Full-link fault detection method and device, storage medium and electronic equipment

Abstract

The embodiment of the invention relates to a full-link fault detection method, a full-link fault detection device, a storage medium and electronic equipment, and relates to the technical field of computers. The encryption method comprises the following steps: acquiring performance data of each node in a video stream full link; for each node, standardizing the performance data of the node through a protocol adaptation layer to obtain performance data in a preset data format; and for each node, according to the standardized performance data, carrying out stability analysis on the node to obtain a fault detection result of the whole link. Therefore, by unifying the performance data of different formats of different nodes, unified management of each node on the whole link is realized; and moreover, by analyzing the performance data with the uniform format, the analysis efficiency is improved, so that the response to the link fault is accelerated.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of computer technology. More specifically, the embodiments of the present disclosure relate to a full-link fault detection method, a full-link fault detection device, a computer-readable storage medium, and an electronic device. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the disclosure that are recited in the claims, and no description herein is admitted to be prior art by inclusion in this section.

[0003] At present, live broadcast service, as a cloud service with increasingly wide applications, has penetrated into all walks of life. In related technologies, stability is mainly ensured by real-time monitoring of the fault monitoring platform of each node in the entire link. Summary of the invention

[0004] However, in related technologies, each service operates independently, background management is decentralized, monitoring data of each node cannot be linked, and problem diagnosis and response are slow.

[0005] Therefore, an improved full-link fault detection method is urgently needed to achieve unified management of all nodes on the entire link and speed up the response to link failures.

[0006] In this context, embodiments of the present disclosure are intended to provide a full-link fault detection method, a full-link fault detection device, a computer-readable storage medium, and an electronic device.

[0007] According to a first aspect of the present disclosure, a full-link fault detection method is provided, the method comprising: collecting performance data of each node in the full link of a video stream; for each of the nodes, standardizing the performance data of the node through a protocol adaptation layer to obtain performance data in a preset data format; for each of the nodes, performing stability analysis on the node based on the standardized performance data to obtain a full-link fault detection result.

[0008] In one embodiment, the node includes a central service node, and the performance data includes first performance data; the collecting performance data of each node in the full link of the video stream includes: collecting the first performance data of the central service node; the first performance data includes at least CPU load data, memory load data, bandwidth load data, current live broadcast channels and current video reception packet loss rate;

[0009] Correspondingly, the preset data format includes a first preset data format, and the performance data of the node is standardized through the protocol adaptation layer to obtain performance data in a preset data format, including: converting the first performance data of the central service node into performance data in a first preset data format; the first preset data format includes at least Version, ServerType, ServerID, CPULoad, MemLoad, BandwidthLoad, Lines, and PacketLossRate fields.

[0010] In one embodiment, the node includes a transcoding service node, and the performance data includes second performance data; the collecting performance data of each node in the full link of the video stream includes: collecting the second performance data of the transcoding service node; the second performance data includes at least CPU load data, GPU load data, memory load data, bandwidth load data and all current transcoding paths;

[0011] Correspondingly, the preset data format includes a second preset data format, and the performance data of the node is standardized through the protocol adaptation layer to obtain performance data in a preset data format, including: converting the second performance data of the transcoding service node into performance data in a second preset data format; the second preset data format includes at least Version, ServerType, ServerID, CPULoad, GPULoad, MemLoad, BandwidthLoad and Lines fields.

[0012] In one embodiment, the node includes a CDN service node, and the performance data includes third performance data; the collecting performance data of each node in the full link of the video stream includes: collecting the third performance data of the CDN service node; the third performance data includes at least CPU load data, memory load data and bandwidth load data;

[0013] Correspondingly, the preset data format includes a third preset data format, and the performance data of the node is standardized through the protocol adaptation layer to obtain performance data in a preset data format, including: converting the third performance data of the CDN service node into performance data in a third preset data format; the third preset data format includes at least Version, ServerType, CDNID, CPULoad, MemLoad and BandwidthLoad fields.

[0014] In one embodiment, the node includes an APM service node, and the performance data includes fourth performance data; the collecting performance data of each node in the full link of the video stream includes: collecting the fourth performance data of the APM service node; the fourth performance data includes at least a reconnection rate, a freeze rate, and a low resolution ratio;

[0015] Correspondingly, the preset data format includes a fourth preset data format, and the performance data of the node is standardized through the protocol adaptation layer to obtain performance data in the preset data format, including: converting the fourth performance data of the APM service node into performance data in the fourth preset data format; the fourth preset data format includes at least Version, ServerType, ServerID, ReconnectionRate, NetworkSlowRate, LowResolutionRate, Operator and CitvCode fields.

[0016] In one embodiment, the stability analysis of the node is performed based on the standardized performance data to obtain a fault detection result for the entire link, including: comparing the standardized performance data with a stable data interval corresponding to the performance data to obtain a fault detection result for the node; and obtaining a fault detection result for the entire link based on the fault detection result of each node.

[0017] In one embodiment, the method further includes: collecting video streams from the streaming end, the central service node, and the CDN service node; performing quality analysis on the video stream, and generating a quality report based on the analysis result; the quality analysis at least includes channel analysis, volume analysis, resolution analysis, bit rate analysis, and comprehensive quality analysis; the data format of the data in the quality report is a fifth preset data format; the fifth preset data format at least includes Version, ServerType, ServerID, channel, volume, resolution, bit rate, and comprehensive quality fields;

[0018] Correspondingly, the stability analysis of the node is performed according to the standardized performance data to obtain the fault detection result of the entire link, including: according to the quality report and the standardized performance data, the stability analysis of the node is performed to obtain the fault detection result of the node; according to the fault detection result of each node, the fault detection result of the entire link is obtained.

[0019] In one embodiment, the method further includes: according to the severity level corresponding to the fault detection result of the full link, using a corresponding preset alarm method to issue an alarm.

[0020] In one embodiment, the method also includes: determining whether to switch the mode of the video stream based on the fault detection result of the full link; if it is determined to be yes, switching the mode of the current video stream; the modes of the video stream include a main video stream mode and a backup video stream mode.

[0021] According to a second aspect of the present disclosure, a full-link fault detection device is provided, the device comprising: a data acquisition module, configured to acquire performance data of each node in the full link of a video stream; a data standardization module, configured to, for each of the nodes, standardize the performance data of the node through a protocol adaptation layer to obtain performance data in a preset data format; a data processing module, configured to, for each of the nodes, perform stability analysis on the node according to the standardized performance data to obtain a full-link fault detection result.

[0022] In one embodiment, the node includes a central service node, and the performance data includes first performance data; the data collection module is configured to: collect the first performance data of the central service node; the first performance data includes at least CPU load data, memory load data, bandwidth load data, current live broadcast channels, and current video reception packet loss rate;

[0023] Correspondingly, the preset data format includes a first preset data format, and the data standardization module is configured to: convert the first performance data of the central service node into performance data in the first preset data format; the first preset data format includes at least Version, ServerType, ServerID, CPULoad, MemLoad, BandwidthLoad, Lines, and PacketLossRate fields.

[0024] In one embodiment, the node includes a transcoding service node, and the performance data includes second performance data; the data collection module is configured to: collect the second performance data of the transcoding service node; the second performance data includes at least CPU load data, GPU load data, memory load data, bandwidth load data and all current transcoding paths;

[0025] Correspondingly, the preset data format includes a second preset data format, and the data standardization module is configured to: convert the second performance data of the transcoding service node into performance data in a second preset data format; the second preset data format includes at least Version, ServerType, ServerID, CPULoad, GPULoad, MemLoad, BandwidthLoad and Lines fields.

[0026] In one embodiment, the node includes a CDN service node, and the performance data includes third performance data; the data collection module is configured to: collect the third performance data of the CDN service node; the third performance data includes at least CPU load data, memory load data, and bandwidth load data;

[0027] Correspondingly, the preset data format includes a third preset data format, and the data standardization module is configured to: convert the third performance data of the CDN service node into performance data in a third preset data format; the third preset data format includes at least Version, ServerType, CDNID, CPULoad, MemLoad and BandwidthLoad fields.

[0028] In one embodiment, the node includes an APM service node, and the performance data includes fourth performance data; the data collection module is configured to: collect the fourth performance data of the APM service node; the fourth performance data includes at least a reconnection rate, a freeze rate, and a low resolution ratio;

[0029] Correspondingly, the preset data format includes a fourth preset data format, and the data standardization module is configured to: convert the fourth performance data of the APM service node into performance data in a fourth preset data format; the fourth preset data format includes at least Version, ServerType, ServerID, ReconnectionRate, NetworkSlowRate, LowResolutionRate, Operator and CitvCode fields.

[0030] In one embodiment, the data processing module is configured to: compare the standardized performance data with the stable data interval corresponding to the performance data to obtain the fault detection result of the node; and obtain the fault detection result of the entire link based on the fault detection result of each node.

[0031] In one embodiment, the device further includes: a video acquisition module, configured to collect video streams from a streaming end, a central service node, and a CDN service node; a video quality analysis module, configured to perform quality analysis on the video stream and generate a quality report based on the analysis result; the quality analysis includes at least channel analysis, volume analysis, resolution analysis, bit rate analysis, and comprehensive quality analysis; the data format of the data in the quality report is a fifth preset data format; the fifth preset data format includes at least Version, ServerType, ServerID, channel, volume, resolution, bit rate, and comprehensive quality fields;

[0032] Correspondingly, the data processing module is configured to: perform stability analysis on the node according to the quality report and the standardized performance data to obtain the fault detection result of the node; and obtain the fault detection result of the entire link according to the fault detection result of each node.

[0033] In one embodiment, the device further includes an alarm module, and the alarm module is configured to: generate an alarm using a corresponding preset alarm method according to a severity level corresponding to the fault detection result of the full link.

[0034] In one embodiment, the device also includes a stream cutting decision module, which is configured to: determine whether to switch the mode of the video stream based on the fault detection result of the full link; if it is determined to be yes, switch the mode of the current video stream; the mode of the video stream includes a main video stream mode and a backup video stream mode.

[0035] According to a third aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, any one of the above methods is implemented.

[0036] According to a fourth aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform any one of the above methods by executing the executable instructions.

[0037] According to the full-link fault detection method, full-link fault detection device, computer-readable storage medium and electronic device of the embodiment of the present disclosure, first, the performance data of each node in the full link of the video stream is collected; secondly, for each of the nodes, the performance data of the node is standardized through the protocol adaptation layer to obtain performance data in a preset data format; finally, for each of the nodes, the stability of the node is analyzed according to the standardized performance data to obtain the fault detection result of the full link. In this way, by unifying the performance data of different nodes in different formats, unified management of each node on the full link is achieved; and by analyzing the performance data in a unified format, the analysis efficiency is improved, thereby speeding up the response to link failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and other purposes, features and advantages of the exemplary embodiments of the present disclosure will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary and non-limiting manner, wherein:

[0039] Figure 1A schematic diagram of a full-link fault detection process architecture in an embodiment of the present disclosure is shown;

[0040] Figure 2 A schematic diagram of a full-link fault detection process architecture in an embodiment of the present disclosure is shown;

[0041] Figure 3 A flow chart of a full-link fault detection method in an embodiment of the present disclosure is shown;

[0042] Figure 4 A schematic diagram of a full-link fault detection process architecture in an embodiment of the present disclosure is shown;

[0043] Figure 5 A flow chart showing a full-link fault detection result obtained in a full-link fault detection method in an embodiment of the present disclosure is shown;

[0044] Figure 6 A flow chart showing generation of a quality report in a full-link fault detection method in an embodiment of the present disclosure is shown;

[0045] Figure 7 A flow chart showing a full-link fault detection result obtained in a full-link fault detection method in an embodiment of the present disclosure is shown;

[0046] Figure 8 A flowchart showing switching of video stream modes in a full-link fault detection method in an embodiment of the present disclosure is shown;

[0047] Fig. 9 A schematic diagram showing the structure of a full-link fault detection device in an embodiment of the present disclosure is shown;

[0048] Fig.10 A schematic structural diagram of an electronic device in an embodiment of the present disclosure is shown.

[0049] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts. DETAILED DESCRIPTION

[0050] The principles and spirit of the present disclosure will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present disclosure, and are not intended to limit the scope of the present disclosure in any way. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0051] Those skilled in the art will appreciate that the embodiments of the present disclosure may be implemented as a system, device, apparatus, method or computer program product. Therefore, the present disclosure may be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0052] According to an embodiment of the present disclosure, a key processing method, a key processing device, a computer-readable storage medium, and an electronic device are provided.

[0053] In this document, any number of elements in the drawings is used for illustration rather than limitation, and any naming is used only for distinction and does not have any limiting meaning.

[0054] The principle and spirit of the present disclosure are explained in detail below with reference to several representative embodiments of the present disclosure. SUMMARY OF THE INVENTION

[0056] At present, in the field of live broadcasting, the stability of each node is mainly guaranteed by manually monitoring the fault monitoring platform of each node in the whole link in real time; however, it is impossible to achieve real-time intercommunication, fault feedback, intelligent problem determination of the whole link, and automatic fault recovery in certain scenarios through manual real-time monitoring of the data and performance of each node. Especially in the whole link, when the suppliers of each node belong to different service providers, it is necessary to monitor the performance, traffic and peak conditions of the central service, transcoding service and content delivery network (CDN) service nodes separately.

[0057] Thus, there are the following problems:

[0058] 1) Each service operates independently, and the management backend is decentralized

[0059] Live broadcasting, especially e-commerce live broadcasting, is a large and complex system. Many services are involved in the whole chain. The stability and performance indicators of each service are related to the smooth completion of abnormal live broadcasting. In order to ensure the overall stability of live broadcasting, it is necessary to ensure that the system of each intermediate link is running normally. Therefore, it is necessary to pay attention to the monitoring background of each technical system at all times during the live broadcast process and report any errors in a timely manner. The problem with this traditional monitoring strategy is that, first, it consumes a lot of manpower and requires high professional quality of personnel; second, upstream system problems are likely to lead to downstream errors, and the real problem point cannot be identified in a short time; third, there are many platforms, which are more complicated to manage and costly.

[0060] 2) The monitoring data of each node cannot be linked, and the problem judgment response is slow

[0061] Due to the large number of nodes in the live broadcast link and the variety of services involved, it is difficult to unify data communication, resulting in isolated monitoring of each link. There is no real-time linkage between service guarantees, which makes it difficult to quickly locate and solve problems once a failure occurs.

[0062] 3) Failure to automatically and intelligently recover from failures

[0063] Live broadcasting scenarios are relatively special, with high real-time requirements and a wide audience. Once a system failure occurs, it will lead to large-scale unavailability and customer complaints, so the recovery of live broadcasting system failures is extremely important. In the traditional monitoring mode, due to the isolation of the system and the lack of data linkage caused by the above-mentioned monitoring methods, it is impossible to achieve fast and intelligent recovery of failures. It is necessary to manually re-analyze and troubleshoot problems from many systems and then try to solve them, which delays a lot of time and leads to serious live broadcast accidents.

[0064] In view of the above, the present disclosure provides a full-link fault detection method, a full-link fault detection device, a computer-readable storage medium, and an electronic device. First, the performance data of each node in the full link of the video stream is collected; secondly, for each of the nodes, the performance data of the node is standardized through the protocol adaptation layer to obtain performance data in a preset data format; finally, for each of the nodes, the stability of the node is analyzed according to the standardized performance data to obtain the fault detection result of the full link. In this way, by unifying the performance data of different nodes in different formats, unified management of each node on the full link is achieved; and by analyzing the performance data in a unified format, the analysis efficiency is improved, thereby speeding up the response to link failures.

[0065] After introducing the basic principles of the present disclosure, various non-limiting embodiments of the present disclosure are described in detail below.

[0066] Application Scenario Overview

[0067] It should be noted that the following application scenarios are only shown to facilitate understanding of the spirit and principle of the present disclosure, and the embodiments of the present disclosure are not limited in this respect. On the contrary, the embodiments of the present disclosure can be applied to any applicable scenario.

[0068] The present disclosure can be applied to full-link fault detection in live broadcast scenarios. First, the performance data of each node in the full link of the video stream is collected; second, for each of the nodes, the performance data of the node is standardized through the protocol adaptation layer to obtain performance data in a preset data format; finally, for each of the nodes, the stability of the node is analyzed based on the standardized performance data to obtain the full-link fault detection result. In this way, by unifying the performance data of different nodes in different formats, unified management of each node on the full link is achieved; and by analyzing the performance data in a unified format, the analysis efficiency is improved, thereby speeding up the response to link failures.

[0069] Exemplary Methods

[0070] Combine the following Figure 1 The system architecture and application scenarios of the operating environment of this exemplary embodiment are exemplarily described.

[0071] Figure 1 A schematic diagram of a system architecture is shown, and the system architecture 100 may include a server 110. The server 110 may generally refer to a background system that provides full-link fault detection related services (such as a full-link fault detection service system), and the server 110 may collect performance data of each node in the full link of the video stream; for each node, the performance data of the node is standardized through the protocol adaptation layer to obtain performance data in a preset data format; for each node, the stability of the node is analyzed according to the standardized performance data to obtain a full-link fault detection result; the server 110 may be a server or a cluster formed by multiple servers.

[0072] like Figure 2 As shown, the live broadcast link of the live broadcast service may include but is not limited to: streaming end, central service node, transcoding service node, CDN service node, and alarm (Alarm Panel Monitor, APM) service node. The above nodes are explained below:

[0073] Streaming End

[0074] In the e-commerce live broadcast scenario, the live streaming end collects image and sound data through a variety of devices (including but not limited to cameras, mobile phones, drones, microphones, etc.), processes them through computer or mobile phone video software, and then pushes the processed live video data to the remote live broadcast center server through the live broadcast software using the Real Time Messaging Protocol (RTMP). This process is the live streaming end. It is a complex system composed of multiple hardware devices and software.

[0075] Central service node

[0076] It is a service used to receive video streams pushed by the live streaming end through the RTMP protocol. Its function is to receive video streams, parse video streams, repackage video data, and forward video data to the corresponding video transcoding service according to the live broadcast configuration.

[0077] Transcoding service node

[0078] In the scenario of e-commerce live broadcast, due to the dispersed geographical locations of users, different network environments, and widely varying equipment conditions, in order to ensure that all users can have a good live viewing experience, it is necessary to allocate live video streams of different qualities to different users. However, it is impossible for the streaming end to stream all quality video streams. The video transcoding service is a server cluster, and its function is to transcode the original video stream on the live broadcast center server into different quality video streams required by different users in real time through the graphics processing unit (GPU) cluster according to the configuration of the administrator.

[0079] CDN service node

[0080] In the video live broadcast scenario, user dispersion and different network environments are basic problems. To solve this problem, live broadcast videos will push videos of various qualities to various CDN services after the transcoding service completes the transcoding. Since CDN services are widely distributed and support various network operators, the C-end live broadcast client can automatically select the optimal node to pull the live video stream according to the device situation.

[0081] APM Service Node

[0082] It is an alarm platform that collects the performance of mobile software (Application, App) used by the client (consumer, C). The App is responsible for reporting the quality data of the currently received video stream. The APM service node comprehensively analyzes the problems encountered in the current live broadcast based on the user's location, operator, transcoding stream used, and other information, and actively and intelligently locates the problem and issues an alarm, providing the necessary conditions for subsequent automatic or manual troubleshooting and recovery.

[0083] The exemplary embodiment of the present disclosure first provides a full-link fault detection method, which may include:

[0084] Collect performance data of each node in the entire video stream link;

[0085] For each node, the performance data of the node is standardized through the protocol adaptation layer to obtain the performance data in a preset data format;

[0086] For each node, a stability analysis is performed on the node based on the standardized performance data to obtain the fault detection results of the entire link.

[0087] Figure 3 The exemplary process of the full-link fault detection method is shown below. Figure 3 Each step in the procedure is described in detail.

[0088] refer to Figure 3 , in step S310, the performance data of each node in the entire link of the video stream is collected.

[0089] Among them, different nodes provide different services, and different services have different performance data; that is, different nodes have different performance data; for example: the performance data of the central service node includes the central processing unit (CPU) load, memory load, etc.; the performance data of the transcoding service node includes the CPU load, GPU load, etc.

[0090] In actual operation, performance data can be collected through the existing monitoring platform of each node, such as: Figure 4 As shown, the performance data of the central service node is collected through a subsystem data collection plug-in set in the central service node; and the performance data of the transcoding service node is collected through another subsystem data collection plug-in set in the transcoding service node.

[0091] Continue to refer Figure 3 In step S320, for each node, the performance data of the node is standardized through the protocol adaptation layer to obtain performance data in a preset data format.

[0092] Among them, the preset data format can be designed based on experience; here, the preset data format can be understood as a standardized protocol; the purpose of this step is to unify the performance data of different formats collected from different nodes; that is, step S320 can be understood as standardizing the performance data of different formats of different nodes to obtain performance data in a unified format (preset data format).

[0093] In actual operation, the plug-in protocol adaptation layer can be used to standardize the performance data of different nodes in different formats to obtain performance data in a unified format (preset data format); for example: Figure 2 As shown, the format of the performance data collected from the central service node is standardized through the central service monitoring protocol adaptation layer set in the central service node; the format of the performance data collected from the transcoding service node is standardized through the transcoding service monitoring protocol adaptation set in the transcoding service node.

[0094] For example, as shown in Table 1, the preset data format may include fields such as the protocol version number, the service type identifier, the unique identifier of the service, specific performance data, and the data collection timestamp.

[0095] Protocol Field size Required illustrate Version 8bit yes The version number of the protocol, which facilitates the extended identification of the log protocol ServerType 8bit yes Service type identifier, such as central service, transcoding service, etc. ServerId 16bit yes Unique identification of the service, convenient for locating faulty units Playload yes Detailed performance data Timestamp yes Data collection timestamp

[0096] Table 1

[0097] Continue to refer Figure 3 In step S330, for each node, a stability analysis is performed on the node based on the standardized performance data to obtain a fault detection result for the entire link.

[0098] Among them, stability analysis can be performed using existing stability analysis methods; for example: comparing different performance data with corresponding stable data intervals. If the performance data falls within the stability data interval, it is stable; otherwise, it is unstable; here, the stable data interval is the data interval corresponding to each performance data when stable.

[0099] The fault detection result of the entire link is determined based on the fault detection result of each node on the entire link. Generally, if all nodes on the entire link are fault-free, the entire link is fault-free. If at least one node on the entire link is faulty, the entire link is faulty.

[0100] In one implementation, the node includes a central service node, and the performance data includes first performance data; specifically, the above step S310 may further include the following steps:

[0101] The first performance data of the central service node is collected.

[0102] Among them, the first performance data includes at least CPU load data, memory load data, bandwidth load data, current live broadcast channels and current video reception packet loss rate.

[0103] The first performance data may be collected using existing technologies; for example, the first performance data may be collected from a monitoring interface of a central service node on a live broadcast link through a subsystem data collection plug-in.

[0104] CPU load refers to the workload of the central processing unit, which is generally measured by CPU utilization and refers to the number of active processes at the current moment.

[0105] Memory load refers to the amount of memory space occupied by programs running on the system.

[0106] Bandwidth load refers to the speed at which data is transmitted over the network at a certain moment.

[0107] The current number of live streaming channels refers to the number of streams currently being pushed, which can be simply understood as the number of live broadcasts currently being broadcast. Each live broadcast requires one stream.

[0108] The current video receiving packet loss rate refers to the ratio of the number of lost data packets to the number of sent data packets.

[0109] In actual operation, the first performance data can be collected by using existing technology; for example: Figure 4 , collect the third performance data from the central service node monitoring interface on the live link through the subsystem data collection plug-in set in the central service node.

[0110] Correspondingly, the above step S320 may further include the following steps:

[0111] The first performance data of the central service node is converted into performance data in a first preset data format.

[0112] Among them, the first preset data format includes at least Version, ServerType, ServerID, CPULoad, MemLoad, BandwidthLoad, Lines, and PacketLossRate fields.

[0113] The first preset data format is a form of expression of the preset data format; that is, the specific performance data field included in the preset data format is replaced with the first performance data field; as shown in Table 2 below, in addition to the version number, service type identifier, and service unique identifier field, the specific performance data field of the first preset data format is replaced with CPULoad, MemLoad, BandwidthLoad, Lines, and PacketLossRate fields; wherein any one of the CPULoad, MemLoad, BandwidthLoad, Lines, and PacketLossRate fields can carry a data collection timestamp field.

[0114] Protocol Field size Required illustrate CPULoad 16bit yes Central service CPU load MemLoad 16bit yes Central service memory load BandwidthLoad 16bit yes Central service bandwidth load Lines 16bit yes Current live broadcast channels of the center service PacketLossRate 16bit yes Current video packet loss rate

[0115] Table 2

[0116] In actual operation, refer to Figure 2 , the collected first performance data of the central service node can be converted into performance data in a first preset data format through a plug-in central service protocol adaptation layer set in the central service node.

[0117] In one implementation, the node includes a transcoding service node, and the performance data includes second performance data; specifically, the above step S310 may further include the following steps:

[0118] Collect second performance data of the transcoding service node.

[0119] The second performance data includes at least CPU load data, GPU load data, memory load data, bandwidth load data and all current transcoding paths.

[0120] The second performance data may be collected using existing technology; for example, the second performance data may be collected from the transcoding service node monitoring interface on the live link through a subsystem data collection plug-in.

[0121] GPU load refers to the workload of the graphics processor, which is generally measured by GPU utilization and is the task being executed or the amount of data being processed.

[0122] The current total number of transcoding paths refers to the number of transcoding tasks being performed by the transcoding service. For example, if one stream needs to be converted into three video streams with different resolutions, then one stream will generate three transcoding paths.

[0123] In actual operation, the second performance data can be collected by using existing technology; for example: Figure 4 , the third performance data is collected from the transcoding service node monitoring interface on the live link through the subsystem data collection plug-in set in the transcoding service node; here, the subsystem data collection plug-in set in the transcoding service node is different from the subsystem data collection plug-in set in the central service node.

[0124] Correspondingly, the above step S320 may further include the following steps:

[0125] The second performance data of the transcoding service node is converted into performance data in a second preset data format.

[0126] The second preset data format includes at least Version, ServerType, ServerID, CPULoad, GPULoad, MemLoad, BandwidthLoad and Lines fields.

[0127] The second preset data format is a form of expression of the preset data format; that is, the specific performance data field included in the preset data format is replaced with the second performance data field; as shown in Table 3 below, in addition to the version number, service type identifier, and service unique identifier field, the specific performance data field in the second preset data format is replaced with CPULoad, GPULoad, MemLoad, BandwidthLoad, and Lines fields; wherein, any one of the CPULoad, GPULoad, MemLoad, BandwidthLoad, and Lines fields can carry a data collection timestamp field.

[0128] Protocol Field size Required illustrate CPULoad 16bit yes Transcoding service CPU load GPULoad 16bit yes Transcoding service GPU load MemLoad 16bit yes Transcoding service memory load BandwidthLoad 16bit yes Transcoding service bandwidth load Lines 16bit yes Current transcoding paths

[0129] Table 3

[0130] In actual operation, refer to Figure 2 , the collected second performance data of the transcoding service node can be converted into performance data in a second preset data format through a plug-in transcoding service protocol adaptation layer set in the transcoding service node.

[0131] In one implementation, the node includes a CDN service node, and the performance data includes third performance data; specifically, the above step S310 may further include the following steps:

[0132] Collect third-party performance data of CDN service nodes.

[0133] The third performance data includes at least CPU load data, memory load data and bandwidth load data.

[0134] The meanings of CPU load, memory load, and bandwidth load have been described above and will not be repeated here.

[0135] In actual operation, the third performance data can be collected by using existing technology; for example: Figure 4 , the third performance data is collected from the CDN service node monitoring interface on the live link through the subsystem data collection plug-in set in the CDN service node; here, the subsystem data collection plug-in set in the CDN service node is different from the subsystem data collection plug-in set in the central service node and the transcoding service node.

[0136] Correspondingly, the above step S320 may further include the following steps:

[0137] The third performance data of the CDN service node is converted into performance data in a third preset data format.

[0138] The third preset data format includes at least Version, ServerType, CDNID, CPULoad, MemLoad and BandwidthLoad fields.

[0139] The third preset data format is a form of expression of the preset data format; that is, the specific performance data fields included in the preset data format are replaced with the third performance data fields; as shown in Table 4 below, in addition to the version number, service type identifier, and service unique identifier fields, the specific performance data fields in the third preset data format are replaced with CPULoad, MemLoad, and BandwidthLoad fields; wherein any one of the CPULoad, MemLoad, and BandwidthLoad fields can carry a data collection timestamp field.

[0140] Protocol Field size Required illustrate CPULoad 16bit yes CDN node CPU load MemLoad 16bit yes CDN node memory load BandwidthLoad 16bit yes CDN node bandwidth load CDN ID 16bit yes Unique identifier of the current CDN node

[0141] Table 4

[0142] In actual operation, refer to Figure 2 , the third performance data collected from the central service node can be converted into performance data in a third preset data format through a plug-in CDN service protocol adaptation layer set in the CDN service node.

[0143] In one implementation, the node includes an APM service node, and the performance data includes fourth performance data; specifically, the above step S310 may further include the following steps:

[0144] Collect the fourth performance data of the APM service node,

[0145] Among them, the fourth performance data at least includes reconnection rate, freeze rate and low resolution ratio.

[0146] The fourth performance data may be collected using existing technology; for example, the fourth performance data may be collected from the APM service node monitoring interface on the live link through a subsystem data collection plug-in.

[0147] The reconnection rate refers to the frequency of reconnection during video playback.

[0148] The stuttering rate refers to the frequency of stuttering during video playback.

[0149] The low-resolution ratio refers to the ratio of low-resolution frames to all frames during video playback.

[0150] In actual operation, the fourth performance data can be collected by using existing technology; for example: Figure 4 , the fourth performance data is collected from the APM service node monitoring interface on the live link through the subsystem data collection plug-in set in the APM service node; here, the subsystem data collection plug-in set in the APM service node is different from the subsystem data collection plug-in set in the central service node, the transcoding service node and the CDN service node.

[0151] Correspondingly, the above step S320 may further include the following steps:

[0152] The fourth performance data of the APM service node is converted into performance data in a fourth preset data format.

[0153] The fourth preset data format includes at least Version, ServerType, ServerID, ReconnectionRate, NetworkSlowRate, LowResolutionRate, Operator and CitvCode fields.

[0154] The fourth preset data format is a form of expression of the preset data format; that is, the specific performance data field included in the preset data format is replaced with the fourth performance data field; as shown in Table 5 below, in addition to the version number, service type identifier, and service unique identifier field, the specific performance data field of the fourth preset data format is replaced with ReconnectionRate, NetworkSlowRate, LowResolutionRate, Operator, and CitvCode fields; wherein, any one of the ReconnectionRate, NetworkSlowRate, and LowResolutionRate fields can carry a data collection timestamp field.

[0155] Protocol Field size Required illustrate ReconnectionRate 16bit yes User focus connection rate NetworkSlowRate 16bit yes User-side lag rate LowResolutionRate 16bit yes User side low resolution ratio Operator 16bit yes Operator CityCode 16bit yes Region

[0156] Table 5

[0157] In actual operation, refer to Figure 2 , the collected fourth performance data of the APM service node can be converted into performance data in a fourth preset data format through a plug-in APM service protocol adaptation layer set in the APM service node.

[0158] It should be noted that the Version, ServerType, and ServerID fields are omitted in Tables 1 to 5; in addition, the subsystem data collection plug-ins set at different nodes are compatible with the monitoring platform of the node.

[0159] In one implementation, the fault detection result of the entire link is determined according to the fault detection result of each node; for details, refer to Figure 5 , the above step S330 may further include the following steps S510 and S520:

[0160] Step S510: Compare the standardized performance data with the stable data interval corresponding to the performance data to obtain a fault detection result of the node.

[0161] Among them, the stable data interval is the data interval corresponding to each performance data when stable; when the performance data falls within the corresponding stable data interval, it is stable; otherwise, it is unstable; for example: the stable data interval of the CPU load is below 80%, when the CPU load data is 90%, it is unstable, and when the CPU load data is 60%, it is stable.

[0162] Step S520: Obtain the fault detection result of the entire link according to the fault detection result of each node.

[0163] If all nodes are fault-free, the entire link is fault-free; if at least one node is faulty, the entire link is faulty.

[0164] In one embodiment, the stability analysis also includes a stability analysis of the video stream; specifically, refer to Figure 6 The above-mentioned full-link fault detection method may further include the following steps S610 and S620:

[0165] Step S610: Collect video streams from the streaming end, the central service node, and the CDN service node.

[0166] The video stream can be collected by setting a data collection module on the streaming side, the central service node, and the CDN service node, such as Figure 2 shown.

[0167] Step S620: Perform quality analysis on the video stream and generate a quality report based on the analysis result.

[0168] Among them, the quality analysis at least includes channel analysis, volume analysis, resolution analysis, bit rate analysis and comprehensive quality analysis; the data format of the data in the quality report is the fifth preset data format; the fifth preset data format at least includes Version, ServerType, ServerID, channel, volume, resolution, bit rate and comprehensive quality fields.

[0169] Channels refer to independent audio signals collected or played back at different spatial locations when recording or playing sounds, so the number of channels is the number of sound sources when recording sounds or the corresponding speaker data when playing back; channel analysis is to determine the number of sound sources.

[0170] Volume, also known as sound intensity or loudness, refers to the human ear's subjective feeling about the size and strength of the sound it hears. Its objective evaluation scale is the amplitude of the sound. Volume analysis is to determine whether the volume adjustment is normal and whether the volume is normal.

[0171] Resolution refers to the precision of a video image within a unit size; resolution analysis is to determine the precision of a video image within a unit size.

[0172] Bit rate refers to the number of data bits transmitted per unit time during data transmission, and the unit generally used is kbps (kilobits per second). Bit rate analysis is to determine the number of data bits transmitted per unit time during data transmission.

[0173] The comprehensive quality analysis determines the final quality analysis based on the analysis results of channel analysis, volume analysis, resolution analysis, and bit rate analysis. The final comprehensive quality analysis result can be determined based on experience, or by setting thresholds or levels, which is not limited here.

[0174] In actual operation, the channel analysis, volume analysis, resolution analysis, and bit rate analysis can be performed using analysis methods in the prior art, which will not be described in detail here.

[0175] Correspondingly, the above step S330 may further include the following steps S710 and S720:

[0176] Step S710: Perform stability analysis on the node according to the quality report and the standardized performance data to obtain a fault detection result of the node.

[0177] Among them, the stability analysis includes video stability analysis, central service node stability analysis, transcoding service node stability analysis, CDN node stability analysis and C-end user stability analysis; among them, the video stream collected from the streaming end, central service node and CDN service node is used for video stability analysis; the performance data in the first preset data format is used for central service node stability analysis; the performance data in the second preset data format is used for transcoding service node stability analysis; the performance data in the third preset data format is used for CDN node stability analysis; the performance data in the fourth preset data format is used for C-end user stability analysis.

[0178] In actual operation, for video stability analysis, the channel data, volume data, resolution data, bit rate data, and comprehensive quality data of the video stream can be compared with the corresponding channel data, volume data, resolution data, bit rate data, and comprehensive quality data when the video stream is stable to determine whether it is stable; for central service node stability analysis, the performance data of the first preset format can be compared with the corresponding stable data interval to determine whether it is stable; transcoding service node stability analysis, CDN node stability analysis, and C-end user stability analysis are similar to the central service node stability analysis and will not be repeated here.

[0179] Step S720: Obtain the fault detection result of the entire link according to the fault detection result of each node.

[0180] Among them, if all nodes on the entire link are fault-free (i.e. stable), the entire link is fault-free; if at least one node on the entire link is faulty (i.e. unstable), the entire link is faulty.

[0181] In one implementation, when a full link fails, an alarm may be issued so that a corresponding strategy can be adopted to handle the failure; specifically, the full link failure detection method may further include the following steps:

[0182] According to the severity level of the fault detection result of the entire link, the corresponding preset alarm method is used to issue an alarm.

[0183] The severity level may be preset based on experience or historical data.

[0184] The preset alarm method can also be set based on experience; generally, the higher the severity level, the more comprehensive the corresponding preset alarm method; for example: when the severity level is low, the alarm can be made via text message; when the severity level is high, the alarm can be made jointly via text message, phone call, automated notification, etc., without limitation here.

[0185] In actual operation, the fault information (fault source) can be sent out together with the preset alarm mode, so that relevant personnel can prepare for fault handling according to the fault information when receiving the alarm, so that the fault can be handled as quickly as possible. Of course, the fault information can also be stored, which is not limited here.

[0186] In one implementation, some faults can be handled intelligently without requiring personnel to handle them; for details, see Figure 8 The above-mentioned full-link fault detection method may further include the following steps S810 and S820:

[0187] Step S810: Determine whether to switch the mode of the video stream according to the fault detection result of the entire link.

[0188] Among them, in order to report the stability of the video stream, if the fault detection result shows that the current video stream is less stable, the mode of the video stream can be switched; here, whether to switch the mode of the video stream can be determined by setting a threshold, comparing with reference data, etc.

[0189] Step S820: If the answer is yes, the mode of the current video stream is switched; the modes of the video stream include a main video stream mode and a backup video stream mode.

[0190] The main video stream mode is a commonly used video stream mode, and the backup video stream mode is a substitute video stream mode. Figure 2Steps S610 and S620 can be determined and switched by setting a streaming switching module.

[0191] Exemplary Devices

[0192] After introducing the full-link fault detection method according to the exemplary embodiment of the present disclosure, next, refer to Fig. 9 A full-link fault detection device according to an exemplary embodiment of the present disclosure is described.

[0193] refer to Fig. 9 As shown, the full-link fault detection device 900 includes:

[0194] The data acquisition module 910 is configured to collect the performance data of each node in the entire link of the video stream; the data standardization module 920 is configured to standardize the performance data of each node through the protocol adaptation layer to obtain the performance data in a preset data format; the data processing module 930 is configured to perform stability analysis on each node according to the standardized performance data to obtain the fault detection result of the entire link.

[0195] In one embodiment, the node includes a central service node, and the performance data includes first performance data; the data collection module 910 is configured to: collect the first performance data of the central service node; the first performance data includes at least CPU load data, memory load data, bandwidth load data, current live broadcast channels, and current video reception packet loss rate;

[0196] Correspondingly, the preset data format includes a first preset data format, and the data standardization module 920 is configured to: convert the first performance data of the central service node into performance data in the first preset data format; the first preset data format includes at least Version, ServerType, ServerID, CPULoad, MemLoad, BandwidthLoad, Lines, and PacketLossRate fields.

[0197] In one implementation, the node includes a transcoding service node, and the performance data includes second performance data; the data collection module 910 is configured to: collect the second performance data of the transcoding service node; the second performance data includes at least CPU load data, GPU load data, memory load data, bandwidth load data, and all current transcoding paths;

[0198] Correspondingly, the preset data format includes a second preset data format, and the data standardization module 920 is configured to: convert the second performance data of the transcoding service node into performance data in a second preset data format; the second preset data format includes at least Version, ServerType, ServerID, CPULoad, GPULoad, MemLoad, BandwidthLoad and Lines fields.

[0199] In one embodiment, the node includes a CDN service node, and the performance data includes third performance data; the data collection module 910 is configured to: collect the third performance data of the CDN service node; the third performance data includes at least CPU load data, memory load data, and bandwidth load data;

[0200] Correspondingly, the preset data format includes a third preset data format, and the data standardization module 920 is configured to: convert the third performance data of the CDN service node into performance data in a third preset data format; the third preset data format includes at least Version, ServerType, CDNID, CPULoad, MemLoad and BandwidthLoad fields.

[0201] In one embodiment, the node includes an APM service node, and the performance data includes fourth performance data; the data collection module 910 is configured to: collect the fourth performance data of the APM service node; the fourth performance data at least includes a reconnection rate, a freeze rate, and a low resolution ratio;

[0202] Correspondingly, the preset data format includes a fourth preset data format, and the data standardization module 920 is configured to: convert the fourth performance data of the APM service node into performance data in a fourth preset data format; the fourth preset data format includes at least Version, ServerType, ServerID, ReconnectionRate, NetworkSlowRate, LowResolutionRate, Operator and CitvCode fields.

[0203] In one embodiment, the data processing module 930 is configured to: compare the standardized performance data with the stable data interval corresponding to the performance data to obtain the fault detection result of the node; and obtain the fault detection result of the entire link based on the fault detection result of each node.

[0204] In one embodiment, the device further includes: a video acquisition module, configured to collect video streams from a streaming end, a central service node, and a CDN service node; a video quality analysis module, configured to perform quality analysis on the video stream and generate a quality report based on the analysis result; the quality analysis includes at least channel analysis, volume analysis, resolution analysis, bit rate analysis, and comprehensive quality analysis; the data format of the data in the quality report is a fifth preset data format; the fifth preset data format includes at least Version, ServerType, ServerID, channel, volume, resolution, bit rate, and comprehensive quality fields;

[0205] Correspondingly, the data processing module 930 is configured to: perform stability analysis on the node according to the quality report and the standardized performance data to obtain the fault detection result of the node; and obtain the fault detection result of the entire link according to the fault detection result of each node.

[0206] In one embodiment, the device further includes an alarm module, and the alarm module is configured to: generate an alarm using a corresponding preset alarm method according to a severity level corresponding to the fault detection result of the full link.

[0207] In one embodiment, the device also includes a stream cutting decision module, which is configured to: determine whether to switch the mode of the video stream based on the fault detection result of the full link; if it is determined to be yes, switch the mode of the current video stream; the mode of the video stream includes a main video stream mode and a backup video stream mode.

[0208] Exemplary Storage Media

[0209] The storage medium according to the exemplary embodiment of the present disclosure is described below.

[0210] In this exemplary embodiment, the above method can be implemented by a program product, such as a portable compact disk read-only memory (CD-ROM) and including program code, and can be run on a device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus, or a device.

[0211] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0212] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0213] The program code contained on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RE, etc., or any suitable combination of the foregoing.

[0214] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user computing device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0215] Exemplary Electronic Devices

[0216] refer to Fig.10 An electronic device according to an exemplary embodiment of the present disclosure is described.

[0217] Fig.10 The electronic device 1000 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0218] like Fig.10 As shown, the electronic device 1000 is in the form of a general computing device. The components of the electronic device 1000 may include, but are not limited to: at least one processing unit 1010, at least one storage unit 1020, a bus 1030 connecting different system components (including the storage unit 1020 and the processing unit 1010), and a display unit 1040.

[0219] The storage unit stores program codes, which can be executed by the processing unit 1010, so that the processing unit 1010 performs the steps according to various exemplary embodiments of the present disclosure described in the above “Exemplary Method” section of this specification. For example, the processing unit 1010 can perform the following steps: Figure 1 The method steps shown, etc.

[0220] The storage unit 1020 may include a volatile storage unit, such as a random access storage unit (RAM) 1021 and / or a cache storage unit 1022 , and may further include a read-only storage unit (ROM) 1023 .

[0221] The storage unit 1020 may also include a program / utility 1024 having a set (at least one) of program modules 1025, such program modules 1025 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0222] The bus 1030 may include a data bus, an address bus, and a control bus.

[0223] The electronic device 1000 may also communicate with one or more external devices 2000 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), and such communication may be performed through an input / output (I / O) interface 1050. The electronic device 1000 also includes a display unit 1040, which is connected to the input / output (I / O) interface 1050 for display. In addition, the electronic device 1000 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 1060. As shown, the network adapter 1060 communicates with other modules of the electronic device 1000 through a bus 1030. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 1000, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0224] It should be noted that although several modules or submodules of the device are mentioned in the above detailed description, such division is merely exemplary and not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules to be embodied.

[0225] In addition, although the operations of the disclosed method are described in a specific order in the drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0226] Although the spirit and principle of the present disclosure have been described with reference to some specific embodiments, it should be understood that the present disclosure is not limited to the disclosed specific embodiments, and the division of various aspects does not mean that the features in these aspects cannot be combined to benefit, and such division is only for the convenience of expression. The present disclosure is intended to cover various modifications and equivalent arrangements included in the spirit and scope of the attached claims.

Claims

1. A full-link fault detection method, characterized in that: The method comprises: Collect performance data of each node in the entire video stream link; For each of the nodes, the performance data of the node is standardized through a protocol adaptation layer to obtain performance data in a preset data format; For each of the nodes, stability analysis is performed on the node based on the standardized performance data to obtain a fault detection result for the entire link.

2. The method according to claim 1, characterized in that: The node includes a central service node, and the performance data includes first performance data; the performance data of each node in the full link of the video stream acquisition includes: Collecting first performance data of the central service node; the first performance data at least includes CPU load data, memory load data, bandwidth load data, current live broadcast channels and current video reception packet loss rate; Correspondingly, the preset data format includes a first preset data format, and the performance data of the node is standardized by the protocol adaptation layer to obtain performance data in the preset data format, including: The first performance data of the central service node is converted into performance data in a first preset data format; the first preset data format includes at least Version, ServerType, ServerID, CPULoad, MemLoad, BandwidthLoad, Lines, and PacketLossRate fields.

3. The method according to claim 1, characterized in that: The node includes a transcoding service node, and the performance data includes second performance data; the performance data of each node in the full link of the video stream acquisition includes: Collecting second performance data of the transcoding service node; the second performance data at least includes CPU load data, GPU load data, memory load data, bandwidth load data and all current transcoding paths; Correspondingly, the preset data format includes a second preset data format, and the performance data of the node is standardized by the protocol adaptation layer to obtain the performance data in the preset data format, including: The second performance data of the transcoding service node is converted into performance data in a second preset data format; the second preset data format includes at least Version, ServerType, ServerID, CPULoad, GPULoad, MemLoad, BandwidthLoad and Lines fields.

4. The method according to claim 1, characterized in that: The node includes a CDN service node, and the performance data includes third performance data; the performance data of each node in the full link of the video stream acquisition includes: Collecting third performance data of the CDN service node; the third performance data at least includes CPU load data, memory load data and bandwidth load data; Correspondingly, the preset data format includes a third preset data format, and the performance data of the node is standardized by the protocol adaptation layer to obtain the performance data in the preset data format, including: The third performance data of the CDN service node is converted into performance data in a third preset data format; the third preset data format at least includes Version, ServerType, CDNID, CPULoad, MemLoad and BandwidthLoad fields.

5. The method according to claim 1, characterized in that The node includes an APM service node, and the performance data includes fourth performance data; the performance data of each node in the full link of the video stream acquisition includes: Collecting fourth performance data of the APM service node; the fourth performance data at least includes a reconnection rate, a freeze rate, and a low resolution ratio; Correspondingly, the preset data format includes a fourth preset data format, and the performance data of the node is standardized by the protocol adaptation layer to obtain the performance data in the preset data format, including: The fourth performance data of the APM service node is converted into performance data in a fourth preset data format; the fourth preset data format includes at least Version, ServerType, ServerID, ReconnectionRate, NetworkSlowRate, LowResolutionRate, Operator and CitvCode fields.

6. The method according to claim 1, characterized in that The step of performing stability analysis on the node according to the standardized performance data to obtain a full-link fault detection result includes: Comparing the standardized performance data with a stable data interval corresponding to the performance data to obtain a fault detection result of the node; According to the fault detection result of each node, the fault detection result of the entire link is obtained.

7. The method according to claim 1, characterized in that The method further comprises: Collect video streams from the streaming end, central service node, and CDN service node; Performing quality analysis on the video stream and generating a quality report based on the analysis result; the quality analysis at least includes channel analysis, volume analysis, resolution analysis, bit rate analysis and comprehensive quality analysis; the data format of the data in the quality report is a fifth preset data format; the fifth preset data format at least includes Version, ServerType, ServerID, channel, volume, resolution, bit rate and comprehensive quality fields; Correspondingly, the stability analysis of the node is performed according to the standardized performance data to obtain a full-link fault detection result, including: Performing stability analysis on the node according to the quality report and the standardized performance data to obtain a fault detection result of the node; According to the fault detection result of each node, the fault detection result of the entire link is obtained.

8. A full-link fault detection device, characterized in that: The device comprises: A data collection module is configured to collect performance data of each node in the entire link of the video stream; A data standardization module is configured to standardize the performance data of each node through a protocol adaptation layer to obtain performance data in a preset data format; The data processing module is configured to perform stability analysis on each of the nodes according to the standardized performance data to obtain a fault detection result for the entire link.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; The processor is configured to perform the method according to any one of claims 1 to 7 by executing the executable instructions.

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