Intelligent collaborative power demand network method

Abstract

The application is a kind of intelligent collaborative power demand network method, which provides a power demand processing mechanism solution based on intelligent collaborative theory, first, a new data packet structure is designed, demand identification and demand attribute information are added, and then the characteristics and requirements of different power grid business demands can be clearly identified and described, providing more basis for subsequent processing and forwarding, second, based on the characteristics of different business demands in the scenes of generation, transmission, transformation, distribution and use, decision number is made according to the communication requirements of business demands, and the binding between them is realized, finally, a demand preference matching table is set in the intermediate equipment, the matching relationship between business demand and preference is obtained according to the matching model, and the preference of demand is determined by table lookup. Combined with demand preference, the best forwarding path is selected, the sustainable control and reasonable allocation of resources are realized, the efficiency of power internet of things business transmission can be effectively improved, and the diversification demand sensing in power internet of things is supported.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of electric power, and more particularly to a smart collaborative power demand network method. BACKGROUND

[0002] With the landing of 5G, the Internet of Things era has officially begun. The continuous accumulation and upgrading of Internet of Things technology have gradually improved and matured the industry chain. Combined with periodic factors such as infrastructure construction, basic industry transformation, and consumption upgrading, fields and industries at different levels of development have alternately promoted the development of the Internet of Things, especially the electric power Internet of Things industry. The electric power Internet of Things will continuously integrate various types of collection, control sensors or controllers with mobile communication, intelligent analysis, and other technologies into each link of the electric power production process, ultimately realizing the upgrading of the traditional electric power industry to a new stage of intelligentization. In terms of application form, the electric power Internet of Things application has the characteristics of real-time, automation, embedded (software), security, and information interconnection. The electric power Internet of Things market is developing rapidly, and more attention is paid to high reliability, low latency, and massive connections, which puts forward higher requirements for the realization of electric power automation.

[0003] The electric power Internet of Things is composed of an electric power backbone communication network, an electric power station real-time monitoring network, and an electric power communication access network. Its demand can be divided into five aspects: power generation, power transmission, power transformation, power distribution, and power consumption. Each aspect has its own characteristics in terms of demand sensing. The electric power Internet of Things needs to realize real-time interconnection and dynamic interaction between devices involved in each link of the power generation, transmission, transformation, distribution, and consumption process of the power system, has dual attributes of network and control, and has the advantages of real-time, reliability, and inclusiveness. Its essence is the combination of information technology such as Internet of Things technology, big data, cloud computing, and artificial intelligence with electric power technology. It is mainly composed of data collection devices such as sensors, computing devices such as computers, and physical devices such as transformers. It deeply integrates advanced technologies such as information collection and transmission, data calculation and processing, and device control and decision-making into physical systems such as electric power-related devices, realizes the interconnection and interaction of people and things in the electric power system, and achieves the interconnection and interaction of people and things in the electric power system.

[0004] On March 7, 2019, State Grid Corporation of China released the outline of ubiquitous electric power Internet of Things construction, which deployed the construction and development of electric power Internet of Things. According to the content of the document, through three years of hard work, the ubiquitous electric power Internet of Things will be initially built by 2021; through three years of improvement, the ubiquitous electric power Internet of Things will be built by 2024. The outline provides the architecture form of the electric power Internet of Things, which is divided into four layers like other forms of Internet of Things systems: the perception layer, the network layer, the platform layer, and the application layer.

[0005] According to the power Internet of Things architecture and functional requirement standards, power Internet of Things demand sensing should have data collection, local communication, collection and forwarding, centralized verification, edge computing, data storage and other functions. The network layer here pays more attention to the remote communication function of data, and designs the network architecture according to the network bearing service type, scale, target coverage area, coverage area, capacity and performance planning target, etc. The network architecture includes network architecture based on private network, network architecture based on public network, and network architecture based on hybrid network. At the same time, software defined network, network virtualization and other technologies can be used to realize the fusion of heterogeneous networks, improve the flexibility of network connection, improve the utilization rate of network resources, realize the logical isolation of sensing layer business data, upload the sensing layer business data to the platform layer, or download the platform layer control command, configuration information to the sensing layer.

[0006] The demand that needs to be sensed in the power Internet of Things has diversified characteristics. On the power supply side, a large number of new energy power generation equipment such as wind power and photovoltaic power generation are connected, which needs to sense temperature, optical and location information, monitor the running state and health of power generation equipment, prevent accidents, improve power generation efficiency and prolong equipment life. In the power grid side, in the scenarios of power transmission, power transformation and power distribution, microclimate, temperature and humidity, tower inclination, icing, sag, wind deviation, partial discharge, vibration and pressure sensing devices are needed to collect power grid operation and equipment state, environment and other auxiliary information, support information comprehensive sensing and intelligent application in power grid production and operation. On the load side, power quality, load monitoring and other sensing devices are needed to collect smart power, new energy vehicle load and other information to support demand side flexible load resource utilization, improve energy utilization rate and user side energy management level.

[0007] Current overall scheme of power demand sensing

[0008] Firstly, through intelligent meters, sensors or monitoring devices, various parameter data in the power system such as current, voltage, power, etc. are collected, and after data cleaning, denoising, correction and standardization and other preprocessing steps, they are transmitted to the central data storage and processing system through the communication network. In the central data storage and processing system, data can be organized and managed according to time stamp to support historical data query and analysis. Statistical analysis, machine learning, time series analysis and other data analysis techniques are applied to real-time data processing, load forecasting, anomaly detection, load curve analysis and other operations on the data, so as to understand and extract power demand. According to the data analysis results, based on historical data and model algorithms, the load demand in the future period is predicted, and according to the prediction results and constraint conditions, the operation strategy of the power system is adjusted to realize more efficient, stable and reliable power supply. Finally, based on the demand prediction and optimization results, load scheduling, power supply strategy, energy management strategy and other decision support and control strategies are formulated to ensure the normal operation and stability of the power system.

[0009] Current routing strategies available for power demand-aware services

[0010] Hybrid Routing: Combines the features of static routing and dynamic routing. A part of static routing can be manually configured, while dynamic routing protocols are used to automatically learn and update the routing information of the other part. Static routing associates specific destination addresses with outbound interfaces, which is suitable for situations where demand preferences do not change frequently, but is not suitable for dynamic network environments; dynamic routing algorithms can dynamically adjust routing paths according to demand preferences, network topology, and link status, which is suitable for situations where demand preferences change frequently, and can provide more flexible and adaptive routing forwarding.

[0011] Policy-based Routing: Selects routing paths based on specific policies or demand preferences. It can match traffic based on source IP address, destination IP address, port number, protocol type, etc., and select the best routing path for forwarding according to predefined policies, which can achieve flexible routing selection based on demand preferences.

[0012] SDN Routing (Software-Defined Networking Routing): SDN routing is a routing method based on software-defined networks. By separating network control and data planes, a centralized controller is used to manage and configure routing policies. SDN routing can perform flexible routing forwarding according to demand preferences, network topology, and real-time data.

[0013] Drawbacks of existing technologies

[0014] 1. Lack of unified description of power grid services and demand, no consistent specifications and standards formed, resulting in high cost and low efficiency in power grid equipment and system processing, and certain safety risks.

[0015] 2. Lack of measurement of power Internet of Things network status, which is not conducive to efficient and reliable transmission of data packets.

[0016] 3. Different power demand characteristics are not considered, and different business scenarios have different requirements for communication and preferences for transmission paths, without considering that this condition may cause more times of data retransmission and loss.

[0017] 4. Sustainable control cannot be performed during transmission, and routing strategies cannot be adaptively selected according to specific service demand characteristics and communication requirements, which may easily cause unbalanced resource utilization, network congestion, and lack of quality of service guarantee. Summary of the invention

[0018] A scheme for describing power grid services and demands is designed, and ENM mode measurement is used to fully obtain the network state information of the power internet of things. In order to improve the efficiency and reliability of the transmission of the demand sensing service of the power internet of things, firstly, a new data packet structure is designed, and demand identification and demand attribute information are added, so that the characteristics and requirements of different power grid service demands can be clearly identified and described, and more basis is provided for subsequent processing and forwarding; secondly, based on the characteristics of different service demands in the scenes of generation, transmission, transformation, distribution and use, decision numbers are decided according to the communication requirements of service demands, so that the binding between them is realized; finally, a demand preference matching table is set in the intermediate device, the matching relationship between service demands and preferences is obtained through a matching model, and the preference of the demand is determined by table lookup. The best forwarding path is selected according to the demand preference, and sustainable control and reasonable allocation of resources are realized. The application provides a kind of intelligent collaborative power demand network method.

[0019] The technical scheme of the application is as follows:

[0020] A kind of intelligent collaborative power demand network method, comprising:

[0021] Step (1): according to the demand in the application of the power internet of things, in the scenes of generation, transmission, transformation, distribution and use, each demand index is measured by using sensor, and the network state information of power grid is measured by using ENM mode.

[0022] Step (2): the power grid service and demand are described, the key words are defined for each demand information item, and the standardization is carried out, the power grid service demand information is bound with the communication requirement through number decision.

[0023] Step (3): the standardized demand attribute information bound is embedded in the service request message, encapsulated as a data packet for transmission, and the corresponding field is added in the data packet header to identify and describe the demand information.

[0024] Step (4): the data packet enters gateway and intermediate forwarding device to execute sequential caching mechanism, stores the received out-of-order data packet in cache area, and determines the correct order of data packet based on the sequence number of data packet using sorting logic.

[0025] Step (5): the demand sensing control workflow is added in the gateway and forwarding device, the data packet is parsed, matched and de-parsed, so as to sense the characteristics and requirements of demand.

[0026] Step (6): a demand preference matching table is set in the forwarding device, the corresponding relationship between demand and preference is obtained through a matching model and stored in the matching table, and the EDA information extracted in the last step is used for table item query.

[0027] Step (7): recombine the parsed data packets, select the optimal routing strategy according to the demand preference, and continue transmission according to the requirements of the power grid service demand.

[0028] Technical effects and advantages of the present application:

[0029] The present application provides a power demand processing mechanism solution based on intelligent collaboration theory, measures the network state information of the power internet of things in an ENM manner, makes decision numbers according to the corresponding relationship between service demand and communication requirements based on the characteristics of power demand in different service scenarios, realizes the binding between the two, matches the demand and preference through a matching model, realizes adaptive routing forwarding based on demand preference, effectively improves the efficiency of power internet of things service transmission by combining existing routing strategies, and can support diversified demand perception in the power internet of things. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 The overall scheme flowchart of the present application.

[0031] Figure 2 The current power internet of things structure diagram.

[0032] Figure 3 Demand perception schematic diagram.

[0033] Figure 4 ENM data report message format diagram.

[0034] Figure 5 Service scenario numbering diagram.

[0035] Figure 6 Transmission requirement schematic diagram of specific service demand under the substation scenario.

[0036] Figure 7 Service demand numbering decision schematic diagram.

[0037] Figure 8 Packet schematic diagram of adding corresponding field data.

[0038] Figure 9 Demand perception control workflow schematic diagram. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0040] Figure 1It is a whole scheme implementation flow chart of power demand processing mechanism based on wisdom coordination theory according to the embodiment of the present application, Figure 2 It is a current power internet of things structure diagram, Figure 3 It is a demand sensing schematic diagram using the present scheme, Figure 4 It is an ENM data report message format diagram, Figure 5 It is a service scene number diagram, Figure 6 It is a transmission requirement schematic diagram of specific service demand under a power transformation scene, Figure 7 It is a service demand number decision schematic diagram, Figure 8 It is a packet schematic diagram of increasing corresponding field data, Figure 9 It is a demand sensing control work flow schematic diagram. Figures 4 to 9 It is a specific description of Figure 3 .

[0041] As shown in Figure 1 , the present application provides a power demand processing mechanism solution based on wisdom coordination theory, which includes seven steps.

[0042] Step (1): Different demands should adopt multiple measurement devices and measurement methods, and a tunneling magnetoresistance (TMR) magnetic sensitive current sensor can be used for high sensitivity detection in aspects of weak current of power grid, high-precision AC / DC, electric energy meter, etc.; a transformer oil dissolved gas separation device is used to separate dissolved gas in transformer oil by using an oil gas separation membrane to realize state detection of the transformer; an overhead transmission line dynamic capacity increasing system based on non-contact sensing technology is used to obtain key parameters such as conductor-to-ground distance and conductor temperature by means of laser radar and infrared temperature measurement technology, to realize conductor load flow evaluation and verification, and to provide basic data for transmission line capacity adjustment.

[0043] In addition, the IPv4 network architecture of the existing power communication system faces serious problems such as insufficient address space and poor scalability, and cannot effectively support the ubiquitous access demand in the power Internet of Things. Compared with IPv4, IPv6 technology has a more massive address space and high scalability, as well as optimized network functions and performance. State awareness of the power Internet of Things using IPv6 network architecture first requires network measurement to timely understand, monitor and master the network state, so as to timely optimize the network structure, improve the network service quality, and realize network fault diagnosis and recovery. Therefore, an electric network measurement (ENM) method is proposed. When a data packet enters the electric network measurement source, the device will encapsulate an ENM header in the message through the pre-defined data plane sampling setting, and define the ENM structure, add ENM data after the ENM header, and fill the network forwarding device information to be collected into the ENM data structure; then the data packet will be forwarded to the next hop of the path, and according to the data structure defined by the ENM, the local ENM metadata information is added; when the data packet passes through all the devices that need to collect state information, it is forwarded to the last network device (ENM sink), and the ENM sink removes the ENM header, and sends the path information to the monitoring server, so that the monitoring server obtains the network link state information, including the switch queue depth, queuing delay, link delay and link utilization, etc., which can quickly locate the fault link and find the bottleneck.

[0044] The upper Figure 4 The ENM datagram message composition is described, which is mainly divided into 9 parts, 8 byte header information and multiple data information, that is, the switch device state information part collected. The meanings of the fields related to the datagram are as follows:

[0045] 1) The first three fields respectively represent the version number, the copy allowed reserved flag and the data type number in the data packet, which respectively occupy 2 bits, 9 bits and 5 bits;

[0046] 2) The next is the maximum number of hops of 8 bits, which represents the maximum number of datagrams that can be added;

[0047] 3) Then is the current total number of hops of 8 bits, which represents the number of received datagrams;

[0048] 4) Data packet content, which represents the specific content of the measurement, is divided into 8 fields. The first 4 bits represent the switch address, ingress port number, forwarding time and queue occupancy, respectively. The last 4 bits represent the ingress timestamp, egress port number, queue congestion status and egress port link utilization, respectively.

[0049] Step (2): Different demand information is set by defining and standardizing keywords. For example, the keyword "TYPE" represents the demand type, which is represented by numbering business scenarios and demands to achieve the binding of power grid business demands with transmission requirements. The keyword "SIZE" represents the size of the demand information. The keywords "DATE" and "TIME" represent the effective date and time of the demand content, respectively. The keyword "PRIO" specifies the following format for the representation of Electricity Demand Attribute (EDA) information:

[0050] EDA={key1:value1,key2:value2,…}

[0051] For example, a sensor measures the voltage in a power transmission scenario. The required content size is 100KB, and the effective date and time are January 20, 2024, at 18:45:23. The business scenario is numbered according to the order of power generation, transmission, substation, distribution, and consumption. Therefore, the substation scenario is numbered 3. Taking the substation scenario as an example... Figure 6 As shown, different business needs have different communication requirements.

[0052] go through Figure 7 The decision-making process shown can then yield the corresponding number for the specific business requirement. If the remote signaling change alarm business requirement in the substation scenario is selected, then this requirement information can be represented as:

[0053] EDA

[0054] ={TYPE:35,SIZE:100KB,DATA:20240120,TIME:184523,PRIO:NONE}

[0055] Step (3): Add a Demand Information (DI) field to the packet header format. This field is embedded between the packet header and the actual transmitted data. It contains an 8-bit binary value representing the length of the information. If the DI field in the packet is set to 0, it indicates that the packet does not contain demand information; otherwise, it contains demand information of the size indicated by the DI field. Encapsulate the added field and EDA content according to the existing protocol to form a complete packet.

[0056] Step (4): When data packets enter the gateway and intermediate forwarding devices, the order caching mechanism is executed to store the received out-of-order data packets in the buffer area and use sorting logic to determine the correct order of data packets based on the sequence number of the data packets.

[0057] Step (5): Add a demand-aware control workflow to the gateway and forwarding device, which consists of a parser, a matching pipeline, and a de-parser. The parser is used to parse the information of the data packets, including MAC address information, IP address information, Port information, and EDA information. The parsed information will be stored in a data structure and type that the forwarding device can read. The IP address and Port information are used to update the routing table and select the transmission path. The demand attributes carried by the EDA information will enter the matching pipeline to match service preferences.

[0058] Step (6): After parsing the data packet in the previous step, the first step is to obtain the correspondence between demand and preference through the matching model. Define two sets: one is the demand set D = {d1, d2, ..., d...} n}, containing n demand elements, with preference set P = {p1, p2, ..., p...} m} contains m preference elements. Matching elements C in matrix C. ij Represents the demand element d i and preference element p j The degree of matching between them. ij This is a decision variable used to indicate whether to include demand element d. i and preference element p j Matching is a correspondence, x ij =1 indicates a match, x ij =0 indicates no match. The goal is to maximize the sum of the weights of the selected matches in the matching matrix:

[0059]

[0060] The constraints of this matching model are:

[0061] Each demand element can only be assigned to one preference element, x ij ∈{0,1}

[0062] After matching, the correspondence between the requirements and preferences is stored in a matching table for easy subsequent querying. The key value from the EDA information can be extracted through the packet parsing process. This key value is then used to query the requirement-preference matching table in the forwarding device. If the match is found, the preferences for time, priority, bandwidth, etc., are known, allowing the forwarding device to select a path based on these preferences.

[0063] Step (7): The parsed data packets are recombined in protocol order and continue to be transmitted according to the routing forwarding strategy determined in the previous step. When the network dynamic changes greatly, within a single autonomous system (AS), the shortest path tree is calculated using Dijkstra algorithm according to the network state information in the power grid state database, and the best path is assigned to each target network. Each router informs each other by exchanging network state update messages. The exchange of routing information between different autonomous systems (AS) through gateways. Each router maintains a routing table containing the network prefixes and path information of other ASs. Path attributes are used to describe AS paths and other attributes to help select the best path. When the network dynamic changes are not great, forwarding rules are defined based on demand preferences, and conditions such as source IP address, destination IP address, protocol, port number, etc. can also be considered to determine which data packets should be routed to a specific path or next hop. On the router or switch, the routing function based on the above rules needs to be configured and associated with a specific path or next hop. When the data packet enters, the router or switch will check the header information of the data packet and match it with the defined rules. If the match is successful, the next hop or path of the data packet is determined according to the established routing configuration. If the match is not successful, the data packet is forwarded according to the default routing table. Flexible adjustment of routing strategy can ensure reasonable allocation of resources.

[0064] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for intelligent collaborative power demand network, characterized in that: Comprise: Step (1): According to the demand in the power internet of things application, in the different scenarios of sending, transmitting, transforming, distributing, and using, the demand indicators are measured by sensors, and the network state information of the power grid is measured by ENM method; Step (2): The power grid business and demand are described, the key words are defined for each demand information item, and the standardization is carried out, the power grid business demand information and the communication requirements are bound through the numbering decision; Step (3): The standardized demand attribute information bound is embedded into the service request message, encapsulated as a data packet for transmission, and the corresponding field is added in the data packet header to identify and describe the demand information; Step (4): The data packet enters the gateway and intermediate forwarding equipment to execute the sequential caching mechanism, stores the received out-of-order data packet in the cache area, and uses the sorting logic to determine the correct order of the data packet based on the sequence number of the data packet; Step (5): The demand awareness control workflow is added in the gateway and forwarding equipment, and the data packet is parsed, matched, and de-parsed to perceive the characteristics and requirements of the demand; Step (6): setting a demand preference matching table in the forwarding device, obtaining the correspondence between the demand and the preference through a matching model and storing it in the matching table, and querying the table using the EDA information extracted in the previous step; specifically, the matching model includes two sets, one is a demand set D = {d1, d2, …, d n n, containing n demand elements, and the other is a preference set P = {p1, p2, …, p m m}, containing m preference elements; an element C ij in a matching matrix C represents the matching degree between a demand element d i and a preference element p j ; x ij is a decision variable, used to represent whether to match the demand element d i and the preference element p j as a corresponding relationship, x ij = 1 representing matching, and x ij = 0 representing non-matching; The target is to maximize the sum of the weights of the selected matching items in the matching matrix: The constraint condition of the matching model is: Each demand element can only be assigned to one preference element, x ij The forwarding device according to the power demand traverses the demand set and the preference set, obtains all possible matching degrees between the demand and the preference, stores the matching degrees to a matching matrix, and calculates a decision variable of the demand and the preference corresponding relationship according to a constraint condition. The correspondence between the demand and the preference obtained after matching is stored in the matching table for subsequent query; The key value in the EDA information is extracted through the data packet analysis just now, and the key value is used to query the demand-preference matching table in the forwarding equipment. If it is queried, the preference of the demand in time, priority, and bandwidth is obtained, so that the forwarding equipment will select the path according to the demand preference; Step (7): The parsed data packet is recombined, the best routing strategy is selected according to the demand preference, and the transmission continues according to the requirements of the power grid business demand.

2. The method of claim 1, wherein: Step (1) is: Different measurement devices and measurement methods should be adopted for different demands, TMR (tunneling magnetoresistance) magnetic current sensor can be used to detect the weak current and high-precision AC / DC of the power grid with high sensitivity; The transformer oil dissolved gas separation device is used to separate the dissolved gas in the transformer oil by using the oil gas separation membrane to detect the state of the transformer; Through the overhead transmission line dynamic capacity increasing system based on non-contact sensing technology, the key parameters of conductor-to-ground distance and conductor temperature are obtained by laser radar and infrared temperature measurement technology, the conductor load flow evaluation and verification are realized, and the basic data for transmission line capacity adjustment are provided.

3. The method of claim 1, wherein: Step (2) is: The different demand information is set by defining and standardizing the key words (keywords), specifically, the key word "TYPE" represents the demand type, which is represented by numbering the business scenarios and demands to realize the binding of the power grid business demand and the transmission requirements, the key word "SIZE" represents the size of the demand information, the key words "DATE" and "TIME" represent the effective date and time of the demand content respectively, and the key word "PRIO"; The representation format of the electricity demand attribute (EDA) information is as follows: EDA = {key1:value1, key2:value2, …}.

4. The method of claim 1, wherein: Step (3) is specifically: adding a demand information field (DI) in the data packet header format, which is embedded between the data packet header and the actual transmission data information, and the field contains an 8-bit binary value indicating the length of the information; if the DI field in the data packet is set to 0, it means that the data packet does not contain demand information, otherwise it contains demand information of the size indicated by the DI field; encapsulate the added field and EDA content according to the original protocol to form a complete data packet.

5. The method of claim 1, wherein: Step (5) is specifically: adding demand-aware control workflow to the gateway and forwarding device, which consists of parser, matching pipeline and de-parser. The role of the parser is to parse the information of the data packet, including MAC address information, IP address information, Port information and EDA information. The parsed information will be stored as data structure and type that can be read by the forwarding device. IP address and Port information are used to update the routing table and select the transmission path. The demand attribute carried by EDA information will enter the matching pipeline for service preference matching.

6. The method of claim 1, wherein: Step (7) is specifically: recombine the parsed data packets in protocol order, and continue transmission according to the routing and forwarding strategy determined in the previous step; when the network changes dynamically, within a single autonomous system (AS), the shortest path tree is calculated using Dijkstra algorithm based on the network state information in the power grid state database, and the best path is assigned to each target network; each router informs each other by exchanging network state update messages; exchange routing information between different autonomous systems (AS) through the gateway; each router maintains a routing table containing network prefixes and path information of other ASs; use path attributes to describe AS paths and other attributes to help select the best path; when the network changes dynamically, define forwarding rules based on demand preferences, consider source IP address, destination IP address, protocol, port number conditions to determine which data packets should be routed to a specific path or next hop; On the router or switch, the routing function based on the above rules needs to be configured and associated with a specific path or next hop; When the data packet enters, the router or switch will check the header information of the data packet and match it with the defined rules; if the match is successful, the next hop or path of the data packet will be determined according to the established routing configuration; if the match is not successful, the data packet will be forwarded according to the default routing table; flexible adjustment of routing strategy can ensure reasonable allocation of resources.

Images