An Optimization Method for Directional Transmission of Network Traffic

Abstract

The present invention discloses an optimization method for network traffic directed transmission, which relates to the technical field of data transmission and includes the following steps: establishing a regional priority model based on regional distribution, dynamically adjusting network links, deploying multi-level caching nodes, clarifying the isolation and multiplexing rules of data traffic, coordinating network resource allocation, establishing a feedback mechanism, and constructing an energy-aware transmission scheduling strategy. This optimization method for network traffic directed transmission significantly improves the flexibility and accuracy of network traffic scheduling through the combination of setting a regional priority model and a dynamic link reconstruction mechanism. It can not only preferentially ensure the transmission efficiency of critical service traffic, but also avoid performance degradation caused by network congestion through real-time link optimization and load prediction. At the same time, it effectively manages low-priority traffic and realizes the reasonable allocation of global network resources. In practical applications, this method can greatly reduce network latency, improve bandwidth utilization, and enhance the user experience.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and particularly to an optimization method for network traffic directed transmission. Background Art

[0002] With the rapid growth of Internet traffic and the diversification of application scenarios, the directed transmission of network traffic faces unprecedented challenges. Traditional traffic management technologies usually rely on static routing strategies and single priority division models, and cannot adapt to the dynamic changes in complex network environments. For example, traffic scheduling based on fixed rules is difficult to respond to sudden traffic congestion in real time. High-priority service traffic may be blocked due to network bottlenecks, and at the same time, excessive competition of low-priority traffic will also affect the overall network performance. In addition, most of the existing technologies adopt a passive approach in link adjustment, only reconstructing the path after the network is severely congested, and failing to predict and avoid potential bottlenecks in advance. Facing the complex network requirements of multiple regions and cross-services, these methods seem powerless. Summary of the Invention

[0003] Aiming at the deficiencies of the existing technology, the present invention provides an optimization method for network traffic directed transmission to solve the problems raised in the above background art.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] In a first aspect, an embodiment of the present invention provides an optimization method for network traffic directed transmission, including the following steps:

[0006] S1. Establish a regional priority model based on regional distribution;

[0007] S2. Dynamically adjust network links based on the regional priority model to achieve rapid traffic diversion;

[0008] S3. After dynamically adjusting the network links, deploy multi-level cache nodes to reduce data transmission pressure;

[0009] S4. On the basis of cache deployment, clarify the isolation and multiplexing rules of data traffic to optimize transmission efficiency;

[0010] S5. After completing the above steps, coordinate network resource allocation based on a distributed controller;

[0011] S6. Establish a feedback mechanism to adjust the transmission strategy of network traffic in real time;

[0012] S7. Construct an energy-aware transmission scheduling strategy to reduce the operating cost of network traffic directed transmission.

[0013] To further optimize this technical solution, in step S1, each node in the network is divided into different regions according to geographical location or virtual topology structure;

[0014] Each region is divided according to physical distance and based on the actual requirements of user behavior, data type, and network bandwidth. A large-scale urban network is divided into several regions according to administrative divisions or according to the usage pattern of network traffic;

[0015] A priority value is assigned to the traffic in each region based on the regional priority model.

[0016] To further optimize this technical solution, in the regional priority model, it is set that within the region there are types of different traffic. The traffic includes video streams, audio streams, and small data packet streams. A weight value and an urgency index are set for each traffic type;

[0017] The regional priority model is as follows:

[0018] ;

[0019] Among them,

[0020] is the total priority value of region ;

[0021] is the total number of traffic types within region ;

[0022] is the weight value of the traffic, which is set based on the business importance of this traffic;

[0023] is the traffic density factor of the traffic type, indicating the transmission volume of this type of traffic within this region. The higher the density, the higher the priority;

[0024] is the urgency coefficient of this type of traffic;

[0025] is the delay tolerance factor of the traffic. For traffic that requires low latency, its delay tolerance is low, and the priority coefficient is high.

[0026] To further optimize this technical solution, in step S2, the dynamic adjustment of the network link adopts the dynamic link reconstruction mechanism. By real-time monitoring the indicators of network bandwidth usage, delay, and packet loss rate, the routing weight of the link is dynamically adjusted;

[0027] In the dynamic link reconstruction mechanism, based on the regional priority model, the priorities of network nodes and links are dynamically calculated. Each network link has two attributes: weight and load. The load and weight of the link are both correlated with the priority of the traffic and the current link state. The calculation results of the link load formula and the link weight adjustment formula are used to determine whether link reconstruction is required.

[0028] To further optimize this technical solution, the link load formula is as follows:

[0029] ;

[0030] where,

[0031] is the comprehensive load value of link ;

[0032] is the number of all traffic types on link ;

[0033] is the priority value of the traffic within region ;

[0034] is the bandwidth occupancy of traffic on the link;

[0035] is the maximum bandwidth of the link, used to standardize the bandwidth occupancy ratio;

[0036] is the current delay of link ;

[0037] is the maximum tolerable delay of the link, used to standardize the impact of the delay;

[0038] is a regulation coefficient, used to control the influence weight of the priority on the load;

[0039] is the current packet loss rate of link ;

[0040] is the maximum packet loss rate of the link, used to standardize the impact of the packet loss rate;

[0041] The link weight adjustment formula is as follows:

[0042] ;

[0043] where,

[0044] For the link The adjusted weight value;

[0045] For the link The current weight of;

[0046] Is the adjustment coefficient, used to control the sensitivity of link adjustment;

[0047] Is the maximum load value of all links, used for standardization.

[0048] To further optimize this technical solution, in step S3, the cache node sets up edge caches in high-priority regions according to the regional priority model and the results of link reconstruction, and at the same time deploys auxiliary caches in low-priority regions for hierarchical transmission on the data request path.

[0049] To further optimize this technical solution, in step S4, by classifying the characteristics of the traffic, the transmission requirements are determined, and traffic with different characteristics is isolated and arranged in different logical links;

[0050] Using time division multiplexing technology, different categories of traffic are preferentially transmitted at different times; or based on frequency division multiplexing technology, dedicated frequency bands are opened for high-priority traffic;

[0051] At the same time, the idle time of low-priority traffic multiplexes high-priority channels to ensure the reasonable utilization of bandwidth.

[0052] To further optimize this technical solution, in step S5, the distributed controller manages the traffic priority, link reconstruction, and cache allocation in different regions by deploying multiple controller nodes;

[0053] In the distributed architecture of the distributed controller, each controller makes decisions independently based on the real-time data it perceives, and at the same time shares information with other controllers through a synchronization protocol.

[0054] To further optimize this technical solution, in step S6, the feedback mechanism includes:

[0055] By taking the link load situation, cache hit rate, and user experience feedback as inputs, the regional priority model and link weights are dynamically updated, thereby adjusting the directional transmission strategy of network traffic.

[0056] To further optimize this technical solution, in step S7, the transmission scheduling strategy includes:

[0057] Deploy sensors and monitor the energy usage of network devices. Combine real-time traffic prediction and load distribution data to turn off some link devices during off-peak hours and direct traffic to more energy-efficient links. For high-priority areas, use nodes supported by green energy to complete the main transmission tasks.

[0058] In a second aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program instructions are executed by the processor, the steps of a network traffic directed transmission optimization method as described in the first aspect of the present invention are implemented.

[0059] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program instructions are executed by the processor, the steps of a network traffic directed transmission optimization method as described in the first aspect of the present invention are implemented.

[0060] Compared with the prior art, the present invention provides a network traffic directed transmission optimization method, which has the following beneficial effects:

[0061] This network traffic directed transmission optimization method, through the combination of setting a regional priority model and a dynamic link reconstruction mechanism, significantly improves the flexibility and accuracy of network traffic scheduling. It can not only give priority to ensuring the transmission efficiency of critical service traffic, but also avoid performance degradation caused by network congestion through real-time link optimization and load prediction. At the same time, it effectively manages low-priority traffic and realizes the reasonable allocation of global network resources. In practical applications, this method can greatly reduce network latency, improve bandwidth utilization, and enhance the user experience, providing a new solution for intelligent traffic management in complex network environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0063] Figure 1 It is a flowchart showing a network traffic directed transmission optimization method proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will describe the specific embodiments of the present invention in detail with reference to the drawings in the specification.

[0065] In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Persons skilled in the art can make similar generalizations without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0066] Secondly, as used herein, "one embodiment" or "an embodiment" refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other. Embodiment 1

[0067] Referring to Figure 1 , which is the first embodiment of the present invention, this embodiment provides an optimization method for network traffic directional transmission, including the following steps:

[0068] S1. Establish a regional priority model based on regional distribution

[0069] In this embodiment, each node in the network is divided into different regions according to geographical location or virtual topology structure;

[0070] Each region is divided according to physical distance and based on the actual requirements of user behavior, data type, and network bandwidth. A large-scale urban network is divided into several regions according to administrative divisions or according to the usage pattern of network traffic.

[0071] Ensure that the priority of traffic scheduling has theoretical support and provide an optimization basis for subsequent steps.

[0072] Assign a priority value to the traffic within each region based on the regional priority model.

[0073] In the regional priority model, it is set that in region , there are different types of traffic. The traffic includes video streams, audio streams, and small data packet streams. A weight value and an urgency index are set for each traffic type;

[0074] The regional priority model is as follows:

[0075] ;

[0076] Wherein,

[0077] is the total priority value of region ;

[0078] is region Total number of internal traffic types;

[0079] is the weight value of the traffic, set based on the business importance of the traffic;

[0080] is the traffic density factor of the traffic type, indicating the transmission volume of this type of traffic in this area. The higher the density, the higher the priority;

[0081] is the urgency coefficient of this type of traffic;

[0082] is the delay tolerance factor of the traffic. For traffic that requires low latency, its delay tolerance is low and the priority coefficient is high.

[0083] Through this model, the traffic priority value of each area can be calculated , and the network resources can be scheduled accordingly. In practical applications, this model can dynamically adjust the weights , density , urgency and delay tolerance values. For example, in a high-traffic area , if the traffic contains a large number of video streams and has a high density and urgency, the priority of this area will be high, meaning that in subsequent network traffic scheduling, the traffic in this area will be given priority.

[0084] In practical applications,

[0085] Assume that in area , there are two types of traffic: video streams and ordinary Web request streams.

[0086] Video stream (traffic type 1):

[0087] Weight (the video stream has a high demand for network resources),

[0088] Traffic density Gbps,

[0089] Urgency (because video streams often require low latency),

[0090] Delay tolerance (video streams are very sensitive to latency).

[0091] Web request stream (traffic type 2):

[0092] Weight ,

[0093] Traffic density Gbps,

[0094] Urgency (The urgency of web traffic is relatively low),

[0095] Delay tolerance (The delay tolerance of web traffic is relatively high).

[0096] Based on this, the priority of area is as follows:

[0097]

[0098] Assume that area mainly carries ordinary web request flows and has a relatively low network density, and its priority may be much lower than .

[0099]

[0100] It can be seen from this that the priority of area is significantly higher than that of area , which provides data support for traffic allocation and link optimization in subsequent steps, ensuring that high-priority areas can obtain sufficient network resources to meet their high-density and low-latency requirements.

[0101] S2. Based on the area priority model, dynamically adjust the network link to achieve fast traffic diversion

[0102] In this embodiment, the dynamic adjustment of the network link adopts a dynamic link reconstruction mechanism. By real-time monitoring the indicators of network bandwidth usage, delay, and packet loss rate, the routing weight of the link is dynamically adjusted;

[0103] In the dynamic link reconstruction mechanism, based on the area priority model, the priorities of network nodes and links are dynamically calculated. Each network link has two attributes of weight and load. The load and weight of the link are both correlated with the priority of the traffic and the current link state. The calculation results of the link load formula and the link weight adjustment formula are used to determine whether link reconstruction is required.

[0104] The link load formula is as follows:

[0105]

[0106] Among them,

[0107] is the link The comprehensive load value;

[0108] is the number of all traffic types on the link ;

[0109] is the priority value of the traffic within the area ;

[0110] is the bandwidth occupancy of the traffic on the link;

[0111] is the maximum link bandwidth, used to standardize the bandwidth occupancy ratio;

[0112] is the current delay of the link ;

[0113] is the maximum tolerable delay of the link, used to standardize the impact degree of the delay;

[0114] is a regulation coefficient, used to control the influence weight of the priority on the load;

[0115] is the current packet loss rate of the link ;

[0116] is the maximum packet loss rate of the link, used to standardize the impact of the packet loss rate.

[0117] When in use,

[0118] Priority influence: The traffic priority value will directly affect the contribution of this traffic to the link load. For example, traffic with a higher priority (such as video streams or medical data) will occupy a greater weight in the link load calculation, meaning that these traffic will occupy the bandwidth first. The bandwidth occupancy ratio of high-priority traffic will significantly affect the load value of the link.

[0119] Bandwidth occupancy: The bandwidth occupancy situation and the total link bandwidth The ratio between is used to evaluate the pressure of the current traffic on the link bandwidth. If a traffic-intensive application on the link (such as high-definition video) occupies too much bandwidth, the load value of the link will increase significantly and may need to be reconstructed.

[0120] Impact of delay and packet loss: Link delay and packet loss rate It will also affect the load value. Links with high latency or high packet loss rate will cause the load value to increase, and may even have an adverse impact on high-priority traffic. Therefore, these factors must be considered.

[0121] The link weight adjustment formula is as follows:

[0122] ;

[0123] Where,

[0124] is the weight value after link adjustment;

[0125] is the current weight of link ;

[0126] is the adjustment coefficient, used to control the sensitivity of link adjustment;

[0127] is the maximum load value of all links, used for normalization.

[0128] When in use,

[0129] dynamically adjust the link weight according to the load calculation result of the link . If a certain link is overloaded, the weight will be adjusted according to the load situation so that traffic can be directed to other links to reduce congestion and bottlenecks.

[0130] The adjustment of the weight is achieved through a formula, where controls the sensitivity of the adjustment. If the load of the link is too high (i.e., is close to ), the weight of this link will decrease significantly, meaning that traffic will tend to choose other links with lighter loads.

[0131] In summary, when using the dynamic link reconstruction mechanism:

[0132] Real-time monitoring: Real-time collect data such as bandwidth occupancy, latency, and packet loss rate of each link, and calculate the comprehensive load value of each link .

[0133] Load evaluation and weight adjustment: Calculate the weight of the link according to the load value, and adjust the network routing through the link reconstruction algorithm. High-priority traffic will be preferentially allocated to links with less load.

[0134] Traffic allocation: Dynamically select the traffic path based on the link weight and network status, avoid congestion or bottlenecks in the network, and ensure low-latency transmission of high-priority traffic.

[0135] S3. After dynamically adjusting the network link, deploy multi-level cache nodes to reduce data transmission pressure

[0136] In this embodiment, according to the regional priority model and the result of link reconstruction, edge caches are set in high-priority regions, while auxiliary caches are deployed in low-priority regions, and hierarchical transmission is performed on the data request path.

[0137] The hierarchical design of the cache should include three levels: edge cache (responding to user requests in real time), regional cache (providing support for local area networks), and main cache (centralized storage of low-frequency data). In this way, hierarchical transmission can be achieved on the data request path, further reducing the load on the core link.

[0138] S4. On the basis of cache deployment, clarify the isolation and multiplexing rules of data traffic to optimize transmission efficiency

[0139] In this embodiment, by classifying the characteristics of traffic, such as video streams, audio streams, small data packet requests, etc., determine their transmission requirements (bandwidth, latency, error tolerance, etc.), and arrange traffic with different characteristics in different logical links.

[0140] Using time-division multiplexing technology, different types of traffic are preferentially transmitted at different times; or based on frequency-division multiplexing technology, dedicated frequency bands are opened for high-priority traffic.

[0141] At the same time, the idle time of low-priority traffic multiplexes the high-priority channel to ensure the reasonable utilization of bandwidth.

[0142] S5. After completing the above steps, coordinate network resource allocation based on a distributed controller

[0143] In this embodiment, the distributed controller deploys multiple controller nodes to manage the traffic priority, link reconstruction, and cache allocation in different regions respectively;

[0144] Under the distributed architecture of the distributed controller, each controller makes independent decisions based on the real-time data it perceives, and at the same time shares information with other controllers through a synchronization protocol to ensure the consistency of global optimization.

[0145] S6. Establish a feedback mechanism to adjust the transmission strategy of network traffic in real time

[0146] In this embodiment, the feedback mechanism includes:

[0147] By taking the execution results of each step (such as link load conditions, cache hit rates, user experience feedback) as inputs, the regional priority model and link weights are dynamically updated. The key to the adaptive optimization mechanism lies in the real-time and accuracy of the feedback, so a low-latency data acquisition and processing framework needs to be introduced. In addition, the optimization algorithm can be based on multi-objective optimization theory, comprehensively considering latency, bandwidth utilization, and energy consumption, and dynamically adjust the network resource allocation scheme.

[0148] S7. Construct an energy-aware transmission scheduling strategy to reduce the operating costs of network traffic directed transmission

[0149] In this embodiment, the transmission scheduling strategy includes:

[0150] Deploy sensors and monitor the energy usage of network devices. Combining real-time traffic prediction and load distribution data, some link devices are turned off during off-peak hours, and the traffic is guided to more energy-efficient links; for high-priority regions, nodes supported by green energy are used to complete the main transmission tasks.

[0151] Through this step, the dual goals of transmission optimization and energy conservation can be achieved. Embodiment 2

[0152] This embodiment also provides a computer device applicable to a network traffic directed transmission optimization method, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the network traffic directed transmission optimization method as proposed in the above embodiment.

[0153] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the network traffic directed transmission optimization method as proposed in the above embodiment.

[0154] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, operator networks, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad set on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0155] When a function 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 invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0156] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions. It can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device.

[0157] More specific examples (nonexhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memories), fiber optic devices, and portable compact disc read-only memories (CDROM). Additionally, a computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or processing it in other suitable ways when necessary, and then storing it in a computer memory.

[0158] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0159] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. An optimization method for directional transmission of network traffic, characterized in that, It includes the following steps: S1. Establish a regional priority model based on regional distribution; divide each node in the network into different regions according to geographical location or virtual topology. Each region is divided according to physical distance and based on the actual requirements of user behavior, data type, and network bandwidth. A large-scale urban network is divided into several regions according to administrative divisions or according to the usage pattern of network traffic. Assign a priority value to the traffic within each region based on the regional priority model; S2. Based on the regional priority model, dynamically adjust the network links to achieve rapid traffic diversion; the dynamic adjustment of network links adopts a dynamic link reconstruction mechanism. By real-time monitoring the indicators of network bandwidth usage, latency, and packet loss rate, dynamically adjust the routing weights of the links; in the dynamic link reconstruction mechanism, based on the regional priority model, the priorities of network nodes and links are dynamically calculated. Each network link has two attributes of weight and load. The load and weight of the link are both correlated with the priority of the traffic and the current link state. Use the calculation results of the link load formula and the link weight adjustment formula to determine whether link reconstruction is required; S3. After dynamically adjusting the network links, deploy multi-level cache nodes to reduce data transmission pressure; the cache nodes, according to the regional priority model and the results of link reconstruction, set up edge caches in high-priority regions and deploy auxiliary caches in low-priority regions for hierarchical transmission on the data request path; S4. On the basis of cache deployment, clarify the isolation and multiplexing rules of data traffic to optimize transmission efficiency; by classifying the characteristics of the traffic, determine its bandwidth, latency, and error tolerance rate, and arrange traffic with different characteristics in different logical links. Use time multiplexing technology to preferentially transmit different types of traffic at different times; or based on frequency multiplexing technology, open up exclusive frequency bands for high-priority traffic, and the idle time of low-priority traffic multiplexes the high-priority channels; S5. After completing the above steps, coordinate network resource allocation based on a distributed controller; by deploying multiple controller nodes to manage the traffic priority, link reconstruction, and cache allocation of different regions respectively. Under the distributed architecture of the distributed controller, each controller makes independent decisions based on the real-time data it perceives, and at the same time shares information with other controllers through a synchronization protocol to ensure the consistency of global optimization; S6. Establish a feedback mechanism to adjust the transmission strategy of network traffic in real time; use the execution results of each step as input to dynamically update the regional priority model and link weights; S7. Build an energy-aware transmission scheduling strategy to reduce the operating cost of network traffic directional transmission; Deploy sensors and monitor the energy usage of network devices. Combine real-time traffic prediction and load distribution data to turn off some link devices during off-peak hours and direct the traffic to links with higher energy efficiency; for high-priority regions, use nodes supported by green energy to complete the main transmission tasks.

2. The optimization method for directed transmission of network traffic according to claim 1, wherein In the described area priority model, it is set that within the area there are different types of traffic, including video traffic, audio traffic, and small data packet traffic. A weight value and an urgency index are set for each traffic type; The regional priority model is as follows: ； Wherein, is the total priority value for the area ; is the total number of flow types within the region; is the weight value of the traffic, which is set based on the business importance of the traffic; The traffic density factor for the traffic type, which represents the transmission volume of this type of traffic in this area. The higher the density, the higher the priority; is the emergency coefficient of this type of traffic; is the delay tolerance factor of the traffic. For traffic that requires low latency, its delay tolerance is low and the priority coefficient is high.

3. A method for optimizing the directional transmission of network traffic according to claim 1, wherein The link load formula is as follows: ； Wherein, For the link of the comprehensive load value; For the link the number of all traffic types; For the priority value of the traffic within the area; is the traffic bandwidth occupancy on the link; is the maximum link bandwidth and is used to standardize the bandwidth occupancy ratio; For the link current delay; is the maximum tolerable delay of the link and is used to standardize the degree of influence of the delay; is a regulation coefficient used to control the influence weight of priority on the load; is the current packet loss rate of the link ; is the maximum link packet loss rate, which is used to standardize the impact of the packet loss rate; The link weight adjustment formula is as follows: ； Wherein, For the link Adjusted weight value; is the current weight of the link ; is an adjustment coefficient used to control the sensitivity of link adjustment; It is the maximum load value for all links and is used for normalization.

4. A method for optimizing the directional transmission of network traffic according to claim 1, characterized in that, In the step S6, the feedback mechanism includes: By taking the link load condition, cache hit rate, and user experience feedback as inputs, the regional priority model and link weights are dynamically updated, thereby adjusting the directional transmission strategy of network traffic.

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