A service information transmission method and system

Abstract

The application provides a service information transmission method and system, which comprises the following steps: calculating a comprehensive service weight factor of a service information packet and a weighted equivalent queue backlog of a current network node; calculating a predicted queue state value of each neighbor network node facing a destination node of the service information packet; calculating a link transmission cost from the network node to each neighbor network node; for each neighbor network node, calculating a weighted pressure difference, obtaining a forwarding decision metric according to the weighted pressure difference and the corresponding link transmission cost; selecting a neighbor network node with the largest forwarding decision metric as a next hop node, and forwarding the service information packet to the next hop node.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of data transmission, and particularly relates to a service information transmission method and system. BACKGROUND

[0002] In a typical multi-hop wireless network environment such as a wireless self-organizing network and an Internet of Things, the network topology structure dynamically changes, the node resources are limited, and the link quality is unstable, which will affect the reliability of service information. A routing algorithm can improve the efficiency of data transmission. In the routing algorithm, a backpressure routing utilizes the queue backlog difference between adjacent nodes facing a specific destination node to obtain the forwarding of a data packet, aims to push the data packet from a node with high queue backlog (high pressure) to a node with low queue backlog (low pressure), and guides the network traffic through a pressure gradient hop by hop, so as to realize network load balancing and maximize the total network throughput in theory. The existing backpressure routing algorithm treats all data packets equally, and cannot provide differentiated quality of service (QoS) guarantee for service information with different service levels, service values or urgency levels (such as long residence time). This easily leads to excessive delay of high-priority services due to congestion of low-priority services, and ignores the actual link channel quality between nodes, which may forward the data packet to a neighbor node with a short queue but poor channel quality, thereby causing frequent retransmission, wasting valuable wireless resources, lacking the ability to predict future queue state changes of neighbor nodes, and not fully considering the overall congestion status of neighbor nodes, resulting in local optimization rather than global optimization. Therefore, how to comprehensively consider service priority, link quality and node congestion prediction has become a problem to be solved in current service information transmission. SUMMARY

[0003] In view of the problem that the service data transmission efficiency is not high when the backlog is large in service transmission, in a first aspect of the application, a service information transmission method applied to a network node is provided, comprising the following steps:

[0004] obtaining a service level, a service value parameter and a current residence time of a to-be-forwarded service information packet; obtaining backlog data of each service level queue facing different destination nodes in the network node and each neighbor network node, a historical dequeue rate of each neighbor network node in a preset time window, a link channel quality parameter from the network node to each neighbor network node, and an overall congestion index of each neighbor network node;

[0005] based on the service level, the service value parameter and the residence time, a comprehensive service weight factor of the service information package is calculated; based on the queue backlog data of each service level queue in the network node facing the destination node of the service information package, a weighted equivalent queue backlog of the current network node is calculated, wherein the queue backlog higher than the service level of the service information package is a first weight, the queue backlog equal to or lower than the service level of the service information package is a second weight, and the first weight is greater than the second weight;

[0006] based on the queue backlog data of each service level of each neighbor network node and the historical dequeue rate, a predicted queue state value of each neighbor network node facing the destination node of the service information package is calculated; based on the link channel quality parameter and the overall congestion index of the neighbor network node, a link transmission cost from the network node to each neighbor network node is calculated;

[0007] for each neighbor network node, a weighted pressure difference is calculated, a forwarding decision metric is obtained according to the weighted pressure difference and the corresponding link transmission cost; the neighbor network node with the largest forwarding decision metric is selected as the next hop node, and the service information package is forwarded to the next hop node.

[0008] Optionally, the calculation formula of the comprehensive service weight factor is:

[0009] ;

[0010] wherein W is the comprehensive service weight factor, 、 and are parameters obtained after normalization processing of the service level, the service value parameter and the current residence time, 、 、 are preset positive weight coefficients.

[0011] Optionally, the calculation formula of the weighted equivalent queue backlog is:

[0012] ;

[0013] wherein W is the weighted equivalent queue backlog, is each queue backlog data higher than the service level of the service information package, is each queue backlog data equal to or lower than the service level of the service information package, is the first weight, is the second weight.

[0014] Optionally, the calculation formula of the predicted queue state value is:

[0015] ​ ;

[0016] wherein, is the predicted queue state value of the neighbor network node n, is the current backlog data sum of all traffic class queues in the neighbor network node facing the destination node, is the historical dequeue rate of the neighbor network node within a preset time window Δt.

[0017] Optionally, the calculation formula of the link transmission cost is:

[0018] ;

[0019] wherein, C(n) is the link transmission cost to the neighbor network node n, E(n) is the overall congestion index of the neighbor network node, S(n) is the link channel quality parameter of the network node to the neighbor network node, and k is a preset cost conversion coefficient.

[0020] Optionally, the calculation formula of the forwarding decision metric is:

[0021] ;

[0022] wherein, M(n) is the forwarding decision metric of the neighbor network node n, is the weighted equivalent queue backlog of the current network node, is the predicted queue state value of the neighbor network node n, W is the comprehensive traffic weight factor of the traffic information packet, C(n) is the link transmission cost to the neighbor network node n, and ε is a small normal number.

[0023] In the second aspect of the present application, a traffic information transmission system is provided, which is applied to a network node and comprises the following modules:

[0024] a data acquisition module, configured to acquire the traffic class, traffic value parameter and current residence time of the traffic information packet to be forwarded; acquire the backlog data of each traffic class queue in the network node and each neighbor network node facing different destination nodes, the historical dequeue rate of each neighbor network node within a preset time window, the link channel quality parameter of the network node to each neighbor network node, and the overall congestion index of each neighbor network node;

[0025] an extrusion calculation module configured to calculate a comprehensive service weight factor of the service information packet based on the service level, the service value parameter and the residence time, and to calculate a weighted equivalent queue backlog of the current network node by using the queue backlog data of each service level queue in the network node facing the destination node of the service information packet, wherein the queue backlog data of each service level higher than the service level of the service information packet is given a first weight, and the queue backlog data of each service level equal to or lower than the service level of the service information packet is given a second weight, and the first weight is greater than the second weight;

[0026] a transmission cost calculation module configured to calculate a predicted queue state value of each neighbor network node facing the destination node of the service information packet based on the queue backlog data of each service level of each neighbor network node and the historical dequeue rate, and to calculate a link transmission cost from the network node to each neighbor network node based on the link channel quality parameter and the overall congestion index of the neighbor network node;

[0027] a forwarding module configured to calculate a weighted pressure difference for each neighbor network node, to obtain a forwarding decision metric according to the weighted pressure difference and the corresponding link transmission cost, to select a neighbor network node with the largest forwarding decision metric as a next hop node, and to forward the service information packet to the next hop node.

[0028] Optionally, the calculation formula of the comprehensive service weight factor is as follows:

[0029] ;

[0030] wherein W is the comprehensive service weight factor, 、 and are parameters obtained by normalizing the service level, the service value parameter and the current residence time respectively, 、 、 are preset positive weight coefficients.

[0031] Optionally, the calculation formula of the weighted equivalent queue backlog is as follows:

[0032] ;

[0033] wherein W is the weighted equivalent queue backlog, is the queue backlog data of each service level higher than the service level of the service information packet, is the queue backlog data of each service level equal to or lower than the service level of the service information packet, is the first weight, is the second weight.

[0034] ​Optionally, the calculation formula of the predicted queue state value is:

[0035] ;

[0036] wherein, is the predicted queue state value of the neighbor network node n, is the current backlog sum of all traffic class queues facing the destination node in the neighbor network node, is the historical dequeue rate of the neighbor network node within a preset time window Δt.

[0037] Optionally, the calculation formula of the link transmission cost is:

[0038] ;

[0039] wherein, C(n) is the link transmission cost to the neighbor network node n, E(n) is the overall congestion index of the neighbor network node, S(n) is the link channel quality parameter of the network node to the neighbor network node, and k is a preset cost conversion coefficient.

[0040] Optionally, the calculation formula of the forwarding decision metric is:

[0041] ;

[0042] wherein, M(n) is the forwarding decision metric of the neighbor network node n, is the weighted equivalent queue backlog of the current network node, is the predicted queue state value of the neighbor network node n, W is the comprehensive traffic weight factor determined by the traffic class, traffic value and residence time, C(n) is the link transmission cost to the neighbor network node n, and ε is a small normal number.

[0043] The application can preferentially guarantee the fast forwarding of high-priority traffic by introducing the comprehensive traffic weight factor determined by the traffic class, traffic value and residence time and performing weighted processing on the queue backlog of the current node, effectively reducing the end-to-end delay and avoiding the problem that high-priority traffic is blocked by low-priority traffic in the traditional algorithm; not only the current queue state of the neighbor node is considered, but also the historical dequeue rate is combined for prediction, so that the routing decision can select the next hop with stronger processing capacity in the future. In addition, by quantifying the link channel quality and the overall congestion condition of the neighbor node as the link transmission cost, the link with poor quality and easy packet loss and the neighbor node with too heavy overall load can be avoided, the data retransmission overhead is reduced, and the transmission success rate and resource utilization are improved. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 is the flowchart of the first embodiment;

[0045] Figure 2 a diagram for a service weight factor;

[0046] Figure 3 a diagram for a weighted equivalent queue backlog calculation result;

[0047] Figure 4 a diagram for a neighbor node predicted queue state. DETAILED DESCRIPTION

[0048] In order to make the purposes, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely below in combination with specific embodiments of the present application and corresponding drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application. It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of the related data need to comply with relevant laws, regulations and standards, and provide corresponding operation portal for the user to choose authorization or refusal.

[0049] In a first embodiment of the present application, a service information transmission method is provided, which is applied to a network node, such as a router, as shown in the following steps: Figure 1

[0050] S1, obtaining the service level, service value parameter and current residence time of the to-be-forwarded service information packet; obtaining the queue backlog data of each service level facing different destination nodes in the network node and each neighbor network node, the historical dequeue rate of each neighbor network node in a preset time window, the link channel quality parameter from the network node to each neighbor network node, and the overall congestion index of each neighbor network node;

[0051] The network node parses the preset service level and service value parameter from the packet header field of the to-be-forwarded service information packet, at the same time, the node records the time when the information packet enters the local queue, and calculates the current residence time by subtracting the entering time from the current system time. The network node periodically broadcasts a state beacon containing its own queue backlog, historical dequeue rate and overall congestion index to its one-hop neighbor, and listens to the state beacon from the neighbor node. At the same time, the node evaluates the link channel quality parameter to the neighbor node by measuring the signal-to-noise ratio or bit error rate of the received neighbor beacon.

[0052] ​S2, calculating a comprehensive service weight factor of the service information packet based on the service level, the service value parameter and the residence time; and calculating a weighted equivalent queue backlog of the current network node by using the queue backlog data of each service level queue in the network node facing the destination node of the service information packet, wherein the queue backlog higher than the service level of the service information packet is given a first weight, and the queue backlog equal to or lower than the service level of the service information packet is given a second weight, the first weight being greater than the second weight;

[0053] The comprehensive service weight factor is positively correlated with the service level, the service value parameter and the residence time. Assuming that the service level is P, the service value parameter is V and the residence time is T, the comprehensive service weight factor is calculated by using a linear weighted summation model , for example , wherein , , are preset normal numbers for adjusting the relative importance of the three parameters. If the service level of the current service information packet is P, the node traverses all service level queues facing the same destination, and for the queue whose level is greater than P, the queue backlog data is multiplied by the first weight W1, and for the queue whose level is less than or equal to P, the queue backlog data is multiplied by the second weight W2, for example, assuming that P1 , , , , , , , , , , ,

[0054] In a preferred embodiment, the calculation formula of the comprehensive service weight factor is as follows:

[0055] ;

[0056] wherein W is the comprehensive service weight factor, , and are parameters obtained by normalizing the service level, the service value parameter and the current residence time respectively, , , are preset positive weight coefficients.

[0057] The three positive weight coefficients in the formula , , may be preset by a network administrator according to an operation strategy, for example, set to equal to 0.5, β equal to 0.3, and γ equal to 0.2, to adjust the relative importance of service level, service value, and latency sensitivity in determining the weight, so that services of different nature can be treated differently, and the quality of service of critical services can be guaranteed.

[0058] For example, a service information packet belonging to real-time voice call has the highest service level, corresponding to a normalized value equal to 1, its user is a premium user, and the service value parameter is also high, corresponding to a normalized value equal to 0.9, and has been waiting in the queue for a period of time, corresponding to a normalized residence time equal to 0.6. Then the calculated result of the comprehensive service weight factor W is 0.89. In contrast, a background data packet of ordinary web browsing has possibly equal to 0.2, equal to 0.1, equal to 0.1, and the calculated W value is only 0.15, as shown in Figure 2 , which is much lower than the former, and thus has a lower priority in the forwarding decision.

[0059] In a preferred embodiment, the formula for calculating the weighted equivalent queue backlog is:

[0060] ;

[0061] wherein, is the weighted equivalent queue backlog, is the queue backlog data higher than the service level of the service information packet, is the queue backlog data equal to or lower than the service level of the service information packet, is the first weight, is the second weight.

[0062] In order to evaluate the queue congestion pressure of the network node, the weights and are introduced to reflect the differentiated influence of queues of different priorities. Generally, the first weight is set to be greater than the second weight , for example, equal to 1.5, equal to 0.8, which means that the queue backlog data higher than the level of the current information packet has a greater effect on the forwarding decision, and the outflow of high-level services is prioritized. Assuming that a network node has three service level queues, from high to low, they are level one, level two, and level three, and the current backlog data are 100M, 300M, and 1000M, respectively. The weighted equivalent queue backlog needs to be calculated for a service information packet of level two. At this time, the queue higher than its level is the level one queue, and its backlog data The backlog is 100M. Queues equal to or lower than this level are classified as Level Two and Level Three queues, with their backlog data... The sum equals 1300M. Therefore, the weighted equivalent queue backlog... The calculation result is 1190, as follows: Figure 3 As shown.

[0063] S3, based on the queue backlog data and historical dequeue rate of each neighboring network node at each service level, calculate the predicted queue status value of each neighboring network node toward the destination node of the service information packet; based on the link channel quality parameters and the overall congestion index of the neighboring network nodes, calculate the link transmission cost from the network node to each neighboring network node.

[0064] For each neighboring node, an exponential moving average model is used to process its announced historical outbound rate, resulting in a smoothed average outbound rate. Then, subtract the average dequeue rate from the current total queue backlog of the neighboring node facing the destination node. With a small prediction time window The predicted queue state value is obtained by multiplying the product of the two factors. Let the link channel quality parameter be L, and the overall congestion index of the neighboring nodes be C. In one embodiment, the link transmission cost M is calculated using the formula... The calculation is performed, where k is a positive constant adjustment factor. For example, L can be the link's signal-to-noise ratio, and C can be the average length of all queues of neighboring nodes. In an optional embodiment, the overall congestion index is calculated from indicators reflecting node busyness and load, such as resource utilization and / or recent packet loss rate; the link channel quality parameters are calculated based on at least one of available bandwidth, signal-to-noise ratio, link delay, and bit error rate / packet loss rate.

[0065] In a preferred embodiment, the formula for calculating the predicted queue state value is:

[0066] ;

[0067] in, Let n be the predicted queue state value of the neighboring network node n. This represents the sum of the current backlog of all service-level queues within the neighboring network node that are directed to the destination node. This represents the historical outbound rate of the neighboring network node within a preset time window Δt.

[0068] To understand the network state after forwarding the packet to neighbor node n, we predict the neighbor node's state in a short future time. Regarding the subsequent queue backlog, this embodiment considers the processing capacity of neighboring nodes, i.e., the historical dequeue rate. , believing that The neighbor node can process a portion of the backlog during the time window. By subtracting this portion of expected processed data from the current total backlog , a predicted value of the queue state that is closer to the real situation in the future can be obtained. The max function ensures that the predicted queue backlog will not be negative.

[0069] For example, a certain neighbor network node n has a total queue backlog of 5000 bits for a specific destination. According to historical statistics, the average dequeue rate of this neighbor node n for this destination is 10000 bits per second. If the set prediction time window is 50 milliseconds, i.e. 0.05 seconds, then the neighbor node is expected to be able to send 500 bits of data during the time window. Therefore, the calculated result of the predicted queue state value is 4500 bits, as shown in the following table. Figure 4

[0070] In a preferred embodiment, the formula for calculating the link transmission cost is:

[0071]

[0072] where C(n) is the link transmission cost to neighbor network node n, E(n) is the overall congestion index of this neighbor network node, S(n) is the link channel quality parameter from the network node to this neighbor network node, and k is a preset cost conversion coefficient.

[0073] An ideal next hop not only has to be idle itself, but also has to have a good path quality connected to it. The higher the congestion index E(n) of a neighbor node, or the lower the channel quality parameter S(n) of the link, the higher the transmission cost C(n) will be. The cost conversion coefficient k is a scaling factor that ensures the calculated cost value matches the unit of the queue backlog, so that they can be meaningfully added or subtracted in decision making. Suppose the link transmission costs to neighbor node A and neighbor node B need to be evaluated. Node A is overall more congested, with a congestion index E(A) of 0.7, but has a good link quality to the node, with a channel quality parameter S(A) of 0.9. Node B is relatively idle, with a congestion index E(B) of 0.2, but the link is interfered and has a poor quality, with S(B) of 0.4. The preset cost conversion coefficient k is 1000. Then the cost C(A) to node A is about 778, and the cost C(B) to node B is 500. Although the link quality to node A is better, its overall transmission cost is higher than that of node B due to its high congestion.​​​​​​​

[0074] S4, for each neighbor network node, a weighted pressure difference is calculated, from which a forwarding decision metric is derived in combination with a corresponding link transmission cost; the neighbor network node with the largest forwarding decision metric is selected as the next hop node, and the service information packet is forwarded to the next hop node.

[0075] the weighted equivalent queue backlog calculated by the current network node minus the predicted queue state value of the target neighbor node the difference multiplied by the comprehensive service weight factor of the service information packet to obtain the corresponding weighted pressure difference of the neighbor. The weighted pressure difference calculated in the previous step for a certain neighbor node is divided by the link transmission cost calculated by the current network node to the neighbor node, to obtain a numerical value as a forwarding decision metric. The network node compares the forwarding decision metrics corresponding to all neighbor nodes, and finds the one with the largest value. Subsequently, the destination MAC address of the service information packet is set to the MAC address of the optimal neighbor node, and is submitted to the data link layer for physical transmission.

[0076] In an optional embodiment, the calculation formula of the forwarding decision metric is: ; wherein, M(n) is the final forwarding decision metric of the neighbor network node n, is the normalized value of the weighted equivalent queue backlog of the current network node, is the normalized value of the predicted queue state value of the neighbor network node n, W is the comprehensive service weight factor of the service information packet, and C(n) is the normalized value of the link transmission cost to the neighbor network node n.

[0077] In a preferred embodiment, the calculation formula of the forwarding decision metric is:

[0078] ;

[0079] wherein, M(n) is the forwarding decision metric of the neighbor network node n, is the weighted equivalent queue backlog of the current network node, is the predicted queue state value of the neighbor network node n, W is the comprehensive service weight factor of the service information packet, C(n) is the link transmission cost to the neighbor network node n, and ε is a small normal number, preferably 0.001.

[0080] A comprehensive score M(n) is calculated for each potential neighbor node n. The neighbor node with the highest score will be selected as the best next hop. The first part of the formula is minus represents how much the network can relieve the congestion of this node after sending out the information packet. It is also multiplied by the weight W of the information packet itself, meaning that the more important the information packet, the more its contribution to relieving the congestion is valued. The link transmission cost C(n) is subtracted to punish those neighbor options with poor link quality or congestion in itself. Following the previous example data, suppose the weighted equivalent queue backlog of the current node is 0.89. For neighbor node A, the predicted queue state value Q(A) is 800, and the link cost C(A) is 200. For neighbor node B, the predicted queue state value Q(B) is 300, and the link cost C(B) is 600. Then the decision metric M(A) of node A is about 1.73. The decision metric M(B) of node B is about 1.32. Since M(B) is greater than M(A), the information packet will be forwarded to neighbor node A.

[0081] In the second embodiment of the present application, a service information transmission system is provided, which is applied to a network node and comprises the following modules:

[0082] A data collection module is configured to acquire the service level, service value parameter and current residence time of a service information packet to be forwarded; acquire the queue backlog data of each service level in the network node and each neighbor network node facing different destination nodes, the historical dequeue rate of each neighbor network node in a preset time window, the link channel quality parameter of the network node to each neighbor network node, and the overall congestion index of each neighbor network node;

[0083] A congestion calculation module is configured to calculate the comprehensive service weight factor of the service information packet based on the service level, service value parameter and residence time; and calculate the weighted equivalent queue backlog of the current network node by using the queue backlog data of each service level in the network node facing the destination node of the service information packet, wherein the queue backlog higher than the service level of the service information packet is a first weight, and the queue backlog equal to or lower than the service level of the service information packet is a second weight, and the first weight is greater than the second weight;

[0084] A transmission cost calculation module is configured to calculate the predicted queue state value of each neighbor network node facing the destination node of the service information packet based on the queue backlog data and historical dequeue rate of each service level of each neighbor network node; and calculate the link transmission cost of the network node to each neighbor network node based on the link channel quality parameter and the overall congestion index of the neighbor network node;

[0085] ​​a forwarding module, configured to calculate a weighted pressure difference for each neighbor network node, and obtain a forwarding decision metric according to the weighted pressure difference and a corresponding link transmission cost; select a neighbor network node with the largest forwarding decision metric as a next hop node, and forward the service information packet to the next hop node.

[0086] In a preferred embodiment, the calculation formula of the comprehensive service weight factor is:

[0087] ;

[0088] wherein W is the comprehensive service weight factor, , and are parameters obtained after normalization processing of the service level, service value parameter and current residence time respectively, , , are preset positive weight coefficients.

[0089] In a preferred embodiment, the calculation formula of the weighted equivalent queue backlog is:

[0090] ;

[0091] wherein is the weighted equivalent queue backlog, is each queue backlog data higher than the service level of the service information packet, is each queue backlog data equal to or lower than the service level thereof, is the first weight, is the second weight.

[0092] In a preferred embodiment, the calculation formula of the predicted queue state value is:

[0093] ;

[0094] wherein is the predicted queue state value of a neighbor network node n, is the current backlog data sum of all service level queues facing the destination node in the neighbor network node, is the historical dequeue rate of the neighbor network node within a preset time window Δt.

[0095] In a preferred embodiment, the calculation formula of the link transmission cost is:

[0096] ;

[0097] Wherein, C(n) is the link transmission cost to the neighbor network node n, E(n) is the overall congestion index of the neighbor network node, S(n) is the link channel quality parameter from the network node to the neighbor network node, and k is a preset cost conversion coefficient.

[0098] In a preferred embodiment, the calculation formula of the forwarding decision metric is:

[0099] ;

[0100] Wherein, M(n) is the forwarding decision metric of the neighbor network node n, is the weighted equivalent queue backlog of the current network node, is the predicted queue state value of the neighbor network node n, W is the comprehensive service weight factor of the service information packet, and C(n) is the link transmission cost to the neighbor network node n.

[0101] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software and the necessary general hardware platform. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in the various embodiments or some parts of the embodiments.

[0102] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, for the system or system embodiments, since it is basically similar to the method embodiments, it is described more simply, and the relevant parts can be referred to the part of the method embodiments. The above-described system and system embodiments are only illustrative, and the units described as separate components can be or can not be physically separated, and the components displayed as units can be or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. According to the actual needs, some or all of the modules can be selected to achieve the purpose of the present embodiment. Those skilled in the art can understand and implement without creative labor.

[0103] The above provides a method for providing commodity object information and an electronic device, and the principles and implementation modes of the present application are described by applying specific examples in this paper. The above example is only used to help understand the method and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A service information transmission method characterized by comprising: The application is applied to a network node and comprises the following steps: Obtaining the service level, service value parameter and current residence time of a service information packet to be forwarded; obtaining the queue backlog data of each service level facing different destination nodes in the network node and each neighbor network node, the historical dequeue rate of each neighbor network node in a preset time window, the link channel quality parameter from the network node to each neighbor network node, and the overall congestion index of each neighbor network node; Based on the service level, service value parameter and residence time, the comprehensive service weight factor of the service information packet is calculated; the weighted equivalent queue backlog of the current network node is calculated by using the queue backlog data of each service level facing the destination node of the service information packet in the network node, wherein the queue backlog higher than the service level of the service information packet is the first weight, and the queue backlog equal to or lower than the service level of the service information packet is the second weight, and the first weight is greater than the second weight; Based on the queue backlog data and historical dequeue rate of each service level of each neighbor network node, the predicted queue state value of each neighbor network node facing the destination node of the service information packet is calculated; based on the link channel quality parameter and the overall congestion index of the neighbor network node, the link transmission cost from the network node to each neighbor network node is calculated; The weighted equivalent queue backlog calculated by the current network node is subtracted from the predicted queue state value of the neighbor network node, and the difference is multiplied by the comprehensive service weight factor of the service information packet to obtain the weighted pressure difference corresponding to the neighbor network node, and the forwarding decision metric is obtained according to the weighted pressure difference and the corresponding link transmission cost; the neighbor network node with the maximum forwarding decision metric is selected as the next hop node, and the service information packet is forwarded to the next hop node.

2. The method of claim 1, wherein, The calculation formula of the comprehensive service weight factor is: ； wherein W is the comprehensive service weight factor, , and are parameters obtained after normalization of the service level, service value parameter and current residence time, respectively, , , is a preset positive weight coefficient.

3. The method of claim 1, wherein, The calculation formula of the weighted equivalent queue backlog is: ； wherein, is the weighted equivalent queue backlog, is the queue backlog data for each queue above the traffic class of the traffic packet, is the queue backlog data for each queue at or below the traffic class of the traffic packet, is the first weight, is the second weight.

4. The method of claim 1, wherein, The calculation formula of the predicted queue state value is: ； wherein, is a predicted queue state value for a neighbor network node n, is a current backlog of data sum for all traffic class queues within the neighbor network node facing the destination node, is a historical de-queue rate for the neighbor network node over a preset time window Δt.

5. The method of claim 1, wherein, The calculation formula of the link transmission cost is: ； Wherein, C(n) is the link transmission cost to the neighbor network node n, E(n) is the overall congestion index of the neighbor network node, S(n) is the link channel quality parameter from the network node to the neighbor network node, and k is a preset cost conversion coefficient.

6. The method of claim 1, wherein, The calculation formula of the forwarding decision metric is: ； where M(n) is the forwarding decision metric of the neighbor network node n, WQ(n) is the weighted equivalent queue backlog of the current network node, WQ(n) is the weighted equivalent queue backlog of the current network node, where WQ(n) is the weighted equivalent queue backlog of the current network node, W is the aggregate traffic weight factor of the traffic information packets, C(n) is the link transmission cost to the neighbor network node n, and ε is a small positive number.

7. A service information transmission system characterized by comprising: The system is applied to a network node and comprises the following modules: A data acquisition module is used to obtain the service level, service value parameter and current residence time of a service information packet to be forwarded; Obtaining the queue backlog data of each service level facing different destination nodes in the network node and each neighbor network node, the historical dequeue rate of each neighbor network node in a preset time window, the link channel quality parameter from the network node to each neighbor network node, and the overall congestion index of each neighbor network node; Based on the service level, service value parameter and residence time, the comprehensive service weight factor of the service information packet is calculated; the weighted equivalent queue backlog of the current network node is calculated by using the queue backlog data of each service level facing the destination node of the service information packet in the network node, wherein the queue backlog higher than the service level of the service information packet is the first weight, and the queue backlog equal to or lower than the service level of the service information packet is the second weight, and the first weight is greater than the second weight; Based on the queue backlog data and historical dequeue rate of each service level of each neighbor network node, the predicted queue state value of each neighbor network node facing the destination node of the service information packet is calculated; based on the link channel quality parameter and the overall congestion index of the neighbor network node, the link transmission cost from the network node to each neighbor network node is calculated; The weighted equivalent queue backlog calculated by the current network node is subtracted from the predicted queue state value of the neighbor network node, and the difference is multiplied by the comprehensive service weight factor of the service information packet to obtain the weighted pressure difference corresponding to the neighbor network node, and the forwarding decision metric is obtained according to the weighted pressure difference and the corresponding link transmission cost; the neighbor network node with the maximum forwarding decision metric is selected as the next hop node, and the service information packet is forwarded to the next hop node. The backlog calculation module is configured to calculate a comprehensive service weight factor of the service information packet based on the service level, the service value parameter and the residence time, and to calculate a weighted equivalent queue backlog of the current network node by using the queue backlog data of each service level queue of the network node facing the destination node of the service information packet, wherein the queue backlog higher than the service level of the service information packet is a first weight, the queue backlog equal to or lower than the service level of the service information packet is a second weight, and the first weight is greater than the second weight; The transmission cost calculation module is configured to calculate a predicted queue state value of each neighbor network node facing the destination node of the service information packet based on the current queue backlog data and the historical dequeue rate of each service level of each neighbor network node, and to calculate a link transmission cost of the network node to each neighbor network node based on the link channel quality parameter and the overall congestion index of the neighbor network node. The forwarding module is configured to obtain a weighted pressure difference of each neighbor network node by subtracting the predicted queue state value of the neighbor network node from the weighted equivalent queue backlog calculated by the current network node, and then multiplying the difference by the comprehensive service weight factor of the service information packet, to obtain a forwarding decision metric according to the weighted pressure difference and the corresponding link transmission cost, to select a neighbor network node with the maximum forwarding decision metric as a next hop node, and to forward the service information packet to the next hop node.

8. The system of claim 7, wherein, The calculation formula of the comprehensive service weight factor is as follows: ； wherein W is the comprehensive service weight factor, , and are parameters obtained after normalization of the service level, service value parameter and current residence time, respectively, , , are preset positive weight coefficients.

9. The system of claim 7, wherein, The calculation formula of the weighted equivalent queue backlog is as follows: ； wherein, is the weighted equivalent queue backlog, is the queue backlog data for each queue above the traffic class of the traffic packet, is the queue backlog data for each queue at or below its traffic class, is the first weight, is the second weight.

10. The system of claim 7, wherein, The calculation formula of the predicted queue state value is as follows: ； wherein, is a predicted queue state value for a neighbor network node n, is a current backlog of data sum for all traffic class queues within the neighbor network node facing the destination node, is a historical de-queue rate for the neighbor network node over a preset time window Δt.

Images