Active distribution network routing dispatching and dynamic selection method and system driven by business needs

Abstract

The present invention discloses a method and system for active distribution network routing scheduling and dynamic selection driven by business demand, and belongs to the field of active distribution network control technology. It includes evaluating the physical importance index and information importance index of each business flow according to the physical impact and information demand of each business flow, and comprehensively considering to obtain the comprehensive importance index, sorting to obtain the sorted business flow set; quantifying the real-time performance and reliability of the routing link, and generating a routing distance matrix; inputting the sorted business flow set and the routing distance matrix into the routing scheduling optimization model, and generating the routing scheduling scheme of the overall business flow based on the principle of matching the business flow importance and routing path performance; the present invention comprehensively considers the physical flow importance index and the information importance index to obtain the comprehensive importance index, which solves the problem of incomplete business flow evaluation in the information-physical coupling scenario.

Description

Technical Field

[0001] The present invention relates to a method and system for active distribution network routing scheduling and dynamic selection driven by business demand, and belongs to the technical field of active distribution network control. Background Art

[0002] In recent years, with the increasing prominence of energy shortages and environmental problems, renewable energy power generation technologies have developed rapidly. A large number of renewable energy power generation devices have been connected to the power grid in the form of distributed generation (DG). While alleviating the pressure on the power grid, it has also brought challenges to the distribution network in terms of power quality, planning, operation, and control. In order to meet the above challenges, people have adopted various active control strategies to alleviate these problems, making the traditional distribution network gradually evolve into an active distribution network (ADN). The active distribution network adopts a mode of actively managing distributed power sources, energy storage equipment, and customer bidirectional loads. It is a public distribution network with a flexible topology structure. The control technology increases and complicates the dependence of the physical system on the information system, making the active distribution network a cyber-physical power system (CPPS).

[0003] There are many control services in the active distribution network, such as voltage regulation, frequency regulation, demand response, etc. These services are generated at the control center, and are dispatched by the control center through the information system to control the physical system in real time, thereby maintaining the safe and stable operation of the active distribution network and providing users with high-quality electricity supply, providing reliable protection for the national economy while meeting users' electricity needs.

[0004] However, on the one hand, due to the different specific requirements of multiple control services in active distribution networks (such as emergency services such as relay protection and safety control pay more attention to real-time performance, while economic services such as power consumption information collection and office automation pay more attention to reliability), and the large number of services, how to coordinate the routing and forwarding of different business flows based on business needs and provide appropriate routing paths for business flows with different needs, so as to meet the real-time and reliability of multiple business flow transmission, has become an urgent problem to be solved. On the other hand, the stability of the physical system and the service quality of users depend on the real-time and accurate issuance of control instructions. As a typical CPPS, the data transmission performance of the active distribution network strongly depends on the normal operation of the information system. Once the information system fails, the control instructions cannot be issued in time, which will seriously affect the stability of the physical system and even cause losses to the national economy. Therefore, how to ensure the real-time issuance of control instructions in the case of sudden failure of some routing communication links due to network attacks or natural disasters is crucial to improving the robustness of the active distribution network and ensuring the safe and stable operation of the system.

[0005] Therefore, the present invention designs a method and device for active distribution network routing scheduling and dynamic selection driven by business needs, which can not only meet the different needs of diversified regulation services and generate the optimal traffic scheduling plan to ensure real-time and reliable transmission of traffic, but also can cope with sudden failures of communication links and quickly reselect routing paths. Summary of the invention

[0006] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method and system for active distribution network routing scheduling and dynamic selection driven by business needs, so as to solve the problem of how to ensure the real-time and reliable transmission of multiple business traffic according to different regulation business needs in the cyber-physical coupling scenario and how to quickly reconstruct the routing path when a link suddenly fails;

[0007] In order to achieve the above purpose / solve the above technical problems, the present invention is implemented by adopting the following technical solutions:

[0008] A first aspect: a method for active distribution network routing scheduling and dynamic selection driven by business demand, the method comprising:

[0009] Evaluate the physical importance index and information importance index of each service flow according to the physical impact and information requirements of each service flow;

[0010] Taking the physical importance index and the information importance index into consideration, we can get the comprehensive business flow importance index;

[0011] Sorting the importance indexes of the comprehensive business traffic to obtain a sorted business traffic set;

[0012] Quantify the real-time performance and reliability of each routing link and generate a routing distance matrix;

[0013] The sorted service flow set and the routing distance matrix are input into the pre-built routing scheduling optimization model, and the optimal routing scheduling solution for the overall service flow is generated based on the service flow importance and routing path performance matching principle;

[0014] The lower-level routers schedule traffic according to the optimal routing scheduling solution.

[0015] Optionally, the evaluating the physical importance index and the information importance index of each service flow according to the physical impact and information demand of each service flow sent includes:

[0016] The improved gravity centrality method is used to evaluate physical importance. The original gravity centrality formula is:

[0017] ;

[0018] in, Expressed as the obtained node influence index, Represents a physical node The k-shell value of Represents a physical node The k-shell value represents its influence in the network topology. Represents a physical node The set of neighbor nodes of Represents a physical node and Topological distance;

[0019] By improving formula (1), we get the improved gravity centrality formula:

[0020] ;

[0021] in, Represents the obtained node physical importance value, Represents the normalized traffic to the physical node The power impact generated, Representation Node A comprehensive indicator of structural influence and physical influence in network topology. Represents the normalized traffic to the physical node The power impact generated, Representation Node A comprehensive indicator of structural influence and physical influence in network topology;

[0022] The method for normalizing all variables that need to be normalized is shown in formula (3):

[0023] ;

[0024] in, represents the normalized variable value, is the variable to be normalized, For variables The minimum value of For variables The maximum value of is the correction parameter;

[0025] The improved gravity centrality formula (2) is used to express the business flow The physical importance of , see formula (4):

[0026] ;

[0027] Considering the specific real-time and reliability requirements of different business flows, the flow information importance formula (5) is constructed:

[0028] ;

[0029] in, Indicates the service flow number. An indicator of the importance of information in traffic. represents the deadline requirement of normalized traffic, Indicates the bit error rate requirement of the normalized traffic. and is the weight coefficient, satisfying .

[0030] Optionally, the physical importance index and the information importance index are comprehensively considered to obtain a comprehensive service flow importance index, including:

[0031] Based on the physical importance of traffic (4) and traffic information importance (5), given the business traffic The cyber-physical comprehensive importance index (6):

[0032] ;

[0033] in, It represents the comprehensive importance of each flow considering both physical importance and information importance. and is the weight coefficient, satisfying ;

[0034] According to the comprehensive importance of each flow Treat the scheduled traffic set The traffic in is arranged in descending order to obtain the comprehensive business traffic importance index.

[0035] Optionally, quantifying the real-time performance and reliability of each routing link and generating a routing distance matrix includes:

[0036] The actual router is abstracted as a communication node, and the link between two communication nodes is abstracted as an edge. The communication topology is expressed as , represents the set of communication nodes, Indicates Communication nodes, represents a collection of communication links, Representation Node and The communication links between them are used to obtain the adjacency matrix of the communication network according to the communication network topology. , when the communication node and When direct communication is possible, ,otherwise, ; Representation Node and The connectivity between Represents the matrix dimension;

[0037] Each communication link Has link status attributes: link available bandwidth , Link transmission delay and link transmission bit error rate , considering the transmission delay and bit error rate in the link state to evaluate each routing link, the evaluation method is shown in formula (7):

[0038] ;

[0039] in, represents the evaluation value of each link, represents the link transmission delay normalized by formula (3), represents the link bit error rate normalized by formula (3), and is the weight coefficient, satisfying , Represents infinity, The smaller the value, the better the performance of the link;

[0040] According to formula (7) and the communication network adjacency matrix , calculate the routing distance matrix .

[0041] Optionally, the method further comprises:

[0042] When a flow is scheduled, a link failure occurs at the communication node where it is located, causing the original routing path to be unusable. At this time, the current communication node will obtain real-time link failure information and Consider it unreachable and modify the adjacency matrix The corresponding element and the corresponding elements in the routing distance matrix ;

[0043] Reselect the optimal path based on the importance of business traffic and the performance of the routing path, thereby achieving rapid reconstruction of the faulty path;

[0044] Among them, considering that the original routing scheduling result is not completely unusable, and the dynamic selection of the path is only performed at the faulty link, it is impossible to form a complete routing path. The optimal scheduling result and the reconstruction result are combined to reconstruct a complete routing path from the service traffic source node to the destination node, which is fed back to the control center and the optimal scheduling routing table is updated. .

[0045] Optionally, the routing scheduling optimization model aims to minimize the average transmission delay and bit error rate of all traffic, and is constructed with the deadline, bit error rate, communication bandwidth and node queue of the service traffic as constraints.

[0046] Optionally, the objective function of the routing scheduling optimization model is:

[0047] ;

[0048] in, Indicates the number of flows in the set of flows to be scheduled. Indicates service traffic The total transmission delay on a complete routing path, Indicates service traffic The total bit error rate on a complete routing path, and is the weight coefficient, satisfying ;

[0049] The total delay and total bit error rate of the service traffic on a complete routing path are (9) and (10) respectively:

[0050] ;

[0051] ;

[0052] in, Indicates a link, Indicates service traffic The complete routing path of Indicates The traffic to be scheduled, is the traffic set to be scheduled;

[0053] Service traffic transmission delay constraint (11): Each flow must arrive within its deadline;

[0054] ;

[0055] in, is the cut-off time for business traffic;

[0056] Service flow bit error rate constraint (12): Each flow must ensure that its total bit error rate does not exceed the maximum allowed bit error rate;

[0057] ;

[0058] in, is the cut-off time for business traffic;

[0059] Communication link bandwidth constraint (13): The total traffic accommodated by each link does not exceed the available link bandwidth;

[0060] ;

[0061] in, For business traffic Whether it passes through the communication link The indicator factor is Via communication link hour, ,otherwise, , The bandwidth required for traffic transmission, Indicates link Total bandwidth;

[0062] Communication node queue constraint (14): The traffic queue length in each communication node does not exceed the maximum allowed queue value;

[0063] ;

[0064] in, For business traffic Whether it passes through the communication node The indicator factor is Through the communication node hour, ,otherwise, , For communication nodes The maximum flow storage queue.

[0065] Optionally, the step of inputting the sorted service flow set and the routing distance matrix into a pre-built routing scheduling optimization model, and generating an optimal routing scheduling solution for the overall traffic based on the service flow importance and routing path performance matching principle, includes:

[0066] For each service flow Based on the principle of matching business traffic importance and routing path performance, the shortest path is solved by the Floyd algorithm to generate the current minimum distance routing path for high-importance business traffic;

[0067] The constraints of the routing path that generates the current minimum distance are judged, and the paths that meet the constraints are retained in the routing table, and the routing distance matrix is ​​updated. Otherwise, the traffic scheduling is considered to have failed, until all the traffic in the traffic set is scheduled and the optimal routing scheduling solution is obtained.

[0068] The second aspect: active distribution network routing dispatching and dynamic selection system driven by business needs, including:

[0069] The traffic importance evaluation module is configured to: evaluate the physical importance index and information importance index of each service flow according to the physical impact and information demand of each service flow;

[0070] The fusion module is configured to: comprehensively consider the physical traffic importance index and the information importance index to obtain a comprehensive business traffic importance index;

[0071] The service flow set module is configured to: sort the service flows according to the comprehensive service flow importance index to obtain a sorted service flow set;

[0072] The routing link quantification module is configured to: quantify the real-time performance and reliability of each routing link and generate a routing distance matrix;

[0073] The route optimization generation module is configured to: input the sorted service flow set and the route distance matrix into a pre-built route scheduling optimization model, and generate an optimal route scheduling solution for the overall service flow based on the service flow importance and the route path performance matching principle;

[0074] The traffic scheduling module is configured to: be used by the lower-layer router to perform traffic scheduling according to the optimal routing scheduling solution.

[0075] Optionally, the system further includes:

[0076] The dynamic routing selection module is configured to: when a link failure occurs, dynamically select a routing path based on real-time link failure information and an original optimization solution.

[0077] Compared with the prior art, the present invention has the following beneficial effects:

[0078] 1. The present invention proposes a method for evaluating the comprehensive information-physical importance of business traffic, which comprehensively considers the physical traffic importance index and the information importance index to obtain a comprehensive importance index, thereby solving the problem of incomplete business traffic evaluation in the information-physical coupling scenario;

[0079] 2. In the present invention, a method for optimizing routing based on the principle of "matching service traffic importance and routing path performance" is proposed, which solves the problem that it is difficult to schedule multiple service traffic on demand;

[0080] 3. The present invention proposes a method for dynamic routing selection based on real-time link failure information and the original optimization plan, which can quickly reselect available paths at the faulty link node, and the reconstruction result can meet the transmission requirements of the traffic, solving the problem of how to ensure on-demand transmission of business traffic under sudden link failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 This is a flow chart of an embodiment of the method for active distribution network routing scheduling and dynamic selection driven by business demand of the present invention;

[0082] Figure 2 It is a schematic diagram of the execution flow of the active power distribution network information physical coupling architecture and method of the present invention;

[0083] Figure 3 It is a flow chart of the routing optimization scheduling algorithm of the present invention;

[0084] Figure 4 It is a flow chart of the dynamic routing selection algorithm of the present invention;

[0085] Figure 5 This is a diagram showing the effect of dynamic routing selection and reconstruction according to the present invention. DETAILED DESCRIPTION

[0086] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0087] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and the like are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", and the like may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0088] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood by specific circumstances.

[0089] Embodiment 1, the present invention designs an active distribution network information-physical coupling architecture and method execution flow diagram, including a top-level control center and control terminal, a middle-level routing communication network, and a bottom-level physical power node; the control center obtains the business requirements of the control terminal and the topology and flow information of the routing communication node, and generates the optimal routing scheduling solution; the routing communication node obtains real-time fault link information and performs dynamic selection. The details are as follows:

[0090] like Figure 1-Figure 2 As shown, a method for active distribution network routing scheduling and dynamic selection driven by business demand is disclosed, and the method includes:

[0091] Step 1: According to the physical impact and information requirements of each service flow, the physical importance index and information importance index of each service flow are evaluated;

[0092] Step 2, comprehensively considering the physical importance index and the information importance index to obtain a comprehensive business flow importance index;

[0093] Step 3, sorting according to the comprehensive business flow importance index to obtain a sorted business flow set;

[0094] Step 4, quantify the real-time performance and reliability of each routing link and generate a routing distance matrix;

[0095] Step 5: Input the sorted service flow set and the routing distance matrix into the pre-built routing scheduling optimization model, and generate the optimal routing scheduling solution for the overall service flow based on the service flow importance and routing path performance matching principle;

[0096] Step 6: The lower-layer router performs traffic scheduling according to the optimal routing scheduling solution.

[0097] For step 1, after multiple types of regulation services are generated in the control center, it is necessary to first conduct a comprehensive assessment of the information requirements (real-time and reliability) of different traffic flows and their impact on the physical system. The system objects actually involved in the power regulation service are abstracted into physical nodes with power attributes to generate Take the business traffic as an example, the overall traffic set is recorded as , each service flow to be scheduled It can be expressed as ,in, and They represent the source node and destination node of the traffic respectively. The bandwidth required for traffic transmission, and the traffic has a deadline requirement and bit error rate requirements , The power impact vector generated by the traffic on the physical node , Expressing the The power impact of each physical node, Indicates the comprehensive importance of traffic. Taking into account the above business needs and impacts, the following evaluation process is designed:

[0098] 1) The improved gravity centrality method is used to evaluate physical importance. The original gravity centrality formula is:

[0099] (1);

[0100] in, Represents a physical node The k-shell value of Represents a physical node The set of neighbor nodes of Represents a physical node and Topological distance. Formula (1) can not only reflect the importance of the node itself in the network topology, but also consider the importance of neighboring nodes, which can better reflect the comprehensive importance of the node.

[0101] Since the object of regulation is the physical node, the impact of different business flows on the physical system is considered, and the improved gravity centrality formula (2) is obtained:

[0102] (2);

[0103] in, Represents the normalized traffic to the physical node The resulting power impact not only considers the topological importance of the node, but also the functional importance of the node from the perspective of the actual grid physical system.

[0104] The normalization method is shown in formula (3):

[0105] (3);

[0106] in, To correct the parameters, prevent the value of 0 from appearing after normalization.

[0107] The improved gravity centrality formula (2) is used to express the business flow The physical importance of , see formula (4):

[0108] (4);

[0109] 2) Considering the specific real-time and reliability requirements of different business flows, we construct the flow information importance formula (5):

[0110] (5);

[0111] in, and They represent the deadline requirement and bit error rate requirement of the normalized traffic, respectively. and is the weight coefficient, satisfying .

[0112] (3) Based on the physical importance of traffic (4) and information importance (5), business traffic is given The comprehensive information-physical importance index of is shown in formula (6):

[0113] (6);

[0114] in, and is the weight coefficient, satisfying The physical traffic importance index and the information importance index are considered comprehensively through the weighted sum form of formula (6) to obtain the comprehensive importance index.

[0115] For step 3, based on the comprehensive importance of each flow Treat the scheduling traffic set The traffic in is sorted in descending order ( The larger the value, the more important the business traffic is). The business traffic set is initially generated in the control center. The sorting here is to sort the traffic in the initial traffic set to obtain the sorted business traffic set;

[0116] For step 4, after step 3 is completed, each routing link needs to be The real-time performance and reliability are quantified and the routing distance matrix is ​​generated. The specific process is as follows:

[0117] 1) The actual router is abstracted as a communication node, and the link between two communication nodes is abstracted as an edge. The communication topology can be expressed as , represents the set of communication nodes, Represents the set of communication links. According to the communication network topology, the adjacency matrix of the communication network can be obtained , when the communication node and When direct communication is possible, ,otherwise, .

[0118] 2) Each communication link Link state attributes: Link available bandwidth , Link transmission delay and link transmission bit error rate Since there is no bandwidth indicator in the business traffic evaluation, only the transmission delay and bit error rate in the link status are considered to evaluate each routing link. The evaluation method is shown in formula (7):

[0119] (7);

[0120] in, and They represent the link transmission delay and bit error rate normalized by formula (3), and is the weight coefficient, satisfying , Represents infinity, that is, the communication node and There is no direct communication link between them. The smaller the value, the better the performance of the link. According to formula (7) and the communication network adjacency matrix , calculate the routing distance matrix .

[0121] For the sorted service traffic set generated in step 5 and the routing distance matrix As the input of the routing scheduling algorithm, the routing scheduling plan for the overall traffic is generated in the control center according to the principle of "matching the importance of business traffic and routing path performance". Meeting the different business needs in the active distribution network is the basic condition for maintaining the safe and stable operation of the system. At the same time, in order to further improve the system control performance and user service quality, all traffic should be allocated to the corresponding optimal link based on traffic importance. However, considering the limitations of actual communication environment resources, the above operation cannot be achieved. Therefore, it is necessary to consider the routing optimization scheduling under constraints and use the fast routing algorithm to schedule the traffic. The specific steps are as follows:

[0122] 1) In order to meet the needs of diversified regulation services in active distribution networks, and considering the limited communication link bandwidth resources and communication node storage queue resources in actual communication network scenarios, the routing scheduling optimization model (8)~(14) is constructed with the deadline, bit error rate, communication bandwidth and node queue of the service traffic as constraints, with the goal of minimizing the average transmission delay and bit error rate of all traffic. The objective function is shown in (8):

[0123] (8);

[0124] in, Indicates flow rate The total transmission delay on a complete routing path, Indicates flow rate The total bit error rate on a complete routing path, and is the weight coefficient, satisfying .

[0125] Business traffic The total delay and total bit error rate on a complete routing path are (9) and (10) respectively:

[0126] (9);

[0127] (10);

[0128] in, Indicates flow rate The complete routing path.

[0129] Service traffic transmission delay constraint (11): Each flow must arrive within its deadline:

[0130] (11);

[0131] in, The cut-off time for business traffic.

[0132] Service flow bit error rate constraint (12): Each flow must ensure that its total bit error rate does not exceed the maximum allowed bit error rate:

[0133] (12);

[0134] in, The cut-off time for business traffic.

[0135] Communication link bandwidth constraint (13): The total traffic accommodated by each link does not exceed the available link bandwidth:

[0136] (13);

[0137] Among them, when the business flow Via communication link hour, ,otherwise, , The bandwidth required for traffic transmission, Indicates link The total bandwidth.

[0138] Communication node queue constraint (14): The traffic queue length in each communication node (router) does not exceed the maximum allowed queue value:

[0139] (14);

[0140] Among them, when the business flow Through the communication node hour, ,otherwise, , For communication nodes The maximum flow storage queue.

[0141] 2) In order to ensure the real-time delivery of control business instructions, not only a fast routing path needs to be allocated for traffic, but the control center also needs to quickly adopt a fast routing scheduling algorithm to generate a routing scheduling solution. Therefore, the present invention adopts the Floyd algorithm for routing scheduling.

[0142] The Floyd algorithm is an algorithm that uses the idea of ​​dynamic programming to find the shortest path between multiple source points in a given weighted graph. First, the sorted service flow set generated in step 2 is and the routing distance matrix As the input of Floyd algorithm; secondly, for each business flow According to the principle of "matching the importance of business traffic and the performance of routing paths", the shortest path is solved by the Floyd algorithm, and a routing path with a short distance (high performance) is allocated to the business traffic with high importance; then, the generated routing path is constrained, and the path that meets the constraints is retained in the routing table, and the routing distance matrix is ​​updated, otherwise the traffic scheduling is considered to have failed; finally, the above process is repeated until the traffic set All flows in the program are scheduled and the final scheduling result is returned. It should be noted that the directly generated solution, for example, if the flow f1 is from a->d, the result is [a,b,c,d]; a path is generated for each flow in sequence;

[0143] The algorithm pseudo code is as follows:

[0144] .

[0145] like Figure 3-Figure 5 As shown, in this embodiment, it also includes when a sudden failure of a communication network link is caused by factors such as a network attack or a natural disaster, so that the routing scheduling result generated by the control center cannot be used. On the one hand, due to the delay in information transmission, the link failure information is not obtained by the control center at this time, and the routing scheduling plan cannot be regenerated; on the other hand, if the scheduling plan is regenerated by the control center and then sent to each communication node, the time period is too long to meet the transmission needs of the business traffic. Therefore, the method proposed in the present invention can quickly reselect a path for the current scheduling traffic only at the faulty link node on the basis of retaining part of the original optimal scheduling results, so as to ensure the real-time requirements of business traffic transmission. The specific process is as follows:

[0146] 1) Consider that when a flow is scheduled, a link failure occurs at the communication node where it is located, causing the original routing path to be unavailable. At this time, the current communication node will obtain real-time link failure information and use the above method to restore the failed link. Consider it unreachable, that is, modify the adjacency matrix The corresponding element and the corresponding elements in the routing distance matrix .

[0147] 2) Considering that the time period for the control center to regenerate the global optimal scheduling plan is relatively long, it will affect the real-time transmission of the business traffic of the current communication node. Therefore, the dynamic selection strategy proposed in the present invention is to use the current communication node as the source node at the faulty link node, and the target node of the business traffic remains unchanged. The optimal path is reselected according to the principles and methods mentioned in step 3, thereby realizing rapid reconstruction of the faulty path.

[0148] 3) Considering that the original routing scheduling result is not completely unusable, and the dynamic selection of the path is only performed at the faulty link, it is impossible to form a complete routing path. Therefore, the optimal scheduling result in step 3 and the routing reconstruction result in step 4 are combined to reconstruct a complete routing path from the service traffic source node to the destination node, which is fed back to the control center and the optimal scheduling routing table is updated. .

[0149] The pseudo code for dynamic route selection is as follows:

[0150] .

[0151] Embodiment 2 discloses a system for active distribution network routing scheduling and dynamic selection driven by business demand, comprising:

[0152] The traffic importance evaluation module is configured to: evaluate the physical importance index and information importance index of each service flow according to the physical impact and information demand of each service flow;

[0153] The fusion module is configured to: comprehensively consider the physical traffic importance index and the information importance index to obtain a comprehensive business traffic importance index;

[0154] The service flow set module is configured to: sort the service flows according to the comprehensive service flow importance index to obtain a sorted service flow set;

[0155] The routing link quantification module is configured to: quantify the real-time performance and reliability of each routing link and generate a routing distance matrix;

[0156] The routing optimization scheduling module is configured to: input the sorted service flow set and the routing distance matrix into a pre-built routing scheduling optimization model, and generate an optimal routing scheduling solution for the overall service flow based on the service flow importance and routing path performance matching principle;

[0157] The traffic scheduling module is configured to: be used by the lower layer router to perform traffic scheduling according to the optimal routing scheduling scheme;

[0158] The dynamic routing selection module is configured to: when a link failure occurs, dynamically select a routing path based on real-time link failure information and an original optimization solution.

[0159] The present invention proposes a method for dynamic route selection based on real-time link failure information and the original optimization scheme, which can quickly reselect available paths at the faulty link node, and the reconstruction result can meet the transmission requirements of the traffic, solving the problem of how to ensure on-demand transmission of business traffic under sudden link failures.

[0160] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. Active distribution network routing scheduling and dynamic selection method driven by business demand, characterized in that: The method comprises: Evaluate the physical importance index and information importance index of each service flow according to the physical impact and information requirements of each service flow; Taking the physical importance index and the information importance index into consideration, we can get the comprehensive business flow importance index; Sorting the importance indexes of the comprehensive business traffic to obtain a sorted business traffic set; Quantify the real-time performance and reliability of each routing link and generate a routing distance matrix; The sorted service flow set and the routing distance matrix are input into the pre-built routing scheduling optimization model, and the optimal routing scheduling solution for the overall service flow is generated based on the service flow importance and routing path performance matching principle; The lower-layer routers perform traffic scheduling according to the optimal routing scheduling scheme; The physical importance index and information importance index of each service flow are evaluated according to the physical impact and information demand of each service flow, including: The improved gravity centrality method is used to evaluate physical importance. The original gravity centrality formula is: Where G(i) represents the node influence index, ks(i) represents the k-shell value of physical node i, ks(j) represents the k-shell value of physical node j, and the k-shell value represents its influence in the network topology. i represents the set of neighbor nodes of physical node i, d ij Represents the topological distance between physical nodes i and j; By improving formula (1), we get the improved gravity centrality formula: Among them, G p (i) represents the obtained node physical importance value, p i，norm represents the power impact of normalized traffic on physical node i, p i，norm ks(i) represents the comprehensive index of the structural influence and physical influence of node i in the network topology, p j，norm represents the power impact of normalized traffic on physical node j, p j，norm ks(j) represents the comprehensive index of the structural influence and physical influence of node j in the network topology; The method for normalizing all variables that need to be normalized is shown in formula (3): Among them, c norm represents the normalized variable value, c is the variable to be normalized, c min is the minimum value of variable c, c max is the maximum value of variable c, and X is the correction parameter; The improved gravity centrality formula (2) is used to express the business flow f k The physical importance of p (k), see formula (4): I p (k)=G p (i) (4) Considering the specific real-time requirements and reliability requirements of different business flows, the flow information importance formula (5) is constructed: Where k represents the number of the service flow, I c (k) represents the information importance index of the traffic, τ k，norm represents the deadline requirement of normalized traffic, e k，norm represents the bit error rate requirement of the normalized traffic, α1 and α2 are weight coefficients, satisfying α1+α2=1; The physical importance index and the information importance index are comprehensively considered to obtain a comprehensive business flow importance index, including: Based on the physical importance of flow I p (k)(4) and traffic information importance (5), given the service traffic f k The comprehensive cyber-physical importance index (6): I k =β1I p (k)+β2I c (k) (6) Among them, I k It represents the comprehensive importance of each flow considering both physical importance and information importance. β1 and β2 are weight coefficients, satisfying β1+β2=1; According to the comprehensive importance of each flow I k Arrange the traffic in the scheduled traffic set F in descending order to obtain the importance index of the comprehensive business traffic; The real-time performance and reliability of each routing link are quantified, and a routing distance matrix is ​​generated, including: The actual router is abstracted as a communication node, and the link between two communication nodes is abstracted as an edge. The communication topology is expressed as V = {v1, v2, ..., v n } represents the set of communication nodes, v n represents the nth communication node, E = {l ij |i≠j} represents the set of communication links, l ij Represents the communication link between nodes i and j. According to the communication network topology, the adjacency matrix A of the communication network is obtained as follows: ij ] n×n , when communication nodes i and j can communicate directly, a ij =1, otherwise, a ij =0; a ij represents the connectivity between nodes i and j, and n represents the matrix dimension; Each communication link ij With link state attribute: link bandwidth b ij , link transmission delay τ ij and link transmission bit error rate e ij , considering the transmission delay and bit error rate in the link state to evaluate each routing link, the evaluation method is shown in formula (7): Among them, distance ij represents the evaluation value of each link, τ ij，norm represents the link transmission delay normalized by formula (3), e ij，norm represents the link bit error rate normalized by formula (3), w1 and ω2 are weight coefficients, satisfying w1+w2=1, inf represents infinity, and distance ij The smaller the value, the better the performance of the link; According to formula (7) and the communication network adjacency matrix A, the routing distance matrix D is calculated as [distance ij ] n×n ; The method further comprises: When a flow is scheduled, a link failure occurs on the communication node where it is located, causing the original routing path to be unusable. At this time, the current communication node will obtain real-time link failure information and send the failed link to the ij Consider it unreachable and modify the corresponding element a in the adjacency matrix A. ij = 0 and the corresponding element distance in the routing distance matrix ij =inf; Reselect the optimal path based on the importance of business traffic and the performance of the routing path, thereby achieving rapid reconstruction of the faulty path; Among them, considering that the original routing scheduling result is not completely unusable, and the dynamic selection of the path is only performed at the faulty link, it is impossible to form a complete routing path, so the optimal scheduling result and the reconstruction result are combined to reconstruct a complete routing path from the service traffic source node to the destination node, which is fed back to the control center and the optimal scheduling routing table S is updated; The objective function of the routing scheduling optimization model is: Among them, |F| represents the number of flows in the flow set to be scheduled, τ k，total Indicates the business flow f k The total transmission delay on a complete routing path, e k，total Indicates the business flow f k The total bit error rate on a complete routing path, o1 and o2 are weight coefficients, satisfying o1+o2=1; The total delay and total bit error rate of the service traffic on a complete routing path are (9) and (10) respectively: Among them, l ij Indicates link, path k Indicates the business flow f k The complete routing path, f k represents the kth flow to be scheduled, and F is the set of flows to be scheduled; Service traffic transmission delay constraint (11): Each flow must arrive within its deadline; Among them, τ k is the cut-off time for business traffic; Service flow bit error rate constraint (12): Each flow must ensure that its total bit error rate does not exceed the maximum allowed bit error rate; Among them, e k is the cut-off time for business traffic; Communication link bandwidth constraint (13): The total traffic accommodated by each link does not exceed the available link bandwidth; in, is the business flow f k Whether it passes through the communication link l ij The indicator factor, when the traffic flow f k Through the communication link ij hour, otherwise, b k is the bandwidth required for traffic transmission, b ij Indicates link l ij Total bandwidth; Communication node queue constraint (14): The length of the traffic queue in each communication node does not exceed the maximum allowed queue value; in, is the business flow f k The indicator factor of whether it passes through the communication node i. k Through the communication node v i hour, otherwise, q i，max is the maximum flow storage queue of communication node i.

2. The method for active distribution network routing scheduling and dynamic selection driven by business demand according to claim 1 is characterized in that: The routing scheduling optimization model aims to minimize the average transmission delay and bit error rate of all traffic, and is constructed with the deadline, bit error rate, communication bandwidth and node queue of the business traffic as constraints.

3. The method for active distribution network routing scheduling and dynamic selection driven by business demand according to claim 1 is characterized in that: The sorted service flow set and the routing distance matrix are input into the pre-built routing scheduling optimization model, and based on the service flow importance and routing path performance matching principle, the optimal routing scheduling solution for the overall flow is generated, including: For each service flow, the shortest path is solved by the Floyd algorithm based on the service flow importance and routing path performance matching principle, and the routing path with the current minimum distance is generated for the service flow with high importance; The constraints of the routing path that generates the current minimum distance are judged, and the paths that meet the constraints are retained in the routing table, and the routing distance matrix is ​​updated. Otherwise, the traffic scheduling is considered to have failed, until all the traffic in the traffic set is scheduled and the optimal routing scheduling solution is obtained.

4. A system for the active distribution network routing scheduling and dynamic selection method driven by business demand according to any one of claims 1 to 3, characterized in that: The system comprises: The traffic importance evaluation module is configured to: evaluate the physical importance index and information importance index of each service flow according to the physical impact and information demand of each service flow; The fusion module is configured to: comprehensively consider the physical traffic importance index and the information importance index to obtain a comprehensive business traffic importance index; The service flow set module is configured to: sort the service flows according to the comprehensive service flow importance index to obtain a sorted service flow set; The routing link quantification module is configured to: quantify the real-time performance and reliability of each routing link and generate a routing distance matrix; The route optimization generation module is configured to: input the sorted service flow set and the route distance matrix into a pre-built route scheduling optimization model, and generate an optimal route scheduling solution for the overall service flow based on the service flow importance and the route path performance matching principle; The traffic scheduling module is configured to: be used by the lower-layer router to perform traffic scheduling according to the optimal routing scheduling solution.

5. The active distribution network routing dispatching and dynamic selection system driven by business demand according to claim 4 is characterized in that: The system further comprises: The dynamic routing selection module is configured to: when a link failure occurs, dynamically select a routing path based on real-time link failure information and an original optimization solution.

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