Network transmission parameter optimization method and device, electronic equipment and storage medium

Abstract

The network transmission parameter optimization method and device, the electronic device and the storage medium provided by the embodiments of the present application comprise the following steps: first, obtaining the traffic transmission demand parameters of at least one network node in a network and the network state information between each two network nodes; then, calculating the maximum transmission delay corresponding to a plurality of different traffic shaping strategies based on the network state information and the traffic transmission demand parameters, and determining an optimized traffic shaping strategy from the plurality of traffic shaping strategies according to the smallest maximum transmission delay; finally, adjusting the network transmission parameters of the at least one network node in the network based on the optimized traffic shaping strategy, so as to effectively improve the flexibility of the transmission parameter configuration of the network nodes in the complex changing network, and effectively improve the transmission rate between the network nodes, thereby meeting the high communication demand of the network nodes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, and particularly relates to a network transmission parameter optimization method and device, electronic equipment and storage medium. BACKGROUND

[0002] With the transformation of digitalization and intelligentization in the field of industrial manufacturing, the determinacy and reliability of industrial network transmission are increasingly strict. In recent years, Time-Sensitive Networking (TSN) technology has become a research hotspot in the academic and industrial fields as a core technology to ensure critical task communication. Through a series of protocols and standards, TSN technology realizes the determinacy and low delay of network communication, and ensures that the data transmission between industrial devices can be completed within the specified time window.

[0003] In the related art, in the network based on the TSN technology, the corresponding transmission parameter scheme is usually configured for the network in advance before the network nodes communicate, such as the scheduling shaper used. However, since the network state in the network is usually changing, and the network nodes in the network may be added or removed, thereby causing the communication demand of the network nodes to change. Therefore, in the changing network, the original transmission parameter scheme is still adopted, which will cause the communication demand of the network nodes to be unable to be met due to insufficient flexibility. SUMMARY

[0004] The embodiments of the present application provide a network transmission parameter optimization method, device, electronic equipment and storage medium, which can improve the flexibility of transmission parameter configuration in a complex changing network to meet the communication demand of network nodes.

[0005] To achieve the above object, a first aspect of the embodiments of the present application provides a network transmission parameter optimization method, which comprises:

[0006] Obtaining the traffic transmission demand parameter of at least one network node in the network and the network state information between each two network nodes;

[0007] Based on the network state information and the traffic transmission demand parameter, calculating the maximum transmission delay corresponding to a plurality of different traffic shaping strategies, and determining an optimized traffic shaping strategy from the plurality of traffic shaping strategies according to the smallest maximum transmission delay;

[0008] Adjusting the network transmission parameter of at least one network node in the network based on the optimized traffic shaping strategy.

[0009] In some embodiments, the calculating the maximum transmission delay corresponding to each of the plurality of traffic shaping strategies based on the network status information and the traffic transmission requirement parameter comprises:

[0010] calculating a data arrival curve function based on the traffic transmission requirement parameter;

[0011] calculating a data service curve function of each of the traffic shaping strategies based on the network status information and the traffic transmission requirement parameter;

[0012] calculating the maximum transmission delay corresponding to each of the traffic shaping strategies based on the data arrival curve function and the data service curve function of each of the traffic shaping strategies.

[0013] In some embodiments, the calculating the data arrival curve function based on the traffic transmission requirement parameter comprises:

[0014] obtaining a plurality of traffic data types and a traffic arrival rate of each traffic data type from the traffic transmission requirement parameter;

[0015] calculating a data arrival curve function of each traffic data type based on the traffic arrival rate.

[0016] In some embodiments, the traffic data types comprise scheduled traffic, high-priority traffic and low-priority traffic, the traffic arrival rate comprises a scheduled traffic arrival period of the scheduled traffic, a high-priority arrival rate of the high-priority traffic and a low-priority arrival rate of the low-priority traffic, and the calculating the data arrival curve function of each traffic data type based on the traffic arrival rate comprises:

[0017] obtaining a maximum scheduled frame length of each scheduled traffic, and calculating the data arrival curve function of the scheduled traffic based on a product of all the maximum scheduled frame lengths, a time parameter and a division result of the scheduled traffic arrival period;

[0018] obtaining a high-priority traffic conflict parameter of the high-priority traffic, and calculating the data arrival curve function of the high-priority traffic based on a sum of the high-priority arrival rate and the high-priority traffic conflict parameter;

[0019] obtaining a low-priority traffic conflict parameter of the low-priority traffic, and calculating the data arrival curve function of the low-priority traffic based on a sum of the low-priority arrival rate and the low-priority traffic conflict parameter.

[0020] In some embodiments, the calculating the data service curve function of each of the traffic shaping strategies based on the network status information and the traffic transmission requirement parameter comprises:

[0021] obtaining a shaper combination strategy of each of the traffic shaping strategies, each of the shaper combination strategies comprising at least one shaper connected in series;

[0022] calculating a shaper service curve function corresponding to each of the shapers based on the network status information and the traffic transmission requirement parameters;

[0023] combining the shaper service curve function and the shaper combination strategy to obtain the data service curve function of each of the traffic shaping strategies.

[0024] In some embodiments, the shapers comprise an asynchronous traffic shaper, a time-aware shaper, and a credit shaper, and the calculating of the shaper service curve function corresponding to each of the shapers based on the network status information and the traffic transmission requirement parameters comprises:

[0025] obtaining a minimum scheduling frame length of the scheduled traffic, a high-priority credit slope of the high-priority traffic, a low-priority burst period of the low-priority traffic, a message queue arrival rate, and a message queue conflict parameter from the traffic transmission requirement parameters, and obtaining a transmission link speed, a scheduling rate accumulation of the scheduled traffic, and a guard band rate from the network status information;

[0026] obtaining a maximum delay of the scheduled traffic based on the minimum scheduling frame length, and obtaining a shaper service curve function of the time-aware shaper based on a difference between the time parameter and the maximum delay multiplied by the transmission link speed;

[0027] obtaining a high-priority effective rate of the high-priority traffic based on the high-priority credit slope, the transmission link speed, the scheduling rate accumulation, and the guard band rate, and obtaining a high-priority service curve function based on a product of the high-priority effective rate and the time parameter;

[0028] obtaining a low-priority delay of the low-priority traffic based on the low-priority burst period, the transmission link speed, and the scheduling rate accumulation, obtaining a low-priority service curve function based on a difference between the time parameter and the low-priority delay, and obtaining a shaper service curve function of the credit shaper based on the high-priority service curve function and the low-priority service curve function;

[0029] calculating a shaper service curve function of the asynchronous traffic shaper based on the message queue arrival rate and the message queue conflict parameter.

[0030] In some embodiments, the obtaining of the high-priority service curve function based on the product of the high-priority effective rate and the time parameter comprises:

[0031] obtaining a total length of the allocation window of the scheduled traffic, and obtaining a minimum window resource item based on a ratio of the total length of the allocation window and the cumulative amount of the scheduling rate;

[0032] obtaining a low-priority minimum window length and a low-priority maximum frame length of the low-priority traffic, and obtaining a low-priority window frame length based on a sum of the low-priority minimum window length and the low-priority maximum frame length;

[0033] obtaining an available link transmission item based on a difference between the transmission link speed and the cumulative amount of the scheduling rate, obtaining a high-priority available window resource item based on a ratio of the low-priority window frame length and the available link transmission item, and accumulating the minimum window resource item and the high-priority available window resource item to obtain a high-priority delay;

[0034] obtaining the high-priority service curve function based on a difference between the time parameter and the high-priority delay, and multiplying the high-priority effective rate.

[0035] In some embodiments, the calculation of the shaper service curve function of the asynchronous traffic shaper based on the message queue arrival rate and the message queue conflict parameter comprises:

[0036] obtaining a message queue output value based on a product of the message queue arrival rate and the time parameter, and adding the message queue conflict parameter, and accumulating the message queue output values of all message queues to obtain an output arrival curve of the asynchronous traffic shaper;

[0037] accumulating the output arrival curves of all traffic data types to obtain an output arrival curve cumulative amount;

[0038] performing priority traffic screening based on the output arrival curve cumulative amount to obtain a reorganized arrival curve;

[0039] obtaining the shaper service curve function of the asynchronous traffic shaper based on a difference between the time parameter and the reorganized arrival curve, and multiplying the transmission link speed.

[0040] In some embodiments, the adjustment of the network transmission parameter of at least one network node in the network based on the optimized traffic shaping strategy comprises:

[0041] generating a configuration file containing the network transmission parameter of the network node based on the optimized traffic shaping strategy;

[0042] performing security verification on the configuration file to obtain a security verification result;

[0043] When the security verification result represents that the configuration file is correct, the configuration file is sent to all the network nodes.

[0044] To achieve the above object, a second aspect of embodiments of the present application provides a network transmission parameter optimization device, the device comprising:

[0045] a data acquisition module configured to acquire traffic transmission demand parameters of at least one network node in a network and network state information between each two network nodes;

[0046] a shaping strategy selection module configured to calculate maximum transmission delays corresponding to a plurality of different traffic shaping strategies based on the network state information and the traffic transmission demand parameters, and determine an optimized traffic shaping strategy from the plurality of traffic shaping strategies according to the smallest maximum transmission delay;

[0047] a configuration optimization module configured to adjust network transmission parameters of at least one network node in the network based on the optimized traffic shaping strategy.

[0048] To achieve the above object, a third aspect of embodiments of the present application provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the network transmission parameter optimization method of the first aspect when executing the computer program.

[0049] To achieve the above object, a fourth aspect of embodiments of the present application provides a storage medium, the storage medium being a computer readable storage medium, the storage medium storing a computer program, and the computer program implementing the network transmission parameter optimization method of the first aspect when executed by a processor.

[0050] The network transmission parameter optimization method, device, electronic equipment and storage medium provided by the embodiment of the application, the method comprises the following steps: firstly, obtaining the traffic transmission demand parameter of at least one network node in a network and the network state information between each two network nodes; then, calculating the maximum transmission delay corresponding to a plurality of different traffic shaping strategies based on the network state information and the traffic transmission demand parameter, determining an optimized traffic shaping strategy from the plurality of traffic shaping strategies according to the minimum maximum transmission delay; and finally, adjusting the network transmission parameter of at least one network node in the network based on the optimized traffic shaping strategy. The embodiment of the application uses the real-time network, the traffic transmission demand parameter of a plurality of network nodes and the network state information between each two network nodes to calculate the maximum transmission delay in the worst case corresponding to a plurality of different traffic shaping strategies, so as to select the optimized traffic shaping strategy with the minimum maximum transmission delay as the transmission parameter configuration under the current network state, thereby effectively improving the flexibility of the transmission parameter configuration of the network nodes in the complex changing network, effectively improving the transmission rate between the network nodes, and further meeting the high communication demand of the network nodes.

[0051] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 is a structure schematic diagram of a network based on TSN technology provided by an embodiment of the application.

[0053] Figure 2 is a flowchart of a network transmission parameter optimization method provided by another embodiment of the application.

[0054] Figure 3 is a mode structure schematic diagram of a plurality of traffic shaping strategies provided by another embodiment of the application.

[0055] Figure 4 is Figure 2 is a flowchart of step 202 in the method.

[0056] Figure 5 is Figure 4 is a flowchart of step 401 in the method.

[0057] Figure 6 is Figure 5 is a flowchart of step 502 in the method.

[0058] Figure 7 is Figure 4 is a flowchart of step 402 in the method.

[0059] Figure 8 is Figure 7 the watershed map in step 702.

[0060] Figure 9 is Figure 8 the watershed map in step 803.

[0061] Figure 10 is Figure 8 the watershed map in step 805.

[0062] Figure 11 is Figure 2 the flow chart in step 203.

[0063] Figure 12 is a flow chart of a network transmission parameter optimization method according to another embodiment of the present application

[0064] Figure 13 is a structural diagram of a network transmission parameter optimization apparatus according to an embodiment of the present application.

[0065] Figure 14 is a hardware structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0066] In order to make the objects, technical solutions and advantages of the present application clearer, 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 described herein are only used to explain the present application and should not be used to limit the present application.

[0067] It should be noted that although the functional modules are divided in the apparatus schematic diagram, and the logical order is shown in the flow chart, in some cases, the steps shown or described can be executed in a manner different from the module division in the apparatus or the order in the flow chart.

[0068] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description herein is for describing the embodiments of the present application only and is not intended to limit the present application.

[0069] First, several terms involved in the present application are analyzed:

[0070] Time-Sensitive Networking (TSN) is a network technology designed specifically for deterministic traffic transmission. It achieves precise time synchronization between devices through a series of IEEE standards (such as 802.1AS rev, IEEE 1588), ensuring that the clocks in the network are accurately aligned. TSN provides low-latency Ethernet services for real-time audio and video services by reserving bandwidth, prioritizing traffic, and synchronizing time. It also introduces time-scheduled traffic and frame preemption mechanisms, allowing time-sensitive and non-time-sensitive data streams to be mixed and interleaved, while obtaining accurate data information at the source, quickly diagnosing and repairing transmission anomalies in the network. TSN also implements bounded low-latency and zero-congestion packet communication through various mechanisms such as traffic scheduling, traffic shaping, and frame preemption, prioritizing the transmission of time-sensitive packets. These mechanisms are reflected in IEEE 802.1 series standards, such as 802.1Qav, 802.1Qbv, 802.1Qbu, etc., which can handle different traffic categories to achieve different quality of service in multi-hop switching networks. In summary, TSN technology provides a reliable network solution for industries that require strict timing and reliability, such as industrial automation, automotive systems, robotics, and critical Internet of Things applications.

[0071] Time-Triggered Symmetric Component (TAS) is a communication method based on time-triggered mechanisms, which uses Gate Control List (GCL) to plan and control the sending time of data. In TAS, the main object of communication planning is the opening and closing time of different queue time windows, ensuring that data is sent within the predetermined time window, thereby achieving global consistency recognition of the transmission time window.

[0072] Asymmetric Time-Triggered Component (ATS) is a shaping method that supports Per-flow, which considers the hop-by-hop transmission process of target traffic and uses interleaving-based shaping to shape and reshape traffic. ATS can implement explicit parameter configuration for specific flows, so that target traffic can meet the expected output constraint target at each transmission node.

[0073] Cyclic Buffer Symmetric Component (CBS) is a Credit Based Shaper, which adopts Per-class shaping method. CBS associates the output sending and queuing process of different categories of traffic with the credit consumption and accumulation process of the corresponding category queue, realizing real-time transmission control of traffic in different category queues. CBS cannot realize explicit parameter configuration for specific flow (Flow), and can only adjust by changing the parameters of the category to which the target traffic belongs.

[0074] With the transformation of digitalization and intelligentization in the field of industrial manufacturing, the determinism and reliability of industrial network transmission are becoming increasingly stringent. Time-Sensitive Networking (TSN) technology, as a core technology to ensure critical task communication, has become a research hotspot in academia and industry in recent years. TSN technology realizes the determinism and low delay of network communication through a series of protocols and standards, ensuring that data transmission between industrial devices can be completed within the specified time window.

[0075] In related technologies, in a network based on TSN technology, a corresponding transmission parameter scheme is usually configured for the network in advance before the network nodes communicate, such as the scheduling shaper used. However, since the network state in the network is usually changing, and there may be addition or removal of network nodes in the network, resulting in changes in the communication requirements of the network nodes. Therefore, in the changing network, the original transmission parameter scheme is still adopted, which will not be flexible enough to meet the communication requirements of the network nodes.

[0076] In order to improve the flexibility of transmission parameter configuration in a complex and changing network to meet the communication requirements of network nodes, the embodiments of the present application utilize the real-time network, the traffic transmission requirement parameters of multiple network nodes, and the network state information between each two network nodes to calculate the maximum transmission delay in the worst case corresponding to multiple different traffic shaping strategies, to select the optimal traffic shaping strategy with the minimum maximum transmission delay as the transmission parameter configuration under the current network state, to effectively improve the flexibility of network node transmission parameter configuration in a complex and changing network, and effectively improve the transmission rate between network nodes, thereby meeting the high communication requirements of network nodes.

[0077] In order to better describe the network transmission parameter optimization method provided by the present application, first, a network based on TSN technology applied to the network transmission parameter optimization method is described below. Referring to Figure 1 is a structural schematic diagram of a network based on TSN technology provided by an embodiment of the present application. As shown inFigure 1 As shown, the TSN-based network includes multiple network nodes, each comprising multiple End Systems (ES) and associated switches (SWs). ESs communicate with SWs, and any two SWs can also communicate with each other. All network nodes are configured with relevant traffic shaping protocol stacks and support precise clock synchronization (such as IEEE 802.1AS-Rev and IEEE 1588). Furthermore, a network controller is connected to all network nodes to adjust their transmission parameter configurations in real time.

[0078] Based on the aforementioned TSN-based network, the network transmission parameter optimization method, apparatus, electronic device, and storage medium provided in the embodiments of this application will be further described below. The network transmission parameter optimization method provided in the embodiments of this application can be applied to any network node with computing capabilities in a network controller network, or to a processing controller connected to a TSN-based network.

[0079] The network transmission parameter optimization method in the embodiments of this application will be described in detail below. (Refer to...) Figure 2 This is an optional flowchart of the network transmission parameter optimization method provided in the embodiments of this application. Figure 2 The method may include, but is not limited to, steps 201 to 203. It is also understood that this embodiment... Figure 2 The order of steps 201 to 203 is not specifically limited. The order of steps can be adjusted or some steps can be reduced or added according to actual needs.

[0080] Step 201: Obtain the traffic transmission demand parameters of at least one network node in the network and the network status information between every two network nodes.

[0081] Step 201 will be described in detail below.

[0082] In some embodiments, for example Figure 1 In the TSN-based network shown, in order to better meet the traffic transmission needs of network nodes, the network controller needs to obtain the traffic transmission requirements of all network nodes in the network and the network status information between every two network nodes in the network in advance through network management protocols (such as SNMP, NETCONF, etc.) or customized monitoring mechanisms, and store them in the controller's memory for analysis and configuration of transmission parameters for network nodes in the network.

[0083] The traffic transmission requirement parameters include the type of traffic data to be transmitted by the network node, the priority of each type of traffic data, the maximum delay of each type of traffic data, and the jitter tolerance of each type of traffic data, etc.

[0084] Generally, the type of traffic data includes control data / scheduled traffic (CDT / Scheduled Traffic, ST), high priority traffic in Audio Video Bridging (AVB) (which includes AVB traffic class A and AVB traffic class B), and best-effort traffic (B.E., i.e., low priority traffic).

[0085] In this embodiment, the control data / scheduled traffic is referred to as scheduled traffic (ST), which has a hard deadline and is characterized by periodic transmission. For example, the value detected by a sensor must be sent to a computing unit within the deadline to provide a feedback value for the control system. This type of data is given the highest priority and is not allowed to be preempted by the transmission of other types of traffic to ensure deterministic transmission of this type of data traffic.

[0086] For AVB traffic class A in high priority traffic, it is non-critical traffic but requires bounded delay and bandwidth guarantees. Data frames from these streams have the characteristics of discrete, large packets, and burst. Visual inspection of the detected product is an example of this type of data traffic in an industrial network. After allocating sufficient bandwidth for critical traffic streams, the remaining bandwidth is shared by non-critical traffic streams. This includes class A traffic. In addition, the network node allocates class A traffic to a high priority queue to guarantee the upper bound of end-to-end delay.

[0087] For AVB traffic class B in high priority traffic, it is non-critical traffic with strict bandwidth requirements. Data frames in class B traffic have large bandwidth requirements and real-time requirements, and consistent allocation of network resources is required to ensure the quality of service requirements. Video traffic flowing through the Industrial Process Monitoring Unit (IPMU) is a typical source of class B frames. Although the network node assigns a lower priority to class B traffic compared to class A traffic, the token-based rate shaping technique applied to class A creates transmission opportunities for class B.

[0088] For low priority traffic (B.E.), it is non-critical traffic with no strict QoS requirements, which can be the result of an Ethernet device or a human-machine interface connected to an IPMU node. The network node allocates unused bandwidth to carry best-effort frames. It can be web browsing data or file transfer data.

[0089] In addition, the network state information between each two network nodes usually includes: 1) link state: performance indicators of each link such as bandwidth utilization, delay, jitter, etc.; 2) device state: processing capacity, queue length, resource usage, etc. of each network node in the network; 3) topology structure: changes of the current network topology, such as newly added or removed network nodes and link failures between network nodes, etc.

[0090] Step 202: calculating the maximum transmission delay corresponding to a plurality of different traffic shaping strategies based on the network state information and the traffic transmission demand parameters, and determining the optimized traffic shaping strategy from the plurality of traffic shaping strategies according to the minimum maximum transmission delay.

[0091] The step 202 is described in detail as follows.

[0092] In some embodiments, after obtaining the traffic transmission demand parameters of all network nodes in the network and the network state information between each two network nodes in real time, all the network state information and the traffic transmission demand parameters are used to calculate the maximum transmission delay corresponding to a plurality of different traffic shaping strategies constructed in advance, so as to select the traffic shaping strategy with the minimum maximum transmission delay from the plurality of traffic shaping strategies as the optimized traffic shaping strategy.

[0093] Reference Figure 3 Fig. 1 is a mode structure schematic diagram of a plurality of traffic shaping strategies provided by the embodiments of the present application. As shown in Fig. 1, the mode structure of the plurality of traffic shaping strategies includes a plurality of traffic shaping strategies 101, 102, 103, 104 and 105. Figure 3In the embodiments of the present application, eight traffic shaping strategies are constructed in advance based on the arrangement and combination of time-triggered symmetric component TAS (i.e. time-aware shaper), asymmetric time-triggered component ATS (i.e. asynchronous traffic shaper) and cyclic buffer symmetric component CBS (i.e. credit shaper). For any network node (i.e. end system ES / switch SW) in the process of traffic transmission, the first traffic shaping strategy is to transmit the transmission data directly through the time-aware shaper TAS, the second traffic shaping strategy is to transmit the transmission data through the time-aware shaper TAS and the credit shaper CBS in sequence, the third traffic shaping strategy is to transmit the transmission data through the time-aware shaper TAS and the asynchronous traffic shaper ATS in parallel, the fourth traffic shaping strategy is to transmit the transmission data through the time-aware shaper TAS and the asynchronous traffic shaper ATS in parallel and then through the credit shaper CBS, the fifth traffic shaping strategy is to transmit the transmission data directly through the asynchronous traffic shaper ATS, the sixth traffic shaping strategy is to transmit the transmission data through the asynchronous traffic shaper ATS and the credit shaper CBS in sequence, the seventh traffic shaping strategy is to transmit the transmission data directly through the credit shaper CBS, and the eighth traffic shaping strategy is to transmit the transmission data directly without using any shaper.

[0094] Based on the above description of the network state information, the traffic parameter demand parameter and the plurality of traffic shaping strategies, how to calculate the maximum transmission delay in the worst case corresponding to different traffic shaping strategies will be further described below.

[0095] With reference to Figure 4 , the maximum transmission delay corresponding to a plurality of different traffic shaping strategies is calculated based on the network state information and the traffic transmission demand parameter, including the following steps 401 to 403.

[0096] Step 401: The data arrival curve function is calculated based on the traffic transmission demand parameter.

[0097] Step 401 will be described in detail below.

[0098] In some embodiments, in order to calculate the maximum transmission delay corresponding to each traffic shaping strategy, the data arrival curve function corresponding to each traffic data type and the data service curve function corresponding to each traffic shaping strategy need to be calculated first, so as to calculate the corresponding maximum transmission delay according to the data arrival curve function and the data service curve function.

[0099] With reference to Figure 5 , the data arrival curve function is calculated based on the traffic transmission demand parameter, including the following steps 501 to 502.

[0100] Step 501: Obtain multiple traffic data types from the traffic transmission requirement parameters, and the traffic arrival rate of each traffic data type.

[0101] Step 502: Calculate the data arrival curve function of each traffic data type based on the traffic arrival rate.

[0102] The steps 501 to 502 are described in detail below.

[0103] In some embodiments, first, multiple traffic data types are obtained from the traffic transmission requirement parameters, including scheduled traffic ST, high-priority traffic A, high-priority traffic B, and low-priority traffic BE; then the traffic arrival rate corresponding to each traffic data type is determined, including the scheduled traffic arrival period T i of the scheduled traffic ST, the high-priority arrival rate r AVB of the high-priority traffic (including r AVB,A of the A class and r AVB,B of the B class), and the low-priority arrival rate r BE of the low-priority traffic.

[0104] Next, based on the traffic arrival rate of each traffic data type, the data arrival curve function of each traffic data type pair is calculated as follows.

[0105] Referring to Figure 6 , based on the traffic arrival rate, the data arrival curve function of each traffic data type is calculated, including the following steps 601 to 603.

[0106] Step 601: Obtain the maximum scheduling frame length of each scheduled traffic, and based on the product of all maximum scheduling frame lengths and a time parameter, and divided by the scheduled traffic arrival period, obtain the data arrival curve function of the scheduled traffic.

[0107] Step 602: Obtain the high-priority traffic conflict parameter of the high-priority traffic, and based on the sum of the high-priority arrival rate and the high-priority traffic conflict parameter, obtain the data arrival curve function of the high-priority traffic.

[0108] Step 603: Obtain the low-priority traffic conflict parameter of the low-priority traffic, and based on the sum of the low-priority arrival rate and the low-priority traffic conflict parameter, obtain the data arrival curve function of the low-priority traffic.

[0109] The steps 601 to 603 are described in detail below.

[0110] In some embodiments, first, the maximum scheduling frame length L iThen, based on the multiplication of all the maximum scheduling frame length and the time parameter t, and the division of the scheduling traffic arrival period, the data arrival curve function of the scheduling traffic is obtained as shown in the following formula (1).

[0111]

[0112] Next, the high-priority traffic conflict parameter b of the high-priority traffic (including the A-class and the B-class) is obtained. AVB Wherein, the arrival curve of the high-priority traffic is subject to the constraint of the token bucket, that is, based on the sum of the high-priority arrival rate and the high-priority traffic conflict parameter, the data arrival curve function of the high-priority traffic is obtained as shown in the following formula (2).

[0113] α AVB (t)=r AVB t+b AVB (2)

[0114] It can be understood that the high-priority traffic includes the A-class data and the B-class data, and therefore the data arrival curve function of the high-priority traffic also includes the pair of α AVB,A (t) corresponding to the A-class data and the pair of α AVB,B (t) corresponding to the B-class data.

[0115] Similarly, the low-priority traffic conflict parameter b of the low-priority traffic is obtained. BE Based on the sum of the low-priority arrival rate and the low-priority traffic conflict parameter, the data arrival curve function of the low-priority traffic is obtained as shown in the following formula (3).

[0116] α BE (t)=r BE t+b BE (3)

[0117] Through the above steps 601 to 603, by accurately calculating the traffic arrival rate and the traffic conflict parameter corresponding to each type of traffic data, the data arrival curve function of each type of traffic data is accurately obtained, so as to accurately calculate the maximum transmission delay corresponding to each type of traffic data by using the data arrival curve function, so as to ensure the real-time and deterministic communication in the network, thereby reducing the delay and conflict, and improving the overall efficiency and reliability of the network.

[0118] Step 402: based on the network state information and the traffic transmission demand parameter, the data service curve function of each traffic shaping strategy is calculated.

[0119] The step 402 is described in detail below.

[0120] In some embodiments, after obtaining the data arrival curve function corresponding to each traffic data type, the data service curve function of each traffic shaping strategy is further calculated by using all the network state information and traffic transmission requirement parameters, so as to subsequently calculate the maximum transmission delay corresponding to each traffic shaping strategy by using the data arrival curve function and the data service curve function. How to calculate the data service curve function of each traffic shaping strategy will be further described below.

[0121] With reference to Figure 7 , the data service curve function of each traffic shaping strategy is calculated based on the network state information and the traffic transmission requirement parameters, including the following steps 701 to 703.

[0122] Step 701: Obtain the shaper combination strategy of each traffic shaping strategy.

[0123] Step 702: Calculate the shaper service curve function corresponding to each shaper based on the network state information and the traffic transmission requirement parameters.

[0124] The steps 701 to 702 will be described in detail below.

[0125] In some embodiments, the shaper combination strategy corresponding to each traffic shaping strategy is first determined as shown in Table 1. Figure 3 As can be understood, each shaper combination strategy includes at least one shaper connected in series in sequence, except for the eighth traffic shaping strategy.

[0126] Next, the shaper service curve function corresponding to each shaper is calculated based on all the network state information and the traffic transmission requirement parameters, which will be described in detail as follows.

[0127] With reference to Figure 8 , the shaper service curve function corresponding to each shaper is calculated based on the network state information and the traffic transmission requirement parameters, including the following steps 801 to 805.

[0128] Step 801: Obtain the minimum scheduling frame length of the scheduling traffic, the high-priority credit slope of the high-priority traffic, the low-priority burst period of the low-priority traffic, the message queue arrival rate and the message queue conflict parameter from the traffic transmission requirement parameters, and obtain the transmission link speed, the scheduling rate cumulative amount of the scheduling traffic, and the guard band rate from the network state information.

[0129] Step 802: Obtain the maximum delay of the scheduling traffic based on the minimum scheduling frame length, obtain the shaper service curve function of the time-aware shaper by multiplying the difference between the time parameter and the maximum delay by the transmission link speed.

[0130] Step 803: Based on the high priority credit slope, transmission link speed, cumulative scheduling rate, and guard band rate, obtain the high priority effective rate of high priority traffic, and obtain the high priority service curve function based on the product of the high priority effective rate and the time parameter.

[0131] Steps 801 to 803 are described in detail below.

[0132] In some embodiments, the service curve function of the Time-Aware Shaper (TAS) is first described, and the minimum scheduling frame length L with hop count k in the scheduled traffic ST is obtained from the traffic transmission demand parameters. minST,k High-priority traffic has a high-priority credit slope (including Class A I) A And Class B I B This refers to the idle credit accumulation slope (bit / s) and the low-priority burst period b of the low-priority traffic BE. BE The message queue arrival rate r corresponding to the asynchronous flow shaper f Message queue conflict parameter b f And obtain the transmission link speed c and the cumulative scheduling rate r of the scheduled traffic ST from the network status information. ST,k , Protection belt speed r GB .

[0133] Among them, the protection belt speed r GB =L minPkt / T minPkt L minPkt The length of the minimum non-preemptible portion of the maximum low-priority frame and T represent the sum of these lengths. minPkt Indicates the duration of the protection band. Cumulative dispatch rate r ST,k This represents the sum of the rates of all scheduled traffic STs that have passed through k hops, i.e., r ST,k =∑ i (L i / T i ).

[0134] Then, based on the minimum scheduling frame length L minST,k The maximum delay T of the scheduled traffic is obtained. ST As shown in the following formula (4).

[0135]

[0136] Among them, L ST,k Let represent the total window length allocated to all scheduled traffic frames with hop count k, and c represent the transmission link speed. Then, based on the difference T between the time parameter t and the maximum delay... ST Multiplying this by the transmission link speed c, we obtain the time-aware shaper service curve function as shown in the following formula (5).

[0137] β ST (t) = c x (t - T ST ) (5)

[0138] It can be understood that different data streams are subject to the service curve β(t) when traversing the network node. In order to guarantee the bounded delay of the scheduled traffic ST, a time-aware shaper, i.e., a TAS scheduler, allocates an exclusive transmission window for the scheduled traffic ST, and the service curve function (5) of the transmission window has a rate-latency form.

[0139] Next, the service curve function of the credit shaper CBS is described, which is based on a high-priority credit slope (which includes the I A of the A-class and the I B of the B-class), a transmission link speed c, a scheduled rate accumulation r ST,k , and a guard rate r GB , to obtain a high-priority effective rate of the high-priority traffic as shown in the following formula (6).

[0140]

[0141] In the formula (6), the first term captures the transmission slope / rate of the A-class traffic outside the transmission window of the scheduled traffic, and the second term represents the guard bandwidth that does not allow the A-class transmission. Further, the high-priority service curve function is obtained based on the product of the high-priority effective rate and a time parameter, which is described as follows.

[0142] Referring to Figure 9 , the high-priority service curve function is obtained based on the product of the high-priority effective rate and a time parameter, including the following steps 901 to 904.

[0143] Step 901: Obtain the total length of the allocated window of the scheduled traffic, and obtain a minimum window resource item based on the ratio of the total length of the allocated window and the scheduled rate accumulation.

[0144] Step 902: Obtain a low-priority minimum window length and a low-priority maximum frame length of the low-priority traffic, and obtain a low-priority window frame length based on the sum of the low-priority minimum window length and the low-priority maximum frame length.

[0145] Step 903: Obtain an available link transmission item based on the difference between the transmission link speed and the scheduled rate accumulation, obtain a high-priority available window resource item based on the ratio of the low-priority window frame length and the available link transmission item, and accumulate the minimum window resource item and the high-priority available window resource item to obtain a high-priority delay.

[0146] Step 904: Based on the difference between the time parameter and the high priority delay, and multiplied by the high priority effective rate, the high priority service curve function is obtained.

[0147] Steps 901 to 904 are described in detail below.

[0148] In some embodiments, the total length L of the allocation window for the scheduled traffic ST is first obtained. ST,k And based on the total length L of the allocation window ST,k and scheduling rate accumulation r ST,k The ratio of the minimum window resource item L is obtained. ST,k / r ST,k This represents the worst-case scenario, where a guard band is present and the Class A credit is negative. In this situation, a Class A frame must wait for the guard band duration, the transmission of all scheduled traffic, and the queued best-effort traffic until it accumulates enough positive credit before being allowed to transmit.

[0149] Then, based on the transmission link speed c and the cumulative scheduling rate r ST,k The difference is used to obtain the available link transmission item cr. ST,k Based on low priority window frame length L BE Add the length L of the minimum non-preemptible portion of the lowest priority frame. minPkt,k Divide by

[0150] The ratio of available link transmission items is used to obtain the high priority available window resource item, and the minimum window resource item and the high priority available window resource item are added together to obtain the high priority delay corresponding to Class A traffic as shown in the following formula (7).

[0151]

[0152] Finally, based on the time parameter t and the high-priority delay T corresponding to Class A traffic. A The difference is multiplied by the high-priority effective rate R corresponding to Class A traffic. A The high priority service curve function is obtained as shown in the following formula (8).

[0153] β A (t)=R A ×(tT A (8)

[0154] Similarly, for Class B traffic in high priority traffic, its high priority service curve function can be shown in the following formula (9).

[0155] β B (t)=R B ×(tT B (9)

[0156] where R B represents the high-priority effective rate of the class B traffic, T B represents the maximum delay (i.e., high-priority delay) encountered by a certain bit to reach the empty class B queue. R B and T B can be shown by the following equations (10) and (11).

[0157]

[0158] where I B represents the idle slope of the class B traffic in the high-priority traffic (i.e., high-priority credit slope), L A represents the maximum frame length of the class A traffic. Equation (10) is similar to equation (7) above, and equation (11) corresponds to the worst case where the guard band occurs and the class B credit is negative. Therefore, considering that the class B traffic still needs to accumulate enough positive credit, the frame of the class B traffic has to wait for the guard band duration, the transmission of all scheduled traffic, the class A transmission window, and the queuing of the best effort traffic.

[0159] Step 804: obtaining a low-priority delay of the low-priority traffic based on a low-priority burst period, a transmission link speed, and a scheduling rate accumulation, obtaining a low-priority service curve function based on a difference between a time parameter and the low-priority delay, and obtaining a shaper service curve function of the credit shaper based on the high-priority service curve function and the low-priority service curve function.

[0160] Step 804 is described in detail as follows.

[0161] In some embodiments, in the credit shaper CBS, similar to the high-priority traffic (including the class A and the class B), the low-priority delay of the low-priority traffic is obtained based on a low-priority burst period b BE , a transmission link speed c, and a scheduling rate accumulation r ST,k , as shown in the following equation (12).

[0162]

[0163] Then, based on a difference between a time parameter and the low-priority delay, and multiplied by the transmission link speed c, a low-priority service curve function of the low-priority traffic BE is obtained, as shown in the following equation (13).

[0164] β BE (t) = c x (t - T BE ) (13)

[0165] Then, the shaper service curve function of the credit shaper CBS is obtained based on the high-priority service curve function of the high-priority traffic (which includes the high-priority service curve function of the A-class traffic (8) and the high-priority service curve function of the B-class traffic (9)) and the low-priority service curve function of the BE traffic (13).

[0166] The shaper service curve function of the asynchronous traffic shaper ATS is described in further detail as follows.

[0167] Step 805: The shaper service curve function of the asynchronous traffic shaper is obtained based on the message queue arrival rate and the message queue conflict parameter.

[0168] The steps 804 to 805 are described in further detail as follows.

[0169] In some embodiments, the shaper mechanism of the asynchronous traffic shaper ATS provides a theoretical basis for the combination of the asynchronous traffic shaper ATS with other shapers, because it does not introduce additional delay to the worst-case delay of the First In First Out (FIFO) system. As shown in Figure 3 The shared queue in the ATS shaper is used for traffic of different priorities, and there are at most 8 shaper queues, i.e. the shaper service curve function of the asynchronous traffic shaper can be obtained based on the message queue arrival rate and the message queue conflict parameter, which is described as follows.

[0170] Referring to Figure 10 , the shaper service curve function of the asynchronous traffic shaper is obtained based on the message queue arrival rate and the message queue conflict parameter, including the steps 1001 to 1004.

[0171] Step 1001: The message queue output value is obtained based on the product of the message queue arrival rate and the time parameter, and the message queue conflict parameter is added, and the message queue output values of all message queues are accumulated to obtain the output arrival curve of the asynchronous traffic shaper.

[0172] Step 1002: The output arrival curves of all traffic data types are accumulated to obtain the output arrival curve accumulation.

[0173] Step 1003: The priority traffic is screened based on the output arrival curve accumulation to obtain a reorganized arrival curve.

[0174] Step 1004: The difference between the time parameter and the reorganized arrival curve is multiplied by the transmission link speed to obtain the shaper service curve function of the asynchronous traffic shaper.

[0175] The steps 1001 to 1004 are described in further detail as follows.

[0176] In some embodiments, first, the message queue arrival rate r f is multiplied by the time parameter t, and the message queue collision parameter is added to obtain the message queue output value, and the output arrival curve of the asynchronous traffic shaper ATS is obtained by accumulating the message queue output values of all message queues q k , k e {1, 2, …, 8} as shown in the following formula (14).

[0177]

[0178] wherein the shaping queue Q i , i e {1, 1, m, n, x, y, z, 8} corresponds to different shaping schemes o i , i e {1, 2, …, 8}, the message queue q k represents different priority traffic data frames.

[0179] In addition, according to the queuing scheme, one or more shaped queues q i are connected to the shared queue. Therefore, the input arrival curve of the aggregated flow is the sum of the output arrival curves of all message queues q i connected to the shaping queue Q i , that is, the output arrival curve accumulation is obtained by accumulating the output arrival curves of all traffic data types as shown in the following formula (15).

[0180]

[0181] The service of the traffic in the integrated queue is subject to strict priority scheduling, that is, only when the queue of high-priority traffic is empty, the low-priority flow can obtain service. Then, based on the output arrival curve accumulation, the priority traffic is screened to obtain the reorganized arrival curve which represents the total arrival curve of the ATS reorganized SP flow with the maximum frame length of the traffic with priority j higher than priority i and priority lower than priority i.

[0182] Further, through ATS shaping, the service curve of the SP service with priority i (i e [1, n]) in the shaping queue Q i is given, that is, based on the difference between the time parameter and the reorganized arrival curve, and multiplied by the transmission link speed, the shaper service curve function of the asynchronous traffic shaper is obtained as shown in the following formula (16).

[0183]

[0184] wherein [x] + = max{0, x}.

[0185] Through the steps 1001 to 1004, the steps 901 to 904, and the steps 801 to 805, the shaping mechanism of the time-aware shaper TAS, the credit-based shaper CBS, and the asynchronous traffic shaper ATS is utilized, and further the current network state information and the traffic transmission requirement parameter, and the different traffic data types are utilized, so as to accurately obtain the shaper service curve function corresponding to each shaper, so as to accurately obtain the maximum delay of each traffic shaping strategy by using the shaper service curve function, so as to select the most suitable optimized traffic shaping strategy, thereby improving the transmission rate of the network.

[0186] Step 703: Based on the shaper service curve function and the shaper combination strategy, the data service curve function of each traffic shaping strategy is obtained by combination.

[0187] The steps 701 to 703 are described in detail as follows.

[0188] In some embodiments, based on the shaper service curve functions of the time-aware shaper TAS, the credit-based shaper CBS, and the asynchronous traffic shaper ATS, and the data arrival curve function corresponding to each traffic data type, and the combination structure of the shaper combination strategy corresponding to the multiple traffic shaping strategies as shown in the following formula (16), the data service curve function of each traffic shaping strategy can be obtained by combination (such as series accumulation). Figure 3

[0189] For the shaper combination strategy of at least two shapers in series, the traffic data arrival service curve of the shaper after the shaper is the port output arrival curve α'(t) of the shaper before the shaper, which is the constraint output cumulative function R * (t), that is, the arrival curve of the input flow of the next hop node is as shown in the following formula (17).

[0190]

[0191] Step 403: Based on the data arrival curve function and the data service curve function of each traffic shaping strategy, the maximum transmission delay corresponding to each traffic shaping strategy is calculated.

[0192] The steps 401 to 403 are described in detail as follows.

[0193] In some embodiments, based on the above description, based on the data arrival curve function of each traffic data type and the data service curve function of each traffic shaping strategy, the maximum transmission delay corresponding to each traffic shaping strategy can be calculated as shown in the following formula (18).

[0194]

[0195] ​That is, according to the network calculus framework, a flow Where the traffic class cls variable includes one or more classes in cls∈{ST,A,B,BE}, the relevant parameters of a single hop k can be defined as: per-hop delay D(α,β), which represents the delay experienced by the data flow, i.e., the maximum horizontal deviation between α(t) and β(t), i.e., the above formula (18).

[0196] It can be understood that sup{.} refers to the minimum value of all upper bounds of a set, that is, the smallest upper bound. The per-hop buffer size B(s) represents the maximum buffer size, i.e., the maximum vertical deviation between α(t) and β(t) as shown in the following formula (19).

[0197]

[0198] Based on this, the traffic shaping strategy with the minimum maximum transmission delay under the condition of the traffic transmission demand parameters of all network nodes and the network state information between each two network nodes can be selected from a plurality of traffic shaping strategies as shown in Figure 3 to optimize the traffic shaping strategy, so as to subsequently configure the transmission parameters of the network nodes in the network by using the optimized traffic shaping strategy to improve the data transmission rate of each network node in the network, thereby flexibly meeting the communication requirements of the network nodes.

[0199] Step 203: Adjust the network transmission parameters of at least one network node in the network based on the optimized traffic shaping strategy.

[0200] The following describes step 203 in detail.

[0201] In some embodiments, after determining the optimized traffic shaping strategy, the network transmission parameters of all relevant network nodes in the network are adjusted by using the optimized traffic shaping strategy to improve the data transmission rate of each network node in the network, thereby flexibly meeting the communication requirements of the network nodes, which will be further described in detail below.

[0202] Referring to Figure 11 , the network transmission parameters of at least one network node in the network are adjusted based on the optimized traffic shaping strategy, including the following steps 1101 to step 1103.

[0203] Step 1101: Based on the optimized traffic shaping strategy, a configuration file containing the network transmission parameters of the network nodes is generated.

[0204] Step 1102: The configuration file is subjected to security verification to obtain a security verification result.

[0205] Step 1103: When the security verification result indicates that the configuration file is correct, the configuration file is sent to all network nodes.

[0206] The steps 1101 to 1103 are described in detail below.

[0207] In some embodiments, based on the optimized traffic shaping strategy, a corresponding configuration file containing network transmission parameters in the network nodes will be generated. Specifically, according to the traffic characteristics and configuration parameters, the configuration parameters include: for the time-aware shaper TAS, it is necessary to generate the GCL table of the time-aware shaper TAS based on the periodicity and priority of the traffic; for the asynchronous traffic shaper ATS, it is necessary to configure the burst allowance and traffic shaping rate of the asynchronous traffic shaper ATS according to the burst characteristics of the traffic; for the credit-based shaper CBS: configure the initial credit limit, credit increment and decrement and other parameters for each traffic. In addition, the configuration parameters should also include the routing table and node identification of each network traffic. All the above configuration parameters are generated into a unified XML configuration file. These files adopt a hierarchical structure and clearly separate the parameter configurations of different traffic shaping schemes.

[0208] Next, the generated XML configuration file needs to be subjected to security verification to obtain a security verification result, so as to ensure that it is correct in format and complete in content. The verification tool will check the legality of the configuration to prevent network failures caused by configuration errors.

[0209] When the verification passes (i.e., the security verification result indicates that the configuration file is correct), the configuration file will be downloaded to each network node through a network management protocol (such as NETCONF, SNMP), and the network node returns a configuration file confirmation frame after receiving it. During network operation, the configuration file can be dynamically adjusted according to the actual traffic changes. For example, the priority of some traffic is increased or decreased, the bandwidth allocation is adjusted, etc. The adjusted configuration file can be updated immediately and re-downloaded to the network node to adapt to the new network requirements.

[0210] In some embodiments, after each network node receives the configuration file, the network transmission parameter configuration process of the network node (including the switch and the end system) is entered. Each network node reads and parses the configuration file, generates configuration instructions, and maps the instructions to the hardware control logic. Specifically, it includes the transmission mode configuration module (such as the TAS module, the ATS module, and the CBS module) of each network node, which configures the network transmission parameters in the network node according to the configuration file. Figure 1As shown, after receiving the XML configuration file, the network node stores it in the non-volatile memory of the node for subsequent parsing and use. Before the network node parses, the network node first performs integrity verification on the received XML configuration file. Using a hash algorithm (such as SHA-256) to verify whether the file has been tampered with or damaged during transmission. After verification, the file parsing link is entered; otherwise, the node will issue an error alarm and request to reissue the configuration file. After that, the software system of the network node starts the XML parser (usually a lightweight XML parsing library such as libxml2), loads the configuration file and prepares to parse the TSN parameter configuration items of each network node. The parser reads and parses the content line by line according to the structure of the XML file, and generates configuration instructions, and maps the instructions to the hardware control logic.

[0211] In some embodiments, after the network transmission parameter configuration of each network node is completed, the basic operating system and the TSN network protocol stack are reloaded to ensure efficient collaborative transmission of various types of traffic in the TSN network. Specifically, each configuration module of the network node generates hardware executable instructions according to the parsed data. The instructions include updating the time schedule table, loading the traffic shaping strategy, configuring the credit mechanism, etc. These instructions are issued to the hardware layer of the TSN node through the high-speed bus, such as FPGA, ASIC or other embedded processors. After the hardware layer of the network node receives the instructions, it configures the internal registers, schedulers and queues according to the instruction content. For example, the time-aware shaper TAS loads the new time schedule table, and the credit shaper CBS updates the credit control logic. After the hardware configuration is completed, the network node enters the working state and is ready to process the actual network traffic.

[0212] Through the above steps 1101 to 1103, the configuration file corresponding to the optimized traffic shaping strategy is generated, and the security verification is performed on the configuration file to avoid unexpected security incidents, thereby improving the reliability and security of the network transmission parameter optimization, and after the security is passed, the configuration file is sent to each network node for configuration, thereby improving the flexibility of the transmission parameter configuration in each network node in the network, and further improving the communication efficiency between the network nodes to meet the communication requirements of the network nodes.

[0213] Reference Figure 12 is a flowchart of a network transmission parameter optimization method provided by an embodiment of the present application. As shown in Figure 12As shown in the figure, the traffic shaping strategy is optimized based on the traffic transmission demand parameters and network state information of the current network nodes in the network, which are obtained in real time in the TSN network controller in the TSN network. Then it is determined whether there is a pre-specified traffic shaping strategy. If not, the end-to-end maximum delay corresponding to each traffic shaping strategy is derived based on different traffic transmission demand parameters and network state information using the corresponding network calculus model, and the traffic shaping strategy with the minimum maximum delay is selected as the optimized traffic shaping strategy. The configuration parameters corresponding to the optimized traffic shaping strategy are obtained to generate a configuration file and store it in XML format and issue it to each network node. After receiving the configuration file, the network node performs the network transmission parameter configuration process and reloads the protocol stack to enter the working state.

[0214] The network transmission parameter optimization method, device, electronic equipment and storage medium provided by the embodiments of the present application comprise the following steps: first, obtaining the traffic transmission demand parameters of at least one network node in the network and the network state information between each two network nodes; then, obtaining multiple traffic data types and the traffic arrival rate of each traffic data type from the traffic transmission demand parameters, obtaining the maximum scheduling frame length of each scheduling traffic, multiplying all the maximum scheduling frame lengths by a time parameter, dividing by the scheduling traffic arrival period to obtain the data arrival curve function of the scheduling traffic, obtaining the high-priority traffic conflict parameter of the high-priority traffic, obtaining the data arrival curve function of the high-priority traffic based on the sum of the high-priority arrival rate and the high-priority traffic conflict parameter, obtaining the low-priority traffic conflict parameter of the low-priority traffic, obtaining the data arrival curve function of the low-priority traffic based on the sum of the low-priority arrival rate and the low-priority traffic conflict parameter, obtaining the shaper combination strategy of each traffic shaping strategy, each shaper combination strategy comprising at least one shaper connected in series, obtaining the minimum scheduling frame length of the scheduling traffic, the high-priority credit slope of the high-priority traffic, the low-priority burst period of the low-priority traffic, the message queue arrival rate and the message queue conflict parameter from the traffic transmission demand parameters, and obtaining the transmission link speed, the scheduling rate cumulative amount of the scheduling traffic and the guard band rate from the network state information, obtaining the maximum delay of the scheduling traffic based on the minimum scheduling frame length, obtaining the shaper service curve function of the time-aware shaper based on the difference between the time parameter and the maximum delay and the product of the transmission link speed, obtaining the high-priority effective rate of the high-priority traffic based on the high-priority credit slope, the transmission link speed, the scheduling rate cumulative amount and the guard band rate, and obtaining the high-priority service curve function based on the product of the high-priority effective rate and the time parameter, obtaining the low-priority delay of the low-priority traffic based on the low-priority burst period, the transmission link speed and the scheduling rate cumulative amount, obtaining the low-priority service curve function based on the difference between the time parameter and the low-priority delay, and obtaining the shaper service curve function of the credit shaper based on the high-priority service curve function and the low-priority service curve function, calculating the shaper service curve function of the asynchronous traffic shaper based on the message queue arrival rate and the message queue conflict parameter, combining the shaper service curve function and the shaper combination strategy to obtain the data service curve function of each traffic shaping strategy, calculating the maximum transmission delay corresponding to each traffic shaping strategy based on the data arrival curve function and the data service curve function of each traffic shaping strategy, and determining the optimized traffic shaping strategy from the multiple traffic shaping strategies according to the smallest maximum transmission delay; finally, generating a configuration file containing the network transmission parameters of the network nodes based on the optimized traffic shaping strategy, performing security verification on the configuration file to obtain a security verification result, and sending the configuration file to all the network nodes when the security verification result indicates that the configuration file is correct.

[0215] The embodiments of the present application utilize the traffic transmission demand parameters of multiple network nodes and the network state information between each two network nodes in real-time network to calculate the maximum transmission delay in the worst case corresponding to multiple different traffic shaping strategies, so as to select the optimal traffic shaping strategy with the minimum maximum transmission delay as the transmission parameter configuration under the current network state, so as to effectively improve the flexibility of transmission parameter configuration of network nodes in the complex changing network, and effectively improve the transmission rate between network nodes, thereby meeting the high communication demand of network nodes; and the traffic arrival rate and traffic conflict parameters corresponding to each traffic data type are accurately calculated, so as to accurately obtain the data arrival curve function of each traffic data type, so as to accurately calculate the maximum transmission delay corresponding to each traffic data type by using the data arrival curve function, so as to ensure the real-time and deterministic communication in the network, thereby reducing the delay and conflict, and improving the overall efficiency and reliability of the network; and the shaping mechanism of the time-aware shaper (TAS), the credit-based shaper (CBS) and the asynchronous traffic shaper (ATS) is utilized, and the current network state information and traffic transmission demand parameters, and different traffic data types are further utilized to accurately obtain the shaper service curve function corresponding to each shaper, so as to accurately obtain the maximum delay of each traffic shaping strategy by using the shaper service curve function, so as to select the most suitable optimal traffic shaping strategy, thereby improving the transmission rate of the network; in addition, the configuration file corresponding to the optimal traffic shaping strategy is generated, and the configuration file is security verified, so as to avoid unexpected security incidents, thereby improving the reliability and security of network transmission parameter optimization, and after security passing, the configuration file is sent to each network node for configuration, so as to improve the flexibility of transmission parameter configuration in each network node in the network, thereby improving the communication efficiency between network nodes, so as to meet the communication demand of network nodes.

[0216] The embodiments of the present application also provide a network transmission parameter optimization device, which can implement the network transmission parameter optimization method. Figure 13 The device 1300 includes:

[0217] The data acquisition module 1310 is configured to acquire the traffic transmission demand parameters of at least one network node in the network and the network state information between each two network nodes.

[0218] The shaping strategy selection module 1320 is configured to calculate the maximum transmission delay corresponding to multiple different traffic shaping strategies based on the network state information and the traffic transmission demand parameters, and determine the optimal traffic shaping strategy from the multiple traffic shaping strategies according to the minimum maximum transmission delay.

[0219] The configuration optimization module 1330 is configured to adjust the network transmission parameters of at least one network node in the network based on the optimal traffic shaping strategy.

[0220] In some embodiments, the shaping policy selection module 1320 is further configured to:

[0221] calculate a data arrival curve function based on the traffic transmission requirement parameters;

[0222] calculate a data service curve function of each traffic shaping policy based on the network status information and the traffic transmission requirement parameters;

[0223] calculate a maximum transmission delay corresponding to each traffic shaping policy based on the data arrival curve function and the data service curve function of each traffic shaping policy.

[0224] In some embodiments, the shaping policy selection module 1320 is further configured to:

[0225] obtain a plurality of traffic data types and a traffic arrival rate of each traffic data type from the traffic transmission requirement parameters;

[0226] calculate a data arrival curve function of each traffic data type based on the traffic arrival rate.

[0227] In some embodiments, the shaping policy selection module 1320 is further configured to:

[0228] obtain a maximum scheduling frame length of each scheduled traffic, and calculate a data arrival curve function of the scheduled traffic based on a product of all the maximum scheduling frame lengths, a time parameter, and a division by a scheduling traffic arrival period;

[0229] obtain a high-priority traffic conflict parameter of the high-priority traffic, and calculate a data arrival curve function of the high-priority traffic based on a sum of the high-priority arrival rate and the high-priority traffic conflict parameter;

[0230] obtain a low-priority traffic conflict parameter of the low-priority traffic, and calculate a data arrival curve function of the low-priority traffic based on a sum of the low-priority arrival rate and the low-priority traffic conflict parameter.

[0231] In some embodiments, the shaping policy selection module 1320 is further configured to:

[0232] obtain a shaper combination strategy of each traffic shaping policy, each shaper combination strategy including at least one shaper connected in series;

[0233] calculate a shaper service curve function corresponding to each shaper based on the network status information and the traffic transmission requirement parameters;

[0234] combine the shaper service curve functions and the shaper combination strategy to obtain a data service curve function of each traffic shaping policy.

[0235] In some embodiments, the shaping policy selection module 1320 is further configured to:

[0236] obtain a minimum scheduling frame length of the scheduled traffic, a high priority credit slope of the high priority traffic, a low priority burst period of the low priority traffic, a message queue arrival rate, and a message queue collision parameter from the traffic transmission requirement parameters, and obtain a transmission link speed, a scheduling rate accumulation of the scheduled traffic, and a guard band rate from the network state information;

[0237] obtain a maximum delay of the scheduled traffic based on the minimum scheduling frame length, and obtain a shaper service curve function of the time-aware shaper based on a difference between the time parameter and the maximum delay, multiplied by the transmission link speed;

[0238] obtain a high priority effective rate of the high priority traffic based on the high priority credit slope, the transmission link speed, the scheduling rate accumulation, and the guard band rate, and obtain a high priority service curve function based on a product of the high priority effective rate and the time parameter;

[0239] obtain a low priority delay of the low priority traffic based on the low priority burst period, the transmission link speed, and the scheduling rate accumulation, obtain a low priority service curve function based on a difference between the time parameter and the low priority delay, and obtain a shaper service curve function of the credit shaper based on the high priority service curve function and the low priority service curve function;

[0240] obtain a shaper service curve function of the asynchronous traffic shaper based on the message queue arrival rate and the message queue collision parameter.

[0241] In some embodiments, the shaping policy selection module 1320 is further configured to:

[0242] obtain a total length of the allocation window of the scheduled traffic, and obtain a minimum window resource item based on a ratio of the total length of the allocation window and the scheduling rate accumulation;

[0243] obtain a low priority minimum window length and a low priority maximum frame length of the low priority traffic, and obtain a low priority window frame length based on a sum of the low priority minimum window length and the low priority maximum frame length;

[0244] obtain an available link transmission item based on a difference between the transmission link speed and the scheduling rate accumulation, obtain a high priority available window resource item based on a ratio of the low priority window frame length and the available link transmission item, and obtain a high priority delay by accumulating the minimum window resource item and the high priority available window resource item;

[0245] obtain a high priority service curve function based on a difference between the time parameter and the high priority delay, multiplied by the high priority effective rate.

[0246] In some embodiments, the shaping policy selection module 1320 is further configured to:

[0247] Based on the product of the message queue arrival rate and the time parameter, and adding the message queue conflict parameter, the message queue output value is obtained, and the message queue output values of all message queues are accumulated to obtain the output arrival curve of the asynchronous traffic shaper;

[0248] The output arrival curves of all traffic data types are accumulated to obtain the output arrival curve cumulative amount;

[0249] Based on the output arrival curve cumulative amount, the priority traffic screening is performed to obtain the reorganized arrival curve;

[0250] Based on the difference between the time parameter and the reorganized arrival curve, and multiplied by the transmission link speed, the shaper service curve function of the asynchronous traffic shaper is obtained.

[0251] In some embodiments, the configuration optimization module 1330 is further configured to:

[0252] Based on the optimized traffic shaping strategy, a configuration file containing network transmission parameters in the network node is generated;

[0253] The configuration file is subjected to security verification to obtain a security verification result;

[0254] When the security verification result indicates that the configuration file is correct, the configuration file is sent to all network nodes.

[0255] In the above embodiments, the description of each embodiment has its own emphasis, and the specific embodiments of the network transmission parameter optimization device are basically the same as the specific embodiments of the above network transmission parameter optimization method, which will not be described here.

[0256] In the embodiments of the present application, the network transmission parameter optimization device utilizes the real-time network, the traffic transmission demand parameters of multiple network nodes and the network state information between each two network nodes to calculate the maximum transmission delay in the worst case corresponding to multiple different traffic shaping strategies, so as to select the optimized traffic shaping strategy with the minimum maximum transmission delay as the transmission parameter configuration under the current network state, so as to effectively improve the flexibility of the network node transmission parameter configuration in the complex changing network, and effectively improve the transmission rate between the network nodes, thereby meeting the high communication demand of the network nodes; and the traffic arrival rate and traffic conflict parameters corresponding to each traffic data type are accurately calculated, so as to accurately obtain the data arrival curve function of each traffic data type, so as to accurately calculate the maximum transmission delay corresponding to each traffic data type by using the data arrival curve function, so as to ensure the real-time and deterministic communication in the network, thereby reducing the delay and conflict, and improving the overall efficiency and reliability of the network; and the shaping mechanism of the time-aware shaper TAS, the credit-based shaper CBS and the asynchronous traffic shaper ATS is utilized, and the current network state information and the traffic transmission demand parameters, and different traffic data types are further utilized to accurately obtain the shaper service curve function corresponding to each shaper, so as to accurately obtain the maximum delay of each traffic shaping strategy by using the shaper service curve function, so as to select the most suitable optimized traffic shaping strategy, thereby improving the transmission rate of the network; in addition, the configuration file corresponding to the optimized traffic shaping strategy is generated, and the configuration file is safety verified to avoid unexpected safety accidents, thereby improving the reliability and safety of the network transmission parameter optimization, and after safety passing, the configuration file is sent to each network node for configuration, thereby improving the flexibility of the transmission parameter configuration in each network node in the network, and thereby improving the communication efficiency between the network nodes, so as to meet the communication demand of the network nodes.

[0257] The embodiments of the present application also provide an electronic device, comprising:

[0258] at least one memory;

[0259] at least one processor;

[0260] at least one program;

[0261] The program is stored in the memory, and the processor executes the at least one program to implement the network transmission parameter optimization method in the embodiments of the present application. The electronic device can be any intelligent terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), a vehicle-mounted computer, etc.

[0262] Please refer to Figure 14 , Figure 14The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0263] The processor 1401 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, and is configured to execute related programs to implement the technical solutions provided by the embodiments of the present application.

[0264] The memory 1402 can be implemented by a ROM (Read Only Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory), etc. The memory 1402 can store an operating system and other application programs. When the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are stored in the memory 1402 and are called and executed by the processor 1401 to implement the network transmission parameter optimization method of the embodiments of the present application.

[0265] The input / output interface 1403 is configured to realize information input and output.

[0266] The communication interface 1404 is configured to realize the communication interaction between the device and other devices. The communication can be realized by a wired manner (for example, a USB, a network cable, etc.) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, etc.).

[0267] The bus 1405 is configured to transmit information between various components (for example, the processor 1401, the memory 1402, the input / output interface 1403, and the communication interface 1404) of the device.

[0268] The processor 1401, the memory 1402, the input / output interface 1403, and the communication interface 1404 are connected to each other by the bus 1405 to realize the communication connection between them in the device.

[0269] The embodiments of the present application further provide a storage medium, which is a computer readable storage medium. The storage medium stores a computer program. When the computer program is executed by a processor, the network transmission parameter optimization method described above is realized.

[0270] The memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory disposed remotely with respect to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0271] The embodiments described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0272] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and can include more or fewer steps than shown in the figures, or combine certain steps, or different steps.

[0273] The device embodiments described above are only schematic, and the units described as separate components can or can not be physically separate, that is, can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0274] Those skilled in the art can understand that all or some of the steps in the above disclosed method, the functional modules / units in the system and the device can be implemented as software, firmware, hardware and their appropriate combinations.

[0275] The terms "first", "second", "third", "fourth" and the like used in the specification of the present application and the above-described drawings (if any) are used to distinguish similar objects, and do not necessarily have to describe a particular order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0276] It should be understood that, in the application, "at least one" refers to one or more, and "multiple" refers to two or more. "And / or" is used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, "A and / or B" can represent three cases of only A, only B and A and B existing at the same time, wherein A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after it. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c can represent a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b and c can be single or multiple.

[0277] In several embodiments provided in the application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the above units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The coupling or direct coupling or communication connection between the displayed or discussed each other can be through some interface, indirect coupling or communication connection between devices or units, which can be electrical, mechanical or other forms.

[0278] The units described above as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment scheme.

[0279] In addition, each functional unit in each embodiment of the application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0280] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in other words, the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program storage media.

[0281] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, and are not limited to the scope of the embodiments of the present application. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the embodiments of the present application.

Claims

1. A method for optimizing network transmission parameters, characterized in that, The method comprises: obtaining traffic transmission demand parameters of at least one network node in a network and network state information between each two network nodes; calculating maximum transmission delays corresponding to a plurality of different traffic shaping strategies based on the network state information and the traffic transmission demand parameters, and determining an optimized traffic shaping strategy from the plurality of traffic shaping strategies according to the smallest maximum transmission delay; adjusting network transmission parameters of at least one network node in the network based on the optimized traffic shaping strategy, wherein the network node adjusts the network transmission parameters based on a configuration file generated based on the optimized traffic shaping strategy; The calculation of the maximum transmission delays corresponding to a plurality of different traffic shaping strategies based on the network state information and the traffic transmission demand parameters comprises: calculating a data arrival curve function based on the traffic transmission demand parameters; calculating a data service curve function of each traffic shaping strategy based on the network state information and the traffic transmission demand parameters; calculating the maximum transmission delay corresponding to each traffic shaping strategy based on the data arrival curve function and the data service curve function of each traffic shaping strategy; The calculation of the data arrival curve function based on the traffic transmission demand parameters comprises: obtaining a plurality of traffic data types and a traffic arrival rate of each traffic data type from the traffic transmission demand parameters; calculating a data arrival curve function of each traffic data type based on the traffic arrival rate.

2. The network transmission parameter optimization method of claim 1, wherein, The traffic data types comprise scheduled traffic, high-priority traffic, and low-priority traffic, the traffic arrival rate comprises a scheduled traffic arrival period of the scheduled traffic, a high-priority arrival rate of the high-priority traffic, and a low-priority arrival rate of the low-priority traffic, and the calculation of the data arrival curve function of each traffic data type based on the traffic arrival rate comprises: obtaining a maximum scheduled frame length of each scheduled traffic, multiplying all the maximum scheduled frame lengths by a time parameter, dividing the product by the scheduled traffic arrival period, and obtaining the data arrival curve function of the scheduled traffic; obtaining a high-priority traffic conflict parameter of the high-priority traffic, obtaining the sum of the high-priority arrival rate and the high-priority traffic conflict parameter, and obtaining the data arrival curve function of the high-priority traffic; obtaining a low-priority traffic conflict parameter of the low-priority traffic, obtaining the sum of the low-priority arrival rate and the low-priority traffic conflict parameter, and obtaining the data arrival curve function of the low-priority traffic.

3. The network transmission parameter optimization method of claim 2, wherein, The calculation of the data service curve function of each traffic shaping strategy based on the network state information and the traffic transmission demand parameters comprises: obtaining a shaper combination strategy of each traffic shaping strategy, wherein each shaper combination strategy comprises at least one shaper connected in series; calculating a shaper service curve function corresponding to each shaper based on the network state information and the traffic transmission demand parameters; Combining the data service curve function of each traffic shaping strategy based on the shaper service curve function and the shaper combination strategy.

4. The network transmission parameter optimization method of claim 3, wherein, The shapers include an asynchronous traffic shaper, a time-aware shaper, and a credit shaper, and the shaper service curve function corresponding to each shaper is calculated based on the network state information and the traffic transmission requirement parameter, including: The minimum scheduling frame length of the scheduling traffic, the high-priority credit slope of the high-priority traffic, the low-priority burst period of the low-priority traffic, the message queue arrival rate, and the message queue conflict parameter are obtained from the traffic transmission requirement parameter, and the transmission link speed, the scheduling rate cumulative amount of the scheduling traffic, and the guard band rate are obtained from the network state information; The maximum time delay of the scheduling traffic is obtained based on the minimum scheduling frame length, and the shaper service curve function of the time-aware shaper is obtained based on the difference between the time parameter and the maximum time delay and multiplied by the transmission link speed; The high-priority effective rate of the high-priority traffic is obtained based on the high-priority credit slope, the transmission link speed, the scheduling rate cumulative amount, and the guard band rate, and the high-priority service curve function is obtained based on the product of the high-priority effective rate and the time parameter; The low-priority delay of the low-priority traffic is obtained based on the low-priority burst period, the transmission link speed, and the scheduling rate cumulative amount, the low-priority service curve function is obtained based on the difference between the time parameter and the low-priority delay, and the shaper service curve function of the credit shaper is obtained based on the high-priority service curve function and the low-priority service curve function; The shaper service curve function of the asynchronous traffic shaper is calculated based on the message queue arrival rate and the message queue conflict parameter.

5. The network transmission parameter optimization method of claim 4, wherein, The high-priority service curve function is obtained based on the product of the high-priority effective rate and the time parameter, including: The allocation window total length of the scheduling traffic is obtained, and the minimum window resource item is obtained based on the ratio of the allocation window total length and the scheduling rate cumulative amount; The low-priority minimum window length and the low-priority maximum frame length of the low-priority traffic are obtained, and the low-priority window frame length is obtained based on the sum of the low-priority minimum window length and the low-priority maximum frame length; The available link transmission item is obtained based on the difference between the transmission link speed and the scheduling rate cumulative amount, the high-priority available window resource item is obtained based on the ratio of the low-priority window frame length and the available link transmission item, and the high-priority delay is obtained by accumulating the minimum window resource item and the high-priority available window resource item; The high-priority service curve function is obtained based on the difference between the time parameter and the high-priority delay and multiplied by the high-priority effective rate.

6. The network transmission parameter optimization method of claim 5, wherein, The shaper service curve function of the asynchronous traffic shaper is calculated based on the message queue arrival rate and the message queue conflict parameter, including: multiplying the message queue arrival rate by the time parameter, and adding the message queue collision parameter to obtain a message queue output value, and accumulating the message queue output values of all message queues to obtain an output arrival curve of the asynchronous traffic shaper; accumulating the output arrival curves of all traffic data types to obtain an output arrival curve cumulative amount; performing priority traffic screening based on the output arrival curve cumulative amount to obtain a reorganized arrival curve; obtaining a shaper service curve function of the asynchronous traffic shaper based on a difference between the time parameter and the reorganized arrival curve, and multiplying the transmission link speed.

7. The network transmission parameter optimization method of claim 1, wherein, The adjusting the network transmission parameters of at least one network node in the network based on the optimized traffic shaping strategy comprises: generating a configuration file containing the network transmission parameters of the network nodes based on the optimized traffic shaping strategy; performing security verification on the configuration file to obtain a security verification result; when the security verification result indicates that the configuration file is correct, sending the configuration file to all the network nodes.

8. A network transmission parameter optimization apparatus, characterized by, The apparatus comprises: a data acquisition module configured to acquire traffic transmission demand parameters of at least one network node in a network and network state information between each two network nodes; a shaping strategy selection module configured to calculate maximum transmission delays corresponding to a plurality of different traffic shaping strategies based on the network state information and the traffic transmission demand parameters, and determine an optimized traffic shaping strategy from the plurality of traffic shaping strategies according to the smallest maximum transmission delay; a configuration optimization module configured to adjust network transmission parameters of at least one network node in the network based on the optimized traffic shaping strategy. The calculating maximum transmission delays corresponding to a plurality of different traffic shaping strategies based on the network state information and the traffic transmission demand parameters comprises: calculating a data arrival curve function based on the traffic transmission demand parameters; calculating a data service curve function of each traffic shaping strategy based on the network state information and the traffic transmission demand parameters; calculating a maximum transmission delay corresponding to each traffic shaping strategy based on the data arrival curve function and the data service curve function of each traffic shaping strategy. The calculating a data arrival curve function based on the traffic transmission demand parameters comprises: acquiring a plurality of traffic data types and a traffic arrival rate of each traffic data type from the traffic transmission demand parameters; calculating a data arrival curve function of each traffic data type based on the traffic arrival rate. 9.An electronic device comprising a memory and a processor, the memory storing a computer program, wherein, The processor executes the computer program to implement the network transmission parameter optimization method in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the network transmission parameter optimization method in any one of claims 1 to 7.

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