Service node quality detection method and device and computing equipment

Abstract

The invention discloses a service node quality detection method and apparatus, and a computing device. The method comprises the following steps: identifying candidate data streams with abnormal playing, and calculating an original stream frame loss rate and a transcoding stream frame loss rate of the candidate data streams; if the original stream frame loss rate is smaller than a first threshold value and the transcoding stream frame loss rate is larger than a second threshold value, determining the candidate data stream as a node abnormal data stream, and calculating a transcoding stream output-input fluctuation ratio of the node abnormal data stream; if the output-input fluctuation ratio of the transcoding stream is smaller than a third threshold value, determining a stream pushing node of the node abnormal data stream as a quality abnormal node; and if the output-input fluctuation ratio of the transcoding stream is greater than or equal to a third threshold value, determining that the transcoding node of the node abnormal data stream is a quality abnormal node. According to the scheme, the quality detection of the service node can be realized without depending on the internal monitoring data of the service node provided by a third party, the application range of the scheme is expanded, and the quality abnormal node and the node abnormal data flow can be accurately and quickly positioned.

Description

Technical Field

[0001] The present application relates to the field of streaming media technology, and particularly relates to a method, apparatus, computing device, computer storage medium and computer program product for detecting the quality of service nodes. Background Art

[0002] With the continuous development of streaming media technology, the emergence of various streaming media service platforms has greatly facilitated the work and life of users. In order to improve the service quality of streaming media service platforms, it is usually necessary to detect the quality of service nodes in the streaming media service platforms, so as to timely discover service nodes with abnormal quality.

[0003] However, the inventors found in the implementation process that there are the following defects in the prior art: the existing streaming media service platforms detect the quality of service nodes through the internal monitoring parameters (such as CPU utilization rate, etc.) of the service nodes. However, in the actual implementation process, some streaming media service platforms use the service nodes of third-party platforms. In this case, the streaming media service platforms need to rely on the internal monitoring parameters of the service nodes provided by the third-party platforms to detect the quality of the service nodes. However, when the third-party platforms are unable or difficult to provide the internal monitoring parameters of the service nodes, the streaming media service platforms cannot detect the quality of the service nodes. Summary of the Invention

[0004] In view of the above problems, the present application is proposed to provide a method, apparatus, computing device, computer storage medium and computer program product for detecting the quality of service nodes that overcomes the above problems or at least partially solves the above problems.

[0005] According to a first aspect of the present application, there is provided a method for detecting the quality of service nodes, including:

[0006] Identifying candidate data streams with abnormal playback, and calculating the original stream packet loss rate and transcoded stream packet loss rate of the candidate data streams;

[0007] If the original stream packet loss rate is less than a first threshold and the transcoded stream packet loss rate is greater than a second threshold, determining that the candidate data stream is a node abnormal data stream, and calculating the transcoded stream output-input fluctuation ratio of the node abnormal data stream;

[0008] If the transcoded stream output-input fluctuation ratio is less than a third threshold, determining that the push node of the node abnormal data stream is a quality abnormal node;

[0009] If the output-input fluctuation ratio of the transcoded stream is greater than or equal to the third threshold, determining that the transcoding node of the node abnormal data stream is a quality abnormal node.

[0010] In an optional implementation manner, the identifying candidate data streams with abnormal playback includes:

[0011] Identify candidate data streams with abnormal playback based on barrage data and / or abnormal report data.

[0012] In an alternative implementation, calculating the original stream frame loss rate and transcoded stream frame loss rate of the candidate data stream includes:

[0013] Calculate the original stream frame loss rate of the candidate data stream according to the monitoring data at the input end of the pushing node of the candidate data stream within the first historical period;

[0014] And / or, calculate the transcoded stream frame loss rate of the candidate data stream according to the monitoring data at the output end of the transcoding node where the transcoded stream of the candidate data stream is located within the first historical period.

[0015] In an alternative implementation, calculating the original stream frame loss rate of the candidate data stream according to the monitoring data at the input end of the pushing node of the candidate data stream within the first historical period includes: obtaining the total number of original frames received by the pushing node at the first moment and the total number of original frames received at the second moment; calculating the difference in the total number of original frames between the second total number of original frames and the first total number of original frames; calculating the original stream frame loss rate according to the difference in the total number of original frames and the set value of the number of original frames per second;

[0016] And / or, calculating the transcoded stream frame loss rate of the candidate data stream according to the monitoring data at the output end of the transcoding node where the transcoded stream of the candidate data stream is located within the first historical period includes: for any transcoded stream, obtaining the total number of transcoded frames output by the transcoding node where the transcoded stream is located at the first moment and the total number of transcoded frames output at the second moment; calculating the difference in the total number of transcoded frames between the second total number of transcoded frames and the first total number of transcoded frames; calculating the transcoded stream frame loss rate corresponding to the transcoded stream according to the difference in the total number of transcoded frames and the set value of the number of transcoded frames per second of the transcoded stream;

[0017] Wherein, the first moment is the start moment of the first historical period, and the second moment is the end moment of the first historical period.

[0018] In an alternative implementation, calculating the transcoded stream input-output fluctuation ratio of the node abnormal data stream includes:

[0019] For any transcoded stream of the node abnormal data stream, calculate the transcoded stream input fluctuation value corresponding to the transcoded stream according to the number of original frames per second at the receiving end of the transcoding node where the transcoded stream is located within the second historical period; calculate the transcoded stream output fluctuation value corresponding to the transcoded stream according to the number of transcoded frames per second at the output end of the transcoding node where the transcoded stream is located within the second historical period;

[0020] Use the ratio of the transcoded stream output fluctuation value to the transcoded stream input fluctuation value corresponding to the transcoded stream as the transcoded stream output-input fluctuation ratio of the transcoded stream.

[0021] In an alternative embodiment, the method further includes:

[0022] Upon receiving the misjudgment feedback data of the node abnormal data stream, lower the first threshold and raise the second threshold;

[0023] And / or, upon receiving the missed judgment feedback data of the node abnormal data stream, raise the first threshold and lower the second threshold.

[0024] In an alternative embodiment, the method further includes:

[0025] Upon receiving the misjudgment feedback data of the streaming node, lower the third threshold;

[0026] Upon receiving the misjudgment feedback data of the transcoding node, raise the third threshold.

[0027] According to a second aspect of the present application, there is provided a service node quality detection device, including:

[0028] An identification module, configured to identify candidate data streams with abnormal playback;

[0029] A calculation module, configured to calculate the original stream packet loss rate and the transcoded stream packet loss rate of the candidate data stream;

[0030] A first detection module, configured to determine that the candidate data stream is a node abnormal data stream if the original stream packet loss rate is less than the first threshold and the transcoded stream packet loss rate is greater than the second threshold, and calculate the transcoded stream output-input fluctuation ratio of the node abnormal data stream;

[0031] A second detection module, configured to determine that the streaming node of the node abnormal data stream is a quality abnormal node if the transcoded stream output-input fluctuation ratio is less than the third threshold; if the output-input fluctuation ratio of the transcoded stream is greater than or equal to the third threshold, determine that the transcoding node of the node abnormal data stream is a quality abnormal node.

[0032] In an alternative embodiment, the identification module is configured to: identify candidate data streams with abnormal playback according to bullet screen data and / or abnormal reporting data.

[0033] In an alternative embodiment, the calculation module is configured to: calculate the original stream packet loss rate of the candidate data stream according to the input-end monitoring data of the streaming node of the candidate data stream within the first historical period;

[0034] And / or, calculate the transcoding stream loss rate of the candidate data stream according to the monitoring data at the output end of the transcoding node where the transcoding stream of the candidate data stream is located within the first historical period.

[0035] In an alternative embodiment, the calculation module is configured to: obtain the total number of original frames of the first original stream received by the streaming node at the first moment and the total number of original frames of the second original stream received at the second moment; calculate the difference in the total number of original frames between the second original stream total number and the first original stream total number; calculate the original stream loss rate based on the difference in the total number of original frames and the set value of the number of original frames per second.

[0036] And / or, for any transcoding stream, obtain the total number of frames of the first transcoding stream output by the transcoding node where the transcoding stream is located at the first moment and the total number of frames of the second transcoding stream output at the second moment; calculate the difference in the total number of frames of the transcoding stream between the second transcoding stream total number and the first transcoding stream total number; calculate the transcoding stream loss rate corresponding to the transcoding stream based on the difference in the total number of frames of the transcoding stream and the set value of the number of frames of the transcoding stream per second.

[0037] Wherein, the first moment is the starting moment of the first historical period, and the second moment is the ending moment of the first historical period.

[0038] In an alternative embodiment, the first detection module is configured to: for any transcoding stream of the node abnormal data stream, calculate the transcoding stream input fluctuation value corresponding to the transcoding stream according to the number of original frames per second at the receiving end of the transcoding node where the transcoding stream is located within the second historical period; calculate the transcoding stream output fluctuation value corresponding to the transcoding stream according to the number of frames of the transcoding stream per second at the output end of the transcoding node where the transcoding stream is located within the second historical period.

[0039] Take the ratio of the transcoding stream output fluctuation value and the transcoding stream input fluctuation value corresponding to the transcoding stream as the transcoding stream output-input fluctuation ratio of the transcoding stream.

[0040] In an alternative embodiment, the device further includes: a threshold adjustment module, configured to receive misjudgment feedback data of the node abnormal data stream, and lower the first threshold and raise the second threshold;

[0041] And / or, receive missed judgment feedback data of the node abnormal data stream, and raise the first threshold and lower the second threshold.

[0042] In an alternative embodiment, the threshold adjustment module is configured to: receive misjudgment feedback data of the streaming node, and lower the third threshold;

[0043] Receive misjudgment feedback data of the transcoding node, and raise the third threshold.

[0044] According to a third aspect of the present application, there is provided a computing device, including: a processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface complete communication with each other through the communication bus;

[0045] The memory is used to store at least one executable instruction, and the executable instruction causes the processor to execute the operations corresponding to the above service node quality detection method.

[0046] According to a fourth aspect of the present application, there is provided a computer storage medium, in which at least one executable instruction is stored, and the executable instruction causes the processor to execute the operations corresponding to the above service node quality detection method.

[0047] According to a fifth aspect of the present application, there is provided a computer program product, including at least one executable instruction, and the executable instruction causes the processor to execute the operations corresponding to the above service node quality detection method.

[0048] The embodiments of the present application determine whether a candidate data stream is a node abnormal data stream of a service node with an abnormality in the processing link according to the original stream frame loss rate and the transcoded stream frame loss rate of the abnormal data stream of the node with a playback abnormality; and for the identified node abnormal data stream, the quality abnormal node is located through the input-output fluctuation ratio of the transcoded stream. The original stream frame loss rate, the transcoded stream frame loss rate, and the input-output fluctuation ratio of the transcoded stream adopted in this solution are not the internal monitoring data of the service node. Therefore, the quality detection of the service node in this solution does not need to rely on the internal monitoring data of the service node provided by a third party, thereby improving the applicable scope of this solution, and being able to accurately and quickly locate the quality abnormal node and the node abnormal data stream.

[0049] The embodiments of the present application identify candidate data streams with playback abnormalities through the barrage data and / or abnormal reporting data on the user side, so as to be able to determine the candidate data streams from the actual playback situation on the user side, and improve the determination accuracy of the candidate data streams.

[0050] The embodiments of the present application set data monitoring points at the input end of the push stream node and the output end of the transcoding node. On the one hand, it does not need to rely on the internal monitoring data of the service node. On the other hand, the transcoding node and the push stream node can be regarded as a whole black box system. The input end of the push stream node is the input port of the black box system, and the output end of the transcoding node is the output port of the black box system. Thus, it is possible to accurately and quickly determine whether the black box system has an abnormality through the original stream frame loss rate at the input port and the transcoded stream frame loss rate at the output port, thereby improving the determination efficiency of the quality abnormal node.

[0051] The embodiments of the present application can accurately calculate the original stream frame loss rate according to the difference between the total number of original stream frames at the first moment and the second moment and the set value of the number of original stream frames per second.

[0052] According to the difference between the total number of frames of the transcoded stream at the first moment and the second moment and the set value of the number of frames per second of the transcoded stream, the embodiment of the present application can accurately calculate the frame loss rate of the transcoded stream.

[0053] The embodiment of the present application calculates the input fluctuation value of the transcoded stream according to the number of frames per second of the original stream received by the transcoding node at the second historical period, calculates the output fluctuation value of the transcoded stream according to the number of frames per second of the transcoded stream at the output end of the transcoding node at the second historical period, and then can accurately obtain the output / input fluctuation ratio of the transcoded stream according to the output fluctuation value and the input fluctuation value of the transcoded stream.

[0054] The embodiment of the present application dynamically adjusts the first threshold and the second threshold according to the data stream feedback data, so as to improve the detection accuracy of abnormal data streams of the node.

[0055] The embodiment of the present application dynamically adjusts the third threshold according to the node feedback data, so as to improve the detection accuracy of the quality abnormal nodes of the abnormal data streams of the node.

[0056] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically illustrates the specific implementation manners of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present application. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0058] Figure 1 A schematic diagram of an operating environment provided for implementing at least one embodiment of the present application is shown;

[0059] Figure 2 A schematic flowchart of a service node quality detection method provided in Embodiment 1 of the present application is shown;

[0060] Figure 3 A schematic diagram of a monitoring point provided in Embodiment 1 of the present application is shown;

[0061] Figure 4 A schematic flowchart of a threshold adjustment method applied to a service node quality detection method provided in Embodiment 2 of the present application is shown;

[0062] Figure 5 A schematic structural diagram of a service node quality detection device provided in Embodiment 3 of the present application is shown;

[0063] Figure 6 The figure shows a schematic structural diagram of a computing device provided in the fourth embodiment of the present application. Detailed implementation manners

[0064] Hereinafter, exemplary embodiments of the present application will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present application can be more thoroughly understood and the scope of the present application can be fully conveyed to those skilled in the art.

[0065] First, a brief explanation of the noun terms related to one or more embodiments of the present application will be given.

[0066] Transcoding is the process of converting a video or audio encoding format into another encoding format in order to adapt to different network bandwidths, different terminal processing capabilities, and / or different user requirements.

[0067] Original stream refers to the data stream pushed by the host end to the streaming node, which can also be called the original data stream, and it is a data stream that has not been transcoded.

[0068] Transcoded stream refers to the data stream output after the original stream has been transcoded.

[0069] Streaming node refers to the service node in the service platform that receives the data stream pushed by the host end.

[0070] Transcoding node refers to the service node in the service platform that executes the transcoding task.

[0071] Transcoding task refers to a program that pulls the original stream through a specific address, performs transcoding through preset configurations to generate a transcoded stream, and outputs the transcoded stream through a specific address.

[0072] Frames per second, abbreviated as FPS, refers to the number of consecutive frames displayed per second.

[0073] Frame loss rate refers to the ratio of the lost frames to the total number of frames when frames are lost in the data stream due to corresponding reasons.

[0074] Figure 1 The figure shows a schematic diagram of an operating environment provided to implement at least one embodiment of the present application. The present application can be applied to application environments including but not limited to the host end, the viewer end, and the service platform.

[0075] Among them:

[0076] The host end is the producer of live broadcast resources, used to generate live broadcast data such as audio and video, and push the generated live broadcast data to the service platform. The host end can run Windows, AndroidTM ) or an electronic device with an operating system such as IOS, such as a smart phone, a tablet device, a laptop computer, a virtual reality device, a gaming device, a set-top box, a vehicle-mounted terminal, a smart TV. Based on the above operating systems, various application programs can be run, such as a browser.

[0077] The viewer side is a consumer of live resources and is used to obtain live data such as audio and video from the service platform. The viewer side can be an electronic device running an operating system such as Windows, Android TM ) or an electronic device with an operating system such as IOS, such as a smart phone, a tablet device, a laptop computer, a virtual reality device, a gaming device, a set-top box, a vehicle-mounted terminal, a smart TV. Based on the above operating systems, various application programs can be run, such as a browser.

[0078] The service platform is a live data processing side, which is used to receive the live data produced by the anchor side, process the live data, and provide the live data to the viewer side. The service platform can be composed of a single or multiple service nodes or service clusters. As Figure 1 shown, the service platform includes at least one streaming node and at least one transcoding node, and each node can be one or more computing devices. The computing device can include a virtualized computing instance. The virtualized computing instance can include a virtual machine, such as an emulation of a computer system, an operating system, a server, etc. The computing device can load the virtual machine based on a virtual image and / or other data that defines a specific software (e.g., an operating system, a dedicated application, a server) for emulation. As the demand for different types of processing services changes, different virtual machines can be loaded and / or terminated on one or more computing devices. A hypervisor can be implemented to manage the use of different virtual machines on the same computing device.

[0079] The service platform can be configured to communicate with the anchor side, the viewer side, etc. through a network. The network can include various network devices, such as routers, switches, multiplexers, hubs, modems, bridges, repeaters, firewalls, proxy devices, and / or the like. The network can also include physical links, such as coaxial cable links, twisted pair cable links, fiber optic links and their combinations, etc., or wireless links, such as cellular links, satellite links, Wi-Fi links, etc.

[0080] Embodiment 1

[0081] Figure 2 shows a schematic flowchart of a method for detecting the quality of a service node provided in Embodiment 1 of the present application. Among them, the method for detecting the quality of a service node provided in the embodiment of the present application can be applied to a service platform, and the service platform can include a live broadcast platform, etc.

[0082] As Figure 2 shown, the method specifically includes the following steps:

[0083] Step S201, identify candidate data streams with abnormal playback.

[0084] The candidate data stream is a data stream with abnormal playback in the service platform. Among them, the abnormal playback may include: playback stuttering, first frame timeout, playback failure, audio-video out of sync, and so on.

[0085] In an optional implementation manner, the embodiments of the present application specifically identify candidate data streams according to the data on the user side (including the host side and / or the viewer side). Specifically, candidate data streams with abnormal playback can be identified according to the barrage data and / or the abnormal reporting data. For example, the barrage message queue and / or the stuttering reporting message queue can be monitored. When a certain barrage or a certain stuttering reporting message with an abnormal playback keyword (such as "stuck", "black screen") is currently monitored, the barrage or the stuttering reporting message is extracted, the data stream corresponding to the barrage or the stuttering reporting message is determined, and the corresponding data stream is used as the candidate data stream with abnormal playback. This implementation manner identifies candidate data streams with abnormal playback through the barrage data and / or the abnormal reporting data on the user side, so as to determine candidate data streams from the actual playback situation on the user side and improve the determination accuracy of candidate data streams.

[0086] Step S202, calculate the original stream packet loss rate and the transcoded stream packet loss rate of the candidate data stream.

[0087] Among them, the candidate data stream is a data stream with abnormal playback. The reasons for the abnormal playback may be the reasons on the user side itself, such as the user side device being stuck, network anomaly, etc.; it may also be that there is a quality anomaly in the service node processing the data stream, and so on. Therefore, after screening out the candidate data stream, further calculate the original stream packet loss rate and the transcoded stream packet loss rate of the candidate data stream, so as to subsequently determine whether the reason for the abnormal playback of the candidate data stream is a service node quality anomaly according to the original stream packet loss rate and the transcoded stream packet loss rate of the candidate data stream.

[0088] Specifically, calculate the packet loss rate of the original stream of the candidate data stream within the first historical period (such as the most recent 1 minute, etc.). This packet loss rate is called the original stream packet loss rate, and the original stream packet loss rate can reflect the packet loss situation of the original stream during the transmission from the host side to the push stream node.

[0089] And calculate the packet loss rate of the transcoded stream of the candidate data stream within the first historical period. This packet loss rate is called the transcoded stream packet loss rate. The transcoded stream packet loss rate can reflect the packet loss situation of the transcoded stream output by the transcoding node. Optionally, if there are multiple transcoded streams of the candidate data stream, calculate the transcoded stream packet loss rate of each transcoded stream respectively.

[0090] It can be seen therefrom that the original stream packet loss rate and the transcoded stream packet loss rate reflect the input and output characteristics of the service node, which can be obtained by monitoring of the streaming media service platform. That is, the original stream packet loss rate and the transcoded stream packet loss rate do not involve the internal monitoring data of the service node, and thus there is no need to rely on the internal monitoring data provided by the third party where the service node is located.

[0091] In an alternative embodiment, specifically according to the input-end monitoring data of the pushing node of the candidate data stream in the first historical period, the original stream packet loss rate of the candidate data stream is calculated; and / or, according to the output-end monitoring data of the transcoding node where the transcoded stream of the candidate data stream is located in the first historical period, the transcoded stream packet loss rate of the candidate data stream is calculated. When there are multiple transcoded streams of the candidate data stream, for each transcoded stream, according to the output-end monitoring data of the transcoding node for this transcoded stream where this transcoded stream is located, the transcoded stream packet loss rate corresponding to this transcoded stream is calculated.

[0092] Reference Figure 3 , the pushing node receives the original stream of the candidate data stream, and this original stream can be transmitted to transcoding node 1 and transcoding node 2 in the way of the pushing node pushing the stream or the transcoding node pulling the stream. Among them, transcoding node 1 outputs transcoded stream 1 and transcoded stream 2 of the candidate data stream, and transcoding node 2 outputs transcoded stream 3 of the candidate data stream. An original stream packet loss rate detection point is set at the input end of the pushing node, so that the input-end monitoring data of the pushing node can be obtained, and then the original stream packet loss rate can be obtained; transcoded stream packet loss rate monitoring points are set at the output ends of transcoding node 1 and transcoding node 2, so that the output-end monitoring data of the transcoding node can be obtained, and then the transcoded stream packet loss rate can be obtained.

[0093] In this embodiment, data monitoring points are set at the input end of the pushing node and the output end of the transcoding node. On the one hand, there is no need to rely on the internal monitoring data of the service node. On the other hand, the transcoding node and the pushing node can be regarded as an overall black box system. The input end of the pushing node is the input port of this black box system, and the output end of the transcoding node is the output port of this black box system. Thus, it can be accurately and quickly determined whether there is an abnormality in this black box system through the original stream packet loss rate of the input port and the transcoded stream packet loss rate of the output port, so as to facilitate quickly determining whether the candidate data stream is a node abnormal data stream.

[0094] Further optionally, the original stream frame loss rate can be specifically calculated as follows: Obtain the total number of original frames of the first original stream received by the streaming node at the first moment and the total number of original frames of the second original stream received at the second moment. Among them, the total number of original frames of the original stream of the abnormal data stream of this node received by the streaming node at the first moment is the total number of the first original frames, and the total number of original frames of the original stream of the abnormal data stream of this node received by the streaming node at the second moment is the total number of the second original frames. The first moment is the start moment of the first historical period, and the second moment is the end moment of the first historical period, that is, the first moment is earlier than the second moment; Calculate the difference in the total number of original frames between the second total number of original frames and the first total number of original frames, that is, the difference between the second total number of original frames and the first total number of original frames is called the difference in the total number of original frames; Finally, according to the difference in the total number of original frames and the set value of the number of original frames per second, calculate the original stream frame loss rate. Among them, the set value of the number of original frames per second is the set parameter for the original stream transmission, and it is also the number of frames per second during the process of the host end pushing the candidate data stream to the streaming node.

[0095] Specifically, the original stream frame loss rate can be calculated using the following formula 1:

[0096]

[0097] Among them, LFR_y represents the original stream frame loss rate; F a represents the total number of the first original frames; F b represents the total number of the second original frames; t ab represents the sampling interval between the first moment and the second moment; FPS_y represents the set value of the number of original frames per second of the candidate data stream.

[0098] Further optionally, the transcoded stream frame loss rate can be specifically calculated as follows: For any transcoded stream, obtain the total number of the first transcoded frames output by the transcoding node where the transcoded stream is located at the first moment and the total number of the second transcoded frames output at the second moment. Among them, the total number of the transcoded frames of this transcoded stream output by the transcoding node where the transcoded stream is located at the first moment is called the total number of the first transcoded frames, and the total number of the transcoded frames of this transcoded stream output by the transcoding node where the transcoded stream is located at the second moment is called the total number of the second transcoded frames; Calculate the difference in the total number of transcoded frames between the second total number of transcoded frames and the first total number of transcoded frames. Among them, the difference between the second total number of transcoded frames and the first total number of transcoded frames is called the difference in the total number of transcoded frames; Finally, according to the difference in the total number of transcoded frames and the set value of the number of transcoded frames per second of this transcoded stream, calculate the transcoded stream frame loss rate corresponding to this transcoded stream. Among them, the set value of the number of transcoded frames per second is the set parameter for the transmission of this transcoded stream, and it is also the ideal number of frames per second during the transmission of this transcoded stream.

[0099] Specifically, the transcoded stream frame loss rate can be calculated using the following formula 2:

[0100]

[0101] Among them, LFR_z represents the frame loss rate of the transcoded stream; f a represents the total number of frames of the first transcoded stream; f b represents the total number of frames of the second transcoded stream; t ab represents the sampling interval between the first moment and the second moment; FPS_z represents the set value of the number of frames per second of the transcoded stream.

[0102] Step S203, determine whether it satisfies that the original stream frame loss rate is less than the first threshold and the transcoded stream frame loss rate is greater than the second threshold; if so, execute step S204.

[0103] If the original stream frame loss rate is less than the first threshold, it indicates that the original stream frame loss rate is low, thus indicating that there is no abnormality in the transmission link between the host end and the push stream node. If the original stream frame loss rate is greater than or equal to the first threshold, it indicates that the original stream frame loss rate exceeds the normal range, thus indicating that there is an abnormality in the transmission link between the host end and the push stream node. If the transcoded stream frame loss rate is greater than the second threshold, it indicates that the transcoded stream frame loss rate is high and exceeds the normal frame loss range; if the transcoded stream frame loss rate is less than or equal to the second threshold, it indicates that the transcoded stream frame loss rate is low and does not exceed the normal frame loss range.

[0104] Therefore, when the original stream frame loss rate is less than the first threshold and the transcoded stream frame loss rate is greater than the second threshold, it indicates that there is a quality abnormality in the processing system composed of the push stream node and the transcoding node of the abnormal data stream of this node. Then determine the current candidate data stream as the node abnormal data stream, that is, execute step S204 and subsequent steps to further determine which service node in this processing system has a quality abnormality. When the current situation does not satisfy that the original stream frame loss rate is less than the first threshold and the transcoded stream frame loss rate is greater than the second threshold, this method ends.

[0105] Among them, when there are multiple transcoded streams corresponding to the candidate data stream, this step specifically determines whether it satisfies: the original stream frame loss rate is less than the first threshold and the frame loss rate of each transcoded stream corresponding to the candidate data stream is greater than the second threshold.

[0106] Step S204, determine the candidate data stream as the node abnormal data stream, and calculate the transcoded stream output-input fluctuation ratio of the node abnormal data stream.

[0107] If the original stream frame loss rate is less than the first threshold and the transcoded stream frame loss rate is greater than the second threshold, it indicates that there is a quality abnormality in the processing system composed of the push stream node and the transcoding node of the abnormal data stream of this node. Then determine this candidate data stream as the node abnormal data stream.

[0108] Further determine the service node with quality abnormality in the processing link of the node abnormal data stream. Specifically, the service node with quality abnormality in this processing system is determined through the transcoded stream output-input fluctuation ratio.

[0109] Specifically, the transcoded stream output-input fluctuation ratio reflects the ratio of the transcoded stream output fluctuation to the input fluctuation. Among them, if the node abnormal data stream corresponds to multiple transcoded streams, the transcoded stream output-input fluctuation ratios corresponding to each transcoded stream are calculated separately.

[0110] In an alternative embodiment, the transcoded stream output-input fluctuation ratio can be obtained specifically in the following manner:

[0111] For any transcoded stream of the node abnormal data stream, according to the number of frames per second of the original stream at the receiving end of the transcoding node where the transcoded stream is located within the second historical period (such as the most recent 5 minutes, etc.), calculate the transcoded stream input fluctuation value corresponding to the transcoded stream. Specifically, the transcoded stream is obtained by transcoding the original stream, so the transcoding node where the transcoded stream is located will receive the original stream. Then, obtain the number of frames per second of the original stream received by the transcoding node at each sampling moment within the second historical period, and further use the fluctuation algorithm to calculate the fluctuation value corresponding to the number of frames per second of the original stream at each sampling moment. This fluctuation value is the transcoded stream input fluctuation value corresponding to the transcoded stream. Among them, the fluctuation value includes but is not limited to: variance, standard deviation, range, etc.

[0112] And, according to the number of frames per second of the transcoded stream at the output end of the transcoding node where the transcoded stream is located within the second historical period, calculate the transcoded stream output fluctuation value corresponding to the transcoded stream. Specifically, obtain the number of frames per second of the transcoded stream output by the transcoding node at different sampling moments within the second historical period, and further use the fluctuation algorithm to calculate the fluctuation value of the number of frames per second of the transcoded stream at each sampling moment. This fluctuation value is the transcoded stream output fluctuation value corresponding to the transcoded stream.

[0113] Finally, take the ratio of the transcoded stream output fluctuation value and the transcoded stream input fluctuation value corresponding to the transcoded stream as the transcoded stream output-input fluctuation ratio of the transcoded stream.

[0114] Step S205, determine whether the transcoded stream output-input fluctuation ratio is less than the third threshold; if so, execute step S206; if not, execute step S207.

[0115] Step S206, determine the pushing node of the node abnormal data stream as a quality abnormal node.

[0116] If the transcoded stream output-input fluctuation ratio is less than the third threshold, it indicates that the difference between the output fluctuation and the input fluctuation of the transcoding node where the transcoded stream is located is small. Therefore, it is determined that the transcoding process of the node abnormal data stream is in a normal state, and thus the pushing node of the node abnormal data stream is determined as a quality abnormal node.

[0117] Step S207, determine the transcoding node of the node abnormal data stream as a quality abnormal node.

[0118] If the output-input fluctuation ratio of the transcoded stream is greater than or equal to the third threshold, it indicates that the difference between the output fluctuation and the input fluctuation of the transcoding node where the transcoded stream is located is relatively large. Thus, it is determined that the transcoding process of the node abnormal data stream is in an abnormal state, and the transcoding node of the node abnormal data stream is determined as a quality abnormal node.

[0119] In an alternative embodiment, if there are multiple transcoded streams of the node abnormal data stream. When the output-input fluctuation ratio of each corresponding transcoded stream is less than the third threshold, the streaming node of the node abnormal data stream is determined as a quality abnormal node. If the output-input fluctuation ratio of each corresponding transcoded stream is greater than or equal to the third threshold, each transcoding node of the node abnormal data stream is determined as a quality abnormal node.

[0120] If the output-input fluctuation ratio of a part of the transcoded streams (referred to as the first type of transcoded streams) of the node abnormal data stream is less than the third threshold, while the output-input fluctuation ratio of another part of the transcoded streams (referred to as the second type of transcoded streams) is greater than or equal to the third threshold, then the streaming node of the node abnormal data stream is determined as a quality abnormal node, and the transcoding node where the second type of transcoded stream is located is determined as a quality abnormal node. In addition, if a certain transcoding node outputs both the first type of transcoded stream and the second type of transcoded stream, then this transcoding node is also regarded as a quality abnormal node.

[0121] It can be seen that the service node quality detection method provided by the embodiments of the present application determines whether the candidate data stream is a node abnormal data stream with an abnormality in the service node in the processing link according to the original stream loss frame rate and the transcoded stream loss frame rate of the node abnormal data stream with abnormal playback; and for the identified node abnormal data stream, the quality abnormal node is located through the output-input fluctuation ratio of the transcoded stream. The original stream loss frame rate, the transcoded stream loss frame rate, and the output-input fluctuation ratio of the transcoded stream adopted in this solution are not the internal monitoring data of the service node. Therefore, this solution for service node quality detection does not need to rely on the internal monitoring data of the service node provided by a third party, improving the applicable scope of this solution, and being able to accurately and quickly locate the quality abnormal node and the node abnormal data stream.

[0122] Embodiment 2

[0123] Figure 4 Fig. shows a schematic flowchart of a threshold adjustment method applied to the service node quality detection method provided by the second embodiment of the present application. Among them, this solution is used to dynamically adjust the threshold in the service node quality detection method.

[0124] As Figure 4 shown, the method specifically includes the following steps:

[0125] Step S401, obtain feedback data.

[0126] The feedback data is the feedback on the detection result of the service node quality detection method.

[0127] In an alternative embodiment, the feedback data may be sourced from manual feedback. For example, when it is determined during manual inspection that the detection result obtained by the service node quality detection method does not match the actual situation, corresponding feedback data is generated.

[0128] In an alternative embodiment, the feedback data may be sourced from client feedback. For example, during the specific implementation process, user feedback information with a feedback type of service quality anomaly can be obtained, and the data streams corresponding to each piece of user feedback information can be determined. If the user feedback information corresponding to any data stream exceeds the fourth threshold, then that data stream is determined to be a real node anomaly data stream. The detection result obtained by the service node quality detection method is searched. If the detection result of the real node anomaly data stream being determined as a node anomaly data stream cannot be found in the detection result, then node anomaly data stream missed detection feedback data corresponding to the real node anomaly data stream is generated.

[0129] In an alternative embodiment, the feedback data can also be generated in the following manner: After identifying candidate data streams with abnormal playback, the push nodes and transcoding nodes of each candidate data stream are respectively determined. For any push node, if the number of candidate data streams corresponding to the push node exceeds the fifth threshold, then that push node is determined to be a real quality anomaly node; for any transcoding node, if the number of candidate data streams corresponding to the transcoding node exceeds the sixth threshold, then that transcoding node is determined to be a real quality anomaly node. The generated real quality anomaly nodes are compared with the quality anomaly node detection results obtained by using the service node quality detection method in Embodiment 1, and corresponding node feedback data is generated according to the comparison result.

[0130] Further optionally, the data streams processed by the identified real quality anomaly nodes are used as real node anomaly data streams, and the real node anomaly data streams are compared with the node anomaly data stream detection results obtained by using the service node quality detection method in Embodiment 1, and corresponding data stream feedback data is generated according to the comparison result.

[0131] Step S402, identify the type of the feedback data; if it is data stream feedback data, then execute step S403; if it is node feedback data, then execute step S406.

[0132] Using the service node quality detection method provided in the first embodiment will generate two types of detection results. The first type of detection result is: the recognition result of whether the candidate data stream is a node abnormal data stream; the second type of detection result is: the recognition result of the service node with quality abnormality of the node abnormal data stream. Therefore, the feedback data for the first type of detection result is data stream feedback data, and there is a difference between this type of feedback data and the detection result of the node abnormal data stream; the feedback data for the second type of detection result is node feedback data, and there is a difference between this type of feedback data and the detection result of the service node with quality abnormality of the node abnormal data stream.

[0133] Step S403, identify the type of the data stream feedback data; if it is misjudgment feedback data of the node abnormal data stream, then execute step S404; if it is missed judgment feedback data of the node abnormal data stream, then execute step S405.

[0134] The type of the data stream feedback data is divided into two types. The first type is the feedback data for the candidate data stream being identified as a node abnormal data stream. It is determined by using the service node quality detection method that the candidate data stream is a node abnormal data stream, but in the feedback data, it is determined that the candidate data stream is not a node abnormal data stream, that is, the candidate data stream is misjudged as a node abnormal data stream. The second type is the feedback data for the candidate data stream not being identified as a node abnormal data stream. It is determined by using the service node quality detection method that the candidate data stream is not a node abnormal data stream, but in the feedback data, it is determined that the candidate data stream is a node abnormal data stream, that is, the candidate data stream is missed judged as a node abnormal data stream.

[0135] Step S404, lower the first threshold and raise the second threshold.

[0136] When receiving the misjudgment feedback data of the node abnormal data stream, it reflects that the non-node abnormal data stream is misjudged as a node abnormal data stream, so the first threshold is lowered and the second threshold is raised. Among them, the first threshold can be lowered and the second threshold can be raised according to a preset step size, so as to reduce the first threshold in the subsequent detection process and increase the second threshold in the subsequent detection process, and reduce the occurrence of misjudgment phenomena.

[0137] Step S405, raise the first threshold and lower the second threshold.

[0138] When receiving the missed judgment feedback data of the node abnormal data stream, it reflects that the real node abnormal data stream is not detected, so the first threshold is raised and the second threshold is lowered. Among them, the first threshold can be raised and the second threshold can be lowered according to a preset step size, so as to increase the first threshold in the subsequent detection process and reduce the second threshold in the subsequent detection process, and reduce the occurrence of missed detection phenomena.

[0139] Step S406: Identify the type of node feedback data. If it is the misjudgment feedback data of the streaming node, execute Step S407; if it is the misjudgment feedback data of the transcoding node, execute Step S408.

[0140] The types of node feedback data are divided into two kinds. The first kind is the feedback data that the streaming node for the abnormal data stream of the node is identified as a quality abnormal node. It is determined by the service node quality detection method that the streaming node of the abnormal data stream of the node is a quality abnormal node, but the feedback data determines that this streaming node is not a quality abnormal node, that is, the streaming node of the abnormal data stream of the node is misjudged as a quality abnormal node. The second kind is the feedback data that the transcoding node for the abnormal data stream of the node is identified as a quality abnormal node. It is determined by the service node quality detection method that the transcoding node of the abnormal data stream of the node is a quality abnormal node, but the feedback data determines that this transcoding node is not a quality abnormal node, that is, the transcoding node of the abnormal data stream of the node is misjudged as a quality abnormal node.

[0141] Step S407: Lower the third threshold.

[0142] When receiving the misjudgment feedback data of the streaming node, which reflects that the streaming node of the abnormal data stream of the node is misjudged as a quality abnormal node, the third threshold is lowered. Among them, the third threshold can be lowered according to a preset step size to reduce the third threshold in the subsequent detection process and reduce the occurrence of misjudgment phenomena.

[0143] Step S408: Raise the third threshold.

[0144] When receiving the misjudgment feedback data of the transcoding node, which reflects that the transcoding node of the abnormal data stream of the node is misjudged as a quality abnormal node, the third threshold is raised. Among them, the third threshold can be raised according to a preset step size to increase the third threshold in the subsequent detection process and reduce the occurrence of misjudgment phenomena.

[0145] It can be seen that the threshold adjustment method provided in the embodiment of the present application for the service node quality detection method can dynamically adjust the first threshold and the second threshold according to the data stream feedback data, thereby improving the detection accuracy of the abnormal data stream of the node; and it can also dynamically adjust the third threshold according to the node feedback data, thereby improving the detection accuracy of the quality abnormal node of the abnormal data stream of the node.

[0146] Embodiment III

[0147] Figure 5 shows a schematic structural diagram of a service node quality detection device provided in Embodiment III of the present application. As Figure 5 shown, the device 500 includes: an identification module 510, a calculation module 520, a first detection module 530, and a second detection module 540.

[0148] An identification module 510, configured to identify candidate data streams with abnormal playback;

[0149] A calculation module 520, configured to calculate the original stream frame loss rate and the transcoded stream frame loss rate of the candidate data stream;

[0150] A first detection module 530, configured to determine that the candidate data stream is a node abnormal data stream if the original stream frame loss rate is less than a first threshold and the transcoded stream frame loss rate is greater than a second threshold, and calculate the transcoded stream output-input fluctuation ratio of the node abnormal data stream;

[0151] A second detection module 540, configured to determine that the push stream node of the node abnormal data stream is a quality abnormal node if the transcoded stream output-input fluctuation ratio is less than a third threshold; if the transcoded stream output-input fluctuation ratio is greater than or equal to the third threshold, determine that the transcoding node of the node abnormal data stream is a quality abnormal node.

[0152] In an optional implementation manner, the identification module 510 is configured to: identify candidate data streams with abnormal playback according to bullet screen data and / or abnormal report data.

[0153] In an optional implementation manner, the calculation module 520 is configured to: calculate the original stream frame loss rate of the candidate data stream according to the input-end monitoring data of the push stream node of the candidate data stream within a first historical period;

[0154] and / or, calculate the transcoded stream frame loss rate of the candidate data stream according to the output-end monitoring data of the transcoding node where the transcoded stream of the candidate data stream is located within the first historical period.

[0155] In an optional implementation manner, the calculation module 520 is configured to: obtain a first total number of original stream frames received by the push stream node at a first moment and a second total number of original stream frames received at a second moment; calculate the difference in the total number of original stream frames between the second total number of original stream frames and the first total number of original stream frames; calculate the original stream frame loss rate according to the difference in the total number of original stream frames and the set value of the number of original stream frames per second;

[0156] and / or, for any transcoded stream, obtain a first total number of transcoded stream frames output by the transcoding node where the transcoded stream is located at a first moment and a second total number of transcoded stream frames output at a second moment; calculate the difference in the total number of transcoded stream frames between the second total number of transcoded stream frames and the first total number of transcoded stream frames; calculate the transcoded stream frame loss rate corresponding to the transcoded stream according to the difference in the total number of transcoded stream frames and the set value of the number of transcoded stream frames per second of the transcoded stream;

[0157] Wherein, the first moment is the start moment of the first historical period, and the second moment is the end moment of the first historical period.

[0158] In an alternative embodiment, the first detection module 530 is configured to: for any transcoded stream of the node abnormal data stream, calculate the transcoded stream input fluctuation value corresponding to the transcoded stream according to the number of frames per second of the original stream at the receiving end of the transcoding node where the transcoded stream is located within the second historical period; calculate the transcoded stream output fluctuation value corresponding to the transcoded stream according to the number of frames per second of the transcoded stream at the output end of the transcoding node where the transcoded stream is located within the second historical period;

[0159] Use the ratio of the transcoded stream output fluctuation value and the transcoded stream input fluctuation value corresponding to the transcoded stream as the transcoded stream output-input fluctuation ratio of the transcoded stream.

[0160] In an alternative embodiment, the apparatus further includes: a threshold adjustment module (not shown in the figure), configured to receive misjudgment feedback data of the node abnormal data stream, and lower the first threshold and raise the second threshold;

[0161] And / or, when receiving missed judgment feedback data of the node abnormal data stream, raise the first threshold and lower the second threshold.

[0162] In an alternative embodiment, the threshold adjustment module is configured to: when receiving misjudgment feedback data of the streaming node, lower the third threshold;

[0163] When receiving misjudgment feedback data of the transcoding node, raise the third threshold.

[0164] It can be seen that the service node quality detection device provided by the embodiments of the present application determines whether the candidate data stream is a node abnormal data stream with an abnormality in the service node in the processing link according to the original stream frame loss rate and the transcoded stream frame loss rate of the node abnormal data stream with a playback abnormality; and for the identified node abnormal data stream, locates the quality abnormal node through the transcoded stream output-input fluctuation ratio. The original stream frame loss rate, the transcoded stream frame loss rate, and the transcoded stream output-input fluctuation ratio adopted by this solution are not the internal monitoring data of the service node. Therefore, this solution does not need to rely on the internal monitoring data of the service node provided by a third party to detect the quality of the service node, improves the applicable scope of this solution, and can accurately and quickly locate the quality abnormal node and the node abnormal data stream.

[0165] Embodiment 4

[0166] Figure 6 FIG. shows a schematic structural diagram of a computing device provided by Embodiment 4 of the present application. The specific implementation of the computing device is not limited in the specific embodiments of the present application.

[0167] As Figure 6As shown, the computing device may include: a processor 602, a communications interface 604, a memory 606, and a communication bus 608.

[0168] Among them: The processor 602, the communications interface 604, and the memory 606 communicate with each other through the communication bus 608. The communications interface 604 is used to communicate with network elements of other devices such as clients or other servers. The processor 602 is used to execute the program 610, and specifically can execute the relevant steps in the above-described method embodiments for quality detection of service nodes of the computing device.

[0169] Specifically, the program 610 may include program code, and the program code includes computer operation instructions.

[0170] The processor 602 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application. One or more processors included in the computing device may be of the same type of processor, such as one or more CPUs; or may be of different types of processors, such as one or more CPUs and one or more ASICs.

[0171] The memory 606 is used to store the program 610. The memory 606 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory. The program 610 is specifically used to cause the processor 602 to perform the operations in any of the above method embodiments.

[0172] Embodiment Five

[0173] Embodiment Five of the present application provides a non-volatile computer storage medium, and the computer storage medium stores at least one executable instruction or computer program, and the executable instruction or computer program can cause the processor to perform the operations corresponding to the service node quality detection method in any of the above method embodiments.

[0174] Embodiment Six

[0175] Embodiment Six of the present application provides a computer program product, and the computer program product includes at least one executable instruction or computer program, and the executable instruction or computer program can cause the processor to perform the operations corresponding to the service node quality detection method in any of the above method embodiments.

[0176] In summary, according to the computing device, computer storage medium, and computer program product provided in this embodiment, it can be seen that the service node quality detection method provided in the embodiments of the present application determines whether the candidate data stream is an abnormal data stream of a service node with an abnormality in the processing link according to the original stream packet loss rate and the transcoded stream packet loss rate of the abnormal data stream of the node with abnormal playback; and for the identified abnormal data stream of the node, the quality abnormal node is located through the input-output fluctuation ratio of the transcoded stream. The original stream packet loss rate, the transcoded stream packet loss rate, and the input-output fluctuation ratio of the transcoded stream adopted in this solution are not the internal monitoring data of the service node. Therefore, the quality detection of the service node in this solution does not need to rely on the internal monitoring data of the service node provided by a third party, which improves the applicable scope of this solution and can accurately and quickly locate the quality abnormal node and the abnormal data stream of the node.

[0177] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings based herein. The structure required to construct such a system will be apparent from the above description. In addition, the embodiments of the present application are not directed to any specific programming language. It should be understood that the content of the present application described herein can be implemented using various programming languages, and the description of the specific language above is to disclose the best mode of the present application.

[0178] In the specification provided herein, a large number of specific details are set forth. However, it can be understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures, and technologies have not been shown in detail so as not to obscure the understanding of this specification.

[0179] Similarly, it should be understood that, in order to streamline the present application and assist in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, the various features of the embodiments of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be construed as reflecting the intention that the claimed present application requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, the inventive aspects lie in less than all the features of the single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate embodiment of the present application.

[0180] Those skilled in the art can understand that the modules in the devices in the embodiments can be adaptively changed and arranged in one or more devices different from those of the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition, they can be divided into multiple sub-modules or sub-units or sub-components. Except that at least some of such features and / or processes or units are mutually exclusive, any combination can be adopted to combine all the features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all the processes or units of any method or device so disclosed. Unless otherwise explicitly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) can be replaced by an alternative feature that provides the same, equivalent, or similar purpose.

[0181] In addition, those skilled in the art can understand that although some of the embodiments herein include certain features included in other embodiments rather than other features, the combination of the features of different embodiments means that it is within the scope of this application and forms different embodiments. For example, in the following claims, any one of the claimed embodiments can be used in any combination.

[0182] Each component embodiment of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present application. The present application can also be implemented as a device or apparatus program (such as a computer program and a computer program product) for executing part or all of the methods described herein. Such a program for implementing the present application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0183] It should be noted that the above embodiments illustrate the present application rather than limit the present application, and those skilled in the art can design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in the claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present application can be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim listing several devices, several of these devices may be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not denote any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A method for detecting the quality of service nodes, characterized in that Including: Identifying candidate data streams with abnormal playback, and calculating the original stream packet loss rate and transcoded stream packet loss rate of the candidate data streams; If the original stream packet loss rate is less than a first threshold and the transcoded stream packet loss rate is greater than a second threshold, determining the candidate data stream as a node abnormal data stream, and calculating the transcoded stream output-input fluctuation ratio of the node abnormal data stream; If the transcoded stream output-input fluctuation ratio is less than a third threshold, determining the push stream node of the node abnormal data stream as a quality abnormal node; If the output-input fluctuation ratio of the transcoded stream is greater than or equal to the third threshold, determining the transcoding node of the node abnormal data stream as a quality abnormal node.

2. The method according to claim 1, characterized in that, The identifying candidate data streams with abnormal playback includes: Identifying candidate data streams with abnormal playback according to barrage data and / or abnormal reporting data.

3. The method according to claim 1 or 2, characterized in that, The calculating the original stream packet loss rate and transcoded stream packet loss rate of the candidate data streams includes: Calculating the original stream packet loss rate of the candidate data stream according to the input end monitoring data of the push stream node of the candidate data stream within a first historical period; And / or, calculating the transcoded stream packet loss rate of the candidate data stream according to the output end monitoring data of the transcoding node where the transcoded stream of the candidate data stream is located within the first historical period.

4. The method according to claim 3, characterized in that, The calculating the original stream packet loss rate of the candidate data stream according to the input end monitoring data of the push stream node of the candidate data stream within a first historical period includes: Obtaining the total number of first original stream frames received by the push stream node at a first moment and the total number of second original stream frames received at a second moment; calculating the difference in the total number of original stream frames between the second original stream total frames and the first original stream total frames; calculating the original stream packet loss rate according to the difference in the total number of original stream frames and the set value of the number of original stream frames per second; And / or, the calculating the transcoded stream packet loss rate of the candidate data stream according to the output end monitoring data of the transcoding node where the transcoded stream of the candidate data stream is located within the first historical period includes: For any transcoded stream, obtaining the total number of first transcoded stream frames output by the transcoding node where the transcoded stream is located at a first moment and the total number of second transcoded stream frames output at a second moment; calculating the difference in the total number of transcoded stream frames between the second transcoded stream total frames and the first transcoded stream total frames; calculating the transcoded stream packet loss rate corresponding to the transcoded stream according to the difference in the total number of transcoded stream frames and the set value of the number of transcoded stream frames per second of the transcoded stream; Wherein, the first moment is the start moment of the first historical period, and the second moment is the end moment of the first historical period.

5. The method according to any one of claims 1-4, characterized in that The calculating the transcoded stream output-input fluctuation ratio of the node abnormal data stream includes: For any transcoded stream of the node abnormal data stream, calculating the transcoded stream input fluctuation value corresponding to the transcoded stream according to the number of original stream frames per second at the receiving end of the transcoding node where the transcoded stream is located within a second historical period; calculating the transcoded stream output fluctuation value corresponding to the transcoded stream according to the number of transcoded stream frames per second at the output end of the transcoding node where the transcoded stream is located within the second historical period; Taking the ratio of the transcoded stream output fluctuation value and the transcoded stream input fluctuation value corresponding to the transcoded stream as the transcoded stream output-input fluctuation ratio of the transcoded stream.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: Upon receiving the misjudgment feedback data of the abnormal data stream of the node, lower the first threshold and raise the second threshold; And / or, upon receiving the missed judgment feedback data of the abnormal data stream of the node, raise the first threshold and lower the second threshold.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: Upon receiving the misjudgment feedback data of the pushing node, lower the third threshold; Upon receiving the misjudgment feedback data of the transcoding node, raise the third threshold.

8. A service node quality detection device, characterized in that, It includes: An identification module for identifying candidate data streams with abnormal playback; A calculation module for calculating the original stream packet loss rate and the transcoded stream packet loss rate of the candidate data stream; A first detection module for determining that the candidate data stream is an abnormal data stream of the node and calculating the transcoded stream output-input fluctuation ratio of the abnormal data stream of the node if the original stream packet loss rate is less than the first threshold and the transcoded stream packet loss rate is greater than the second threshold; A second detection module for determining that the pushing node of the abnormal data stream of the node is a quality abnormal node if the transcoded stream output-input fluctuation ratio is less than the third threshold; and determining that the transcoding node of the abnormal data stream of the node is a quality abnormal node if the output-input fluctuation ratio of the transcoded stream is greater than or equal to the third threshold.

9. A computing device, characterized in that, It includes: A processor, a memory, a communication interface, and a communication bus. The processor, the memory, and the communication interface complete communication with each other through the communication bus; The memory is used to store at least one executable instruction, and the executable instruction causes the processor to perform the operations corresponding to the service node quality detection method according to any one of claims 1-7.

10. A computer storage medium, characterized in that, At least one executable instruction is stored in the storage medium, and the executable instruction causes the processor to perform the operations corresponding to the service node quality detection method according to any one of claims 1-7.

11. A computer program product, characterized in that, It includes at least one executable instruction, and the executable instruction causes the processor to perform the operations corresponding to the service node quality detection method according to any one of claims 1-7.

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