CN118337694B - A routing optimization method and system based on service function chain - Google Patents

Abstract

The invention relates to the technical field of electric power communication, and discloses a route optimization method and a system based on a service function chain, wherein the method comprises the steps of calculating the importance of the function chain of the service function chain according to the information physical sensitivity of an electric power system and the cascading failure indication coefficient of a transmission line; calculating a link risk value of the physical link according to the importance of the functional link and the availability of the physical link, and calculating a risk route weight value of the physical link according to the link risk value and the link length; and establishing a service function link route optimization model according to the importance degree of the function link and the risk route weight value, and solving to obtain an optimal route strategy of the power system. The routing strategy of the invention preferentially distributes the high-importance SFC to the network path with low risk value, avoids the concentration of the high-importance information in the local topology of the network, and improves the robustness of the routing mechanism and realizes the self-adaption in the fault scene by searching the solution in the most serious fault scene.

Description

A routing optimization method and system based on service function chain

Technical Field

The present invention relates to the field of power communication technology, and in particular to a routing optimization method and system based on a service function chain.

Background Art

As the digitalization of power grids accelerates, State Grid has laid a digital foundation for the cross-domain upgrade of traditional power grids to energy Internet and the transformation of power grid enterprises to energy Internet enterprises through new digital infrastructure, so as to further promote the deep coupling of power systems and power communication systems from functional to topological levels. At present, the more typical emerging network technologies such as network slicing technology, software-defined network technology, and network function virtualization technology have been implemented in power communication systems.

The continuous evolution of power grid safety production and emerging services towards IP and broadband has greatly improved the flexibility and control capabilities of power communication systems, and also provided new solutions to the cascading failure problem in cyber-physical fusion scenarios. As a typical cyber-physical coupling system, the new power system, while improving efficiency due to information empowerment, also faces the risk of cascading failures due to the deep coupling of the power system and the power communication system. For example, due to hacker attacks on the power grid, some links of the power communication system failed (the control server was shut down), which affected the operation of the physical power grid and caused serious consequences such as regional power outages. In actual operation, the business most affected by the communication system is the emergency control business carried on the transmission layer. The loss of relevant business information will directly affect the operation adjustment and topology structure of the power system after the fault. In extreme cases, it will cause the scope of the fault to expand and even cause the system to become unstable.

As far as the regulation of new power systems is concerned, accurately adjusting the energy distribution on the power grid lines is far more difficult than optimizing the information flow transmission on the information side. In order to address the problem of information service loss caused by communication failures, which in turn deteriorates a single failure into a cascading failure, most of the current cascading failure resistance mechanisms on the power grid side are adopted. However, this mechanism cannot effectively solve the above problems. Therefore, a routing optimization strategy is urgently needed to reduce the possibility of power system failures evolving into cascading failures due to the blocking of emergency control services due to communication failures.

Summary of the invention

In order to solve the above technical problems, the present invention provides a routing optimization method and system based on a service function chain, which improves the phenomenon of high-importance information being concentrated in a local network by optimizing the routing strategy from the information side, so as to achieve the technical effect of enhancing the robustness of information transmission and reducing the possibility of communication failures becoming the cause of cascading failures in the power system.

In a first aspect, the present invention provides a route optimization method based on a service function chain, the method comprising:

According to the information physical sensitivity of the power system and the cascading fault indication coefficient of the transmission line, the function chain importance of the service function chain is calculated;

Calculating a link risk value of the physical link according to the importance of the function chain and the availability of the physical link, and calculating a risk routing weight value of the physical link according to the link risk value and the link length;

According to the function chain importance and the risk routing weight value, a service function chain routing optimization model is established, and the service function chain routing optimization model is solved to obtain an optimal routing strategy for the power system.

Furthermore, before the step of calculating the function chain importance of the service function chain according to the information physical sensitivity of the power system and the cascading fault indication coefficient of the transmission line, the method further includes:

According to the mapping relationship between information quantity and physical quantity in the power system, the information-physical sensitivity of the power system is calculated;

According to the limit power proximity and power fluctuation amplitude of the transmission line in the power system, the cascading fault indication coefficient of the transmission line is calculated.

Furthermore, the step of establishing a service function chain routing optimization model according to the function chain importance and the risk routing weight value includes:

Using the risk routing weight value as the weight value of the routing decision variable, and establishing a first objective function according to the function chain importance;

Determine the interruption indication coefficient of the service function chain according to the uncertain variable of the link failure, and establish a second objective function according to the importance of the function chain;

Establishing a routing optimization objective function according to the first objective function and the second objective function;

Establishing constraints of the service function chain according to the virtual network function node requirements, bandwidth resource requirements and routing selection requirements of the service function chain;

The routing optimization objective function and the constraint conditions are used as a service function chain routing optimization model.

Furthermore, the step of establishing the constraint conditions of the service function chain according to the virtual network function node requirements, bandwidth resource requirements and route selection requirements of the service function chain includes:

Establish node constraints based on the uniqueness of node deployment of virtual network functions in the service function chain;

Establish bandwidth resource constraint conditions based on the maximum bandwidth resource of the service function chain;

Establish access sequence constraints based on the node access sequence requirements of the selected routes in the service function chain;

Establish routing topology constraints based on the network topology requirements of the routes selected in the service function chain.

Furthermore, the physical sensitivity of the information is expressed by the following formula:

In the formula, A vector representing the physical state variables; represents a vector of information variables, represents the vector of physical side control variables, represents the vector of information-side control variables;

The limit power proximity is expressed by the following formula:

In the formula, Indicates the transmission line before the fault The power flowing through Indicates transmission line The reference power value, Indicates transmission line The power transfer limit is represents the total number of transmission lines;

The power fluctuation amplitude is expressed by the following formula:

In the formula, Indicates transmission line Transmission line after fault Power fluctuation caused by

The cascade fault indication coefficient is expressed by the following formula:

In the formula, Indicates transmission line The cascading fault indication coefficient, represents the equilibrium factor;

The following formula is used to express the importance of the function chain:

In the formula, represents the function chain importance of the kth service function chain, represents the information-physical sensitivity of the kth service function chain, Indicates the fault scenario The probability of occurrence, S represents the uncertainty set of the fault matrix of the communication network, Indicates a failure scenario Transmission lines Cascading fault indication factor.

Furthermore, the link risk value is expressed by the following formula:

In the formula, Indicates the physical link The link risk value is Indicates the physical link The availability rate, Represents a physical node and physical nodes The physical link between represents the function chain importance of the kth service function chain;

The risk routing weight value is expressed by the following formula:

In the formula, Indicates the physical link The risk routing weight value is Indicates the physical link Length, represents the link availability per unit length, Indicates the physical link The maximum risk value that can be carried, Indicates transmission line Physical Link The link risk value.

Furthermore, the first objective function is expressed by the following formula:

In the formula, represents the function chain importance of the kth service function chain, K represents the total number of service function chains, Represents a physical node and physical nodes The physical link between Represents a set of physical links. Indicates the physical link The risk routing weight value is Represents the logical node in the kth service function chain and logical nodes Does the main logical link between the two need to pass through the physical link? , Represents the logical node in the kth service function chain and logical nodes Do the backup logical links between the two need to pass through the physical link? , Represents a logical node and logical nodes The logical link between represents the first threshold;

The second objective function is expressed by the following formula:

In the formula, Indicates the physical link status, Indicates the kth service function chain based on the physical link The interruption indication coefficient of the state;

The routing optimization objective function is expressed by the following formula:

In the formula, represents the second threshold, S represents the uncertainty set of the fault matrix of the communication network;

The constraint condition is expressed by the following formula:

In the formula, Indicates whether the mth virtual network function is deployed on the physical node i, M represents the set of virtual network functions in the communication network, represents a collection of physical nodes in a communication network, Represents a logical node Whether it is handled by the mth virtual network function, represents the node set in the logical topology of the kth service function chain, represents the set of links in the logical topology of the kth service function chain, Indicates the physical link Available bandwidth, represents the source node of the kth service function chain, represents the destination node of the kth service function chain, Represents the logical node in the kth service function chain and logical nodes Does the main logical link between the two need to pass through the physical link? , Represents the logical node in the kth service function chain and logical nodes Do the backup logical links between the two need to pass through the physical link? .

In a second aspect, the present invention provides a route optimization system based on a service function chain, the system comprising:

An importance calculation module, used to calculate the function chain importance of the service function chain according to the information physical sensitivity of the power system and the cascade fault indication coefficient of the transmission line;

A weight value calculation module, used to calculate the link risk value of the physical link according to the importance of the function chain and the availability of the physical link, and to calculate the risk routing weight value of the physical link according to the link risk value and the link length;

The routing optimization module is used to establish a service function chain routing optimization model according to the function chain importance and the risk routing weight value, and solve the service function chain routing optimization model to obtain the optimal routing strategy of the power system.

Furthermore, the importance calculation module is also used to calculate the information-physical sensitivity of the power system based on the mapping relationship between the information quantity and the physical quantity in the power system; and calculate the cascading fault indication coefficient of the transmission line based on the limit power proximity and power fluctuation amplitude of the transmission line in the power system.

Furthermore, the routing optimization module is also used to use the risk routing weight value as the weight value of the routing decision variable, and establish a first objective function based on the importance of the function chain; determine the interruption indication coefficient of the service function chain based on the uncertain variables of the link failure, and establish a second objective function based on the importance of the function chain; establish a routing optimization objective function based on the first objective function and the second objective function; establish constraints of the service function chain based on the virtual network function node requirements, bandwidth resource requirements and routing selection requirements of the service function chain; and use the routing optimization objective function and the constraints as a service function chain routing optimization model.

The present invention provides a method and system for routing optimization based on service function chain. Through the method, high-importance SFCs are preferentially allocated to network paths with low risk values, which can avoid the concentration of high-importance information in the local network topology. At the same time, uncertainty disturbances are introduced into the model, and the robustness of the routing mechanism is improved by finding solutions under the most serious fault scenarios, so as to achieve self-adaptation under fault scenarios.

BRIEF DESCRIPTION OF THE DRAWINGS

1 is a schematic diagram of a flow chart of a route optimization method based on a service function chain in an embodiment of the present invention;

2 is a communication network topology diagram of an IEEE 30-node system in a simulation experiment according to an embodiment of the present invention;

FIG3 is a comparison diagram of information accessibility in simulation experiment analysis results according to an embodiment of the present invention;

FIG4 is a comparison diagram of load loss rates in simulation experiment analysis results in an embodiment of the present invention;

FIG5 is a comparison diagram of branch risk values in simulation experiment analysis results in an embodiment of the present invention;

6 is a comparison diagram of the importance of blocked SFCs in the simulation experiment analysis results according to an embodiment of the present invention;

FIG. 7 is a schematic diagram of the structure of a route optimization system based on a service function chain in an embodiment of the present invention.

DETAILED DESCRIPTION

In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

Before describing the technical solution of the present invention, the technical keywords involved in the technical solution are first described:

NFV: Network Function Virtualization;

SFC: Service Function Chain, service function chain;

VNF: Virtualized Network Function, virtual network function;

CFI: Cascading Failure Index, Cascading Failure Index;

SFC-RS: SFC-Routing Strategy, routing strategy of service function chain;

SPRM: Shortest-Path Routing Model, shortest path routing algorithm.

Please refer to FIG. 1 , which shows a route optimization method based on a service function chain according to a first embodiment of the present invention, wherein the method comprises steps S10 to S30:

Step S10, calculating the function chain importance of the service function chain according to the information physical sensitivity of the power system and the cascade fault indication coefficient of the transmission line;

Step S20, calculating the link risk value of the physical link according to the importance of the function chain and the availability of the physical link, and calculating the risk routing weight value of the physical link according to the link risk value and the link length;

Step S30, establishing a service function chain routing optimization model according to the function chain importance and the risk routing weight value, and solving the service function chain routing optimization model to obtain an optimal routing strategy for the power system.

The technical solution of the present invention is applied to a new type of power system. The new type of power system is a typical cyber-physical coupling system, which can be divided into two levels: power network and communication network. The nodes and transmission lines of the power grid and the communication nodes and links of the communication network are two different entities located in the same spatial position, belonging to the power grid and the communication network respectively. The power grid can be described as a network composed of nodes. and lines The composition of the picture .

The communication network mainly reflects the feedback process of the information control system from local measurement to the decision-making of the control center. The information flow of concern is not the closed-loop feedback of local protection control, but the "network flow" that needs to consider the characteristics of communication network nodes and branches. In the network function virtualization NFV network, the communication layer includes the physical topology network and the logical topology network that represents the service function chain SFC request. The physical topology network consists of an undirected graph definition, and They represent the set of entity nodes and the set of links in the communication network respectively. Represents any two physical nodes, Representation Node and The physical link between The logical topology network consists of a directed graph Definition, where represents the kth SFC, and Respectively The set of nodes and links in the logical topology, Represents any two logical nodes, Represents a logical link between two nodes.

one The logical link is composed of an ingress node, an egress node, and several virtual network functions VNF. VNF is deployed on NFV, which corresponds to the physical node of the communication network. Since the virtual functions that should be included in the formation of a specific network service have not yet been standardized, the present invention uses the following general expression to represent the function chain and its constituent VNFs:

definition is the collection of VNFs in the communication network, represents the mth NVF, For any communication network , which is defined as:

in, For The length of is the number of VNFs in the SFC. For No. VNFs.

When a power grid fails, the plant and station whose emergency control service is blocked cannot make feedforward control of the fault in time, which will expand the scale of the fault and cause cascading failure. The possibility of causing cascading failure directly reflects the importance of the service function chain SFC that carries the corresponding information. In a preferred embodiment provided by the present invention, the importance of SFC is calculated based on the information physical sensitivity of the power system and the cascading fault indication coefficient of the transmission line. The specific calculation steps include:

According to the mapping relationship between information quantity and physical quantity in the power system, the information-physical sensitivity of the power system is calculated;

According to the limit power proximity and power fluctuation amplitude of the transmission line in the power system, the cascading fault indication coefficient of the transmission line is calculated.

Considering the deep coupling between power system and communication system under the current trend of digital development, the process of cascading failure caused by communication failure is actually a cyber-physical interaction process. In order to quantify the impact of these blocked SFCs on the power system, this embodiment adopts a cyber-physical sensitivity analysis method to characterize the impact of information flow on physical state variables. Specifically, cyber-physical sensitivity can be obtained by the mapping relationship between information quantity and physical quantity change:

In the formula, For the The vector of physical state variables for each control cycle, is the vector of information variables, is the vector of physical side control variables, is the vector of information side control variables, is the sensitivity of the information variable to the physical state variable, To control the relationship matrix, is the optimization function mapping from measured variables to controlled variables.

Since the importance of the physical state variables themselves also varies during the cyber-physical interaction process, the importance of calculating the SFC should consider both the cyber-physical sensitivity and the importance of the physical state variables themselves. Therefore, this embodiment defines the cascading fault indication coefficient CFI to measure the risk of cascading failures in each branch of the power system affected by the information fault, so as to describe the importance of the physical state variables themselves.

When a power grid failure occurs and emergency control services are lost, load transfer within the system will cause overloads on some lines. Since the power transmission limits of each transmission line are different, lines with lower transmission limits are not necessarily overloaded because they share the power of the failed line. On the contrary, some lines with higher power transmission limits but heavy loads are more prone to failure. Therefore, the potential failure risk of a line can be measured by calculating the degree of proximity of the line transmission power before the failure to the line transmission limit.

Transmission lines The degree of approach of the power to the line transmission limit during normal transmission, i.e., the limit power proximity, is recorded as :

In the formula, Indicates the transmission line before the fault The power flowing through Indicates transmission line The reference power value, Indicates transmission line The power transfer limit is Represents the total number of transmission lines.

After a fault occurs, the power of the failed line will be redistributed in the network. For transmission lines Power fluctuation range after power flow redistribution:

In the formula, Indicates transmission line Transmission line after fault The power fluctuation caused by

in, is the branch breaking distribution factor, is the generator transmission power transfer distribution factor, Before the fault The power flowing through To be hindered The absolute value of the power at the destination node.

After obtaining the above two parameters, the transmission line Cascading fault indication factor It can be defined as:

in, is the balancing factor.

By integrating the indicators of line power before and after the fault, It can reflect the possibility of cascading failures in normal lines after an information-physical coupling failure occurs. The larger the value, the more likely the line is to have a cascading failure. That is, the present invention uses CFI to measure the importance of the physical state variable itself.

After calculating the physical sensitivity of the information and the importance of the physical state variables themselves, The importance of function chain can be defined as:

In the formula, represents the function chain importance of the kth service function chain, represents the information-physical sensitivity of the kth service function chain, Indicates the fault scenario The probability of occurrence, the failure scenario of the power communication network can be expressed as , S represents the uncertainty set of the fault matrix of the communication network, Indicates a failure scenario Transmission lines Cascading fault indication factor.

At present, the traditional risk balance strategy is to divide the information business into S types, among which: , Indicates the physical link Whether a failure has occurred, but this method ignores the fact that information belonging to the same business type also has different importance. Because the importance of different nodes to the power system in actual power grid operation is different, the importance of information flows of these nodes also differs. Therefore, the present invention proposes a more targeted risk balancing strategy based on the above-mentioned SFC importance, and applies it to the SFC routing strategy.

Traditional risk balancing strategies link The risk is defined as the product of the service importance and the link failure rate. In this embodiment, in order to accurately identify different information under the same service type, the SFC importance is used to replace the importance divided according to the service type. Therefore, the improved link risk value can be defined as:

In the formula, The length is Physical link When a physical link carries multiple SFCs, its risk value is the sum of the risk values of each SFC:

Furthermore, in this embodiment, the link risk value is constructed in the form of a weight, so the link Risk routing weight value is defined as:

In the formula, Indicates the physical link The risk routing weight value is Indicates the physical link Length, represents the link availability per unit length, Indicates the physical link The maximum risk value that can be carried, Indicates transmission line Physical Link The link risk value.

When the risk value of the link increases, the corresponding weight will increase significantly. Therefore, the risk routing weight value can sensitively reflect the changes in the line status.

Like traditional networks, network function virtualization NFV networks also face the problem of information transmission reliability. Since virtual network functions VNFs are executed on virtualized platforms through software, this form has a higher risk of failure compared to dedicated hardware. An effective way to ensure reliability is to provide redundant backup for SFC. When the main VNF instance fails, the backup will be activated. Therefore, the present invention proposes a routing strategy SFC-RS for the service function chain. By considering the joint problem of reliable deployment of the main and backup routes of SFC and network risk balance, and introducing the influence of uncertainty in fault scenarios, the routing strategy of the power communication service function chain affected by cascading failures of the power system is optimized, so as to achieve the purpose of reducing the centralized distribution of high-importance information in the local topology of the network while improving the robustness of information transmission, thereby avoiding the situation where a single failure deteriorates into a cascading failure due to obstruction of communication services.

In this embodiment, by constructing a service function chain routing optimization model, an optimal routing strategy that considers the impact of cascading failures and risk balancing strategy is obtained. The specific steps of constructing the routing optimization model include:

Using the risk routing weight value as the weight value of the routing decision variable, and establishing a first objective function according to the function chain importance;

Determine the interruption indication coefficient of the service function chain according to the uncertain variable of the link failure, and establish a second objective function according to the importance of the function chain;

Establishing a routing optimization objective function according to the first objective function and the second objective function;

Establishing constraints of the service function chain according to the virtual network function node requirements, bandwidth resource requirements and routing selection requirements of the service function chain;

The routing optimization objective function and the constraint conditions are used as a service function chain routing optimization model.

In this embodiment, the SFC routing deployment with the improved risk balancing strategy is first modeled as a mathematical optimization problem. Based on the model structure of the general expression for representing the function chain and its constituent VNFs, a binary variable is defined. To represent the mth virtual network function Whether it is deployed on physical node i. When the value is 1, it indicates Deployed on physical nodes Otherwise, it is 0; binary variables are also defined To represent the logical node Whether the mth virtual network function Processing, when the value is 1, it indicates a logical node Depend on Processing, otherwise 0; In addition, the definition of binary variables and , respectively Primary and backup logical links Whether a physical link is required , the value is 1 when needed, otherwise it is 0.

In order to achieve risk balance, in this embodiment, the risk routing weight value As decision variables and The weight of the virtual link is dynamically updated according to the network status. The carrying relationship is constructed to construct the first objective function as follows:

In the formula, K represents the total number of service function chains SFC, represents the first threshold, where It is defined as a small value to ensure that the objective function prioritizes the main route.

Considering the uncertain impact of failures, this embodiment also introduces an uncertain scenario set of link failures as an uncertainty factor to improve the robustness of information transmission. Therefore, the second objective function is defined as:

In the formula, Indicates the physical link status, Indicates the kth service function chain based on the physical link The interruption indication coefficient of the state is 1 when interrupted, otherwise it is 0. The value of depends on the uncertain variable :

Specific, uncertain variables Indicates the physical link The status of the link is 1, otherwise it is 0. Each fault scenario corresponds to a specific set of ; Auxiliary variables and Used to represent Whether the primary and backup routes are interrupted, 1 if interrupted, otherwise 0; is a sufficiently large value.

Based on the above two objective functions, the SFC-RC routing strategy proposed in the present invention can be constructed as a two-stage robust optimization problem, and its objective function can be expressed as:

In the formula, represents the second threshold, S represents the uncertainty set of the fault matrix of the communication network, here, It is defined as a small value to prioritize the robustness goal.

Furthermore, according to the requirements of the NFC network nodes, bandwidth resources, and route selection, the SFC needs to be constrained. The steps for constructing the constraint conditions include:

Establish node constraints based on the uniqueness of node deployment of virtual network functions in the service function chain;

Establish bandwidth resource constraint conditions based on the maximum bandwidth resource of the service function chain;

Establish access sequence constraints based on the node access sequence requirements of the selected routes in the service function chain;

Establish routing topology constraints based on the network topology requirements of the routes selected in the service function chain.

In this embodiment, based on the network function virtualization NFC network of the power system, each VNF should be uniquely deployed on a physical node and Each VNF corresponds to at most one logical node. The node constraint condition based on this requirement can be expressed as:

In the formula, Indicates the mth virtual network function Whether it is deployed on physical node i, M represents the set of virtual network functions VNF in the communication network, represents a collection of physical nodes in a communication network, Represents a logical node Whether the mth virtual network function deal with, Represents the kth service function chain A collection of nodes in a logical topology.

For physical links ,The bandwidth resources occupied by SFC cannot exceed its available bandwidth: ,Therefore, the bandwidth resource constraint can be expressed as:

In the formula, Indicates the physical link of available bandwidth.

For each , it is necessary to ensure that the selected path accesses the included VNFs in a specific order. Therefore, the access order constraint can be defined as:

Take the main logical routing as an example. When it is 1, it means that the information flows from the virtual node Flow to virtual node , in this constraint, when the path is selected, the starting node of this path The corresponding VNF is accessed first, and only the node As the next path Therefore, when a path is determined, it can always be guaranteed that the starting node of the path is visited first, so as to ensure the access order of VNF.

In addition, it should be ensured that the selected physical links are connected end to end in the network topology, and the primary and backup paths of the SFC should not overlap. Therefore, the routing topology constraints can be defined as:

In the formula, represents the destination node of the kth service function chain, Represents the logical node in the kth service function chain and logical nodes Does the main logical link between the two need to pass through the physical link? , Represents the logical node in the kth service function chain and logical nodes Do the backup logical links between the two need to pass through the physical link? .

Regarding the constraints to ensure that the link ends and is connected, for the source node , the information flow it sends has only out-degree but no in-degree. For information flow k, its out-degree at the source node is 1; similarly, at the destination node , the information flow has only in-degree but no out-degree, and its out-degree is -1; for the intermediate nodes, the out-degree = in-degree, so the value is 0. When the information flow meets the in-degree requirements of each node type, the links through which the information flow passes are naturally connected, thus forming a self- arrive The full path of .

By using the above constraints as the constraints of the objective function, we can get a complete service function chain routing optimization model. Then, by using the solver to solve the routing optimization model, we can get the optimal value of the decision variable, that is, the optimal routing strategy of the power system. The specific solution process can refer to the conventional model solution process, which will not be repeated here.

The routing strategy SFC-RS of the service function chain provided by the present invention dynamically updates the weights according to the improved risk balancing strategy and network changes, and preferentially allocates high-importance SFCs to network paths with low risk values, thereby avoiding the concentration of high-importance information in the local topology of the network. At the same time, uncertain disturbances are introduced into the model, and the robustness of the routing mechanism is improved by finding solutions to the most serious fault scenarios, thereby achieving self-adaptation under fault scenarios.

The effect of the routing optimization method provided by the present invention is verified by simulation experiments. In the simulation experiments, the routing optimization method proposed by the present invention is applied to the IEEE 30-node system and the corresponding communication network, and the simulation results are analyzed. Specifically, according to the structure of the IEEE 30-node system, the communication network topology of the system is shown in Figure 2, wherein the IEEE 30-node system includes n nodes, represented by 1 to 30 respectively, and the number of nodes in the corresponding communication network topology is 21, represented by S1 to S20 and CC. According to the connectivity of the network, the node CC with the highest node degree is selected as the control master station. Use Python 3.7.0 to perform simulation on a laptop equipped with an AMD Ryzen 7 5800H processor and 16GB of memory, and call Gurobi 9.5.2 to solve the optimization problem. Convergence threshold of the solver and small values Set to There are 4 types of VNFs in the network, the length of SFC is randomly distributed between 2 and 4, the same type of VNF is generated only once in each SFC, and the link bandwidth is set to 30. Since the main goal of SFC-RS is to affect the operation of the power system by regulating the SFC path, the communication delay problem is not considered here, and VNFs are allowed to be pre-deployed on communication nodes, which represents the usage scenario of shared communication infrastructure.

According to the requirements of relevant standards, the production implementation control service must meet the planning target of N-2. In this experiment, the power communication network failure is preset as N-2 failure, and the power grid failure is preset as N-1 failure. When the physical links carrying the SFC main and backup paths are interrupted, the SFC transmission is blocked. In actual power grid operation, the existing emergency control service routing optimization is mainly based on the shortest path model and is manually designed according to the experience of the operating personnel. Therefore, this experiment compares the effect of applying the shortest path routing algorithm SPRM in the IEEE 30-node system and the routing strategy SFC-RS of the service function chain provided by the present invention.

Figure 3 shows the information transmission reachability of the two methods in response to the N-2 failure of the communication system. Analysis of Figure 3 shows that in the most serious fault scenario, SFC-RS has 3 nodes disconnected, and the maximum number of disconnected nodes of SPRM reaches 5. When dealing with N-2 faults, SFC-RS only reaches the maximum number of node disconnections in three scenarios, and the number of disconnected nodes in the remaining scenarios does not exceed 2, which is much less than the number of scenarios in which SPRM loses more than two nodes. In addition, when more than 3 SFCs are blocked, that is, the number of disconnected nodes is greater than 3, at least 15% of the control services in the system will fail. When SPRM is applied, if 4 and 8 fail at the same time, 7 and 16 fail at the same time, or 11 and 16 fail at the same time, the service interruption rate will be as high as 25%, which seriously threatens the safe and stable operation of the power system.

Figure 4 shows the load loss rates of the two methods when dealing with the N-2 failure of the communication system. The average and maximum values when SFC-RS is applied are lower than those of SPRM. From the effects of information reachability and load loss rate, it can be seen that SFC-RS effectively improves the robustness of the system.

Figure 5 shows the SFC risk distribution of each physical link when two algorithms are applied in the IEEE 30-node communication network topology. Comparing Figure 5 (a) and Figure 5 (b), it is found that when SPRM is used, the information is mainly concentrated on links 7 and 8, and the risk value of link 8 is much higher than that of other links. This is because SPRM preferentially selects the path with the smallest cost, so that the information is gathered on the shortest path, resulting in too concentrated distribution of SFC. In contrast, since SFC-RS uses the risk routing weight as the weight and dynamically updates the risk weight in the network after allocating a path to the previous SFC, it will look for the path with the smallest risk weight when allocating a path to the new SFC, rather than the path with the smallest cost, to avoid the information being concentrated in a local area or a certain line of the network.

However, compared with (b) in FIG. 5 , the overall network risk value of (a) in FIG. 5 is slightly higher. This is because SFC-RS is essentially also a type of shortest path algorithm. The difference is that this method seeks the minimum link risk, which results in a single SFC-RS allocated path being longer than the path when SPRM is applied, thereby increasing the overall risk value. However, the original intention of SFC-RS is to reduce the possibility of a single fault on the power grid side deteriorating into a cascading fault due to a communication fault by effectively regulating the information side. For this goal, the excessively high risk value of a local area or a single line is more dangerous than a slight increase in the overall risk of the network. If an information fault occurs, it will have a serious impact on the system. SFC-RS reduces the risk value of a local area or line, that is, the algorithm of the present invention slightly increases the average risk of the network in exchange for the robustness of communication and the balance of network risk distribution, avoiding a significant increase in the risk value of a single point and inducing more serious consequences after an information fault occurs. From the actual effect, such a sacrifice is very worthwhile. The comparison between (c) in FIG. 5 and (d) in FIG. 5 further confirms this fact.

In order to verify the effect of SFC-RS in reducing the probability of cascading failures in power systems, combined with the application background, the present invention borrows the concept of information entropy and proposes a weighted fault risk entropy to evaluate the optimization effect.

Calculate the sum of the importance of the blocked SFCs under the N-2 fault scenario, and give the importance sequence of the blocked SFCs ,use Indicates importance The number of fault scenarios is in the range Probability for:

Then the weighted risk distribution entropy is defined as:

in, For importance The average importance value of all scenes, assuming Include There are several fault scenarios, and I _mn represents the importance of the nth fault scenario. Then:

According to the definition of entropy, the fewer high-risk scenarios there are in the system, the smaller the corresponding entropy value is, which means that the probability of causing cascading failures in the system is lower.

Select the blocked SFC, and its importance sequence is ,By traversing the N-2 fault scenarios of the IEEE30-node system, the importance values corresponding to the blocked SFCs of the two methods in these fault scenarios are counted, as shown in Figure 6. As can be seen from the figure, due to the consideration of the robustness of information transmission and the improvement of risk balance, the importance of the blocked SFCs when applying SFC-RS is much less than that when applying SPRM.

The weighted risk distribution entropy is used to calculate these data. It can be seen that the entropy value of SFC-RS is 495.057, which is much smaller than the entropy value of SPRM 601.126. This shows that under the same conditions, SFC-RS can effectively reduce the risk of cascading failures caused by the obstruction of emergency control services due to communication failures in the system, thereby proving that SFC-RS can effectively achieve the objectives of the present invention.

This embodiment provides a routing optimization method based on a service function chain. By considering the joint problem of reliable deployment of the main and backup routes of the SFC and network risk balance and introducing the influence of the uncertainty of the fault scenario, a routing strategy based on the service function chain is proposed. The routing strategy of the present invention improves the robustness of information transmission while effectively reducing the concentrated distribution of high-importance information in the local topology of the network, thereby avoiding the situation where a single fault deteriorates into a cascading fault due to the obstruction of communication services.

Referring to FIG. 7 , based on the same inventive concept, a route optimization system based on a service function chain is proposed in a second embodiment of the present invention, including:

The importance calculation module 10 is used to calculate the function chain importance of the service function chain according to the information physical sensitivity of the power system and the cascade fault indication coefficient of the transmission line;

The weight value calculation module 20 is used to calculate the link risk value of the physical link according to the importance of the function chain and the availability of the physical link, and calculate the risk routing weight value of the physical link according to the link risk value and the link length;

The routing optimization module 30 is used to establish a service function chain routing optimization model according to the function chain importance and the risk routing weight value, and solve the service function chain routing optimization model to obtain the optimal routing strategy of the power system.

In a preferred embodiment, the present invention further comprises:

The importance calculation module 10 is also used to calculate the information-physical sensitivity of the power system based on the mapping relationship between the information quantity and the physical quantity in the power system; and calculate the cascading fault indication coefficient of the transmission line based on the limit power proximity and power fluctuation amplitude of the transmission line in the power system.

In another preferred embodiment, the present invention further comprises:

The routing optimization module 30 is also used to use the risk routing weight value as the weight value of the routing decision variable, and establish a first objective function according to the importance of the function chain; determine the interruption indication coefficient of the service function chain according to the uncertain variables of the link failure, and establish a second objective function according to the importance of the function chain; establish a routing optimization objective function according to the first objective function and the second objective function; establish constraints of the service function chain according to the virtual network function node requirements, bandwidth resource requirements and route selection requirements of the service function chain; and use the routing optimization objective function and the constraints as a service function chain routing optimization model.

The technical features and technical effects of the route optimization system based on the service function chain proposed in the embodiment of the present invention are the same as those of the method proposed in the embodiment of the present invention, and are not described in detail here. Each module in the route optimization system based on the service function chain can be implemented in whole or in part by software, hardware, and a combination thereof. Each of the above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

In summary, the embodiment of the present invention proposes a routing optimization method and system based on a service function chain. The method calculates the function chain importance of the service function chain according to the information physical sensitivity of the power system and the cascading fault indication coefficient of the transmission line; calculates the link risk value of the physical link according to the function chain importance and the availability of the physical link, and calculates the risk routing weight value of the physical link according to the link risk value and the link length; establishes a service function chain routing optimization model according to the function chain importance and the risk routing weight value, and solves the service function chain routing optimization model to obtain the optimal routing strategy of the power system. The present invention introduces the influence of the uncertainty of the fault scenario by considering the joint problem of the reliable deployment of the main and backup routes of the SFC and the network risk balance, and proposes a routing strategy based on the service function chain. The routing strategy of the present invention preferentially allocates the high-importance SFC to the network path with low risk value, avoiding the concentration of high-importance information in the local topology of the network. At the same time, by finding the solution under the most serious fault scenario, the robustness of the routing mechanism is improved, and the adaptation under the fault scenario is realized, thereby avoiding the situation where a single fault deteriorates into a cascading fault due to the obstruction of the communication service.

Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

The above-mentioned embodiments only express several preferred implementation modes of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in the technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be based on the protection scope of the claims.

Claims (6)

1. A route optimization method based on a service function chain, characterized by comprising: According to the mapping relationship between information quantity and physical quantity in the power system, the information-physical sensitivity of the power system is calculated; Calculate the cascading fault indication coefficient of the transmission line according to the limit power proximity and power fluctuation amplitude of the transmission line in the power system; According to the information physical sensitivity of the power system and the cascading fault indication coefficient of the transmission line, the function chain importance of the service function chain is calculated; Calculating a link risk value of the physical link according to the importance of the function chain and the availability of the physical link, and calculating a risk routing weight value of the physical link according to the link risk value and the link length; According to the importance of the function chain and the risk routing weight value, a service function chain routing optimization model is established, and the service function chain routing optimization model is solved to obtain an optimal routing strategy for the power system; The step of establishing a service function chain routing optimization model according to the function chain importance and the risk routing weight value comprises: Using the risk routing weight value as the weight value of the routing decision variable, and establishing a first objective function according to the function chain importance; Determine the interruption indication coefficient of the service function chain according to the uncertain variable of the link failure, and establish a second objective function according to the importance of the function chain; Establishing a routing optimization objective function according to the first objective function and the second objective function; Establishing constraints of the service function chain according to the virtual network function node requirements, bandwidth resource requirements and routing selection requirements of the service function chain; The routing optimization objective function and the constraint conditions are used as a service function chain routing optimization model. 2. The route optimization method based on the service function chain according to claim 1 is characterized in that the step of establishing the constraint conditions of the service function chain according to the virtual network function node requirements, bandwidth resource requirements and route selection requirements of the service function chain comprises: Establish node constraints based on the uniqueness of node deployment of virtual network functions in the service function chain; Establish bandwidth resource constraint conditions based on the maximum bandwidth resource of the service function chain; Establish access sequence constraints based on the node access sequence requirements of the selected routes in the service function chain; Establish routing topology constraints based on the network topology requirements of the routes selected in the service function chain. 3. The route optimization method based on service function chain according to claim 1 is characterized in that the information physical sensitivity is expressed by the following formula: In the formula, A vector representing the physical state variables; represents a vector of information variables, represents the vector of physical side control variables, represents the vector of information-side control variables; The limit power proximity is expressed by the following formula: In the formula, Indicates the transmission line before the fault The power flowing through Indicates transmission line The reference power value, Indicates transmission line The power transfer limit is represents the total number of transmission lines; The power fluctuation amplitude is expressed by the following formula: In the formula, Indicates transmission line Transmission line after fault Power fluctuation caused by The cascade fault indication coefficient is expressed by the following formula: In the formula, Indicates transmission line The cascading fault indication coefficient, represents the equilibrium factor; The following formula is used to express the importance of the function chain: In the formula, represents the function chain importance of the kth service function chain, represents the information-physical sensitivity of the kth service function chain, Indicates the fault scenario The probability of occurrence, S represents the uncertainty set of the fault matrix of the communication network, Indicates a failure scenario Transmission lines Cascading fault indication factor. 4. The route optimization method based on service function chain according to claim 1 is characterized in that the link risk value is expressed by the following formula: In the formula, Indicates the physical link The link risk value is Indicates the physical link The availability rate, Represents a physical node and physical nodes The physical link between represents the function chain importance of the kth service function chain; The risk routing weight value is expressed by the following formula: In the formula, Indicates the physical link The risk routing weight value is Indicates the physical link Length, represents the link availability per unit length, Indicates the physical link The maximum risk value that can be carried, Indicates transmission line Physical Link The link risk value. 5. The route optimization method based on service function chain according to claim 1 is characterized in that the first objective function is expressed by the following formula: In the formula, represents the function chain importance of the kth service function chain, K represents the total number of service function chains, Represents a physical node and physical nodes The physical link between Represents a set of physical links. Indicates the physical link The risk routing weight value is Represents the logical node in the kth service function chain and logical nodes Does the main logical link between the two need to pass through the physical link? , Represents the logical node in the kth service function chain and logical nodes Do the backup logical links between the two need to pass through the physical link? , Represents a logical node and logical nodes The logical link between represents the first threshold; The second objective function is expressed by the following formula: In the formula, Indicates the physical link status, Indicates the kth service function chain based on the physical link The interruption indication coefficient of the state; The routing optimization objective function is expressed by the following formula: In the formula, represents the second threshold, S represents the uncertainty set of the fault matrix of the communication network; The constraint condition is expressed by the following formula: In the formula, Indicates whether the mth virtual network function is deployed on the physical node i, M represents the set of virtual network functions in the communication network, represents a collection of physical nodes in a communication network, Represents a logical node Whether it is handled by the mth virtual network function, represents the node set in the logical topology of the kth service function chain, represents the set of links in the logical topology of the kth service function chain, Indicates the physical link Available bandwidth, represents the source node of the kth service function chain, represents the destination node of the kth service function chain, Represents the logical node in the kth service function chain and logical nodes Does the main logical link between the two need to pass through the physical link? , Represents the logical node in the kth service function chain and logical nodes Do the backup logical links between the two need to pass through the physical link? . 6. A route optimization system based on a service function chain, characterized by comprising: The importance calculation module is used to calculate the information-physical sensitivity of the power system according to the mapping relationship between the information quantity and the physical quantity in the power system; calculate the cascading fault indication coefficient of the transmission line according to the limit power proximity and power fluctuation amplitude of the transmission line in the power system; calculate the function chain importance of the service function chain according to the information-physical sensitivity of the power system and the cascading fault indication coefficient of the transmission line; A weight value calculation module, used to calculate the link risk value of the physical link according to the importance of the function chain and the availability of the physical link, and to calculate the risk routing weight value of the physical link according to the link risk value and the link length; A routing optimization module, used to establish a service function chain routing optimization model according to the function chain importance and the risk routing weight value, and solve the service function chain routing optimization model to obtain an optimal routing strategy for the power system; The routing optimization module is also used to use the risk routing weight value as the weight value of the routing decision variable, and establish a first objective function according to the function chain importance; determine the interruption indication coefficient of the service function chain according to the uncertain variable of the link failure, and establish a second objective function according to the function chain importance; establish a routing optimization objective function according to the first objective function and the second objective function; establish constraints of the service function chain according to the virtual network function node requirements, bandwidth resource requirements and route selection requirements of the service function chain; and use the routing optimization objective function and the constraints as a service function chain routing optimization model.