Vehicle network interaction edge intelligent regulation and control method and system

Abstract

The invention provides a vehicle network interaction edge intelligent regulation and control method and system. The method is applied to a vehicle network interaction edge intelligent regulation and control system. The system comprises a communication management module, a load detection module, a strategy making module and an instruction execution module. The method comprises the following steps: establishing communication with user equipment by using the communication management module, and sending a signal to the load detection module in response to the establishment of communication with the user equipment; the load detection module is used for collecting real-time charging data of the user equipment and transmitting the collected real-time charging data to the strategy making module in real time; the strategy making module is used for making a regulation and control strategy according to the real-time charging data and sending the regulation and control strategy to the instruction execution module; and the instruction execution module is utilized to adjust the user equipment according to the regulation and control strategy, and an execution result corresponding to the regulation and control strategy is fed back to the strategy making module.

Description

Technical Field

[0001] This application relates to the field of vehicle-to-everything (V2X) interaction technology, and in particular to a method and system for edge intelligent control of V2X interaction. Background Technology

[0002] Vehicle-to-grid (V2G) technology enables bidirectional energy flow between electric vehicles and the power grid, allowing charging during off-peak hours and discharging during peak hours. This effectively regulates grid load, smooths peak flows, enhances the absorption capacity of new energy sources, reduces reliance on fossil fuels, and lowers carbon emissions. Existing technologies typically employ a three-tiered collaborative architecture: terminal-edge-cloud. The terminal layer handles data collection, the edge layer optimizes computing resource allocation, and the cloud layer performs global model training and resource scheduling. This architecture excels in improving system response efficiency and resource utilization, and is currently the mainstream solution for V2G technology.

[0003] However, existing technologies still have significant shortcomings. The massive amounts of heterogeneous data collected at the terminal layer consume a large amount of storage space, leading to resource waste. Furthermore, the complexity of this data makes it difficult for the system to achieve dynamic scheduling across time, resulting in a lack of flexibility. In addition, the existing system falls short in combining global optimization with personalized control, failing to fully utilize historical and real-time data for precise analysis and control, thus limiting the practical effectiveness of vehicle-to-everything (V2X) interaction technology. Summary of the Invention

[0004] In view of this, the purpose of this application is to propose a vehicle-to-grid (V2G) intelligent edge control method and system.

[0005] To achieve the above objectives, this application provides a vehicle-to-grid (V2G) interaction edge intelligent control method, which is applied to a V2G interaction edge intelligent control system. The system includes: a communication management module, a load detection module, a strategy formulation module, and an instruction execution module. The method includes: The communication management module establishes communication with the user equipment, and in response to establishing communication with the user equipment, sends a signal to the load detection module; The load detection module collects real-time charging data of the user equipment and transmits the collected real-time charging data to the strategy formulation module in real time. The strategy formulation module formulates a control strategy based on the real-time charging data and sends it to the instruction execution module; The instruction execution module adjusts the user equipment according to the control strategy and feeds back the execution result of the control strategy to the strategy formulation module.

[0006] In one possible implementation, the system further includes an intelligent computing module; the intelligent computing module is connected to the communication management module, the load detection module, and the strategy formulation module, respectively. The method further includes: using the intelligent computing module to adjust the control strategy of the strategy formulation module.

[0007] In one possible implementation, establishing communication with the user equipment using the communication management module includes: Load the corresponding protocol library and driver according to the communication protocol type of the user equipment; The link quality is dynamically assessed by link status monitoring, and the assessment results are obtained. Communication between the communication management module and the user equipment is established based on the link with the best quality in the evaluation results.

[0008] In one possible implementation, the step of collecting real-time charging data of the user equipment using the load detection module includes: The load detection module collects real-time charging data of the user equipment at preset sampling intervals; the real-time charging data includes the user equipment's charging time, charging power, battery level, battery voltage, battery current, battery health status, real-time load, and user equipment energy consumption. The real-time charging data is cleaned and noise-reduced, and the processed real-time charging data is extracted to obtain data features.

[0009] In one possible implementation, the method further includes: The load detection module sends the acquired real-time charging data to the strategy formulation module, and the control strategy is updated in real time based on the real-time charging data.

[0010] In one possible implementation, adjusting the control strategy of the strategy formulation module using the intelligent computing module includes: The intelligent computing module is used to collect on-site charging data; the on-site charging data includes: charging and swapping behavior, load, power consumption, and emission factors; Based on the on-site charging data and the real-time charging data, the intelligent computing module is used to perform macroscopic analysis to obtain macroscopic analysis results. The macroscopic analysis results are transmitted to the strategy formulation module.

[0011] In one possible implementation, the step of using the strategy formulation module to formulate a control strategy based on the real-time charging data and sending it to the instruction execution module includes: The strategy formulation module receives the real-time charging data transmitted by the load detection module and retrieves historical data corresponding to the current user equipment from the historical database. Based on the historical data and using a time series analysis algorithm, the load changes of the user equipment are predicted to obtain a load curve; Statistical characteristics were extracted based on the load curve; An initial strategy is generated based on the statistical characteristics and the real-time charging data; The initial strategy is adjusted based on the macroscopic analysis results to obtain the control strategy, and the control strategy is sent to the instruction execution module; In one possible implementation, adjusting the user equipment using the instruction execution module according to the control strategy includes: The control strategy is verified using the instruction execution module. In response to the successful verification, the control strategy is parsed and the corresponding user equipment information is obtained; The parsed control strategy is converted into a recognizable control signal for the user equipment, and the recognizable control signal is sent to the user equipment corresponding to the user equipment information to adjust the user equipment.

[0012] In one possible implementation, feeding back the execution result corresponding to the control strategy to the strategy formulation module includes: In response to the completion of the user equipment adjustment, a termination signal is sent to the instruction execution module; The instruction execution module records the termination signal and feeds it back to the strategy formulation module.

[0013] Based on the same inventive concept, this application also provides a vehicle-to-grid interactive edge intelligent control system, including: a communication management module, a load detection module, a strategy formulation module, and an instruction execution module; The communication management module is used to establish communication with the user equipment, and in response to establishing communication with the user equipment, sends a signal to the load detection module; The load detection module is used to collect real-time charging data of the user equipment and transmit the collected real-time charging data to the strategy formulation module in real time. The strategy formulation module is used to formulate a control strategy based on the real-time charging data and send it to the instruction execution module; The instruction execution module is used to adjust the user equipment according to the control strategy and feed back the execution result corresponding to the control strategy to the strategy formulation module.

[0014] As can be seen from the above, the vehicle-to-grid (V2G) edge intelligent control method provided in this application establishes communication with the user equipment using the communication management module, and sends a signal to the load detection module in response to establishing communication with the user equipment; the load detection module collects real-time charging data of the user equipment and transmits the collected real-time charging data to the strategy formulation module in real time; the strategy formulation module formulates a control strategy based on the real-time charging data and sends it to the instruction execution module; the instruction execution module adjusts the user equipment according to the control strategy and feeds back the execution result of the control strategy to the strategy formulation module. This embodiment of the application achieves efficient energy interaction between electric vehicles and the power grid through the V2G edge intelligent control method, significantly improving system flexibility and control accuracy. The communication management module establishes stable communication with the user equipment, dynamically evaluates link quality, and selects the optimal link to ensure the reliability of data transmission. The load detection module collects charging data in real time and performs cleaning and feature extraction to provide high-quality data support for the control strategy. The intelligent computing module combines on-site charging data and real-time data for macroscopic analysis to generate a globally optimized macroscopic adjustment strategy, improving the power grid load allocation capability. The strategy formulation module combines historical and real-time data to predict load changes and generate precise control strategies, while also adjusting them based on macroeconomic analysis results. The instruction execution module verifies and parses the control strategies, converts them into control signals for user equipment, and sends them out to achieve precise charging adjustments. A feedback mechanism dynamically optimizes the strategies. This method solves the storage waste problem caused by massive heterogeneous data, improves cross-time dynamic scheduling capabilities, significantly enhances vehicle-to-grid interaction efficiency and renewable energy absorption capacity, reduces carbon emissions, and maximizes energy utilization efficiency. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the vehicle-to-grid interaction edge intelligent control method according to an embodiment of this application; Figure 2 This is a schematic diagram of the vehicle-to-grid interaction edge intelligent control system according to an embodiment of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0018] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] It is understood that before using the technical solutions of the various embodiments in this disclosure, users will be informed of the type, scope of use, and usage scenarios of the personal information involved in an appropriate manner, and user authorization will be obtained.

[0020] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose, based on the prompt message, whether to provide personal information to the software or hardware such as electronic devices, applications, servers, or storage media performing the operations of this disclosed technical solution.

[0021] As an optional but not limited implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0022] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0023] As described in the background section, vehicle-to-grid (V2G) interaction technology enables bidirectional energy flow between electric vehicles and the power grid, achieving off-peak charging and peak discharging, regulating grid load, peak shaving and valley filling, improving the absorption capacity of new energy sources, and reducing carbon emissions. Existing technologies employ a three-tiered collaborative architecture of "terminal-edge-cloud," with the terminal layer collecting data, the edge layer optimizing computing resources, and the cloud layer responsible for global scheduling, thus improving overall system efficiency and resource utilization. However, the massive amounts of heterogeneous data collected at the terminal layer consume significant storage space, leading to resource waste. Furthermore, the complex data structure makes it difficult for the system to achieve dynamic scheduling across time, lacking flexibility. In addition, existing technologies have shortcomings in combining global optimization with personalized control, failing to provide precise analysis and control, thus limiting practical effectiveness.

[0024] Based on the above considerations, this application proposes an edge intelligent control method for vehicle-to-grid interaction. The method utilizes a communication management module to establish communication with a user equipment (UE), and in response to establishing communication with the UE, sends a signal to a load detection module. The load detection module collects real-time charging data from the UE and transmits this data to a strategy formulation module in real time. The strategy formulation module formulates a control strategy based on the real-time charging data and sends it to an instruction execution module. The instruction execution module adjusts the UE according to the control strategy and feeds back the execution result to the strategy formulation module. This application, through the edge intelligent control method for vehicle-to-grid interaction, achieves efficient energy interaction between electric vehicles and the power grid, significantly improving the system's flexibility and control accuracy. Establishing efficient and stable communication with the UE through the communication management module allows for dynamic evaluation of link quality and selection of the optimal link for data transmission, ensuring the reliability and real-time performance of data transmission. The load detection module collects charging data from the UE in real time, cleans and extracts features from the data, ensuring data quality and validity, and providing accurate basic data support for the subsequent formulation of control strategies. The intelligent computing module combines on-site charging data and real-time charging data for macroscopic analysis, generating a globally optimized macroscopic adjustment strategy to improve the system's ability to rationally allocate grid load. The strategy formulation module combines historical and real-time data, using time series analysis to predict load changes, generating an initial control strategy based on statistical characteristics, and adjusting it based on macroscopic analysis results to ensure the accuracy and adaptability of the control strategy. The instruction execution module verifies and parses the control strategy, converting it into control signals recognizable by user equipment and issuing them, achieving precise adjustments to user equipment charging behavior. Simultaneously, the instruction execution module receives feedback from user equipment and transmits it to the strategy formulation module, providing a basis for dynamic updates to the control strategy and achieving closed-loop system control. This effectively solves the resource waste problem caused by massive heterogeneous data in existing technologies, significantly reducing storage requirements through data cleaning and feature extraction. Furthermore, combining real-time data, historical data, and macroscopic analysis results enhances the ability for cross-time dynamic scheduling, ensuring that the control strategy can flexibly respond to changes in grid load. Furthermore, through a closed-loop feedback mechanism, the system can optimize control strategies in real time, further improving the efficiency of vehicle-to-grid interaction and the absorption capacity of new energy sources, reducing carbon emissions, and maximizing energy utilization efficiency.

[0025] The technical solutions of the embodiments of this application will be described in detail below through specific examples.

[0026] refer to Figure 1 The vehicle-to-grid interaction edge intelligent control method of this application includes the following steps: Step S101: Establish communication with the user equipment using the communication management module, and in response to establishing communication with the user equipment, send a signal to the load detection module; Step S102: The load detection module is used to collect real-time charging data of the user equipment, and the collected real-time charging data is transmitted to the strategy formulation module in real time. Step S103: The strategy formulation module formulates a control strategy based on the real-time charging data and sends it to the instruction execution module. Step S104: The instruction execution module adjusts the user equipment according to the control strategy, and feeds back the execution result corresponding to the control strategy to the strategy formulation module.

[0027] Regarding step S101, in some embodiments, establishing communication between the communication management module and the user equipment includes: loading the corresponding protocol library and driver according to the communication protocol type of the user equipment; dynamically evaluating the link quality through link status monitoring to obtain the evaluation result; and establishing communication between the communication management module and the user equipment based on the link with the best quality in the evaluation result.

[0028] In this embodiment, a stable communication connection is established with the user equipment through the communication management module, ensuring the reliability and real-time performance of data transmission, and laying the foundation for subsequent data acquisition, processing, and control. During implementation, the communication management module first needs to identify and load the corresponding protocol library and driver based on the communication protocol type of the user equipment. Since different user equipment may use different communication protocols (such as Modbus, TCP / IP, etc.), the communication management module needs to have automatic protocol identification capabilities and dynamically load the appropriate protocol library and driver based on the identification results. The key to this step is ensuring seamless compatibility between the system and multiple types and brands of equipment, providing a unified communication interface for subsequent data interaction.

[0029] After successfully loading the protocol library and driver, the communication management module dynamically evaluates the quality of the communication links through link status monitoring. The purpose of link status monitoring is to collect and analyze key performance parameters of the links in real time, such as latency, packet loss rate, and bandwidth utilization. Through a comprehensive evaluation of these parameters, the communication management module can accurately determine the current status and stability of each link and generate link quality evaluation results. The dynamic evaluation process is continuous and real-time to adapt to rapid changes in link status. The evaluation results clearly indicate the quality level of different links, providing data support for subsequent link selection.

[0030] Based on the link quality assessment results, the communication management module selects the highest quality link from the available links and establishes communication with the user equipment through this link. The core objective of link selection is to ensure communication stability and efficiency, reducing data loss or transmission delays caused by poor link performance. During the communication establishment process, the communication management module sends a handshake signal to confirm the connection with the user equipment, including verifying whether the device's network address, port number, and communication parameters match. A communication connection will only be successfully established if the communication parameters are completely matched and the link is stable.

[0031] Once communication is established, the communication management module immediately sends a communication establishment signal to the load detection module, notifying other modules in the system that communication is ready. The purpose of sending the communication establishment signal is to trigger the load detection module to begin data acquisition, enabling it to obtain charging-related data from the user equipment in real time. This design ensures that all modules in the system can work collaboratively according to a predetermined logical flow, avoiding interruptions in data acquisition or processing due to communication delays between modules. Furthermore, the communication management module monitors the link status in real time during communication establishment to ensure continuous communication stability. If the link quality deteriorates due to faults or interference, the communication management module will dynamically switch to a backup link based on the latest link status assessment results, ensuring uninterrupted communication.

[0032] Through the above steps, the communication management module not only establishes communication with the user equipment but also lays a solid foundation for data transmission and control of the entire system. The dynamic evaluation and switching mechanism of the communication link effectively improves the system's reliability and stability, enabling subsequent data acquisition and control strategies to be implemented efficiently and accurately.

[0033] In one specific embodiment, the communication management module establishes a communication connection with a charging station for an electric vehicle to enable real-time data acquisition and execution of control strategies. First, the communication management module detects the communication protocol type of the charging station; for example, if the charging station uses the Modbus protocol. The communication management module automatically identifies this protocol type through its protocol identification function and loads the corresponding Modbus protocol library and driver to ensure correct parsing of the data transmitted by the charging station and sending valid control commands. Simultaneously with loading the protocol library and driver, the communication management module also initializes basic communication parameters, such as baud rate, device ID, and data frame format. This step ensures that the communication management module can interact with the charging station using the same communication protocol.

[0034] After the protocol loading is complete, the communication management module initiates the link status monitoring function to evaluate the quality of the communication link between the charging pile and the system in real time. The core of link status monitoring is collecting key performance parameters of the link, such as communication latency, packet loss rate, and bandwidth utilization. In this embodiment, by analyzing the real-time collected link parameters, the communication management module found that there are currently three available links, one of which has the lowest latency and a packet loss rate close to zero. The evaluation result shows that this is the link with the best current quality. Based on this evaluation result, the communication management module selected this link as the main channel for communication with the charging pile and allocated high-priority communication resources to it.

[0035] After selecting the optimal link, the communication management module sends a handshake signal through that link to confirm the connection with the charging pile. The handshake signal includes information such as the network address, port number, and communication parameters, used to verify whether the charging pile can correctly receive and respond. Upon receiving the handshake signal, the charging pile returns a connection confirmation response, indicating that the communication parameters match and the link is stable. At this point, the communication connection between the communication management module and the charging pile is successfully established, and the system can transmit data through this link.

[0036] After communication is established, the communication management module immediately sends a communication establishment signal to the load detection module, notifying the load detection module that it can begin collecting real-time data from the charging pile. In this embodiment, the load detection module obtains real-time charging data from the charging pile through the communication management module, including key parameters such as charging power, battery level, battery voltage, and charging duration. This communication establishment signal ensures coordinated operation between the various modules of the system, enabling the data acquisition and processing flow to proceed smoothly.

[0037] Furthermore, the communication management module continues to monitor the link status in real time after communication is established. If a degradation in the quality of the current link is detected during communication, such as a significant increase in latency or a packet loss rate exceeding a set threshold, the communication management module will dynamically switch to another alternative link based on the latest link status assessment results, thereby ensuring the continuity and stability of communication. Through this dynamic link switching mechanism, the system can effectively avoid data loss or control delays caused by link interruptions.

[0038] In this embodiment, the communication management module's protocol identification, link status monitoring, and dynamic switching functions not only successfully achieved stable communication with the charging pile but also laid a reliable foundation for subsequent data acquisition and the execution of control strategies. The communication management module ensures the system can maintain efficient operation in complex network environments, while dynamic evaluation and link optimization mechanisms improve the system's reliability and real-time performance.

[0039] Furthermore, regarding step S102, in some embodiments, the step of collecting real-time charging data of the user equipment using the load detection module includes: periodically collecting the real-time charging data of the user equipment using the load detection module according to a preset sampling interval; the real-time charging data includes the user equipment's charging time, charging power, battery level, battery voltage, battery current, battery health status, real-time load, and user equipment energy consumption; performing data cleaning and noise reduction processing on the real-time charging data, and extracting data features from the processed real-time charging data.

[0040] In some embodiments, the method further includes: using the load detection module to send the acquired real-time charging data to the strategy formulation module, and updating the control strategy in real time based on the real-time charging data.

[0041] In this embodiment, a load detection module efficiently and accurately collects real-time charging data from user equipment and transmits the collected data to a strategy formulation module in real time to support the formulation and real-time updating of control strategies. In the specific implementation, the load detection module first establishes a physical connection with the user equipment through sensors and periodically collects real-time charging data from the user equipment according to a preset sampling interval. The sampling interval is typically determined based on system requirements and the operating characteristics of the equipment, ensuring data real-time performance while avoiding excessively high sampling frequencies that could waste system resources. The load detection module collects a wide range of data, covering multiple key parameters during the user equipment charging process, including charging time, charging power, battery level, battery voltage, battery current, battery health status, real-time load, and the user equipment's energy consumption. This data comprehensively reflects the charging status and operating characteristics of the user equipment, providing rich information for subsequent data processing and decision-making.

[0042] After collecting the aforementioned real-time charging data, the load detection module cleaned and denoised the data to ensure its reliability and accuracy. The cleaning process primarily involved identifying and removing outliers, such as excluding abnormal or duplicate data that exceeded reasonable limits. Noise reduction employed filtering algorithms (such as mean filtering or Kalman filtering) to remove noise interference from the data, resulting in a cleaner and more accurate signal. The cleaned and denoised data was then further used to extract key feature parameters, such as load change trends, charging efficiency, and battery status indicators. These data features are crucial inputs for subsequent control strategy calculations, significantly improving the accuracy and practicality of the control strategy.

[0043] After processing, the load detection module transmits the real-time charging data to the strategy formulation module via the communication management module. Real-time transmission ensures that the strategy formulation module can make dynamic decisions based on the latest device data, thereby responding quickly to changes in the user's device status. In some embodiments, the load detection module not only performs data acquisition but also supplements and expands the real-time charging data based on feedback from the strategy formulation module to adapt to specific control requirements.

[0044] Furthermore, based on the transmission of real-time charging data, the strategy formulation module can update the control strategy in real time. Specifically, after receiving real-time charging data, the strategy formulation module combines historical data and the current system state to recalculate the control strategy using time series analysis and prediction algorithms. The data interaction between the load detection module and the strategy formulation module is a continuous closed-loop process, which not only improves the real-time performance and accuracy of the control strategy but also enhances the system's adaptability to dynamic changes. This design enables the system to quickly respond to fluctuations in grid load or changes in user charging demand, achieving efficient vehicle-grid interaction and resource optimization.

[0045] In one specific embodiment, a load detection module efficiently collects real-time charging data from a charging station for an electric vehicle, supporting the dynamic updating of control strategies. First, the load detection module establishes a physical connection with the charging station via sensors, which can perceive the charging station's operating status in real time. The system presets a sampling interval, for example, collecting the charging station's operating parameters every 10 seconds. This sampling interval ensures data real-time performance while avoiding excessive consumption of system resources due to high sampling frequency. During sampling, the load detection module collects several key parameters of the charging station, including charging time, current charging power, remaining battery capacity, battery voltage, battery current, battery health status, real-time load, and overall energy consumption. Through these parameters, the system can comprehensively understand the current charging status of the electric vehicle and the operating characteristics of the charging station.

[0046] After collecting the aforementioned real-time charging data, the load detection module cleans and denoises the data to ensure its accuracy and reliability. During cleaning, the module identifies and removes outliers, such as abnormally high power values ​​within a specific time period or duplicate data recordings. Outliers exceeding reasonable limits are marked as invalid and removed from the dataset. Subsequently, the load detection module denoises the data, using methods such as mean filtering or Kalman filtering to remove noise interference from the charging current or voltage signals, resulting in smoother and more accurate signals. This processed data is then used to extract key feature parameters, such as load variation trends during charging, battery charging efficiency, and real-time battery performance indicators. These feature parameters provide crucial input data for subsequent control strategies, significantly improving the accuracy and practicality of strategy formulation.

[0047] After data processing is complete, the load detection module transmits real-time charging data to the strategy formulation module via the communication management module. This real-time data transmission ensures that the strategy formulation module can make decisions based on the latest user equipment status. For example, when the real-time load of the charging pile approaches its peak, the strategy formulation module can quickly adjust the charging power to avoid overload. In some embodiments, the load detection module can also collect more data based on feedback from the strategy formulation module to meet specific control needs. For example, when the strategy formulation module requires more detailed battery health information, the load detection module can increase the sampling frequency or expand the data collection range to meet the needs of strategy formulation.

[0048] Furthermore, in some embodiments, the system further includes an intelligent computing module; the intelligent computing module is connected to the communication management module, the load detection module, and the strategy formulation module respectively; the method further includes: using the intelligent computing module to adjust the control strategy of the strategy formulation module.

[0049] In some embodiments, adjusting the control strategy of the strategy formulation module using the intelligent computing module includes: collecting on-site charging data using the intelligent computing module; the on-site charging data includes: charging and swapping behavior, load, power consumption, and emission factors; performing macroscopic analysis using the intelligent computing module based on the on-site charging data and the real-time charging data to obtain macroscopic analysis results; and transmitting the macroscopic analysis results to the strategy formulation module.

[0050] In this embodiment, the intelligent computing module, connected to the communication management module, load detection module, and strategy formulation module, serves as the core analysis and optimization unit of the entire system. The intelligent computing module plays a crucial role in optimizing and adjusting the control strategy. Working collaboratively with the load detection module through the communication management module, the intelligent computing module collects on-site charging data. This on-site charging data is extensive, covering key parameters such as charging and swapping behavior, real-time load, power consumption, and carbon emission factors. This data reflects the operating status and energy efficiency of multiple charging devices within the area, providing an important foundation for subsequent macro-level analysis.

[0051] The intelligent computing module integrates on-site charging data with real-time charging data provided by the load detection module, utilizing advanced analytical algorithms for in-depth macro-level analysis. This macro-level analysis goes beyond the current system status, incorporating historical data and predictive models to uncover trends in load changes, patterns in energy consumption, and dynamic characteristics of carbon emissions. During the analysis, the intelligent computing module employs various algorithms and technologies, such as big data analytics, time series forecasting, and machine learning models, to ensure the accuracy and reliability of the results. Through this step, the intelligent computing module generates macro-level analysis results that include not only the regional charging load distribution and energy consumption but also identify potential bottlenecks and optimization points.

[0052] After completing the macro-level analysis, the intelligent computing module transmits the results to the strategy formulation module, providing it with a global perspective for optimization. Upon receiving the macro-level analysis results, the strategy formulation module combines current real-time charging data with historical data to readjust or optimize the control strategy. Through this combination, the control strategy not only adapts to the real-time needs of individual user devices but also meets the requirements of global load distribution and carbon emission optimization. This design enables the system to achieve precise coordination between local and global aspects, satisfying individual needs while optimizing overall resource utilization efficiency.

[0053] The intelligent computing module acts as a bridge in the entire system process. Its macroscopic analysis results guide the adjustment of the strategy formulation module, enabling the control strategy to remain adaptable and accurate in a dynamically changing environment. This modular and collaborative design significantly improves flexibility and efficiency, while further promoting the efficient utilization of new energy sources and the achievement of carbon emission reduction targets through the analysis and optimization of carbon emission factors.

[0054] In one specific embodiment, the intelligent computing module, as the core analysis and optimization unit of the system, connects to the communication management module, load detection module, and strategy formulation module, respectively, and is responsible for the global optimization and dynamic adjustment of the control strategy. In practical applications, the intelligent computing module accesses the system through the communication management module and works in conjunction with the load detection module to achieve comprehensive collection of on-site charging data. This data includes key parameters such as charging and swapping behavior, real-time load, power consumption, and carbon emission factors, reflecting the operating status and energy efficiency of multiple charging devices in the area. For example, the intelligent computing module can collect the current load status of multiple charging piles, the battery capacity of charging vehicles, and the corresponding carbon emission levels in a certain area. This on-site data provides a detailed foundation for subsequent macro-analysis.

[0055] After collecting on-site charging data, the intelligent computing module further integrates the real-time charging data transmitted by the load detection module, including detailed information such as charging time, power, and battery status of individual user devices. Through comprehensive processing of on-site and real-time charging data, the intelligent computing module utilizes advanced analytical algorithms to conduct in-depth macro-level analysis of the system. Specifically, this macro-level analysis is not limited to the current operating status of the system but also incorporates historical data and predictive models to uncover patterns in load changes, trends in energy consumption, and dynamic characteristics of carbon emissions. For example, the intelligent computing module can predict peak load periods for the next few hours in the region and identify potential overload risks at certain charging stations. These analytical results, based on big data analytics, time series prediction models, and machine learning algorithms, ensure the accuracy and reliability of the analysis.

[0056] After completing the macro-analysis, the intelligent computing module generates macro-analysis results, including the load distribution of charging equipment in the current region, energy consumption, and potential optimization directions. For example, the analysis results may show that the renewable energy consumption rate in a certain region is low, while some charging equipment is overloaded. These macro-analysis results are transmitted to the strategy formulation module as an important basis for optimizing control strategies. After receiving these macro-analysis results, the strategy formulation module combines real-time charging data and historical data to adjust the existing control strategies. For example, if the macro-analysis results indicate that the carbon emission factor in the region is high, the strategy formulation module may optimize the strategy to prioritize the use of low-carbon energy for charging electric vehicles; if some charging equipment is overloaded, the strategy formulation module will adjust power allocation or delay some charging tasks to achieve a balanced load distribution.

[0057] Furthermore, regarding step S103, in some embodiments, the step of using the strategy formulation module to formulate a control strategy based on the real-time charging data and sending it to the instruction execution module includes: using the strategy formulation module to receive the real-time charging data transmitted by the load detection module, and obtaining historical data corresponding to the current user equipment from the historical database; based on the historical data, predicting the load change of the user equipment based on a time series analysis algorithm to obtain a load curve; extracting statistical features based on the load curve; generating an initial strategy based on the statistical features and the real-time charging data; adjusting the initial strategy based on the macroscopic analysis results to obtain the control strategy, and sending the control strategy to the instruction execution module.

[0058] In this embodiment, the strategy formulation module plays a crucial decision-making role in the system. Its core function is to generate control strategies based on real-time charging data and historical data, and then send the optimized control strategies to the instruction execution module for specific control. In its implementation, the strategy formulation module first receives real-time charging data transmitted by the load detection module. This data includes key parameters such as charging time, charging power, battery level, battery voltage, and battery current, reflecting the real-time operating status of the user equipment. Simultaneously, the strategy formulation module also extracts historical data corresponding to the current user equipment from the historical database. This historical data includes the equipment's operating characteristics at different time periods, load change trends, and user charging behavior patterns, providing long-term data support for the formulation of control strategies.

[0059] After acquiring real-time and historical charging data, the strategy formulation module predicts load changes in user equipment using time series analysis algorithms. These algorithms predict future load changes based on historical data and current trends, thus generating a load curve. The load curve reflects the dynamic changes in equipment load over time, providing an intuitive reference for further strategy optimization. Building on this, the strategy formulation module extracts key statistical features from the load curve, such as load peaks, troughs, average load levels, and load fluctuation amplitudes. These statistical features help the system comprehensively understand the load characteristics of the equipment, providing higher-dimensional information for the precise formulation of control strategies.

[0060] Based on the extracted statistical features and real-time charging data, the strategy formulation module generates an initial control strategy. The initial strategy is primarily based on the current operating status of the equipment and load forecast results, aiming to meet the current charging needs of the equipment while optimizing charging power and time slot allocation. However, the initial strategy, based only on data from local equipment, may be insufficient to meet global load balancing or energy optimization requirements. Therefore, after generating the initial strategy, the strategy formulation module further adjusts it by incorporating macroscopic analysis results provided by the intelligent computing module. These macroscopic analysis results reflect the overall operating status of the system, including load distribution within the region, energy consumption levels, and carbon emission optimization targets. By combining the macroscopic analysis results with the initial strategy, the strategy formulation module can generate a control strategy that meets both local needs and global optimization requirements.

[0061] Finally, the strategy formulation module sends the optimized control strategy to the instruction execution module. The control strategy includes specific control parameters and execution instructions, such as adjusting charging power, switching charging periods, and load limiting. These instructions are parsed and verified by the instruction execution module and can be accurately executed by the user equipment, thus achieving precise control over the equipment's operating status. Through this strategy formulation process that combines real-time data, historical data, and macro-analysis results, the system can achieve precise and flexible dynamic control, effectively responding to changes in user equipment demand and grid load, improving the overall efficiency of vehicle-to-grid interaction and the utilization rate of new energy sources.

[0062] In one specific embodiment, a strategy formulation module is used to regulate the charging process of an electric vehicle to achieve precise dynamic control. First, the strategy formulation module receives real-time charging data transmitted from the load detection module. This data includes the current charging time, charging power, battery level, battery voltage, and battery current. This real-time data comprehensively reflects the current charging status of the vehicle, such as whether the real-time power is stable, the remaining battery capacity, and the current load. Simultaneously, the strategy formulation module also retrieves historical data related to the vehicle from a historical database. This historical data records the vehicle's charging behavior over different time periods, such as load change patterns, common charging power ranges, and user charging habits. This historical data provides long-term behavioral references for formulating control strategies.

[0063] After acquiring real-time and historical data, the strategy formulation module uses time series analysis algorithms to predict vehicle load changes. For example, by combining the patterns of load changes in historical data with the trends in current real-time data, the algorithm predicts the vehicle's load change curve for the next half hour. This load curve visually displays the dynamic changes in vehicle load, such as the trend of gradually decreasing power in the later stages of charging or the possibility of a sudden increase in load during a specific period. Based on the predicted load curve, the strategy formulation module further extracts key statistical features, such as peak load, trough load, average load level, and load fluctuation range. These statistical features help the strategy formulation module better understand the vehicle's load characteristics, providing data support for the precise formulation of control strategies.

[0064] Based on extracted statistical characteristics and real-time charging data, the strategy formulation module generates an initial control strategy. For example, the initial strategy might include reducing charging power during peak load periods to avoid overload, or increasing charging power during off-peak periods to accelerate the charging process. However, this initial control strategy, primarily based on real-time and historical data for individual vehicles, may not meet the load balancing requirements of the entire regional power grid. Therefore, the strategy formulation module further optimizes the initial strategy by incorporating macroscopic analysis results provided by the intelligent computing module. These macroscopic analysis results come from the analysis of the operating status of all charging equipment and the overall load distribution within the region, such as whether there is a risk of overload in the current area, whether energy absorption efficiency meets expectations, and whether carbon emissions comply with environmental protection targets. By combining these macroscopic analysis results with the initial strategy, the strategy formulation module adjusts the initial control strategy to effectively meet vehicle charging needs while simultaneously optimizing the overall load of the regional power grid and maximizing energy utilization efficiency.

[0065] Finally, the strategy formulation module sends the optimized control strategy to the instruction execution module. The control strategy includes specific execution instructions, such as adjusting the vehicle's charging power to 5 kilowatts during a certain period or suspending charging during periods of high load. These instructions, after verification and parsing by the instruction execution module, are then sent to the vehicle's charging control system for execution. Through this strategy formulation process that combines real-time data, historical data, and macro-analysis results, the control strategy can dynamically adapt to changes in vehicle charging demand and regional power grid load, achieving precise control of the vehicle charging process while effectively optimizing regional energy efficiency and reducing carbon emissions.

[0066] Furthermore, regarding step S104, in some embodiments, adjusting the user equipment using the instruction execution module according to the control strategy includes: verifying the control strategy using the instruction execution module; in response to the successful verification, parsing the control strategy and obtaining the corresponding user equipment information; converting the parsed control strategy into a identifiable control signal for the user equipment, and sending the identifiable control signal to the user equipment corresponding to the user equipment information to adjust the user equipment.

[0067] In some embodiments, feeding back the execution result corresponding to the control strategy to the strategy formulation module includes: in response to the user equipment adjustment ending, sending an end signal to the instruction execution module; using the instruction execution module to record the end signal and feeding it back to the strategy formulation module.

[0068] In this embodiment, the instruction execution module plays a crucial role in translating control strategies into specific user equipment operations. Its function is to precisely adjust the operating state of the user equipment according to the control strategies generated by the strategy formulation module, and then feed the execution results back to the strategy formulation module, forming a closed-loop control. In specific implementation, after receiving the control strategy sent by the strategy formulation module, the instruction execution module verifies the control strategy. The verification process includes checking the integrity, legality, and credibility of the instruction's source to ensure that the control strategy has not been damaged or tampered with during transmission, and verifying whether the strategy is applicable to the current user equipment. Only after the control strategy passes verification will the system proceed to the subsequent parsing and execution stages.

[0069] After successful verification, the instruction execution module parses the received control strategy. This parsing process includes extracting specific control parameters from the strategy, such as charging power adjustment values, charging period switching signals, or load allocation limits. Simultaneously, the parsing module identifies user equipment information associated with the control strategy, such as the device's address, communication protocol, and current status, to ensure subsequent operations are accurately applied to the target device. After parsing, the instruction execution module converts the parameters and instructions in the control strategy into control signals recognizable by the user equipment. This conversion process requires considering the user equipment's specific communication protocol and control logic, translating high-level strategy language into low-level device operation instructions, such as charging power control signals or charging mode switching signals.

[0070] After instruction conversion is completed, the instruction execution module sends the generated control signals to the target user equipment via the communication management module, and monitors the instruction issuance status and equipment response in real time. Upon receiving the control signals, the user equipment adjusts its operating status according to the instructions, such as adjusting charging power, switching charging periods, or limiting load output. During instruction execution, the instruction execution module continuously listens for feedback signals from the user equipment to confirm whether the equipment has performed the expected control operations.

[0071] After the user equipment completes the adjustment, it sends an adjustment completion signal to the instruction execution module. This signal includes the execution result and the latest operating status of the equipment, such as the adjusted charging power, battery level, and current load. While recording the completion signal, the instruction execution module organizes the relevant information into feedback data and transmits it to the strategy formulation module. This feedback data transmission provides the strategy formulation module with real-time execution feedback, helping it evaluate the effectiveness and execution results of the control strategy. If the feedback data indicates that the user equipment's adjustment result meets expectations, the strategy formulation module can use this to verify the applicability of the current control strategy; if the feedback data indicates that the adjustment result does not meet expectations, the strategy formulation module will combine the feedback data to optimize and dynamically update the strategy.

[0072] Through the parsing, issuing, and feedback functions of the instruction execution module, the system achieves closed-loop control from strategy generation to equipment adjustment. This process not only ensures the accurate execution of the control strategy but also provides continuous data support for strategy optimization through the feedback mechanism, enabling the system to dynamically adapt and optimize. This allows it to effectively cope with complex and ever-changing grid load demands and changes in user equipment status, further improving the intelligence level and operational efficiency of the vehicle-to-grid interaction system.

[0073] In one specific embodiment, an instruction execution module precisely adjusts the operating status of an electric vehicle charging station and feeds the execution results back to a strategy formulation module, forming a complete closed-loop control. First, after the strategy formulation module generates a control strategy for the charging station, the instruction execution module receives and verifies this strategy. The verification process includes checking the integrity of the control strategy to ensure no data loss or corruption occurred during transmission; simultaneously, it verifies the legality and credibility of the strategy's source to ensure that the strategy indeed originates from the strategy formulation module. The instruction execution module also checks whether the strategy is applicable to the current charging station, for example, confirming whether the charging station's device address, communication protocol, and current status match the strategy requirements. Only after the control strategy passes all verification stages will the instruction execution module proceed to the subsequent parsing and execution phase.

[0074] After successful verification, the instruction execution module parses the control strategy, extracting specific control parameters and execution instructions. For example, in this embodiment, the strategy might require adjusting the charging power of the charging pile to 6 kilowatts and temporarily suspending charging during periods of high load. During parsing, the instruction execution module also extracts device information related to the control target, such as confirming the charging pile's address, protocol type, and operating status, to ensure that the parsed instructions are accurately applied to the target device. After parsing, the instruction execution module converts the high-level control strategy into low-level control signals recognizable by the charging pile, such as generating specific charging power adjustment instructions and pause operation signals via the Modbus protocol.

[0075] After instruction conversion, the instruction execution module sends the generated control signal to the target charging pile via the communication management module, while simultaneously monitoring the signal transmission status and the charging pile's response in real time. In this embodiment, upon receiving the control signal, the charging pile adjusts its charging power to 6 kilowatts according to the instruction and suspends charging during periods of high load. Throughout the execution process, the instruction execution module continuously listens to the charging pile's feedback signals to confirm whether it has completed the adjustment according to the control strategy. For example, the feedback signals may include information such as the current charging power, battery status, and load conditions, used to verify the actual execution effect of the charging pile.

[0076] After the charging pile completes its adjustment, it sends an adjustment completion signal to the instruction execution module. This signal includes status information such as whether the charging power has been adjusted to 6 kW, changes in battery charge level, and the current load level. Upon receiving the completion signal, the instruction execution module records it as feedback data, processes it, and transmits it to the strategy formulation module. This feedback data provides the strategy formulation module with real-time execution feedback, enabling it to evaluate the effectiveness of the control strategy. If the feedback data indicates that the charging pile's adjustment results are as expected—for example, the charging power was successfully adjusted to 6 kW and charging was successfully paused during peak load periods—the strategy formulation module can confirm that the current control strategy is effective. If the feedback data indicates that the adjustment results deviate from expectations—for example, the charging power adjustment did not take effect in a timely manner due to communication delays—the strategy formulation module can combine this feedback data to optimize and dynamically update the control strategy.

[0077] As can be seen from the above embodiments, the vehicle-to-grid (V2G) edge intelligent control method described in this application establishes communication with the user equipment using the communication management module, and sends a signal to the load detection module in response to establishing communication with the user equipment; the load detection module collects real-time charging data of the user equipment and transmits the collected real-time charging data to the strategy formulation module in real time; the strategy formulation module formulates a control strategy based on the real-time charging data and sends it to the instruction execution module; the instruction execution module adjusts the user equipment according to the control strategy and feeds back the execution result of the control strategy to the strategy formulation module. This application embodiment, through the V2G edge intelligent control method, achieves efficient energy interaction between electric vehicles and the power grid, significantly improving the system's flexibility and control accuracy. Establishing efficient and stable communication with the user equipment through the communication management module enables dynamic evaluation of link quality and selection of the optimal link for data transmission, ensuring the reliability and real-time performance of data transmission. The load detection module can collect charging data from the user equipment in real time, and perform data cleaning and feature extraction to ensure data quality and effectiveness, providing accurate basic data support for the subsequent formulation of control strategies. The intelligent computing module combines on-site charging data and real-time charging data for macroscopic analysis, generating a globally optimized macroscopic adjustment strategy to improve the system's ability to rationally allocate grid load. The strategy formulation module combines historical and real-time data, using time series analysis to predict load changes, generating an initial control strategy based on statistical characteristics, and adjusting it based on macroscopic analysis results to ensure the accuracy and adaptability of the control strategy. The instruction execution module verifies and parses the control strategy, converting it into control signals recognizable by user equipment and issuing them, achieving precise adjustments to user equipment charging behavior. Simultaneously, the instruction execution module receives feedback from user equipment and transmits it to the strategy formulation module, providing a basis for dynamic updates to the control strategy and achieving closed-loop system control. This effectively solves the resource waste problem caused by massive heterogeneous data in existing technologies, significantly reducing storage requirements through data cleaning and feature extraction. Furthermore, combining real-time data, historical data, and macroscopic analysis results enhances the ability for cross-time dynamic scheduling, ensuring that the control strategy can flexibly respond to changes in grid load. Furthermore, through a closed-loop feedback mechanism, the system can optimize control strategies in real time, further improving the efficiency of vehicle-to-grid interaction and the absorption capacity of new energy sources, reducing carbon emissions, and maximizing energy utilization efficiency.

[0078] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0079] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0080] Based on the same inventive concept, and corresponding to any of the above embodiments, this application also provides a vehicle-to-everything (V2X) interactive edge intelligent control system.

[0081] refer to Figure 2 The vehicle-to-grid interactive edge intelligent control system includes: a communication management module, a load detection module, a strategy formulation module, and an instruction execution module; The communication management module is used to establish communication with the user equipment, and in response to establishing communication with the user equipment, sends a signal to the load detection module; The load detection module is used to collect real-time charging data of the user equipment and transmit the collected real-time charging data to the strategy formulation module in real time. The strategy formulation module is used to formulate a control strategy based on the real-time charging data and send it to the instruction execution module; The instruction execution module is used to adjust the user equipment according to the control strategy and feed back the execution result corresponding to the control strategy to the strategy formulation module.

[0082] The vehicle-to-grid (V2G) interactive intelligent carbon emission control terminal includes a communication management module, a load detection module, a strategy formulation module, an instruction execution module, and an intelligent computing module. Multiple communication management modules are mounted on charging equipment, such as charging piles or distribution boxes, and employ multi-link redundancy transmission technology to establish communication with user equipment and acquire data. The communication modules ensure communication stability by monitoring relevant parameters based on link status and are connected to the corresponding load detection modules. The communication management modules identify the communication protocols required by the entire system, determining whether Modbus is used for industrial equipment communication or TCP / IP for network connections. Based on the identification results, they load the corresponding protocol libraries and drivers to establish the basic communication environment. After protocol initialization, the communication modules establish contact with the user equipment and send signals to the load detection modules.

[0083] The load detection module collects real-time data from user equipment (UAEs) by acquiring charging data such as power consumption and battery status through sensors. Upon receiving a notification of a communication connection establishment, the load detection module establishes a connection with the sensors on the charging equipment. Analog sensors connect via analog input interfaces, and the ADC is initialized with parameters such as sampling frequency and resolution. Digital sensors connect via digital interfaces, and their bus and device addresses are configured, performing corresponding initialization operations based on their type. The load detection module reads the device's operating parameters according to the set parameters, converts these parameters into actual physical quantities, and uses threshold judgment to remove obviously abnormal data and filtering algorithms to reduce noise interference. The load detection module then transmits the real-time data to the corresponding strategy formulation module.

[0084] The strategy formulation module receives real-time data from the load detection module and issues control strategies based on this data. The module preprocesses the collected data, including missing value imputation and outlier handling, plots load curves based on the cleaned data, and obtains load statistical characteristics for data analysis. Based on this, the strategy formulation module formulates a preliminary strategy and uses a model predictive control algorithm to determine the load control strategy, then organizes it into a standardized instruction format.

[0085] The instruction execution module adjusts the charging equipment according to the control strategy issued by the strategy formulation module and feeds back the execution results to the strategy formulation module. The instruction execution module transmits the instruction format issued by the strategy formulation module to the user equipment, adjusts the charging status of the user equipment, and receives feedback data from the user equipment. The instruction execution module transmits the feedback data to the strategy formulation module, which updates its strategy based on the feedback results and sends it back to the instruction execution module. The instruction execution module continuously monitors the strategy formulation module to ensure it can promptly capture newly generated control instructions, guaranteeing the integrity and accuracy of instruction transmission and preventing instruction loss or corruption.

[0086] The intelligent computing module connects to multiple communication management modules, load detection modules, and strategy formulation modules. The intelligent computing module collects on-site charging data based on signals from the communication management modules, which in turn send signals to the intelligent computing module after establishing a basic communication platform. The intelligent computing module collects on-site charging data from the activated communication modules, including parameters such as charging / swapping behavior, load, power consumption, and emission factors. The intelligent computing module collects on-site charging data and receives data from the load detection module, integrating and performing macro-level analysis. The load detection module simultaneously transmits user equipment data to the intelligent computing module. The intelligent computing module combines the actual charging data with the user equipment data to perform calculations and analysis, and formulate macro-level adjustment strategies. The intelligent computing module feeds back the macro-level analysis results to the strategy formulation module for its use. The intelligent computing module then transmits the macro-level adjustment strategies to the strategy formulation module, which formulates specific adjustment strategies for the user equipment based on the macro-level adjustment strategies.

[0087] The load detection module periodically collects data from user equipment and sends the collected data to the strategy formulation module for real-time updates to the adjustment strategy. This, combined with feedback from the instruction execution module, enables dynamic strategy adjustments. The intelligent computing module is divided into a collection layer, a data processing layer, and an application layer. The collection layer collects on-site charging data, primarily focusing on real-time acquisition of various carbon emission-related data, such as charging / swapping behavior, load, power consumption, emission factors, and key parameters related to renewable energy consumption. The data processing layer processes, analyzes, and stores on-site charging data and user equipment data, employing big data analytics and cloud computing technologies to achieve efficient and accurate data processing. The data processing layer uses big data analytics to clean, denoise, and extract features from the collected data, providing a more accurate foundation for subsequent data processing. By designing appropriate algorithms and models, the data is analyzed and processed in depth to extract carbon emission-related features and information, formulate macro-level strategies, and transmit these strategies to the strategy formulation module. The application layer displays the data based on the data analysis results and further analyzes the data. It has developed a data visualization interface and functional modules. The interface and modules intuitively display carbon emission data and key indicators, enabling users to quickly obtain the information they need. At the same time, it provides a variety of data analysis and mining tools to help users deeply explore the potential value of the data.

[0088] The strategy formulation module includes a reading unit, a processing unit, and a predictive control algorithm unit. The reading unit accesses the data processing layer to obtain historical data, such as recent on-site charging data and user equipment operating status information. The processing unit cleans and preprocesses existing and historical data, plots load curves based on the cleaned data, obtains load statistical characteristics, and performs data analysis. The predictive control algorithm unit receives the calculation results from the data processing layer and combines them with the load data from the processing unit to formulate a control strategy. Under the macro-level strategy sent by the data processing module, the predictive control algorithm unit, combined with current user equipment data, performs calculations using a model predictive control algorithm to determine the load control strategy and organize it into a standardized instruction format.

[0089] The reading unit performs time-series analysis by reading historical data, facilitating in-depth data analysis and mining to discover patterns and trends, providing more accurate and valuable decision support for subsequent applications. The instruction execution module includes a parsing unit, a conversion unit, and a feedback unit. The parsing unit receives and verifies the control strategy, validating instructions from the strategy formulation module to ensure the completeness of the instruction format and the legality of its source. After completion, it parses the instructions and obtains user equipment information. The conversion unit converts the parsed data into control signals recognizable by the user equipment and verifies, connects, and monitors these control signals. The feedback unit receives feedback signals from the user equipment and sends them to the processing unit through the conversion unit. After the user equipment executes the adjustment strategy, the feedback unit records various execution details and feeds them back to the strategy formulation module.

[0090] For ease of description, the above system is described by dividing it into various modules based on their functions. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0091] The system described in the above embodiments is used to implement the corresponding vehicle-to-grid interaction edge intelligent control method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0092] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0093] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

Claims

1. A method for intelligent edge control of vehicle-to-everything (V2X) interaction, characterized in that, An intelligent edge control system for vehicle-to-grid interaction is applied, the system comprising: a communication management module, a load detection module, a strategy formulation module, and an instruction execution module; The method includes: The communication management module establishes communication with the user equipment, and in response to establishing communication with the user equipment, sends a signal to the load detection module; The load detection module collects real-time charging data of the user equipment and transmits the collected real-time charging data to the strategy formulation module in real time. The strategy formulation module formulates a control strategy based on the real-time charging data and sends it to the instruction execution module; The instruction execution module adjusts the user equipment according to the control strategy and feeds back the execution result of the control strategy to the strategy formulation module.

2. The method according to claim 1, characterized in that, The system also includes an intelligent computing module; the intelligent computing module is connected to the communication management module, the load detection module, and the strategy formulation module respectively. The method further includes: using the intelligent computing module to adjust the control strategy of the strategy formulation module.

3. The method according to claim 2, characterized in that, The step of establishing communication with the user equipment using the communication management module includes: Load the corresponding protocol library and driver according to the communication protocol type of the user equipment; The link quality is dynamically assessed by link status monitoring, and the assessment results are obtained. Communication between the communication management module and the user equipment is established based on the link with the best quality in the evaluation results.

4. The method according to claim 3, characterized in that, The process of collecting real-time charging data of the user equipment using the load detection module includes: The load detection module collects real-time charging data of the user equipment at preset sampling intervals; the real-time charging data includes the user equipment's charging time, charging power, battery level, battery voltage, battery current, battery health status, real-time load, and user equipment energy consumption. The real-time charging data is cleaned and noise-reduced, and the processed real-time charging data is extracted to obtain data features.

5. The method according to claim 4, characterized in that, The method further includes: The load detection module sends the acquired real-time charging data to the strategy formulation module, and the control strategy is updated in real time based on the real-time charging data.

6. The method according to claim 5, characterized in that, The step of adjusting the control strategy of the strategy formulation module using the intelligent computing module includes: The intelligent computing module is used to collect on-site charging data; the on-site charging data includes: charging and swapping behavior, load, power consumption, and emission factors; Based on the on-site charging data and the real-time charging data, the intelligent computing module is used to perform macroscopic analysis to obtain macroscopic analysis results. The macroscopic analysis results are transmitted to the strategy formulation module.

7. The method according to claim 6, characterized in that, The step of using the strategy formulation module to formulate a control strategy based on the real-time charging data and sending it to the instruction execution module includes: The strategy formulation module receives the real-time charging data transmitted by the load detection module and retrieves historical data corresponding to the current user equipment from the historical database. Based on the historical data and using a time series analysis algorithm, the load changes of the user equipment are predicted to obtain a load curve; Statistical characteristics were extracted based on the load curve; An initial strategy is generated based on the statistical characteristics and the real-time charging data; The initial strategy is adjusted based on the macroscopic analysis results to obtain the control strategy, and the control strategy is sent to the instruction execution module.

8. The method according to claim 1, characterized in that, The step of adjusting the user equipment using the instruction execution module according to the control strategy includes: The control strategy is verified using the instruction execution module. In response to the successful verification, the control strategy is parsed and the corresponding user equipment information is obtained; The parsed control strategy is converted into a recognizable control signal for the user equipment, and the recognizable control signal is sent to the user equipment corresponding to the user equipment information to adjust the user equipment.

9. The method according to claim 8, characterized in that, The step of feeding back the execution result corresponding to the control strategy to the strategy formulation module includes: In response to the completion of the user equipment adjustment, a termination signal is sent to the instruction execution module; The instruction execution module records the termination signal and feeds it back to the strategy formulation module.

10. A vehicle-to-everything (V2X) intelligent edge control system, characterized in that, include: The module includes a communication management module, a load detection module, a policy formulation module, and an instruction execution module. The communication management module is used to establish communication with the user equipment, and in response to establishing communication with the user equipment, sends a signal to the load detection module; The load detection module is used to collect real-time charging data of the user equipment and transmit the collected real-time charging data to the strategy formulation module in real time. The strategy formulation module is used to formulate a control strategy based on the real-time charging data and send it to the instruction execution module; The instruction execution module is used to adjust the user equipment according to the control strategy and feed back the execution result corresponding to the control strategy to the strategy formulation module.

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