Edge control system and policy generation method

Abstract

This application provides an edge control system and a strategy generation method. The edge control system is located at the edge node of vehicle-to-grid (V2G) interaction and connected to multiple devices, including at least an on-board charger and a charging pile. The system includes: a data transmission device for receiving device data sent by the multiple devices; a strategy determination device connected to the data transmission device for determining control strategies corresponding to the multiple devices based on vehicle data, charging pile data, and power grid data; and a local control device connected to the strategy determination device and the data transmission device for generating control commands for the multiple devices based on the control strategies and sending the control commands to the multiple devices through the data transmission device. This application solves the technical problem of high control latency in V2G interaction in related technologies.

Description

Technical Field

[0001] This application relates to the field of vehicle-to-everything (V2X) interaction, and more specifically, to an edge control system and a strategy generation method. Background Technology

[0002] With the rapid popularization of new energy vehicles, energy interaction between the power grid and transportation system is becoming more frequent. Vehicle-to-grid (V2G) interaction is receiving increasing attention; however, how to efficiently achieve power regulation is an urgent problem to be solved.

[0003] Related technologies typically employ centralized scheduling, which relies on centralized control. However, as the load increases and the network environment becomes unstable, centralized scheduling may suffer from significant control latency.

[0004] There is currently no good solution to the above problems. Summary of the Invention

[0005] This application provides an edge control system and a strategy generation method to at least solve the technical problem of high control latency for vehicle-to-grid interaction in related technologies.

[0006] According to one aspect of the embodiments of this application, an edge control system is provided, disposed at an edge node of vehicle-to-grid interaction, and connected to multiple devices, the multiple devices including at least an on-board charger and a charging pile, comprising: a data transmission device for receiving device data sent by the multiple devices, wherein the device data includes: vehicle data, charging pile data, and power grid data; a strategy determination device connected to the data transmission device for determining control strategies corresponding to the multiple devices based on the vehicle data, charging pile data, and power grid data; and a local control device connected to the strategy determination device and the data transmission device for generating control commands for the multiple devices based on the control strategies, and sending the control commands to the multiple devices through the data transmission device, wherein the control commands are used to control the operation of the multiple devices.

[0007] Optionally, the data transmission device includes: multiple communication interface modules, each connected to multiple devices; and a data acquisition module, connected to the multiple communication interface modules, for receiving device data through the multiple communication interface modules.

[0008] Optionally, the strategy determination device includes: a data fusion and identification module connected to the data transmission device, used to fuse vehicle data, charging pile data, and power grid data to obtain fused data, and to identify the status of multiple devices based on the fused data to obtain status data of multiple devices; an edge computing module connected to the data fusion and identification module, used to determine a first control strategy for multiple devices based on the fused data and status data; and a cloud-edge collaboration module connected to the edge computing module and the cloud, used to determine a control strategy based on the connection status with the cloud and the first control strategy; preferably, the cloud-edge collaboration module is further used to determine the first control strategy as the control strategy when the connection status is abnormal; preferably, the cloud-edge collaboration module is further used to receive a second control strategy sent by the cloud and determine the second control strategy as the control strategy when the connection status is normal.

[0009] Optionally, the system further includes: a security management module, located between the data transmission device and the local control device, used to perform identity authentication on multiple devices based on device data upon receiving an authentication command issued by the local control device, and obtain authentication results, wherein the authentication results are used to indicate whether multiple devices have passed authentication; preferably, the security management module is also used to perform fault detection on multiple devices based on device data upon receiving a detection command issued by the local control device, and obtain detection results, wherein the detection results are used to indicate whether multiple devices have faults.

[0010] Optionally, the system further includes a data relay device, which is located between the data transmission device and the policy determination device, for transmitting device data to the cloud in real time via the policy determination device.

[0011] According to another aspect of the embodiments of this application, a strategy generation method is also provided, applied to a strategy determination device in an edge control system in various embodiments of this application, comprising: receiving device data sent by a data transmission device, wherein the device data includes vehicle data, charging pile data, and power grid data, the device data being sent by multiple devices connected to the edge control system, the multiple devices including at least an on-board charger and a charging pile; determining control strategies corresponding to the multiple devices based on the vehicle data, charging pile data, and power grid data; and sending the control strategies to a local control device so that the local control device controls the multiple devices to operate based on the control strategies.

[0012] Optionally, based on vehicle data, charging pile data, and power grid data, control strategies for multiple devices are determined, including: fusing vehicle data, charging pile data, and power grid data to obtain fused data; identifying the status of multiple devices based on the fused data to obtain status data of multiple devices; and determining control strategies based on the connection status with the cloud, as well as the fused data and status data.

[0013] Optionally, a control strategy is determined based on the connection status with the cloud, as well as the fused data and status data, including: determining a first control strategy based on the fused data and status data; and determining a control strategy based on the connection status and the first control strategy. Preferably, determining a control strategy based on the connection status and the first control strategy includes: determining the first control strategy as the control strategy when the connection status is an abnormal connection. Preferably, the above method further includes: receiving a second control strategy issued by the cloud when the connection status is a normal connection, and determining the second control strategy as the control strategy.

[0014] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0015] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0016] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0017] According to another aspect of the embodiments of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0018] According to another aspect of the embodiments of this application, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of this application.

[0019] In this embodiment, the edge control system is located at the edge node of the vehicle-to-grid interaction and connected to multiple devices, including at least an on-board charger and a charging pile. The system includes: a data transmission device for receiving device data sent by the multiple devices; a strategy determination device connected to the data transmission device for determining control strategies corresponding to the multiple devices based on vehicle data, charging pile data, and power grid data; and a local control device connected to the strategy determination device and the data transmission device for generating control commands for the multiple devices based on the control strategies and sending the control commands to the multiple devices via the data transmission device. The edge control system of this application is located at the edge node of the vehicle-to-grid interaction. By connecting to multiple devices, including at least an on-board charger and a charging pile, it can receive device data sent by the multiple devices in a timely manner through the data transmission device. The strategy determination device then analyzes the control strategies corresponding to each device locally based on vehicle data, charging pile data, and power grid data, thereby generating control commands in the local control device and sending them to each device in a timely manner. This achieves efficient control of the multiple devices in the vehicle-to-grid interaction, reducing control latency and solving the technical problem of high control latency in related technologies. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0021] Figure 1 This is a schematic diagram of an edge control system according to an embodiment of this application;

[0022] Figure 2 This is a schematic diagram of an optional V2G edge control system according to an embodiment of this application;

[0023] Figure 3 This is a schematic diagram of an optional edge device according to an embodiment of this application;

[0024] Figure 4 This is a schematic diagram of an optional edge control method according to an embodiment of this application;

[0025] Figure 5 This is a flowchart of a strategy generation method according to an embodiment of this application;

[0026] Figure 6 This is a schematic diagram of a strategy generation apparatus according to an embodiment of this application. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] According to an embodiment of this application, an embodiment of an edge control system is provided. Figure 1 This is a schematic diagram of an edge control system according to an embodiment of this application, such as... Figure 1 As shown, the edge control system 100 is located at the edge node of the vehicle-to-everything (V2X) interaction and is connected to multiple devices 200. Figure 1 (Taking two devices as an example for demonstration), multiple devices include at least a vehicle charger and a charging station, including:

[0030] The data transmission device 102 is used to receive device data sent by multiple devices, including vehicle data, charging pile data, and power grid data.

[0031] The strategy determination device 104 is connected to the data transmission device and is used to determine the control strategies corresponding to multiple devices based on vehicle data, charging pile data and power grid data.

[0032] The local control device 106 is connected to the strategy determination device and the data transmission device. It is used to generate control commands for multiple devices based on the control strategy and send the control commands to the multiple devices through the data transmission device. The control commands are used to control the operation of the multiple devices.

[0033] The aforementioned vehicle-to-grid (V2G) interaction is an important application model of the energy internet, referring to the bidirectional energy flow between vehicles and the power grid. In the V2G model, vehicles equipped with power batteries can not only act as energy consumers, obtaining electricity from the grid for charging, but also as mobile energy storage units, releasing electricity back to the grid and participating in grid regulation. By tightly connecting electric vehicles and the power grid through V2G interaction, dynamic energy balance and efficient utilization are achieved.

[0034] Vehicle-to-grid (V2G) interaction may involve the bidirectional flow of energy; during the charging phase, the grid supplies electricity to the vehicle, while during the discharging phase, the vehicle feeds electricity back to the grid. As an energy storage unit, the vehicle can supplement the grid's energy storage during off-peak hours, helping to peak shaving and valley filling, improving grid stability and the absorption capacity of renewable energy. Through V2G technology, the grid can intelligently schedule vehicle charging and discharging behavior, for example, charging during periods of low electricity prices and discharging during periods of high electricity prices or grid congestion. Furthermore, users can set charging and discharging preferences through the V2G system, such as reserving full charge for a specific time or allowing vehicles to participate in discharging under specific conditions for economic gain.

[0035] The aforementioned edge nodes refer to computing and data processing entities located close to data sources and end users in a distributed network. Compared to traditional centralized cloud data centers, edge nodes are closer to network terminal devices, such as charging piles and vehicle sensors, enabling them to perform data processing and real-time decision-making locally, reducing data transmission latency and bandwidth consumption.

[0036] In a V2G edge control system, edge nodes can collect and process local data, including preprocessing such as data cleaning, format conversion, and feature recognition. Based on the collected data, they can quickly calculate control strategies and translate these strategies into specific control commands, which are then sent to charging piles, inverters, and other devices to execute charging and discharging operations.

[0037] The role of edge nodes in vehicle-to-grid (V2G) interaction is to move the V2G control logic to the end of the network, achieving low latency in control decisions and increasing the system's responsiveness and flexibility. Especially when facing rapidly changing power grid conditions or personalized user needs, edge control systems deployed at edge nodes can respond quickly while ensuring data privacy and security, laying a solid foundation for building an intelligent, efficient, and secure V2G ecosystem.

[0038] The aforementioned data transmission device connects the edge control device in the V2G edge control system to various external devices, such as charging piles, vehicle on-board chargers (OBCs), inverters, and power management systems, ensuring smooth and accurate data interaction between these devices and the control device. The data transmission device can adapt to different data formats and communication protocols. It can transmit vehicle data, charging pile data, and power grid data. Furthermore, it can also transmit ground lock system protocol data to facilitate control of the ground lock system. The efficient transmission of this real-time data is a prerequisite for data fusion and strategy calculation by the edge control system and directly affects the accuracy and timeliness of V2G operation.

[0039] Vehicle data may include, but is not limited to, vehicle status data and user-defined data. Vehicle status data may include, but is not limited to, onboard battery parameters, battery state of charge (SOC), battery state of health (SOH), and maximum permissible power. User-defined data may include, but is not limited to, scheduled travel time and minimum SOC threshold. Charging station data may include, but is not limited to, voltage, current, frequency, and load. Grid data can reflect energy output, such as photovoltaic power and remaining energy storage capacity.

[0040] The aforementioned strategy determination device can receive multi-source data from a data transmission device and calculate a control strategy by processing and analyzing this data. The control strategy may involve determining charging or discharging modes, setting power output, and how to participate in grid peak shaving and valley filling. The goal of the strategy determination device is to maximize energy efficiency while considering user needs and equipment safety.

[0041] For example, if multi-source data indicates that the current period is a peak period for grid load and the vehicle's SOC is high, the strategy determination device may decide to put the vehicle into discharge mode to help the grid reduce peak loads. At the same time, considering that the vehicle will be used in 2 hours, the strategy determination device may also output a strategy to reserve enough charging time to ensure that the vehicle's SOC reaches the predetermined level before the user travels.

[0042] The aforementioned control strategy is a scheme generated in the edge control system for vehicle-to-grid interaction, based on collected vehicle data, charging pile data, power grid data, etc., through data fusion and analysis, model prediction, and algorithm calculation, to coordinate the charging and discharging operations of multiple devices (such as electric vehicles and charging piles).

[0043] Control strategies can include, but are not limited to, charging and discharging modes, such as determining whether to charge, discharge, or remain in standby mode based on the real-time state of the power grid and user demand. Power allocation determines the power level at which vehicles discharge to or charge from the grid to achieve goals such as peak shaving, energy cost savings, or meeting specific user needs. Prioritizing different charging tasks or discharging demands ensures that critical loads or urgent needs are met first. Control strategies can be used for precise control of charging and discharging modes and power, maximizing the use of renewable energy, reducing dependence on fossil fuels, and lowering energy costs. Balancing grid load, such as through scheduling priorities and power allocation strategies, helps the grid reduce load during peak periods and increase energy storage during off-peak periods to maintain stable grid operation.

[0044] The aforementioned local control device can receive control policies from the policy determination device and convert these policies into specific operational instructions to guide the behavior of multiple devices. Compared to cloud-based control, local control places greater emphasis on real-time performance and adaptability to local conditions, enabling it to independently perform basic charging and discharging control and services even under network constraints or cloud response delays.

[0045] The local control device may include, but is not limited to: a processor and memory: used to run the control logic program and store device status and control strategies. This can be a dedicated microcontroller or digital signal processor equipped with non-volatile memory to save programs and data. A communication module can be connected to the strategy determination device and data transmission device to receive control strategies and send execution status. The communication module can support multiple communication protocols to exchange data with different types of devices and systems. A control output interface can be used to send control commands to the device. The control output interface can be a digital signal output, analog signal output, serial communication port, etc., depending on the communication capabilities and control requirements of the target device.

[0046] Local control devices can concretize abstract control strategies into actual equipment operations, ensuring the safe, efficient, and orderly conduct of V2G energy interaction activities. These devices can parse control strategies into control commands for devices such as charging piles and inverters, including initiating charging, switching discharging modes, and adjusting power, enabling local execution of the strategy. Furthermore, the local control device can respond rapidly the instant the strategy is generated, without waiting for cloud feedback, thus reducing control latency and improving system real-time performance. Local control devices can also coordinate the charging and discharging operations of multiple devices, preventing conflicts or incompatibilities between them, such as ensuring that the power grid is not overloaded when multiple vehicles participate in V2G simultaneously.

[0047] The aforementioned control commands can be used to instruct specific device settings. Control commands can be charging commands, discharging commands, or extended commands. Extended commands can be emergency power supply commands, instructing the vehicle to supply power to specific loads, such as household appliances or critical facilities, to ensure basic living and safety needs during grid failures or power outages. Intelligent pre-charging / pre-discharging commands, such as those based on weather forecasts and user travel plans, adjust charging and discharging plans in advance to cope with future energy demands or electricity price changes. Device self-check and maintenance commands periodically send instructions to the charging pile or on-board charger for self-diagnosis and maintenance, such as battery health checks and charging pile status monitoring, to ensure the device is in good working condition.

[0048] In one alternative embodiment, the data transmission device can receive device data sent by multiple devices via Ethernet communication, wireless communication, or short-range communication protocols, and then send the device data to the policy determination device. Ethernet communication supports multiple protocols, ensuring data integrity and security. Wireless communication enables remote communication between edge nodes and devices. Short-range communication can be used for communication with user devices to obtain user charging / discharging preferences or authentication, etc.

[0049] Then, the data transmission device sends the received device data to the policy determination device, so that the policy determination device can determine the control policy locally. Based on the received vehicle data, charging pile data, and power grid data, the policy determination device can determine the control policy for each device. This decision-making process fully demonstrates the advantages of edge computing—fast response, low latency, and high reliability—allowing for independent policy calculation locally without waiting for feedback from the cloud.

[0050] After determining the control strategy, the strategy determination device sends it to the local control device, which then executes the local protection strategy. Upon receiving the control strategy, the local control device generates a series of control commands for the charging pile, power conversion equipment, and even the on-board charger. These commands may include, but are not limited to, starting charging, stopping discharging, adjusting charging and discharging power, and switching operating modes. Furthermore, the local control device is responsible for executing the local protection strategy. It can react quickly to emergencies such as grid anomalies, equipment overload, low battery SOC, and fire warnings, such as automatically disconnecting charging and discharging connections, limiting power output, and triggering fire safety mechanisms, ensuring the safe and stable operation of the entire system.

[0051] In this embodiment, the edge control system is located at the edge node of the vehicle-to-grid interaction and connected to multiple devices, including at least an on-board charger and a charging pile. The system includes: a data transmission device for receiving device data sent by the multiple devices; a strategy determination device connected to the data transmission device for determining control strategies corresponding to the multiple devices based on vehicle data, charging pile data, and power grid data; and a local control device connected to the strategy determination device and the data transmission device for generating control commands for the multiple devices based on the control strategies and sending the control commands to the multiple devices via the data transmission device. The edge control system of this application is located at the edge node of the vehicle-to-grid interaction. By connecting to multiple devices, including at least an on-board charger and a charging pile, it can receive device data sent by the multiple devices in a timely manner through the data transmission device. The strategy determination device then analyzes the control strategies corresponding to each device locally based on vehicle data, charging pile data, and power grid data, thereby generating control commands in the local control device and sending them to each device in a timely manner. This achieves efficient control of the multiple devices in the vehicle-to-grid interaction, reducing control latency and solving the technical problem of high control latency in related technologies.

[0052] Optionally, the data transmission device includes: multiple communication interface modules, each connected to multiple devices; and a data acquisition module, connected to the multiple communication interface modules, for receiving device data through the multiple communication interface modules.

[0053] The aforementioned communication interface module provides a range of data interfaces and protocol support for connecting edge control devices with external equipment. The communication interface module can cover various scenarios, from low-speed serial communication to high-speed network communication.

[0054] In a V2G system, the communication interface module can communicate with the OBC (On-Board Cell) to acquire battery status data in real time and feed it back to the data acquisition module for further processing. Simultaneously, the communication interface module can also interact with local power grid monitoring equipment to read real-time voltage, current, frequency, and other parameters of the power grid, thereby transmitting power grid status information to enable precise energy management and scheduling.

[0055] The aforementioned data acquisition module can acquire device data from multiple devices via communication interfaces and transmit it to other devices in the system. This module collects and integrates data from distributed devices, providing a comprehensive and multi-dimensional perspective for edge computing and helping the system better understand the current operating environment. Through efficient cooperation with multiple communication interface modules, the data acquisition module can capture changes in device status in real time. Whether it's the charging and discharging needs of vehicles or fluctuations in grid load, it can provide immediate feedback to the strategy determination device, ensuring the real-time nature of the control strategy.

[0056] In one optional embodiment, the communication interface module is a fundamental component of the data transmission device, used to establish and maintain communication links between the edge control system and external devices. In V2G scenarios, external devices include, but are not limited to, electric vehicles, charging piles, power grid monitoring facilities, distributed energy systems, and user interaction terminals. These devices may use different communication protocols and technical standards; therefore, multiple communication interface modules are provided to accommodate diverse device communication needs. Each communication interface module is specifically designed to interface with a particular type of device, ensuring accurate and efficient data transmission. Using the communication interface module, data can be transmitted from the device to the data acquisition module, enabling real-time information sharing. Furthermore, it allows control over the opening and closing of the communication link, management of communication quality and security, and ensures the stability and confidentiality of data transmission.

[0057] The data acquisition module in the data transmission device is connected to multiple communication interface modules to receive and integrate device data from different devices. Through the data acquisition module, the edge control system monitors the devices in real time, integrating and correlating data from different sources to form a unified data view, facilitating subsequent analysis and decision-making. All data received by the data acquisition module, after preliminary processing, can be sent to the strategy determination device in a unified format and structure.

[0058] Optionally, the strategy determination device includes: a data fusion and identification module, connected to the data transmission device, for fusing vehicle data, charging pile data, and power grid data to obtain fused data, and identifying the status of multiple devices based on the fused data to obtain status data of multiple devices; an edge computing module, connected to the data fusion and identification module, for determining a first control strategy for multiple devices based on the fused data and status data; and a cloud-edge collaboration module, connected to the edge computing module and the cloud, for determining a control strategy based on the connection status with the cloud and the first control strategy.

[0059] The aforementioned data fusion and identification module can collect and integrate equipment data from multiple sources. Since equipment data can be heterogeneous, the module can preprocess it, such as removing noise, correcting data, and standardizing formats. Then, through data fusion and identification, it can output structured fused data and status data. Status data may include, but is not limited to, available charging and discharging power range, equipment health status, grid load status, and user demand priorities. The fused data and status data form the basis for the strategy determination device to make intelligent decisions, ensuring that the control strategy is both real-time and fully considers the overall system status.

[0060] The aforementioned edge computing module is a component in the strategy determination device that performs real-time calculations based on the state data and fused data provided by the data fusion and identification module, generating a superior first control strategy. This first control strategy may include, but is not limited to, the selection of charging or discharging modes, specific power settings, and priority in grid dispatching; all aimed at adjusting the energy interaction between the vehicle and the grid while simultaneously meeting user needs and grid stability requirements. The edge computing module responds rapidly within milliseconds to seconds, calculating a charging and discharging strategy suitable for the current situation, reducing reliance on the cloud and improving system real-time performance and response speed. Furthermore, the edge computing module can quickly adjust the strategy based on local conditions (such as sudden grid fluctuations or changes in vehicle status), making the V2G system more flexible and secure.

[0061] The aforementioned cloud-edge collaboration module is a component within the policy determination device used for data synchronization and policy interaction with the cloud. This module periodically uploads data from the edge to the cloud and simultaneously receives updated policies from the cloud, such as the latest electricity price information and scheduling priority adjustments. Through this module, synchronized updates of data and policies are achieved, ensuring the system can adapt promptly to changes in global scheduling. The module also periodically or continuously checks the communication status between the edge control device and the cloud server to confirm a normal connection, ensuring the V2G edge control system can flexibly adjust its operating mode according to the current network conditions.

[0062] In one optional embodiment, the strategy determination device can ensure that the V2G edge control system can make better decisions in a changing environment through close cooperation between the data fusion and recognition module, the edge computing module, and the cloud-edge collaboration module. This utilizes local real-time data and computing power while fully integrating global strategies from the cloud, thereby effectively improving the efficiency and stability of vehicle-to-grid interaction, while ensuring the system's security and reliability.

[0063] Specifically, the data fusion and identification module receives device data sent by the data transmission device and integrates this heterogeneous device data into unified fused data. Data fusion overcomes the limitations of a single data source by combining information from multiple sources, improving data accuracy and completeness, and thus obtaining more comprehensive data. Based on the fused data, the status of multiple devices is identified, such as determining whether a vehicle is ready to participate in V2G, whether a charging station is capable of performing charging and discharging tasks, and whether the power grid is in a stable state, generating status data to provide a reference for subsequent strategy formulation. Furthermore, status identification based on multi-dimensional data can also promptly identify potential problems, such as battery overheating, charging station malfunctions, and power grid anomalies, allowing for advance preparation for preventative and corrective actions.

[0064] The edge computing module can calculate a primary control strategy adapted to the current operating conditions based on the fused data and identified status data. This primary control strategy can be determined locally in real time, allowing the edge control system to maintain efficient and safe operation even when the cloud-based strategy has not yet arrived or is unavailable.

[0065] The cloud-edge collaboration module connects the edge computing module and the cloud server to monitor the communication status between them. Based on network connectivity, it determines whether the initial control policy generated by the edge computing module needs to be synchronized with the global policy issued by the cloud. This module enables bidirectional interaction between the cloud and the edge. The cloud, with its more powerful data processing and analysis capabilities, can formulate policies based on a wider range of data; while edge nodes can make immediate adjustments based on local real-time data. This collaboration allows the policy to have both a global perspective and local flexibility. The cloud-edge collaboration module not only ensures the consistency and continuity of the policy but also dynamically adjusts the operating mode according to the network environment, effectively responding to unexpected situations such as network latency and signal interruptions, ensuring that the V2G edge control system provides optimal service under all conditions.

[0066] Preferably, the cloud-edge collaboration module is further configured to determine the first control strategy as the control strategy when the connection status is an abnormal connection.

[0067] In one optional embodiment, when the cloud-edge collaboration module detects an abnormal connection status with the cloud, such as network latency, interruption, or instability, the first control strategy generated by the edge computing module can be directly determined as the final control strategy to guide the real-time operation of each device. This mechanism ensures that even in the event of unreliable cloud communication, the V2G edge control system can make timely and effective decisions based on local data, guaranteeing the continuous operation and stability of the system.

[0068] Preferably, the cloud-edge collaboration module is further configured to receive a second control policy sent by the cloud when the connection status is normal, and to determine the second control policy as the control policy.

[0069] In one optional embodiment, under normal connection, the cloud-edge collaboration module can receive a globally processed second control strategy from the cloud and determine it as the control strategy to guide edge node devices in performing specific charging and discharging operations, power regulation, device protection, and other tasks. The second control strategy is determined based on cloud-based analytical capabilities. This second control strategy can improve the overall efficiency of the V2G system, enabling energy management and scheduling, and taking into account demand response and energy supply and demand relationships between different regions. Receiving the second control strategy provides the edge control system with a more comprehensive decision-making perspective, enabling more efficient device control strategies at edge nodes, reducing energy waste, improving user satisfaction, and enhancing the system's global collaboration capabilities. This ensures that edge node devices can execute the cloud-based global strategy, achieving efficient interaction between the V2G system and the power grid, while meeting users' personalized needs. By applying the cloud-based second control strategy locally, the edge control system can leverage cloud-based intelligent decision support to improve charging and discharging accuracy and real-time response capabilities, enhancing system safety and economy.

[0070] Optionally, the system further includes a security management module, which is located between the data transmission device and the local control device. This module is used to perform identity authentication on multiple devices based on device data upon receiving an authentication command from the local control device, and to obtain authentication results. The authentication results are used to indicate whether multiple devices have passed authentication.

[0071] The aforementioned security management module is a component designed to ensure the safe operation of the V2G edge control system. Located between the data transmission device and the local control device, the security management module ensures, through an authentication process, that only verified devices can access the system and perform corresponding charging and discharging operations. This enhances system security, prevents unauthorized device access, and avoids data leakage due to device malfunctions, malicious attacks, or other reasons.

[0072] In one alternative embodiment, a security management module ensures that interacting devices are authenticated, protecting the system from security threats. This front-end security component effectively prevents potential device spoofing or other forms of unauthorized access. It enhances the access security of the V2G system, increases the system's defense layers, ensures the integrity and confidentiality of data transmission and control commands, and provides a security barrier for the system.

[0073] Upon receiving an authentication command, the security management module performs a series of security checks and authentication operations based on device data received from the data transmission device. Information such as device identifiers, encrypted signatures, and historical activity records within the device data can be used to determine the authenticity and legitimacy of the device. By analyzing the device data, the security management module can verify the device's identity, prevent counterfeit or compromised devices from accessing the system, and ensure that the exchange of data and control commands within the system takes place in a secure and reliable environment.

[0074] After security checks and authentication, the security management module can generate an authentication result. This result clearly indicates which devices have passed authentication and which have failed. The authentication result can then be fed back to the local control device to ensure that the local control device sends control commands to those certified devices, avoiding security risks and unnecessary system burden.

[0075] Preferably, the safety management module is further configured to perform fault detection on multiple devices based on device data upon receiving a detection command from the local control device, and obtain detection results, wherein the detection results are used to characterize whether multiple devices are faulty.

[0076] In one optional embodiment, the local control device can send a detection command to the safety management module to ensure the safe operation of the system and the health status of the equipment. The detection command can be sent at system startup, before or after equipment operation, or periodically within a certain cycle to check the current status of the equipment. Using the detection command to initiate a fault detection process ensures that devices connected to the V2G system are healthy and safe before and during energy interaction. Timely detection of equipment faults can prevent unexpected problems during system operation and reduce risks.

[0077] Upon receiving a detection command, the safety management module can analyze equipment data, including operation logs, performance indicators, temperature readings, voltage and current levels, to detect any malfunctions or performance degradation in the equipment and obtain the detection results. Through real-time analysis of equipment data, the safety management module can detect phenomena indicating changes in equipment health, such as abnormal voltage fluctuations, excessively high temperatures, and current readings exceeding normal ranges, allowing for timely intervention. This process is crucial for ensuring the safe operation of the system. Fault detection can prevent energy exchange interruptions, equipment damage, and even more serious safety incidents caused by equipment failures. The detection results can include equipment with existing or potential faults, as well as equipment in normal condition.

[0078] Optionally, the system further includes a data relay device, which is located between the data transmission device and the policy determination device, for transmitting device data to the cloud in real time via the policy determination device.

[0079] The aforementioned data relay device is used in V2G systems to forward real-time data from devices, such as video surveillance and power quality system data streams, to the cloud. These data streams typically contain large amounts of raw data, which could face bandwidth bottlenecks and data security issues if transmitted directly. Therefore, the data relay device can preprocess the device data, such as compressing it to reduce the data volume, encrypting it to ensure data security during transmission, and encapsulating it to facilitate network transmission. Furthermore, the relay device also has network communication capabilities, enabling it to establish a stable data transmission channel with the cloud.

[0080] In one alternative embodiment, the data relay device can transmit data to the cloud via a policy determination device. This increases the controllability and security of data flow. The policy determination device can perform necessary processing on the data to comply with the cloud's data reception standards and security requirements, while also ensuring that data transmission does not interfere with the ongoing charge / discharge control process.

[0081] Real-time performance means that data relay devices can quickly capture and forward data, ensuring that the cloud receives real-time device data. This is beneficial for global scheduling, demand response, energy market forecasting, and security monitoring. Data relay devices can ensure real-time data transmission through efficient transmission mechanisms, reducing data transmission latency and thus improving the response speed and accuracy of the entire V2G system.

[0082] By employing strategy-based mechanisms, data relay devices can manage data flows more intelligently, avoiding the transmission of invalid data, reducing network load, and enhancing data confidentiality and integrity. This enables the cloud to receive processed, valid data, allowing for better global analysis and strategy formulation.

[0083] like Figure 2 As shown, an optional V2G edge control system is illustrated, which connects to both the terminal and the cloud. The edge control system may include a communication interface module, a data acquisition module, a data fusion and status recognition module, an edge computing module, a local control execution module, a cloud-edge collaboration module, a security management module, and a data relay module. The edge control system can interact with the terminal and the cloud via the communication interface module. In this system, data flows sequentially to the communication interface module, data acquisition module, data fusion and status recognition module, edge computing module, and local control execution module, and then from the communication interface module, data acquisition module, and data relay module to the cloud-edge collaboration module. Data interaction occurs between the local control execution module and the cloud-edge collaboration module, which can also connect to the communication interface module for data exchange.

[0084] like Figure 3The image shows an edge device that can interact with a cloud platform. The edge device may include a communication module, a processor, a power module, a memory, a security chip, and a communication interface. The communication module can perform energy conversion control, power quality monitoring, and video surveillance. The communication module interacts with the processor, which in turn interacts with the security chip, the power module, and the memory. The processor can also interact with local security sensors via the communication interface.

[0085] like Figure 4 This illustrates an edge control method. The method includes sequentially executed steps such as data acquisition, fusion analysis, policy calculation, cloud-edge collaboration, deployment and execution, and iterative execution. After the fusion analysis step, an exception handling step and a data relay transmission step can be performed. Following exception handling and data relay transmission, deployment and iterative execution steps can continue.

[0086] According to an embodiment of this application, a strategy generation method is provided. This embodiment provides a strategy generation method applied to a strategy determination device in an edge control system according to various embodiments of this application. Figure 5 This is a flowchart of an edge control system according to an embodiment of this application, such as... Figure 5 As shown, the method includes the following steps:

[0087] Step S502: Receive device data sent by the data transmission device.

[0088] The device data includes vehicle data, charging pile data, and power grid data. The device data is sent by multiple devices connected to the edge control system, including at least an on-board charger and a charging pile.

[0089] In one alternative embodiment, the policy determination device can receive device data via a hardware interface. Alternatively, the policy determination device can also physically connect to a data transmission device via optical fiber for high-speed data transmission.

[0090] In another alternative embodiment, the policy determination device can receive device data via wireless communication technology. Through wireless transmission technology, the data transmission device wirelessly transmits the device data to the policy determination device without a physical connection.

[0091] Step S504: Based on vehicle data, charging pile data, and power grid data, determine the control strategies corresponding to multiple devices.

[0092] In one alternative embodiment, the control strategy can be determined based on a rule-based decision tree. The decision tree can be determined by constructing a series of decision rules. Using a decision tree, vehicle data, charging pile data, and power grid data can be effectively analyzed, and the control strategy can be automatically derived based on the characteristics and status of this data.

[0093] In another alternative embodiment, a multilayer sensor can be used to analyze vehicle data, charging pile data, and power grid data to determine a control strategy. The multilayer sensor can employ a multilayer neuron structure to learn the nonlinear relationships between data, thereby analyzing and obtaining the control strategy.

[0094] Step S506: The control strategy is sent to the local control device so that the local control device can control multiple devices to work based on the control strategy.

[0095] In one alternative embodiment, the policy determination device can send the control policy to the local control device via a hardware interface. Alternatively, the policy determination device can also send the control policy to the local control device via a physical fiber optic connection.

[0096] In another alternative embodiment, the policy determination device can connect to a local control device via wireless communication technology to transmit the control policy. For example, the transmission of the control policy can be achieved via a local area network or a cellular network.

[0097] Optionally, based on vehicle data, charging pile data, and power grid data, control strategies for multiple devices are determined, including: fusing vehicle data, charging pile data, and power grid data to obtain fused data; identifying the status of multiple devices based on the fused data to obtain status data of multiple devices; and determining control strategies based on the connection status with the cloud, as well as the fused data and status data.

[0098] In one alternative embodiment, to determine a control strategy, device data collected from different sources can be converted into a unified data representation to facilitate subsequent analysis and strategy development. Data fusion can identify redundant information in the data, fill in missing values, and reduce inconsistencies between data.

[0099] Then, based on data fusion, the status of the devices is analyzed and identified in depth, transforming the fused data into meaningful and specific status descriptions, such as vehicle charging needs, charging station availability, and grid load levels. Status identification allows for a more accurate understanding of the actual operating status of devices participating in V2G interactions. It also determines which devices can participate in V2G activities, which devices need priority charging or discharging, and how to adjust the operating mode of charging stations. Status identification improves the targeting and effectiveness of strategies. For example, if status identification indicates a poor vehicle battery condition, the strategy may tend to restrict vehicle discharging activity to protect battery health; conversely, if charging stations are idle while the grid load is high, the strategy may encourage vehicle discharging to help balance the grid load.

[0100] After data fusion and status identification are completed, the control strategy for each device in the V2G system can be further determined based on the current connection status with the cloud. This ensures that the V2G system operates efficiently and securely, whether under normal network conditions or offline conditions. By considering the cloud connection status, the control strategy can be flexibly adjusted to fully utilize cloud resources and intelligence.

[0101] In this way, the V2G edge control system can effectively integrate and analyze data from vehicles, charging piles and the power grid, and can independently respond to network anomalies while taking cloud scheduling into account.

[0102] Optionally, a control strategy is determined based on the connection status with the cloud, as well as the fused data and status data, including: determining a first control strategy based on the fused data and status data; and determining a control strategy based on the connection status and the first control strategy.

[0103] In one alternative embodiment, the edge control device can utilize fused data and state data to generate a first control strategy locally. Using this first control strategy, the decision-making device can quickly respond to changes in the current environment and make preliminary charge / discharge control decisions based on local information, even without real-time guidance from the cloud.

[0104] After defining the primary control strategy, it can be adjusted or confirmed based on the connection status between the edge device and the cloud to ultimately determine the control strategy. Through cloud-edge collaborative control, the powerful computing capabilities and more comprehensive information of the cloud can be utilized to adjust the control strategy of the edge nodes. Cloud-edge collaboration allows for the combination of local real-time information and the global perspective of the cloud to make more accurate and efficient charging and discharging decisions. The cloud can provide longer-term load forecasts, electricity price information, and user behavior analysis, helping edge nodes plan their charging and discharging strategies in advance.

[0105] Preferably, the control strategy is determined based on the connection state and the first control strategy, including:

[0106] In the case of an abnormal connection, the first control policy will be determined as the control policy.

[0107] When the connection status is normal, receive the second control policy issued by the cloud and determine the second control policy as the control policy.

[0108] The technical solution proposed in this application will be described below in conjunction with an optional embodiment. This application proposes a multifunctional V2G edge control system.

[0109] The V2G system suffers from several drawbacks. First, it relies heavily on real-time cloud server computing, which can lead to charging and discharging control failures due to network anomalies. This highlights the system's high dependence on centralized scheduling, significant control latency, and inability to meet real-time requirements. Second, most systems lack real-time mechanisms for handling local faults and fires, resulting in inadequate safety and fault handling. Furthermore, these systems lack data relay functionality for peripheral devices, such as for site security monitoring, and fail to achieve cloud-based video recognition and security monitoring. Finally, they lack access to the site ground lock control system and data upload to the cloud platform for control.

[0110] The system in this embodiment can solve the problem of local control and cloud-based collaborative control of ground locks at the site, realizing edge integration of the ground lock system. It can reduce control latency, improve real-time performance, and perform policy calculations and power regulation at the edge, without complete reliance on the cloud, thus improving response speed. It also enables real-time processing of local security policies. Furthermore, it can process data from on-site sensors such as those used for fire detection in real time, reducing response time and increasing safety assurance. The system integration reduces overall system complexity and increases user ease of operation. It also enhances site utilization by adding a ground lock control system, simplifying operation procedures. Additionally, it can integrate site video surveillance to increase site security, utilize the processing power of the cloud platform to handle safety incidents in real time, and combine with on-site fire early warning devices to achieve dual fire protection, enabling multi-disaster detection and handling.

[0111] Specifically, this multi-functional V2G edge control system can be installed near the edge node of the charging pile, including but not limited to the following modules:

[0112] The data acquisition module is used to acquire various real-time data required for the V2G process, such as vehicle battery parameters, site ground lock data, local power monitoring device data, user charging intentions, electricity price signals, and cloud dispatch instructions.

[0113] Real-time data can include, but is not limited to: vehicle-side data, such as onboard battery parameters, SOC, SOH, and maximum permissible power; grid-side data, such as voltage and electricity price signals; data from local power monitoring devices, such as smart meters and power quality monitors; local distributed energy data, such as photovoltaic power generation and energy storage status; site ground lock system protocol data; and user-side data, such as scheduled travel time and charging / discharging preferences. The data acquisition module can connect to charging piles, vehicle OBCs, inverters, and power management systems via communication interfaces.

[0114] The data stream relay module is used to compress, encrypt, and encapsulate the acquired video surveillance data stream, power quality system data stream, etc., and relay the data to the cloud control platform.

[0115] The data fusion and status identification module is used to extract, filter, and fuse features from multi-source data to identify the current vehicle status, power grid status, and local energy status. This module can output information such as available power range and equipment status to subsequent modules.

[0116] The edge computing module is used to calculate real-time V2G policies based on fused data. V2G policies may include, but are not limited to: the current charging or discharging mode to be executed; charging / discharging power settings; scheduling priorities for participating in demand response or peak shaving; and matching and adjustment of differences with cloud policies. Policy calculations can be performed within millisecond / second intervals to meet real-time requirements.

[0117] The local control execution module is used to perform real-time control of charging piles, inverters, or energy routers, as well as ground lock control at the site, based on V2G commands output by the strategy calculation module. V2G commands may include, but are not limited to: issuing power commands; executing start / stop charging / discharging; controlling the V2G mode switching of charging piles; controlling the ground lock system; and executing local protection strategies, such as overload protection, undervoltage protection, SOC lower limit limiting, and power-off in case of fire.

[0118] The cloud-edge collaboration module is used for data synchronization, policy distribution, and status reporting with the cloud platform, enabling collaboration between cloud-based global scheduling and local edge computing. When the network is normal, the edge device maintains periodic interaction with the cloud platform. When the network is abnormal, the cloud-edge collaboration module automatically switches to local independent control mode to ensure continuous system operation.

[0119] The security management module ensures the integrity and security of data interaction and control links, including functions such as identity authentication, access control, encrypted transmission, and equipment anomaly detection. Based on on-site inspection signals, it monitors the site's electrical safety, fire safety, and personnel safety in real time, promptly handles emergencies, and uploads data to the cloud.

[0120] A communication interface module is used to connect to external devices. The communication interface module may include, but is not limited to: a communication interface with the charging pile / vehicle side; an interface with the power conversion equipment; and a network interface with the cloud platform.

[0121] The technical solution proposed in this application will be described below with reference to an optional embodiment. This application proposes a V2G control system, which includes a cloud layer, an edge layer, and a terminal device layer.

[0122] The cloud layer can provide global scheduling, policy adjustment, and data management functions. For example, it can house a global load forecasting and scheduling module, a unified V2G policy publishing module, a big data analysis and user behavior prediction module, an energy management platform, an operations management platform, and an alarm system. The cloud layer can periodically send scheduling instructions, electricity price information, and security policies to the edge.

[0123] The terminal device layer can include various devices that can communicate directly with the edge control system, such as V2G charging piles / bidirectional DC charging piles, vehicle OBC or DC ports, photovoltaic module inverters, energy storage devices, and local power monitoring devices (such as smart meters, power quality monitors, etc.). The terminal device layer can execute the actual charging or discharging process according to the instructions issued by the edge layer and transmit status information back to the edge layer in real time.

[0124] The edge layer is the control execution module of the control system. It communicates directly with charging piles, vehicles, energy storage systems, and power monitoring equipment, and maintains policy interaction with the cloud layer. Edge control devices can be set up in the edge layer. These edge control devices may include the following functional modules: data acquisition module, data fusion and status recognition module, edge policy calculation module, local control execution module, cloud-edge collaboration module, security management module, data relay module, and communication interface module. These modules interact with each other through a bus or internal message mechanism.

[0125] Specifically, the data acquisition module can collect data through peripheral I / O ports, ADC sampling ports, and communication interface modules. The collected data includes: vehicle parameters (SOC, SOH, maximum rechargeable / dischargeable power), grid parameters (voltage, current, frequency, load), distributed energy output (photovoltaic power, remaining energy storage capacity), user-defined parameters (scheduled travel time, minimum SOC threshold), cloud-based dispatching strategies (demand response level, electricity price curve, etc.), and video data. The data acquisition module can also perform preprocessing operations such as noise reduction, filtering, and time alignment on the data.

[0126] The data fusion and status recognition module can perform the following functions based on multi-source data: status judgment (chargeable, non-chargeable, dischargeable, non-dischargeable), local load forecasting, electricity price status recognition (peak, flat, valley), user demand priority analysis, safety threshold judgment (such as SOC lower limit, voltage upper limit, power limit), and the module outputs structured status information, including: the feasibility of the current vehicle participating in V2G, the maximum feedback or maximum charging power allowed by the current grid side, the support capability of the local energy system, and whether the V2G action triggers the limiting conditions.

[0127] The edge policy calculation module can generate V2G control policies based on fused data. These policies may include: power allocation: calculating feasible power ranges based on current grid load, vehicle SOC, and price signals; mode selection: switching between charging / discharging / holding modes; collaborative processing: using cloud-based policies as upper-layer targets; locally correcting real-time power to meet current device capabilities and safety limitations; and user demand constraints: stopping discharging when about to leave the site and prohibiting discharging when the SOC is below a user threshold. The policy calculation module can be implemented using rule-based calculations, gradient algorithms, heuristic methods, etc., without limiting the specific algorithm. The final policy output may include, but is not limited to: the current V2G mode, charging or discharging power values, and device action commands (start, stop, power reduction, etc.).

[0128] The local control execution module can convert strategies into executable instructions and send them to the device, such as sending V2G power control instructions to the charging pile, sending grid-connected / off-grid control instructions to the inverter, controlling the charging pile to switch to V2G session mode, and executing protection actions (disconnecting output, current limiting, voltage limiting). Control instructions may include: starting charging, stopping charging, starting discharging, stopping discharging, setting target power, and switching operating states. The local control execution module can also monitor the execution results and send back execution feedback.

[0129] The cloud-edge collaboration module can operate in two modes. Normal network mode: The edge uploads data, such as device status, power data, and V2G session records; the edge receives data from the cloud, such as electricity price curves, policies, and scheduling commands; both sides maintain periodic communication and policy synchronization. Weak network / network outage mode: The edge control device automatically switches to independent operation, uses local policies for vehicle-to-network interaction, caches data during network outages locally, and automatically synchronizes cached data after network recovery.

[0130] The security management module can implement security policies including: communication authentication (certificate verification, encrypted channels), command validity checks, policy security verification (prohibition of exceeding power limits and discharge limits), and anomaly detection (device offline, data loss). The security management module can also trigger protective actions upon detecting anomalies, including stopping discharge and disconnecting connections.

[0131] The communication interface module can be used to connect to external devices. The communication interface module may include, but is not limited to: a communication interface with the charging pile / vehicle side, an interface with the power conversion equipment, and a network interface with the cloud platform.

[0132] Furthermore, the edge control device at the edge layer may include, but is not limited to, the following hardware components: a processor, used to run the V2G control program and realize functions such as data fusion calculation, strategy adjustment, command issuance, and cloud-edge communication; a memory, used to store system programs, control logic, data acquisition records, security certificates, etc.; a communication interface module, used to connect different types of external devices; an input / output interface module, used to connect local devices such as sensors, relays, and switch input / output, with optional digital and analog interfaces; a power management module, providing stable power supply to the device, which may include DC / DC conversion, uninterruptible power supply backup, surge protection circuits, etc.; a security encryption module, used to ensure the security of communication and control, which may include: an encryption acceleration module, a security chip, and a certificate storage module; and a system bus, used to connect the processor and various functional modules to realize internal data interaction. The edge control device can implement the control method of the V2G edge control system by running the control program in the memory.

[0133] The edge control system can implement the following methods: data acquisition, fusion analysis, strategy calculation, cloud-edge collaboration, anomaly handling, data relay transmission, execution, and cyclic execution.

[0134] Specifically, edge control devices can collect real-time operational data from the vehicle side, the power grid side, charging piles, local distributed energy sources, and the user side. This data may include, but is not limited to: vehicle battery status parameters (SOC, SOH, available power, etc.); grid voltage, current, frequency, load, and electricity price signals; power generation / storage status of distributed energy sources such as photovoltaics and energy storage; user-defined charging and discharging preferences and scheduled travel plans; and dispatch instructions or strategy information issued from the cloud.

[0135] Then, the fusion analysis step can preprocess, perform feature analysis and fusion calculation on the acquired data to identify the current system operating status for subsequent strategy calculation. The current system operating status may include, but is not limited to: vehicle status (chargeable / dischargeable / not eligible for V2G); local power grid status (high / low load, voltage stability); distributed energy output prediction; user intent judgment; and safety threshold detection (such as SOC lower limit, power quality threshold).

[0136] The strategy calculation step can perform local calculations based on the state identification results to generate the current V2G strategy. The strategy may include, but is not limited to: prioritizing the execution of charging or discharging modes; specific charging / discharging power values; scheduling response levels (such as peak shaving, valley filling, and load balancing); whether to participate in demand response or dynamic electricity price scheduling issued by the cloud; and equipment protection strategies (limiting power, stopping feedback, etc.). Under normal network conditions, this strategy can be synchronized or modified with the cloud strategy.

[0137] The anomaly handling steps can execute corresponding protection strategies when an anomaly is detected. These strategies may include, but are not limited to: switching to local independent control if the network is abnormal; stopping discharge if the SOC is below the threshold; reducing power or stopping output if the mains voltage or frequency exceeds limits; and triggering an emergency power-off logic strategy if monitoring sensor switching signals for fire, smoke, over-temperature, or leakage current: disconnecting the V2G pile; cutting off the main power supply within the station; issuing an alarm; and reporting to the cloud. This process ensures the stability and security of the entire V2G interaction process.

[0138] The data relay transmission process can classify, package, and compress the video data in the relay data.

[0139] The execution steps can send V2G power control commands to the charging pile, send grid-connected / off-grid control commands to the inverter, control the charging pile to switch to V2G session mode, and execute protection actions (disconnect output, current limiting, voltage limiting); send synchronization data, resume data, and relay data (including video stream data, power quality data, etc.) to the cloud; and send fault handling actions to fault-handling components, including power outage, audible and visual alarms, and telephone alarms.

[0140] The cyclic execution step involves returning to the data acquisition step to perform the next round of data acquisition based on the system's operating cycle, thus achieving real-time closed-loop control. This step can be repeated within a second-level or sub-second-level cycle to achieve real-time closed-loop control.

[0141] The technical solution proposed in this application is described below with reference to an optional embodiment. This application also proposes a V2G edge control method, which may include: a data acquisition step to collect vehicle, grid-side data, local distributed energy data, and local and user-side data in real time; data relay transmission to integrate the data formats of the collected and transmitted data, encrypt and encode the data, and then transmit it to the cloud server; a data fusion step to extract features, perform correlation analysis, and determine the status of multi-source data; a policy calculation step to calculate the V2G power policy at the edge, including charging power, discharging power, scheduling priority, etc.; a local execution step to issue control commands and safety management commands to charging piles or power conversion equipment according to the policy; a cloud-edge collaboration step to synchronize data and policies when the network is normal and automatically switch to independent operation mode when it is abnormal; and an anomaly handling and safety control step to provide real-time protection and policy adjustment for situations such as overload, low battery, communication anomalies, and site fires.

[0142] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0143] According to an embodiment of this application, a device embodiment for a strategy generation apparatus is provided. It should be noted that the apparatus can be used to execute the above-described strategy generation method. Figure 6 This is a schematic diagram of a strategy generation apparatus according to an embodiment of this application, such as... Figure 6 As shown, the device includes:

[0144] The receiving module 62 is used to receive device data sent by the data transmission device. The device data includes vehicle data, charging pile data and power grid data. The device data is sent by multiple devices connected to the edge control system. The multiple devices include at least an on-board charger and a charging pile.

[0145] The determination module 64 is used to determine the control strategies for multiple devices based on vehicle data, charging pile data, and power grid data.

[0146] The control module 66 is used to send the control strategy to the local control device so that the local control device can control multiple devices to work based on the control strategy.

[0147] Optionally, the determination module is also used to fuse vehicle data, charging pile data, and power grid data to obtain fused data; identify the status of multiple devices based on the fused data to obtain status data of multiple devices; and determine the control strategy based on the connection status with the cloud, as well as the fused data and status data.

[0148] Optionally, the determining module is further configured to determine a first control strategy based on fused data and status data; and to determine a control strategy based on the connection status and the first control strategy. Preferably, the determining module is further configured to determine the first control strategy as the control strategy when the connection status is an abnormal connection. Preferably, the determining module is further configured to receive a second control strategy sent from the cloud when the connection status is a normal connection, and to determine the second control strategy as the control strategy.

[0149] Embodiments of this application also provide an electronic device, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0150] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0151] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0152] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0153] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of this application.

[0154] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0155] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0156] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0157] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0158] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0159] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. An edge control system, characterized in that, Located at the edge node of vehicle-to-grid interaction, it connects to multiple devices, including at least an on-board charger and a charging pile, comprising: A data transmission device is used to receive device data sent by the plurality of devices, wherein the device data includes: vehicle data, charging pile data, and power grid data; A strategy determination device, connected to the data transmission device, is used to determine the control strategy corresponding to the multiple devices based on the vehicle data, the charging pile data, and the power grid data. A local control device, connected to the strategy determination device and the data transmission device, is used to generate control instructions for the plurality of devices based on the control strategy, and to send the control instructions to the plurality of devices through the data transmission device, wherein the control instructions are used to control the operation of the plurality of devices.

2. The system according to claim 1, characterized in that, The data transmission device includes: Multiple communication interface modules are connected to the multiple devices respectively; The data acquisition module is connected to the plurality of communication interface modules and is used to receive device data through the plurality of communication interface modules.

3. The system according to claim 1, characterized in that, The strategy determination device includes: A data fusion and identification module, connected to the data transmission device, is used to fuse the vehicle data, the charging pile data, and the power grid data to obtain fused data, and to identify the status of the multiple devices based on the fused data to obtain the status data of the multiple devices; An edge computing module, connected to the data fusion and recognition module, is used to determine a first control strategy for the multiple devices based on the fused data and the status data; The cloud-edge collaboration module is connected to the edge computing module and the cloud, and is used to determine the control strategy based on the connection status with the cloud and the first control strategy; Preferably, the cloud-edge collaboration module is further configured to determine the first control strategy as the control strategy when the connection status is an abnormal connection; Preferably, the cloud-edge collaboration module is further configured to receive a second control policy sent by the cloud when the connection status is a normal connection, and to determine the second control policy as the control policy.

4. The system according to claim 1, characterized in that, The system also includes: A security management module is located between the data transmission device and the local control device. Upon receiving an authentication command issued by the local control device, the module performs identity authentication on the multiple devices based on the device data and obtains an authentication result. The authentication result is used to indicate whether the multiple devices have passed authentication. Preferably, the safety management module is further configured to, upon receiving a detection command issued by the local control device, perform fault detection on the plurality of devices based on the device data and obtain detection results, wherein the detection results are used to characterize whether the plurality of devices are faulty.

5. The system according to claim 1, characterized in that, The system also includes: A data relay device is disposed between the data transmission device and the policy determination device, and is used to transmit the device data to the cloud in real time through the policy determination device.

6. A strategy generation method, characterized in that, The strategy determination apparatus applied in the edge control system according to any one of claims 1-5 includes: The device receives device data sent by a data transmission device, wherein the device data includes vehicle data, charging pile data, and power grid data, and the device data is sent by multiple devices connected to the edge control system, the multiple devices including at least an on-board charger and a charging pile; Based on the vehicle data, the charging pile data, and the power grid data, control strategies for multiple devices are determined. The control strategy is sent to a local control device so that the local control device controls the operation of the multiple devices based on the control strategy.

7. The method according to claim 6, characterized in that, Based on the vehicle data, the charging pile data, and the power grid data, control strategies corresponding to the multiple devices are determined, including: The vehicle data, the charging pile data, and the power grid data are fused to obtain fused data. Based on the fused data, the status of the multiple devices is identified to obtain the status data of the multiple devices; The control strategy is determined based on the connection status with the cloud, as well as the fused data and the status data.

8. The method according to claim 7, characterized in that, Based on the connection status with the cloud, as well as the fused data and the status data, the control strategy is determined, including: Based on the fused data and the state data, a first control strategy is determined; The control strategy is determined based on the connection state and the first control strategy; Preferably, determining the control strategy based on the connection state and the first control strategy includes: If the connection status is an abnormal connection, the first control strategy will be determined as the control strategy. Preferably, the method further includes: When the connection status is normal, the system receives the second control policy issued by the cloud and determines the second control policy as the control policy.

9. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method of any one of claims 6-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method of any one of claims 6-8.

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