Network transmission quality detection method and device, equipment and storage medium

Abstract

The invention provides a network transmission quality detection method and device, equipment and a storage medium, and the method comprises the steps: obtaining a to-be-detected multimedia stream, and determining a target measurement type included in the to-be-detected multimedia stream; the target measurement type is a message type used for network transmission quality detection; determining a target association type corresponding to the target measurement type according to a first target message corresponding to the target measurement type; and determining target quality data of the to-be-detected multimedia stream according to a target calculation mode corresponding to the target association type and a second target message corresponding to the target association type. According to the invention, the network equipment can achieve the effective detection of the transmission quality of the multimedia stream network, and improves the success rate and accuracy of the detection of the transmission quality of the network.

Description

Technical Field

[0001] This application relates to the field of network communication technologies, and in particular, to a method, apparatus, device, and storage medium for detecting network transmission quality. Background Art

[0002] With the continuous development of Internet technologies, the transmission of data streams has become increasingly common, such as the transmission of multimedia streams in audio and video applications. During the transmission of data streams, in order to improve the user's viewing experience, an effective and accurate network quality transmission method is urgently needed in related technologies. Summary of the Invention

[0003] This application provides a method, apparatus, device, and storage medium for detecting network transmission quality, which can effectively detect the network transmission quality of multimedia streams and improve the accuracy of detecting the network transmission quality of multimedia streams.

[0004] In a first aspect, an embodiment of this application provides a method for detecting network transmission quality, including:

[0005] Obtain a multimedia stream to be detected, and determine a target metric type included in the multimedia stream to be detected; the target metric type is a packet type used for detecting network transmission quality;

[0006] Determine a target association type corresponding to the target metric type according to the target packet corresponding to the target metric type;

[0007] Determine target quality data of the multimedia stream to be detected according to the target association type and the target packet.

[0008] In a possible implementation manner, the obtaining a multimedia stream to be detected and determining a target metric type included in the multimedia stream to be detected includes:

[0009] Receive packet data of the multimedia stream to be detected through a preset classifier; the preset classifier includes a receiver classifier and a sender classifier;

[0010] In a case where the type of the packet data does not match a preset type in the preset classifier, create a first category in the preset classifier;

[0011] In a case where the number of the first categories meets a first preset condition, generate a secondary classifier in the preset classifier;

[0012] Create a second category in the secondary classifier according to first packet data corresponding to each of the first categories, and determine second packet data included in the second category;

[0013] Determine the target marker byte corresponding to the second category according to each second message data in the second category, and determine the target metric type in the second category according to the target marker byte.

[0014] In the embodiments of the present application, based on a preset classifier and a secondary classifier, a network device can comprehensively and accurately classify message data, determine the target metric type corresponding to the multimedia stream to be detected, and ensure the effectiveness and success rate of network transmission quality detection.

[0015] In a possible implementation manner, when the type of the message data does not match the preset type in the preset classifier, creating a first category in the preset classifier includes:

[0016] Extract the first M bytes of the message data, and compare and match the first M bytes with the first M bytes of the preset message corresponding to the preset type in the preset classifier to obtain a first matching result; where M is a positive integer;

[0017] When the first matching result does not meet the second preset condition, determine that the type of the message data does not match the preset type, and create the first category in the preset classifier.

[0018] In the embodiments of the present application, the network device compares and matches the first M bytes of the message data with the first M bytes of the preset message corresponding to the preset type, and determines whether to create a new first category according to the matching result, which can improve the accuracy of classifying the message data.

[0019] In a possible implementation manner, creating a second category in the secondary classifier according to the first message data corresponding to each first category includes:

[0020] For the first message data corresponding to the first category, pairwise match the first message data to determine the number of consecutive identical bytes with the same value;

[0021] Create a second category in the secondary classifier based on the first message data corresponding to the largest number of consecutive identical bytes.

[0022] In the embodiments of the present application, the network device creates a second category in the secondary classifier according to the number of consecutive identical bytes with the same value, which can further refine the classification of the first category and further improve the accuracy of classifying the message data.

[0023] In a possible implementation, determining a target marker byte corresponding to the second category according to each second message data in the second category, and determining a target metric type in the second category according to the target marker byte includes:

[0024] For each of the second categories, continuously receive the second message data based on the secondary classifier;

[0025] Determine the target marker byte according to the difference in the numerical values of each byte between the current second message data and the previous second message data;

[0026] If the number of second message data including the target marker byte meets a third preset condition, determine the second category corresponding to the target marker byte as the target metric type.

[0027] In the embodiments of the present application, the network device determines the target marker byte based on the difference in the numerical values of each byte between the current second message data and the previous adjacent second message data, and verifies the target marker byte, thereby determining the target metric type that can be used for network transmission quality detection, which can improve the accuracy and rationality of determining the target metric type.

[0028] In a possible implementation, the target association type includes an interaction association metric type; determining the target association type corresponding to the target metric type according to the first target message corresponding to the target metric type includes:

[0029] Create a message receiver, and continuously receive the first target message through the message receiver;

[0030] When the number of the first target messages in the message receiver meets a fourth preset condition, determine a first value corresponding to the target marker byte in the first message receiver, and determine a second value corresponding to the target marker byte in the second message receiver;

[0031] If the number of the first target messages with the same first value and second value meets a fifth preset condition, determine the target association type corresponding to the target metric type as the interaction association metric type.

[0032] In an embodiment of the present application, the network device uses a packet receiver to match and associate the first value of the first target packet in the first target packet in the first packet receiver with the second value of the second target packet in the second target packet in the second packet receiver. If the number of first target packets with the same first value and second value meets the fifth preset condition, the network device can determine that the target metric type is an interaction correlation metric type, and subsequently, the target calculation method corresponding to the interaction correlation metric type can be used for network transmission quality detection, which can ensure the effectiveness and accuracy of network transmission quality detection.

[0033] In a possible implementation manner, the packet receiver includes a first receiver and a second packet receiver; the method further includes:

[0034] When the number of packets of the first target packet in the first packet receiver meets the fourth preset condition and the number of packets of the first target packet in the second packet receiver meets the fourth preset condition, it is determined that the number of packets of the first target packet in the packet receiver meets the fourth preset condition.

[0035] In an embodiment of the present application, the network device creates a first packet receiver and a second packet receiver, and performs verification of the interaction correlation metric type when the number of packets in both meets the fourth preset condition, which can improve the accuracy of determining the correlation type.

[0036] In a possible implementation manner, the determining the target quality data of the multimedia stream to be detected according to the target calculation method corresponding to the target correlation type and the second target packet corresponding to the target correlation type includes:

[0037] Determine the first received packet and the second received packet corresponding to the interaction correlation metric type;

[0038] Determine the target quality data according to the matching relationship of the target marker bytes in the first received packet and the second received packet.

[0039] In an embodiment of the present application, for the interaction correlation metric type, the network device can obtain the first received packet and the second received packet, and determine the target quality data such as the delay and packet loss of the multimedia stream to be detected according to the matching relationship of the target marker bytes of the two, which can effectively and accurately detect the network transmission quality.

[0040] In a possible implementation manner, the target correlation type includes a time correlation metric type; the determining the target correlation type corresponding to the target metric type according to the first target packet corresponding to the target metric type includes:

[0041] Create a message receiver and continuously receive the first target message through the message receiver;

[0042] When the number of messages of the first target message in the message receiver meets the fourth preset condition, determine the interval time series corresponding to the first target message;

[0043] When the interval time series meets the sixth preset condition, determine that the target association type corresponding to the target metric type is the time - associated metric type, and determine the target interval time corresponding to the target association type based on the interval time series.

[0044] In the embodiments of the present application, the network device determines that the target metric type is the time - associated metric type according to the interval time series of the first target message received by the message receiver, and when the interval time series meets the sixth preset condition, the subsequent target quality data can be determined based on the target calculation method corresponding to the time - associated metric type, which can improve the accuracy and effectiveness of network transmission quality detection.

[0045] In a possible implementation manner, the message receiver includes a third message receiver and a fourth message receiver; the method further includes:

[0046] When the number of messages of the first target message in the third message receiver meets the fourth preset condition, or the number of messages of the first target message in the fourth message receiver meets the fourth preset condition, determine that the number of messages of the first target message in the message receiver meets the fourth preset condition.

[0047] In the embodiments of the present application, the network device creates a third message receiver and a fourth message receiver, and verifies the time - interaction metric type when the number of messages in any one of them meets the fourth preset condition, which can improve the accuracy of metric type determination.

[0048] In a possible implementation manner, the determining the target quality data of the multimedia stream to be detected according to the target calculation method corresponding to the target association type and the second target message corresponding to the target association type includes:

[0049] Determine the first reception time of the second target message corresponding to the time - associated metric type and the second reception time of the previous second target message corresponding to the second target message;

[0050] Determine the target quality data according to the first reception time, the second reception time, and the target interval time.

[0051] In the embodiments of the present application, for the time correlation type, the network device can determine target quality data such as the delay of the multimedia stream to be detected based on the reception time of adjacent second target packets and the target interval time, which can improve the accuracy and effectiveness of network transmission quality detection.

[0052] In a possible implementation manner, the multimedia stream to be detected is a multimedia stream transmitted based on the User Datagram Protocol (UDP).

[0053] In the embodiments of the present application, for the multimedia stream of UDP packets, the network device can implement network transmission quality detection, improving the success rate and accuracy of network transmission quality detection.

[0054] In a second aspect, the embodiments of the present application provide a network transmission quality detection device, including:

[0055] An obtaining module, configured to obtain a multimedia stream to be detected and determine a target metric type included in the multimedia stream to be detected; the target metric type is a packet type used for network transmission quality detection.

[0056] A first determination module, configured to determine a target correlation type corresponding to the target metric type according to a first target packet corresponding to the target metric type.

[0057] A second determination module, configured to determine target quality data of the multimedia stream to be detected according to a target calculation method corresponding to the target correlation type and a second target packet corresponding to the target correlation type.

[0058] In a possible implementation manner, the obtaining module is specifically configured to:

[0059] Receive packet data of the multimedia stream to be detected through a preset classifier; the preset classifier includes a receiver classifier and a sender classifier.

[0060] In a case where the type of the packet data does not match a preset type in the preset classifier, create a first category in the preset classifier.

[0061] In a case where the number of the first categories meets a first preset condition, generate a secondary classifier in the preset classifier.

[0062] Create a second category in the secondary classifier according to first packet data corresponding to each of the first categories, and determine second packet data included in the second category.

[0063] Determine a target marker byte corresponding to the second category according to each second packet data in the second category, and determine a target metric type in the second category according to the target marker byte.

[0064] In a possible implementation, the obtaining module is specifically configured to:

[0065] Extract the first M bytes of the message data, and compare and match the first M bytes with the first M bytes of a preset message corresponding to a preset type in the preset classifier to obtain a first matching result; where M is a positive integer;

[0066] In the case where the first matching result does not meet the second preset condition, determine that the type of the message data does not match the preset type, and create the first category in the preset classifier.

[0067] In a possible implementation, the obtaining module is specifically configured to:

[0068] Pairwise match the first message data corresponding to the first category, and determine the number of consecutive identical bytes with the same value;

[0069] Based on the first message data corresponding to the largest number of consecutive identical bytes, create a second category in the secondary classifier.

[0070] In a possible implementation, the obtaining module is specifically configured to:

[0071] For each second category, continuously receive the second message data based on the secondary classifier;

[0072] Determine a target marker byte according to the difference in the values of each byte between the current second message data and the previous second message data;

[0073] If the number of second message data including the target marker byte meets the third preset condition, determine the second category corresponding to the target marker byte as the target metric type.

[0074] In a possible implementation, the target association type includes an interaction association metric type; the first determining module is specifically configured to:

[0075] Create a message receiver, and continuously receive the first target message through the message receiver;

[0076] In the case where the number of messages of the first target message in the message receiver meets the fourth preset condition, determine a first value corresponding to the target marker byte in the first message receiver, and determine a second value corresponding to the target marker byte in the second message receiver;

[0077] If the number of first target messages where the first value is the same as the second value meets the fifth preset condition, determine that the target association type corresponding to the target metric type is an interactive association metric type.

[0078] In a possible implementation, the message receiver includes a first message receiver and a second message receiver; the first determination module is specifically configured to:

[0079] When the number of first target messages in the first message receiver meets the fourth preset condition and the number of first target messages in the second message receiver meets the fourth preset condition, determine that the number of first target messages in the message receiver meets the fourth preset condition.

[0080] In a possible implementation, the second determination module is specifically configured to:

[0081] Determine the first received message and the second received message corresponding to the interactive association metric type;

[0082] Determine the target quality data according to the matching relationship of target marker bytes in the first received message and the second received message.

[0083] In a possible implementation, the target association type includes a time association metric type; the first determination module is specifically configured to:

[0084] Create a message receiver and continuously receive the first target message through the message receiver;

[0085] When the number of first target messages in the message receiver meets the fourth preset condition, determine the interval time sequence corresponding to the first target message;

[0086] When the interval time sequence meets the sixth preset condition, determine that the target association type corresponding to the target metric type is a time association metric type, and determine the target interval time corresponding to the target association type based on the interval time sequence.

[0087] In a possible implementation, the message receiver includes a third message receiver and a fourth message receiver; the first determination module is specifically configured to:

[0088] When the number of first target messages in the third message receiver meets the fourth preset condition, or the number of first target messages in the fourth message receiver meets the fourth preset condition, determine that the number of first target messages in the message receiver meets the fourth preset condition.

[0089] In a possible implementation manner, the second determination module is specifically configured to:

[0090] Determine a first reception time of a second target message corresponding to the time correlation metric type and a second reception time of the previous second target message corresponding to the second target message;

[0091] Determine the target quality data according to the first reception time, the second reception time, and the target interval time.

[0092] In a possible implementation manner, the multimedia stream to be detected is a multimedia stream transmitted based on the User Datagram Protocol (UDP).

[0093] In a third aspect, an embodiment of the present application provides a network transmission quality detection device, including: a processor and a memory;

[0094] The memory stores computer-executable instructions;

[0095] The processor executes the computer-executable instructions stored in the memory to implement the network transmission quality detection method according to any one of the first aspects.

[0096] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed, they are used to implement the network transmission quality detection method according to any one of the first aspects.

[0097] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, and when the computer program is executed, it implements the network transmission quality detection method according to any one of the first aspects.

[0098] In a sixth aspect, an embodiment of the present application provides a chip, on which a computer program is stored, and when the computer program is executed by the chip, it implements the network transmission quality detection method according to any one of the first aspects.

[0099] The network transmission quality detection method, device, equipment, and storage medium provided by the embodiments of the present application obtain a multimedia stream to be detected and determine the target metric type included in the multimedia stream to be detected; the target metric type is the packet type used for network transmission quality detection; according to the first target packet corresponding to the target metric type, determine the target association type corresponding to the target metric type; according to the target calculation method corresponding to the target association type and the second target packet corresponding to the target association type, determine the target quality data of the multimedia stream to be detected. In the embodiments of the present application, the network device obtains the multimedia stream to be detected, determines the target metric type capable of performing network transmission quality detection in the multimedia stream to be detected, and then can further determine the target association type corresponding to the target metric type according to the first target packet corresponding to each target metric type; then the network device performs detection calculations on the second target packet corresponding to the target association type according to the target calculation method corresponding to each target association type to obtain the target quality data of the multimedia stream to be detected, which can effectively detect the network transmission quality of the multimedia stream and improve the success rate and accuracy of network transmission quality detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] Figure 1 It is a schematic diagram of an application scenario provided by the embodiments of the present application;

[0101] Figure 2 It is a schematic flowchart of a network transmission quality detection method provided by the embodiments of the present application;

[0102] Figure 3 It is a schematic flowchart of another network transmission quality detection method provided by the embodiments of the present application;

[0103] Figure 4 It is a modular schematic diagram of a network transmission quality detection method provided by the embodiments of the present application;

[0104] Figure 5 It is a schematic diagram for determining a target metric type provided by the embodiments of the present application;

[0105] Figure 6 It is a schematic diagram for determining an interaction correlation metric type provided by the embodiments of the present application;

[0106] Figure 7 It is a schematic diagram for determining a time correlation metric type provided by the embodiments of the present application;

[0107] Figure 8 It is a schematic diagram of the process for determining a target association type provided by the embodiments of the present application;

[0108] Figure 9 It is a schematic diagram for calculating the target quality data of an interaction correlation metric type provided by the embodiments of the present application;

[0109] Figure 10 Schematic diagram for calculating target quality data of another type of interaction correlation metric provided by an embodiment of the present application;

[0110] Figure 11 Schematic diagram for calculating target quality data of a type of time correlation metric provided by an embodiment of the present application;

[0111] Figure 12 Schematic diagram of interaction shared by a type of multi-device metric provided by an embodiment of the present application;

[0112] Figure 13 Schematic diagram of the structure of a network transmission quality detection device provided by an embodiment of the present application;

[0113] Figure 14 Schematic diagram of the structure of a network transmission quality detection device provided by an embodiment of the present application. Detailed implementation manners

[0114] To enable those skilled in the art to better understand the technical solutions of the present application, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments and drawings described herein are only used to explain the present application, rather than limiting the present application. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant laws, regulations, and standards, and corresponding operation entrances are provided for users to choose to authorize or refuse.

[0115] With the continuous development of the Internet and communication technologies, the transmission of data streams has become increasingly common. For example, audio and video communication applications usually require multimedia stream transmission based on the network. During the transmission of data streams based on the network, in order to ensure the user experience, it is usually necessary to detect the network transmission quality in related technologies.

[0116] Specifically, the network transmission quality may include indicators such as latency and packet loss. Among them, latency refers to the time required for a data packet to be sent and received. For real-time applications, such as voice calls, video conferences, and online games, a high latency will cause delays and lags, affecting the fluency and immediacy of real-time communication. Packet loss refers to the data packets that fail to be successfully transmitted to the receiving party during the data transmission process. In real-time streaming media applications, a high packet loss rate will cause video lags, audio interruptions, or distortions, reducing the user experience.

[0117] Most audio and video applications perform data transmission based on the User Datagram Protocol (UDP). When sending data, UDP does not establish a connection, nor does it perform packet confirmation and retransmission. Therefore, in the case of an unstable or congested network environment, UDP packets may be lost, out of order, or arrive at the destination repeatedly.

[0118] In the related art, developers of applications usually implement a reliable transmission mechanism through a custom protocol at the application layer, such as packet sequence number confirmation, retransmission, etc., to ensure the reliable transmission and reception of data. At the same time, the network transmission quality between the two communication parties can be detected based on this custom protocol. However, developers usually do not disclose the custom protocol. Therefore, it is difficult for various devices on the network link, such as routers and gateways, to detect the network transmission quality of audio and video communication based on the custom protocol, and the detection quality will also vary.

[0119] In addition, in the related art, the Internet Control Message Protocol (ICMP) can also be used for network transmission quality detection, that is, the ICMP detection method. Specifically, the network device actively sends multiple ICMP detection packets per second according to the Internet Protocol (IP) addresses of the two communication parties, estimates the delay based on the average time difference between the request and the response from the other party, and approximately estimates the packet loss situation based on the number of lost packets. In the related art, the method for detecting network transmission quality based on the ICMP detection method, on the one hand, because the detection packets actively constructed by the network device may have a different forwarding path in the network from the communication traffic path between the two parties, resulting in inaccurate detection; on the other hand, security devices in the network or security software is usually installed on the terminals of the two communication parties, and the ICMP detection is regarded as an attack and discarded, resulting in detection failure.

[0120] It can be seen that the success rate of the network transmission quality detection method in the related art is low, the detection difficulty is large, and the detection accuracy is not high.

[0121] To solve the above problems, the embodiments of the present application provide a network transmission instruction detection method, device, equipment, and storage medium. The network device obtains the multimedia stream to be detected, determines the target metric type capable of performing network transmission quality detection in the multimedia stream to be detected, and then can further determine the target association type corresponding to the target metric type according to the first target packet corresponding to each target metric type; then the network device performs detection calculation on the second target packet corresponding to the target association type according to the target calculation method corresponding to each target association type, and obtains the target quality data of the multimedia stream to be detected, which can effectively detect the network transmission quality of the multimedia stream and improve the success rate and accuracy of the network transmission quality detection.

[0122] Figure 1 This is a schematic diagram of the application scenario provided by the embodiments of the present application. As Figure 1 shown, in the related art, when performing network transmission quality detection, it is usually implemented based on a custom protocol or the ICMP detection method. Since it is difficult to obtain the custom protocol, the network transmission quality detection is difficult, and there are also problems of low success rate and low accuracy in ICMP detection.

[0123] In the embodiments of the present application, the network device classifies the packets in the multimedia stream to be detected, determines the target metric type that can be used for network transmission quality detection, and then determines the target association type according to the first target packet corresponding to the target metric type. After that, the network device can perform detection and calculation on the second target packet corresponding to the target association type according to the calculation method corresponding to the target association type to obtain the target quality data of the multimedia stream to be detected, which can effectively and accurately detect the network transmission quality.

[0124] The following details the solution shown in the present application through specific embodiments. It should be noted that the following several embodiments can exist independently or be combined with each other. For the same or similar content, it will not be repeated in different embodiments.

[0125] Figure 2 This is a flowchart of a network transmission quality detection method provided by the embodiments of the present application. Please refer to Figure 2 , the network transmission quality detection method may include:

[0126] S201. Obtain the multimedia stream to be detected and determine the target metric type included in the multimedia stream to be detected; the target metric type is the packet type used for network transmission quality detection.

[0127] The execution subject of the embodiments of the present application can be a network device or a network transmission quality detection device set in the network device. The network transmission quality detection device can be implemented by software or by a combination of software and hardware. For the convenience of understanding, hereinafter, the execution subject is taken as an example of a network device for description. The network device can specifically refer to the device corresponding to any node on the network path of the multimedia stream communication parties, such as the terminal devices of the communication parties, network forwarding devices, etc. The network forwarding devices can include routers, gateways, etc., and can be flexibly set according to actual needs. The specific type of the network device is not limited in the embodiments of the present application.

[0128] In the embodiments of the present application, the multimedia stream to be detected may refer to a multimedia stream that needs to be detected for network transmission quality. For example, it may refer to the audio and video stream of an online meeting application, etc. The target metric type may refer to the type of packets used for network transmission quality detection in the multimedia stream to be detected. The packet data corresponding to the target metric type may have an increasing relationship at specific byte positions and can be used to characterize the continuity of the packet data.

[0129] Specifically, in this step, after the network device obtains the multimedia stream to be detected, it can classify and identify the packets based on the packet characteristics of each packet in the multimedia stream to be detected through a preset classification algorithm, and obtain the type of packets that can be used for network transmission quality detection.

[0130] S202. Determine the target association type corresponding to the target metric type according to the first target packets corresponding to the target metric type.

[0131] In the embodiments of the present application, the first target packets may refer to the packet data included in each target metric type in the multimedia stream to be detected. One, two, or more target metric types may be included in the same multimedia stream to be detected, and each target metric type corresponds to multiple first target packets. The target association type may refer to the specific associated packet type for network transmission quality detection calculation corresponding to the target metric type. Different target association types may correspond to different target calculation methods, and the network device can subsequently implement the detection calculation of network transmission quality according to the target calculation method corresponding to the target association type.

[0132] Exemplarily, the target association type may specifically include an interactive association metric type and a time association metric type, etc. Among them, the interactive association metric type may refer to a metric type with an association relationship between the sender and the receiver in both directions. Subsequently, network transmission quality data such as the delay and packet loss of the multimedia stream to be detected can be determined based on the interactive association metric type. The time association metric type may refer to a metric type that has no association relationship between the sender and the receiver, but has a specific time association in a single-directional transmission or a single-directional reception (such as sending at a specific time interval or receiving at a specific time interval, etc.). Subsequently, the delay of the multimedia stream to be detected can be determined based on the time association metric type. Of course, the target association type may also include other types, and the embodiments of the present application do not limit this.

[0133] In this step, after determining the target metric type in the multimedia stream to be detected, the network device can further identify and determine the target association type corresponding to the target metric type based on detection algorithms such as the interaction correlation detection algorithm and the time correlation detection algorithm. Then, the network device can perform subsequent detection calculations according to the target calculation method corresponding to the target association type.

[0134] S203. Determine the target quality data of the multimedia stream to be detected according to the target calculation method corresponding to the target association type and the second target packet corresponding to the target association type.

[0135] In the embodiments of the present application, the second target packet may refer to the packet data corresponding to the target association type and can be used for calculating network transmission quality data such as delay data and packet loss data. Exemplarily, for the interaction correlation metric type, the second target packet may refer to the first received packet corresponding to the sender and the second received packet corresponding to the receiver received by the network device; for the time correlation metric type, the second target packet may refer to the received packet in one direction of the receiver or the sent packet in one direction of the sender, etc.

[0136] It should be noted that since the first target packet is the packet data corresponding to the target metric type, and the second target packet is the packet data corresponding to the target association type, and the target association type is determined based on the target metric type, the second target packet is a part of the packet data in the first target packet and is used to calculate the network transmission quality according to the target calculation method corresponding to the target metric type. That is, the first target packet includes the second target packet.

[0137] The target quality data may refer to the network transmission quality data of the multimedia stream to be detected, which may include delay data, packet loss data, etc. Of course, it may also include other types of quality data, which are not limited in the embodiments of the present application.

[0138] In the embodiments of the present application, after determining the target association type corresponding to the target metric type included in the multimedia stream to be detected, the network device can calculate the network transmission quality based on the target calculation method corresponding to the target association type and the target packet, and obtain the target quality data corresponding to the multimedia stream to be detected. Specifically, the network device can determine the delay data based on the sending time and receiving time of the second target packet, or determine the packet loss data based on the matching relationship of the numerical values at specific byte positions. Of course, it may also include other calculation methods, which can be flexibly set based on actual requirements and are not limited in the embodiments of the present application.

[0139] The network transmission quality detection method provided by the embodiment of the present application, where a network device obtains a multimedia stream to be detected and determines a target metric type included in the multimedia stream to be detected; the target metric type is a message type used for network transmission quality detection; according to a first target message corresponding to the target metric type, a target association type corresponding to the target metric type is determined; according to a target calculation method corresponding to the target association type and a second target message corresponding to the target association type, target quality data of the multimedia stream to be detected is determined. In the present application, the network device obtains the multimedia stream to be detected, determines a target metric type capable of performing network transmission quality detection in the multimedia stream to be detected, and then can further determine a target association type corresponding to the target metric type according to a first target message corresponding to each target metric type; then the network device performs detection calculation on the second target message corresponding to the target association type according to the target calculation method corresponding to each target association type to obtain the target quality data of the multimedia stream to be detected, which can effectively detect the network transmission quality of the multimedia stream and improve the success rate and accuracy of network transmission quality detection.

[0140] Based on the above embodiment, Figure 3 is a schematic flowchart of another network transmission quality detection method provided by the embodiment of the present application. Please refer to Figure 3 , and this network transmission quality detection method may include:

[0141] S301. Obtain an alternative data stream and determine identification information corresponding to the alternative data stream; in the case where the identification information matches the target identification information, determine the alternative data stream as the multimedia stream to be detected.

[0142] In the embodiment of the present application, the alternative data stream may refer to application traffic received by the network device. The identification information may include an identification corresponding to the alternative data stream, specifically including source IP, destination IP, source port, destination port, and transmission protocol, etc. Of course, it may also include other types of identifications, and the embodiment of the present application does not limit this. The target identification information may refer to specific identification information corresponding to the multimedia stream of the target application program, specifically including source IP, destination IP, source port, destination port, and transmission protocol, etc. The target application program may refer to an audio and video communication application program, etc.

[0143] In this step, after receiving the alternative data stream, the network device may determine the identification information corresponding to the alternative data stream, and then perform a matching judgment on the identification information and the target identification information. In the case where the identification information matches the target identification information, the network device may determine that the alternative data stream is the multimedia stream to be detected corresponding to the target application program. In this way, the network device determines the multimedia stream to be detected by identifying and judging the received alternative data stream, which can improve the accuracy of subsequent network transmission quality detection and avoid waste of computing resources.

[0144] In a possible implementation, the multimedia stream to be detected is a multimedia stream transmitted based on the User Datagram Protocol (UDP).

[0145] In the embodiments of the present application, the multimedia stream to be detected may refer to a multimedia stream transmitted based on UDP. The network transmission quality detection method in the present application may refer to the transmission quality detection process for UDP audio and video data streams, which can classify the packets of UDP audio and video communication data streams and detect the transmission quality, improving the success rate and accuracy of UDP multimedia stream transmission quality detection. Of course, the multimedia stream to be detected may also be audio and video transmitted based on other protocols, and can be specifically configured flexibly according to actual needs. The embodiments of the present application do not limit this.

[0146] Exemplarily, Figure 4 is a modular schematic diagram of a network transmission quality detection method provided by the embodiments of the present application. As Figure 4 shown, the network device 40 may include an application identification module 401, a metric packet type discovery module 402, a network quality detection module 403, and a database 404. Among them, the application identification module 401 receives the alternative data stream (i.e., application traffic) and determines the identification information of the alternative data stream. If the identification information matches the target identification information, the application identification module 401 may determine that the alternative data stream is the multimedia stream to be detected for a UDP audio and video communication application, such as the audio and video stream of an online meeting, etc. At this time, the application identification module 401 may introduce the multimedia stream to be detected into the metric packet type discovery module 402, and the metric packet type discovery module 402 determines the target metric type in the multimedia stream to be detected and further determines the target association type corresponding to the target metric type.

[0147] After that, the metric packet type discovery module 402 may send the target association type to the network quality detection module 403, and the network quality detection module 403 calculates the target quality data of the multimedia stream to be detected based on the target association type and its corresponding target packets. Finally, the network quality detection module 403 may store the target quality data of the multimedia stream to be detected in the database 404 for subsequent other functions. Of course, Figure 4 the module settings in

[0148] S302, receiving the packet data of the multimedia stream to be detected through a preset classifier; the preset classifier includes a receiver classifier and a sender classifier.

[0149] In the embodiments of the present application, the message data may refer to the message data included in the multimedia stream to be detected. The preset classifier may refer to a classifier set in advance, and the preset classifier may be created based on a preset classification algorithm (such as a deep learning algorithm, a neural network algorithm, etc.). The embodiments of the present application do not limit the specific algorithm type of the preset classifier. It should be noted that during the network transmission of the multimedia stream, since the multimedia stream is transmitted bidirectionally, for example, in scenarios such as video conferencing, the sender and the receiver are not fixed. Therefore, the preset classifier in this step may include a sender classifier and a receiver classifier, so as to realize the bidirectional classification and recognition of the message data.

[0150] In this step, the network device may create a preset classifier based on a preset classification algorithm, and the preset classifier may include a receiver classifier and a sender classifier. It should be noted that the subsequent classification steps of the receiver classifier and the sender classifier for the message data of the multimedia stream to be detected are the same, and will not be distinguished and described later. The network device may continuously receive the message data of the multimedia stream to be detected through the preset classifier, and then may determine the target metric type included in the multimedia stream to be detected based on the message data.

[0151] S303. In the case where the type of the message data does not match the preset type in the preset classifier, create a first category in the preset classifier.

[0152] In the embodiments of the present application, the preset type may refer to the message type that already exists in the preset classifier. In the initial state, the preset type does not exist in the preset classifier. At this time, the preset classifier may use the type of the first received message data as the preset type, and subsequent message data may be compared and matched with the first message data. The first category may refer to the category of the message data created in the preset classifier.

[0153] In this step, after receiving the message data, the preset classifier may compare and match the type of the message data with the preset type already existing in the preset classifier. If they do not match, the network device may determine through the preset classifier that the type of the message data and the preset type do not belong to the same message type, and may create a new first category in the preset classifier to represent the message type of the message data. The specific matching process of the preset classifier may refer to the matching of the numerical characteristics of each byte of the message data, etc., and the embodiments of the present application do not limit this.

[0154] In a possible implementation manner, step S303 may be implemented through the following steps (1) to (2):

[0155] (1). Extract the first M bytes of the packet data, and compare and match the first M bytes with the first M bytes of the preset packet corresponding to the preset type in the preset classifier to obtain a first matching result; where M is a positive integer.

[0156] In the embodiments of the present application, the M bytes may refer to the first M bytes of the data part in the packet data. For example, the first 20 bytes of the data part (Data) in a UDP packet, etc. The value of M may also be 10, 30, etc., and can be specifically set based on actual requirements and the performance of the network device. The present application does not limit this. The preset packet may refer to the preset packet corresponding to the preset type existing in the preset classifier. The first matching result may refer to the matching result of the first M bytes of the packet data and the first M bytes of the preset packet of the preset type, and may specifically refer to the same number of bytes, etc.

[0157] Specifically, after receiving the packet data through the preset classifier, the network device may extract the first M bytes of the packet data, and then match and compare the first M bytes with the first M bytes of the preset packet corresponding to the preset type to determine the first matching result. Subsequently, based on the first matching result, it can be determined whether the type of the packet data and the preset type belong to the same type of packet.

[0158] (2). In the case where the first matching result does not meet the second preset condition, determine that the type of the packet data does not match the preset type, and create a first category in the preset classifier.

[0159] In the embodiments of the present application, the second preset condition may refer to a pre-set judgment condition for whether the type of the packet data matches the preset type. For example, the second preset condition may be that the number of the same bytes in the first matching result is greater than or equal to 2 bytes, etc. If the first matching result does not meet the second preset condition, the network device may determine that the type of the packet data does not match the preset type, and the packet data belongs to a new type of packet. The network device may create a first category in the preset classifier. In this way, the network device performs comparison and matching on the first M bytes of the packet data through the preset classifier, and then creates a first category based on the new type of packet, which can realize the preliminary classification of the packet data in the multimedia stream to be detected and improve the accuracy of subsequent classification and recognition.

[0160] S304. In the case where the number of the first category meets the first preset condition, generate a secondary classifier in the preset classifier.

[0161] In the embodiments of the present application, the first preset condition may refer to the creation condition of the secondary classifier. Specifically, it may refer to that the number of the first category reaches a preset number threshold or there is no new first category in the preset number of message data. For example, the first preset condition may refer to that the number of the first category reaches 30 or there is no new first category in 100 message data. Of course, the first preset condition may also be other judgment conditions, and the embodiments of the present application do not limit this. The secondary classifier may refer to a classifier that performs secondary classification on message data. After the number of the first category in the preset classifier meets the first preset condition, the network device may generate a secondary classifier to perform further refined classification on multiple first categories, improving the accuracy of message data classification and recognition.

[0162] Specifically, the network device continuously receives message data through the preset classifier, and creates a first category in the preset classifier when the type of the message data does not match the preset type. As the number of the first category gradually increases, when the number of the first category meets the first preset condition, the network device may create or generate a secondary classifier in the preset classifier, and subsequently perform secondary refined classification on the message data based on the secondary classifier to improve the accuracy of classification and recognition.

[0163] S305. Create a second category in the secondary classifier according to the first message data corresponding to each first category, and determine the second message data included in the second category.

[0164] In the embodiments of the present application, the first message data may refer to the message data correspondingly included in each first category. The second category may refer to a secondary classification category for the first message data corresponding to the first category, and the first message data correspondingly included in each second category may be used as the second message data. Specifically, after the network device generates a secondary classifier through the preset classifier, it may perform secondary classification on the first message data in the first category according to features such as the same byte count to obtain the second category, and each second category correspondingly includes the second message data under this category.

[0165] In a possible implementation manner, the specific creation process of the second category in S305 may be implemented through the following steps (3) to (4):

[0166] (3). For the first message data corresponding to the first category, match the first message data pairwise to determine the continuous same byte count with the same value.

[0167] (4). Based on the first message data corresponding to the largest continuous same byte count, create a second category in the secondary classifier.

[0168] In the embodiments of the present application, the number of consecutive identical bytes may refer to the number of consecutive bytes with the same value in two first message data. Specifically, the network device pairwise matches and compares any two first message data in the first category, determines the number of consecutive identical bytes with the same value in the two first message data, and creates a second category in the secondary classifier for the first message data corresponding to the largest number of consecutive identical bytes.

[0169] Exemplarily, if the number of consecutive identical bytes between the first message data A and the first message data B is 4, the number of consecutive identical bytes between the first message data A and the first message data C is 7, and the number of consecutive identical bytes between the first message data A and other first message data is not greater than 7, then the network device can create a second category in the secondary classifier based on the first message data A, the first message data C, and other first message data whose number of consecutive identical bytes with the first message data A is equal to 7. At this time, the first message data A, the first message data C, and other first message data whose number of consecutive identical bytes with the first message data A is equal to 7 can be used as the second message data included in the second category. In this way, the network device classifies and identifies according to the numerical characteristics of each byte of the first message data in the secondary classifier, which can improve the rationality and accuracy of message data classification.

[0170] S306. Determine the target marker byte corresponding to the second category according to each second message data in the second category, and determine the target metric type in the second category according to the target marker byte.

[0171] In the embodiments of the present application, the target marker byte may refer to the byte position with an increasing relationship (or a decreasing relationship, etc.) in the second message data of the second category. Specifically, after the network device creates the second category in the secondary classifier, it can determine whether there is a situation where a certain byte has an increasing data value through the comparison and calculation of the numerical values of each byte of each second message data in the second category. If so, the network device can determine the target marker byte through the secondary classifier, and then verify the target marker byte. If the verification passes, it can be determined that the second message data has an increasing relationship, and the second category corresponding to the target marker byte that passes the verification can be used as the target metric type. In this way, in the embodiments of the present application, the network device performs feature recognition on the second message data of the second category through the secondary classifier, determines the target marker byte, and further determines the target metric type according to the target marker byte, which can accurately discover the numerical characteristics of the message data and improve the accuracy of determining the target metric type.

[0172] In a possible implementation manner, step S306 may be specifically implemented through the following steps (5) to (7):

[0173] (5) For each second category, continuously receive second message data based on the secondary classifier.

[0174] (6) Determine the target marked byte according to the difference between the values of each byte between the current second message data and the previous second message data.

[0175] In the embodiments of the present application, for each second category, the network device can continuously receive second message data through the secondary classifier. Then, through the secondary classifier, the network device can perform a subtraction operation one by one on the values of the bytes at the same positions of the received current second message data and the previous second message data to determine the difference between the current second message data and the previous second message data. If there is a byte position where the difference is 01, then mark the byte position of this second classification to obtain the target marked byte of this second category.

[0176] (7) If the number of second message data including the target marked byte meets the third preset condition, then determine the second category corresponding to the target marked byte as the target metric type.

[0177] In the embodiments of the present application, the third preset condition may refer to the judgment condition of the target metric type, that is, the judgment condition of the credibility of the target marked byte. Specifically, the third preset condition may be that the ratio of the number of times the target marked byte passes the verification to the total number of verifications is greater than 0.9, for example, the ratio of the number of second message data including the target marked byte with an increasing relationship to the total number of second message data is greater than 0.9. Of course, the third preset condition may also be other verification conditions, and the embodiments of the present application do not limit this.

[0178] Specifically, after determining the target marked byte of the second category, since the target marked byte may be accidental, the network device can continuously receive second message data through the secondary classifier to verify the target marked byte. If the number of second message data including the target marked byte meets the third preset condition, for example, among 100 second message data, the number of verified ones is 95. At this time, the network device can determine that the target marked byte passes the verification, and there is an increasing pattern in the position of the target marked byte in the second message data. Then, the second category corresponding to the target marked byte can be used as the target metric type, and subsequent network transmission quality detection can be performed based on this target metric type.

[0179] Exemplarily, Figure 5 FIG. is a schematic diagram for determining a target metric type provided by an embodiment of the present application.

[0180] As Figure 5 shown, the specific determination process of the target metric type is as follows:

[0181] S1. Create preset classifiers (including sender classifier and receiver classifier), and continuously receive the packet data of the multimedia stream to be detected through the preset classifiers.

[0182] S2. Extract the first M (e.g., 20) bytes of the packet data, and perform a byte-by-byte comparison with the first M bytes of the preset packets of the preset types existing in the preset classifier to obtain the first matching result; if the first matching result does not meet the second preset condition, determine that the packet data corresponds to a new packet type, and create a first category in the preset classifier.

[0183] S3. Repeat S2 until the number of the first categories meets the first preset condition (the number of the first categories exceeds 30, or there is no new first category within 100 packet data), and generate a secondary classifier in the preset classifier.

[0184] S4. Perform a maximum same-byte matching pairwise on the first packet data of the first categories in the preset classifier, and create the first packet data corresponding to the maximum number of consecutive same bytes as the second category in the secondary classifier. Each second category can correspond to a secondary classifier.

[0185] S5. Continuously monitor the packets, i.e., the second packet data, based on each secondary classifier, perform a subtraction operation byte by byte on the same-position bytes of the current second packet data and the previously received second packet data. If there is a byte position where the calculation result is 01, mark the byte position of this classification, obtain the target marked byte, and enter S6. If there is no such situation in all secondary classifiers, end the preset classification algorithm. It should be noted that Figure 5 in it, "Y" corresponds to "Yes", and "N" corresponds to "No".

[0186] S6. The secondary classifier continuously monitors the second packet data, verifies the target marked byte, and performs a subtraction operation on the target marked byte positions of the current second packet data and the previously received second packet data. When the calculation result is 01, the verification pass count is incremented by one.

[0187] S7. Continuously verify 100 packets (second packet data), and when the verification pass count / total verification times is greater than or equal to 0.9, it can be determined that the verification of the target marked byte passes and meets the third preset condition. The network device can determine the second category corresponding to the verified target marked byte as the target metric type and output it. If there are still other secondary classifiers to be verified, re-enter S5; otherwise, end the algorithm. When the verification result is less than 0.9 and there are still traceable secondary classifiers, re-enter S5; otherwise, end the algorithm.

[0188] In the embodiments of the present application, the network device performs two-level classification and recognition on the packet data in the multimedia stream to be detected based on a preset classifier and a secondary classifier, and finally determines the target metric type with an increasing index rule, which can accurately identify and classify the packet data in the multimedia stream to be detected. Subsequently, based on the target metric type, accurate detection of the network transmission quality can be achieved. Of course, Figure 5 The above process for determining the target metric type is only an example, and the judgment conditions therein can be flexibly set, or other algorithms can be used for classification and recognition. The embodiments of the present application do not limit this.

[0189] S307. Determine the target association type corresponding to the target metric type according to the first target packet corresponding to the target metric type.

[0190] In the embodiments of the present application, after the network device determines the target metric type in the multimedia stream to be detected, it can further determine the target association type corresponding to the target metric type to determine the target calculation method for the corresponding network transmission quality. Specifically, the network device can perform association determination according to the first target packet corresponding to the target metric type. For example, it can perform identification and verification based on the interactive association detection algorithm and the time association detection algorithm to determine the target association type corresponding to the target metric type. Exemplarily, the interactive association may mean that the sender sends a packet with a target marker byte value of A to the receiver, and the receiver also returns a packet with a target marker byte value of A to the sender; the time association may mean that the sender sends packets to the receiver at a fixed time interval, and the target marker byte values can be A - 1, A, A + 1, etc. in sequence.

[0191] In a possible implementation manner, the target association type includes an interactive association metric type; step S307 can be specifically implemented through the following steps (8) to (10):

[0192] (8). Create a packet receiver and continuously receive the first target packet through the packet receiver.

[0193] In the embodiments of the present application, the packet receiver can be used to receive and cache the first target packet.

[0194] Specifically, when both the sending classifier and the receiving classifier determine the target metric type from the multimedia stream to be detected, there may be an interactive association for this target metric type at this time. The network device can verify based on the interactive association detection algorithm. The network device can create a packet receiver and continuously receive the first target packet corresponding to the target metric type based on the packet receiver.

[0195] In a possible implementation manner, the packet receiver includes a first receiver and a second packet receiver; the method further includes:

[0196] When the number of the first target messages in the first message receiver meets the fourth preset condition and the number of the first target messages in the second message receiver meets the fourth preset condition, it is determined that the number of the first target messages in the message receiver meets the fourth preset condition.

[0197] In the embodiments of the present application, when verifying whether the target metric type is an interactive correlation metric type, the message receivers created by the network device may include a first message receiver and a second message receiver. The first message receiver may refer to a sender target metric type message receiver for receiving the first target messages of the target metric type sent by the sender to the receiver, and the second message receiver may refer to a receiver target metric type message receiver for caching the first target messages of the target metric type returned by the receiver.

[0198] The fourth preset condition may refer to the received upper limit condition of the number of messages. Specifically, it may refer to that the number of messages reaches 30 or no new first target messages are received within 1 minute, etc. The fourth preset condition can be flexibly set based on actual requirements and the performance of the network device such as its memory, and the embodiments of the present application do not limit this.

[0199] Specifically, since the interactive correlation metric type requires interactive correlation verification of the first target messages at both the sender and the receiver ends, when the number of the first target messages in the first message receiver meets the fourth preset condition and the number of the first target messages in the second message receiver meets the fourth preset condition, the network device determines that the number of the first target messages in the message receiver meets the fourth preset condition, which can achieve the interactive correlation verification at both the sender and the receiver ends and improve the accuracy of determining the correlation type.

[0200] (9) When the number of the first target messages in the message receiver meets the fourth preset condition, determine the first value corresponding to the target flag byte in the first message receiver and determine the second value corresponding to the target flag byte in the second message receiver.

[0201] In the embodiments of the present application, the first value may refer to the value corresponding to the target flag byte in the first target message in the first message receiver. The second value may refer to the value corresponding to the target flag byte in the first target message.

[0202] Specifically, after the number of first target messages in the message receiver meets the fourth preset condition, the network device can determine the corresponding numerical values of the first target messages in the sender and receiver message receivers at the target marker byte, namely the first numerical value and the second numerical value. Subsequently, the first numerical value and the second numerical value can be compared and judged to determine whether there is an association relationship between the target metric types in the sender and the receiver.

[0203] (10) If the number of first target messages with the same first numerical value and second numerical value meets the fifth preset condition, determine that the target association type corresponding to the target metric type is an interactive association metric type.

[0204] In the embodiments of the present application, the fifth preset condition may refer to the judgment condition of the interactive association metric type. Specifically, it may refer to that the ratio of the number of first target messages with successful association (the first numerical value and the second numerical value are the same) to the total number of first target messages is greater than 0.8, etc. Specifically, the network device can traverse the first target messages cached in the first message receiver to obtain the first numerical value at the target marker byte position, and traverse in the second message receiver to find the second numerical value with the same value as the first numerical value at the target marker byte position, and finally obtain the number of first target messages with the same first numerical value and second numerical value in the first message receiver and the second message receiver. If the number of first target messages with the same first numerical value and second numerical value meets the fifth preset condition, the network device can determine that the target metric type is an interactive association metric type.

[0205] Exemplarily, Figure 6 is a schematic diagram for determining an interactive association metric type provided by an embodiment of the present application. As Figure 6 shown, the network device creates message receivers, which may specifically include a first message receiver ( Figure 6 the sender classifier A therein) and a second message receiver ( Figure 6 the receiver classifier 1, receiver classifier 2, and receiver classifier 3 therein), and receives the first target messages based on the message receivers. When the number of first target messages cached in each message receiver meets the fourth preset condition, the network device can traverse the first target messages cached in the first message receiver to obtain the first numerical value of the target marker byte, and traverse in the second message receiver to find the first target messages with the same numerical value at the target marker byte position, and finally obtain the number of first target messages with the same first numerical value and second numerical value. As Figure 6As shown in , if the first value in the sender classifier A is the same as the second value in the receiver classifier 3, it can be determined that the two target messages are successfully associated. After that, the network device can determine whether the number of the first target messages whose first value and second value are successfully associated (i.e., the values are the same) meets the fifth preset condition. If it meets, the network device can determine that the target metric type is an interactive association metric type. If it does not meet and there are other receivers to be verified, the above pairwise traversal and comparison process can be continued.

[0206] In the embodiments of the present application, by determining the interactive association relationship of the target metric type between the sender and the receiver, the network device can subsequently calculate the delay data and packet loss data of both parties based on this association relationship according to the corresponding sending messages and receiving messages, and can accurately obtain the network transmission quality data of the multimedia stream to be detected.

[0207] In another possible implementation manner, the target association type includes a time association metric type; step S307 can be specifically implemented through the following steps (11) to (13):

[0208] (11) Create a message receiver and continuously receive the first target messages through the message receiver.

[0209] In the embodiments of the present application, the message receiver can be used to receive and cache the first target messages. After that, a message receiver can be created for each metric type. The network device can continuously receive the first target messages of the target metric type through the message receiver.

[0210] Specifically, when the sender and the receiver fail to fully discover the target metric type from the multimedia stream to be detected, or when the interactive correlation detection algorithm determines that the target metric type is not an interactive association metric type, the network device can identify and determine the target metric type based on the time correlation detection algorithm to determine whether there is a fixed time interval for the metric type in one direction, which can be used to calculate the delay data of the message data, etc. later.

[0211] In a possible implementation manner, the message receiver includes a third message receiver and a fourth message receiver; the method further includes:

[0212] When the number of the first target messages in the third message receiver meets the fourth preset condition, or when the number of the first target messages in the fourth message receiver meets the fourth preset condition, it is determined that the number of the first target messages in the message receiver meets the fourth preset condition.

[0213] In an embodiment of the present application, when verifying whether the target metric type is a time-correlated metric type, the packet receivers created by the network device may include a third packet receiver and a fourth packet receiver, where the third packet receiver may refer to a sender target metric type packet receiver, and the fourth packet receiver may refer to a receiver target metric type packet receiver.

[0214] Specifically, since the time-correlated metric type verifies the first target packets in one direction, there are two cases of one-way sending and one-way receiving. When the number of first target packets in the third packet receiver meets the fourth preset condition, or the number of first target packets in the fourth packet receiver meets the fourth preset condition, the network device can determine that the number of first target packets in the packet receiver meets the fourth preset condition. In this way, subsequent verification of the time-correlated metric type can be performed, improving the accuracy of determining the correlation type.

[0215] (12) When the number of first target packets in the packet receiver meets the fourth preset condition, determine the interval time series corresponding to the first target packets.

[0216] In an embodiment of the present application, the interval time series may refer to a time series composed of the time differences between adjacent first target packets received. Each packet receiver can continuously receive the first target packets and record the time differences before and after the first target packets. The time difference can be specifically determined based on the reception time of the first target packets. When the number of first target packets meets the fourth preset condition, for example, when the number of first target packets reaches 30 or no new first target packets are received within 1 minute, the network device can determine the interval time series corresponding to the first target packets of the target metric type. Subsequently, based on this interval time series, it can be determined whether there is a fixed time interval for the target metric type.

[0217] (13) When the interval time series meets the sixth preset condition, determine that the target correlation type corresponding to the target metric type is a time-correlated metric type, and determine the target interval time corresponding to the target correlation type based on the interval time series.

[0218] In an embodiment of the present application, the sixth preset condition may refer to a judgment condition for the time-correlated metric type set in advance. Specifically, it may refer to that the variance of the interval time series is less than a preset value (such as 5), etc. Of course, the sixth preset condition may also be other conditions, such as standard deviation, range, etc., and the preset value can also be set flexibly. This embodiment of the present application does not limit this. The target interval time may be the sending interval time of the target packets corresponding to the time-correlated metric type.

[0219] In this step, after the network device determines the interval time series corresponding to the first target packet of the target metric type, it can further determine whether the interval time series meets the sixth preset condition. If it meets, the network device can determine that the target metric type is a time-correlated metric type. At the same time, based on the average value of the interval time series, the network device can determine the target interval time of the time-correlated metric type. Subsequently, based on the target interval time, the network device can determine the delay data of the multimedia stream to be detected, improving the accuracy and success rate of network transmission quality detection.

[0220] Exemplarily, Figure 7 FIG. 5 is a schematic diagram for determining a time-correlated metric type provided by an embodiment of the present application. As Figure 7 shown, the network device creates packet receivers, which may specifically include a third packet receiver and a fourth packet receiver. For example, Figure 7 sender classifier A in FIG. 5. Each packet receiver continuously receives the first target packet and records the time intervals before and after the first target packet. For example, the time interval t1 between byte value 52 and byte value 51, and the time interval tn between byte value n and byte value n - 1.

[0221] When the number of packets of the first target packet meets the fourth preset condition (more than 30 or no new first target packets are received by the packet receiver within 1 minute), the network device can determine the interval time series corresponding to the target metric type. For example, the interval time series t1, t2 until tn of the first target packet in sender classifier A. Then, the network device can determine whether the interval time series meets the sixth preset condition. For example, whether the variance of the interval time series is less than 5. If it meets, the network device can determine that the target metric type is a time-correlated metric type, and at the same time, use the average value of the interval time series as the target interval time of the time-correlated metric type. If it does not meet, the network device can continue to judge other packet receivers until all target metric types are judged.

[0222] In the embodiment of the present application, for the first target packet of the target metric type in one direction, the network device determines whether the target metric type has time correlation by determining the time interval of the first target packet, which can accurately identify periodically sent packets and improve the accuracy and success rate of subsequent multimedia stream quality detection to be detected.

[0223] Based on the above embodiments, Figure 8 FIG. 6 is a schematic diagram of a process for determining a target association type provided by an embodiment of the present application. The determination of the target metric type and the determination of the target association type in the foregoing steps S302 to S307 can be implemented by Figure 4 the metric packet type discovery module 402 shown in FIG. 6. As Figure 8As shown, the metric message type discovery module 402 may include a classification algorithm, an interaction correlation detection algorithm, and a time correlation detection algorithm. Specifically, the network device first classifies and identifies the packet data of the multimedia stream to be detected based on the classification algorithm, and determines the target metric type included in the multimedia stream to be detected. If the target metric type is determined in the multimedia stream to be detected, the network device may determine whether there is a two-way interaction correlation for the target metric type based on the interaction correlation detection algorithm. If so, it may be determined that the target correlation type of the target metric type is an interaction correlation metric type.

[0224] If there is no two-way interaction correlation for the target metric type or the target metric type cannot be fully detected for the sender and receiver in the multimedia stream to be detected, the network device may determine whether there is a time correlation for the target metric type in one direction based on the time correlation algorithm. If so, it is determined that the target metric type is a time correlation metric type. In this way, by combining multiple algorithms, the network device can classify and identify the packet data in the multimedia stream to be detected, determine the target correlation type that can be used for network transmission quality detection, effectively detect the network transmission quality of the multimedia stream, and also improve the accuracy of subsequent determination of network transmission quality data.

[0225] S308. Determine the target quality data of the multimedia stream to be detected according to the target calculation method corresponding to the target correlation type and the second target packet corresponding to the target correlation type.

[0226] In a possible implementation manner, when the target correlation type is an interaction correlation metric type, step S308 may be specifically implemented through the following steps (14) to (15):

[0227] (14). Determine the first received packet and the second received packet corresponding to the interaction correlation metric type.

[0228] (15). Determine the target quality data according to the matching relationship of the target marker bytes in the first received packet and the second received packet.

[0229] In the embodiment of the present application, the first received packet may refer to the packet of the interaction correlation metric type sent by the sender of the multimedia stream to be detected received by the network device. The first received packet may refer to the packet corresponding to the interaction correlation metric type sent by the receiver of the multimedia stream to be detected.

[0230] In this step, after the metric message type discovery module in the network device determines the target association type, it can send the target association type to the network quality detection module, and the network quality detection module determines the target quality data. Specifically, the network device can first determine the first received message and the second received message corresponding to the interaction association type, and then determine the first value of the target marker byte in the first received message and the first reception time t1. At the same time, it can determine the second value of the target marker byte in the second received message and the second reception time t2. After that, the network device can determine the packet loss data according to the correspondence between the first value and the second value, and at the same time, based on the correspondence between the first reception time and the second reception time, determine the delay data, and then can obtain the target quality data based on the packet loss data and the delay data.

[0231] Exemplarily, Figure 9 is a schematic diagram for calculating the target quality data of an interaction association metric type provided by an embodiment of this application. As Figure 9 shown, when the first value of the target marker byte in the first received message is the same as the second value of the target marker byte in the second received message (both are byte value A), the network device can determine that there is no packet loss at this time, and the network device can obtain the delay data according to (t2 - t1) / 2.

[0232] Exemplarily, Figure 10 is another schematic diagram for calculating the target quality data of an interaction association metric type provided by an embodiment of this application. As Figure 10 shown, when the first value of the target marker byte in the first received message is different from the second value of the target marker byte in the second received message, that is, the first value is byte value A and the second value is byte value A - 1, the network device can traverse the sender buffer queue to find the buffer with the equal value (A - 1). If it exists in the queue, the buffer reception time t0 is taken out, and the delay data is calculated based on (t2 - t0) / 2. If it does not exist in the queue, the packet loss data is incremented by one. Before time t3, the buffer queue can include byte value A - 1 and byte value A. At time t3, byte value A - 1 has been successfully matched, and the buffer queue can remove byte value A - 1, only retaining the unmatched byte value, which can ensure the accuracy of the comparison and matching.

[0233] In the embodiment of this application, for the interaction association metric type, the network device determines the delay data and the packet loss data according to the messages in two directions, that is, the first received message and the second received message, and obtains the target quality data of the multimedia stream to be detected according to the matching relationship of the target marker byte, which can improve the accuracy of network transmission quality detection.

[0234] In a possible implementation manner, when the target association type is a time association metric type, step S308 may specifically be implemented through the following steps (15) to (16):

[0235] (15) Determine the first reception time of the second target message corresponding to the time association metric type and the second reception time of the previous second target message corresponding to the second target message.

[0236] (16) Determine the target quality data according to the first reception time, the second reception time, and the target interval time.

[0237] In the embodiments of the present application, the second target message may refer to a message of the time association metric type received by a network device. The first reception time may be the reception time corresponding to the current second target message, which may be represented by t1. The second reception time may refer to the reception time corresponding to the previous second target message, which may be represented by t2. Based on the first reception time t1, the second reception time t2, and the target time interval t, the network device may calculate the delay data corresponding to the time association metric type, and further may obtain the target quality data based on the delay data. Exemplarily, the delay data corresponding to the time association metric type may be calculated by t2 - (t1 + t).

[0238] Exemplarily, Figure 11 is a schematic diagram for calculating the target quality data of a time association metric type provided by the embodiments of the present application. As Figure 11 shown, when the network quality detection module obtains that the target association type is a time association metric type and the target interval time is t, it may record the first reception time t1 of the second target message, subtract the reception time t2 of the previous second target message, and calculate t2 - (t1 + t) as the delay.

[0239] In the embodiments of the present application, for the time association metric type, the network device can accurately determine the delay data of the time association metric type based on the reception times of two consecutive second target messages and in combination with the target interval time of the time association metric type, realizing accurate and effective detection of the transmission quality of the multimedia stream to be detected.

[0240] On the basis of the above embodiments, after the network device determines the metric type (target metric type and target association type) based on the classification algorithm, it may send the metric type to other network devices for use, so that other network devices do not need to perform classification and identification again, which can save computing resources. Exemplarily, Figure 12 is an interaction schematic diagram for sharing the metric type among multiple devices provided by the embodiments of the present application. As Figure 12As shown, network device A uploads the determined metric type to the cloud platform; network device B can request the metric type from the cloud platform, and the cloud platform returns the metric type to network device B. Network device B then detects the quality of multimedia stream transmission based on the metric type, determines latency, packet loss, jitter, etc., and at the same time determines the credibility of the metric type, which can be determined based on the accuracy and rationality of the data calculated by the metric type. After that, network device B can upload the credibility of the metric type to the cloud platform, and the cloud platform updates the credibility of the metric type. In this way, through the interaction and sharing of multiple network devices, the rapid application of the metric type can be realized, without repeatedly executing the classification algorithm, and the accuracy and detection efficiency of multimedia stream transmission quality detection can be effectively improved.

[0241] In the related art, traditional application recognition can usually only identify which application program a multimedia stream belongs to. In the embodiments of the present application, network devices can classify and identify the multimedia stream to be detected, determine the target metric type, and achieve more refined traffic recognition, and can further classify and present the message composition in the application stream, such as video conference video interaction stream, video conference video heartbeat message stream, and video conference video data stream, etc.

[0242] In the embodiments of the present application, network devices can perform refined classification on UDP audio and video communication data streams, identify the message types available for transmission quality detection, and further determine the associated types, and obtain quality data such as latency and packet loss of audio and video communication in real time based on the associated types, realizing the effective detection of audio and video network transmission quality and improving the accuracy of audio and video network transmission quality detection.

[0243] Figure 13 It is a schematic structural diagram of a network transmission quality detection device provided by the embodiments of the present application. Please refer to Figure 13 , the network transmission quality detection device 130 may include:

[0244] An acquisition module 131, configured to acquire the multimedia stream to be detected and determine the target metric type included in the multimedia stream to be detected; the target metric type is the message type used for network transmission quality detection;

[0245] A first determination module 132, configured to determine the target association type corresponding to the target metric type according to the first target message corresponding to the target metric type;

[0246] A second determination module 133, configured to determine the target quality data of the multimedia stream to be detected according to the target calculation method corresponding to the target association type and the second target message corresponding to the target association type.

[0247] In a possible implementation manner, the acquisition module 131 is specifically configured to:

[0248] Receiving the packet data of the multimedia stream to be detected through a preset classifier; the preset classifier includes a receiver classifier and a sender classifier;

[0249] In the case where the type of the packet data does not match the preset type in the preset classifier, creating a first category in the preset classifier;

[0250] In the case where the quantity of the first category meets the first preset condition, generating a secondary classifier in the preset classifier;

[0251] According to the first packet data corresponding to each first category, creating a second category in the secondary classifier and determining the second packet data included in the second category;

[0252] According to each second packet data in the second category, determining the target marker byte corresponding to the second category, and determining the target metric type in the second category according to the target marker byte.

[0253] In a possible implementation manner, the obtaining module 131 is specifically configured to:

[0254] Extracting the first M bytes of the packet data, and comparing and matching the first M bytes with the first M bytes of the preset packet corresponding to the preset type in the preset classifier to obtain a first matching result; where M is a positive integer;

[0255] In the case where the first matching result does not meet the second preset condition, determining that the type of the packet data does not match the preset type, and creating a first category in the preset classifier.

[0256] In a possible implementation manner, the obtaining module 131 is specifically configured to:

[0257] Pairwise matching the first packet data corresponding to the first category, and determining the number of consecutive identical bytes with the same value;

[0258] Based on the first packet data corresponding to the maximum number of consecutive identical bytes, creating a second category in the secondary classifier.

[0259] In a possible implementation manner, the obtaining module 131 is specifically configured to:

[0260] For each second category, continuously receiving second packet data based on the secondary classifier;

[0261] Determining the target marker byte according to the difference between the values of each byte between the current second packet data and the previous second packet data;

[0262] If the quantity of the second message data including the target marker byte meets the third preset condition, determine the second category corresponding to the target marker byte as the target metric type.

[0263] In a possible implementation manner, the target association type includes an interaction association metric type; the first determination module 132 is specifically configured to:

[0264] Create a message receiver and continuously receive the first target message through the message receiver;

[0265] When the quantity of the first target message in the message receiver meets the fourth preset condition, determine the first value corresponding to the target marker byte in the first message receiver and determine the second value corresponding to the target marker byte in the second message receiver;

[0266] If the quantity of the first target messages with the same first value and second value meets the fifth preset condition, determine that the target association type corresponding to the target metric type is the interaction association metric type.

[0267] In a possible implementation manner, the message receiver includes a first message receiver and a second message receiver; the first determination module 132 is specifically configured to:

[0268] When the quantity of the first target message in the first message receiver meets the fourth preset condition and the quantity of the first target message in the second message receiver meets the fourth preset condition, determine that the quantity of the first target message in the message receiver meets the fourth preset condition.

[0269] In a possible implementation manner, the second determination module 133 is specifically configured to:

[0270] Determine the first received message and the second received message corresponding to the interaction association metric type;

[0271] Determine the target quality data according to the matching relationship of the target marker bytes in the first received message and the second received message.

[0272] In a possible implementation manner, the target association type includes a time association metric type; the first determination module 132 is specifically configured to:

[0273] Create a message receiver and continuously receive the first target message through the message receiver;

[0274] When the quantity of the first target message in the message receiver meets the fourth preset condition, determine the interval time sequence corresponding to the first target message;

[0275] When the interval time series meets the sixth preset condition, determine that the target correlation type corresponding to the target metric type is the time correlation metric type, and determine the target interval time corresponding to the target correlation type based on the interval time series.

[0276] In a possible implementation manner, the message receiver includes a third message receiver and a fourth message receiver; the first determination module 132 is specifically configured to:

[0277] When the number of messages of the first target message in the third message receiver meets the fourth preset condition, or the number of messages of the first target message in the fourth message receiver meets the fourth preset condition, determine that the number of messages of the first target message in the message receiver meets the fourth preset condition.

[0278] In a possible implementation manner, the second determination module 133 is specifically configured to:

[0279] Determine the first reception time of the second target message corresponding to the time correlation metric type and the second reception time of the previous second target message corresponding to the second message;

[0280] Determine the target quality data according to the first reception time, the second reception time, and the target interval time.

[0281] In a possible implementation manner, the multimedia stream to be detected is a multimedia stream transmitted based on the User Datagram Protocol (UDP).

[0282] The network transmission quality detection device 130 provided by the embodiments of the present application can execute the technical solutions shown in the above method embodiments, and its implementation principle and beneficial effects are similar, which will not be elaborated here.

[0283] Figure 14 It is a schematic structural diagram of a network transmission quality detection device provided by an embodiment of the present application. Please refer to Figure 14 , the network transmission quality detection device 140 may include: a memory 141 and a processor 142. Exemplarily, the memory 141 and the processor 142 are connected to each other through a bus 143.

[0284] The memory 141 is used to store program instructions;

[0285] The processor 142 is used to execute the program instructions stored in the memory to implement the network transmission quality detection method shown in the above embodiments.

[0286] Figure 14 The network transmission quality detection device 140 shown can execute the technical solutions shown in the above method embodiments, and its implementation principle and beneficial effects are similar, which will not be elaborated here.

[0287] An embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the above-mentioned network transmission quality detection method.

[0288] An embodiment of the present application may also provide a computer program product, including a computer program, which, when executed by a processor, can implement the above-mentioned network transmission quality detection method.

[0289] An embodiment of the present application provides a chip, on which a computer program is stored, and when the computer program is executed by the chip, the above-mentioned network transmission quality detection method is implemented.

[0290] It should be noted that the processor mentioned in the embodiments of the present application may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0291] It should be understood that the memory mentioned in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synch link dynamic random access memory (SLDRAM), and direct ram bus random access memory (DR RAM). It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA, or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, the memory (storage module) is integrated in the processor. It should be noted that the memory described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0292] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution, and the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0293] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processing unit of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processing unit of the computer or other programmable data processing devices generate for implementation in the processFigure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0294] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, and the instruction device implements the process Figure 1 one process or multiple processes and / or blocks Figure 1 the functions specified in one block or multiple blocks.

[0295] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 the functions specified in one block or multiple blocks.

[0296] Regarding each device and each module / unit included in the product described in the above embodiments, it can be a software module / unit, a hardware module / unit, or it can also be partly a software module / unit and partly a hardware module / unit. Each device and product can be applied to or integrated into a chip, a chip module, or a terminal device. Exemplarily, for each device and product applied to or integrated into a chip, each module / chip included therein can be implemented in a hardware manner such as a circuit, or at least part of the modules / units can be implemented in a software program manner, and the software program runs on a processor integrated inside the chip, and the remaining part of the modules / units can be implemented in a hardware manner such as a circuit.

[0297] In this application, the term "including" and its variations can refer to non-restrictive inclusion; the term "or" and its variations can refer to "and / or". In this application, terms such as "first" and "second" are used to distinguish similar objects and do not necessarily have to describe a specific order or sequence. In this application, "a plurality of" means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0298] The above are only some embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. A method for detecting network transmission quality, characterized in that Including: Obtain a multimedia stream to be detected and determine the target metric type included in the multimedia stream to be detected; The target metric type is a packet type for network transmission quality detection; Determine the target association type corresponding to the target metric type according to the first target packet corresponding to the target metric type; Determine the target quality data of the multimedia stream to be detected according to the target calculation method corresponding to the target association type and the second target packet corresponding to the target association type.

2. The method according to claim 1, wherein The obtaining the multimedia stream to be detected and determining the target metric type included in the multimedia stream to be detected includes: Receive the packet data of the multimedia stream to be detected through a preset classifier; the preset classifier includes a receiver classifier and a sender classifier; When the type of the packet data does not match the preset type in the preset classifier, create a first category in the preset classifier; When the number of the first categories meets a first preset condition, generate a secondary classifier in the preset classifier; Create a second category in the secondary classifier according to the first packet data corresponding to each of the first categories, and determine the second packet data included in the second category; Determine the target marker byte corresponding to the second category according to each second packet data in the second category, and determine the target metric type in the second category according to the target marker byte.

3. The method according to claim 2, wherein The creating a first category in the preset classifier when the type of the packet data does not match the preset type in the preset classifier includes: Extract the first M bytes of the packet data, and compare and match the first M bytes with the first M bytes of the preset packet corresponding to the preset type in the preset classifier to obtain a first matching result; where M is a positive integer; When the first matching result does not meet a second preset condition, determine that the type of the packet data does not match the preset type, and create the first category in the preset classifier.

4. The method according to claim 2, wherein The creating a second category in the secondary classifier according to the first packet data corresponding to each of the first categories includes: For the first packet data corresponding to the first category, match the first packet data pairwise to determine the number of consecutive identical bytes with the same value; Create a second category in the secondary classifier based on the first packet data corresponding to the largest number of consecutive identical bytes.

5. The method according to claim 2, wherein The determining the target marker byte corresponding to the second category according to each second packet data in the second category and determining the target metric type in the second category according to the target marker byte includes: For each second category, continuously receive the second packet data based on the secondary classifier; Determine the target marker byte according to the difference in the numerical values of each byte between the current second packet data and the previous second packet data; If the number of second packet data including the target marker byte meets a third preset condition, determine the second category corresponding to the target marker byte as the target metric type.

6. The method according to claim 5, characterized in that, The target association type includes an interaction association metric type; Determining the target association type corresponding to the target metric type according to the first target message described above includes: Creating a message receiver and continuously receiving the first target message through the message receiver; When the number of messages of the first target message in the message receiver meets the fourth preset condition, determining a first value corresponding to the target marker byte in the first message receiver and determining a second value corresponding to the target marker byte in the second message receiver; If the number of first target messages where the first value is the same as the second value meets the fifth preset condition, determining that the target association type corresponding to the target metric type is an interactive association metric type.

7. The method according to claim 6, characterized in that, The message receiver includes a first message receiver and a second message receiver; the method further includes: When the number of messages of the first target message in the first message receiver meets the fourth preset condition and the number of messages of the first target message in the second message receiver meets the fourth preset condition, determining that the number of messages of the first target message in the message receiver meets the fourth preset condition.

8. The method according to claim 6, characterized in that, Determining the target quality data of the multimedia stream to be detected according to the target calculation method corresponding to the target association type and the second target message corresponding to the target association type includes: Determining a first received message and a second received message corresponding to the interactive association metric type; Determining the target quality data according to the matching relationship of the target marker bytes in the first received message and the second received message.

9. The method according to claim 5, characterized in that The target association type includes a time association metric type; Determining the target association type corresponding to the target metric type according to the first target message corresponding to the target metric type includes: Creating a message receiver and continuously receiving the first target message through the message receiver; When the number of messages of the first target message in the message receiver meets the fourth preset condition, determining the corresponding interval time series of the first target message; When the interval time series meets the sixth preset condition, determining that the target association type corresponding to the target metric type is a time association metric type and determining the target interval time corresponding to the target association type based on the interval time series.

10. The method according to claim 9, characterized in that The message receiver includes a third message receiver and a fourth message receiver; the method further includes: When the number of messages of the first target message in the third message receiver meets the fourth preset condition or the number of messages of the first target message in the fourth message receiver meets the fourth preset condition, determining that the number of messages of the first target message in the message receiver meets the fourth preset condition.

11. The method according to claim 9, wherein Determining the target quality data of the multimedia stream to be detected according to the target calculation method corresponding to the target association type and the second target message corresponding to the target association type includes: Determine the first reception time of the second target packet corresponding to the time correlation metric type and the second reception time of the previous second target packet corresponding to the second target packet; Determine the target quality data according to the first reception time, the second reception time, and the target interval time.

12. The method according to any one of claims 1 to 11, characterized in that, The multimedia stream to be detected is a multimedia stream transmitted based on the User Datagram Protocol (UDP).

13. A network transmission quality detection device, characterized in that, Comprising: An acquisition module, configured to acquire a multimedia stream to be detected and determine a target metric type included in the multimedia stream to be detected; The target metric type is a packet type used for network transmission quality detection; A first determination module, configured to determine a target correlation type corresponding to the target metric type according to a first target packet corresponding to the target metric type; A second determination module, configured to determine target quality data of the multimedia stream to be detected according to a target calculation method corresponding to the target correlation type and a second target packet corresponding to the target correlation type.

14. A network transmission quality detection device, characterized in that Comprising: A processor and a memory; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the network transmission quality detection method according to any one of claims 1 to 12.

15. A computer-readable storage medium, characterized in that, Computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed, they are used to implement the network transmission quality detection method according to any one of claims 1 to 12.

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