Optimization method for directional transmission of network traffic

Abstract

The invention discloses a network flow directional transmission optimization method, and relates to the technical field of data transmission. Comprising the following steps: establishing a regional priority model based on regional distribution, dynamically adjusting a network link, deploying multi-level cache nodes, determining isolation and multiplexing rules of data traffic, coordinating network resource allocation, establishing a feedback mechanism, and constructing an energy-aware transmission scheduling strategy. According to the network traffic directional transmission optimization method, the flexibility and the accuracy of network traffic scheduling are remarkably improved by setting the combination of a regional priority model and a dynamic link reconstruction mechanism, the transmission efficiency of key service traffic can be guaranteed preferentially, and the network traffic directional transmission optimization method can be used for optimizing the network traffic through real-time link optimization and load prediction. And performance reduction caused by network congestion is avoided. And meanwhile, the low-priority flow is effectively managed, and the reasonable distribution of global network resources is realized. In practical application, the method can greatly reduce the network delay, improve the bandwidth utilization rate and improve the user experience.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular to a method for optimizing directional transmission of network traffic. Background Art

[0002] With the rapid growth of Internet traffic and the diversification of application scenarios, the directional transmission of network traffic faces unprecedented challenges. Traditional traffic management technologies usually rely on static routing strategies and a single priority division model, which cannot adapt to 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 business traffic may be blocked by network bottlenecks, and excessive competition for low-priority traffic will also affect the overall network performance. In addition, most existing technologies use a passive approach to link adjustment, and only reconstruct paths after severe network congestion, but fail to predict and avoid potential bottlenecks in advance. Faced with complex network requirements across multiple regions and businesses, these methods seem to be powerless. Summary of the invention

[0003] In view of the deficiencies of the prior art, the present invention provides a method for optimizing directional transmission of network traffic to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a method for optimizing network traffic directional transmission, comprising the following steps: S1. Establish a regional priority model based on regional distribution; S2: Based on the regional priority model, dynamically adjust network links to achieve rapid traffic diversion; S3, after dynamically adjusting the network link, deploy multi-level cache nodes to reduce data transmission pressure; S4. Based on cache deployment, clarify the isolation and reuse rules of data traffic to optimize transmission efficiency; S5. After completing the above steps, coordinate network resource allocation based on the distributed controller; S6. Establish a feedback mechanism to adjust the transmission strategy of network traffic in real time; S7. Build an energy-aware transmission scheduling strategy to reduce the operating cost of directional transmission of network traffic.

[0005] To further optimize the technical solution, in step S1, each node in the network is divided into different areas according to the geographical location or virtual topology structure; Each area is divided according to physical distance and based on user behavior, data type and actual demand for network bandwidth. Large-scale urban networks are divided into several areas according to administrative divisions or according to the usage pattern of network traffic. Traffic within each zone is assigned a priority value based on the zone priority model.

[0006] To further optimize this technical solution, in the regional priority model, set Inside, there is Different types of traffic, including video streams, audio streams, and small data packet streams, are classified, and a weight value and urgency index are set for each traffic type; The regional priority model is as follows: ; in, For Region The total priority value of It is a region Total number of internal traffic types; It is The weight value of the traffic is set based on the business importance of the traffic; The traffic density factor of the traffic type indicates the transmission volume of this type of traffic in the area. The higher the density, the higher the priority. is the urgency coefficient of this type of traffic; It is the delay tolerance factor of the traffic. For traffic that requires low latency, its delay tolerance is lower and its priority coefficient is higher.

[0007] Further optimizing the technical solution, in step S2, the network link is dynamically adjusted by adopting a dynamic link reconstruction mechanism, and the routing weight of the link is dynamically adjusted by real-time monitoring of the network's bandwidth usage, delay, and packet loss rate indicators; 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 correlated with the priority of the traffic and the current link status. The calculation results of the link load formula and the link weight adjustment formula are used to determine whether link reconstruction is needed.

[0008] To further optimize this technical solution, the link load formula is as follows: ; in, For Link The comprehensive load value of For Link The number of all traffic types on For Region Priority value of internal traffic; For flow Bandwidth usage on the link; is the maximum link bandwidth, used to standardize the bandwidth occupancy ratio; For Link The current delay of is the maximum tolerable delay of the link, used to standardize the impact of delay; It is an adjustment coefficient used to control the weight of the impact of priority on load; For Link The current packet loss rate; is the maximum packet loss rate of the link, used to standardize the impact of packet loss rate; The link weight adjustment formula is as follows: ; in, For Link The adjusted weight value; For Link The current weight of is the adjustment coefficient, used to control the sensitivity of link adjustment; is the maximum load value of all links, used for standardization.

[0009] To further optimize the technical solution, in step S3, the cache node sets an edge cache in a high-priority area according to the regional priority model and the result of link reconstruction, and deploys an auxiliary cache in a low-priority area, and performs hierarchical transmission on the path of the data request.

[0010] To further optimize the technical solution, in step S4, the transmission requirements are determined by classifying the characteristics of the traffic, and traffic with different characteristics is isolated and arranged in different logical links; Use time multiplexing technology to prioritize transmission of different types of traffic at different times; or use frequency multiplexing technology to open up exclusive frequency bands for high-priority traffic; At the same time, the idle time of low-priority traffic is multiplexed with high-priority channels to ensure the rational use of bandwidth.

[0011] To further optimize the technical solution, in step S5, the distributed controller manages the traffic priority, link reconstruction and cache allocation of different areas by deploying multiple controller nodes; In the distributed architecture of distributed controllers, each controller makes independent decisions based on the real-time data it perceives, and shares information with other controllers through synchronization protocols.

[0012] To further optimize the technical solution, in step S6, the feedback mechanism includes: By taking link load conditions, cache hit rate, and user experience feedback as input, the regional priority model and link weight are dynamically updated to adjust the directional transmission strategy of network traffic.

[0013] To further optimize the technical solution, in step S7, the transmission scheduling strategy includes: Deploy sensors and monitor the energy usage of network devices. Combined with real-time traffic forecasts and load distribution data, shut down 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.

[0014] In a second aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of a network traffic directional transmission optimization method as described in the first aspect of the present invention are implemented.

[0015] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of a network traffic directional transmission optimization method as described in the first aspect of the present invention are implemented.

[0016] Compared with the prior art, the present invention provides a method for optimizing network traffic directional transmission, which has the following beneficial effects: This network traffic directional transmission optimization method significantly improves the flexibility and accuracy of network traffic scheduling by combining the regional priority model and the dynamic link reconstruction mechanism. It can not only prioritize the transmission efficiency of key business traffic, but also avoid performance degradation caused by network congestion through real-time link optimization and load prediction. At the same time, low-priority traffic is effectively managed to achieve a reasonable allocation of global network resources. In practical applications, this method can significantly reduce network latency, improve bandwidth utilization, and improve user experience, providing a new solution for intelligent traffic management in complex network environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 The present invention is a flowchart of a method for optimizing network traffic directional transmission. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments. Embodiment 1

[0022] Reference Figure 1 , which is the first embodiment of the present invention, and provides a method for optimizing directional transmission of network traffic, comprising the following steps: S1. Establish a regional priority model based on regional distribution In this embodiment, each node in the network is divided into different areas according to geographical location or virtual topology structure; Each area is divided according to physical distance and based on user behavior, data type and actual demand for network bandwidth. Large-scale urban networks are divided into several areas according to administrative divisions or according to network traffic usage patterns.

[0023] Ensure that the priority of traffic scheduling has theoretical support to provide an optimization basis for subsequent steps.

[0024] Traffic within each zone is assigned a priority value based on the zone priority model.

[0025] In the regional priority model, the Inside, there is Different types of traffic, including video streams, audio streams, and small data packet streams, are classified, and a weight value and urgency index are set for each traffic type; The regional priority model is as follows: ; in, For Region The total priority value of It is a region Total number of internal traffic types; It is The weight value of the traffic is set based on the business importance of the traffic; The traffic density factor of the traffic type indicates the transmission volume of this type of traffic in the area. The higher the density, the higher the priority. is the urgency coefficient of this type of traffic; It is the delay tolerance factor of the traffic. For traffic that requires low latency, its delay tolerance is lower and its priority coefficient is higher.

[0026] Through this model, the traffic priority value of each area can be calculated , and schedule network resources accordingly. In practical applications, this model can dynamically adjust weights according to different scenarios. ,density , Urgency and delay tolerance For example, in a high traffic area If the traffic contains a large number of video streams with high density and urgency, the priority of the area It will be higher, which means that in the subsequent network traffic scheduling, the traffic in this area will be given priority.

[0027] In practical applications, Assume that in the area There are two types of traffic: video streaming and common web request streaming.

[0028] Video stream (traffic type 1): Weight (Video streaming has high demand for network resources), Traffic density Gbps, Urgency (because video streaming often requires low latency), Delay Tolerance (Video streaming is very sensitive to latency).

[0029] Web request flow (traffic type 2): Weight , Traffic density Gbps, Urgency (Web traffic has a lower urgency), Delay Tolerance (Web traffic has a higher tolerance for latency).

[0030] Based on this, the region Priority for:

[0031] Assumption Area Mainly carries common Web request flows, and the network density is low, its priority May be much lower than .

[0032]

[0033] It can be seen that the area The priority is significantly higher than the region This 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.

[0034] S2: Based on the regional priority model, dynamically adjust network links to achieve rapid traffic diversion In this embodiment, the dynamic adjustment of network links adopts a dynamic link reconstruction mechanism, which dynamically adjusts the routing weight of the link by real-time monitoring of the network's bandwidth usage, delay, and packet loss rate indicators; 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 correlated with the priority of the traffic and the current link status. The calculation results of the link load formula and the link weight adjustment formula are used to determine whether link reconstruction is needed.

[0035] The link load formula is as follows:

[0036] in, For Link The comprehensive load value of For Link The number of all traffic types on For Region Priority value of internal traffic; For flow Bandwidth usage on the link; is the maximum link bandwidth, used to standardize the bandwidth occupancy ratio; For Link The current delay of is the maximum tolerable delay of the link, used to standardize the impact of delay; It is an adjustment coefficient used to control the weight of the impact of priority on load; For Link The current packet loss rate; is the maximum packet loss rate of the link, which is used to standardize the impact of the packet loss rate.

[0037] When using, Priority Impact: Traffic Priority Value It directly affects the contribution of the traffic to the link load. For example, higher priority traffic (such as video streaming or medical data) will have a greater weight in the link load calculation, which means that these traffic will take up bandwidth first. This will significantly affect the link load value.

[0038] Bandwidth usage: Bandwidth usage and the total link bandwidth The ratio between Used to evaluate the pressure of current traffic on link bandwidth. If a traffic-intensive application (such as high-definition video) on a link occupies too much bandwidth, the link load value will increase significantly and may need to be restructured.

[0039] Effects of latency and packet loss: Link latency and packet loss rate Links with high latency or high packet loss will cause the load value to increase and may even have a negative impact on high priority traffic, so these factors must be taken into account.

[0040] The link weight adjustment formula is as follows: ; in, For Link The adjusted weight value; For Link The current weight of is the adjustment coefficient, used to control the sensitivity of link adjustment; is the maximum load value of all links, used for standardization.

[0041] When using, Dynamically adjust link weights based on link load calculation results If a link is overloaded, the weight Adjustments will be made based on load conditions so that traffic can be directed to other links to reduce congestion and bottlenecks.

[0042] The weight adjustment is achieved through the formula, where Controls the sensitivity of the adjustment. If the link load is too high (i.e. near ), the weight of the link will drop significantly, which means that traffic will tend to choose other links with lighter loads.

[0043] In summary, when the dynamic link reconstruction mechanism is used: Real-time monitoring: collects data such as bandwidth usage, latency, and packet loss rate of each link in real time, and calculates the comprehensive load value of each link .

[0044] Load evaluation and weight adjustment: Calculate the link weight based on the load value, and adjust the network route through the link reconstruction algorithm. High-priority traffic will be preferentially distributed to links with less load.

[0045] Traffic distribution: Dynamically select traffic paths based on link weights and network status to avoid congestion or bottlenecks in the network and ensure low-latency transmission of high-priority traffic.

[0046] S3. After dynamically adjusting the network link, deploy multi-level cache nodes to reduce data transmission pressure In this embodiment, the cache node sets edge cache in the high priority area according to the regional priority model and the result of link reconstruction, and deploys auxiliary cache in the low priority area, and performs hierarchical transmission on the path of data request.

[0047] The cache hierarchy design should include three levels: edge cache (real-time response to user requests), regional cache (providing support for local area networks), and primary cache (centralized storage of low-frequency data). In this way, hierarchical transmission can be achieved on the path of data requests, further reducing the load on the core link.

[0048] S4. Based on cache deployment, clarify the isolation and reuse rules of data traffic to optimize transmission efficiency In this embodiment, by classifying the characteristics of traffic, such as video stream, audio stream, small data packet request, etc., its transmission requirements (bandwidth, delay, fault tolerance, etc.) are determined, and traffic with different characteristics is arranged in different logical links.

[0049] Use time multiplexing technology to prioritize the transmission of different types of traffic at different time periods; or use frequency multiplexing technology to open up exclusive frequency bands for high-priority traffic.

[0050] At the same time, the idle time of low-priority traffic is multiplexed with high-priority channels to ensure the rational use of bandwidth.

[0051] S5. After completing the above steps, coordinate network resource allocation based on distributed controllers In this embodiment, the distributed controller manages the traffic priority, link reconstruction and cache allocation of different areas by deploying multiple controller nodes; Under the distributed architecture of distributed controllers, each controller makes independent decisions based on the real-time data it perceives, and shares information with other controllers through synchronization protocols to ensure the consistency of global optimization.

[0052] S6. Establish a feedback mechanism to adjust the transmission strategy of network traffic in real time In this embodiment, the feedback mechanism includes: By taking the execution results of each step (such as link load, cache hit rate, and user experience feedback) as input, the regional priority model and link weight are dynamically updated. The key to the adaptive optimization mechanism lies in the real-time and accuracy of feedback, so a low-latency data collection and processing framework needs to be introduced. In addition, the optimization algorithm can be based on multi-objective optimization theory, comprehensively consider latency, bandwidth utilization, and energy consumption, and dynamically adjust the network resource allocation plan.

[0053] S7. Build energy-aware transmission scheduling strategies to reduce the operating costs of directional transmission of network traffic In this embodiment, the transmission scheduling strategy includes: Deploy sensors and monitor the energy usage of network devices. Combined with real-time traffic forecasts and load distribution data, shut down 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.

[0054] Through this step, the dual goals of transmission optimization and energy conservation can be achieved. Embodiment 2

[0055] This embodiment also provides a computer device, which is suitable for a network traffic directional 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 computer executable instructions to implement the network traffic directional transmission optimization method proposed in the above embodiment.

[0056] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the network traffic directional transmission optimization method proposed in the above embodiment is implemented.

[0057] The computer device may 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 an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0058] If the 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, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0059] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0060] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk case (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0061] It should be understood that the 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, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0062] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing network traffic directional transmission, characterized in that: The following steps are involved: S1. Establish a regional priority model based on regional distribution; S2: Based on the regional priority model, dynamically adjust network links to achieve rapid traffic diversion; S3, after dynamically adjusting the network link, deploy multi-level cache nodes to reduce data transmission pressure; S4. Based on cache deployment, clarify the isolation and reuse rules of data traffic to optimize transmission efficiency; S5. After completing the above steps, coordinate network resource allocation based on the distributed controller; S6. Establish a feedback mechanism to adjust the transmission strategy of network traffic in real time; S7. Build an energy-aware transmission scheduling strategy to reduce the operating cost of directional transmission of network traffic.

2. A network traffic directional transmission optimization method according to claim 1, characterized in that: In the step S1, each node in the network is divided into different areas according to geographical location or virtual topology structure; Each area is divided according to physical distance and based on user behavior, data type and actual demand for network bandwidth. Large-scale urban networks are divided into several areas according to administrative divisions or according to the usage pattern of network traffic. Traffic within each zone is assigned a priority value based on the zone priority model.

3. A network traffic directional transmission optimization method according to claim 2, characterized in that: In the regional priority model, the Inside, there is Different types of traffic, including video streams, audio streams, and small data packet streams, are classified, and a weight value and urgency index are set for each traffic type; The regional priority model is as follows: ； in, For Region The total priority value of It is a region Total number of internal traffic types; It is The weight value of the traffic is set based on the business importance of the traffic; The traffic density factor of the traffic type indicates the transmission volume of this type of traffic in the area. The higher the density, the higher the priority. is the urgency coefficient of this type of traffic; It is the delay tolerance factor of the traffic. For traffic that requires low latency, its delay tolerance is lower and its priority coefficient is higher.

4. A method for optimizing network traffic directional transmission according to claim 1, characterized in that: In step S2, the network link is dynamically adjusted by adopting a dynamic link reconstruction mechanism, and the routing weight of the link is dynamically adjusted by real-time monitoring of the network's bandwidth usage, delay, and packet loss rate indicators; 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 correlated with the priority of the traffic and the current link status. The calculation results of the link load formula and the link weight adjustment formula are used to determine whether link reconstruction is needed.

5. A method for optimizing network traffic directional transmission according to claim 4, characterized in that: The link load formula is as follows: ； in, For Link The comprehensive load value of For Link The number of all traffic types on For Region Priority value of internal traffic; For flow Bandwidth usage on the link; is the maximum link bandwidth, used to standardize the bandwidth occupancy ratio; For Link The current delay of is the maximum tolerable delay of the link, used to standardize the impact of delay; It is an adjustment coefficient used to control the weight of the impact of priority on load; For Link The current packet loss rate; is the maximum packet loss rate of the link, used to standardize the impact of packet loss rate; The link weight adjustment formula is as follows: ； in, For Link The adjusted weight value; For Link The current weight of is the adjustment coefficient, used to control the sensitivity of link adjustment; is the maximum load value of all links, used for standardization.

6. A method for optimizing network traffic directional transmission according to claim 1, characterized in that: In step S3, the cache node sets edge cache in the high priority area according to the regional priority model and the result of link reconstruction, and deploys auxiliary cache in the low priority area, and performs hierarchical transmission on the path of data request.

7. A method for optimizing network traffic directional transmission according to claim 1, characterized in that: In step S4, the transmission requirements are determined by classifying the characteristics of the traffic, and the traffic with different characteristics is isolated and arranged in different logical links; Use time multiplexing technology to prioritize transmission of different types of traffic at different times; or use frequency multiplexing technology to open up exclusive frequency bands for high-priority traffic; At the same time, the idle time of low-priority traffic is multiplexed with high-priority channels to ensure the rational use of bandwidth.

8. A method for optimizing network traffic directional transmission according to claim 1, characterized in that: In step S5, the distributed controller manages the traffic priority, link reconstruction and cache allocation of different areas by deploying multiple controller nodes; In the distributed architecture of distributed controllers, each controller makes independent decisions based on the real-time data it perceives, and shares information with other controllers through synchronization protocols.

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

10. A method for optimizing network traffic directional transmission according to claim 1, characterized in that: In step S7, the transmission scheduling strategy includes: Deploy sensors and monitor the energy usage of network devices. Combined with real-time traffic forecasts and load distribution data, shut down 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.

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