A cross-domain routing method for power communication network

Abstract

The application relates to the technical field of communication, and particularly discloses a cross-domain routing method for a power communication network, which comprises the following steps: S1, obtaining a historical service flow data set to be classified, and constructing a classification evaluation matrix according to evaluation indexes corresponding to each service flow in the historical service flow data set; S2, obtaining a subjective evaluation matrix, and determining a score of each service flow according to the subjective evaluation matrix and the classification evaluation matrix; S3, classifying the service flows according to the scores of the service flows, and determining a category of each service flow; S4, traversing network nodes and links of the power communication network, and abstracting a network topology with an upper-layer network and a lower-layer network; S5, determining a starting point, an ending point and a category to which each incoming service flow belongs according to the category, determining a routing selection mechanism according to the starting point, the ending point and the category, and determining a route of each service flow through the routing selection mechanism and the network topology.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a cross-domain routing method for a power communication network. Background Art

[0002] The internet has evolved into an infrastructure that supports diverse applications, enterprise computing, and other complex network services. The continued growth of the internet's network scale and backbone network capacity is driving changes in routing technology for large-scale networks, such as power grid communications networks. These networks and applications require end-to-end connectivity, involving both cross-domain and intra-domain routing. For example, overlay networks require robust bandwidth performance, while real-time interactive applications have stringent network QoS requirements. Therefore, applying dynamic and flexible policies for efficient traffic scheduling across large-scale network architectures is a pressing challenge.

[0003] With the advancement of society and science and technology, and the widespread application of information technologies such as cloud computing, the Internet of Things, mobile internet, and big data, the world has entered the era of the digital economy. The rise of the digital economy, coupled with the advent of a new technological cycle, has become a key driving force for economic development. However, in practice, cross-regional business traffic transmission still has significant drawbacks. How to better control balanced transmission within and between regions and minimize or avoid congestion during transmission remains a major challenge.

[0004] Existing technologies often use controllers for centralized or distributed control across domains, focusing on network node efficiency and controller load. However, they often overlook link load balancing and the inherent properties of service traffic. In the context of cross-domain transmission, this can easily lead to inter-domain link congestion, resulting in lower subsequent service returns. Summary of the Invention

[0005] In view of the above problems, the purpose of the present invention is to provide a cross-domain routing method for power communication networks, which effectively reduces the congestion of inter-domain links by comprehensively considering domain-based and hierarchical resources.

[0006] The present invention provides a cross-domain routing method for a power communication network, comprising:

[0007] Step S1, obtaining a historical service flow data set for service classification, and constructing a classification evaluation matrix based on evaluation indicators corresponding to each service flow in the historical service flow data set; the evaluation indicators include at least: delay requirement, bandwidth requirement, jitter requirement, throughput requirement and packet loss rate requirement;

[0008] Step S2, obtaining a subjective evaluation matrix, and determining a score for each service flow based on the subjective evaluation matrix and the classification evaluation matrix;

[0009] Step S3, classifying the service flows according to the scores of the service flows, and determining the category of each service flow;

[0010] Step S4, abstracting the network topology with the upper network and the lower network by traversing the network nodes and links of the power communication network;

[0011] Step S5, determining the start point, end point and category of each incoming service flow according to the category, determining the routing mechanism according to the start point, end point and category, and determining the route of each service flow through the routing mechanism and the network topology.

[0012] In a possible implementation, the S1 comprises:

[0013] According to the evaluation indexes corresponding to each service flow in the historical service flow dataset, an objective index matrix is constructed;

[0014] The objective index matrix is normalized and dimensionless to obtain a plurality of classification evaluation matrices.

[0015] In a possible implementation, the S1 further comprises:

[0016] The objective index matrix is normalized and dimensionless, and a plurality of classification evaluation matrices are constructed according to the specific index number, and the formula is as follows:

[0017] S={s1,s2,…,s k ,…s K}

[0018]

[0019] Wherein, S represents the historical service flow dataset, s k represents the kth service flow, A represents the objective index matrix, M represents the number of classification indexes, K represents the total number of service flows, a km represents the mth evaluation index of the kth service flow, represents the matrix element after the normalization processing, represents the matrix element after the dimensionless processing, represents the element of the i th row and the j th column in the classification evaluation matrix A m of the evaluation index m.

[0020] In a possible implementation, the S2 comprises:

[0021] An objective evaluation matrix is obtained;

[0022] Each row of the objective evaluation index is averaged to obtain the weight of each evaluation index;

[0023] averaging each row of the classification evaluation index to obtain a baseline score of each evaluation index;

[0024] performing weighted summation on the baseline score and the weight of each evaluation index of each service flow to obtain a score of each service flow.

[0025] In a possible implementation, the S2 further includes:

[0026] performing weighted summation on the baseline score and the weight of each evaluation index of each service flow to obtain a score of each service flow, and the formula is as follows:

[0027]

[0028] wherein B represents a subjective evaluation matrix, b ij represents an element in the i-th row and the j-th column of the subjective evaluation matrix, i, j ∈ [1, M], M represents a number of classification indexes, ω m represents a weight of the evaluation index m, represents a baseline score of each index of each service flow, s k represents a score.

[0029] In a possible implementation, the S3 includes:

[0030] when the score of the service flow is greater than a first threshold value, determining that the service flow is a flood flow;

[0031] when the score of the service flow is greater than a second threshold value and less than or equal to the first threshold value, determining that the service flow is a turbulent flow;

[0032] when the score of the service flow is greater than the second threshold value, determining that the service flow is a slow flow;

[0033] the first threshold value is greater than the second threshold value.

[0034] In a possible implementation, the S3 further includes:

[0035] obtaining a new service flow;

[0036] performing normalization and standardization on the new service flow to obtain index values of the new service;

[0037] calculating a confluence degree of the new service and each historical service flow according to the index values and the weight;

[0038] taking a score of a historical service flow with the highest confluence degree as the score of the new service flow.

[0039] In a possible implementation, the S3 includes:

[0040] The convergence degree is calculated according to the following formula:

[0041]

[0042] wherein, θ k represents the category of the service flow k, θ FLOOD represents the category of the flood flow, θ RAPID represents the category of the turbulent flow, θ SUBDUED represents the category of the slow flow, represents the convergence degree between the newly-arrived service flow i and the flow k in the historical service flow set, represents the index value of the service flow i after being normalized and standardized.

[0043] In a possible implementation, the S5 comprises:

[0044] When the transmission is within the domain, the shortest distance algorithm with shared weight is adopted to determine the route of the lower-layer network;

[0045] When the transmission is cross-domain, the route of the upper-layer network is determined according to the category of the service flow, the controller load, the voltage level of the inter-domain switch, and the inter-domain link load;

[0046] According to the route of the lower-layer network and the route of the upper-layer network, the route of each service flow is determined.

[0047] In a possible implementation, the S5 comprises:

[0048] The route selection mechanism is determined according to the following formula:

[0049]

[0050]

[0051]

[0052] wherein, ξ k represents the field selection factor, ξ F represents the field selection factor of the flood flow, ξ R represents the field selection factor of the turbulent flow, ξ S represents the field selection factor of the slow flow, θ k represents the category of the service flow k, θ FLOOD represents the category of the flood flow, θ RAPID represents the category of the turbulent flow, θ SUBDUED represents the category of the slow flow, α1, α2, α3 represent weight constants, and α1+α2+α3=1, represents the total capacity of the neighborhood controller, C domj represents the remaining capacity of the neighborhood controller, LE maxIndicates the highest voltage level in the network, LE a Indicates the inter-domain switch of the initial domain Voltage level, LE b Indicates the inter-domain switches of the neighboring domains The voltage level, represents the total link bandwidth between two inter-domain switches, B ab represents the remaining bandwidth of the link between two inter-domain switches, expressed as a threshold constant, α R ∈[0,1] represents a random number, η1 is a limiting constant;

[0053] The sharing weight is calculated according to the following formula:

[0054]

[0055] in, represents the link weight between node a and node b, B ab represents the remaining bandwidth of the link between node a and node b, Indicates the total bandwidth between node a and node b, Indicates the entire selected path path i The shared weight value of .

[0056] The cross-domain routing method for the electric power communication network provided by the present invention divides business categories based on historical business flow data sets according to specific business requirements, determines the category for each newly arrived business flow, and transmits different categories using different transmission mechanisms. It comprehensively considers domain-based and hierarchical resources, can effectively reduce the congestion of inter-domain links, and better avoid business flow transmission problems caused by long queuing delays. In addition, it has good feasibility for the goal of hierarchical and domain-based resource sharing. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic diagram of a cross-domain routing method according to an embodiment of the present invention;

[0058] Figure 2 A schematic diagram of the topology of the upper and lower layer networks provided in an embodiment of the present invention;

[0059] Figure 3 This is a simulation diagram of resource sharing balance provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following detailed description of the embodiments of the present invention is provided in conjunction with the accompanying drawings and examples. The following detailed description of the embodiments and the accompanying drawings are intended to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention. That is, the present invention is not limited to the preferred embodiments described, and the scope of the present invention is defined by the claims.

[0061] In the description of the present invention, it should be noted that, unless otherwise specified, “plurality” means two or more; the terms “first”, “second”, etc. are used for descriptive purposes only and cannot be understood as indicating or implying relative importance; for ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0062] Figure 1 A schematic diagram of a cross-domain routing method according to an embodiment of the present invention is provided. Figure 2 The topology diagram of the upper and lower layer networks provided by the embodiment of the present invention is combined with Figure 1 and Figure 2 The present invention provides a cross-domain routing method for a power communication network, comprising:

[0063] Step S1, obtaining a historical business flow data set to be classified, and constructing a classification evaluation matrix based on the evaluation indicators corresponding to each business flow in the historical business flow data set;

[0064] Evaluation indicators include at least: latency requirements, bandwidth requirements, jitter requirements, throughput requirements, and packet loss rate requirements;

[0065] In a possible implementation, S1 includes:

[0066] Construct an objective indicator matrix based on the evaluation indicators corresponding to each business flow in the historical business flow data set;

[0067] The objective indicator matrix is ​​normalized and dimensionless to obtain multiple classification evaluation matrices.

[0068] In a possible implementation, S1 further includes:

[0069] The objective indicator matrix is ​​normalized and dimensionless, and multiple classification evaluation matrices are constructed according to the specific number of indicators. The formula is as follows:

[0070] S={s1,s2,…,s k ,…s K}

[0071]

[0072] Among them, S represents the historical business flow dataset, s k represents the kth business flow, A represents the objective indicator matrix, M represents the number of classification indicators, in this invention, M=5, K represents the total number of business flows, a km represents the mth evaluation index of the kth business flow, represents the matrix elements after forward processing, represents the matrix elements after dimensionless transformation, Represents the classification evaluation matrix A of the evaluation index m m The element at row i and column j in .

[0073] Step S2: Obtain a subjective evaluation matrix, and determine the score of each service flow based on the subjective evaluation matrix and the classification evaluation matrix;

[0074] In one possible implementation, S2 includes:

[0075] Obtain subjective evaluation matrix;

[0076] The average of each row of subjective evaluation indicators is calculated to obtain the weight of each evaluation indicator;

[0077] Perform the average calculation for each row of the classification evaluation index to obtain the benchmark score of each evaluation index;

[0078] The benchmark scores and weights of each evaluation indicator of each business flow are weighted and summed to obtain the score of each business flow.

[0079] In a possible implementation, S2 further includes:

[0080] The weighted sum of the benchmark scores and weights of each evaluation indicator for each business flow is used to obtain the score of each business flow. The formula is as follows:

[0081]

[0082] Among them, B represents the subjective evaluation matrix, b ij Represents the element in the i-th row and j-th column of the subjective evaluation matrix, i,j∈[1,M], M represents the number of classification indicators, ω m represents the weight of the evaluation index m, Indicates the benchmark score of each indicator of each business flow, s k Indicates a fraction.

[0083] Step S3, classifying the service flows according to their scores and determining the category of each service flow;

[0084] In one possible implementation, S3 includes:

[0085] When the score of the service flow is greater than a first threshold, determining that the service flow is a flood;

[0086] When the score of the business flow is greater than the second threshold and less than or equal to the first threshold, determining that the business flow is turbulent;

[0087] When the score of the service flow is greater than a second threshold, determining that the service flow is a slow flow;

[0088] The first threshold is greater than the second threshold.

[0089] In one example, traffic with a score of 85 or more is classified as flood, traffic with a score between 50 and 85 is classified as rapid, and traffic with a score below 50 is classified as subdued. The formula is as follows:

[0090]

[0091] For each incoming business flow, it is positively oriented and standardized according to the indicators, and the convergence degree is used to obtain the historical business flow that is closest to it. This is used as the score of the newly arrived business flow and classified according to the score. After a period of time, the historical business flow matrix is ​​updated offline and the score is recalculated for continuous updating.

[0092] In one possible implementation, S3 also includes:

[0093] Acquire new business flows;

[0094] Perform positive and standardization on new business flows to obtain various indicator values ​​of new business;

[0095] Calculate the degree of convergence of new business and historical business flows based on the values ​​and weights of various indicators;

[0096] The score of the historical business flow with the highest degree of convergence with the new business flow is used as the score of the new business flow.

[0097] In one possible implementation, S3 includes:

[0098] Confluence is calculated using the following formula:

[0099]

[0100] Among them, θ k represents the category of business flow k, θ FLOOD Indicates that the category is flood, θ RAPID Indicates that the category is turbulent, θ SUBDUED Indicates that the category is slow flow, represents the confluence degree between the newly arrived business flow i and the flow k in the historical business flow set, Indicates the indicator values ​​of business flow i after normalization and standardization.

[0101] Step S4, traversing the network nodes and links of the electric power communication network, and abstracting the network topology having an upper network and a lower network;

[0102] In a possible implementation, according to the total network topology G = (V, E) and the control domain planning set DOM = {dom1, dom2, ..., dom nThe network nodes and links are traversed to build the upper-layer topology C on the multi-domain.

[0103] It is judged whether there is any switch connected between any control domain i and control domain j.

[0104] If there is, the number of pairs of switches adjacent to each other is counted, and matrix elements C[i][j]=C[j][i]=c are recorded, where c represents the logarithm of the adjacent switches. If there is not, C[i][j]=C[j][i]=0 is recorded.

[0105] The upper-layer topology C is as follows:

[0106]

[0107] In step S5, the source and destination and the category of each incoming service flow are determined according to the category, the routing mechanism is determined according to the source, the destination and the category, and the route of each service flow is determined through the routing mechanism and the network topology.

[0108] In a possible implementation, S5 includes:

[0109] When the transmission is in the domain, the shortest distance algorithm with a shared weight is used to determine the route of the lower-layer network;

[0110] When the transmission is cross-domain, the route of the upper-layer network is determined according to the category of the service flow, the controller load, the voltage level of the inter-domain switch, the inter-domain link load, etc.

[0111] The route of each service flow is determined according to the route of the lower-layer network and the route of the upper-layer network.

[0112] Specifically, the category, the source switch and the destination switch of the newly incoming service flow are determined, and it is judged whether the transmission is cross-domain according to the network topology and the control domain set. If the transmission is cross-domain, different routing mechanisms are selected according to the category of the service flow. The selection variable of the next-hop neighborhood selection factor ξ k is constructed to select the route of the flood, the neighborhood of the next hop is selected according to the switch node where the service flow initially arrives, the largest neighborhood of ξ k is selected as the next hop through the improved next-hop neighborhood search method, and the process is repeated until the destination node is reached, i.e., the transmission ends, and the path is recorded.

[0113] ​​Confirm the routing selection of the upper-layer network. The routing within the domain is selected based on the shortest distance algorithm with shared weights. Based on the shortest distance, the link with the largest shared weight is selected. For services arriving at the same time, the order of route selection is determined based on the scores of the services.

[0114] In a possible implementation, S5 includes:

[0115] The routing mechanism is determined according to the following formula:

[0116]

[0117]

[0118]

[0119] Among them, ξ k represents the domain selection factor, ξ F represents the domain selection factor of the torrent, ξ R represents the field selection factor of turbulence, ξ S represents the domain selection factor of slow flow, θ k represents the category of business flow k, θ FLOOD Indicates that the category is flood, θ RAPID Indicates that the category is turbulent, θ SUBDUED Indicates that the category is slow flow, α1, α2, α3 represent weight constants, and α1+α2+α3=1, represents the total capacity of the neighborhood controller, C domj represents the remaining capacity of the neighborhood controller, LE max Indicates the highest voltage level in the network, LE a Indicates the inter-domain switch of the initial domain Voltage level, LE b Indicates the inter-domain switches of the neighboring domains The voltage level, represents the total link bandwidth between two inter-domain switches, B ab represents the remaining bandwidth of the link between two inter-domain switches, expressed as a threshold constant, α R ∈[0,1] represents a random number, η1 is a limiting constant;

[0120] The sharing weight is calculated according to the following formula:

[0121]

[0122] in, represents the link weight between node a and node b, B ab represents the remaining bandwidth of the link between node a and node b, represents the total bandwidth between node a and node b, represents the sharing weight value of the whole selected path path i .

[0123] In summary, the overall concept of the present application is as follows: a historical service flow data set to be classified is determined, the delay requirement, bandwidth requirement, jitter requirement, throughput requirement and packet loss rate requirement of each service flow in the data set are obtained. The analytic hierarchy process is used to determine the influence weight of each index, and the specific score of each service flow is calculated. Based on the specific score of the service flow set, the service flow is classified into three categories, which are called flood flow, turbulent flow and slow flow respectively. The upper and lower layers of the power communication network are determined, and the topologies of the upper layer network and the lower layer network are abstracted. According to the classification, the starting point and end point and the category of each incoming service flow are determined, and different routing mechanisms are determined according to this. The service flows arriving at the same time are preferentially processed for the service flows with higher scores. The weight shortest distance algorithm is used for intra-domain transmission, and the link bandwidth load is used as the weight for routing selection in the lower layer network. According to different categories, the controller load, the inter-domain switch voltage level and the inter-domain link load are comprehensively considered for routing selection in the upper layer network, and finally the route of each service flow is determined.

[0124] In the present application, the cross-domain involved is different from the sub-domain of autonomous system, and is defined as follows: each domain, also called upper layer node, refers to the inter-provincial network under the jurisdiction of the headquarters of State Grid, and the range under the jurisdiction of each provincial company site is called a domain, and the intra-domain network is composed of the next level of city networks. The contact link between the upper and lower layers is the use and distribution management of logical resources such as provincial AS number, IP address, code number resource and video conference GK code uniformly distributed by the headquarters of the provincial company, and the cross-domain means that the service of the first level network can be transmitted through the lines of the second and third level networks.

[0125] The present application is helpful for constructing a new type of power system path, improving the operation guarantee capability of the communication backbone network and the access network, supporting the construction and development of the new type of power system, combining with the existing various communication resources, continuously doing well in resource sharing research, continuously enhancing the resource sharing and mutual aid capability, and improving the guarantee capability of the communication network end to end, so as to promote the practical needs of the transformation of the power system.

[0126] The present application also includes a system for implementing the above method, including a scoring and classification unit, a historical database unit, an inter-domain link unit and an intra-domain link unit.

[0127] The scoring and classification units are shown in Table 1. Businesses are primarily categorized into three types. Drawing on the principle of a dam releasing floodwater, incoming business flows can be likened to incoming water. The more turbulent the flow, the greater the volume, and the more stringent the environmental requirements, the more cautious the flow. Conversely, slower flows can be treated relatively slowly, hence the definitions of torrent, turbulent flow, and slow flow. Based on the single-level analytic hierarchy process (AHP), a subjective weight matrix is ​​constructed, and based on the historical business flow dataset, an objective judgment matrix is ​​constructed. Using these two matrices, combined with the subjective and objective matrices, historical business flows are classified into the three categories described above, using the AHP as a foundation. Each incoming business flow is normalized and dimensionless, and the distance method is used to calculate the closest historical business. The score of this historical business is then used to assign a score to the incoming business and determine its category.

[0128] The main business areas covered in this invention include production control services such as relay protection, distribution network differential protection, precise load control, and distribution network automation; production operation information services such as protection management information, wide-area phasor measurement systems, metering automation, and dispatch management; and management information and office services. Production control services naturally constitute the majority of the torrent, as they place very high demands on communication channel latency, reliability, and security. For example, ultra-high voltage and ultra-high voltage relay protection services, which may cross domains, constitute the majority of cross-domain services.

[0129] Table 1

[0130]

[0131] The historical database unit is mainly a unit for storing and inputting business flow demand information. It is responsible for collecting the latency requirements, bandwidth requirements, jitter requirements, throughput requirements and packet loss rate requirements of historical services. After each service arrives, the newly arrived service is stored in the service library to be updated, and an update cycle is defined. At the beginning of each update cycle, the service in the service library to be updated is updated to the historical database, and the objective judgment matrix is ​​updated to re-score and classify the services in the historical database.

[0132] The inter-domain routing unit and the intra-domain routing unit are collectively referred to as the service routing unit. The cross-domain routing model can be divided into two layers: the upper layer for inter-domain routing, where each node represents a domain, and the lower layer for intra-domain routing. As the name suggests, this unit is primarily responsible for service processing and transmission. After determining the type of incoming service, it first determines the inter-domain route based on the type. Then, through intra-domain routing, it connects all traversed domains to form a complete route.

[0133] The optimization goal of the present invention is the resource sharing load balancing degree in the power communication network. The target formula is as follows:

[0134]

[0135] Among them, δ B represents the average resource sharing balance, θ i B represents the maximum link utilization variance in the i-th group of services, represents the maximum link utilization variance in the M-group service set, n represents the total number of nodes in the network, σ ab =1 means that when the link between node a and node b has been used, B ab represents the total bandwidth of the link between node a and node b, Indicates that the link between nodes A and B has used bandwidth resources. A higher degree of resource sharing balance means better network load balancing under service deployment, more balanced resource utilization, and better network response to emergencies. It also helps reduce queuing delays and improve service processing capabilities.

[0136] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0137] like Figure 3 As shown, by comparing with the existing DI J algorithm and TLF algorithm on the market, the present invention has a more significant advantage in terms of resource sharing balance, which is helpful to build a new power system path, improve the operation guarantee capability of the communication backbone network and access network, support the construction and development of the new power system, combine various existing communication resources, continue to do a good job in resource sharing research, continuously enhance the resource sharing mutual assistance capability, and improve the end-to-end guarantee capability of the communication network, so as to promote the actual needs of power system transformation.

[0138] Compared with the existing DIJ algorithm and TLF algorithm on the market, the present invention has a greater advantage in load balancing. The DIJ algorithm searches for upper-level inter-domain routing through the shortest path method, and selects lower-level intra-domain routing with the same mechanism. The TLF algorithm constructs upper-level inter-domain routing by greedily selecting the domain with the smallest controller load, and constructs intra-domain routing through the adjacent node with the smallest node load. The present invention provides a cross-domain routing selection mechanism for an electric power communication network. According to the specific requirements of the business, the business categories are divided by using the historical business flow data set, and the category is determined for each newly arrived business flow. Different transmission mechanisms are used to transmit for different categories, and the domain-based and hierarchical resources are comprehensively considered. It can effectively reduce the congestion of inter-domain links, better avoid the business flow transmission problem caused by the long queuing delay of the business, and has good feasibility for the hierarchical and domain-based resource sharing goals.

[0139] The cross-domain routing method for the electric power communication network provided by the present invention divides business categories based on historical business flow data sets according to specific business requirements, determines the category for each newly arrived business flow, and transmits different categories using different transmission mechanisms. It comprehensively considers domain-based and hierarchical resources, can effectively reduce the congestion of inter-domain links, and better avoid business flow transmission problems caused by long queuing delays. In addition, it has good feasibility for the goal of hierarchical and domain-based resource sharing.

[0140] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A cross-domain routing method for power communication network, characterized in that: include: Step S1, obtaining a historical business flow data set to be classified, and constructing a classification evaluation matrix according to the evaluation indicators corresponding to each business flow in the historical business flow data set; The evaluation indicators include at least: delay requirement, bandwidth requirement, jitter requirement, throughput requirement and packet loss rate requirement; Step S2, obtaining a subjective evaluation matrix, and determining a score for each service flow based on the subjective evaluation matrix and the classification evaluation matrix; Step S3, classifying the service flows according to the scores of the service flows, and determining the category of each service flow; Step S4, traversing the network nodes and links of the electric power communication network, and abstracting the network topology having an upper network and a lower network; Step S5, determining the starting point, end point, and category of each incoming service flow based on the category, determining a routing mechanism based on the starting point, end point, and category, and determining the route of each service flow based on the routing mechanism and the network topology; Said S1 comprises: Constructing an objective indicator matrix based on the evaluation indicators corresponding to each business flow in the historical business flow data set; Performing forward transformation and dimensionless transformation on the objective indicator matrix to obtain multiple classification evaluation matrices; The S1 further includes: The objective indicator matrix is ​​normalized and dimensionless, and multiple classification evaluation matrices are constructed according to the specific number of indicators. The formula is as follows: S={s1,s2,…,s k ,…s K } Among them, S represents the historical business flow dataset, s k represents the kth business flow, A represents the objective indicator matrix, M represents the number of classification indicators, K represents the total number of business flows, a km represents the mth evaluation index of the kth business flow, represents the matrix elements after forward processing, represents the matrix elements after dimensionless transformation, Represents the classification evaluation matrix A of the evaluation index m m The element at row i and column j in .

2. The cross-domain routing method according to claim 1, characterized in that: The S2 includes: Obtain subjective evaluation matrix; Performing an average calculation on each row of the subjective evaluation indicators to obtain the weight of each evaluation indicator; Calculate the average of each row of the classification evaluation indicators to obtain the benchmark score of each evaluation indicator; The benchmark scores and weights of each evaluation indicator of each business flow are weighted and summed to obtain the score of each business flow.

3. The cross-domain routing method according to claim 2, characterized in that: Said S2 further comprises: The weighted sum of the benchmark scores and weights of each evaluation indicator for each business flow is used to obtain the score of each business flow. The formula is as follows: Among them, B represents the subjective evaluation matrix, b ij Represents the element in the i-th row and j-th column of the subjective evaluation matrix, i,j∈[1,M], M represents the number of classification indicators, ω m represents the weight of the evaluation index m, Indicates the benchmark score of each indicator of each business flow, s k Indicates a fraction.

4. The cross-domain routing method according to claim 1, wherein: The S3 includes: When the score of the service flow is greater than a first threshold, determining that the service flow is a flood; When the score of the business flow is greater than the second threshold and less than or equal to the first threshold, determining that the business flow is turbulent; When the score of the service flow is greater than a second threshold, determining that the service flow is a slow flow; The first threshold is greater than the second threshold.

5. The cross-domain routing method according to claim 1, wherein: Said S3 further comprises: Acquire new business flows; Performing normalization and standardization on the new business flow to obtain various indicator values ​​of the new business; Calculating the degree of convergence of the new business and each historical business flow based on the indicator values ​​and weights; The score of the historical business flow with the highest degree of convergence with the new business flow is used as the score of the new business flow.

6. The cross-domain routing method according to claim 1, wherein: The S3 includes: Confluence is calculated using the following formula: Among them, θ k represents the category of business flow k, θ FLOOD Indicates that the category is flood, θ RAPID Indicates that the category is turbulent, θ SUBDUED Indicates that the category is slow flow, represents the confluence degree between the newly arrived business flow i and the flow k in the historical business flow set, Indicates the indicator values ​​of business flow i after normalization and standardization.

7. The cross-domain routing method according to claim 1, wherein: The S5 includes: When transmitting within the domain, the shortest distance algorithm with shared weights is used to determine the routing of the underlying network; When transmitting across domains, the upper-layer network routing is determined based on the service flow type, controller load, inter-domain switch voltage level, and inter-domain link load. The route of each service flow is determined based on the routes of the lower-layer network and the upper-layer network.

8. The cross-domain routing method according to claim 1, wherein: The S5 includes: The routing mechanism is determined according to the following formula: Among them, ξ k represents the domain selection factor, ξ F represents the domain selection factor of the torrent, ξ R represents the field selection factor of turbulence, ξ S represents the domain selection factor of slow flow, θ k represents the category of business flow k, θ FLOOD Indicates that the category is flood, θ RAPID Indicates that the category is turbulent, θ SUBDUED Indicates that the category is slow flow, α1, α2, α3 represent weight constants, and α1+α2+α3=1, represents the total capacity of the neighborhood controller, C domj represents the remaining capacity of the neighborhood controller, LE max Indicates the highest voltage level in the network, LE a Indicates the inter-domain switch of the initial domain Voltage level, LE b Indicates the inter-domain switches of the neighboring domains The voltage level, represents the total link bandwidth between two inter-domain switches, B ab represents the remaining bandwidth of the link between two inter-domain switches, expressed as a threshold constant, α R ∈[0,1] represents a random number, η1 is a limiting constant; The sharing weight is calculated according to the following formula: in, represents the link weight between node a and node b, B ab represents the remaining bandwidth of the link between node a and node b, Indicates the total bandwidth between node a and node b, Indicates the entire selected path path i The shared weight value of .

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