closely related to solving the technical problems proposed by the present invention are not introduced in this embodiment, but this does not mean that there are no other units in this embodiment.

[0070] The fourth embodiment of the present invention relates to a service flow detection system, as Figure 5 shown, including: a data collection end 502 and a data analysis end 503, such as the service flow detection device 501 described in the third embodiment. The data collection end 502 and the data analysis end 503 are both communicatively connected to the service flow detection device 501, where the data collection end 502 is further communicatively connected to the bearer device of the target network.

[0071] Among them, the service flow detection device 501 further includes: an indication module, configured to instruct the data collection end to obtain the IP flow information of all service flows in the target network from the bearer device of the target network.

[0072] The data collection end 502 is configured to obtain the IP flow information of all service flows in the target network from the bearer device of the target network and send the obtained IP flow information to the data analysis end.

[0073] The data analysis end 503 is configured to query the relevant information of all service flows in the network management system of the target network according to the IP flow information, generate an association model according to the relevant information of all service flows, and save the association model in the local database for the query module of the service flow detection device to retrieve.

[0074] In an example, the service flow detection device 501 is further configured to, when it is queried that there is an in-band detection task established for the modified IP flow information, modify or delete the in-band detection task established for the modified IP flow information.

[0075] In another example, the service flow detection device 501 is further configured to receive the relevant information of the input service flow; search for the service flow corresponding to the relevant information of the service flow in the association model according to the relevant information of the service flow; and determine the selected service flow as the service flow to be detected in the corresponding service flow according to the received selection instruction.

[0076] In another example, the service flow detection device 501 is further configured to search for the service flow corresponding to the network element information in the association model according to the network element information.

[0077] In one example, when there is a difference between the newly obtained IP flow information and the existing IP flow information, the data analysis end 503 is further configured to modify the IP flow information and update the association model according to the modified IP flow information.

[0078] It should be noted that each module involved in this embodiment is a logical module. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or implemented as a combination of multiple physical units. In addition, in order to highlight the innovative part of the present invention, units that are not closely related to solving the technical problems proposed by the present invention are not introduced in this embodiment, but this does not mean that there are no other units in this embodiment.

[0079] The fifth embodiment of the present invention relates to a terminal, as Figure 6 shown, including: at least one processor 601; and a memory 602 communicatively connected to the at least one processor 601; wherein, the memory 602 stores instructions executable by the at least one processor 601, and the instructions are executed by the at least one processor 601 to enable the at least one processor 601 to execute the service flow detection method in the first or second embodiment.

[0080] Among them, the memory 602 and the processor 601 are connected in a bus manner. The bus can include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors 601 and the memory 602 together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, etc., which are well known in the art, so they will not be further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a component or multiple components, such as multiple receivers and transmitters, and provides a unit for communicating with various other devices on the transmission medium. The data processed by the processor 601 is transmitted on the wireless medium through the antenna. Further, the antenna also receives data and transmits the data to the processor 601. The processor 601 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. The memory 602 can be used to store the data used by the processor 601 when executing operations.

[0081] The sixth embodiment of the present invention relates to a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the above method embodiment is implemented.

[0082] That is, those skilled in the art can understand that all or part of the steps in implementing the above method embodiments can be completed by instructing relevant hardware through a program. The program is stored in a storage medium and includes several instructions to enable a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, etc., which can store program codes.

[0083] Those of ordinary skill in the art can understand that the above embodiments are specific embodiments for implementing the present invention, and in practical applications, various changes can be made in form and details without departing from the spirit and scope of the present invention.

Claims

1. A service flow detection method, characterized in that, Including: Issuing an IP flow information collection task to the data collection end, instructing the data collection end to obtain the IP flow information of all service flows in the target network from the bearer device of the target network; The IP flow information collection task includes a collection period; When the terminal deploying the in-band detection task receives the relevant information of the input service flow, query the corresponding service flow in the network management system according to the relevant information of the service flow as the service flow to be detected, where the relevant information of the service flow includes at least one of the service name to which the service flow belongs, the identifier of the service flow, the identifier of the access device on the bearer side of the service flow, the access interface identifier, the network element nodes related to the service flow, and the network element interfaces; Search for the IP flow information of the service flow to be detected in the pre-generated association model; where the association model is used to describe the correspondence between the relevant information of the service flow in the target network and the IP flow information; Automatically configure an in-band detection task for the service flow to be detected according to the IP flow information; Execute the in-band detection task and obtain the performance data collected according to the in-band detection task; If there is changed IP flow information in the IP flow information of all service flows collected periodically, modify the changed IP flow information and update the association model according to the modified IP flow information.

2. The service flow detection method according to claim 1, wherein The association model is generated in the following manner: Obtain the IP flow information of all service flows in the target network from the bearer device of the target network; Query the relevant information of all service flows in the network management system of the target network according to the IP flow information; Generate the association model according to the relevant information of all service flows and save the association model in the database.

3. The service flow detection method according to claim 2, wherein After saving the association model in the database, it further includes: If it is queried that there is currently an in-band detection task established for the IP flow information before modification, modify or delete the in-band detection task established for the IP flow information before modification.

4. The service flow detection method according to claim 1, characterized in that Before determining the service flow to be detected according to the relevant information of the service flow, it further includes: Receiving the relevant information of the input service flow; Determining the service flow to be detected in the target network according to the relevant information of the service flow includes: Searching in the association model for the service flow in the target network that matches the relevant information of the input service flow; Determining the selected service flow as the service flow to be detected among the matching service flows according to the received selection instruction.

5. The service flow detection method according to claim 4, wherein The relevant information of the service flow includes network element information and / or service characteristics; Searching in the association model for the service flow corresponding to the relevant information of the service flow according to the relevant information of the service flow includes: Searching in the association model for the service flow that matches the input network element information and / or service characteristics.

6. The service flow detection method according to any one of claims 1 to 5, characterized in that The IP flow information is represented in the form of a five-tuple, including: source IP address, source port number, destination IP address, destination port number, protocol number.

7. A service flow detection device, characterized in that Including: A determination module, configured to, when a terminal for deploying an in-band detection task receives relevant information of an input service flow, query a corresponding service flow in a network management system according to the relevant information of the service flow as a service flow to be detected, where the relevant information of the service flow includes at least one of a service name to which the service flow belongs, an identifier of the service flow, an identifier of a bearer-side access device of the service flow, an access interface identifier, a network element node related to the service flow, and a network element interface; A query module, configured to find IP flow information of the service flow to be detected in a pre-generated association model; where the association model is used to describe the correspondence between relevant information of a service flow in a target network and the IP flow information; A configuration module, configured to automatically configure an in-band detection task for the service flow to be detected according to the IP flow information; An execution module, configured to execute the in-band detection task and obtain performance data collected according to the in-band detection task; if there is changed IP flow information among the IP flow information of all service flows periodically collected, modify the changed IP flow information and update the association model according to the modified IP flow information; The apparatus further includes: sending an IP flow information collection task to a data collection end, instructing the data collection end to obtain the IP flow information of all service flows in the target network from a bearer device of the target network; the IP flow information collection task includes a collection period.

8. A terminal, characterized in that, Comprising: At least one processor; And, A memory communicatively connected to the at least one processor; where The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the service flow detection method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, The computer program, when executed by a processor, implements the service flow detection method according to any one of claims 1 to 6.

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