Energy Internet architecture and energy router thereof

Abstract

The invention relates to the technical field of energy management, and discloses an energy internet architecture and an energy router thereof, and the energy internet architecture comprises a data acquisition and monitoring module, an energy management module, a fault detection and response module, a user interface and control module and an intelligent optimization module. The data acquisition and monitoring module is used for monitoring and acquiring various parameters in the system in real time, processing and transmitting data, and ensuring that the state of the system is transparent and controllable; the energy management module is used for predicting future energy demands, optimizing distribution of energy resources and managing the charging and discharging process of the energy storage system, and the fault detection and response module is used for monitoring the system state in real time, detecting, diagnosing and identifying faults and executing preset emergency treatment measures. Accurate prediction and optimal scheduling of energy demands are achieved through the first converter, the second converter and the energy management module, so that efficient utilization of energy and energy balance of the system are ensured, the optimal operation state is achieved under different load conditions, and the overall energy efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to an energy Internet architecture and an energy router thereof. Background Art

[0002] Energy is the foundation for the survival and development of modern society. Whether it is family life, industrial production, or transportation, it is inseparable from the support of energy. Traditional energy such as coal, oil and natural gas, although providing abundant power sources for human beings, also brings about problems of environmental pollution and resource depletion. With the global emphasis on sustainable development, clean energy such as solar energy, wind energy, and hydropower are gradually gaining favor. However, how to efficiently utilize these diverse energy resources has become a major challenge. To meet this challenge, the energy Internet architecture came into being. Energy Internet is a system that organically combines energy production, transmission, storage and consumption through advanced Internet technology and smart grid technology. It aims to achieve efficient configuration and use of energy, and improve the overall efficiency and stability of the energy system through intelligent scheduling and management. In order to integrate the functions of the energy Internet architecture, we will use an energy router, which makes full use of modern Internet technology and smart grid technology. Through efficient energy conversion, precise energy management, real-time system monitoring, reliable security protection and intelligent optimization operation, it significantly improves the overall performance, stability and user experience of the energy system, and provides a solid foundation for future smart energy management.

[0003] First, the traditional energy Internet architecture and its energy routers are often inefficient when processing multiple external power sources, resulting in a large amount of energy being wasted during the conversion process. For example, when converting from AC to DC or from different voltage levels, the conversion efficiency of traditional converters may be only 80%-90%, which means that 10%-20% of the energy is lost during the conversion process. In addition, they are significantly incompatible with different types of power sources and cannot flexibly adapt to multiple power forms (such as solar energy, wind energy, city electricity and backup power). This limitation makes it impossible for the system to effectively integrate and utilize energy from different sources, limiting the flexibility and optimization space of energy utilization. Secondly, the traditional energy Internet architecture has low accuracy in load forecasting, making it difficult to accurately predict future energy demand; traditional methods rely on simple historical data regression analysis and lack comprehensive consideration of complex influencing factors. Inaccurate load forecasting leads to the system's inability to adjust energy supply in a timely manner during actual operation, resulting in unbalanced energy distribution and waste of resources. It is also difficult to achieve optimal allocation of energy resources, and scheduling decisions are often made based on static or outdated data, which cannot adapt to dynamically changing loads and energy supply conditions; the lack of scheduling optimization leads to the system's inability to achieve optimal operation under different load conditions; Finally, the monitoring methods of traditional energy Internet architecture are relatively simple, mainly relying on limited sensors and manual data records, making it difficult to comprehensively and accurately monitor the operating status and parameters of the system. This limitation makes it impossible for the system to detect and respond in a timely manner when abnormal situations occur. The real-time and accuracy of the monitoring data are insufficient, resulting in users being unable to obtain immediate status feedback of the system, increasing the difficulty and risk of operation and management. Users' understanding of the actual working conditions of the system depends on cumbersome data reports and manual records, and lacks intuitive status display tools. Traditional systems often do not have integrated visual user interfaces, making it difficult for users to quickly understand the operating conditions and potential problems of the system. In addition, traditional systems usually lack remote access and operation capabilities, and users can only monitor and operate the system on site, limiting the management flexibility of the system. Especially in distributed energy systems, the limitations of on-site management are more obvious. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an energy Internet architecture and an energy router thereof, which solve the problem that devices cannot reasonably schedule energy.

[0005] To achieve the above objectives, according to a first aspect of the present invention, the present invention provides an energy internet architecture, including a data acquisition and monitoring module, an energy management module, a fault detection and response module, a user interface and control module, and an intelligent optimization module; The data acquisition and monitoring module is used to monitor and collect various parameters in the system in real time, process and transmit data, and ensure the transparency and controllability of the system status; The energy management module is used to predict future energy demand, optimize the allocation of energy resources, and manage the charging and discharging process of the energy storage system; The fault detection and response module is used to monitor the system status in real time, detect and identify faults, diagnose the fault type and location, and execute preset emergency treatment measures; The user interface and control module is used to provide real-time data and system status display, allowing users to remotely access and operate, and help users understand and manage the system through data visualization tools; The intelligent optimization module is used to run the optimization algorithm and prediction model, perform load and energy output prediction, and dynamically adjust the control strategy.

[0006] Preferably, the data acquisition and monitoring module includes a sensor unit, a data processing unit and a communication unit; The sensor unit is used to monitor parameters in the system, and the parameters include: voltage, current, and temperature; The data processing unit is used to perform preliminary processing on the collected data, and the preliminary processing includes filtering; The communication unit is used to realize data transmission and communication.

[0007] Preferably, the energy management module includes a load forecasting unit, an energy dispatching unit and an energy storage management unit; The load forecasting unit is used to forecast future energy demand; The energy scheduling unit is used to optimize scheduling among different energy resources; The energy storage management unit is used to manage the charging and discharging process of the energy storage system.

[0008] Preferably, the fault detection and response module includes a fault detection unit, a fault diagnosis unit and an emergency response unit; The fault detection unit is used to monitor the system status in real time, detect and identify faults; The fault diagnosis unit is used to determine the fault type and location; The emergency response unit is used to execute preset emergency handling measures.

[0009] Preferably, the user interface and control module include a real-time monitoring interface, a remote control unit and a data visualization unit; The real-time monitoring interface is used to display real-time data and system status; The remote control unit is used to allow the user to remotely access and operate via a mobile device or computer; The data visualization unit is used to provide intuitive data charts and reports.

[0010] Preferably, the intelligent optimization module includes an optimization algorithm unit, a prediction model unit and an adaptive control unit; The optimization algorithm unit is used to develop and run the optimization algorithm; The prediction model unit is used to train and run load prediction and energy output prediction models; The adaptive control unit is used to dynamically adjust the control strategy according to real-time data.

[0011] Preferably, the optimization algorithm unit adopts a genetic algorithm for optimization, and the specific formula is: in, is the weight, Is an individual Energy efficiency, is the total energy efficiency after optimization, is the total number of units involved in the calculation in the system, with a value range of 1≤ ≤N, where N is the total number of units actually existing in the system; The genetic algorithm optimization comprises the following steps: S11. Selection: Use roulette selection method or tournament selection method to select excellent individuals; S22, crossover: perform crossover operation on the selected individuals to generate new offspring individuals; S33, mutation: perform mutation operations on new individuals to increase population diversity; S44, iterative process: repeat fitness evaluation, selection, crossover and mutation until the termination condition is met.

[0012] Preferably, the prediction model unit adopts a random forest model, and the corresponding loss function formula is: in, is the actual value, is the predicted value, is the loss function value, is the total number of samples, ranging from 10≤ 1000000; The random forest model training includes the following steps: S21, data preprocessing: collect historical energy output data, perform feature extraction and normalization; S22, model construction: build a random forest model and set the number and depth parameters of the tree; S23, model training: Use the training data set to train the random forest model and adjust the number of trees and the maximum depth; S24. Model validation: Use validation data sets to evaluate model performance and avoid overfitting; S25, model adjustment: adjust the model structure and the number and maximum depth of trees according to the verification results; S26. Model testing: Use the test dataset to test the final model and evaluate its generalization ability.

[0013] According to a second aspect of the present invention, there is provided an energy router, comprising a body, a converter 1 is provided on one side of the body, a converter 2 is provided on the other side of the body, a battery module is provided inside the body, a heat conductive strip is provided on one side of the battery module, a cooling fan is provided on one side of the heat conductive strip, a cooling hole is provided inside the body, a display module is provided in the middle of the body, a display screen is provided on the front side of the body, the display module is electrically connected to the display screen, a management module is provided inside the body, a conversion module is provided on one side of the management module, the management module and the conversion module are electrically connected, an intelligent module is provided inside the body, a detection module is provided on one side of the intelligent module, the intelligent module and the detection module are electrically connected, external access interfaces are provided on both sides of the body, and a monitoring module is provided inside the body.

[0014] The present invention provides an energy internet architecture and an energy router thereof, which have the following beneficial effects: 1. The present invention efficiently converts a variety of external power sources into electrical energy suitable for storage and use by the internal battery module through converter 1 and converter 2, and then realizes accurate prediction and optimized scheduling of energy demand through the energy management module, thereby ensuring efficient utilization of energy and energy balance of the system, enabling it to achieve optimal operating state under different load conditions, thereby improving overall energy efficiency.

[0015] 2. The present invention uses real-time monitoring through the data acquisition and monitoring module to ensure the accuracy and reliability of the data, and then uses the display module and display screen to display the operating status and parameters of the equipment in real time, so that users can intuitively understand the working condition of the equipment. The external access interface allows external devices or users to access the energy router to configure and read data, so that users can efficiently manage and operate the system, thereby improving the operability of the equipment and user experience.

[0016] 3. The present invention improves the safety and reliability of the system through the fault detection and response module and the intelligent optimization module, can quickly respond to faults, reduce downtime, ensure the normal operation of the system, and improve the intelligence level and operating efficiency of the system, ensuring that the energy system achieves optimal performance under various dynamic conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a distribution diagram of the energy internet architecture modules of the present invention; Figure 2 It is a schematic diagram of the data acquisition and monitoring module of the present invention; Figure 3 It is a schematic diagram of the energy management module of the present invention; Figure 4 It is a schematic diagram of a fault detection and response module of the present invention; Figure 5 A schematic diagram of a user interface and a control module of the present invention; Figure 6 It is a schematic diagram of the intelligent optimization module of the present invention; Figure 7 A three-dimensional diagram of the energy router of the present invention; Figure 8 It is the internal structure distribution diagram of the body of the present invention.

[0018] Among them, 1. body; 2. converter 1; 3. converter 2; 4. battery module; 5. thermal conductive strip; 6. cooling fan; 7. heat dissipation hole; 8. display module; 9. display screen; 10. management module; 11. conversion module; 12. intelligent module; 13. detection module; 14. external access interface; 15. monitoring module. DETAILED DESCRIPTION

[0019] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Embodiment 1: See also Figure 1 ,This embodiment provides an energy Internet architecture, including a data acquisition and monitoring module, an energy management module, a fault detection and response module, a user interface and control module, and an intelligent optimization module; The data acquisition and monitoring module is used to monitor and collect various system parameters in real time, process and transmit data, and ensure the transparency and controllability of the system status; The energy management module is used to predict future energy demand, optimize the allocation of energy resources, and manage the charging and discharging process of the energy storage system; The fault detection and response module is used to monitor the system status in real time, detect and identify faults, diagnose the fault type and location, and execute preset emergency response measures; The user interface and control module is used to provide real-time data and system status display, allowing users to remotely access and operate, and help users understand and manage the system through data visualization tools; The intelligent optimization module is used to run optimization algorithms and prediction models, make load and energy output forecasts, and dynamically adjust control strategies.

[0021] Specifically, the data acquisition and monitoring module uses sensors distributed throughout the system to collect data and transmits it to the central processing unit through the communication network for analysis and processing. The system can alarm for abnormal data and transmit the processed data to other modules for further operation, thereby ensuring the transparency and controllability of the system status, providing necessary data support for other modules, and helping to improve the stability and reliability of the system; the energy management module uses predictive algorithms to analyze historical energy usage data and current status, predict future energy demand, and optimize the allocation of energy resources based on the predicted results. In addition, the energy management module is also responsible for managing the energy storage system and controlling the battery charging and discharging process to maintain the energy balance of the system. Through prediction and optimization, the energy management module can reduce energy waste, improve system efficiency, and extend the service life of energy storage equipment, while ensuring the stability and reliability of energy supply; the fault detection and response module uses real-time monitoring data, analyzes the system operation status through algorithms, identifies abnormal situations and To locate the fault point, the module has built-in multiple emergency response measures and can automatically select the appropriate response plan according to the fault type, which can significantly improve the safety and reliability of the system, reduce the downtime caused by faults, and protect the normal operation of equipment and systems; the user interface and control module displays the system's operating status, historical data and forecast information through a graphical interface. Users can operate and set up through the interface. The module supports remote access, and users can monitor and manage the system at any location, thereby improving the system's operability and user experience, enabling users to monitor and manage the energy system more conveniently, and enhancing the transparency and controllability of the system; the intelligent optimization module is based on real-time data and historical data. The intelligent optimization module predicts load demand and energy output through machine learning and optimization algorithms, formulates the optimal control strategy, and adjusts the system operating parameters in real time, thereby improving the system's intelligence level and operating efficiency, and can adapt to dynamically changing energy demand and supply conditions to achieve the best performance of the energy system.

[0022] See also Figure 2-Figure 5 ,The data acquisition and monitoring module includes a sensor unit, a data processing unit and a communication unit; The sensor unit is used to monitor the parameters in the system, including: voltage, current, temperature; The data processing unit is used to perform preliminary processing on the collected data, and the preliminary processing includes filtering; The communication unit is used to realize data transmission and communication; The energy management module includes a load forecasting unit, an energy dispatching unit, and an energy storage management unit; The load forecasting unit is used to predict future energy demand; The energy dispatching unit is used to optimize the dispatching among different energy resources; The energy storage management unit is used to manage the charging and discharging process of the energy storage system; The fault detection and response module includes a fault detection unit, a fault diagnosis unit and an emergency response unit; The fault detection unit is used to monitor the system status in real time, detect and identify faults; The fault diagnosis unit is used to determine the fault type and location; The emergency response unit is used to execute preset emergency treatment measures; The user interface and control module includes a real-time monitoring interface, a remote control unit, and a data visualization unit; The real-time monitoring interface is used to display real-time data and system status; The remote control unit is used to allow users to remotely access and operate via a mobile device or computer; The data visualization unit is used to provide intuitive data charts and reports.

[0023] Specifically, the sensor unit collects data through sensing devices, converts analog signals into digital signals, and transmits them to the data processing unit, so as to accurately and in real time obtain key information on the system operation status and provide basic data support for the stable operation of the system; the data processing unit pre-processes the collected raw data, including noise filtering, data smoothing and outlier detection, to ensure data quality, thereby improving data reliability, reducing false alarms and missed alarms, and providing a high-quality data foundation for subsequent data analysis and processing; the communication unit uses wireless or wired communication technology such as WiFi to transmit the processed data to the central control system or other modules, thereby ensuring real-time transmission of data. Improve the response speed and overall efficiency of the system; the load forecasting unit uses big data analysis and machine learning algorithms to analyze historical energy usage data and current loads to predict future energy demand, so as to plan energy distribution in advance, improve energy utilization efficiency, and avoid energy waste; the energy dispatching unit dynamically adjusts the allocation of energy resources and optimizes the dispatch between energy sources based on load forecast results and real-time energy supply conditions, so as to achieve optimal utilization of energy resources, balance energy supply and demand, and improve the overall efficiency of the system; the energy storage management unit monitors the energy storage system in real time, intelligently controls the charging and discharging process, maintains the energy balance of the system, and thus extends the service life of the energy storage equipment. Ensure the stability and reliability of the system; the fault detection unit uses sensors and monitoring equipment to collect system operation data in real time, and detects anomalies through algorithm analysis, so as to quickly discover faults and improve the safety and operational reliability of the system; the fault diagnosis unit determines the specific type and location of the fault through data analysis and pattern recognition, and provides detailed fault reports to improve the accuracy and efficiency of fault handling and reduce fault downtime; the emergency response unit automatically or manually initiates emergency handling measures according to the preset emergency response strategy, such as switching to backup power supply and shutdown protection, to ensure that the system can respond quickly when a fault occurs, avoid the spread of faults, and ensure system safety; real-time The monitoring interface uses a graphical interface to display the system's real-time data and status information to users in the form of charts and dashboards, thereby improving users' understanding of the system status and facilitating real-time monitoring and management; the remote control unit uses the Internet and communication technology, and users can access the system interface through a remote connection to adjust parameters and control operations, thereby enhancing the system's operational flexibility and convenience, allowing users to manage the system anytime, anywhere; the data visualization unit uses data visualization technology to convert complex data into easy-to-understand charts and reports, showing the system's operating conditions and historical trends, thereby improving data readability and analysis efficiency, and helping users make more informed decisions.

[0024] See also Figure 6 ,The intelligent optimization module includes an optimization algorithm unit, a prediction model unit and an adaptive control unit; The optimization algorithm unit is used to develop and run optimization algorithms; The forecast model unit is used to train and run load forecast and energy output forecast models; The adaptive control unit is used to dynamically adjust the control strategy according to real-time data; The optimization algorithm unit uses genetic algorithm for optimization, and the specific formula is: in, is the weight, Is an individual Energy efficiency, is the total energy efficiency after optimization, is the total number of units involved in the calculation in the system, with a value range of 1≤ ≤N, where N is the total number of units actually existing in the system; Genetic algorithm optimization includes the following steps: S11. Selection: Use roulette selection method or tournament selection method to select excellent individuals; S22, crossover: perform crossover operation on the selected individuals to generate new offspring individuals; S33, mutation: perform mutation operations on new individuals to increase population diversity; S44, iterative process: repeat fitness evaluation, selection, crossover and mutation until the termination condition is met; The prediction model unit adopts the random forest model, and the corresponding loss function formula is: in, is the actual value, is the predicted value, is the loss function value, is the total number of samples, ranging from 10≤ 1000000; Random forest model training consists of the following steps: S21, data preprocessing: collect historical energy output data, perform feature extraction and normalization; S22, model construction: build a random forest model and set the number and depth parameters of the tree; S23, model training: Use the training data set to train the random forest model and adjust the number of trees and the maximum depth; S24. Model validation: Use validation data sets to evaluate model performance and avoid overfitting; S25, model adjustment: adjust the model structure and the number and maximum depth of trees according to the verification results; S26. Model testing: Use the test dataset to test the final model and evaluate its generalization ability.

[0025] Specifically, the optimization algorithm unit is used to develop and run the optimization algorithm, and adopts the genetic algorithm for optimization. The genetic algorithm is an optimization algorithm that simulates the natural selection process and is suitable for solving complex optimization problems. In this way, the genetic algorithm can find the optimal or near-optimal solution in a complex energy management environment, thereby improving the overall energy efficiency and stability of the system; the prediction model unit adopts the random forest model for prediction. The random forest model is an integrated learning method that improves the prediction accuracy by constructing multiple decision trees. The random forest model can effectively improve the accuracy and robustness of the prediction by integrating multiple decision trees, and is suitable for dealing with complex energy demand and output prediction problems; the adaptive control unit monitors the system's operating data in real time, and dynamically adjusts the control strategy according to the output of the optimization algorithm and the prediction model. For example, the energy allocation strategy is adjusted according to the load forecast, and the charging and discharging process of the energy storage system is adjusted according to the energy output forecast. In this way, the system can be dynamically adjusted according to the real-time changing working conditions, thereby improving the system's response speed and overall operating efficiency.

[0026] Embodiment 2: See also Figure 7-Figure 8 The present embodiment provides an energy router, including a body 1, a converter 1 2 is arranged on one side of the body 1, a converter 2 3 is arranged on the other side of the body 1, a battery module 4 is arranged inside the body 1, a heat conducting strip 5 is arranged on one side of the battery module 4, a cooling fan 6 is arranged on one side of the heat conducting strip 5, a cooling hole 7 is opened inside the body 1, a display module 8 is arranged in the middle of the body 1, a display screen 9 is arranged on the front side of the body 1, the display module 8 is electrically connected to the display screen 9, a management module 10 is arranged inside the body 1, a conversion module 11 is arranged on one side of the management module 10, and the management module 10 and the conversion module 11 are electrically connected, an intelligent module 12 is arranged inside the body 1, a detection module 13 is arranged on one side of the intelligent module 12, and the intelligent module 12 and the detection module 13 are electrically connected, external access interfaces 14 are arranged on both sides of the body 1, and a monitoring module 15 is arranged inside the body 1.

[0027] Specifically, the device is responsible for converting the external power supply into electric energy suitable for storage and use by the internal battery module 4 through the converter 1 2 and the converter 2 3. The converter 1 2 and the converter 2 3 can efficiently convert different types of external power supplies into stable electric energy, thereby meeting the charging requirements of the battery module 4. The converted electric energy is stored in the battery module 4. A heat-conducting strip 5 is provided on one side of the module to conduct away the heat generated during the operation of the battery to prevent the battery from overheating and affecting its performance or life; a cooling fan 6 is installed on one side of the heat-conducting strip 5 to further enhance the heat dissipation effect by forcing air flow. The heat dissipation holes 7 opened inside the body 1 also help to dissipate the heat of the device, thereby ensuring that the device operates within a safe temperature range to avoid performance degradation or failure due to overheating. The design of the heat dissipation system ensures that the device can still work stably and reliably under high load or long-term operation; a display module 8 is provided in the middle of the body 1. The module displays the operating status and parameters of the device in real time through a display screen 9, such as voltage, current, temperature and battery power, which not only enables the user to intuitively understand the working status of the device, but also to discover in time and solve potential problems. The presence of the display screen 9 improves the operability and user experience of the device. A management module 10 is arranged inside the body 1, which is responsible for the overall management of the device, including energy scheduling, fault detection and system maintenance. The management module 10 and the conversion module 11 are electrically connected to transmit data and control signals to ensure coordinated work between the modules. The intelligent module 12 and the detection module 13 interact through electrical connection to realize intelligent control and detection functions. The intelligent module 12 can adjust the control strategy according to real-time data to improve the response speed and efficiency of the system. When it is necessary to access the body 1 from the outside, the external access interface 14 can be used to allow external devices or users to access and configure and read data from the energy router. This design provides great flexibility and convenience, allowing users to remotely monitor and manage the device. In addition, the monitoring module 15 can monitor the status of each component inside the device when the device is running to ensure the normal operation of the device. The monitoring module 15 can promptly detect and report any abnormal situation, so as to quickly take measures to prevent the spread of faults and ensure the safety and reliability of the equipment.

[0028] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An energy internet architecture, characterized in that: It includes data acquisition and monitoring module, energy management module, fault detection and response module, user interface and control module and intelligent optimization module; The data acquisition and monitoring module is used to monitor and collect various parameters in the system in real time, process and transmit data, and ensure the transparency and controllability of the system status; The energy management module is used to predict future energy demand, optimize the allocation of energy resources, and manage the charging and discharging process of the energy storage system; The fault detection and response module is used to monitor the system status in real time, detect and identify faults, diagnose the fault type and location, and execute preset emergency treatment measures; The user interface and control module is used to provide real-time data and system status display, allowing users to remotely access and operate, and help users understand and manage the system through data visualization tools; The intelligent optimization module is used to run the optimization algorithm and prediction model, perform load and energy output prediction, and dynamically adjust the control strategy.

2. The energy internet architecture according to claim 1, characterized in that: The data acquisition and monitoring module includes a sensor unit, a data processing unit and a communication unit; The sensor unit is used to monitor parameters in the system, and the parameters include: voltage, current, and temperature; The data processing unit is used to perform preliminary processing on the collected data, and the preliminary processing includes filtering; The communication unit is used to realize data transmission and communication.

3. The energy internet architecture according to claim 1, characterized in that: The energy management module includes a load forecasting unit, an energy dispatching unit and an energy storage management unit; The load forecasting unit is used to forecast future energy demand; The energy scheduling unit is used to optimize scheduling among different energy resources; The energy storage management unit is used to manage the charging and discharging process of the energy storage system.

4. The energy internet architecture according to claim 1, characterized in that: The fault detection and response module includes a fault detection unit, a fault diagnosis unit and an emergency response unit; The fault detection unit is used to monitor the system status in real time, detect and identify faults; The fault diagnosis unit is used to determine the fault type and location; The emergency response unit is used to execute preset emergency handling measures.

5. The energy internet architecture according to claim 1, characterized in that: The user interface and control module includes a real-time monitoring interface, a remote control unit and a data visualization unit; The real-time monitoring interface is used to display real-time data and system status; The remote control unit is used to allow the user to remotely access and operate via a mobile device or computer; The data visualization unit is used to provide intuitive data charts and reports.

6. The energy internet architecture according to claim 1, characterized in that: The intelligent optimization module includes an optimization algorithm unit, a prediction model unit and an adaptive control unit; The optimization algorithm unit is used to develop and run the optimization algorithm; The prediction model unit is used to train and run load prediction and energy output prediction models; The adaptive control unit is used to dynamically adjust the control strategy according to real-time data.

7. The energy internet architecture according to claim 1, characterized in that: The optimization algorithm unit adopts genetic algorithm for optimization, and the specific formula is: in, is the weight, Is an individual Energy efficiency, is the total energy efficiency after optimization, is the total number of units involved in the calculation in the system, with a value range of 1≤ ≤N, where N is the total number of units actually existing in the system; The genetic algorithm optimization comprises the following steps: S11. Selection: Use roulette selection method or tournament selection method to select excellent individuals; S22, crossover: perform crossover operation on the selected individuals to generate new offspring individuals; S33, mutation: perform mutation operations on new individuals to increase population diversity; S44, iterative process: repeat fitness evaluation, selection, crossover and mutation until the termination condition is met.

8. The energy internet architecture according to claim 1, characterized in that: The prediction model unit adopts a random forest model, and the corresponding loss function formula is: in, is the actual value, is the predicted value, is the loss function value, is the total number of samples, ranging from 10≤ 1000000; The random forest model training includes the following steps: S21, data preprocessing: collect historical energy output data, perform feature extraction and normalization; S22, model construction: build a random forest model and set the number and depth parameters of the tree; S23, model training: Use the training data set to train the random forest model and adjust the number of trees and the maximum depth; S24. Model validation: Use validation data sets to evaluate model performance and avoid overfitting; S25, model adjustment: adjust the model structure and the number and maximum depth of trees according to the verification results; S26. Model testing: Use the test dataset to test the final model and evaluate its generalization ability.

9. An energy router, according to the energy internet architecture according to any one of claims 1 to 9, comprising a body (1), characterized in that: A converter 1 (2) is arranged on one side of the body (1), a converter 2 (3) is arranged on the other side of the body (1), a battery module (4) is arranged inside the body (1), a heat conducting strip (5) is arranged on one side of the battery module (4), a cooling fan (6) is arranged on one side of the heat conducting strip (5), a cooling hole (7) is opened inside the body (1), a display module (8) is arranged in the middle of the body (1), a display screen (9) is arranged on the front side of the body (1), the display module (8) is electrically connected to the display screen (9), and the body (1) is provided with a management module (10) inside, a conversion module (11) is provided on one side of the management module (10), the management module (10) and the conversion module (11) are electrically connected, an intelligent module (12) is provided inside the body (1), a detection module (13) is provided on one side of the intelligent module (12), the intelligent module (12) and the detection module (13) are electrically connected, external access interfaces (14) are provided on both sides of the body (1), and a monitoring module (15) is provided inside the body (1).

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