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

Abstract

The embodiment of the invention provides a network transmission parameter optimization method and device, electronic equipment and a storage medium, and the method comprises the steps: firstly, obtaining a traffic transmission demand parameter of at least one network node in a network and network state information between every two network nodes; then, calculating maximum transmission time 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 minimum maximum transmission time delay; and finally, network transmission parameters of at least one network node in the network are adjusted based on the optimized traffic shaping strategy, so that the flexibility of configuration of the transmission parameters of the network nodes in the complex changing network is effectively improved, the transmission rate between the network nodes is effectively improved, and the high communication requirement of the network nodes is further met.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, device, electronic device and storage medium for optimizing network transmission parameters. Background Art

[0002] With the digital and intelligent transformation of industrial manufacturing, the requirements for the determinism and reliability of industrial network transmission are becoming more and more stringent. As a core technology to ensure mission-critical communications, Time-Sensitive Networking (TSN) technology has become a research hotspot in academia and industry in recent years. Through a series of protocols and standards, TSN technology achieves determinism and low latency in network communications, ensuring that data transmission between industrial devices can be completed within the specified time window.

[0003] In the related art, in a network based on TSN technology, before the network nodes communicate, the network is usually configured with corresponding transmission parameter schemes, such as the scheduling shaper, according to the communication requirements of the network nodes. However, since the network status in the network is usually changing, and the network nodes in the network may be added or removed, the communication requirements of the network nodes also change accordingly. Therefore, if the original transmission parameter scheme is still adopted in the changing network, it will not be able to meet the communication requirements of the network nodes due to insufficient flexibility. Summary of the invention

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

[0005] To achieve the above object, a first aspect of an embodiment of the present application proposes a network transmission parameter optimization method, the method comprising:

[0006] Acquire traffic transmission demand parameters of at least one network node in the network and network status information between every two of the network nodes;

[0007] Calculate the maximum transmission delays corresponding to a plurality of different traffic shaping strategies based on the network status information and the traffic transmission demand parameters, and determine the optimized traffic shaping strategy from the plurality of traffic shaping strategies according to the minimum maximum transmission delay;

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

[0009] In some embodiments, the calculating the maximum transmission delays corresponding to a plurality of different traffic shaping strategies based on the network state information and the traffic transmission demand parameter includes:

[0010] Calculate the data arrival curve function based on the traffic transmission demand parameter;

[0011] Based on the network status information and the traffic transmission demand parameters, a data service curve function of each traffic shaping strategy is calculated;

[0012] 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.

[0013] In some embodiments, the step of calculating the data arrival curve function based on the traffic transmission demand parameter includes:

[0014] Obtaining multiple traffic data types and a traffic arrival rate of each traffic data type from the traffic transmission demand parameters;

[0015] Based on the traffic arrival rate, a data arrival curve function of each traffic data type is calculated.

[0016] In some embodiments, the traffic data type includes scheduled traffic, high priority traffic, and low priority traffic, the traffic arrival rate includes the scheduled traffic arrival period of the scheduled traffic, the high priority arrival rate of the high priority traffic, and the low priority arrival rate of the low priority traffic, and the data arrival curve function of each traffic data type is calculated based on the traffic arrival rate, including:

[0017] Obtaining the maximum scheduling frame length of each of the scheduling flows, and obtaining the data arrival curve function of the scheduling flows based on multiplying all the maximum scheduling frame lengths by a time parameter and dividing by the scheduling flow arrival period;

[0018] Acquire a high priority flow conflict parameter of the high priority flow, and obtain the data arrival curve function of the high priority flow based on the sum of the high priority arrival rate and the high priority flow conflict parameter;

[0019] A low priority traffic conflict parameter of the low priority traffic is obtained, and 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.

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

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

[0022] Based on the network status information and the traffic transmission demand parameters, a shaper service curve function corresponding to each shaper is calculated;

[0023] Based on the shaper service curve function and the shaper combination strategy, the data service curve function of each traffic shaping strategy is combined to obtain.

[0024] In some embodiments, the shaper includes 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 demand parameter, including:

[0025] Obtaining the minimum scheduling frame length of the scheduled 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 accumulation of the scheduled traffic and the guard band rate from the network status information;

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

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

[0028] Based on the low priority burst period, the transmission link speed and the scheduling rate accumulation, a low priority delay of the low priority traffic is obtained, a low priority service curve function is obtained based on the difference between the time parameter and the low priority delay, and a 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;

[0029] Based on the message queue arrival rate and the message queue conflict parameter, a shaper service curve function of the asynchronous traffic shaper is calculated.

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

[0031] Obtaining the total length of the allocation window of the scheduling traffic, and obtaining a minimum window resource item based on the ratio of the total length of the allocation window to 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 the sum of the low priority minimum window length and the low priority maximum frame length;

[0033] Based on the difference between the transmission link speed and the scheduling rate accumulation, an available link transmission item is obtained, based on the ratio of the low priority window frame length to the available link transmission item, a high priority available window resource item is obtained, and the minimum window resource item and the high priority available window resource item are accumulated to obtain a high priority delay;

[0034] 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.

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

[0036] Based on the product of the message queue arrival rate and the time parameter, and adding the message queue conflict parameter, a message queue output value is obtained, and the message queue output values ​​of all message queues are accumulated to obtain an output arrival curve of the asynchronous traffic shaper;

[0037] Accumulating the output arrival curves of all flow data types to obtain the output arrival curve accumulation;

[0038] Performing priority flow screening based on the output arrival curve accumulation to obtain a reorganized arrival curve;

[0039] Based on the difference between the time parameter and the reassembled arrival curve and multiplied by the transmission link speed, a shaper service curve function of the asynchronous traffic shaper is obtained.

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

[0041] Based on the optimized traffic shaping strategy, generating a configuration file including the network transmission parameters in the network node;

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

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

[0044] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application proposes a network transmission parameter optimization device, the device comprising:

[0045] A data acquisition module, used to acquire the flow transmission demand parameters of at least one network node in the network and the network status information between every two network nodes;

[0046] A shaping strategy selection module, used to calculate the maximum transmission delays corresponding to multiple different traffic shaping strategies based on the network status information and the traffic transmission demand parameters, and determine the optimized traffic shaping strategy from the multiple traffic shaping strategies according to the minimum maximum transmission delay;

[0047] A configuration optimization module is 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-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the network transmission parameter optimization method described in the first aspect when executing the computer program.

[0049] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium, and the storage medium stores a computer program. When the computer program is executed by a processor, the network transmission parameter optimization method described in the first aspect is implemented.

[0050] The network transmission parameter optimization method, device, electronic device and storage medium proposed in the embodiment of the present application include: first, obtaining the traffic transmission requirement parameters of at least one network node in the network and the network status information between every two network nodes; then, calculating the maximum transmission delay corresponding to multiple different traffic shaping strategies based on the network status information and the traffic transmission requirement parameters, and determining the optimized traffic shaping strategy from multiple traffic shaping strategies according to the minimum maximum transmission delay; finally, adjusting the network transmission parameters of at least one network node in the network based on the optimized traffic shaping strategy. The embodiment of the present application uses the traffic transmission requirement parameters of multiple network nodes and the network status information between every two network nodes in a real-time network to calculate the worst-case maximum transmission delay 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 a complex and changing network, and effectively improve the transmission rate between network nodes, thereby meeting the high communication requirements of network nodes.

[0051] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0054] Figure 3 This is a schematic diagram of a mode structure of multiple traffic shaping strategies provided in another embodiment of the present application.

[0055] Figure 4 yes Figure 2 Flow chart of step 202 in FIG.

[0056] Figure 5 yes Figure 4 The watershed diagram of step 401 in FIG.

[0057] Figure 6 yes Figure 5 Flowchart of step 502 in FIG.

[0058] Figure 7 yes Figure 4 The watershed diagram of step 402 in FIG.

[0059] Figure 8 yes Figure 7 The watershed diagram of step 702 in FIG.

[0060] Fig. 9 yes Figure 8 The watershed diagram of step 803 in FIG.

[0061] Fig.10 yes Figure 8 The watershed diagram of step 805 in FIG.

[0062] Fig.11 yes Figure 2 Flow chart of step 203 in FIG.

[0063] Fig.12 This is a flow chart of a network transmission parameter optimization method provided by another embodiment of the present application.

[0064] Fig.13 It is a structural diagram of a network transmission parameter optimization device provided in one embodiment of the present application.

[0065] Fig.14 It is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0067] It should be noted that although the functional modules are divided in the device schematic and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart.

[0068] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0069] First, some nouns involved in this application are analyzed:

[0070] Time-Sensitive Networking (TSN) is a network technology designed for deterministic traffic transmission. It uses a series of IEEE standards (such as 802.1ASrev, IEEE 1588) to achieve precise time synchronization between devices and ensure that the clocks in the network can be accurately aligned. TSN provides Ethernet services with low transmission latency through technologies such as reserved bandwidth, traffic priority and time synchronization to meet the data transmission requirements of real-time audio and video services. It also introduces time-scheduling traffic and frame preemption mechanisms, allowing time-sensitive data streams and non-time-sensitive data streams to be mixed and interleaved, while obtaining accurate data information at the data source, quickly diagnosing and promptly repairing transmission anomalies in the network. TSN also achieves bounded low latency and zero congestion packet loss communication through various mechanisms such as traffic scheduling, traffic shaping and frame presetting, and prioritizes the transmission of time-sensitive data packets. These mechanisms are reflected in the IEEE 802.1 series of standards, such as 802.1Qav, 802.1Qbv, 802.1Qbu, etc., which can handle different traffic categories to achieve different service qualities in multi-hop switching networks. Overall, TSN technology provides a reliable network solution for industries that require strict timing and reliability, such as industrial automation, automotive systems, robotics, and critical IoT applications.

[0071] Time-Triggered Symmetric Component (TAS) is a communication method based on the time-triggered mechanism, which plans and controls the data transmission time through the gate control list (GCL). 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 per-flow shaping method that considers the hop-by-hop transmission process of the target traffic and uses traffic processing methods based on interleaving shaping to shape and reshape the traffic. ATS can implement clear parameter configuration for specific flows, so that the target traffic can meet the expected output constraint target at each transmission node.

[0073] The Cyclic Buffer Symmetric Component (CBS) is a credit-based shaper that uses a per-class shaping method. CBS associates the output and queuing process of different types of traffic with the credit consumption and accumulation process of the corresponding queues, thus achieving real-time transmission control of traffic in different queues. CBS cannot implement clear parameter configuration for specific flows, and can only be adjusted by changing the parameters of the class to which the target traffic belongs.

[0074] With the digital and intelligent transformation of industrial manufacturing, the requirements for the determinism and reliability of industrial network transmission are becoming more and more stringent. As a core technology to ensure mission-critical communications, Time-Sensitive Networking (TSN) technology has become a research hotspot in academia and industry in recent years. Through a series of protocols and standards, TSN technology achieves determinism and low latency in network communications, ensuring that data transmission between industrial devices can be completed within the specified time window.

[0075] In the related art, in a network based on TSN technology, before the network nodes communicate, the network is usually configured with corresponding transmission parameter schemes, such as the scheduling shaper, according to the communication requirements of the network nodes. However, since the network status in the network is usually changing, and the network nodes in the network may be added or removed, the communication requirements of the network nodes also change accordingly. Therefore, if the original transmission parameter scheme is still adopted in the changing network, it will not be able to meet the communication requirements of the network nodes due to insufficient flexibility.

[0076] In order to improve the flexibility of transmission parameter configuration in a complex and changing network to meet the communication needs of network nodes, the embodiment of the present application uses the traffic transmission demand parameters of multiple network nodes in a real-time network and the network status information between every two network nodes to calculate the worst-case maximum transmission delay corresponding to a variety of different traffic shaping strategies, and selects the optimized traffic shaping strategy with the smallest maximum transmission delay as the transmission parameter configuration under the current network state, so as 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 needs of network nodes.

[0077] In order to better describe the network transmission parameter optimization method provided by the present application, the following first describes a network based on TSN technology applied to the network transmission parameter optimization method. Figure 1 , is a schematic diagram of a network structure based on TSN technology provided in an embodiment of the present application. Figure 1 As shown in , a network based on TSN technology includes multiple network nodes, which include multiple end systems (ES) and switches (SW) associated with multiple end systems ES, wherein the end system ES communicates data with the switch SW, and data communication can also be performed between every two switches SW, and 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). In addition, a network controller is also equipped in the network, which is connected to all network nodes and is used to adjust the transmission parameter configuration of the network nodes in real time.

[0078] In conjunction with the above-mentioned TSN-based network, the network transmission parameter optimization method, device, electronic device and storage medium provided in the embodiments of the present application will be further described below. The network transmission parameter optimization method provided in the embodiments of the present application can be applied to any network node with computing function in the network controller network, or a processing controller connected to the TSN-based network.

[0079] The following is a detailed description of the network transmission parameter optimization method in the embodiment of the present application. Figure 2 , is an optional flowchart of the network transmission parameter optimization method provided in an embodiment of the present application, Figure 2 The method in the embodiment may include but is not limited to steps 201 to 203. Figure 2 The order of step 201 to step 203 is not specifically limited, and the order of steps can be adjusted or some steps can be reduced or added according to actual needs.

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

[0081] The following is a detailed description of step 201.

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

[0083] The traffic transmission requirement parameters include the type of traffic data that the network node needs to transmit, the priority of each traffic data type, the maximum delay of each traffic data type, and the jitter tolerance of each traffic data type.

[0084] Generally speaking, traffic data types include control data / scheduled traffic (CDT / Scheduled Traffic, ST), high priority traffic in audio and video bridging technology (AVB) (which includes AVB traffic class A and AVB traffic class B), and best-effort traffic (BE, i.e. low priority traffic).

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

[0086] AVB traffic class A in high priority traffic is non-critical but requires bounded latency and bandwidth guarantees. Data frames from these flows are discrete, large packets, and bursty. Visual inspection of inspected products is an example of this type of data traffic in industrial networks. After allocating enough bandwidth for critical business flows, the remaining bandwidth is shared by non-critical business flows. Including class A traffic. In addition, network nodes assign class A traffic to high priority queues to guarantee an upper bound on end-to-end latency.

[0087] For AVB traffic type B in high priority traffic, it is non-critical traffic with strict bandwidth requirements. The data frames in class B traffic have large bandwidth requirements and real-time nature, and network resources need to be allocated consistently to ensure quality of service requirements. Video traffic flowing through industrial process monitoring units (IPMUs) is a typical source of class B frames. Although network nodes give class B traffic a lower priority compared to class A traffic, token-based rate shaping technology applied to class A creates transmission opportunities for class B.

[0088] For low priority traffic (BE), it is a non-critical business flow without strict QoS requirements, which may be the result of Ethernet devices or human-machine interfaces connected to IPMU nodes. 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 status information between every two network nodes usually includes: 1) link status: performance indicators such as bandwidth utilization, delay, jitter, etc. of each link; 2) device status: processing capacity, queue length, resource usage, etc. of each network node in the network; 3) topology structure: changes in the current network topology, such as newly added or removed network nodes and link failures between network nodes.

[0090] Step 202: Calculate the maximum transmission delays corresponding to multiple different traffic shaping strategies based on network status information and traffic transmission demand parameters, and determine the optimal traffic shaping strategy from the multiple traffic shaping strategies according to the minimum maximum transmission delay.

[0091] Step 202 is described in detail below.

[0092] In some embodiments, based on the traffic transmission demand parameters of all network nodes in the network and the network status information between every two network nodes obtained in real time, it is necessary to use all the network status information and traffic transmission demand parameters to calculate the maximum transmission delays corresponding to multiple pre-built different traffic shaping strategies, so as to select the traffic shaping strategy with the smallest maximum transmission delay from multiple traffic shaping strategies as the optimized traffic shaping strategy.

[0093] Reference Figure 3 , is a schematic diagram of a mode structure of multiple traffic shaping strategies provided in an embodiment of the present application. Figure 3As shown in, in an embodiment of the present application, eight traffic shaping strategies are pre-constructed based on the permutations and combinations of the time-triggered symmetric component TAS (i.e., time-aware shaper), the asymmetric time-triggered component ATS (i.e., asynchronous traffic shaper), and the circular buffer symmetric component CBS (i.e., credit shaper). For any network node (that is, the end system ES / switch SW) when transmitting traffic, 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 in sequence through the time-aware shaper TAS and the credit shaper CBS, the third traffic shaping strategy is to transmit the transmission data in parallel through the time-aware shaper TAS and the asynchronous traffic shaper ATS, the fourth traffic shaping strategy is to transmit the transmission data in parallel through the time-aware shaper TAS and the asynchronous traffic shaper ATS, and then transmit it 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 in sequence through the asynchronous traffic shaper ATS and the credit shaper CBS, 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 network status information, traffic parameter requirement parameters and various traffic shaping strategies, the following will further describe how to calculate the worst-case maximum transmission delay corresponding to different traffic shaping strategies.

[0095] Reference Figure 4 , based on network status information and traffic transmission demand parameters, calculating the maximum transmission delay corresponding to multiple different traffic shaping strategies, including the following steps 401 to 403.

[0096] Step 401: Calculate and obtain a data arrival curve function based on traffic transmission demand parameters.

[0097] Step 401 is described in detail below.

[0098] In some embodiments, in order to calculate the maximum transmission delay corresponding to each traffic shaping strategy, it is necessary to first calculate the data arrival curve function corresponding to each traffic data type and the data service curve function corresponding to each traffic shaping strategy, so as to calculate the corresponding maximum transmission delay based on the data arrival curve function and the data service curve function.

[0099] Reference Figure 5 , calculating the data arrival curve function based on the flow transmission demand parameters, including the following steps 501 to 502.

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

[0101] Step 502: Based on the traffic arrival rate, a data arrival curve function of each traffic data type is calculated.

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

[0103] In some embodiments, firstly, a plurality of traffic data flows are obtained from the traffic transmission demand parameters, namely, the scheduled traffic ST, the high priority traffic class A, the high priority traffic class B, and the low priority traffic BE; then, the traffic arrival rate corresponding to each traffic data type is determined, which includes the scheduled traffic arrival period T of the scheduled traffic ST. i , the high priority arrival rate r of high priority traffic AVB (It includes r of type A AVB,A and r of class B AVB,B ) and the low priority arrival rate r of low priority traffic BE .

[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 described below.

[0105] Reference Figure 6 , based on the traffic arrival rate, a 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 scheduling flow, and obtain the data arrival curve function of the scheduling flow based on multiplying all maximum scheduling frame lengths by a time parameter and dividing by the scheduling flow arrival period.

[0107] Step 602: Obtain a high priority traffic conflict parameter of the high priority traffic, and obtain a 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.

[0108] Step 603: Obtain the low priority traffic conflict parameter of the low priority traffic, and obtain 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.

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

[0110] In some embodiments, the maximum scheduling frame length L of each scheduled flow (including the i-th scheduled flow ST flow) is first obtained. i, then based on all the maximum scheduled frame lengths multiplied by the time parameter t and divided by the scheduled traffic arrival period, the data arrival curve function of the scheduled traffic is obtained as shown in the following formula (1).

[0111]

[0112] Next, obtain the high priority traffic conflict parameter b of the high priority traffic (which includes class A and class B). AVB Among them, the arrival curve of high priority traffic is subject to the constraints 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 high priority traffic includes class A data and class B data, so the data arrival curve function of high priority traffic also includes the α corresponding to class A data. AVB,A (t) and α of the B data pair AVB,B (t).

[0115] Similarly, obtain the low priority traffic conflict parameter b of the low priority traffic 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, the traffic arrival rate and traffic conflict parameters corresponding to each traffic data type are accurately calculated to accurately obtain the data arrival curve function of each traffic data type, so as to facilitate the subsequent use of the data arrival curve function to accurately calculate the maximum transmission delay corresponding to each traffic data type, so as to ensure real-time and deterministic communication in the network, thereby reducing delays and conflicts and improving the overall efficiency and reliability of the network.

[0118] Step 402: Based on the network status information and the traffic transmission demand parameters, a data service curve function of each traffic shaping strategy is calculated.

[0119] Step 402 is described in detail below.

[0120] In some embodiments, after obtaining the data arrival curve function corresponding to each traffic data type, all network status information and traffic transmission demand parameters are further used to calculate the data service curve function of each traffic shaping strategy, so as to subsequently calculate the maximum transmission delay corresponding to each traffic shaping strategy using the data arrival curve function and the data service curve function. The following will further describe how to calculate the data service curve function of each traffic shaping strategy.

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

[0122] Step 701: Obtain a shaper combination strategy for each traffic shaping strategy.

[0123] Step 702: Based on the network status information and the traffic transmission demand parameters, a shaper service curve function corresponding to each shaper is calculated.

[0124] Steps 701 to 702 are described in detail below.

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

[0126] Next, based on all network status information and traffic transmission demand parameters, a shaper service curve function corresponding to each shaper is calculated, as described in detail below.

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

[0128] Step 801: Obtain the minimum scheduling frame length of the scheduled 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 parameters from the traffic transmission demand parameters, and obtain the transmission link speed, the cumulative scheduling rate of the scheduled traffic, and the protection band rate from the network status information.

[0129] Step 802: The maximum delay of the scheduled traffic is obtained based on the minimum scheduled 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 delay and multiplied by the transmission link speed.

[0130] Step 803: Based on the high priority credit slope, transmission link speed, scheduling rate accumulation and protection band rate, obtain the high priority effective rate of the 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 a hop number k in the scheduling traffic ST is obtained from the traffic transmission demand parameter. minST,k , high priority credit slope for high priority traffic (which includes I A and Class B I B , which is the idle credit accumulation slope (bit / s)), the low priority burst period b of the low priority traffic BE BE , the message queue arrival rate r corresponding to the asynchronous traffic shaper f Conflict parameter b with message queue f , and obtain the transmission link speed c and the scheduling rate accumulation r of the scheduling traffic ST from the network status information ST,k , guard band rate r GB .

[0133] Among them, the guard band rate r GB =L minPkt / T minPkt , where L minPkt Indicates the length of the minimum non-preemptible portion of the low-priority frame and T minPkt represents the duration of the guard band. The scheduling rate accumulator r ST,k represents the sum of the rates of all scheduled traffic STs that pass 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 Get the maximum delay T of the scheduled traffic ST As shown in the following formula (4).

[0135]

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

[0137] β ST (t) = c × (tT ST ) (5)

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

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

[0140]

[0141] Wherein, formula (6) represents the effective rate of class A traffic data in high priority traffic (i.e., the high priority effective rate of class A traffic data), the first term in formula (6) captures the transmission slope / rate of class A traffic outside the scheduled traffic transmission window, and the second term represents the protection bandwidth that does not allow class A transmission. Further, the high priority service curve function is obtained based on the product of the high priority effective rate and the time parameter, as described below.

[0142] Reference Fig. 9 , obtaining a high priority service curve function based on the product of the high priority effective rate and the time parameter, includes the following steps 901 to 904.

[0143] Step 901: Obtain the total length of the allocation window of the scheduled traffic, and obtain the minimum window resource item based on the ratio of the total length of the allocation window to the cumulative amount of the scheduling rate.

[0144] Step 902: Obtain the low priority minimum window length and the low priority maximum frame length of the low priority traffic, and obtain the 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: Based on the difference between the transmission link speed and the cumulative amount of the scheduling rate, the available link transmission item is obtained, and based on the ratio of the low priority window frame length and the available link transmission item, the high priority available window resource item is obtained, and the minimum window resource item and the high priority available window resource item are accumulated to obtain the 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, a high priority service curve function is obtained.

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

[0148] In some embodiments, first obtain the total length L of the allocation window of the scheduling flow ST ST,k , and based on the total length L of the allocation window ST,k and the scheduling rate cumulant r ST,k The ratio of the minimum window resource item L ST,k / r ST,k , which represents the worst case, when the guard band occurs and the Class A credit is negative. In this case, a Class A frame must wait for the guard band duration, the transmission of all scheduled traffic, and queued best-effort traffic until it accumulates enough positive credit to be allowed to transmit.

[0149] Then, based on the transmission link speed c and the scheduling rate accumulation r ST,k The difference between the available link transmission term cr ST,k , based on the low priority window frame length L BE plus the length L of the minimum non-preemptible portion of the low-priority frame minPkt,k , and then divided by

[0150] The ratio of the 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 accumulated to obtain the high priority delay corresponding to the 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 among high priority traffic, its high priority service curve function can be shown as the following formula (9).

[0155] β B (t) = R B ×(tT B ) (9)

[0156] Where R B Indicates the high priority effective rate of class B traffic, T B Indicates the maximum delay encountered by a bit reaching an empty Class B queue (i.e., high priority delay). B and T B This can be expressed by the following formulas (10) and (11).

[0157]

[0158] Among them I B Indicates the idle slope of Class B traffic in high priority traffic (i.e., high priority credit slope), L A represents the maximum frame length of class A traffic. Formula (10) is similar to the above formula (7), and formula (11) corresponds to the worst case where the guard band appears and the class B credit is negative. Therefore, considering that class B traffic still needs to accumulate sufficient positive credits, the frames of class B traffic must wait for the guard band duration, the transmission of all scheduled traffic, the class A transmission window, and the queued best-effort traffic.

[0159] Step 804: Based on the low priority burst period, the transmission link speed and the accumulated scheduling rate, the low priority delay of the low priority traffic is obtained, 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.

[0160] Step 804 is described in detail below.

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

[0162]

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

[0164] β BE (t) = c × (tT 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 high priority traffic (which includes the high priority service curve function (8) of class A traffic and the high priority service curve function (9) of class B traffic) and the low priority service curve function (13) of low priority traffic BE.

[0166] The shaper service curve function of the asynchronous traffic shaper ATS is further described below.

[0167] Step 805: Based on the message queue arrival rate and the message queue conflict parameter, a shaper service curve function of the asynchronous traffic shaper is calculated.

[0168] Steps 804 to 805 are described in detail below.

[0169] In some embodiments, for the shaping mechanism of the asynchronous traffic shaper ATS, since it does not introduce additional delay to the worst-case delay of the First In First Out (FIFO) system, this feature provides a theoretical basis for combining the asynchronous traffic shaper ATS with other shapers. Figure 3 The shared queues in the ATS shaper shown are used for traffic of different priorities, and there are up to 8 shaping queues. That is, the shaper service curve function of the asynchronous traffic shaper can be calculated based on the message queue arrival rate and the message queue conflict parameters, as described below.

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

[0171] Step 1001: 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.

[0172] Step 1002: Accumulate the output arrival curves of all flow data types to obtain the output arrival curve accumulation.

[0173] Step 1003: Prioritize traffic screening based on the output arrival curve accumulation to obtain a reorganized arrival curve.

[0174] Step 1004: Based on the time parameter and the difference between the reassembled arrival curve and multiplying by the transmission link speed, a shaper service curve function of the asynchronous traffic shaper is obtained.

[0175] Steps 1001 to 1004 are described in detail below.

[0176] In some embodiments, first, based on the message queue arrival rate r f The product of the time parameter t and the message queue conflict parameter is added to obtain the message queue output value, and the accumulated value of all message queues q k ,k∈{1,2,…,8}, the message queue output value, and the output arrival curve of the asynchronous traffic shaper ATS is shown in the following formula (14).

[0177]

[0178] Among them, the shaping queue Q i ,i∈{1,l,m,n,x,y,z,8} corresponds to different shaping schemesο i ,i∈{1,2,…,8}, message queue q k Represents traffic data frames of different priorities.

[0179] Furthermore, depending on the queuing scheme, there will be one or more queues q of shape i Connected to the shared queue. Therefore, the input arrival curve of the aggregate flow is connected to the shaping queue Q i All message queues q i The sum of the output arrival curves, that is, the output arrival curves of all traffic data types are accumulated to obtain the cumulative output arrival curve 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, low-priority traffic can only be served when the queue of high-priority traffic is empty. Then, priority traffic is screened based on the cumulative amount of output arrival curves to obtain the reorganized arrival curve It is expressed as the maximum frame length of traffic with priority j higher than priority i and lower than priority i Total arrival curve of the SP flow after ATS reorganization.

[0182] Furthermore, through ATS shaping, the corresponding shaping queue Q is given i The service curve of the SP service with priority i (i∈[1,n]) is based on the difference between the time parameter and the reassembled arrival curve, and multiplied by the transmission link speed, to obtain the shaper service curve function of the asynchronous traffic shaper as shown in the following formula (16).

[0183]

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

[0185] Through the above steps 1001 to 1004, steps 901 to 904, and steps 801 to 805, the shaping mechanisms of the time-aware shaper TAS, the credit shaper CBS and the asynchronous traffic shaper ATS are utilized, and further utilizing the current network status information and traffic transmission demand parameters, as well as different traffic data types, the shaper service curve function corresponding to each shaper is accurately obtained, so that the shaper service curve function can be used to accurately obtain the maximum delay of each traffic shaping strategy in the subsequent use, so as to select the most appropriate 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, a data service curve function of each traffic shaping strategy is obtained by combination.

[0187] Steps 701 to 703 are described in detail below.

[0188] In some embodiments, based on the shaper service curve function of the time-aware shaper TAS, the credit shaper CBS and the asynchronous traffic shaper ATS, and the data arrival curve function corresponding to each traffic data type, as well as Figure 3 The combination structure of shaper combination strategies corresponding to the multiple traffic shaping strategies shown can be combined (such as serially accumulated) to obtain the data service curve function of each traffic shaping strategy.

[0189] Among them, for the shaper combination strategy of at least two shapers in series, the flow data arrival service curve of the rear shaper is the port output arrival curve α′(t) of the front shaper, and its constraint output cumulative function R * (t), that is, the arrival curve of the input flow of the next hop node is 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] Steps 401 to 403 are described in detail below.

[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 The traffic category cls variable includes one or more categories in cls∈{ST,A,B,BE}, and the relevant parameters of a single hop k can be defined as: the delay per hop D(α,β), which represents the delay experienced by the data flow, that is, the maximum horizontal deviation between α(t) and β(t), which is 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 cache size per hop B(s) represents the maximum cache size, that is, the maximum vertical deviation between α(t) and β(t) as shown in the following formula (19).

[0197]

[0198] Based on this, we can Figure 3 Among the multiple traffic shaping strategies shown, the traffic shaping strategy with the smallest maximum transmission delay is selected as the optimized traffic shaping strategy under the current traffic transmission demand parameters of all network nodes and the network status information between every two network nodes, so as to facilitate the subsequent use of the optimized traffic shaping strategy in the network to configure the transmission parameters of the network nodes to improve the data transmission rate of each network node in the network, thereby flexibly meeting the communication needs of the network nodes.

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

[0200] Step 203 is described in detail below.

[0201] In some embodiments, after determining the optimized traffic shaping strategy, the optimized traffic shaping strategy is used to adjust the network transmission parameters of all relevant network nodes in the network to increase the data transmission rate of each network node in the network, thereby flexibly meeting the communication needs of the network nodes, which will be further described in detail below.

[0202] Reference Fig.11 , adjusting the network transmission parameters of at least one network node in the network based on the optimized traffic shaping strategy, including the following steps 1101 to 1103.

[0203] Step 1101: Based on the optimized traffic shaping strategy, a configuration file including network transmission parameters in the network node is generated.

[0204] Step 1102: Perform security verification on the configuration file 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] 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 node 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 tolerance and traffic shaping rate of the asynchronous traffic shaper ATS according to the burst characteristics of the traffic; for the credit 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 identifier of each network traffic. Generate a unified XML configuration file for all the above configuration parameters. These files adopt a hierarchical structure to clearly separate the parameter configurations of different traffic shaping schemes.

[0208] Next, the generated XML configuration file needs to undergo security verification to obtain security verification results to ensure that its format is correct and the content is complete. The verification tool will check the legitimacy of the configuration to prevent network failures caused by configuration errors.

[0209] When the verification is passed (that is, when the security verification result indicates that the configuration file is correct), the configuration file will be sent to each network node through the network management protocol (such as NETCONF, SNMP), and the network node will return 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, increase or decrease the priority of certain traffic, adjust bandwidth allocation, etc. The adjusted configuration file can be updated in real time and re-sent to the network node to adapt to new network requirements.

[0210] In some embodiments, after each network node receives the configuration file, it enters the network transmission parameter configuration process of the network node (including the switch and the end system). Each network node reads and parses the configuration file, generates configuration instructions, and maps the instructions to the hardware control logic. Specifically, each network node's transmission mode configuration module (such as Figure 1As shown in the figure, it is usually run by an embedded operating system or dedicated software) and receives the XML configuration file, which is stored in the non-volatile memory of the node for subsequent parsing and use. Before the network node parses it, the network node first verifies the integrity of the received XML configuration file. A hash algorithm (such as SHA-256) is used to verify whether the file has been tampered with or damaged during transmission. After the verification is passed, the file parsing phase begins; otherwise, the node will issue an error alarm and request the configuration file to be reissued. 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, generates configuration instructions, and maps the instructions to the hardware control logic.

[0211] In some embodiments, after the network transmission parameters of each network node are configured, the basic operating system and the TSN network protocol stack are reloaded to ensure that all types of traffic in the TSN network can be efficiently and collaboratively transmitted. Specifically, each configuration module of the network node generates hardware executable instructions based on the parsed data. The instructions include updating the time schedule, loading the traffic shaping strategy, configuring the credit mechanism, etc. These instructions are sent to the hardware layer of the TSN node through a high-speed bus, such as FPGA, ASIC or other embedded processors. After receiving the instructions, the hardware layer of the network node configures the internal registers, schedulers and queues according to the content of the instructions. For example, the time-aware shaper TAS will load a new time schedule, and the credit shaper CBS will update 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, a configuration file corresponding to the optimized traffic shaping strategy is generated, and the configuration file is security verified to avoid unexpected security incidents, thereby improving the reliability and security of network transmission parameter optimization, and after passing the security verification, the configuration file is sent to each network node for configuration, thereby improving the flexibility of transmission parameter configuration in each network node in the network, and then improving the communication efficiency between network nodes to meet the communication needs of network nodes.

[0213] Reference Fig.12 , is a flow chart of a network transmission parameter optimization method provided by an embodiment of the present application. Fig.12As shown in , based on the TSN network controller in the TSN network, the traffic transmission demand parameters and network status information of the current network nodes in the network are obtained in real time, and then it is confirmed whether there is a pre-specified traffic shaping strategy. If not, the corresponding network calculation model is used to derive the end-to-end maximum delay corresponding to each traffic shaping strategy based on different traffic transmission demand parameters and network status information, and the traffic shaping strategy with the smallest maximum delay is selected as the optimized traffic shaping strategy, and the configuration parameters corresponding to the optimized traffic shaping strategy are obtained to generate a configuration file and store it in XML format and send 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 device and storage medium proposed in the embodiments of the present application include: first, obtaining the flow transmission demand parameters of at least one network node in the network and the network status information between every two network nodes; then, obtaining multiple flow data types and the flow arrival rate of each flow data type from the flow transmission demand parameters, obtaining the maximum scheduling frame length of each scheduling flow, and obtaining the data arrival curve function of the scheduling flow based on all the maximum scheduling frame lengths multiplied by the time parameter and divided by the scheduling flow arrival period, obtaining the high priority flow conflict parameter of the high priority flow, and obtaining the data arrival curve function of the high priority flow based on the sum of the high priority arrival rate and the high priority flow conflict parameter. Line function, obtain low priority traffic conflict parameters of low priority traffic, obtain data arrival curve function of low priority traffic based on the sum of low priority arrival rate and low priority traffic conflict parameters, obtain shaper combination strategy of each traffic shaping strategy, each shaper combination strategy includes at least one shaper connected in series in sequence, obtain the minimum scheduling frame length of scheduling traffic, high priority credit slope of high priority traffic, low priority burst period of low priority traffic, message queue arrival rate and message queue conflict parameters from traffic transmission demand parameters, and obtain transmission link speed, scheduling rate accumulation of scheduling traffic, and protection band rate from network status information, obtain the maximum delay of scheduling traffic based on the minimum scheduling frame length, and obtain the maximum delay of scheduling traffic based on time parameters and The difference in the maximum delay is multiplied by the transmission link speed to obtain the shaper service curve function of the time-aware shaper. Based on the high priority credit slope, the transmission link speed, the cumulative amount of scheduling rate, and the guard band rate, the high priority effective rate of the high priority traffic is obtained, and the high priority service curve function is obtained based on the product of the high priority effective rate and the time parameter. Based on the low priority burst period, the transmission link speed, and the cumulative amount of scheduling rate, the low priority delay of the low priority traffic is obtained. 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. Based on the message queue arrival rate and the message queue burst rate, the high priority service curve function is obtained. The shaper service curve function of the asynchronous traffic shaper is calculated based on the shaper service curve function and the shaper combination strategy. The data service curve function of each traffic shaping strategy is combined. The maximum transmission delay corresponding to each traffic shaping strategy is calculated based on the data arrival curve function and the data service curve function of each traffic shaping strategy. The optimized traffic shaping strategy is determined from multiple traffic shaping strategies according to the minimum maximum transmission delay. Finally, based on the optimized traffic shaping strategy, a configuration file containing network transmission parameters in the network nodes is generated, and the configuration file is security verified to obtain a security verification result. When the security verification result indicates that the configuration file is correct, the configuration file is sent to all network nodes.

[0215] The embodiments of the present application utilize the traffic transmission demand parameters of multiple network nodes in a real-time network and the network status information between every two network nodes to calculate the worst-case maximum transmission delay corresponding to multiple different traffic shaping strategies, and select the optimized traffic shaping strategy with the smallest 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 a complex and changing network, and effectively improve the transmission rate between network nodes, thereby meeting the high communication requirements of network nodes; and, by accurately calculating the traffic arrival rate and traffic conflict parameters corresponding to each traffic data type, the data arrival curve function of each traffic data type is accurately obtained, so as to facilitate the subsequent use of the data arrival curve function to accurately calculate the maximum transmission delay corresponding to each traffic data type, so as to ensure real-time and deterministic communication in the network, thereby reducing delays and conflicts, and improving the overall efficiency and reliability of the network. reliability; and, utilizing the shaping mechanisms of the time-aware shaper TAS, the credit shaper CBS and the asynchronous traffic shaper ATS, and further utilizing the current network status information and traffic transmission demand parameters, as well as different traffic data types, to accurately obtain the shaper service curve function corresponding to each shaper, so as to facilitate the subsequent use of the shaper service curve function to accurately obtain the maximum delay of each traffic shaping strategy, so as to select the most appropriate optimized traffic shaping strategy, thereby improving the transmission rate of the network; in addition, generating a configuration file corresponding to the optimized traffic shaping strategy, and performing security verification on the configuration file to avoid unexpected security incidents, thereby improving the reliability and security of network transmission parameter optimization, and sending the configuration file to each network node for configuration after security passes, thereby improving the flexibility of transmission parameter configuration in each network node in the network, thereby improving the communication efficiency between network nodes to meet the communication needs of network nodes.

[0216] The present application also provides a network transmission parameter optimization device, which can implement the above network transmission parameter optimization method. Fig.13 , the device 1300 comprises:

[0217] The data acquisition module 1310 is used to acquire the traffic transmission demand parameters of at least one network node in the network and the network status information between every two network nodes;

[0218] A shaping strategy selection module 1320 is used to calculate the maximum transmission delays corresponding to multiple different traffic shaping strategies based on network status information and traffic transmission demand parameters, and determine the optimized traffic shaping strategy from multiple traffic shaping strategies according to the minimum maximum transmission delay;

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

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

[0221] The data arrival curve function is calculated based on the traffic transmission demand parameters;

[0222] Based on the network status information and traffic transmission demand parameters, a data service curve function of each traffic shaping strategy is calculated;

[0223] 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.

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

[0225] Obtaining multiple traffic data types and a traffic arrival rate of each traffic data type from traffic transmission demand parameters;

[0226] Based on the traffic arrival rate, the data arrival curve function of each traffic data type is calculated.

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

[0228] The maximum scheduling frame length of each scheduling flow is obtained, and based on all the maximum scheduling frame lengths multiplied by the time parameter and divided by the scheduling flow arrival period, the data arrival curve function of the scheduling flow is obtained;

[0229] Obtain a high priority traffic conflict parameter of the high priority traffic, and obtain a 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;

[0230] A low priority traffic conflict parameter of the low priority traffic is obtained, and a data arrival curve function of the low priority traffic is obtained based on the sum of the low priority arrival rate and the low priority traffic conflict parameter.

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

[0232] Obtaining a shaper combination strategy for each traffic shaping strategy, each shaper combination strategy comprising at least one shaper connected in series in sequence;

[0233] Based on the network status information and the traffic transmission demand parameters, a shaper service curve function corresponding to each shaper is calculated;

[0234] Based on the shaper service curve function and the shaper combination strategy, a data service curve function of each traffic shaping strategy is obtained by combination.

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

[0236] The minimum scheduling frame length of the scheduled 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 demand parameters, and the transmission link speed, the scheduling rate accumulation of the scheduled traffic and the guard band rate are obtained from the network status information;

[0237] The maximum delay of the scheduled traffic is obtained based on the minimum scheduled 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 delay and multiplied by the transmission link speed;

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

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

[0240] Based on the message queue arrival rate and the message queue conflict parameter, a shaper service curve function of the asynchronous traffic shaper is calculated.

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

[0242] Obtain the total length of the allocation window of the scheduled traffic, and obtain the minimum window resource item based on the ratio of the total length of the allocation window to the cumulative amount of the scheduling rate;

[0243] 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 the sum of the low priority minimum window length and the low priority maximum frame length;

[0244] Based on the difference between the transmission link speed and the cumulative amount of the scheduling rate, an available link transmission item is obtained, based on the ratio of the low priority window frame length and the available link transmission item, a high priority available window resource item is obtained, and the minimum window resource item and the high priority available window resource item are accumulated to obtain a high priority delay;

[0245] 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.

[0246] In some embodiments, the shaping strategy selection module 1320 is further used 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] Accumulate the output arrival curves of all traffic data types to obtain the output arrival curve accumulation;

[0249] Priority traffic screening is performed based on the cumulative amount of output arrival curves to obtain a reorganized arrival curve;

[0250] Based on the difference between the time parameter and the reassembled 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 nodes is generated;

[0253] Perform security verification on the configuration file and obtain the 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. For the part that is not described in detail in a certain embodiment, the specific implementation method of the network transmission parameter optimization device is basically the same as the specific implementation method of the above network transmission parameter optimization method, and will not be repeated here.

[0256] In an embodiment of the present application, a network transmission parameter optimization device utilizes the traffic transmission demand parameters of multiple network nodes in a real-time network and the network status information between every two network nodes to calculate the worst-case maximum transmission delay corresponding to a plurality of different traffic shaping strategies, and selects the optimized traffic shaping strategy with the smallest 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 a complex and changing network, and effectively improve the transmission rate between network nodes, thereby meeting the high communication requirements of network nodes; and, by accurately calculating the traffic arrival rate and traffic conflict parameters corresponding to each traffic data type, the data arrival curve function of each traffic data type is accurately obtained, so as to facilitate the subsequent use of the data arrival curve function to accurately calculate the maximum transmission delay corresponding to each traffic data type, so as to ensure real-time and deterministic communication in the network, thereby reducing delays and conflicts and improving the network. Overall efficiency and reliability; and, utilizing the shaping mechanisms of the time-aware shaper TAS, the credit shaper CBS and the asynchronous traffic shaper ATS, and further utilizing the current network status information and traffic transmission demand parameters, as well as different traffic data types, to accurately obtain the shaper service curve function corresponding to each shaper, so as to facilitate the subsequent use of the shaper service curve function to accurately obtain the maximum delay of each traffic shaping strategy, so as to select the most appropriate optimized traffic shaping strategy, thereby improving the transmission rate of the network; in addition, generating a configuration file corresponding to the optimized traffic shaping strategy, and performing security verification on the configuration file to avoid unexpected security incidents, thereby improving the reliability and security of network transmission parameter optimization, and sending the configuration file to each network node for configuration after security passes, thereby improving the flexibility of transmission parameter configuration in each network node in the network, thereby improving the communication efficiency between network nodes to meet the communication needs of network nodes.

[0257] The present application also provides an electronic device, including:

[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 implemented in the present application. The electronic device can be any intelligent terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), a car computer, etc.

[0262] See also Fig.14 , Fig.14The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

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

[0264] The memory 1402 may be implemented in the form of a ROM (Read Only Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory). The memory 1402 may store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1402, and the processor 1401 calls and executes the network transmission parameter optimization method of the embodiment of this application;

[0265] Input / output interface 1403, used to implement information input and output;

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

[0267] A bus 1405 that transmits information between the various components of the device (e.g., the processor 1401, the memory 1402, the input / output interface 1403, and the communication interface 1404);

[0268] The processor 1401 , the memory 1402 , the input / output interface 1403 and the communication interface 1404 are connected to each other in communication within the device via a bus 1405 .

[0269] An embodiment of the present application further provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the above-mentioned network transmission parameter optimization method is implemented.

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

[0271] The embodiments described in the embodiments of the present application are intended 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 in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

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

[0273] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0274] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0275] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0276] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0277] In the several embodiments provided in the present 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 schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

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

[0279] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0280] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0281] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A network transmission parameter optimization method, characterized in that: The method comprises: Acquire traffic transmission demand parameters of at least one network node in the network and network status information between every two of the network nodes; Calculate the maximum transmission delays corresponding to a plurality of different traffic shaping strategies based on the network status information and the traffic transmission demand parameters, and determine the optimized traffic shaping strategy from the plurality of traffic shaping strategies according to the minimum maximum transmission delay; A network transmission parameter of at least one network node in the network is adjusted based on the optimized traffic shaping strategy.

2. The network transmission parameter optimization method according to claim 1, characterized in that: The calculating the maximum transmission delays corresponding to a plurality of different traffic shaping strategies based on the network state information and the traffic transmission demand parameter includes: Calculate the data arrival curve function based on the traffic transmission demand parameter; Based on the network status information and the traffic transmission demand parameters, a data service curve function of each traffic shaping strategy is calculated; 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.

3. The network transmission parameter optimization method according to claim 2, characterized in that: The calculating and obtaining the data arrival curve function based on the traffic transmission demand parameter includes: Obtaining multiple traffic data types and a traffic arrival rate of each traffic data type from the traffic transmission demand parameters; Based on the traffic arrival rate, a data arrival curve function of each traffic data type is calculated.

4. The method for optimizing network transmission parameters according to claim 3, characterized in that: The traffic data type includes scheduling traffic, high priority traffic and low priority traffic, the traffic arrival rate includes the scheduling traffic arrival period of the scheduling traffic, the high priority arrival rate of the high priority traffic and the low priority arrival rate of the low priority traffic, and the data arrival curve function of each traffic data type is calculated based on the traffic arrival rate, including: Obtaining the maximum scheduling frame length of each of the scheduling flows, and obtaining the data arrival curve function of the scheduling flows based on multiplying all the maximum scheduling frame lengths by a time parameter and dividing by the scheduling flow arrival period; Acquire a high priority flow conflict parameter of the high priority flow, and obtain the data arrival curve function of the high priority flow based on the sum of the high priority arrival rate and the high priority flow conflict parameter; A low priority traffic conflict parameter of the low priority traffic is obtained, and 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.

5. The network transmission parameter optimization method according to claim 4, characterized in that: The step of calculating the data service curve function of each traffic shaping strategy based on the network status information and the traffic transmission requirement parameter includes: Obtaining a shaper combination strategy for each of the traffic shaping strategies, each of the shaper combination strategies comprising at least one shaper connected in series in sequence; Based on the network status information and the traffic transmission demand parameters, a shaper service curve function corresponding to each shaper is calculated; Based on the shaper service curve function and the shaper combination strategy, the data service curve function of each traffic shaping strategy is combined to obtain.

6. The network transmission parameter optimization method according to claim 5, characterized in that: The shaper includes an asynchronous traffic shaper, a time-aware shaper, and a credit shaper. The shaper service curve function corresponding to each shaper is calculated based on the network state information and the traffic transmission demand parameter, including: Obtaining the minimum scheduling frame length of the scheduled 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 accumulation of the scheduled traffic and the guard band rate from the network status information; A maximum delay of the scheduled traffic is obtained based on the minimum scheduled frame length, and a shaper service curve function of the time-aware shaper is obtained based on a difference between the time parameter and the maximum delay and multiplied by the transmission link speed; Based on the high priority credit slope, the transmission link speed, the scheduling rate accumulation and the guard band rate, a high priority effective rate of the high priority traffic is obtained, and based on the product of the high priority effective rate and the time parameter, a high priority service curve function is obtained; Based on the low priority burst period, the transmission link speed and the scheduling rate accumulation, a low priority delay of the low priority traffic is obtained, a low priority service curve function is obtained based on the difference between the time parameter and the low priority delay, and a 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; Based on the message queue arrival rate and the message queue conflict parameter, a shaper service curve function of the asynchronous traffic shaper is calculated.

7. The method for optimizing network transmission parameters according to claim 6, characterized in that: The obtaining of a high priority service curve function based on the product of the high priority effective rate and the time parameter comprises: Obtaining the total length of the allocation window of the scheduling traffic, and obtaining a minimum window resource item based on the ratio of the total length of the allocation window to the cumulative amount of the scheduling rate; 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 the sum of the low priority minimum window length and the low priority maximum frame length; Based on the difference between the transmission link speed and the scheduling rate accumulation, an available link transmission item is obtained, based on the ratio of the low priority window frame length to the available link transmission item, a high priority available window resource item is obtained, and the minimum window resource item and the high priority available window resource item are accumulated to obtain a high priority delay; 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.

8. The method for optimizing network transmission parameters according to claim 7, characterized in that: The calculating and obtaining the shaper service curve function of the asynchronous traffic shaper based on the message queue arrival rate and the message queue conflict parameter includes: Based on the product of the message queue arrival rate and the time parameter, and adding the message queue conflict parameter, a message queue output value is obtained, and the message queue output values ​​of all message queues are accumulated to obtain an output arrival curve of the asynchronous traffic shaper; Accumulating the output arrival curves of all flow data types to obtain the output arrival curve accumulation; Performing priority flow screening based on the output arrival curve accumulation to obtain a reorganized arrival curve; Based on the difference between the time parameter and the reassembled arrival curve and multiplied by the transmission link speed, a shaper service curve function of the asynchronous traffic shaper is obtained.

9. The method for optimizing network transmission parameters according to claim 1, characterized in that: The adjusting a network transmission parameter of at least one network node in the network based on the optimized traffic shaping strategy includes: Based on the optimized traffic shaping strategy, generating a configuration file including the network transmission parameters in the network node; 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, the configuration file is sent to all the network nodes.

10. A network transmission parameter optimization device, characterized in that: The device comprises: A data acquisition module, used to acquire the flow transmission demand parameters of at least one network node in the network and the network status information between every two network nodes; A shaping strategy selection module, used to calculate the maximum transmission delays corresponding to multiple different traffic shaping strategies based on the network status information and the traffic transmission demand parameters, and determine the optimized traffic shaping strategy from the multiple traffic shaping strategies according to the minimum maximum transmission delay; A configuration optimization module is configured to adjust network transmission parameters of at least one network node in the network based on the optimized traffic shaping strategy.

11. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the network transmission parameter optimization method according to any one of claims 1 to 9 is implemented.

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

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