Power main and distribution network dynamic communication resource configuration system based on real-time data monitoring and analysis and optimization method thereof

Abstract

A power main and distribution network dynamic communication resource configuration system based on real-time data monitoring and analysis and an optimization method thereof aim to realize dynamic configuration of power resources through real-time data monitoring and analysis, and the system is composed of a data acquisition module, a data analysis module, a resource scheduling module and an optimization algorithm module. The method has the following beneficial effects: 1, the energy utilization efficiency is improved: the communication capacity requirements of the power grid in different time periods can be more accurately predicted and met by establishing a dynamic capacity distribution system based on real-time data monitoring and analysis, so that the power resource configuration is optimized, and the energy utilization efficiency is improved; and 2, the operation efficiency and reliability of the power grid are enhanced: centralized management and dynamic adjustment of network resources and automatic discovery and optimization of network topology are realized by adopting SDN and NFV technologies, efficient connection and cooperation of the network are ensured, and the operation efficiency and reliability of the power grid are enhanced.

Description

Technical Field

[0001] This invention mainly relates to the fields of smart grid technology and network resource optimization and allocation technology, specifically to a dynamic communication resource allocation system for power main and distribution networks based on real-time data monitoring and analysis, and its optimization method. Background Technology

[0002] With the rapid development of power systems, optimizing power resource allocation and improving grid operation efficiency have become crucial issues for the power industry. Optimizing power resource allocation not only affects the efficient use of energy but also directly impacts the stability and reliability of the power grid. Therefore, achieving dynamic allocation of power resources and improving grid operation efficiency and reliability has become a key challenge in the field of power technology.

[0003] Currently, power resource allocation primarily relies on traditional static planning methods. These methods typically allocate resources based on historical data and experience, lacking real-time performance and flexibility. For example, some grid operators adjust power resources through periodic grid load forecasting and manual dispatching. This approach cannot respond promptly to real-time changes in grid operation, leading to inefficient resource allocation. Furthermore, traditional grid operation monitoring systems also have limitations; they often provide only limited real-time data and cannot comprehensively capture dynamic changes in the grid, thus restricting improvements in grid operation efficiency and reliability.

[0004] While existing power resource allocation and grid operation monitoring technologies have supported the stable operation of power systems to some extent, several significant problems and shortcomings remain. First, traditional static planning methods cannot adapt to rapid changes in grid operation, leading to inaccurate resource allocation and low energy utilization efficiency. Second, existing grid operation monitoring systems have limited data acquisition capabilities, failing to provide sufficient real-time data support, making real-time optimization of grid operation difficult. Furthermore, existing technologies are slow to respond to grid faults or congestion, lacking effective multi-path selection and rapid switching mechanisms, thus impacting grid reliability. Therefore, developing a dynamic power resource allocation system and its optimization method based on real-time data monitoring and analysis is of significant practical importance and application value for improving the operational efficiency and reliability of power systems. Summary of the Invention

[0005] The technical problems to be solved by this invention are: 1) Traditional static planning methods cannot adapt to rapid changes in power grid operation, resulting in inaccurate resource allocation and low energy utilization efficiency; 2) Existing power grid operation monitoring systems have limited data acquisition capabilities and cannot provide sufficient real-time data support, making real-time optimization of power grid operation difficult; 3) Existing inventions are slow to respond when dealing with power grid faults or congestion, lack effective multi-path selection and fast switching mechanisms, which affect the reliability of the power grid.

[0006] To achieve the above objectives, the present invention is implemented through the following invention: A dynamic communication resource allocation system for power main and distribution networks based on real-time data monitoring and analysis consists of a data acquisition module, a data analysis module, a resource scheduling module, and an optimization algorithm module. The data acquisition module refers to the deployment of flow monitoring equipment in the main network and distribution network to collect communication flow data in real time. The flow monitoring equipment includes sensors, data acquisition units and communication interfaces. The sensors are responsible for monitoring the real-time operating status of the power grid, and the data acquisition unit preprocesses the data collected by the sensors and sends it to the data analysis module through the communication interface. Data analysis module: Receives data from the data acquisition module and processes and analyzes the data using data analysis algorithms; Resource scheduling module: The resource scheduling module receives the prediction results from the data analysis module and dynamically adjusts the communication capacity allocation between the main network and the distribution network through software-defined networking (SDN) and network function virtualization (NFV) technologies to achieve centralized management and scheduling of network resources. Optimization Algorithm Module: An intelligent decision-making module is set up in the software-defined network (SDN) controller. The intelligent decision-making module automatically generates the optimal network resource allocation strategy based on real-time network status and capacity demand prediction results.

[0007] A method for optimizing the dynamic communication resource allocation of the power main and distribution networks based on real-time data monitoring and analysis includes the following steps: Step 1: Deploy traffic monitoring devices in the main grid and distribution network to collect communication traffic data in real time; the traffic monitoring devices include sensors, data acquisition units and communication interfaces, wherein each traffic monitoring device is equipped with 10 sensors, which are responsible for monitoring the real-time operating status of the power grid; the data acquisition unit preprocesses the data collected by the sensors and sends it to the data analysis module through the communication interface; Step 2: The data analysis module uses data analysis algorithms to predict the communication capacity requirements of the main network and distribution network at different time periods; Step 3: Based on the prediction results of the data analysis module, dynamically adjust the communication capacity allocation between the main network and the distribution network; Step 4: The resource scheduling module receives the prediction results from the data analysis module and dynamically adjusts the communication capacity allocation between the main network and the distribution network through software-defined networking (SDN) and network function virtualization (NFV) technologies to achieve centralized management and scheduling of network resources.

[0008] Traffic monitoring devices are distributed and deployed at key nodes of the main network and distribution network. Preferably, there are 100 traffic monitoring devices, and each traffic monitoring device can automatically form a network with adjacent traffic monitoring devices.

[0009] The sensor is a high-precision, low-latency flow monitoring sensor; the flow monitoring sensor has a built-in microcontroller and internal registers, which automatically adjust the sampling frequency according to the flow fluctuation of the main network, increasing the sampling frequency when the flow is high and decreasing the sampling frequency when the flow is low.

[0010] Step 2 specifically includes: Step 2.1: Send the data collected by the flow monitoring device to the data analysis module; Step 2.2: Establish a Long Short-Term Memory (LSTM) network, introduce deep learning algorithms, and train the LSTM model using historical traffic data so that it can learn the time-series characteristics of the communication capacity requirements of the main network and distribution network. Step 2.3: Use a Long Short-Term Memory (LSTM) network to extract features and perform pattern recognition on the data; Step 2.4: In real-time monitoring, the current traffic data is input into the trained LSTM model. The LSTM model can more accurately predict the communication capacity requirements of the main network and distribution network at different time periods. Step 2.5: Correct the prediction results by combining fuzzy logic algorithms.

[0011] Step 3 includes: Step 3.1: Assess the communication capacity requirements between the main network and the distribution network based on time; Step 3.2: Establish a priority allocation strategy: Set different priorities for different types of communication services; at the same time, establish an emergency capacity reserve mechanism to reserve a certain proportion of the main network communication capacity as an emergency reserve.

[0012] Step 4 specifically involves: Step 4.1: Establish an SDN / NFV network with the following structure: Infrastructure layer: The physical network consists of network devices and general-purpose servers; Control Layer: The SDN controller has a complete understanding of the network topology and status, and exposes it to power grid applications through the Northbound API; Application layer: Runs power grid business applications and intelligent dispatching algorithms, which dynamically calculate communication requirements based on power grid status and issue strategies through the controller; Step 4.2: Based on the real-time data collected by the traffic monitoring devices in the main network and distribution network, detect sudden changes in the main network and distribution network, and then generate a configuration strategy; Step 4.3: Configure and distribute policy conversion; The application layer submits the above configuration strategy to the SDN controller through the northbound API, and the SDN controller calculates one or more paths from the source to the destination; Step 4.4: Network resource reconfiguration: A. SDN enables dynamic bandwidth / path adjustment: The controller distributes new flow table entries to all SDNs along the path via the southbound interface, and the SDNs immediately update their forwarding rules. For high-priority traffic, it is placed in a strict priority queue. If the primary path fails, the SDN controller quickly calculates and distributes a backup path within a specified time to achieve seamless switching. B. NFV enables flexible deployment of network functions: NFV is dynamically created while capacity is adjusted.

[0013] For the control layer, a blockchain-based distributed SDN controller architecture is established. In this architecture, each SDN controller node is a node in the blockchain network. The blockchain network adopts the form of a consortium blockchain, and only authorized SDN controller nodes can join. Each SDN controller node records its own status information and control commands on the blockchain, forming an immutable distributed ledger. When an SDN controller node fails or needs to perform resource scheduling, other nodes can query the information on the blockchain.

[0014] It also includes step 5: adding an intelligent decision-making module to the SDN controller to automatically generate the optimal network resource allocation strategy using the ant colony algorithm.

[0015] Compared with existing technologies, this invention has the following beneficial effects: 1. Improved energy utilization efficiency: By establishing a dynamic capacity allocation system based on real-time data monitoring and analysis, the communication capacity demand of the power grid at different time periods can be predicted and met more accurately, thereby optimizing power resource allocation and improving energy utilization efficiency. 2. Enhanced power grid operation efficiency and reliability: By adopting SDN and NFV technologies, centralized management and dynamic adjustment of network resources, as well as automatic discovery and optimization of network topology, are achieved, ensuring efficient network connection and coordination, and enhancing the efficiency and reliability of power grid operation. 3. Improved intelligence level of network resource allocation: The intelligent decision-making module of the SDN controller can automatically generate the optimal resource allocation strategy based on real-time network status and prediction results, improving the intelligence level of network resource allocation. Attached Figure Description

[0016] Figure 1This is a schematic diagram of the present invention; Figure 2 This refers to the network resource scheduling process for NFV. Detailed Implementation

[0017] The invention will now be clearly and completely described with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0019] like Figure 1 As shown, the dynamic power resource allocation system based on real-time data monitoring and analysis aims to achieve dynamic allocation of power resources through real-time data monitoring and analysis. The system consists of a data acquisition module, a data analysis module, a resource scheduling module, and an optimization algorithm module.

[0020] The data acquisition module refers to the deployment of flow monitoring equipment in the main grid and distribution network to collect communication flow data in real time. The flow monitoring equipment includes sensors, data acquisition units, and communication interfaces. The sensors are responsible for monitoring the real-time operating status of the power grid, including parameters such as voltage, current, and power. The data acquisition unit preprocesses the data collected by the sensors and then sends it to the data analysis module through the communication interface.

[0021] Data Analysis Module: This module receives data from the data acquisition module and processes and analyzes the data using data analysis algorithms. Within the data analysis module, machine learning algorithms, such as neural networks and support vector machines, are used to extract features and recognize patterns from the acquired data, predicting the power grid's communication capacity requirements at different times.

[0022] Resource scheduling module: The resource scheduling module receives the prediction results from the data analysis module and dynamically adjusts the communication capacity allocation between the main network and the distribution network through software-defined networking (SDN) and network function virtualization (NFV) technologies to achieve centralized management and scheduling of network resources.

[0023] Optimization Algorithm Module: An intelligent decision-making module is set up in the Software-Defined Networking (SDN) controller. This module automatically generates the optimal network resource allocation strategy based on real-time network status and capacity demand predictions. Within this intelligent decision-making module, multi-path selection strategies such as ant colony optimization are employed to quickly find the optimal backup path when network failures or congestion occur, ensuring communication continuity.

[0024] like Figure 1 As shown, a dynamic power resource allocation optimization method based on real-time data monitoring and analysis includes the following steps: Step 1: Deploy traffic monitoring devices in the main grid and distribution network to collect communication traffic data in real time. The traffic monitoring devices include sensors, data acquisition units, and communication interfaces. Each traffic monitoring device is equipped with 10 sensors, which are responsible for monitoring the real-time operating status of the power grid, including parameters such as voltage, current, and power. The data acquisition unit preprocesses the data collected by the sensors and then sends it to the data analysis module through the communication interface.

[0025] Furthermore, flow monitoring devices are distributed and deployed at key nodes of the main network and distribution network (such as substations and distribution rooms). Preferably, there are 100 flow monitoring devices, and each flow monitoring device can automatically network with adjacent flow monitoring devices to achieve rapid data transmission and sharing.

[0026] Furthermore, the sensor is a high-precision, low-latency traffic monitoring sensor, improving the accuracy and real-time performance of communication traffic data acquisition. The traffic monitoring sensor has a built-in microcontroller and internal registers that automatically adjust the sampling frequency based on main network traffic fluctuations. During periods of high traffic, the sampling frequency is increased to obtain more detailed data, while during periods of low traffic, the sampling frequency is decreased to reduce data processing pressure. Simultaneously, the traffic monitoring sensor is equipped with local storage and caching functions, temporarily storing collected data when the network is unstable and uploading it again once the network returns to normal, ensuring data integrity.

[0027] In addition, the flow monitoring equipment has intelligent learning capabilities, which can automatically adjust the monitoring accuracy and frequency based on historical flow data, thereby improving the accuracy and efficiency of flow data collection.

[0028] Step 2: Use data analysis algorithms to predict the communication capacity requirements of the main network and distribution network at different time periods. This specifically includes: Step 2.1: Send the data collected by the flow monitoring device to the data analysis module; ideally, the collected data should include power grid operation data from the past year. Step 2.2: Establish a Long Short-Term Memory (LSTM) network, introduce deep learning algorithms, and train the LSTM model using historical traffic data so that it can learn the time-series characteristics of the communication capacity requirements of the main network and distribution network. Step 2.3: Use a Long Short-Term Memory (LSTM) network to extract features and perform pattern recognition on the data; Step 2.4: In real-time monitoring, the current traffic data is input into the trained LSTM model. The LSTM model can more accurately predict the communication capacity requirements of the main network and distribution network at different time periods.

[0029] Step 2.5: Combine fuzzy logic algorithm to correct the prediction results, taking into account the impact of some uncertain factors (such as sudden failures, weather changes, etc.) on communication capacity requirements.

[0030] Step 3: Based on the prediction results of the data analysis module, dynamically adjust the communication capacity allocation between the main network and the distribution network.

[0031] Step 3.1: Assess the communication capacity requirements between the main network and the distribution network based on time: During morning and evening peak hours, allocate 20% of the main network's communication capacity to the distribution network; Step 3.2: Establish a priority allocation strategy: Set different priorities for different types of communication services (such as real-time monitoring, metering, and control services). When communication capacity is tight, prioritize the communication needs of high-priority services. Simultaneously, establish an emergency capacity reserve mechanism, reserving a certain proportion of the main network communication capacity as an emergency reserve. When a sudden surge in communication demand occurs in the distribution network, the emergency reserve capacity can be quickly activated.

[0032] Step 4: The resource scheduling module receives the prediction results from the data analysis module and dynamically adjusts the communication capacity allocation between the main network and the distribution network through Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies to achieve centralized management and scheduling of network resources. Specifically: Step 4.1: Establish an SDN / NFV network with the following structure: Infrastructure layer: The physical network consists of network devices (SDN switches, routers) and general-purpose servers (carrying virtualized network functions).

[0033] Control layer: The SDN controller has a grasp of the entire network topology and status, and exposes it to power grid applications through the Northbound API.

[0034] Application layer: Runs various power grid business applications and intelligent scheduling algorithms. They dynamically calculate communication requirements based on power grid conditions (such as faults, load forecasts, and new energy fluctuations) and issue strategies through the controller.

[0035] At the infrastructure layer, a container-based resource isolation scheme is adopted. Traditional network device functions are virtualized, with each Virtual Network Function (VNF) running in an independent container. Container technology provides a lightweight virtualization environment that effectively isolates resources between different VNFs. Resource isolation within containers is achieved using the Linux kernel's namespace and cgroups mechanisms. Namespaces isolate resources such as processes, networks, and file systems, ensuring that each VNF can only access its own resource space. Cgroups can limit and monitor container resources such as CPU, memory, and disk I / O.

[0036] Better still, for the control layer, a blockchain-based distributed SDN controller architecture is established. In this architecture, each SDN controller node acts as a node in the blockchain network. The blockchain network adopts a consortium blockchain format, and only authorized SDN controller nodes can join. Each SDN controller node records its state information and control commands on the blockchain, forming an immutable distributed ledger. When an SDN controller node fails or requires resource scheduling, other nodes can quickly understand the overall network status and historical operation records by querying the information on the blockchain. Simultaneously, smart contracts are used to achieve automatic collaboration and resource sharing among nodes. A smart contract is an automatically executed contract that automatically triggers corresponding operations based on preset rules and conditions when those conditions are met. For example, when a node's resources are insufficient, a smart contract can automatically allocate resources from other nodes.

[0037] Step 4.2: Based on the real-time data collected by the traffic monitoring devices in the main network and distribution network, detect sudden changes in the main network and distribution network, and then generate a configuration policy; the sudden changes in the main network and distribution network can be: Power grid energy management system: Issues an early warning, predicting an active power deficit of XX MW in a certain distribution network area in the next hour; Distribution automation system: The system detected that the load on critical protection channels in this area has reached 70%. Network monitoring system: detected increased latency in relevant communication links.

[0038] Step 4.3: Configure and distribute policy conversion; The application layer submits the above configuration strategy to the SDN controller via a northbound API (such as RESTful). The SDN controller calculates one or more paths from the source (dispatch center gateway) to the destination (region A distribution network substation). Factors considered include: existing bandwidth, hop count, latency, and reliability.

[0039] Resource reservation: Determine which switch ports and link bandwidth are needed.

[0040] Generate flow table: Convert path instructions into specific OpenFlow flow table entries, such as matching source / destination IP, VLAN tag, setting the action to "forward from specified port" and "add to high priority queue".

[0041] Step 4.4: Network resource reconfiguration (SDN and NFV collaboration). This is the core step in dynamic adjustment, involving collaborative adjustments at the forwarding layer and the functional layer.

[0042] A. SDN enables dynamic bandwidth / path adjustment (data plane reconstruction): The controller distributes new flow table entries to all SDNs along the path via a southbound interface (such as OpenFlow), and the SDNs immediately update their forwarding rules. High-priority traffic is placed in a strict priority queue to ensure absolute priority forwarding, unaffected by other data flows. This instantly "carves out" a logical dedicated line on the physical network, dynamically allocating critical control traffic and guaranteeing communication capacity. If the primary path fails, the SDN controller quickly calculates and distributes a backup path within a specified time, achieving seamless failover.

[0043] B. NFV enables flexible deployment of network functions (functional plane reconfiguration): While adjusting capacity, it may be necessary to perform specific traffic processing, and NFV can be dynamically created.

[0044] Step 5: Add an intelligent decision-making module to the SDN controller to automatically generate the optimal network resource allocation strategy using the ant colony algorithm. Specifically, this involves introducing a multi-path selection strategy based on the ant colony algorithm. In the SDN controller, each network node is considered an "ant" activity point in the ant colony. When a network failure or congestion occurs, the SDN controller activates the ant colony algorithm. Ants start from the failed or congested node and search for paths in the network. When selecting a path, ants make decisions based on the pheromone concentration on the path and the path's real-time status (such as bandwidth and latency). The pheromone concentration reflects the historical quality of the path, while the real-time status reflects the current network condition. Ants leave pheromones along the paths they traverse, and the pheromone update rules are adjusted based on path usage and performance. After multiple iterations, the ant colony algorithm finds the optimal backup path, and the SDN controller quickly switches communication traffic to this backup path, ensuring communication continuity.

[0045] The algorithm's iteration count is set to 100, and the pheromone update rule is adjusted based on path usage and performance.

[0046] Through the above inventions and embodiments, this invention enables dynamic allocation of power resources, improving the operational efficiency and reliability of the power grid. Compared with traditional static planning methods, this system can respond in real time to rapid changes in power grid operation, achieving precise resource allocation and improved energy utilization efficiency. Simultaneously, by introducing advanced technologies such as SDN, NFV, and ant colony optimization, this system effectively solves the problems of limited data acquisition capabilities and slow response to power grid faults or congestion in existing power grid operation monitoring systems, providing strong technical support for the sustainable development of the power industry.

[0047] Compared with existing technologies, the beneficial effects of this invention are as follows: 1. Improved energy utilization efficiency: By establishing a dynamic capacity allocation system based on real-time data monitoring and analysis, the communication capacity demand of the power grid at different time periods can be predicted and met more accurately, thereby optimizing power resource allocation and improving energy utilization efficiency. 2. Enhanced power grid operation efficiency and reliability: By adopting SDN and NFV technologies, centralized management and dynamic adjustment of network resources, as well as automatic discovery and optimization of network topology, are achieved, ensuring efficient network connection and coordination, and enhancing the efficiency and reliability of power grid operation. 3. Improved intelligence level of network resource allocation: The intelligent decision-making module of the SDN controller can automatically generate the optimal resource allocation strategy based on real-time network status and prediction results, improving the intelligence level of network resource allocation. 4. Enhanced network adaptability and fault tolerance: By introducing a multi-path selection strategy based on ant colony algorithm, the SDN controller can quickly find the optimal backup path and switch when network failures or congestion occur, enhancing the network's adaptability and fault tolerance. 5. Improve resource isolation and monitoring efficiency: Adopting a container-based resource isolation solution effectively isolates resources between different virtual network functions, and the resource monitoring module monitors resource usage in real time, improving the efficiency of resource isolation and monitoring. 6. Enhance system reliability and scalability: The SDN controller supports distributed deployment, improving system reliability and scalability. 7. Achieve automatic collaboration and resource sharing among distributed SDN controllers: Design a blockchain-based distributed SDN controller architecture, using smart contracts to achieve automatic collaboration and resource sharing among nodes, improving the efficiency and accuracy of resource scheduling.

[0048] Due to its advanced nature, this invention has wide applications in smart grids, data centers, cloud computing, and other related fields. With technological advancements, smart grids place increasingly higher demands on the optimized allocation of power resources and energy utilization efficiency. This invention can effectively improve the allocation efficiency of power resources, meeting the needs of smart grids. Simultaneously, the development of data centers and cloud computing places higher demands on the efficient operation and reliability of networks. The application of this invention can improve the network operating efficiency and reliability of data centers and cloud computing, promoting their development. Furthermore, with the rapid development of new technologies such as the Internet of Things and 5G, higher demands are placed on the dynamic adjustment and resource optimization of networks. The application of this invention can improve the network's dynamic adjustment capabilities and resource optimization level, driving the development of new technologies. Therefore, this invention has broad market demand and promising application prospects.

[0049] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several changes and improvements without departing from the overall concept of the present invention, and these should also be considered within the scope of protection of the present invention.

Claims

1. A dynamic communication resource allocation system for power main and distribution networks based on real-time data monitoring and analysis, characterized in that: It consists of a data acquisition module, a data analysis module, a resource scheduling module, and an optimization algorithm module; The data acquisition module refers to the deployment of flow monitoring equipment in the main network and distribution network to collect communication flow data in real time. The flow monitoring equipment includes sensors, data acquisition units and communication interfaces. The sensors are responsible for monitoring the real-time operating status of the power grid, and the data acquisition unit preprocesses the data collected by the sensors and sends it to the data analysis module through the communication interface. Data analysis module: Receives data from the data acquisition module and processes and analyzes the data using data analysis algorithms; Resource scheduling module: The resource scheduling module receives the prediction results from the data analysis module and dynamically adjusts the communication capacity allocation between the main network and the distribution network through software-defined networking (SDN) and network function virtualization (NFV) technologies to achieve centralized management and scheduling of network resources. Optimization Algorithm Module: An intelligent decision-making module is set up in the software-defined network (SDN) controller. The intelligent decision-making module automatically generates the optimal network resource allocation strategy based on real-time network status and capacity demand prediction results.

2. A method for optimizing the dynamic communication resource allocation of power main and distribution networks based on real-time data monitoring and analysis, characterized in that: Includes the following steps: Step 1: Deploy traffic monitoring devices in the main grid and distribution network to collect communication traffic data in real time; the traffic monitoring devices include sensors, data acquisition units and communication interfaces, wherein each traffic monitoring device is equipped with 10 sensors, which are responsible for monitoring the real-time operating status of the power grid; the data acquisition unit preprocesses the data collected by the sensors and sends it to the data analysis module through the communication interface; Step 2: The data analysis module uses data analysis algorithms to predict the communication capacity requirements of the main network and distribution network at different time periods; Step 3: Based on the prediction results of the data analysis module, dynamically adjust the communication capacity allocation between the main network and the distribution network; Step 4: The resource scheduling module receives the prediction results from the data analysis module and dynamically adjusts the communication capacity allocation between the main network and the distribution network through software-defined networking (SDN) and network function virtualization (NFV) technologies to achieve centralized management and scheduling of network resources.

3. The method for optimizing dynamic communication resource allocation in power main and distribution networks based on real-time data monitoring and analysis according to claim 2, characterized in that: Traffic monitoring devices are distributed and deployed at key nodes of the main network and distribution network. Preferably, there are 100 traffic monitoring devices, and each traffic monitoring device can automatically form a network with adjacent traffic monitoring devices.

4. The method for optimizing dynamic communication resource allocation in power main and distribution networks based on real-time data monitoring and analysis according to claim 3, characterized in that: The sensor is a high-precision, low-latency flow monitoring sensor; the flow monitoring sensor has a built-in microcontroller and internal registers, which automatically adjust the sampling frequency according to the flow fluctuation of the main network, increasing the sampling frequency when the flow is high and decreasing the sampling frequency when the flow is low.

5. The method for optimizing dynamic communication resource allocation in power main and distribution networks based on real-time data monitoring and analysis according to claim 2, characterized in that: Step 2 specifically includes: Step 2.1: Send the data collected by the flow monitoring device to the data analysis module; Step 2.2: Establish a Long Short-Term Memory (LSTM) network, introduce deep learning algorithms, and train the LSTM model using historical traffic data so that it can learn the time-series characteristics of the communication capacity requirements of the main network and distribution network. Step 2.3: Use a Long Short-Term Memory (LSTM) network to extract features and perform pattern recognition on the data; Step 2.4: In real-time monitoring, the current traffic data is input into the trained LSTM model. The LSTM model can more accurately predict the communication capacity requirements of the main network and distribution network at different time periods. Step 2.5: Correct the prediction results by combining fuzzy logic algorithms.

6. The method for optimizing dynamic communication resource allocation in power main and distribution networks based on real-time data monitoring and analysis according to claim 2, characterized in that: Step 3 includes: Step 3.1: Assess the communication capacity requirements between the main network and the distribution network based on time; Step 3.2: Establish a priority allocation strategy: Set different priorities for different types of communication services; at the same time, establish an emergency capacity reserve mechanism to reserve a certain proportion of the main network communication capacity as an emergency reserve.

7. The method for optimizing dynamic communication resource allocation in power main and distribution networks based on real-time data monitoring and analysis according to claim 2, characterized in that: Step 4 specifically involves: Step 4.1: Establish an SDN / NFV network with the following structure: Infrastructure layer: The physical network consists of network devices and general-purpose servers; Control Layer: The SDN controller has a complete understanding of the network topology and status, and exposes it to power grid applications through the Northbound API; Application layer: Runs power grid business applications and intelligent dispatching algorithms, which dynamically calculate communication requirements based on power grid status and issue strategies through the controller; Step 4.2: Based on the real-time data collected by the traffic monitoring devices in the main network and distribution network, detect sudden changes in the main network and distribution network, and then generate a configuration strategy; Step 4.3: Configure and distribute policy conversion; The application layer submits the above configuration strategy to the SDN controller through the northbound API, and the SDN controller calculates one or more paths from the source to the destination; Step 4.4: Network resource reconfiguration: A. SDN enables dynamic bandwidth / path adjustment: The controller distributes new flow table entries to all SDNs along the path via the southbound interface, and the SDNs immediately update their forwarding rules. For high-priority traffic, it is placed in a strict priority queue. If the primary path fails, the SDN controller quickly calculates and distributes a backup path within a specified time to achieve seamless switching. B. NFV enables flexible deployment of network functions: NFV is dynamically created while capacity is adjusted.

8. The method for optimizing dynamic communication resource allocation in power main and distribution networks based on real-time data monitoring and analysis according to claim 7, characterized in that: For the control layer, a blockchain-based distributed SDN controller architecture is established. In this architecture, each SDN controller node is a node in the blockchain network. The blockchain network adopts the form of a consortium blockchain, and only authorized SDN controller nodes can join. Each SDN controller node records its own status information and control commands on the blockchain, forming an immutable distributed ledger. When an SDN controller node fails or needs to perform resource scheduling, other nodes can query the information on the blockchain.

9. The method for optimizing dynamic communication resource allocation in power main and distribution networks based on real-time data monitoring and analysis according to claim 2, characterized in that: It also includes step 5: adding an intelligent decision-making module to the SDN controller to automatically generate the optimal network resource allocation strategy using the ant colony algorithm.

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