技术领域Technical Field
本发明涉及保护路径规划方法技术领域，尤其涉及一种城域传送网络自适应保护路径规划方法及装置。The present invention relates to the technical field of protection path planning methods, and in particular to a method and device for adaptive protection path planning in a metropolitan area transmission network.
背景技术Background technique
城域传送网络是一种覆盖城市范围、提供丰富业务和支持多种通信协议的网络。在当前城域传送网的体系结构中，确保数据传输的稳定性和连续性对于实现无缝通信服务至关重要。随着第五代移动通信技术的推广及未来技术的迅速发展，城域传送网承载的数据流量增多，传统的静态保护机制已不足以适应日益增长的网络负载和动态变化的网络拓扑。The metropolitan area transport network is a network that covers the city, provides rich services and supports multiple communication protocols. In the current metropolitan area transport network architecture, ensuring the stability and continuity of data transmission is crucial to achieving seamless communication services. With the promotion of the fifth generation of mobile communication technology and the rapid development of future technologies, the data traffic carried by the metropolitan area transport network has increased, and the traditional static protection mechanism is no longer sufficient to adapt to the growing network load and dynamically changing network topology.
现有技术在处理非预期的网络故障、动态负载平衡以及资源利用率方面存在显著不足。目前通常使用常规的主动保护方法实现网络保护，主动方法的目标是为故障做好准备，沿着多条路径发送相同的数据，以便当故障发生时，网络内部不需要重新配置，这虽然为网络故障提供了基本的安全网，但方案并不能动态适应网络流量的实时变化，而且容易导致资源的过度预留或在关键时刻的资源不足，反应式方法在故障发生后虽然可以节省资源，但在故障发生到恢复的响应时间较长，影响了网络的响应时效性，传统的优化算法由于解空间庞大难以找到最优解或解的时间过长，不适合于实时应用场景。这些方法未能充分解决在共享保护机制下的资源冲突、大规模故障时可能造成的信号处理延迟，无法建立一个资源利用率高、负载平衡和传输稳定且连续的网络保护机制，实现更高效的资源利用和保护路径选择。Existing technologies have significant deficiencies in handling unexpected network failures, dynamic load balancing, and resource utilization. At present, conventional active protection methods are usually used to achieve network protection. The goal of the active method is to prepare for failures and send the same data along multiple paths so that when a failure occurs, the network does not need to be reconfigured. Although this provides a basic safety net for network failures, the solution cannot dynamically adapt to real-time changes in network traffic and is prone to excessive resource reservation or insufficient resources at critical moments. Although the reactive method can save resources after a failure occurs, the response time from the occurrence of the failure to recovery is long, which affects the timeliness of the network response. Traditional optimization algorithms are difficult to find the optimal solution or the solution time is too long due to the huge solution space, and are not suitable for real-time application scenarios. These methods fail to fully solve the resource conflicts under the shared protection mechanism and the signal processing delays that may be caused by large-scale failures. It is impossible to establish a network protection mechanism with high resource utilization, load balancing, stable and continuous transmission, and achieve more efficient resource utilization and protection path selection.
发明内容Summary of the invention
鉴于此，本发明实施例提供了一种城域传送网络自适应保护路径规划方法及装置，以消除或改善现有技术中存在的一个或更多个缺陷，本发明通过图卷积网络(GCN)对所述城域传送网络进行下一时间段流量预测并为每一个业务流生成保护路径，实现保护路径负载均衡、最小化阻塞率和最小化资源消耗，解决了现有技术中网络状况不稳定情况下资源浪费、故障恢复和保护路径选择效率低的问题，从而实现更高效的资源利用率、故障恢复和保护路径选择。In view of this, an embodiment of the present invention provides a method and device for adaptive protection path planning of a metropolitan area transmission network to eliminate or improve one or more defects existing in the prior art. The present invention uses a graph convolutional network (GCN) to predict the traffic flow in the next time period of the metropolitan area transmission network and generate a protection path for each service flow, thereby achieving load balancing of the protection path, minimizing the blocking rate and minimizing resource consumption, solving the problems of resource waste, fault recovery and low efficiency of protection path selection when the network condition is unstable in the prior art, thereby achieving more efficient resource utilization, fault recovery and protection path selection.
本发明的一个方面提供了一种城域传送网络自适应保护路径规划方法，所述方法在城域传送网络上执行，所述城域传送网络由多个节点组成，该方法包括以下步骤：One aspect of the present invention provides a method for adaptive protection path planning in a metropolitan area transmission network, the method being performed on the metropolitan area transmission network, the metropolitan area transmission network being composed of a plurality of nodes, the method comprising the following steps:
获取所述城域传送网络的网络拓扑信息并存储为图数据，获取多个业务流的历史流量信息，所述历史流量信息用于记录所述业务流在各时间段的时间戳、传输字节数和互联网协议地址；Acquire network topology information of the metropolitan area transport network and store it as graph data, and acquire historical traffic information of multiple service flows, wherein the historical traffic information is used to record timestamps, transmission bytes, and Internet Protocol addresses of the service flows in each time period;
基于预训练的流量预测模型，根据各业务流的所述历史流量信息预测下一时间段各业务流的流量负载，并根据所述流量负载估算所述城域传送网络中下一时间段各链路的剩余容量、可用时隙种类及对应的数量；所述流量预测模型采用图卷积网络对网络拓扑图的结构进行多层特征提取，其中，每一层的特征通过聚合各节点及其邻居的特征信息得到；Based on the pre-trained traffic prediction model, the traffic load of each service flow in the next time period is predicted according to the historical traffic information of each service flow, and the remaining capacity, available time slot types and corresponding quantities of each link in the metropolitan area transmission network in the next time period are estimated according to the traffic load; the traffic prediction model uses a graph convolutional network to extract multi-layer features of the structure of the network topology graph, wherein the features of each layer are obtained by aggregating the feature information of each node and its neighbors;
构建负载均衡约束、最小化阻塞率约束和最小化资源消耗约束，并根据下一时间段各链路的所述剩余容量、所述可用时隙种类及对应的数量，为每一个业务流生成保护路径以及保护时隙分配方案。Load balancing constraints, minimization of blocking rate constraints and minimization of resource consumption constraints are constructed, and a protection path and protection time slot allocation plan are generated for each service flow based on the remaining capacity of each link in the next time period, the types of available time slots and the corresponding quantities.
在一些实施例中，所述流量预测模型的预训练方法包括：In some embodiments, the pre-training method of the traffic prediction model includes:
获取网络拓扑图训练样本集，所述训练样本集包括网络拓扑的样本特征节点；Acquire a network topology graph training sample set, wherein the training sample set includes sample feature nodes of the network topology;
获取初始图卷积网络，所述初始图卷积网络以l层所述特征节点为输入，以经过了l层所述特征节点迭代更新后的最终节点特征为输出；Obtain an initial graph convolutional network, wherein the initial graph convolutional network takes the feature nodes of the l-th layer as input and takes the final node features after iterative update of the feature nodes of the l-th layer as output;
获取预测层训练样本集，所述训练样本集包括网络拓扑的最终节点特征和多个业务流的样本流量数据；Acquire a prediction layer training sample set, wherein the training sample set includes final node features of the network topology and sample traffic data of multiple service flows;
采用所述预测层训练样本集对初始化流量预测模型进行训练，所述初始化流量预测模型以各业务流t-p至t时间段网络拓扑的所述最终节点特征和所述样本流量数据为输入，以t+1时刻所述样本流量数据为输出，基于均方误差损失函数对所述初始化流量预测模型进行参数更新，得到流量预测模型。The prediction layer training sample set is used to train the initialized traffic prediction model. The initialized traffic prediction model takes the final node characteristics of the network topology of each business flow from t-p to t time period and the sample traffic data as input, and the sample traffic data at time t+1 as output. The parameters of the initialized traffic prediction model are updated based on the mean square error loss function to obtain a traffic prediction model.
在一些实施例中，所述初始图卷积网络采用随机初始化。In some embodiments, the initial graph convolutional network is randomly initialized.
在一些实施例中，所述初始图卷积网络包括设定数量个特征层和一个预测层，所述预测层基于最后一个特征层输出的各节点特征和当前时间段样本流量信息预测下一时间段各节点流量信息，下一时间段流量负载的表达式为：In some embodiments, the initial graph convolution network includes a set number of feature layers and a prediction layer, wherein the prediction layer predicts the traffic information of each node in the next time period based on the features of each node output by the last feature layer and the sample traffic information of the current time period, and the traffic load of the next time period The expression is:
其中，W(pred)表示所述预测层的权重，b(pred)表示所述预测层的偏置，L表示所述特征层的层数，H(L)表示在t时间段第L层所述特征层的节点特征，Ft表示在t时间段的流量信息，ReLU表示非线性激活函数。Wherein, W(pred) represents the weight of the prediction layer, b(pred) represents the bias of the prediction layer, L represents the number of layers of the feature layer, H(L) represents the node features of the Lth layer of the feature layer in time period t, Ft represents the flow information in time period t, and ReLU represents a nonlinear activation function.
在一些实施例中，每一层的特征通过聚合各节点及其邻居的特征信息得到，聚合了各节点及其邻居的特征信息表达式为：In some embodiments, the features of each layer are obtained by aggregating the feature information of each node and its neighbors. The feature information of each node and its neighbors is expressed as:
其中，A表示所述城域传送网络拓扑图的邻接矩阵，I表示单位矩阵，表示添加自环的邻接矩阵，/>是/>的度矩阵，/>表示第l个所述特征层中节点v的节点特征，W(l)表示所述第l个特征层可学习的权重矩阵，σ表示激活函数。Wherein, A represents the adjacency matrix of the metropolitan area transport network topology graph, I represents the identity matrix, represents the adjacency matrix with self-loops added,/> Yes/> The degree matrix of represents the node feature of node v in the lth feature layer, W(l) represents the learnable weight matrix of the lth feature layer, and σ represents the activation function.
在一些实施例中，所述负载均衡约束的表达式为：In some embodiments, the expression of the load balancing constraint is:
其中，E表示所述城域传送网络中链路的集合，e表示所述链路集合中的一条链路，Ue表示所述城域传送网络中链路e的流量负载情况，表示所述城域传送网络中链路的平均流量负载，/>表示下一时间段业务流fi的流量负载，/>表示下一时间段链路e的可用容量。Wherein, E represents a set of links in the metropolitan area transmission network, e represents a link in the link set, Ue represents the traffic load of link e in the metropolitan area transmission network, represents the average traffic load of the links in the metropolitan area transport network,/> represents the traffic load of service flowfi in the next time period,/> Indicates the available capacity of link e in the next time period.
在一些实施例中，所述最小化阻塞率约束的表达式为：In some embodiments, the expression for minimizing the blocking rate constraint is:
其中，B表示所有请求中被阻塞请求的比例，Iblock(fi)定义第i个业务流fi是否被阻塞，Iblock(fi)等于1表示业务流fi由于资源不足不能成功处理，Iblock(fi)等于0表示业务流fi由于资源充足能够成功处理，Nf表示业务流总数。Wherein, B represents the proportion of blocked requests in all requests, Iblock (fi ) defines whether the i-th service flowfi is blocked, Iblock (fi ) equals 1, indicating that the service flowfi cannot be successfully processed due to insufficient resources, Iblock (fi ) equals 0, indicating that the service flowfi can be successfully processed due to sufficient resources, and Nf represents the total number of service flows.
在一些实施例中，所述最小化资源消耗约束的表达式为：In some embodiments, the expression for minimizing the resource consumption constraint is:
其中，表示所述业务流fi的保护路径长度，/>表示所述业务流fi保护路径上所需时隙的数量，Nf表示业务流总数，wL表示所述保护路径长度的权重系数，wS表示所述保护路径上所需时隙数量的权重系数。in, represents the protection path length of the service flowfi ,/> represents the number of time slots required on the protection path of the service flowfi ,Nf represents the total number of service flows,wL represents the weight coefficient of the protection path length, andwS represents the weight coefficient of the number of time slots required on the protection path.
在一些实施例中，所述方法还包括：In some embodiments, the method further comprises:
将所述负载均衡约束、所述最小化阻塞率约束和所述最小化资源消耗约束结合构建一个综合约束，所述综合约束的表达式为：The load balancing constraint, the minimization of the blocking rate constraint and the minimization of the resource consumption constraint are combined to form a comprehensive constraint, and the expression of the comprehensive constraint is:
其中，表示所述业务流fi保护路径上所需时隙的数量，/>表示所述业务流fi在下一时间段的保护路径，σU表示所述负载均衡约束，B表示所有请求中被阻塞请求的比例，wL表示所述保护路径长度的权重系数，wS表示所述保护路径上所需时隙数量的权重系数，/>表示所述业务流fi的保护路径长度，α、β和γ是平衡所述负载均衡约束、所述阻塞率约束和所述资源消耗约束的权重系数。in, represents the number of time slots required on the protection path of the service flowfi ,/> represents the protection path of the service flowfi in the next time period, σU represents the load balancing constraint, B represents the proportion of blocked requests in all requests, wL represents the weight coefficient of the protection path length, wS represents the weight coefficient of the number of time slots required on the protection path, /> represents the protection path length of the service flowfi , α, β and γ are weight coefficients for balancing the load balancing constraint, the blocking rate constraint and the resource consumption constraint.
另一方面，本发明还提供一种计算机可读存储介质，其上存储有计算机程序，其特征在于，该程序被处理器执行时实现上述方法的步骤。On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that the program implements the steps of the above method when executed by a processor.
本发明的有益效果至少是：The beneficial effects of the present invention are at least:
本发明提供一种城域传送网络自适应保护路径规划方法和装置，所述方法在城域传送网络上执行，所述城域传送网络由多个节点组成。将所述城域传送网络的网络拓扑信息存储为图数据，基于预训练的流量预测模型以及获取的历史流量信息预测下一时间段各业务流的流量负载，并估算所述城域传送网络中下一时间段各链路的剩余容量、可用时隙种类及对应的数量，所述流量预测模型采用图卷积网络对网络拓扑图的结构进行多层特征提取，构建负载均衡约束、最小化阻塞率约束和最小化资源消耗约束，并根据下一时间段各链路的所述剩余容量、所述可用时隙种类及对应的数量为每一个业务流生成保护路径以及保护时隙分配方案，实现更高效的故障恢复和保护路径选择，避免产生资源浪费、故障恢复和保护路径选择效率低的问题。The present invention provides a method and device for adaptive protection path planning of a metropolitan area transmission network, the method is performed on a metropolitan area transmission network, and the metropolitan area transmission network is composed of multiple nodes. The network topology information of the metropolitan area transmission network is stored as graph data, and the traffic load of each service flow in the next time period is predicted based on a pre-trained traffic prediction model and the acquired historical traffic information, and the remaining capacity, available time slot type and corresponding quantity of each link in the next time period in the metropolitan area transmission network are estimated. The traffic prediction model uses a graph convolutional network to perform multi-layer feature extraction on the structure of the network topology graph, constructs load balancing constraints, minimization of blocking rate constraints and minimization of resource consumption constraints, and generates a protection path and a protection time slot allocation scheme for each service flow according to the remaining capacity, available time slot type and corresponding quantity of each link in the next time period, so as to achieve more efficient fault recovery and protection path selection, and avoid the problems of resource waste, low efficiency of fault recovery and protection path selection.
进一步的，本发明采用Node2Vec模型学习网络中各节点的低维度特征向量，通过所述低维度特征向量可以获取节点的领域结构和节点间的高阶相似性，由此获得更为深入的网络结构信息，简化模型以及后续模型中的计算。Furthermore, the present invention adopts the Node2Vec model to learn the low-dimensional feature vectors of each node in the network. The domain structure of the node and the high-order similarity between nodes can be obtained through the low-dimensional feature vectors, thereby obtaining more in-depth network structure information and simplifying the model and calculations in subsequent models.
本发明的附加优点、目的，以及特征将在下面的描述中将部分地加以阐述，且将对于本领域普通技术人员在研究下文后部分地变得明显，或者可以根据本发明的实践而获知。本发明的目的和其它优点可以通过在说明书以及附图中具体指出的结构实现到并获得。Additional advantages, purposes, and features of the present invention will be described in part in the following description, and will become apparent to those skilled in the art after studying the following, or may be learned from the practice of the present invention. The purposes and other advantages of the present invention may be achieved and obtained by the structures specifically indicated in the specification and the accompanying drawings.
本领域技术人员将会理解的是，能够用本发明实现的目的和优点不限于以上具体所述，并且根据以下详细说明将更清楚地理解本发明能够实现的上述和其他目的。Those skilled in the art will appreciate that the objectives and advantages that can be achieved with the present invention are not limited to the above specific description, and the above and other objectives that can be achieved by the present invention will be more clearly understood from the following detailed description.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
此处所说明的附图用来提供对本发明的进一步理解，构成本申请的一部分，并不构成对本发明的限定。在附图中：The drawings described herein are used to provide a further understanding of the present invention, constitute a part of the present application, and do not constitute a limitation of the present invention. In the drawings:
图1为本发明一实施例所述城域传送网络自适应保护路径规划方法的流程示意图。FIG1 is a schematic flow chart of a method for adaptive protection path planning in a metropolitan area transport network according to an embodiment of the present invention.
图2为本发明一实施例所述流量预测模型的预训练方法。FIG. 2 is a diagram showing a pre-training method for a traffic prediction model according to an embodiment of the present invention.
具体实施方式Detailed ways
为使本发明的目的、技术方案和优点更加清楚明白，下面结合实施方式和附图，对本发明做进一步详细说明。在此，本发明的示意性实施方式及其说明用于解释本发明，但并不作为对本发明的限定。In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.
在此，还需要说明的是，为了避免因不必要的细节而模糊了本发明，在附图中仅仅示出了与根据本发明的方案密切相关的结构和/或处理步骤，而省略了与本发明关系不大的其他细节。It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and/or processing steps closely related to the solutions according to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.
应该强调，术语“包括/包含”在本文使用时指特征、要素、步骤或组件的存在，但并不排除一个或更多个其它特征、要素、步骤或组件的存在或附加。It should be emphasized that the term “include/comprises” when used herein refers to the presence of features, elements, steps or components, but does not exclude the presence or addition of one or more other features, elements, steps or components.
在此，还需要说明的是，如果没有特殊说明，术语“连接”在本文不仅可以指直接连接，也可以表示存在中间物的间接连接。It should also be noted that, unless otherwise specified, the term “connection” herein may refer not only to a direct connection but also to an indirect connection with an intermediate.
在下文中，将参考附图描述本发明的实施例。在附图中，相同的附图标记代表相同或类似的部件，或者相同或类似的步骤。Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the accompanying drawings, the same reference numerals represent the same or similar components, or the same or similar steps.
现有技术中，常规的主动保护方法是沿着多条路径发送相同的数据，以便当故障发生时，网络内部不需要重新配置，但并不能动态适应网络流量的实时变化，而且容易导致资源的过度预留或在关键时刻资源不足，反应式方法在故障发生后虽然可以节省资源，但在故障发生到恢复的响应时间较长，影响了网络的响应时效性，这些方法无法实现高效的资源利用、故障恢复和保护路径选择；本发明提出了一种城域传送网络自适应保护路径规划方法，通过图卷积网络对所述城域传送网络进行下一时间段流量预测并为每一个业务流生成保护路径，实现保护路径负载均衡、最小化阻塞率和最小化资源消耗，解决了现有技术中网络状况不稳定情况下资源浪费、故障恢复和保护路径选择效率低的问题。In the prior art, the conventional active protection method is to send the same data along multiple paths so that when a fault occurs, the network does not need to be reconfigured, but it cannot dynamically adapt to the real-time changes in network traffic and is prone to excessive resource reservation or insufficient resources at critical moments. Although the reactive method can save resources after a fault occurs, the response time from the occurrence of the fault to recovery is long, which affects the response timeliness of the network. These methods cannot achieve efficient resource utilization, fault recovery and protection path selection. The present invention proposes an adaptive protection path planning method for a metropolitan area transmission network, which predicts the traffic of the metropolitan area transmission network in the next time period through a graph convolutional network and generates a protection path for each business flow, thereby achieving load balancing of the protection path, minimizing the blocking rate and minimizing resource consumption, and solving the problems of resource waste, fault recovery and low efficiency of protection path selection under unstable network conditions in the prior art.
图1为本发明一实施例所述城域传送网络自适应保护路径规划方法的流程示意图。图2为本发明一实施例所述流量预测模型的预训练方法。具体的，本申请提供了一种城域传送网络自适应保护路径规划方法，该方法在城域传送网络上执行，城域传送网络由多个节点组成，该方法包括如下步骤S101～S103：FIG1 is a flow chart of a method for adaptive protection path planning of a metropolitan area transmission network according to an embodiment of the present invention. FIG2 is a pre-training method for a traffic prediction model according to an embodiment of the present invention. Specifically, the present application provides a method for adaptive protection path planning of a metropolitan area transmission network, the method is performed on a metropolitan area transmission network, the metropolitan area transmission network is composed of multiple nodes, and the method includes the following steps S101 to S103:
步骤S101：获取城域传送网络的网络拓扑信息并存储为图数据，获取多个业务流的历史流量信息，历史流量信息用于记录业务流在各时间段的时间戳、传输字节数和互联网协议地址。Step S101: obtain network topology information of the metropolitan area transport network and store it as graph data, obtain historical traffic information of multiple service flows, and the historical traffic information is used to record the timestamp, number of transmitted bytes and Internet Protocol address of the service flow in each time period.
步骤S102：基于预训练的流量预测模型，根据各业务流的历史流量信息预测下一时间段各业务流的流量负载，并根据流量负载估算城域传送网络中下一时间段各链路的剩余容量、可用时隙种类及对应的数量；流量预测模型采用图卷积网络对网络拓扑图的结构进行多层特征提取，其中，每一层的特征通过聚合各节点及其邻居的特征信息得到。Step S102: Based on the pre-trained traffic prediction model, the traffic load of each service flow in the next time period is predicted according to the historical traffic information of each service flow, and the remaining capacity, available time slot types and corresponding quantities of each link in the metropolitan area transmission network in the next time period are estimated according to the traffic load; the traffic prediction model uses a graph convolutional network to perform multi-layer feature extraction on the structure of the network topology graph, wherein the features of each layer are obtained by aggregating the feature information of each node and its neighbors.
步骤S103：构建负载均衡约束、最小化阻塞率约束和最小化资源消耗约束，并根据下一时间段各链路的剩余容量、可用时隙种类及对应的数量，为每一个业务流生成保护路径以及保护时隙分配方案。Step S103: construct load balancing constraints, minimization of blocking rate constraints and minimization of resource consumption constraints, and generate a protection path and protection time slot allocation plan for each service flow according to the remaining capacity of each link in the next time period, the type and corresponding number of available time slots.
在步骤S101中，本发明的在城域传送网络上执行，业务流是传输过程中经过网络拓扑图中各个节点和链路的数据、信号和信息，将网络拓扑信息存储为图数据，可以获取网络拓扑中的节点关联性，为后续预测流量提供更为精确的数据信息，图数据是一种复杂的数据结构，由节点集合和链路集合组成，图数据的存储结构包括邻接矩阵、邻接矩阵、十字链表、邻接多重表和边集数组，本发明网络拓扑中的节点关联性用邻接矩阵表示，当邻接矩阵等于1的时候表示两个节点间存在直接的链路连接。In step S101, the present invention is executed on a metropolitan area transmission network. The business flow is the data, signals and information passing through each node and link in the network topology diagram during the transmission process. The network topology information is stored as graph data, and the node association in the network topology can be obtained to provide more accurate data information for subsequent traffic prediction. Graph data is a complex data structure composed of a node set and a link set. The storage structure of graph data includes an adjacency matrix, an adjacency matrix, a cross linked list, an adjacency multi-list and an edge set array. The node association in the network topology of the present invention is represented by an adjacency matrix. When the adjacency matrix is equal to 1, it indicates that there is a direct link connection between two nodes.
历史流量信息是在一个时间段内包含在流量数据中的信息，记录业务流在各时间段的时间戳、传输字节数和互联网协议地址。Historical traffic information is the information contained in the traffic data within a time period, recording the timestamp, number of transmitted bytes and Internet Protocol address of the business flow in each time period.
在步骤S102中，流量预测模型的预训练方法包括以下步骤S201～S204：In step S102, the pre-training method of the traffic prediction model includes the following steps S201 to S204:
步骤S201：获取网络拓扑图训练样本集，训练样本集包括网络拓扑的样本特征节点。Step S201: Acquire a network topology graph training sample set, where the training sample set includes sample feature nodes of the network topology.
步骤S202：获取初始图卷积网络，初始图卷积网络以l层特征节点为输入，以经过了l层特征节点迭代更新后的最终节点特征为输出。Step S202: Obtain an initial graph convolutional network, which takes l layers of feature nodes as input and takes final node features after iterative update of l layers of feature nodes as output.
步骤S203：获取预测层训练样本集，训练样本集包括网络拓扑的最终节点特征和多个业务流的样本流量数据。Step S203: Obtain a prediction layer training sample set, where the training sample set includes final node features of the network topology and sample traffic data of multiple service flows.
步骤S204：采用预测层训练样本集对初始化流量预测模型进行训练，初始化流量预测模型以各业务流t-p至t时间段网络拓扑的最终节点特征和样本流量数据为输入，以t+1时刻样本流量数据为输出，基于均方误差损失函数对初始化流量预测模型进行参数更新，得到流量预测模型。Step S204: Use the prediction layer training sample set to train the initialized traffic prediction model. The initialized traffic prediction model takes the final node characteristics and sample traffic data of the network topology of each business flow from t-p to t time period as input, and the sample traffic data at time t+1 as output. The parameters of the initialized traffic prediction model are updated based on the mean square error loss function to obtain the traffic prediction model.
具体的，存储为图数据的网络拓扑信息中包含节点信息和链路信息，构建流量预测模型主要关注网络拓扑视图在节点和链路之间的拓扑结构关系。在一些实施例中，初始图卷积网络采用随机初始化。进一步的，采用Node2Vec模型学习网络中各节点的低维度特征向量，通过低维度特征向量可以获取节点的领域结构和节点间的高阶相似性，高阶相似性可以提供更为深入的网络结构信息，由此简化模型以及后续模型中的计算并减少储存空间。采用图卷积网络对网络拓扑图中的特征进行多次更新并将自身及其邻居的节点特征进行加权平均得到最后的特征。Specifically, the network topology information stored as graph data includes node information and link information, and the construction of the traffic prediction model mainly focuses on the topological structure relationship between nodes and links in the network topology view. In some embodiments, the initial graph convolutional network is randomly initialized. Furthermore, the Node2Vec model is used to learn the low-dimensional feature vectors of each node in the network. The domain structure of the node and the high-order similarity between nodes can be obtained through the low-dimensional feature vectors. The high-order similarity can provide more in-depth network structure information, thereby simplifying the calculation in the model and subsequent models and reducing storage space. A graph convolutional network is used to update the features in the network topology graph multiple times and perform weighted averaging of the node features of itself and its neighbors to obtain the final features.
初始图卷积网络基于均方误差损失函数进行参数更新，均方误差损失函数LMSE表达式为：The initial graph convolutional network updates parameters based on the mean square error loss function. The mean square error loss function LMSE is expressed as:
其中，N表示预训练流量预测模型的样本数量；yi表示第i个样本的实际流量值；表示图卷积网络流量预测模型的流量值。Where N represents the number of samples of the pre-trained traffic prediction model;yi represents the actual traffic value of the i-th sample; Represents the traffic value of the graph convolutional network traffic prediction model.
在一些实施例中，每一层的特征通过聚合各节点及其邻居的特征信息得到，聚合了各节点及其邻居的特征信息表达式为：In some embodiments, the features of each layer are obtained by aggregating the feature information of each node and its neighbors. The feature information of each node and its neighbors is expressed as:
其中，A表示城域传送网络拓扑图的邻接矩阵，I表示单位矩阵，表示添加自环的邻接矩阵，/>是/>的度矩阵，/>表示第l个特征层中节点v的节点特征，W(l)表示第l个特征层可学习的权重矩阵，σ表示激活函数。Where A represents the adjacency matrix of the metropolitan area transport network topology graph, I represents the identity matrix, represents the adjacency matrix with self-loops added,/> Yes/> The degree matrix of represents the node feature of node v in the l-th feature layer, W(l) represents the learnable weight matrix of the l-th feature layer, and σ represents the activation function.
在一些实施例中，初始图卷积网络包括设定数量个特征层和一个预测层，预测层基于最后一个特征层输出的各节点特征和当前时间段样本流量信息预测下一时间段各节点流量信息，下一时间段流量负载的表达式为：In some embodiments, the initial graph convolutional network includes a set number of feature layers and a prediction layer. The prediction layer predicts the traffic information of each node in the next time period based on the features of each node output by the last feature layer and the sample traffic information of the current time period. The traffic load of the next time period The expression is:
其中，W(pred)表示预测层的权重，b(pred)表示预测层的偏置，L表示特征层的层数，H(L)表示在t时间段第L层特征层的节点特征，Ft表示在t时间段的流量信息，ReLU表示非线性激活函数。Among them, W(pred) represents the weight of the prediction layer, b(pred) represents the bias of the prediction layer, L represents the number of feature layers, H(L) represents the node features of the Lth feature layer in time period t, Ft represents the flow information in time period t, and ReLU represents the nonlinear activation function.
在步骤S103中，本发明保护路径的选择要求负载均衡、最小化阻塞率和最小化资源消耗，负载均衡约束要求城域传送网的网络拓扑图中链路均衡地分配下一时间段业务流的传输流量和负载，最小化阻塞率要求业务流有充足的传输资源进行传输，最小化资源消耗要求业务流在负载均衡和传输资源充足的情况下提高资源利用率，在网络发生故障时可以避免传统保护方法造成的负载不平衡、业务流传输不稳定和资源浪费的问题。In step S103, the selection of the protection path of the present invention requires load balancing, minimizing the blocking rate and minimizing the resource consumption. The load balancing constraint requires that the links in the network topology diagram of the metropolitan area transmission network evenly distribute the transmission traffic and load of the service flow in the next time period. Minimizing the blocking rate requires that the service flow has sufficient transmission resources for transmission. Minimizing resource consumption requires that the service flow improves resource utilization under the conditions of load balancing and sufficient transmission resources. When a network failure occurs, the problems of load imbalance, unstable service flow transmission and resource waste caused by traditional protection methods can be avoided.
在一些实施例中，负载均衡约束的表达式为：In some embodiments, the expression of the load balancing constraint is:
其中，E表示城域传送网络中链路的集合，e表示链路集合中的一条链路，Ue表示城域传送网络中链路e的流量负载情况，表示城域传送网络中链路的平均流量负载，/>表示下一时间段业务流fi的流量负载，/>表示下一时间段链路e的可用容量。Where E represents the set of links in the metropolitan area transport network, e represents a link in the link set,Ue represents the traffic load of link e in the metropolitan area transport network, represents the average traffic load of the links in the metropolitan area transport network,/> represents the traffic load of service flowfi in the next time period,/> Indicates the available capacity of link e in the next time period.
在一些实施例中，最小化阻塞率约束的表达式为：In some embodiments, the expression for minimizing the blocking rate constraint is:
其中，B表示所有请求中被阻塞请求的比例，Iblock(fi)定义第i个业务流fi是否被阻塞，Iblock(fi)等于1表示业务流fi由于资源不足不能成功处理，Iblock(fi)等于0表示业务流fi由于资源充足能够成功处理，Nf表示业务流总数。Wherein, B represents the proportion of blocked requests in all requests, Iblock (fi ) defines whether the i-th service flowfi is blocked, Iblock (fi ) equals 1, indicating that the service flowfi cannot be successfully processed due to insufficient resources, Iblock (fi ) equals 0, indicating that the service flowfi can be successfully processed due to sufficient resources, and Nf represents the total number of service flows.
在一些实施例中，最小化资源消耗约束的表达式为：In some embodiments, the expression for minimizing the resource consumption constraint is:
其中，表示业务流fi的保护路径长度，/>表示业务流fi保护路径上所需时隙的数量，Nf表示业务流总数，wL表示保护路径长度的权重系数，wS表示保护路径上所需时隙数量的权重系数。in, represents the protection path length of service flowfi ,/> represents the number of time slots required on the protection path of service flowfi ,Nf represents the total number of service flows,wL represents the weight coefficient of the protection path length, andwS represents the weight coefficient of the number of time slots required on the protection path.
在一些实施例中，方法还包括：In some embodiments, the method further comprises:
将负载均衡约束、最小化阻塞率约束和最小化资源消耗约束结合构建一个综合约束，综合约束的表达式为：The load balancing constraint, the minimization of the blocking rate constraint, and the minimization of the resource consumption constraint are combined to form a comprehensive constraint. The expression of the comprehensive constraint is:
其中，表示业务流fi保护路径上所需时隙的数量，/>表示业务流fi在下一时间段的保护路径，σU表示负载均衡约束，B表示所有请求中被阻塞请求的比例，wL表示保护路径长度的权重系数，wS表示保护路径上所需时隙数量的权重系数，/>表示业务流fi的保护路径长度，α、β和γ是平衡负载均衡约束、阻塞率约束和资源消耗约束的权重系数。in, represents the number of time slots required on the protection path of service flowfi ,/> represents the protection path of service flowfi in the next time period, σU represents the load balancing constraint, B represents the proportion of blocked requests in all requests, wL represents the weight coefficient of the protection path length, wS represents the weight coefficient of the number of time slots required on the protection path, /> represents the protection path length of service flowfi , α, β and γ are the weight coefficients for balancing load balancing constraints, blocking rate constraints and resource consumption constraints.
进一步的，客户流fi时隙分配方案的表达式为：Furthermore, the expression of the time slot allocation scheme of client flowfi is:
其中，表示下一时间段业务流fi的流量负载，/>表示业务流fi保护路径上所需时隙的数量。in, represents the traffic load of service flowfi in the next time period,/> Indicates the number of time slots required on the protection path of service flowfi .
具体的，SDP是基于动态规划的混合时隙分配方法，根据网络中业务流的数量将本发明所需要解决的时隙分配问题划分为多个阶段，每个阶段独立处理一个业务流的时隙分配问题，在各阶段定义状态变量来表示当前阶段可用的时隙资源并根据当前阶段的状态做出时隙分配的决策，根据当前状态和决策确定状态转移方程并定义用于评估每个阶段决策结果的指标函数，通过建立动态规划基本方程获取每一个阶段的最优决策，最终获得全过程的最优时隙分配方案。Specifically,SDP is a hybrid time slot allocation method based on dynamic programming. The time slot allocation problem to be solved by the present invention is divided into multiple stages according to the number of business flows in the network. Each stage independently handles the time slot allocation problem of a business flow. State variables are defined in each stage to represent the time slot resources available in the current stage and make time slot allocation decisions according to the state of the current stage. The state transition equation is determined according to the current state and the decision, and an indicator function for evaluating the decision results of each stage is defined. The optimal decision of each stage is obtained by establishing the basic equation of dynamic programming, and finally the optimal time slot allocation plan for the whole process is obtained.
另一方面，本发明还提供一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现上述方法的步骤。On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.
下面结合一具体实施例对本发明进行说明：The present invention is described below in conjunction with a specific embodiment:
1.参数描述：1. Parameter description:
本发明中，将网络拓扑表示为图G＝(V,E)，其中V是节点集合，E是链路集合。每条链路e∈E的容量为Be，链路e在下一时间段t+1的可用容量记为节点间的连接关系用邻接矩A来表示，Aij＝1表示节点i和j之间有直接的链路。业务流fi在时间段t和t+1时间段的保护路径分别表示为/>和/>其具体形式可用节点序列表示，即(vs,...,vu)，vs,vu∈V。vs和vu分别表示保护路径的起始节点、中间节点和目的节点。例如/>表示第1个业务流的路径从起始节点v1到目的节点v5，经过中间节点v2和v4。每条链路上的时隙种类表示为k∈(1,2,...,J)，每种时隙对应的数量表示为Nk。客户流fi的时隙分配方案表示为In the present invention, the network topology is represented as a graph G = (V, E), where V is a set of nodes and E is a set of links. The capacity of each link e∈E is Be , and the available capacity of link e in the next time period t+1 is recorded as The connection relationship between nodes is represented by the adjacency matrix A. Aij = 1 indicates that there is a direct link between nodes i and j. The protection paths of service flowfi in time period t and time period t+1 are respectively represented as/> and/> Its specific form can be represented by a node sequence, that is, (vs , ...,vu ),vs ,vu ∈ V.vs andvu represent the starting node, intermediate node and destination node of the protection path respectively. For example/> The path of the first service flow is from the starting nodev1 to the destination nodev5 , passing through the intermediate nodesv2 andv4 . The type of time slots on each link is denoted by k∈(1,2,...,J), and the number of each time slot is denoted byNk . The time slot allocation scheme of customer flowfi is expressed as
其中与/>分别表示第i个客户流fi所使用到的时隙及相应的数量。定义/>为fi使用第k种时隙的数量。为客户流fi分配的保护时隙带宽记为/>不小于客户流的预测带宽：in With/> Respectively represent the time slots and corresponding quantities used by the i-th client flowfi . Definition/> The number of k-th time slots used forfi . The bandwidth of the protection time slot allocated to the client flowfi is denoted as/> Not less than the predicted bandwidth of the client flow:
定义业务流量集合F＝{fi|i＝1,2,…,Nf}，fi表示第i个业务流，fi在t-p到t时间段的历史流量信息记为流量预测机制将根据Ft和学习到的拓扑信息预测该业务流在时间段t+1的流量带宽，记为/>Define the service flow set F = {fi |i = 1, 2, ...,Nf }, wherefi represents the ith service flow and the historical flow information offi in the time period from tp to t is recorded as The traffic prediction mechanism predicts the traffic bandwidth of the service flow in time period t+1 based onFt and the learned topology information, which is denoted as/>
在构建基于图卷积网络(GCN)的流量预测模型时，主要关注如何利用图的图的拓扑结构和节点间的关系，为此，首先采用Node2Vec方法以学习网络中各节点的低维度特征向量。这些向量捕获了节点的邻域结构以及节点间的高阶相似性。接着，对于图中的任一节点v，在图卷积网络框架下，节点v在第l+1层的特征更新通过聚合其邻居节点特征的加权平均来实现：When building a traffic prediction model based on graph convolutional networks (GCN), we mainly focus on how to use the topological structure of the graph and the relationship between nodes. To this end, we first use the Node2Vec method to learn the low-dimensional feature vectors of each node in the network. These vectors capture the neighborhood structure of the nodes and the high-order similarities between nodes. Then, for any node v in the graph, in the framework of the graph convolutional network, the feature update of node v at the l+1 layer is This is achieved by aggregating the weighted average of its neighbor node features:
其中，(A为城域传送网络拓扑图的邻接矩阵，I是单位矩阵，用于添加自环)，/>是/>的度矩阵，H(l)是第l个特征层中节点v的特征表示，W(l)是第l个特征层可学习的权重矩阵，σ是激活函数。in, (A is the adjacency matrix of the metropolitan area transport network topology graph, and I is the identity matrix used to add self-loops),/> Yes/> , H(l) is the feature representation of node v in the l-th feature layer, W(l) is the learnable weight matrix of the l-th feature layer, and σ is the activation function.
在经过L层的图卷积后，得到节点v的最终特征表示为了预测下一时间段的流量，使用一个预测层，该层结合了节点特征和历史流量信息，预测的目标是估计每个业务流在下一时间段内的流量负载/>并根据/>估算出链路的剩余容量/>和可用时隙。After L layers of graph convolution, the final feature representation of node v is obtained In order to predict the traffic in the next time period, a prediction layer is used, which combines node characteristics and historical traffic information. The goal of the prediction is to estimate the traffic load of each business flow in the next time period./> And according to/> Estimate the remaining capacity of the link/> and available time slots.
其中，H(L)是图卷积网络的最后一层的输出，表示在t时间段的节点特征。这些特征与当前的流量信息Ft联合表示为[H(L),Ft]，作为预测层的输入。W(pred)和b(pred)分别是预测层的权重和偏置。ReLU激活函数确保预测的流量是非负的。Among them, H(L) is the output of the last layer of the graph convolutional network, which represents the node features in time period t. These features are jointly represented with the current traffic information Ft as [H(L) ,Ft ], which serves as the input of the prediction layer.W (pred) and b(pred) are the weight and bias of the prediction layer respectively. The ReLU activation function ensures that the predicted traffic is non-negative.
2.目标函数：2. Objective function:
为了更全面地评估网络的总体效率，需要考虑包括负载均衡、阻塞率、带宽利用率和时隙利用率等指标。我们设计了一个综合目标函数，该函数在多个重要维度上进行权衡，进而优化路由规划与时隙分配决策。In order to more comprehensively evaluate the overall efficiency of the network, it is necessary to consider indicators such as load balancing, blocking rate, bandwidth utilization, and time slot utilization. We designed a comprehensive objective function that weighs multiple important dimensions to optimize routing planning and time slot allocation decisions.
2.1负载均衡目标：2.1 Load balancing objectives:
链路e在t+1时间段的流量负载情况通过来量化。其中/>为业务流的预测带宽，/>为链路可用容量。则负载均衡目标为：The traffic load of link e in time period t+1 is shown in To quantify. is the predicted bandwidth of the service flow,/> is the available capacity of the link. Then the load balancing target is:
其中，E是城域传送网络中链路的集合，为城域传送网络中链路e的平均流量负载。Where E is the set of links in the metropolitan area transport network, is the average traffic load of link e in the metropolitan area transport network.
2.2阻塞率目标：2.2 Blocking rate target:
定义指示函数Iblock(fi)表示业务流fi是否被阻塞：Define an indicator function Iblock (fi ) to indicate whether the service flowfi is blocked:
阻塞率B可以定义为在所有请求中被阻塞请求的比例：The blocking rate B can be defined as the proportion of blocked requests among all requests:
其中Nf为业务流总数，fi为单个业务流。阻塞率部分的目标函数为：WhereNf is the total number of service flows and fiis a single service flow. The objective function of the blocking rate part is:
2.3资源消耗目标：2.3 Resource consumption targets:
资源消耗部分的目标是最小化网络中的保护路径长度和时隙资源消耗。定义业务流fi的保护路径长度为定义/>为该路径上所需时隙的数量。该部分的目标函数专注于上述两种资源的使用，以确保在满足传输需求的同时，网络资源得到有效利用：The goal of the resource consumption part is to minimize the protection path length and time slot resource consumption in the network. The protection path length of service flowfi is defined as Definition/> is the number of time slots required on the path. The objective function of this part focuses on the use of the above two resources to ensure that network resources are effectively utilized while meeting transmission requirements:
其中，wL和wS分别是保护路径长度的权重系数和时隙资源数量的权重系数。Wherein,wL andwS are the weight coefficient of the protection path length and the weight coefficient of the number of time slot resources, respectively.
为了提高上述网络性能，我们提出了一个综合的目标函数，该函数结合了网络负载均衡、业务流阻塞概率，以及业务流的保护路径长度和时隙资源消耗:In order to improve the above network performance, we propose a comprehensive objective function that combines network load balancing, traffic flow blocking probability, and the protection path length and time slot resource consumption of traffic flows:
综合目标函数通过权重系数α、β和γ来平衡这些不同的性能指标，从而实现全局最优的保护路径规划方案。The comprehensive objective function balances these different performance indicators through weight coefficients α, β and γ to achieve the global optimal protection path planning solution.
3.自适应保护路径规划算法：3. Adaptive protection path planning algorithm:
本发明提出的基于图卷积网络的自适应保护路由规划算法由以下两个子算法组成，包括基于图卷积网络的流量预测算法和保护路径规划算法。首个子算法运用图卷积网络深入学习网络拓扑与历史流量数据，以预测未来的业务流带宽需求。此流量预测为保护路径规划提供了关键的输入参数。第二个子算法利用这些预测结果，结合当前网络状态，通过计算最优保护路径和时隙分配，来制定出旨在提高网络鲁棒性和资源利用效率的保护路由策略。整个算法框架致力于确保业务连续性和服务质量的同时实现网络性能的最大化。The adaptive protection routing planning algorithm based on graph convolutional network proposed in the present invention consists of the following two sub-algorithms, including a traffic prediction algorithm based on graph convolutional network and a protection path planning algorithm. The first sub-algorithm uses graph convolutional network to deeply learn network topology and historical traffic data to predict future business flow bandwidth requirements. This traffic prediction provides key input parameters for protection path planning. The second sub-algorithm uses these prediction results, combined with the current network status, to calculate the optimal protection path and time slot allocation to formulate a protection routing strategy aimed at improving network robustness and resource utilization efficiency. The entire algorithm framework is committed to maximizing network performance while ensuring business continuity and service quality.
3.1业务流量预测算法：3.1 Business traffic prediction algorithm:
本算法提出了一种基于图卷积网络(GCN)的方法，用于预测网络中各业务流在下一时间段的带宽需求。通过历史数据分析，该方法能够学习到网络流量的复杂模式，并据此预测未来的带宽需求，从而为保护路径规划提供决策支持。该模型利用图结构数据，能够捕捉网络拓扑中的节点关联性，为每个业务流提供精确的带宽预测。This algorithm proposes a method based on graph convolutional network (GCN) to predict the bandwidth requirements of each service flow in the network in the next time period. Through historical data analysis, this method can learn the complex patterns of network traffic and predict future bandwidth requirements based on them, thereby providing decision support for protection path planning. The model uses graph structure data to capture the node association in the network topology and provide accurate bandwidth prediction for each service flow.
在构建基于图卷积网络(GCN)的流量预测模型的过程中，首先为图中的每个节点构建包含其静态属性的特征向量，主要包含节点的静态属性，如节点类型或位置等。利用图的拓扑结构，模型通过一系列图卷积层迭代地更新节点特征，其中每层的更新基于对节点及其邻居信息的聚合以及非线性激活函数的应用，这有助于捕捉节点在图中的结构关系。GCN层的主要作用是提取图结构特征，而历史流量信息则在模型的后续预测阶段被整合，用于最终的流量预测。In the process of building a traffic prediction model based on graph convolutional networks (GCN), a feature vector containing its static properties is first constructed for each node in the graph, mainly including the static properties of the node, such as node type or location. Using the topological structure of the graph, the model iteratively updates the node features through a series of graph convolutional layers, where the update of each layer is based on the aggregation of node and its neighbor information and the application of nonlinear activation functions, which helps to capture the structural relationship of nodes in the graph. The main function of the GCN layer is to extract graph structural features, while historical traffic information is integrated in the subsequent prediction stage of the model for the final traffic prediction.
3.2保护路径规划算法：3.2 Protection path planning algorithm:
该算法利用了算法1的结果，同样利用图神经网络的思想，将网络拓扑特征，带宽容量和预测的业务流量等信息结合，构建了主动保护路由算法。This algorithm utilizes the results of Algorithm 1 and also uses the idea of graph neural network to combine network topology characteristics, bandwidth capacity, predicted business traffic and other information to construct an active protection routing algorithm.
首先，算法计算每个业务流的当前和预测带宽需求，确定网络链路的剩余容量。接着，它通过负载均衡函数评估网络负载情况，并为每个业务流计算阻塞概率。算法进一步测算保护路径的长度，基于此信息及网络负载和容量状态，为每个业务流生成保护路由。此外，算法还处理时隙资源分配，采用灵活的时隙分配策略以适应各业务流的保护需求。最后，算法输出保护路由和时隙分配结果，以备在网络中实施，确保数据传输的可靠性和网络的鲁棒性。First, the algorithm calculates the current and predicted bandwidth requirements for each service flow and determines the remaining capacity of the network links. Next, it evaluates the network load through a load balancing function and calculates the blocking probability for each service flow. The algorithm further measures the length of the protection path and generates a protection route for each service flow based on this information and the network load and capacity status. In addition, the algorithm also handles time slot resource allocation and adopts a flexible time slot allocation strategy to adapt to the protection requirements of each service flow. Finally, the algorithm outputs the protection route and time slot allocation results for implementation in the network to ensure the reliability of data transmission and the robustness of the network.
综上所述，本发明提供一种城域传送网络自适应保护路径规划方法和装置，所述方法在城域传送网络上执行，所述城域传送网络由多个节点组成。将所述城域传送网络的网络拓扑信息存储为图数据，基于预训练的流量预测模型以及获取的历史流量信息预测下一时间段各业务流的流量负载，并估算所述城域传送网络中下一时间段各链路的剩余容量、可用时隙种类及对应的数量，所述流量预测模型采用图卷积网络对所述网络拓扑图的结构进行多层特征提取，构建负载均衡约束、最小化阻塞率约束和最小化资源消耗约束，并根据下一时间段各链路的所述剩余容量、所述可用时隙种类及对应的数量，为每一个业务流生成保护路径以及保护时隙分配方案，实现更高效的故障恢复和保护路径选择，避免产生资源浪费、故障恢复和保护路径选择效率低的问题。In summary, the present invention provides a method and device for adaptive protection path planning of a metropolitan area transmission network, the method is performed on a metropolitan area transmission network, and the metropolitan area transmission network is composed of multiple nodes. The network topology information of the metropolitan area transmission network is stored as graph data, and the traffic load of each service flow in the next time period is predicted based on a pre-trained traffic prediction model and the acquired historical traffic information, and the remaining capacity, available time slot type and corresponding quantity of each link in the metropolitan area transmission network in the next time period are estimated. The traffic prediction model uses a graph convolutional network to perform multi-layer feature extraction on the structure of the network topology graph, constructs load balancing constraints, minimizes blocking rate constraints and minimizes resource consumption constraints, and generates a protection path and a protection time slot allocation plan for each service flow according to the remaining capacity, available time slot type and corresponding quantity of each link in the next time period, so as to achieve more efficient fault recovery and protection path selection, and avoid the problems of resource waste, low efficiency of fault recovery and protection path selection.
进一步的，本发明采用Node2Vec模型学习网络中各节点的低维度特征向量，通过所述低维度特征向量可以获取节点的领域结构和节点间的高阶相似性，由此获得更为深入的网络结构信息，简化模型以及后续模型中的计算。Furthermore, the present invention adopts the Node2Vec model to learn the low-dimensional feature vectors of each node in the network. The domain structure of the node and the high-order similarity between nodes can be obtained through the low-dimensional feature vectors, thereby obtaining more in-depth network structure information and simplifying the model and calculations in subsequent models.
本发明实施例还提供了一种计算机设备，该计算机设备可以包括处理器、存储器，其中处理器和存储器可以通过总线或者其他方式连接。An embodiment of the present invention further provides a computer device, which may include a processor and a memory, wherein the processor and the memory may be connected via a bus or in other ways.
处理器可以为中央处理器(Central Processing Unit，CPU)。处理器还可以为其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现场可编程门阵列(Field-Programmable GateArray，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等芯片，或者上述各类芯片的组合。The processor may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.
存储器作为一种非暂态计算机可读存储介质，可用于存储非暂态软件程序、非暂态计算机可执行程序以及模块，如本发明实施例中的车载显示装置按键屏蔽方法对应的程序指令/模块。处理器通过运行存储在存储器中的非暂态软件程序、指令以及模块，从而执行处理器的各种功能应用以及数据处理。As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as the program instructions/modules corresponding to the key shielding method of the vehicle-mounted display device in the embodiment of the present invention. The processor executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory.
存储器可以包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需要的应用程序；存储数据区可存储处理器所创建的数据等。此外，存储器可以包括高速随机存取存储器，还可以包括非暂态存储器，例如至少一个磁盘存储器件、闪存器件、或其他非暂态固态存储器件。在一些实施例中，存储器可选包括相对于处理器远程设置的存储器，这些远程存储器可以通过网络连接至处理器。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required by at least one function; the data storage area may store data created by the processor, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
所述一个或者多个模块存储在所述存储器中，当被所述处理器执行时，执行本实施例中所述的方法。The one or more modules are stored in the memory, and when executed by the processor, the method described in this embodiment is performed.
本发明实施例还提供一种计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时以实现前述边缘计算服务器部署方法的步骤。该计算机可读存储介质可以是有形存储介质，诸如随机存储器(RAM)、内存、只读存储器(ROM)、电可编程ROM、电可擦除可编程ROM、寄存器、软盘、硬盘、可移动存储盘、CD-ROM、或技术领域内所公知的任意其它形式的存储介质。The embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned edge computing server deployment method. The computer-readable storage medium may be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the technical field.
本领域普通技术人员应该可以明白，结合本文中所公开的实施方式描述的各示例性的组成部分、系统和方法，能够以硬件、软件或者二者的结合来实现。具体究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。当以硬件方式实现时，其可以例如是电子电路、专用集成电路(ASIC)、适当的固件、插件、功能卡等等。当以软件方式实现时，本发明的元素是被用于执行所需任务的程序或者代码段。程序或者代码段可以存储在机器可读介质中，或者通过载波中携带的数据信号在传输介质或者通信链路上传送。It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.
需要明确的是，本发明并不局限于上文所描述并在图中示出的特定配置和处理。为了简明起见，这里省略了对已知方法的详细描述。在上述实施例中，描述和示出了若干具体的步骤作为示例。但是，本发明的方法过程并不限于所描述和示出的具体步骤，本领域的技术人员可以在领会本发明的精神后，作出各种改变、修改和添加，或者改变步骤之间的顺序。It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.
本发明中，针对一个实施方式描述和/或例示的特征，可以在一个或更多个其它实施方式中以相同方式或以类似方式使用，和/或与其他实施方式的特征相结合或代替其他实施方式的特征。In the present invention, features described and/or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and/or combined with features of other embodiments or replace features of other embodiments.
以上所述仅为本发明的优选实施例，并不用于限制本发明，对于本领域的技术人员来说，本发明实施例可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
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| CN118660014A (en)* | 2024-08-19 | 2024-09-17 | 苏州爱雄斯通信技术有限公司 | Dynamic load balancing method and system for optical communication device |
| CN119629067A (en)* | 2024-11-28 | 2025-03-14 | 广西电网有限责任公司 | A lossless network construction method supporting multiple power scenarios |
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| CN119629067A (en)* | 2024-11-28 | 2025-03-14 | 广西电网有限责任公司 | A lossless network construction method supporting multiple power scenarios |
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