Method and device for routing planning of real-time audio and video network

Abstract

The embodiment of the present invention provides a method and device for routing planning of a real-time audio and video network. The method includes: collecting data of each edge node in the RTN according to the real-time network topology information of the audio and video network RTN obtained; mapping the data into the quantitative scores of the links between the edge nodes according to the routing strategies of different demand types, and obtaining a multi-strategy full RTN link quantitative network topology; performing a first routing planning for multiple edge nodes in the multi-strategy full RTN link quantitative network topology, and based on the first edge node in the first routing, performing a second routing planning for the data instance in the first edge node. Through the present invention, the problem of inaccurate planned routing links caused by the inability to plan routing according to the real-time network topology in the related technology is solved, and the accuracy of routing planning is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of real-time audio and video communication, and in particular to a method and device for routing planning of a real-time audio and video network. Background Art

[0002] Real-time Communication (RTC) is a technology for real-time interaction of video, audio and other data. In recent years, with the development of digital technologies such as 5G, artificial intelligence (AI), and extended reality (XR), as well as the objective demand for onlineization, the integration of the digital world and the physical world is also accelerating. People's experience of interactive modes is also undergoing fundamental changes. The demand for stronger interactivity and higher immersion is driving the transformation of the carrier network of RTC applications - Real-time Network (RTN), which is evolving towards lower latency and better quality.

[0003] The routing planning between network element nodes is the core technology of RTN. Reasonable routing planning can provide faster and better data transmission experience for RTC applications, thus providing important competitiveness for RTN systems. However, the routing planning technology in RTN in the industry still has many pain points:

[0004] First, the real-time performance is poor. The routing planning in the related technology only selects the route to the node cluster. When the business data is forwarded according to the routing table, it is necessary to plan and select the data forwarding instance from the cluster, which results in high latency in the data forwarding stage, a long time to establish the data link, poor real-time performance, and thus reduces the user experience.

[0005] Second, the accuracy is low. The routing planning in related technologies is generally a static planning, which cannot be reasonably planned dynamically and globally according to the real-time changes in network topology, changes in network detection indicators, changes in network element node load, etc. At the same time, there are problems with single routing rules and calculation methods, which cannot meet different business needs. All these have caused the planned routing links to be inaccurate, which are only suitable for private network transmission, not for the Internet. Summary of the invention

[0006] The embodiments of the present invention provide a method and device for real-time audio and video network routing planning, so as to at least solve the problem in the related art that the routing link is inaccurate due to the inability to plan the routing according to the real-time network topology.

[0007] According to an embodiment of the present invention, a method for routing planning of a real-time audio and video network is provided, comprising: collecting data of each edge node in the RTN according to the acquired real-time network topology information of the audio and video network RTN; mapping the data into a quantized score of a link between each edge node according to routing strategies of different demand types to obtain a multi-strategy full RTN link quantized network topology; performing a first routing planning for multiple edge nodes in the multi-strategy full RTN link quantized network topology, and based on the first edge node in the first routing, performing a second routing planning for the data instance in the first edge node.

[0008] According to another embodiment of the present invention, a route planning device for a real-time audio and video network is provided, comprising: a collection module for collecting data of each edge node in the RTN according to the acquired real-time network topology information of the audio and video network RTN; a mapping module for mapping the data into a quantized score of a link between each edge node according to routing strategies of different demand types, so as to obtain a multi-strategy full RTN link quantized network topology; a planning module for performing a first route planning for multiple edge nodes in the multi-strategy full RTN link quantized network topology, and performing a second route planning for a data instance in the first edge node based on the first edge node in the first route.

[0009] According to yet another embodiment of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any one of the above method embodiments when running.

[0010] According to yet another embodiment of the present invention, there is provided an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0011] Through the present invention, the topology information and edge node information of the real-time audio and video network RTN are obtained to generate a multi-strategy full RTN link quantized network topology, so as to perform dynamic routing planning according to the multi-strategy full RTN link quantized network topology. Therefore, the problem of inaccurate routing links due to the inability to plan routing according to the real-time network topology in the related art can be solved, and the accuracy of routing planning can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a hardware structure block diagram of a computer terminal for running a routing planning method for a real-time audio and video network according to an embodiment of the present invention;

[0013] Figure 2is a schematic diagram of a network architecture of an RTN according to an embodiment of the present invention;

[0014] Figure 3 is a flow chart of a method for routing planning of a real-time audio and video network according to an embodiment of the present invention;

[0015] Figure 4 is a structural block diagram of a routing planning device for a real-time audio and video network according to an embodiment of the present invention;

[0016] Figure 5 is a structural block diagram of a routing planning device for a real-time audio and video network according to another embodiment of the present invention;

[0017] Figure 6 is a structural block diagram of a routing planning device for a real-time audio and video network according to another embodiment of the present invention;

[0018] Figure 7 is a structural block diagram of a routing planning device for a real-time audio and video network according to another embodiment of the present invention;

[0019] Figure 8 is a flow chart of a routing planning method according to an embodiment of the present invention;

[0020] Fig. 9 is a schematic diagram of an initial network topology according to an embodiment of the present invention;

[0021] Fig.10 is a schematic diagram of a method for acquiring real-time edge data according to an embodiment of the present invention;

[0022] Fig.11 is a schematic diagram of a multi-strategy quantization network topology according to an embodiment of the present invention;

[0023] Fig.12 is a schematic diagram of primary routing according to an embodiment of the present invention;

[0024] Fig.13 The diagram is a schematic diagram of an optimal routing table for secondary routing according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings and in combination with the embodiments.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0027] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a computer terminal as an example, Figure 11 is a hardware structure block diagram of a computer terminal for running a routing planning method for a real-time audio and video network according to an embodiment of the present invention. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor (Central Processing Unit, MCU) or a programmable logic device (Field Programmable Gate Array, FPGA)) and a memory 104 for storing data, wherein the above-mentioned computer terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0028] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the routing planning method of the real-time audio and video network in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0029] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0030] The present application embodiment can be run on Figure 2 In the RTN network architecture shown in Figure 2As shown, the network architecture includes: a central node and multiple edge nodes. The central node also includes: a topology management and routing center, and each edge node also includes multiple forwarding instances (ie, data instances).

[0031] Specifically, the central node is the brain of RTN and is responsible for computing, controlling, and scheduling edge nodes in RTN.

[0032] Among them, topology management configures the RTN network topology information, and the routing center performs intelligent routing planning for edge nodes.

[0033] Edge nodes: are neurons of RTN, responsible for forwarding business data. Among them, forwarding instances are applications that actually forward data in edge nodes. An edge node represents a forwarding instance cluster, which can contain multiple forwarding instances.

[0034] In this embodiment, a method for routing a real-time audio and video network running on the above-mentioned computer terminal or network architecture is provided. Figure 3 is a flow chart of a method for routing planning of a real-time audio and video network according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0035] Step S302, collecting data of each edge node in the audio and video network RTN according to the acquired real-time network topology information of the RTN;

[0036] Step S304, mapping the data into quantized scores of links between edge nodes according to routing strategies of different demand types, and obtaining a multi-strategy full RTN link quantized network topology;

[0037] Step S306: Perform a first route planning for multiple edge nodes in the multi-strategy full RTN link quantization network topology, and perform a second route planning for the data instance in the first edge node based on the first edge node in the first route.

[0038] Before step S302 of this embodiment, the method further includes: obtaining an initial network topology of the RTN; updating the initial network topology of the RTN in real time according to a notification of a message subscription mechanism to obtain a real-time network topology; or periodically querying the network topology of the RTN to obtain the real-time network topology.

[0039] In step S302 of this embodiment, it includes: sending a data detection request to each edge node according to the real-time network topology information; and sending a data collection request to each edge node at a regular interval to collect detection data information and instance data information.

[0040] In this embodiment, the detection data information includes at least one of the following detection indicators: packet loss rate, delay, jitter, and link bandwidth of the RTN.

[0041] In step S304 of this embodiment, it includes: according to the requirement type of the routing strategy, the data of multiple detection indicators are input into a quantitative model so that the data is mapped into a quantitative score of the link between each edge node; wherein each type of routing strategy corresponds to one quantitative model.

[0042] In an exemplary embodiment, the requirement type of the routing strategy includes at least one of the following: a quality priority strategy, a cost priority strategy, or a comprehensive strategy of quality priority and cost priority.

[0043] In step S304 of this embodiment, it includes: when the change rate of the quantization score does not exceed the preset threshold, keeping the quantization score of the corresponding link in the multi-strategy full RTN link quantization network topology unchanged; when the change rate of the quantization score exceeds the preset threshold, updating the quantization score of the corresponding link in the multi-strategy full RTN link quantization network topology.

[0044] In step S306 of this embodiment, it includes: selecting a link with the smallest quantization score between any two or more edge nodes to plan a first routing link.

[0045] In step S306 of this embodiment, it includes: obtaining instance data information in the first edge node; and selecting the data instance with the lightest load in the first edge node for planning the second route according to the instance data information.

[0046] Through the above steps, the topology information and edge node information of the real-time audio and video network RTN are obtained to generate a multi-strategy full RTN link quantized network topology, so as to perform dynamic routing planning according to the multi-strategy full RTN link quantized network topology. Therefore, the problem of inaccurate routing links caused by the inability to plan routes according to the real-time network topology in the related technology can be solved, and the accuracy of routing planning is improved.

[0047] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory (ROM / RAM), a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0048] In the present embodiment, a route planning device for a real-time audio and video network is also provided, and the device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made are not repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware is also possible and conceived.

[0049] Figure 4 is a structural block diagram of a routing planning device for a real-time audio and video network according to an embodiment of the present invention, such as Figure 4 As shown, the device includes: a collection module 10, a mapping module 20 and a planning module 30.

[0050] The collection module 10 is used to collect data of each edge node in the RTN according to the acquired real-time network topology information of the audio and video network RTN;

[0051] A mapping module 20 is used to map the data into quantized scores of links between edge nodes according to routing strategies of different demand types, so as to obtain a multi-strategy full RTN link quantized network topology;

[0052] The planning module 30 is used to perform a first route planning for multiple edge nodes in the multi-strategy full RTN link quantization network topology, and based on the first edge node in the first route, perform a second route planning for the data instance in the first edge node.

[0053] Figure 5 is a structural block diagram of a routing planning device for a real-time audio and video network according to an embodiment of the present invention, such as Figure 5 As shown, the device includes Figure 4 All modules shown, plus:

[0054] A first acquisition module 40, configured to acquire an initial network topology of the RTN;

[0055] The second acquisition module 50 is used to update the initial network topology of the RTN in real time according to the notification of the message subscription mechanism to obtain the real-time network topology; or to query the network topology of the RTN at regular intervals to obtain the real-time network topology.

[0056] Figure 6 is a structural block diagram of a routing planning device for a real-time audio and video network according to an embodiment of the present invention, such as Figure 6 As shown, the device includes Figure 5 In addition to all the modules shown, the acquisition module 10 also includes:

[0057] A first sending unit 11 is used to send a data detection request to each edge node according to the real-time network topology information;

[0058] The second sending unit 12 is used to periodically send a data collection request to each edge node to collect detection data information and instance data information.

[0059] Figure 7 is a structural block diagram of a routing planning device for a real-time audio and video network according to an embodiment of the present invention, such as Figure 7 As shown, the device includes Figure 6 In addition to all modules shown, the planning module 30 also includes:

[0060] The first selection unit 31 is configured to select a link with a smallest quantization score between the plurality of edge nodes to plan a first routing link.

[0061] An acquiring unit 32, configured to acquire instance data information in the first edge node;

[0062] The second selection unit 33 is configured to select, according to the instance data information, the data instance with the lightest load in the first edge node for planning a second route.

[0063] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0064] To facilitate the understanding of the technical solution provided by the present invention, a detailed description will be given below in conjunction with embodiments of specific scenarios.

[0065] The embodiment of the present invention provides a method that can be operated on Figure 2 The routing planning method of the network architecture is Figure 8is a flow chart of a routing planning method according to an embodiment of the present invention. Figure 8 As shown, the method comprises the following steps:

[0066] Step S801: Acquire the RTN initial network topology or network topology change.

[0067] Specifically, the routing center needs to dynamically obtain the topology information of the RTN, that is, obtain the network topology information in real time.

[0068] Step S802: Send a data detection request to the edge node to collect edge data.

[0069] Specifically, the routing center sends data detection requests to edge nodes based on the real-time network topology. After that, each edge node performs network detection on the edge nodes connected to it. At the same time, the routing center periodically initiates data collection requests to edge nodes to collect detection data, instance data and other information.

[0070] Step S803, determining whether to return detection data;

[0071] Specifically, if it is determined that no detection data is returned, go to step S804;

[0072] If it is determined that the detection data has been returned, go to step S805.

[0073] Step S804: initiate a data detection request again.

[0074] Specifically, when it is determined that no detection data is returned, a data detection request is sent to the edge node again.

[0075] Step S805 , mapping edge data into a multi-strategy quantitative network topology.

[0076] Specifically, the routing center maps edge data into a quantitative network topology for the entire network based on different routing strategies.

[0077] Step S806, filtering and selecting the quantization scores in the multi-strategy quantization network topology.

[0078] Step S807: primary routing.

[0079] Specifically, the routing center plans the primary routing between nodes based on real-time multi-strategy quantitative network topology;

[0080] Step S808: secondary routing.

[0081] Specifically, the routing center plans secondary routing between instances based on real-time edge node instance data.

[0082] The following is a detailed description of the relevant steps in the above routing planning method in combination with a specific implementation scenario.

[0083] 1. About obtaining real-time network topology

[0084] The network topology represents the distribution of edge nodes in the RTN and the connection status of the edges between edge nodes. In the embodiment of the present invention, the network topology needs to be configured in the topology management of the central node, which is completely configured according to the actual node distribution and connection status, and also includes information such as the distance between edge nodes and the total allocable bandwidth.

[0085] Specifically, during the initialization phase, the routing center needs to obtain the RTN initialization network topology from the topology management.

[0086] For example: Fig. 9 As shown, the initial network topology is a mesh structure consisting of 6 edge nodes, the edge nodes are A, B, C, D, E, F, and the connected edges are AB, AD, AE, BC, BF, CD, DE, DF

[0087] During the RTN network topology change phase, if nodes are added or deleted, or connected edges are added or deleted, the topology management will notify the routing center of the topology change in real time through the Kafka message subscription mechanism. In addition, the routing center will also query the topology management and update the network topology regularly. In this embodiment, the Kafka mechanism can ensure real-time performance, and the timing mechanism can ensure reliability.

[0088] 2. About obtaining real-time edge data

[0089] The routing center of the central node will periodically collect detection data, instance data and other information from the edge nodes based on the real-time RTN network topology.

[0090] Specifically, regarding sending data detection requests:

[0091] The routing center analyzes the connection status between edge nodes based on the current real-time RTN network topology and sends data detection requests to each node; each node parses the request data and performs network detection on its connected nodes, including network packet loss rate, latency, jitter, bandwidth and other detection indicator data.

[0092] When the RTN network topology changes, the routing center will again send a new data detection request based on the real-time node connection status.

[0093] About timing data collection:

[0094] The routing center periodically sends data collection requests to edge nodes. In addition to the above-mentioned detection data, the data that needs to be collected also includes the forwarding instance information under each node, such as the instance load, CPU, etc. The collection time granularity is in seconds, that is, it is collected once per second. If it is found that there is no detection data in the collected data, it is necessary to resend the data detection request.

[0095] For example: Fig.10 As shown, the method for obtaining real-time edge data includes the following steps:

[0096] Detection phase:

[0097] Step 1.1: According to the initial network topology of RTN, the routing center of the central node initiates a data detection request to the edge node A;

[0098] Step 1.2: After receiving the request, edge node A performs network detection on edge node B, edge node D, and edge node E.

[0099] Collection phase:

[0100] Step 2.1, the routing center periodically initiates a data collection request to edge node A;

[0101] In step 2.2, edge node A returns the detection data information of edge node B, edge node D, and edge node E and the instance data information of this node. Similarly, the detection and collection process of the remaining edge nodes is similar to that of edge node A.

[0102] 3. About Multi-Strategy Quantitative Network Topology

[0103] The routing center will select edge data based on different routing strategies, map it into a quantitative network topology for the entire network, and filter the quantitative scores.

[0104] Specifically, regarding mapping quantized network topology:

[0105] In order to meet the different needs of different businesses, the routing center has formulated a variety of routing strategies. Each routing strategy has a corresponding link quantification mathematical model. The multiple edge indicators obtained above are input into the quantification model, which will be mapped into the quantification score of the edge links connected between nodes, and then the quantified network topology of the entire network is obtained. The routing center will traverse all routing strategies and quantification models based on real-time edge data to generate a multi-strategy quantified network topology for the entire network.

[0106] For example: when the routing strategy is a quality-first strategy, the goal is to pursue quality and require the selection of the link with the lowest transmission delay. The quantitative model will select packet loss rate (%), delay (ms), jitter (ms), etc. as indicators, and map these network indicators to link scores; when the routing strategy is a cost-first strategy, the goal is to save costs and require the selection of the link with the lowest transmission cost. The node distance, link bandwidth, etc. will be selected as indicators; when the routing strategy is a comprehensive strategy, the goal is to balance quality and cost, and the model indicators of quality strategy and cost strategy are required to be comprehensively considered. The routing center will generate multi-strategy quantitative network topology in real time, such as Fig.11 shown.

[0107] By formulating a variety of strategies to meet the different needs of different businesses, real-time edge data is mapped into a real-time multi-strategy quantitative network topology, ensuring the accuracy of the quantitative scores of the edges connecting the nodes.

[0108] About filtering quantization scores:

[0109] In order to reduce the useless work done due to the link score change being too small, resulting in the newly planned route link not changing, and to reduce the calculation pressure when planning routes, it is necessary to reduce the frequency of updating the link score. The routing center will filter the link quantization score of each edge. When the score change rate does not exceed the set threshold, the link score will not be updated in the route planning calculation model.

[0110] For example, the last link score from node A to node B was 100. New edge data is now arriving, and the link score from node A to node B calculated by the routing center based on the latest quantized network topology is 101. The score change rate threshold is set to 10%, and the change rate between the current score and the last score is 1%, which does not exceed 1%, so it is not updated to the calculation model. By filtering the quantized scores, the useless work of the newly planned routing links that remain unchanged due to too small changes in the link scores is reduced, and the calculation pressure when planning the route is also reduced.

[0111] In this embodiment, by acquiring information such as RTN network topology and edge data in real time, a multi-strategy quantitative network topology is generated for dynamic multi-level routing planning. In this way, problems such as high latency in the data forwarding stage and non-real-time planning of routes can be solved, thereby improving the timeliness of routing planning. At the same time, problems such as single and simple routing rules and calculation methods are also solved, thereby improving the accuracy of routing planning.

[0112] 4. Multi-level routing planning

[0113] The computing model of the routing center plans the routing between nodes based on the real-time quantitative network topology, i.e., primary routing; and plans the routing between instances based on the real-time edge node instance data, i.e., secondary routing.

[0114] About primary routing

[0115] The routing center plans the optimal route between edge nodes, i.e., first-level routing, based on the filtered and screened multi-strategy quantitative network topology. First, a node is selected as the starting node, and the optimal route from the node to other nodes is calculated. It is agreed that if the node can be directly connected to other nodes, the link score is used as its distance, and if it cannot be directly connected to other nodes, infinity is used as its distance. Next, the node with the smallest distance is selected as the next point. Then, the distance from the starting point through the selected node to other nodes is recalculated, and the minimum distance data is updated. Finally, this process is repeated until all nodes are traversed. The selected node is the optimal routing node that has been determined and will no longer participate in subsequent calculations.

[0116] The routing center quantifies the network topology based on multi-strategy updates in real time, and calculates the optimal route from any node to any other node in real time.

[0117] For example, taking the quality priority strategy as an example, the optimal route from node A to any node is planned. The optimal route from node A to node B is "A→B", the optimal route from node A to node C is "A→E→D→C", the optimal route from node A to node D is "A→E→D", the optimal route from node A to node E is "A→E", and the optimal route from node A to node F is "A→E→D→F". Fig.12 As shown. Similarly, the optimal routes of the remaining nodes to any node are as follows Fig.12 shown.

[0118] About secondary routing:

[0119] The routing center plans the optimal route between instances under the node based on the results of the first-level routing, that is, the second-level routing. First, when the routing center receives a routing query request, it selects an optimal routing link based on the routing strategy, source node, and target node information. Then, the routing node is determined based on the optimal routing link, and the instance data information under the node is determined based on the collected edge data information. Finally, the instance with the lightest load under the routing node is selected in real time, and the optimal routing table is finally generated by traversing the nodes.

[0120] For example, taking the quality priority strategy as an example, according to the above-mentioned first-level routing planning method, the first-level routing from source node A to target node B is planned, that is, "A→B". Among them, the instance with the lightest load under node A is a1, and the instance with the lightest load under node B is b1, so the planned second-level routing is "a1→b1". The final routing expression is as follows Fig.13 As shown. Similarly, the optimal routing table of the remaining nodes to any node is as follows Fig.13 shown.

[0121] The above embodiments of the present invention are applicable to RTC-related industries, such as video conferencing, cloud computing, interactive live broadcasting, and extended reality (Extended Reality, XR).

[0122] By planning routes through dynamic multi-level routing, the instance routing process of edge nodes is moved to the central node for unified calculation and planning, which can avoid high latency in the data forwarding stage and improve the real-time performance of business transmission. In addition, the data for multi-level routing comes from real-time network topology and edge data, which further ensures the real-time performance of data. Dynamically planning new routes based on real-time data changes improves the accuracy of route planning.

[0123] An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0124] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0125] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0126] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0127] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0128] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0129] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for routing planning of a real-time audio and video network, characterized in that: include: Collect data of each edge node in the RTN according to the acquired real-time network topology information of the audio and video network RTN; According to the routing strategies of different demand types, the data is mapped into the quantitative scores of the links between the edge nodes to obtain the multi-strategy full RTN link quantitative network topology; Performing a first route planning for multiple edge nodes in the multi-strategy full RTN link quantization network topology, and performing a second route planning for a data instance in the first edge node based on the first edge node in the first route; The step of obtaining a multi-strategy full RTN link quantized network topology includes: When the rate of change of the quantization score does not exceed a preset threshold, maintaining the quantization score of the corresponding link in the multi-strategy full RTN link quantization network topology unchanged; When the rate of change of the quantization score exceeds a preset threshold, the quantization score of the corresponding link in the multi-strategy full RTN link quantization network topology is updated.

2. The method according to claim 1, characterized in that Before collecting data of each edge node in the RTN network according to the acquired real-time network topology information of the RTN, the method further includes: Obtaining an initial network topology of the RTN; The initial network topology of the RTN is updated in real time according to the notification of the message subscription mechanism to obtain the real-time network topology information; or the network topology of the RTN is queried periodically to obtain the real-time network topology information.

3. The method according to claim 1, characterized in that Collecting data of each edge node in the RTN according to the acquired real-time network topology information of the RTN includes: Sending a data detection request to each edge node according to the real-time network topology information; A data collection request is sent to each edge node at a regular interval to collect detection data information and instance data information.

4. The method according to claim 3, characterized in that in, The detection data information includes at least one of the following detection indicators: The packet loss rate, delay, jitter, and link bandwidth of the RTN.

5. The method according to claim 1, characterized in that According to the routing strategies of different demand types, the data is mapped into the quantitative scores of the links between the edge nodes, including: According to the requirement type of the routing strategy, the data of the plurality of detection indicators are input into a quantitative model so that the data is mapped into a quantitative score of the link between each edge node; wherein each type of routing strategy corresponds to one quantitative model.

6. The method according to claim 5, characterized in that in, The requirement type of the routing policy includes at least one of the following: Quality priority strategy, cost priority strategy, and a comprehensive strategy of quality priority and cost priority.

7. The method according to claim 1, characterized in that Performing a first route planning on multiple edge nodes in the multi-strategy full RTN link quantization network topology includes: A link with the smallest quantization score between any two or more edge nodes is selected to plan a first routing link.

8. The method according to claim 1, characterized in that The second route planning performed on the data instance in the first edge node includes: Obtaining instance data information in the first edge node; According to the instance data information, a data instance with the lightest load in the first edge node is selected for planning a second route.

9. A routing planning device for a real-time audio and video network, characterized in that: include: A collection module, used to collect data of each edge node in the RTN according to the acquired real-time network topology information of the audio and video network RTN; A mapping module, used to map the data into quantized scores of links between edge nodes according to routing strategies of different demand types, and obtain a multi-strategy full RTN link quantized network topology; A planning module, configured to perform a first route planning for a plurality of edge nodes in the multi-strategy full RTN link quantization network topology, and perform a second route planning for a data instance in the first edge node based on the first edge node in the first route; The device is further configured to keep the quantization score of the corresponding link in the multi-strategy full RTN link quantization network topology unchanged when the change rate of the quantization score does not exceed a preset threshold; When the rate of change of the quantization score exceeds a preset threshold, the quantization score of the corresponding link in the multi-strategy full RTN link quantization network topology is updated.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the method described in any one of claims 1 to 8 when executed by a processor.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method described in any one of claims 1 to 8 are implemented.

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