Distributed power device networking based on active registration and control method thereof

Abstract

The application discloses a kind of distributed power equipment networking based on active registration, each distributed power equipment sends registration request and registration information to main grid register;Other distributed power equipment is found by main grid register and obtains other equipment data, historical data of itself and receives the request instruction sent by other equipment;Predictive equipment state using neural network model;According to the predicted equipment state, the equipment data obtained and energy demand, energy scheduling strategy is formulated, and after optimization using neural network, energy scheduling strategy is executed, and according to energy scheduling strategy, the request instruction of execution demand is sent to other equipment;Or execute the request instruction received.Active registration mechanism is used, new equipment can be dynamically joined to network, so that the system has strong flexibility and expansibility;Each equipment is registered in network independently, without centralized control node, so that the system is more decentralized, and the risk of single point failure is reduced.

Description

Technical Field

[0001] This invention relates to the networking of distribution networks, specifically to a distributed power equipment networking and control method based on active registration. Background Technology

[0002] With the development and popularization of renewable energy technologies, more and more distributed energy devices (such as solar panels and wind turbines) are being connected to the power system. As the scale and complexity of the power system continue to increase, the traditional centralized management model can no longer meet the needs of the power system. Traditional power equipment networking usually relies on manual configuration and management, which is inefficient and prone to errors. Summary of the Invention

[0003] Purpose of the invention: To address the above-mentioned shortcomings, this invention provides a distributed power equipment networking system based on active registration to improve the efficiency, reliability, and intelligence of energy systems.

[0004] The present invention also provides a control method for microgrid secondary system networking based on flexible matching.

[0005] Technical solution: To solve the above problems, the present invention adopts a distributed power device networking based on active registration, including a main grid registrar, which is used to send commands to the distributed power devices to activate active registration, and to receive registration requests and registration information of the distributed power devices themselves; and to manage the registration information sent by all registered distributed power devices.

[0006] Each distributed power device (DPD) is used to send a registration request and its own registration information to the main grid registrar based on the active registration command. After registration, it obtains the registration information managed by the main grid registrar, and obtains the device data of other registered DPDs based on the obtained registration information managed by the main grid registrar. It is used to predict its own device status using a neural network model based on its own historical data, formulate an energy dispatch strategy based on its own device status, the device data of other DPDs, and energy demand, and optimize the energy dispatch strategy using a neural network. It executes the optimized energy dispatch strategy and sends execution request instructions to other registered DPDs based on the optimized energy dispatch strategy. It is also used to execute the request instructions received from other registered DPDs.

[0007] Furthermore, the main grid registrar is used to determine whether it receives registration information from the distributed power equipment within a first set time after sending the command to activate active registration to the distributed power equipment. If no registration information is received, it queries the active registration progress of the distributed power equipment and performs the corresponding registration operation based on the queried active registration progress of the distributed power equipment.

[0008] Furthermore, the formula for calculating the first set time T is as follows:

[0009] T = T base +ω1×W+ω2×T type +ω3×S+ω4×H

[0010] Among them, T base The first set time is the basic value, ω1, ω2, ω3, and ω4 are adjustment factors, W is the weight of the distributed power equipment slave node, T is the node type weight, S is the node status parameter, and H is...

[0011] type

[0012] Historical registration time decay factor.

[0013] Furthermore, the step of performing the corresponding registration operation based on the queried active registration progress of the distributed power equipment includes, upon receiving an instruction from the distributed power equipment that the active registration has ended, replying with an instruction confirmation frame and sending a termination instruction for the active registration of the slave node to the distributed power equipment. The instruction confirmation frame includes information confirming that the distributed power equipment has received the termination signal.

[0014] Furthermore, it also includes a network maintenance and update module, used to monitor the communication operation status between the main grid registrar and the distributed power equipment, and periodically test and verify the communication operation status between the main grid registrar and the distributed power equipment, with a test period length T. test The calculation formula is:

[0015] T test = [1-(M1 / M+N1 / N)×T] c / T z ]×T0

[0016] Where M1 is the number of communication connections; M is the number of communication connection drops; N1 is the total number of registrations; N is the number of registration failures; T c T represents the average timeout duration, calculated as the actual communication duration relative to the theoretical communication duration across all communication connections. z T0 represents the average duration of a single communication for all communication connections; T0 is the initial cycle time length, used to measure the communication operation status between the main grid registrar and distributed power equipment in the initial stage.

[0017] Furthermore, it also includes a fault detection module, which uses a convolutional neural network to detect faults in the device.

[0018] This invention also employs a control method for distributed power equipment networking based on active registration, comprising the following steps:

[0019] Distributed power devices send a registration request and their own registration information to the main grid registrar based on the received active registration command;

[0020] After registration, distributed power devices obtain registration information managed by the main grid registrar, and then obtain device data of other registered distributed power devices based on the obtained registration information managed by the main grid registrar; the registration information managed by the main grid registrar includes registration information sent by all registered distributed power devices.

[0021] Distributed power equipment uses a neural network model to predict its own equipment status based on its historical data, and formulates an energy dispatch strategy based on its own equipment status, the equipment data of other distributed power equipment, and energy demand, and then uses a neural network to optimize the energy dispatch strategy.

[0022] Distributed power devices execute optimized energy dispatch strategies and send request instructions to other registered distributed power devices to fulfill their needs, based on the optimized energy dispatch strategies; or execute request instructions received from other registered distributed power devices.

[0023] The present invention also employs a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the above method when executing the computer program.

[0024] The present invention also employs a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of the above-described method.

[0025] Beneficial Effects: Compared with existing technologies, the significant advantages of this invention are: the adoption of an active registration mechanism allows new devices to dynamically join the network, giving the system greater flexibility and scalability; each device can autonomously register to the network, eliminating the need for a centralized control node, making the system more decentralized and reducing the risk of single points of failure; devices can register to the network instantly and respond to requests from other devices in real time, giving the system high real-time performance and responsiveness; devices can share information and collaborate on tasks such as energy management and optimized scheduling through the network, making the system more coordinated and efficient; the network can employ secure encryption mechanisms to protect the security of communication data and user privacy, ensuring the safe operation of the system; and the use of neural networks can better achieve device status prediction, fault detection, and energy scheduling optimization, thereby further improving the intelligence level, operational efficiency, and reliability of the distributed power equipment self-organizing network based on the active registration mechanism. Attached Figure Description

[0026] Figure 1 This is a flowchart of the distributed power equipment self-organizing network technology in this invention.

[0027] Figure 2 This is a structural diagram of the device registration module in this invention.

[0028] Figure 3 This is a schematic diagram illustrating the working principle of intelligent optimization and prediction in this invention. Detailed Implementation

[0029] Example 1

[0030] like Figure 1As shown in this embodiment, a distributed power device network based on active registration includes a device registration module, a registrar management module, a device discovery and communication module, a data sharing and collaboration module, and a network maintenance and update module. Each distributed power device sends a registration request to the main grid registrar according to preset rules and conditions, including the device's unique identifier, type, location, and other information. The main grid registrar receives and manages these registration requests, maintains the device registry, and notifies other devices of the addition of new devices. Each device discovers other devices by querying the main grid registrar or monitoring network communication, and exchanges and communicates data through network protocols, sharing local data and collaboratively completing tasks such as energy dispatching and load balancing. Each device maintains its online status through regular network maintenance and updates, ensuring communication security and network stability. Each device incorporates an intelligent optimization and prediction module, and utilizes various neural network algorithms for device status prediction, fault detection, and energy dispatch optimization, thereby improving the system's intelligence level. The main grid registrar (master node) manages the registration process for the entire network. When a distributed power device completes its active registration, it notifies the master node (main grid registrar). The master node then confirms and sends instructions to the slave nodes to terminate their active registration. This signifies that the slave node's registration process is complete, and the master node has confirmed this. Slave nodes refer to non-master devices in the distributed power device network; they receive and execute instructions from the master node during the active registration process.

[0031] like Figure 2 As shown, the main grid registrar includes:

[0032] The startup module sends a command to the distributed power devices to activate active registration, thus initiating the active registration process for the distributed power devices.

[0033] The storage and processing module receives registration information from distributed power devices and stores and processes the registration information. The stored registration information includes: device topology, device association, device registration time, device communication key, device type, device location, device status, and other specific information.

[0034] The progress judgment module queries the active registration progress of distributed power equipment when the main grid registrar does not receive registration information reported by distributed power equipment within a first set time. The formula for calculating the first set time is:

[0035] T = T base +ω1×W+ω2×T type +ω3×S+ω4×H (1)

[0036] In the formula, T represents the first set time; T baseThis represents the first set time base value; ω1, ω2, ω3, and ω4 are adjustment factors; W represents the slave node weight; T type S represents the node type weight; H represents the node status parameter; and H represents the historical registration time decay factor.

[0037] The registration module performs corresponding registration operations based on the queried progress of the distributed power equipment's active registration. When the main grid registrar receives a notification from the distributed power equipment indicating the end of active registration, the main grid registrar replies with a confirmation frame and sends a termination of active registration command to the distributed power equipment. The confirmation frame includes information confirming that the distributed power equipment has received the termination signal. Specific steps include:

[0038] The main grid registration module is responsible for managing the registration information of distributed power equipment and maintaining the registration registry for each device. Its workflow includes:

[0039] Step B1: Establish a simulated front-end on the main grid registrar;

[0040] Step B2: Simulate the front end receiving request information sent by distributed power equipment in real time;

[0041] Step B3: Parse the request information to obtain the simulated operation content contained in the request information;

[0042] Step B4: Control the simulation front end to perform simulation operations on the simulation operation content and obtain the simulation operation results;

[0043] Step B5: Control the simulation front end to send the simulation operation results to the distributed power equipment;

[0044] In step B6, after receiving the simulation operation results, the distributed power equipment returns the simulation operation results to the main grid registrar via a TCP connection. By having the distributed power equipment return the simulation operation results, the main grid registrar can verify whether the results received by the distributed equipment are consistent with the results sent, ensuring that no data is lost or tampered with during transmission, confirming whether the equipment has correctly executed the simulation operation, and understanding the current status of the equipment.

[0045] Step B7 involves data management of the data generated during the communication process between the main grid registrar and distributed power equipment.

[0046] The benefits of the above technical solution are as follows: During the communication process between the main grid registrar and distributed power equipment, various data will be generated, including requests, responses and possible log data. Data management involves the recording, storage, transmission and processing of data to ensure the integrity of communication and the accuracy of data.

[0047] Distributed power equipment includes:

[0048] The device registration module is used to send a registration request to the main grid registrar according to the preset rules and conditions of the distributed power equipment. This request includes information such as the device's communication security key, type, and location. The registration information sent by the device registration module to the main grid registrar includes:

[0049] Device communication key: Information used to uniquely identify each device. It is usually a unique string or number. A secure encryption mechanism can ensure that the main grid registrar can accurately and securely identify each distributed power device.

[0050] Equipment type: This indicates the type or category of equipment, such as solar panels, wind turbines, fuel cells, etc. This helps other equipment users identify and understand the functions and characteristics of new equipment.

[0051] Equipment location: This indicates the location information of the equipment, which can be geographic coordinates (latitude and longitude), physical address (street, building, etc.), or other forms of location description;

[0052] Equipment status: This includes information such as the equipment's operating status, working mode, and energy production. This helps other equipment understand the current operating status and performance of the equipment.

[0053] Other specific information: Depending on the actual needs, other specific equipment information may also be included, such as the equipment manufacturer, model, rated power, voltage, etc.

[0054] The device registration module's working steps include:

[0055] Step S1: Establish a communication architecture between the main grid registrar and distributed power devices;

[0056] Step S2: Establish a simulation front-end on the distributed power equipment and perform simulation operations;

[0057] The benefits of the above technical solution are as follows: the establishment of network connection, protocol settings and communication channels can ensure that distributed power equipment can communicate with the main grid registrar; the establishment of the distributed power equipment simulation front end means that the distributed power equipment will simulate the operation of the main grid registrar in order to realize simulated access to the main grid registrar. This process can include simulating user input, requests and operations.

[0058] The device discovery and communication module is used to query the main grid registrar or network communication protocol for data exchange and communication, including data querying, status updates, and event notifications, thereby discovering newly added devices. This includes: defining the communication protocol between the main grid registrar and distributed power devices; defining the data format between the main grid registrar and distributed power devices; using Secure Sockets Layer (SSL) to encrypt communication data between the main grid registrar and distributed power devices; the main grid registrar establishing a registration interface for distributed power devices to provide registration information; and establishing a long-lived connection between the main grid registrar and distributed power devices when registration is successful, ensuring that only authorized applications can communicate with the main grid registrar. The network communication protocol used is MQTT.

[0059] The benefits of the above technical solution are as follows: Secure communication: By defining communication protocols, data formats, and using encrypted communication, this method ensures the security of communication and prevents unauthorized access to communication data.

[0060] The data sharing and collaboration module is used to share data from distributed power equipment, such as energy generation, energy consumption, and grid load. Based on the status and needs of other devices in the network, it collaboratively performs operations such as energy scheduling and load balancing to optimize energy utilization efficiency. This includes:

[0061] Data sharing and query: The device sends data query requests to other devices through network communication protocols to obtain information such as the energy production and energy consumption of other devices; after receiving the query request, the device sends the corresponding data to the requesting device according to the content and permissions of the request.

[0062] Data update and status synchronization: The device periodically updates its own status information such as energy generation and energy consumption, and broadcasts or sends update notifications to other devices; the device that receives the update notification updates its own status information and broadcasts the updated status information to other devices;

[0063] Event notification and collaborative tasks: When a device detects a specific event (such as an energy supply alarm, a change in grid load, etc.), it sends an event notification to other devices; the device receiving the event notification performs corresponding collaborative tasks according to the content and requirements of the notification, such as adjusting energy production or reducing energy consumption.

[0064] Data processing and analysis: The data collected by the equipment can be used to analyze the operation of the energy system, identify potential problems and optimization opportunities; the equipment can take corresponding measures based on the analysis results, adjust its own working mode and parameters, and realize intelligent management and optimized scheduling of the energy system.

[0065] Testing and Verification: Test the normal operation of distributed power equipment, verify its functionality, and also test for abnormal conditions to assess the stability and robustness of the main power grid.

[0066] Shared distributed power equipment data includes:

[0067] Total energy output of distributed power equipment E gen :

[0068] E gen =P gen ×T gen (2)

[0069] In the formula, P gen T represents the energy output of distributed power equipment per unit time. gen Indicates production capacity over time;

[0070] Total energy consumption of distributed power equipment E con :

[0071] E con =P con ×T con (3)

[0072] In the formula, P con T represents the energy consumption of distributed power equipment per unit time. con Indicates energy consumption time;

[0073] Total load of the power grid L grid :

[0074] L grid =E gen -E con (4)

[0075] Energy storage status of distributed power equipment S storage :

[0076] S storage =C storage -E con +E gen (5)

[0077] In the formula, C storage This indicates the total energy storage capacity of distributed power equipment.

[0078] The benefits of the above technical solution are: it can realize data sharing and collaboration of distributed power equipment, intelligent management and optimized scheduling of energy systems, and improve energy utilization efficiency and system reliability.

[0079] The network maintenance and update module is used to periodically check the online status of devices, facilitating timely updates to the registry by the main grid registrar and deletion of offline device information. Distributed power devices maintain their online status by periodically sending heartbeat signals to the registrar; the steps include:

[0080] Step E1: Monitor the communication operation status between distributed power equipment and the main grid registrar in real time;

[0081] Step E2: Determine whether the communication operation status between the distributed power equipment and the main grid registrar has resulted in connection disconnection, timeout, and / or registration failure. When the communication operation status between the distributed power equipment and the main grid registrar results in connection disconnection, timeout, and / or registration failure, the occurrence of connection disconnection, timeout, and / or registration failure will be recorded in the log.

[0082] Periodic testing and verification are performed on distributed power equipment and the main grid registrar to determine whether the communication operation between the distributed power equipment and the main grid registrar has good functionality and operational status. The systems embedded in the connecting devices between the distributed power equipment and the main grid registrar are also updated and maintained periodically. This may include software updates, firmware upgrades, and security maintenance to ensure the reliability and security of the connected devices. The formula for obtaining the test cycle length is:

[0083] T test = [1-(M1 / M+N1 / N)×T] c / T z ]×T0 (6)

[0084] In the formula, T test Indicates the test period duration; M1 represents the number of communication connections; M represents the number of communication connection drops; N1 represents the total number of registrations; N represents the number of registration failures; T c T represents the average timeout duration corresponding to the actual communication duration relative to the theoretical communication duration across all communication connections. z T0 represents the average duration of a single communication for all communication connections, while T0 refers to the initial cycle time length, used to measure the communication operation status between the main grid registrar and distributed power equipment in the initial stage.

[0085] Based on the communication operation status information within the initial period, the system will calculate the test period length (T). test This period is determined based on whether the communication connection is broken, registration fails, or communication times out.

[0086] The advantages of the above technical solution are as follows: By calculating the test cycle length, the system can adaptively adjust the test frequency according to the actual communication situation. If communication is running normally, the test cycle can be extended, thereby reducing resource consumption. If problems occur, the test cycle can be shortened to detect and respond to problems more quickly.

[0087] like Figure 3 As shown, the intelligent optimization and prediction module is used to predict and optimize equipment status, energy demand, and supply using a neural network model, achieving intelligent scheduling and fault detection. It selects a suitable neural network model for equipment status prediction, fault detection, and energy scheduling optimization; trains and updates the neural network model, optimizing it using historical and real-time data; and uses the trained neural network model for real-time prediction and optimized scheduling. The neural network model selection module specifically includes:

[0088] A Long Short-Term Memory (LSTM) network model is used for time-series forecasting of equipment status and energy demand; the equipment status forecasting formula is as follows:

[0089]

[0090] In the formula, x represents the predicted device state at time t+1; t The input data represents time t; h t and c t f represents the hidden state and the cell state at time t, respectively; LSTM This represents the LSTM network function.

[0091] A Convolutional Neural Network (CNN) model is used for equipment fault detection and image recognition; the equipment fault detection formula is as follows:

[0092]

[0093] In the formula, Indicates the fault detection result; I represents the input image or information data; f CNN This represents the CNN network function.

[0094] Deep reinforcement learning (DRL) models are used to optimize energy dispatch strategies. The energy dispatch optimization formula is as follows:

[0095]

[0096] In the formula, a t s represents the optimal action at time t; t Q(s) represents the state at time t. t ,a;θ) represents the Q-value function of the DRL model, where θ is the model parameter.

[0097] Example 2

[0098] This embodiment presents a control method for distributed power equipment networking based on active registration, comprising the following steps:

[0099] Step 1: The main grid registrar sends a command to the distributed power equipment to activate active registration;

[0100] Step 2: Distributed power devices send registration requests and registration information to the main grid registrar according to the active registration command;

[0101] Step 3: The main grid registrar receives registration requests from distributed power devices and manages the registration information of distributed power devices;

[0102] Step 4: Distributed power devices discover other distributed power devices through the main grid registrar;

[0103] Step 5: After registration, distributed power devices obtain registration information managed by the main grid registrar. Based on this information, they acquire device data from other registered distributed power devices, as well as their own historical data. Distributed power devices collect data on energy generation, consumption, grid load, and environmental parameters (such as weather and temperature). The collected data undergoes preprocessing, including data cleaning, normalization, and feature extraction. Through an active registration mechanism, each device registers its preprocessed data and shares it with the central server or distributed database. The MQTT secure communication protocol ensures secure and real-time data transmission between devices.

[0104] Step 6: Distributed power equipment uses a neural network model to predict equipment status based on its historical data; selects an appropriate neural network model according to specific needs, such as LSTM for time series forecasting, CNN for fault detection, and DRL for optimized scheduling; trains the neural network model on a central server or cloud platform using a shared dataset, and performs model training and cross-validation using historical data; deploys the trained model to the distributed power equipment to ensure real-time performance and low latency. The LSTM model deployed on the distributed power equipment is used to predict equipment status and energy demand in real time.

[0105] Step 7: Distributed power equipment formulates energy dispatch strategies based on its own predicted equipment status, the equipment data of other distributed power equipment, and energy demand. Based on the prediction results, it uses the DRL model to optimize energy dispatch and allocation, and adjusts equipment operation strategies to improve energy utilization efficiency.

[0106] Step 8: Distributed power devices execute the optimized energy dispatch strategy and send request instructions to other registered distributed power devices to fulfill their needs according to the optimized energy dispatch strategy; or execute the request instructions received from other registered distributed power devices.

[0107] Step 9: Anomaly Detection and Self-Healing. A CNN model is used to detect anomalies in equipment operation and identify fault signals. The system executes self-healing strategies based on the detection results, such as switching to backup equipment or adjusting operating parameters.

[0108] Step 10: Data Analysis and Feedback. Collect the prediction and optimization results of the neural network model, as well as feedback data from actual operation. Analyze the model's performance and effectiveness, update and optimize the model, and improve the system's intelligence level and operational efficiency.

[0109] The beneficial effects of adopting this technical solution are as follows: by combining the active registration mechanism and neural network technology, the self-organizing network, intelligent management and optimized scheduling of distributed power equipment are realized. This not only improves the communication efficiency and data sharing capabilities between devices, but also enhances the system's prediction and fault detection capabilities, optimizes energy utilization efficiency, and ensures the stability and reliability of the power grid, thereby significantly improving the intelligence level and operating efficiency of the entire energy system.

[0110] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0111] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0112] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0113] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0114] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A distributed power equipment networking method based on active registration, characterized in that, include: The main grid registrar is used to send commands to distributed power devices to activate active registration, and to receive registration requests from distributed power devices and their own registration information. It also manages the registration information sent by all registered distributed power devices; Each distributed power device (DPD) is used to send a registration request and its own registration information to the main grid registrar based on the active registration command. After registration, it obtains the registration information managed by the main grid registrar, and obtains the device data of other registered DPDs based on the obtained registration information managed by the main grid registrar. It is used to predict its own device status using a neural network model based on its own historical data, formulate an energy dispatch strategy based on its own device status, the device data of other DPDs, and energy demand, and optimize the energy dispatch strategy using a neural network. It executes the optimized energy dispatch strategy and sends execution request instructions to other registered DPDs based on the optimized energy dispatch strategy. It is also used to execute the request instructions received from other registered DPDs.

2. The distributed power equipment networking based on active registration according to claim 1, characterized in that, The main grid registrar is used to determine whether it receives registration information from the distributed power equipment within a first set time after sending a command to activate active registration to the distributed power equipment. If no registration information is received, it queries the active registration progress of the distributed power equipment and performs the corresponding registration operation based on the queried active registration progress of the distributed power equipment.

3. The distributed power equipment networking based on active registration according to claim 2, characterized in that, The formula for calculating the first set time T is: T=T base +ω1×W+ω2×T type +ω3×S+ω4×H Among them, T base The first set time is the basic value, ω1, ω2, ω3, and ω4 are adjustment factors, W is the weight of the distributed power equipment slave node, T is the node type weight, S is the node status parameter, and H is the type. Historical registration time decay factor.

4. The distributed power equipment networking based on active registration according to claim 2, characterized in that, The step of performing the corresponding registration operation based on the queried active registration progress of the distributed power equipment includes replying with an instruction confirmation frame after receiving an instruction from the distributed power equipment that the active registration has ended, and sending a termination instruction for the slave node's active registration to the distributed power equipment. The instruction confirmation frame includes information confirming that the distributed power equipment has received the termination signal.

5. The distributed power equipment networking based on active registration according to claim 1, characterized in that, It also includes a network maintenance and update module, used to monitor the communication operation status between the main grid registrar and the distributed power equipment, and periodically test and verify the communication operation status between the main grid registrar and the distributed power equipment, with a test period length T. test The calculation formula is: T test =[1-(M1 / M+N1 / N)×T c / T z ]×T0 Where M1 is the number of communication connections; M is the number of communication connection drops; N1 is the total number of registrations; N is the number of registration failures; T c T represents the average timeout duration, calculated as the actual communication duration relative to the theoretical communication duration across all communication connections. z T0 represents the average duration of a single communication session for all communication connections; T0 represents the initial period duration.

6. The distributed power equipment networking based on active registration according to claim 1, characterized in that, It also includes a fault detection module, which uses a deep reinforcement learning model to detect faults in the device.

7. A control method for distributed power equipment networking based on active registration, characterized in that, Includes the following steps: Distributed power devices send a registration request and their own registration information to the main grid registrar based on the received active registration command; After a distributed power device registers, it obtains the registration information managed by the main grid registrar, and then obtains the device data of other registered distributed power devices based on the obtained registration information managed by the main grid registrar. The registration information managed by the main grid registrar includes registration information sent by all registered distributed power devices; Distributed power equipment uses a neural network model to predict its own equipment status based on its historical data, and formulates an energy dispatch strategy based on its own equipment status, the equipment data of other distributed power equipment, and energy demand, and then uses a neural network to optimize the energy dispatch strategy. Distributed power devices execute optimized energy dispatch strategies and send request instructions to other registered distributed power devices to fulfill their needs, based on the optimized energy dispatch strategies; or execute request instructions received from other registered distributed power devices.

8. The control method according to claim 7, characterized in that, After the main grid registrar determines whether to send a command to activate active registration to the distributed power equipment, it checks whether it receives registration information from the distributed power equipment within a first set time. If no registration information is received, it queries the active registration progress of the distributed power equipment and performs the corresponding registration operation based on the queried active registration progress of the distributed power equipment.

9. The control method according to claim 8, characterized in that, The formula for calculating the first set time T is: T=T base +ω1×W+ω2×T type +ω3×S+ω4×H Among them, T base The first set time is the basic value, ω1, ω2, ω3, and ω4 are adjustment factors, W is the weight of the distributed power equipment slave node, T is the node type weight, S is the node status parameter, and H is the type. Historical registration time decay factor.

10. The control method according to claim 8, characterized in that, The step of performing the corresponding registration operation based on the queried active registration progress of the distributed power equipment includes replying with an instruction confirmation frame after receiving an instruction from the distributed power equipment that the active registration has ended, and sending a termination instruction for the slave node's active registration to the distributed power equipment. The instruction confirmation frame includes information confirming that the distributed power equipment has received the termination signal.

11. The control method according to claim 7, characterized in that, This also includes monitoring the communication operation status between the main grid registrar and distributed power equipment, and periodically testing and verifying the communication operation status between the main grid registrar and distributed power equipment, with a test cycle length T. test The calculation formula is: T test =[1-(M1 / M+N1 / N)×T c / T z ]×T0 Where M1 is the number of communication connections; M is the number of communication connection drops; N1 is the total number of registrations; N is the number of registration failures; T c T represents the average timeout duration, calculated as the actual communication duration relative to the theoretical communication duration across all communication connections. z T0 represents the average duration of a single communication session for all communication connections; T0 represents the initial period duration.

12. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 7 to 11.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 7 to 11.

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