Cross-domain routing method for power communication network

Abstract

The invention 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, acquiring a historical service flow data set to be subjected to service classification, and constructing a classification evaluation matrix according to an evaluation index corresponding to each service flow in the historical service flow data set; s2, obtaining a subjective evaluation matrix, and determining the 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 the 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; and S5, according to the category, determining a starting point, an end point and a category to which each service flow to arrive belongs, determining a routing mechanism according to the starting point, the end point and the category, and determining the routing of each service flow through the routing 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 developed into an infrastructure that supports complex network services such as multi-type applications and enterprise computing. At present, the network scale and backbone network capacity of the Internet continue to increase, prompting changes in routing technology for large networks, such as power communication networks. These networks and applications require end-to-end network connections, involving cross-domain and intra-domain routing. For example, overlay networks require good bandwidth performance guarantees; real-time interactive applications have strict network QoS requirements. Therefore, how to apply dynamic and flexible strategies for large-scale network architectures to perform efficient traffic scheduling is an urgent problem that needs to be solved.

[0003] With the progress of society and science and technology, 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 digital economy. The rise of digital economy and the advent of new technology cycles have become one of the important forces driving economic development. In the actual environment, there are still major drawbacks in the transmission of cross-regional business traffic. How to better control the balanced transmission within and between regions and minimize or avoid congestion during transmission is still a major problem.

[0004] In the existing technology, cross-domain controllers are mostly used for centralized or distributed control, focusing on node efficiency in the network, controller load, etc. However, the link load balancing and the attributes of the service traffic itself are ignored to a certain extent. In the context of cross-domain transmission, it is easy to cause congestion of inter-domain links, resulting in low subsequent service benefits. 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 according to evaluation indicators corresponding to each service flow in the historical service flow data set; the evaluation indicators at least include: 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 according to 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, traversing the network nodes and links of the power communication network, and abstracting the network topology having an upper network and a lower network;

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

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

[0013] Constructing an objective indicator matrix according to the evaluation indicators corresponding to each business flow in the historical business flow data set;

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

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

[0016] 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:

[0017] S={s1,s2,…,s k ,…s K}

[0018]

[0019] Among them, S represents the historical business flow dataset, s k represents the kth service flow, A represents the objective indicator matrix, M represents the number of classification indicators, K represents the total number of service flows, and a km represents the mth evaluation index of the kth service flow, represents the matrix elements after forward processing, represents the dimensionless matrix element, Represents the classification evaluation matrix A of the evaluation index m m The element at row i and column j in .

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

[0021] Obtain subjective evaluation matrix;

[0022] Performing an average calculation on each row of the subjective evaluation index to obtain the weight of each evaluation index;

[0023] Calculate the average of each row of the classification evaluation index to obtain a benchmark score for each evaluation index;

[0024] The benchmark score and weight of each evaluation indicator of each business flow are weighted and summed to obtain the score of each business flow.

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

[0026] The weighted sum of the benchmark score and weight of each evaluation indicator of each business flow is performed to obtain the score of each business flow. The formula is as follows:

[0027]

[0028] 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.

[0029] In a possible implementation, S3 includes:

[0030] When the score of the service flow is greater than a first threshold, determining that the service flow is a torrent;

[0031] 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;

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

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

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

[0035] Acquire new business flows;

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

[0037] Calculating the degree of convergence of the new business and each historical business flow according to the index values ​​and weights;

[0038] 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.

[0039] In a possible implementation, S3 includes:

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

[0041]

[0042] Among them, θ k represents the category of service 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, It represents the values ​​of various indicators after business flow i is normalized and standardized.

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

[0044] When transmitting within the domain, the shortest distance algorithm with shared weights is used to determine the routing of the underlying network;

[0045] When transmitting across domains, the upper-layer network routing is determined based on the service flow category, controller load, inter-domain switch voltage level, and inter-domain link load;

[0046] The route of each service flow is determined based on the route of the lower-layer network and the route of the upper-layer network.

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

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

[0049]

[0050]

[0051]

[0052] 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 service 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 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 domain The voltage level, represents the total bandwidth of the link 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 restriction 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, represents the total bandwidth between node a and node b, Represents 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 the business categories according to the specific requirements of the business by means of the historical business flow data set, and determines the category for each newly arrived business flow, and transmits different categories with different transmission mechanisms, comprehensively considering the domain-based and hierarchical resources, can effectively reduce the congestion of the 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 sharing goals of resources. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0059] Figure 3 A simulation diagram of resource sharing balance provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following detailed description of the embodiments of the present invention is further described in detail in conjunction with the accompanying drawings and examples. The detailed description of the following embodiments and the accompanying drawings are used to exemplarily illustrate the principles of the present invention, but cannot be used 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 than two; the terms “first”, “second”, etc. are only used for descriptive purposes 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 provided by an embodiment of the present invention. Figure 2 A topological diagram of the upper and lower layer networks provided in the embodiment of the present invention, 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 for business classification, and constructing a classification evaluation matrix according to the evaluation indicators corresponding to each business flow in the historical business flow data set;

[0064] The evaluation indicators shall at least include: delay requirement, bandwidth requirement, jitter requirement, throughput requirement and packet loss rate requirement;

[0065] In a possible implementation, S1 includes:

[0066] According to the evaluation indicators corresponding to each business flow in the historical business flow data set, an objective indicator matrix is ​​constructed;

[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 service flow, A represents the objective indicator matrix, M represents the number of classification indicators, M=5 in the present invention, K represents the total number of service flows, a km represents the mth evaluation index of the kth service flow, represents the matrix elements after forward processing, represents the dimensionless matrix element, Represents the classification evaluation matrix A of the evaluation index m m The element at row i and column j in .

[0073] Step S2, obtaining a subjective evaluation matrix, and determining the score of each service flow according to the subjective evaluation matrix and the classification evaluation matrix;

[0074] In a possible implementation, S2 includes:

[0075] Obtain subjective evaluation matrix;

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

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

[0078] The benchmark score and weight 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 score and weight of each evaluation indicator of each business flow is performed 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 the scores of the service flows, and determining the category of each service flow;

[0084] In a 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 torrent;

[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 the second threshold, determining the service flow as a slow flow;

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

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

[0090]

[0091] For each business flow that arrives later, 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 a possible implementation, S3 further includes:

[0093] Acquire new business flows;

[0094] Forward and standardize the new business flow to obtain the various indicator values ​​of the new business;

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

[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 a possible implementation, S3 includes:

[0098] The confluence is calculated according to the following formula:

[0099]

[0100] Among them, θ k represents the category of service 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, It represents the values ​​of various indicators after business flow i is normalized and standardized.

[0101] Step S4, traversing the network nodes and links of the 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 n} to traverse each network node and link to build a multi-domain upper layer topology C.

[0103] Determine whether there is any switch between any control domain i and any control domain j With switch Connected,

[0104] If it exists, count how many pairs of switches are adjacent to each other, and record the matrix element C[i][j]=C[j][i]=c, where c represents the number of pairs of adjacent switches. If it does not exist, record C[i][j]=C[j][i]=0.

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

[0106]

[0107] Step S5, according to the category, determine the starting point, end point and category of each incoming business flow, determine the routing mechanism according to the starting point, end point and category, and determine the route of each business flow through the routing mechanism and network topology.

[0108] In a possible implementation, S5 includes:

[0109] When transmitting within the domain, the shortest distance algorithm with shared weights is used to determine the routing of the underlying network;

[0110] When transmitting across domains, the upper-layer network routing is determined based on the service flow category, controller load, inter-domain switch voltage level, and inter-domain link load;

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

[0112] Specifically, determine the category of the newly arrived service flow, the source switch and the destination switch, and determine whether it belongs to cross-domain transmission based on the network topology and control domain set. If it belongs to cross-domain transmission, select different routing mechanisms according to the category of the service flow. Construct the neighborhood selection factor ξ of the next hop k The selection variable is used to select the routing of the flood, based on the switch node where the business flow initially arrives. To select the next hop neighborhood, through the improved next hop neighborhood search method, traverse the upper network and select ξ k The largest neighboring area is taken as the next hop, and so on, until the destination node is reached. This ends and the path taken 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. On the basis of selecting 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 selection 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 service 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 domain The voltage level, represents the total bandwidth of the link 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 restriction 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 entire selected path path i The shared weight value of .

[0123] In summary, the overall concept of the present invention is: determine the historical business flow data set to be classified, and obtain the delay requirements, bandwidth requirements, jitter requirements, throughput requirements and packet loss rate requirements of each business flow in the data set. Use the hierarchical analysis method to determine the influence weight of each indicator, and calculate the specific score of each business flow. Based on the specific scores of the business flow set, the business flow is classified into three categories, namely torrent, turbulent flow and slow flow. Determine the upper and lower network of the power communication network, and abstract the upper network and lower network topology. According to the classification category, determine the starting point, end point and category of each incoming business flow, and use this to determine different routing selection mechanisms, and the business flows arriving at the same time give priority to the business flows with higher scores. The intra-domain transmission adopts the weighted shortest distance algorithm, and uses the link bandwidth load as the weight to perform routing selection of the lower network. Cross-domain transmission is based on different categories, comprehensively considering the controller load, the voltage level of the inter-domain switch, and the inter-domain link load, and performs routing selection of the upper network, and finally determines the routing of each business flow.

[0124] In the present invention, the cross-domain involved is different from the sub-domain of the autonomous system, and is defined as follows: each domain is also called an upper node, which refers to the inter-provincial networks under the jurisdiction of the State Grid Information and Communication Headquarters. The scope under the jurisdiction of each provincial company site is called a domain, and the intra-domain network is composed of the next level of prefecture-level city networks. The link between the upper and lower layers is the use and allocation management of logical resources such as the provincial AS number, IP address, code number resources, and video conference venue GK coding uniformly allocated by the provincial company headquarters. Cross-domain means that the services of the first-level network can be transmitted through the lines of the second-level and third-level networks.

[0125] The present invention helps 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 the power system transformation.

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

[0127] The scoring and classification units are as shown in Table 1. The business is mainly divided into three categories. Referring to the principle of dam flood discharge, the incoming business flow is compared to the incoming water. The more turbulent the water flow, the larger the water volume, and the more stringent the environmental requirements of the water flow, the more cautious it needs to be treated. On the contrary, it can be relatively slowed down, so it is defined as torrent, turbulent flow and slow flow respectively. Based on the single-level analytic method, a subjective weight matrix is ​​constructed, and an objective judgment matrix is ​​constructed based on the historical business flow data set. Through these two matrices, based on the hierarchical analytic method, combined with the subjective and objective matrices, the historical business flow is divided into the above three categories. Each newly arrived business flow is standardized and dimensionless, and the distance method is used to calculate the closest historical business. The score of the historical business is used to assign points to the newly arrived business and obtain the category to which it belongs.

[0128] In the present invention, the main business scope involved is as follows: production control relay protection, distribution network differential protection, precise load control, distribution network automation, etc.; production operation information protection management information, wide area phasor measurement system, metering automation, dispatching management, etc.; it also includes management information and management office services. Among them, production control services are naturally the main part of the torrent, because production control services have very high requirements for the delay, reliability and security of the communication channel, and for example, ultra-high voltage and ultra-high voltage relay protection in relay protection services may be cross-domain, and similar services are the main part 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 delay 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 can be collectively referred to as the service routing selection unit. The cross-domain routing model can be divided into two layers. The upper layer is the selection of inter-domain routing, where each node represents a domain, and the lower layer is the intra-domain routing selection. As the name implies, this unit is mainly for the processing and transmission of services. After determining the type of incoming services, the inter-domain routing is determined based on the type, and then all the domains passed through are connected in series through the intra-domain routing selection 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 is used, B ab represents the total bandwidth of the link between node a and node b, Indicates that the link between node a and node b has used bandwidth resources. The higher the resource sharing balance, the better the network load balancing under service deployment, the more balanced the resource utilization, and the better the network's ability to respond to emergencies. It also helps to 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 resource sharing balance, which is helpful to build a new power system path, improve the operation guarantee capability of the communication backbone network and the 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 the power system transformation.

[0138] Compared with the existing DI J algorithm and TLF algorithm on the market, the present invention has a greater advantage in load balancing. The DI J 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 the business categories according to the specific requirements of the business by means of the historical business flow data set, and determines the category for each newly arrived business flow, and transmits different categories with different transmission mechanisms, comprehensively considering the domain-based and hierarchical resources, can effectively reduce the congestion of the 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 sharing goals of resources.

[0140] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope 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 for business classification, and constructing a classification evaluation matrix according to the evaluation index 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 according to 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 power communication network, and abstracting the network topology having an upper network and a lower network; Step S5, according to the category, determine the starting point, end point and category of each incoming business flow, determine the routing mechanism according to the starting point, end point and category, and determine the route of each business flow through the routing mechanism and network topology.

2. The cross-domain routing method according to claim 1, characterized in that: The S1 includes: Constructing an objective indicator matrix according to the evaluation indicators corresponding to each business flow in the historical business flow data set; The objective indicator matrix is ​​normalized and dimensionless to obtain a plurality of classification evaluation matrices.

3. The cross-domain routing method according to claim 2, characterized in that: The S1 also 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 service flow, A represents the objective indicator matrix, M represents the number of classification indicators, K represents the total number of service flows, and a km represents the mth evaluation index of the kth service flow, represents the matrix elements after forward processing, represents the dimensionless matrix element, Represents the classification evaluation matrix A of the evaluation index m m The element at row i and column j in .

4. 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 index to obtain the weight of each evaluation index; Calculate the average of each row of the classification evaluation index to obtain a benchmark score for each evaluation index; The benchmark score and weight of each evaluation indicator of each business flow are weighted and summed to obtain the score of each business flow.

5. The cross-domain routing method according to claim 4, characterized in that: The S2 further includes: The weighted sum of the benchmark score and weight of each evaluation indicator of each business flow is performed 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.

6. The cross-domain routing method according to claim 1, characterized in that: The S3 includes: When the score of the service flow is greater than a first threshold, determining that the service flow is a torrent; 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.

7. The cross-domain routing method according to claim 1, characterized in that: The S3 further includes: Acquire new business flows; Performing normalization and standardization on the new service flow to obtain various index values ​​of the new service; Calculating the degree of convergence of the new business and each historical business flow according to the index 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.

8. The cross-domain routing method according to claim 1, characterized in that: The S3 includes: The confluence is calculated according to the following formula: Among them, θ k represents the category of service 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, It represents the values ​​of various indicators after business flow i is normalized and standardized.

9. The cross-domain routing method according to claim 1, characterized in that: 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 category, controller load, inter-domain switch voltage level, and inter-domain link load; The route of each service flow is determined based on the route of the lower-layer network and the route of the upper-layer network.

10. The cross-domain routing method according to claim 1, characterized in that: The S5 includes: The routing selection 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 service 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 domain The voltage level, represents the total bandwidth of the link 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, represents the total bandwidth between node a and node b, Represents the entire selected path path i The shared weight value of .

Images