Digital twin-driven water and fertilizer real-time dynamic balance transfer system

Abstract

The invention discloses a digital twin-driven water and fertilizer real-time dynamic balance transfer system, which comprises a sensing and data acquisition module for monitoring moisture, salinity, pH value, illumination and rainfall meteorological data of soil in real time through various sensors deployed in a farmland; and the digital twinborn modeling and simulation module is used for constructing a digital twinborn model of the farmland based on the real-time data collected by the perception and data acquisition module. According to the invention, by arranging the perception and data acquisition module, the digital twin modeling and simulation module, the intelligent decision-making and regulation module, the execution and feedback module, the historical data tracing and system optimization module, the expansion module, the networking module and the edge-cloud cooperative computing architecture, the farmland environment and the crop growth condition can be monitored in real time; the digital twinborn modeling and simulation module constructs a digital twinborn model of a farmland based on real-time data, simulates a soil environment and crop growth, and optimizes a water and fertilizer proportioning strategy.

Description

Technical Field

[0001] This invention belongs to the field of water and fertilizer regulation technology, and particularly relates to a digital twin-driven real-time dynamic balance regulation system for water and fertilizer. Background Technology

[0002] Water and fertilizer management generally refers to the integrated management of water and fertilizer for crops in agricultural production. Proper water and fertilizer management is crucial for crop growth and development, and can improve crop yield and quality. During irrigation, it is important to pay attention to the amount and timing, avoiding excessive or insufficient water that could adversely affect crops. Simultaneously, various fertilizers, such as nitrogen, phosphorus, and potassium fertilizers, should be applied rationally according to the crop's nutritional needs and soil fertility to meet the crop's growth requirements. Integrated water and fertilizer management technology is a commonly used management model in modern agriculture. By integrating the supply of water and fertilizer, it achieves precise fertilization and water-saving irrigation, improving water and fertilizer utilization efficiency and reducing environmental pollution.

[0003] In existing technologies, traditional water and fertilizer systems rely on static threshold control, which cannot dynamically respond to soil moisture and crop fertilizer requirements. Sensor data is isolated, lacks multi-physics coupling analysis (water-fertilizer-air-heat-crop growth coupling), and has serious regulation lag (>2 hours), making it impossible to achieve real-time optimization of the "perception-decision-execution" closed loop. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a digital twin-driven real-time dynamic balance adjustment system for water and fertilizer, comprising: The sensing and data acquisition module uses various sensors deployed in farmland to monitor soil moisture, salinity, pH, as well as sunlight and rainfall meteorological data in real time. The digital twin modeling and simulation module, based on real-time data collected by the sensing and data acquisition module, constructs a digital twin model of farmland, simulates the soil environment, crop growth status, and water and fertilizer requirements of farmland in real time, and finds the optimal water and fertilizer ratio strategy by simulating the effects of different irrigation and fertilization schemes. The intelligent decision-making and control module automatically generates irrigation and fertilization decision schemes based on the simulation results of the digital twin modeling and simulation module, combined with crop growth needs and water and fertilizer utilization factors. When the sensor detects that the soil moisture or nutrient content is lower than the set threshold, the system will automatically calculate the amount of water and fertilizer to be added and adjust it through actuators such as variable frequency water pumps and fertilization devices to ensure the precise supply of water and fertilizer. The execution and feedback module transforms the decision-making schemes generated by the intelligent decision-making and control module into actual irrigation and fertilization operations. By controlling the execution mechanisms such as irrigation water pumps and fertilization devices, the system can achieve precise irrigation and fertilization of farmland. This module also collects data in real time during the execution process, such as irrigation volume and fertilization volume, and feeds this data back to the digital twin modeling and simulation module so that the system can continuously optimize the decision-making schemes. The historical data tracing and system optimization module records various historical data during system operation. It analyzes and mines this data through machine learning algorithms to predict future water and fertilizer demand trends, providing a reference for future irrigation and fertilization decisions. This module can also continuously optimize the system's decision-making algorithms and control strategies based on feedback from historical data, thereby improving the system's intelligence level and operating efficiency. The expansion module is used to connect new sensors and actuators to expand the system's functions and application scope. Through flexible interface design and modular design, the system can easily cope with future changes in farmland environment and crop planting needs. The networking module transmits the data collected by the sensors to the cloud server in real time, and allows for remote access and monitoring via mobile APP, computer client and smart terminal. Users can view real-time environmental data of farmland, crop growth status and irrigation and fertilization records anytime and anywhere, realizing comprehensive management and control of farmland. The edge-cloud collaborative computing architecture deploys edge computing nodes in farmland to perform preliminary processing and outlier removal of sensor data, reducing the burden on cloud servers. Edge computing nodes can also achieve real-time data synchronization and backup, ensuring data security and reliability. Cloud servers are responsible for in-depth data analysis and mining, providing strong support for intelligent decision-making and regulation. Through the application of the edge-cloud collaborative computing architecture, the system can achieve real-time monitoring and precise management of the farmland environment, improving water and fertilizer utilization and crop yield.

[0005] As a preferred embodiment of the present invention, the sensors in the sensing and data acquisition module include one or more combinations of temperature sensors, humidity sensors, and light sensors.

[0006] As a preferred embodiment of the present invention, the digital twin modeling and simulation module further includes the following functions: Data acquisition and integration is used to collect data (such as from sensors, IoT devices, databases, third-party systems, etc.), and to integrate and process it. Through data cleaning, format standardization and other means, the consistency and accuracy of the data are ensured, providing a solid data foundation for subsequent modeling and simulation. 3D modeling and simulation uses 3D modeling tools to accurately model physical objects, forming a visualized virtual twin. Based on real physical parameters, it performs high-precision simulation of the system's motion, mechanics, and thermodynamics, including the physical system's shape and geometric features, and also restores its physical characteristics and operating behavior as much as possible to ensure that the virtual twin's behavior is consistent with the physical object. Through simulation testing, it can predict the system's performance, optimize parameter design, and reduce risks in practical applications. Data analysis and insights involve in-depth mining and processing of collected data, analyzing historical data through machine learning algorithms, predicting future development trends, providing system optimization data, offering valuable insights for managers, and displaying key data indicators using charts, dashboards, and other formats to help users understand data trends. 3D visualization and interaction: The virtual 3D graphical interface presents the state and changes of physical objects, providing multi-view viewing functions (such as top view, perspective, cross-section, etc.), allowing users to observe the state of the digital twin from different angles. It also supports interactive interfaces, allowing users to interact with the visualized objects through mouse, touch, etc. (such as rotation, scaling, roaming, etc.), achieving more intuitive and convenient system monitoring and management. Platform management and collaboration manages all resources of the entire system and supports collaborative work between different roles, including user permission management (ensuring system security and data privacy), collaborative work support (multiple users can view, analyze and operate the platform simultaneously), data storage (providing distributed storage solutions to ensure high reliability and high availability of data storage), and encryption and access control (preventing unauthorized access and data leakage). API interfaces are integrated with the system, providing standard API interfaces and integration middleware, enabling third-party applications to easily call platform functions for data interaction. It supports integration with systems such as ERP and MES, enhancing the platform's functional applicability and ensuring that the digital twin modeling and simulation module can be seamlessly integrated with other systems and applications, enabling wider application and collaboration.

[0007] As a preferred embodiment of the present invention, the intelligent decision-making and control module further includes the following functions: The intelligent decision-making module is used for task decomposition and orchestration. It can analyze and decompose power grid events to obtain a set of tasks for handling the event. The intelligent decision-making module also includes a business function model library to match tasks with business. For different power grid events, the intelligent decision-making module adopts an auxiliary decision-making mode to quickly and accurately locate and identify disturbance sources, and generates control instructions based on wide-area information, which are directly issued to the intelligent execution module. The intelligent monitoring module is responsible for data acquisition, processing, and perception of the power grid's operating status. Utilizing big data analytics, information filtering, and integration technologies, it achieves interactive fusion of multi-source heterogeneous data, extracts key information features, and performs knowledge mining on structured data (such as SCADA systems, PMUs, transmission plans, alarm data, equipment monitoring data, etc.) and unstructured data (such as control logs, video surveillance, external environment, etc.) to obtain information on power grid operation, external environment, and personnel behavior. This information is then transmitted to the intelligent decision-making module. The intelligent execution module interacts with the automatic generation control (AGC), automatic voltage control (AVC), equipment operation, information release, and management system modules to complete corresponding operation tasks. It includes a task execution engine and a process execution engine. The former realizes cross-platform and cross-system interface calls, while the latter completes the corresponding tasks through the task execution engine according to the task arrangement. The intelligent execution module uses semantic information analysis to match the various functional modules and adopts general interface description theory and reasoning technology to realize intelligent calling and issuing instructions to different modules. The intelligent interaction module acquires events sensed by the intelligent monitoring module, decision suggestions generated by the intelligent decision-making module, and result information output by the intelligent execution module. It interacts with control personnel and other systems. The intelligent interaction module has functions such as reading, listening, understanding, neural center, intelligent search, and comprehensive display. It uses virtual reality, 3D simulation, voice broadcast or holographic images to display operational information in a panoramic way, improving interaction efficiency and convenience. The database and database management module store and manage various data related to decision-making. The model library and model library management module store and manage various decision-making models; The module manages the method library, experts, and knowledge base, storing and managing expert knowledge, experience, and various decision-making methods.

[0008] As a preferred embodiment of the present invention, the execution and feedback module further includes the following functions: The execution module receives instructions from the intelligent decision-making and control module and controls physical devices or virtual entities to perform corresponding operations. The specific functions of the execution module include: Action generation: Based on the received instructions, generate specific actions to be executed; Motion control: Precisely control the execution of actions to ensure that the actions are performed as expected; Device interaction: Interacting with physical devices related to the execution of actions, such as controlling motor operation or adjusting valve opening and closing; The feedback module is responsible for monitoring the execution results and system status, and feeding back relevant information to the decision-making module or execution module. The specific functions of the feedback module include: Status monitoring: Real-time monitoring of the system's operating status and execution results, such as parameters like temperature, pressure, and liquid level.

[0009] Information acquisition: Collect system status information through sensors and other devices, and convert it into digital signals for processing; Data analysis: Analyzing and processing the collected data to extract useful information for system adjustment and optimization; Feedback signal generation: Generate feedback signals based on the analysis results and send them to the decision-making module or execution module to guide subsequent operations.

[0010] As a preferred embodiment of the present invention, the historical data tracing and system optimization module further includes the following functions: Data preservation and integrity: Automatically save all relevant data records to ensure data integrity and security, including production data, financial data and other types of business data, for easy traceability and auditing later; Timeline and Quick Location: The timeline function allows you to quickly locate data at any point in time for detailed viewing and analysis, helping companies respond quickly to verification and auditing needs and providing accurate data support. Multi-dimensional query and traceability: Supports querying and tracing by different dimensions (such as product batch, production date, department, etc.), helping enterprises to fully understand the business scenarios and processes behind the data; Data visualization and trend analysis: Visualize historical data intuitively through charts, reports, and other means to help enterprises discover data trends and anomalies, providing support for decision-making; Real-time monitoring and feedback: Real-time monitoring of business data and processes, timely detection of anomalies and feedback to users help enterprises respond quickly to problems and prevent losses from escalating; Intelligent analysis and prediction: By leveraging machine learning and artificial intelligence technologies, historical data is deeply mined and intelligently analyzed to predict future trends and potential risks, helping enterprises to make more scientific decisions and plans; Process optimization and automation: By analyzing historical data and business processes, the system can identify bottlenecks and problems, and propose optimization suggestions. The system can also automate some processes to improve work efficiency and accuracy. Cost control and budget management: Used by enterprises to establish a cost control system and budget management mechanism, and to discover potential cost savings through data analysis and optimize resource allocation; Supply chain optimization and collaboration: By integrating with enterprise ERP, MES, SCM and other systems, data traceability and collaborative management of the entire supply chain process can be achieved, which helps to improve the transparency and efficiency of the supply chain and reduce operating costs. Decision support and performance evaluation: Based on historical and real-time data, it provides enterprises with decision support and performance evaluation functions, which helps enterprises formulate more scientific strategies and goals and improve overall competitiveness.

[0011] As a preferred embodiment of the present invention, the edge-cloud collaborative computing architecture further includes the following functions: Resource optimization and dynamic scheduling: The edge-cloud collaborative computing architecture achieves resource optimization through a layered design; edge nodes act as the first layer to process real-time data, while the cloud acts as the second layer for global analysis and storage. This layered mechanism can significantly reduce cloud load and shorten edge response time. The architecture also includes dynamic load balancing and intelligent scheduling algorithms, which can dynamically adjust task allocation based on real-time resource usage and task priority to ensure efficient system operation. Data preprocessing and compression: In the edge-cloud collaborative computing architecture, edge devices are responsible for data preprocessing and compression. By processing and analyzing data locally, edge devices can filter out invalid information and upload only key data to the cloud. This not only reduces the latency and bandwidth consumption of data transmission to the cloud, but also reduces the storage costs of the cloud. Data preprocessing at the edge can also improve data security and privacy protection. Task offloading and collaborative processing: When the computing resources of edge devices are insufficient, the edge-cloud collaborative computing architecture supports offloading some tasks to the cloud computing platform for processing; this task offloading mechanism can be dynamically adjusted according to real-time resource usage and task priority to ensure the efficient operation of the system. Edge devices and the cloud can also collaboratively process complex tasks, achieving rapid response and efficient completion of tasks through division of labor and cooperation. Model training and inference collaboration: In the edge-cloud collaborative computing architecture, edge devices can collect real-time data and perform preliminary processing, and then transmit the data to the cloud computing platform for model training; the trained model can be deployed to the edge device for real-time inference, thereby achieving rapid response. This collaborative mechanism of model training and inference can make full use of the real-time data processing capabilities of edge devices and the powerful computing capabilities of the cloud, thereby improving the overall performance and efficiency of the system. Highly efficient data synchronization and consistency protocol: It has a data synchronization mechanism and consistency protocol to ensure that data between edge devices and the cloud can be synchronized in real time and accurately; this not only improves the reliability and stability of the system, but also provides users with more consistent and accurate data services. Security and privacy protection: Ensuring the security and privacy of data during transmission and storage, including various security measures and technologies such as data encryption, access control, and identity authentication.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention, through the establishment of a sensing and data acquisition module, a digital twin modeling and simulation module, an intelligent decision-making and control module, an execution and feedback module, a historical data tracing and system optimization module, an expansion module, a networking module, and an edge-cloud collaborative computing architecture, can monitor farmland environment and crop growth status in real time, providing necessary data support for precision agriculture. The digital twin modeling and simulation module constructs a digital twin model of farmland based on real-time data, simulates soil environment and crop growth, optimizes water and fertilizer ratio strategies, and improves resource utilization efficiency. The intelligent decision-making and control module combines simulation results and crop needs to automatically generate irrigation and fertilization plans, achieving precise supply and ensuring healthy crop growth. The execution and feedback module translates decision-making schemes into actual operations and collects execution data feedback in real time to continuously optimize decision-making schemes. The historical data tracing and system optimization module analyzes historical data through machine learning, predicts future needs, optimizes decision-making algorithms, and improves the system's intelligence level. The expansion module allows the connection of new devices, enhancing system functionality and adaptability. The networking module enables remote data access and monitoring, facilitating user management. The edge-cloud collaborative computing architecture processes data initially through edge computing nodes, reducing the burden on the cloud while ensuring data security and reliability. The cloud server performs in-depth analysis, supporting intelligent decision-making, achieving efficient and precise management, and improving water and fertilizer utilization and crop yield.

[0013] 2. By setting sensors in the sensing and data acquisition module, this invention improves the flexibility and accuracy of data acquisition. Appropriate sensor combinations can be selected according to actual needs to meet the monitoring requirements in different scenarios, enhancing the intelligence level of the system and providing rich and reliable raw data for subsequent data processing and analysis, which helps to improve the overall system performance and user experience.

[0014] 3. This invention, through the establishment of a digital twin modeling and simulation module and data acquisition and integration functions, enables the system to collect and integrate various data from physical objects in real time, ensuring data integrity and accuracy. This provides a solid foundation for subsequent analysis and simulation. The 3D modeling and simulation function, through precise 3D modeling tools, creates a virtual twin that is highly consistent with the physical object, facilitating an intuitive understanding of the physical object's structure and function. Furthermore, through high-precision simulation based on real physical parameters, in-depth analysis of the system's motion, mechanical, and thermodynamic behavior can be conducted, thereby optimizing design and operational processes. The data analysis and insight function, through in-depth mining and processing of collected data, combined with machine learning algorithms to analyze historical data, can predict future development trends and provide data support for system optimization, helping to anticipate future trends. By identifying problems and developing corresponding improvement measures, the system's efficiency and reliability are enhanced. The 3D visualization and interactive functions, through a virtual 3D graphical interface, intuitively present the state and changes of physical objects, providing multi-view functionality. This allows users to more intuitively understand complex information and make more informed decisions. Platform management and collaboration functions ensure effective management of the entire system's resources and support collaborative work between different roles, helping to improve team efficiency and ensure smooth project progress. API interfaces and system integration functions, by providing standard API interfaces and integration middleware, allow third-party applications to easily call platform functions for data interaction, enhancing the system's flexibility and scalability. Furthermore, it promotes compatibility and integration with other systems, enabling broader business applications.

[0015] 4. This invention, by setting up an intelligent decision-making and control module, enables rapid analysis and processing of power grid events, improving the intelligence level of power grid operation. The intelligent decision-making module, through task decomposition and arrangement, combined with a business function model library, can quickly and accurately locate and identify power grid disturbance sources and generate effective control commands. The intelligent monitoring module utilizes big data analysis technology to integrate multi-source heterogeneous data, extract key information features, and provide comprehensive data support for intelligent decision-making. The intelligent execution module completes operational tasks through interaction with multiple system modules and uses semantic information analysis to achieve intelligent invocation and command issuance between modules. The intelligent interaction module improves the efficiency and convenience of interaction with control personnel and other systems through various interaction methods. The database and model library management module ensures the storage and management of decision-related data and models, while the method library and expert and knowledge base management module stores expert knowledge and decision-making methods, providing support for intelligent decision-making.

[0016] 5. This invention, by setting up execution and feedback modules, enables the specific execution of instructions from the intelligent decision-making and control modules in the physical world. The action generation function of the execution module ensures the generation of specific executable actions based on instructions, while the action control function ensures that these actions are executed precisely as expected, thereby improving the accuracy and reliability of operation. The device interaction function enables the execution module to effectively communicate and control various physical devices, such as motor operation and valve switching, which provides a foundation for the automation and intelligence of the system. The status monitoring function of the feedback module can track the system's operating status and execution results in real time, ensuring that the system can respond to various changes in a timely manner. The information acquisition function collects system status information through sensors and other devices and converts it into digital signals, providing basic data for data analysis. The data analysis function performs in-depth analysis on these data, extracting valuable information for system adjustment and optimization. The feedback signal generation function converts the analysis results into feedback signals, which can guide the decision-making or execution module to adjust subsequent operations, thereby achieving closed-loop control and improving the system's adaptability and overall performance.

[0017] 6. This invention, by setting up a historical data tracing and system optimization module, automatically saves all relevant data records, ensuring data integrity and security, facilitating subsequent tracing and auditing, and providing enterprises with a reliable data foundation. The addition of timeline and multi-dimensional query and tracing functions enables enterprises to quickly locate and analyze data at any point in time, thereby rapidly responding to verification and auditing needs and providing accurate data support. Data visualization and trend analysis functions, through intuitive charts and reports, help enterprises discover data trends and anomalies, providing strong support for decision-making. Real-time monitoring and feedback mechanisms ensure real-time monitoring of business data and processes, timely detection and feedback of anomalies, helping enterprises respond quickly to problems and prevent escalation of losses. Intelligent analysis and prediction functions utilize advanced machine learning and artificial intelligence technologies to deeply mine and intelligently analyze historical data. Predicting future trends and potential risks helps enterprises make more scientific decisions and plans. Process optimization and automation functions analyze historical data and business processes to identify bottlenecks and problems, and propose optimization suggestions to automate parts of the process, improving work efficiency and accuracy. Cost control and budget management functions help enterprises establish cost control systems and budget management mechanisms, discover potential cost savings through data analysis, and optimize resource allocation. Supply chain optimization and collaboration functions integrate with enterprise ERP, MES, SCM, and other systems to achieve data traceability and collaborative management throughout the supply chain, improving supply chain transparency and efficiency, and reducing operating costs. Decision support and performance evaluation functions, based on historical and real-time data, provide enterprises with decision support and performance evaluation, helping them formulate more scientific strategies and goals, and improve overall competitiveness.

[0018] This invention establishes an edge-cloud collaborative computing architecture. Resource optimization and dynamic scheduling ensure efficient utilization of computing resources. Through layered design and intelligent scheduling algorithms, it reduces the load on the cloud and shortens the response time on the edge, thereby improving the overall system performance and efficiency. Data preprocessing and compression are performed at the edge, effectively reducing the transmission of invalid data, lowering bandwidth consumption and cloud storage costs. Edge-side data processing enhances data security and privacy protection because sensitive data does not need to be transmitted to the cloud. Task offloading and collaborative processing mechanisms allow some tasks to be transferred to the cloud when edge device resources are scarce. This dynamic adjustment ensures efficient task completion, and the collaborative work between edge devices and the cloud enables… It can quickly respond to complex tasks, improve processing speed and efficiency, and the collaborative mechanism of model training and inference fully utilizes the real-time data processing capabilities of edge devices and the powerful computing capabilities of the cloud. By training models in the cloud and deploying the trained models to edge devices for real-time inference, it achieves rapid response and efficient data processing. The efficient data synchronization and consistency protocol ensures real-time and accurate data synchronization between edge devices and the cloud, improving the reliability and stability of the system and providing users with consistent and accurate data services. Security and privacy protection measures ensure the security and privacy of data during transmission and storage, using a variety of security means and technical measures, including data encryption, access control, and identity authentication. Attached Figure Description

[0019] Figure 1 This is the system control diagram of the present invention. Detailed Implementation

[0020] To further understand the invention's content, features, and effects, the following embodiments are provided, and detailed descriptions are given in conjunction with the accompanying drawings.

[0021] The structure of the present invention will now be described in detail with reference to the accompanying drawings.

[0022] like Figure 1 The present invention provides a digital twin-driven real-time dynamic balance adjustment system for water and fertilizer, comprising: The sensing and data acquisition module uses various sensors deployed in farmland to monitor soil moisture, salinity, pH, as well as sunlight and rainfall meteorological data in real time. The digital twin modeling and simulation module, based on real-time data collected by the sensing and data acquisition module, constructs a digital twin model of farmland, simulates the soil environment, crop growth status, and water and fertilizer requirements of farmland in real time, and finds the optimal water and fertilizer ratio strategy by simulating the effects of different irrigation and fertilization schemes. The intelligent decision-making and control module automatically generates irrigation and fertilization decision schemes based on the simulation results of the digital twin modeling and simulation module, combined with crop growth needs and water and fertilizer utilization factors. When the sensor detects that the soil moisture or nutrient content is lower than the set threshold, the system will automatically calculate the amount of water and fertilizer to be added and adjust it through actuators such as variable frequency water pumps and fertilization devices to ensure the precise supply of water and fertilizer. The execution and feedback module transforms the decision-making schemes generated by the intelligent decision-making and control module into actual irrigation and fertilization operations. By controlling the execution mechanisms such as irrigation water pumps and fertilization devices, the system can achieve precise irrigation and fertilization of farmland. This module also collects data in real time during the execution process, such as irrigation volume and fertilization volume, and feeds this data back to the digital twin modeling and simulation module so that the system can continuously optimize the decision-making schemes. The historical data tracing and system optimization module records various historical data during system operation. It analyzes and mines this data through machine learning algorithms to predict future water and fertilizer demand trends, providing a reference for future irrigation and fertilization decisions. This module can also continuously optimize the system's decision-making algorithms and control strategies based on feedback from historical data, thereby improving the system's intelligence level and operating efficiency. The expansion module is used to connect new sensors and actuators to expand the system's functions and application scope. Through flexible interface design and modular design, the system can easily cope with future changes in farmland environment and crop planting needs. The networking module transmits the data collected by the sensors to the cloud server in real time, and allows for remote access and monitoring via mobile APP, computer client and smart terminal. Users can view real-time environmental data of farmland, crop growth status and irrigation and fertilization records anytime and anywhere, realizing comprehensive management and control of farmland. The edge-cloud collaborative computing architecture deploys edge computing nodes in farmland to perform preliminary processing and outlier removal of sensor data, reducing the burden on cloud servers. Edge computing nodes can also achieve real-time data synchronization and backup, ensuring data security and reliability. Cloud servers are responsible for in-depth data analysis and mining, providing strong support for intelligent decision-making and regulation. Through the application of the edge-cloud collaborative computing architecture, the system can achieve real-time monitoring and precise management of the farmland environment, improving water and fertilizer utilization and crop yield.

[0023] The sensors in the sensing and data acquisition module include one or more combinations of temperature sensors, humidity sensors, and light sensors.

[0024] The above approach improves the flexibility and accuracy of data acquisition, allowing for the selection of appropriate sensor combinations to meet monitoring needs in different scenarios. It also enhances the system's intelligence level and provides rich and reliable raw data for subsequent data processing and analysis, thereby improving the overall system performance and user experience.

[0025] The digital twin modeling and simulation module also includes the following functions: Data acquisition and integration is used to collect data (such as from sensors, IoT devices, databases, third-party systems, etc.), and to integrate and process it. Through data cleaning, format standardization and other means, the consistency and accuracy of the data are ensured, providing a solid data foundation for subsequent modeling and simulation. 3D modeling and simulation uses 3D modeling tools to accurately model physical objects, forming a visualized virtual twin. Based on real physical parameters, it performs high-precision simulation of the system's motion, mechanics, and thermodynamics, including the physical system's shape and geometric features, and also restores its physical characteristics and operating behavior as much as possible to ensure that the virtual twin's behavior is consistent with the physical object. Through simulation testing, it can predict the system's performance, optimize parameter design, and reduce risks in practical applications. Data analysis and insights involve in-depth mining and processing of collected data, analyzing historical data through machine learning algorithms, predicting future development trends, providing system optimization data, offering valuable insights for managers, and displaying key data indicators using charts, dashboards, and other formats to help users understand data trends. 3D visualization and interaction: The virtual 3D graphical interface presents the state and changes of physical objects, providing multi-view viewing functions (such as top view, perspective, cross-section, etc.), allowing users to observe the state of the digital twin from different angles. It also supports interactive interfaces, allowing users to interact with the visualized objects through mouse, touch, etc. (such as rotation, scaling, roaming, etc.), achieving more intuitive and convenient system monitoring and management. Platform management and collaboration manages all resources of the entire system and supports collaborative work between different roles, including user permission management (ensuring system security and data privacy), collaborative work support (multiple users can view, analyze and operate the platform simultaneously), data storage (providing distributed storage solutions to ensure high reliability and high availability of data storage), and encryption and access control (preventing unauthorized access and data leakage). API interfaces are integrated with the system, providing standard API interfaces and integration middleware, enabling third-party applications to easily call platform functions for data interaction. It supports integration with systems such as ERP and MES, enhancing the platform's functional applicability and ensuring that the digital twin modeling and simulation module can be seamlessly integrated with other systems and applications, enabling wider application and collaboration.

[0026] The above solution employs the following features: Data acquisition and integration capabilities enable the system to collect and integrate various data from physical objects in real time, ensuring data integrity and accuracy and providing a solid foundation for subsequent analysis and simulation. The 3D modeling and simulation capabilities, through precise 3D modeling tools, create a virtual twin highly consistent with the physical object, facilitating an intuitive understanding of the object's structure and function. Furthermore, high-precision simulation based on real physical parameters allows for in-depth analysis of the system's motion, mechanical, and thermodynamic behavior, thereby optimizing design and operational processes. The data analysis and insight capabilities, through in-depth data mining and processing, combined with machine learning algorithms to analyze historical data, can predict future development trends and provide data support for system optimization, helping to identify problems early and formulate solutions. Corresponding improvements enhance system efficiency and reliability. The 3D visualization and interactive functions, through a virtual 3D graphical interface, intuitively present the state and changes of physical objects, providing multi-view functionality. This allows users to more intuitively understand complex information and make more informed decisions. Platform management and collaboration functions ensure effective management of system resources and support collaborative work between different roles, helping to improve team efficiency and ensure smooth project progress. API interfaces and system integration functions, by providing standard API interfaces and integration middleware, allow third-party applications to easily call platform functions for data interaction, enhancing system flexibility and scalability. Furthermore, it promotes compatibility and integration with other systems, enabling broader business applications.

[0027] The intelligent decision-making and control module also includes the following functions: The intelligent decision-making module is used for task decomposition and orchestration. It can analyze and decompose power grid events to obtain a set of tasks for handling the event. The intelligent decision-making module also includes a business function model library to match tasks with business. For different power grid events, the intelligent decision-making module adopts an auxiliary decision-making mode to quickly and accurately locate and identify disturbance sources, and generates control instructions based on wide-area information, which are directly issued to the intelligent execution module. The intelligent monitoring module is responsible for data acquisition, processing, and perception of the power grid's operating status. Utilizing big data analytics, information filtering, and integration technologies, it achieves interactive fusion of multi-source heterogeneous data, extracts key information features, and performs knowledge mining on structured data (such as SCADA systems, PMUs, transmission plans, alarm data, equipment monitoring data, etc.) and unstructured data (such as control logs, video surveillance, external environment, etc.) to obtain information on power grid operation, external environment, and personnel behavior. This information is then transmitted to the intelligent decision-making module. The intelligent execution module interacts with the automatic generation control (AGC), automatic voltage control (AVC), equipment operation, information release, and management system modules to complete corresponding operation tasks. It includes a task execution engine and a process execution engine. The former realizes cross-platform and cross-system interface calls, while the latter completes the corresponding tasks through the task execution engine according to the task arrangement. The intelligent execution module uses semantic information analysis to match the various functional modules and adopts general interface description theory and reasoning technology to realize intelligent calling and issuing instructions to different modules. The intelligent interaction module acquires events sensed by the intelligent monitoring module, decision suggestions generated by the intelligent decision-making module, and result information output by the intelligent execution module. It interacts with control personnel and other systems. The intelligent interaction module has functions such as reading, listening, understanding, neural center, intelligent search, and comprehensive display. It uses virtual reality, 3D simulation, voice broadcast or holographic images to display operational information in a panoramic way, improving interaction efficiency and convenience. The database and database management module store and manage various data related to decision-making. The model library and model library management module store and manage various decision-making models; The module manages the method library, experts, and knowledge base, storing and managing expert knowledge, experience, and various decision-making methods.

[0028] The above solution enables rapid analysis and processing of power grid events, improving the intelligence level of power grid operation. The intelligent decision-making module, through task decomposition and orchestration combined with a business function model library, can quickly and accurately locate and identify power grid disturbance sources and generate effective control commands. The intelligent monitoring module utilizes big data analytics to integrate multi-source heterogeneous data, extract key information features, and provide comprehensive data support for intelligent decision-making. The intelligent execution module completes operational tasks through interaction with multiple system modules and uses semantic information analysis to achieve intelligent inter-module calls and command issuance. The intelligent interaction module improves the efficiency and convenience of interaction with control personnel and other systems through various interaction methods. The database and model library management module ensures the storage and management of decision-related data and models, while the method library and expert / knowledge base management module stores expert knowledge and decision-making methods, providing support for intelligent decision-making.

[0029] The execution and feedback module also includes the following functions: The execution module receives instructions from the intelligent decision-making and control module and controls physical devices or virtual entities to perform corresponding operations. The specific functions of the execution module include: Action generation: Based on the received instructions, generate specific actions to be executed; Motion control: Precisely control the execution of actions to ensure that the actions are performed as expected; Device interaction: Interacting with physical devices related to the execution of actions, such as controlling motor operation or adjusting valve opening and closing; The feedback module is responsible for monitoring the execution results and system status, and feeding back relevant information to the decision-making module or execution module. The specific functions of the feedback module include: Status monitoring: Real-time monitoring of the system's operating status and execution results, such as parameters like temperature, pressure, and liquid level.

[0030] Information acquisition: Collect system status information through sensors and other devices, and convert it into digital signals for processing; Data analysis: Analyzing and processing the collected data to extract useful information for system adjustment and optimization; Feedback signal generation: Generate feedback signals based on the analysis results and send them to the decision-making module or execution module to guide subsequent operations.

[0031] The above scheme enables the execution of instructions from the intelligent decision-making and control module in the physical world. The action generation function of the execution module ensures the generation of specific executable actions based on the instructions, while the action control function guarantees that these actions are executed precisely as expected, thereby improving the accuracy and reliability of the operation. The device interaction function allows the execution module to effectively communicate and control various physical devices, such as motor operation and valve switching, which provides a foundation for the system's automation and intelligence. The status monitoring function of the feedback module can track the system's operating status and execution results in real time, ensuring that the system can respond to various changes in a timely manner. The information acquisition function collects system status information through sensors and other devices and converts it into digital signals, providing basic data for data analysis. The data analysis function performs in-depth analysis on this data, extracting valuable information for system adjustment and optimization. The feedback signal generation function converts the analysis results into feedback signals, which can guide the decision-making or execution module to adjust subsequent operations, thereby achieving closed-loop control and improving the system's adaptability and overall performance.

[0032] The historical data tracing and system optimization module also includes the following functions: Data preservation and integrity: Automatically save all relevant data records to ensure data integrity and security, including production data, financial data and other types of business data, for easy traceability and auditing later; Timeline and Quick Location: The timeline function allows you to quickly locate data at any point in time for detailed viewing and analysis, helping companies respond quickly to verification and auditing needs and providing accurate data support. Multi-dimensional query and traceability: Supports querying and tracing by different dimensions (such as product batch, production date, department, etc.), helping enterprises to fully understand the business scenarios and processes behind the data; Data visualization and trend analysis: Visualize historical data intuitively through charts, reports, and other means to help enterprises discover data trends and anomalies, providing support for decision-making; Real-time monitoring and feedback: Real-time monitoring of business data and processes, timely detection of anomalies and feedback to users help enterprises respond quickly to problems and prevent losses from escalating; Intelligent analysis and prediction: By leveraging machine learning and artificial intelligence technologies, historical data is deeply mined and intelligently analyzed to predict future trends and potential risks, helping enterprises to make more scientific decisions and plans; Process optimization and automation: By analyzing historical data and business processes, the system can identify bottlenecks and problems, and propose optimization suggestions. The system can also automate some processes to improve work efficiency and accuracy. Cost control and budget management: Used by enterprises to establish a cost control system and budget management mechanism, and to discover potential cost savings through data analysis and optimize resource allocation; Supply chain optimization and collaboration: By integrating with enterprise ERP, MES, SCM and other systems, data traceability and collaborative management of the entire supply chain process can be achieved, which helps to improve the transparency and efficiency of the supply chain and reduce operating costs. Decision support and performance evaluation: Based on historical and real-time data, it provides enterprises with decision support and performance evaluation functions, which helps enterprises formulate more scientific strategies and goals and improve overall competitiveness.

[0033] The above solution ensures data integrity and security by automatically saving all relevant data records, facilitating subsequent traceability and auditing, and providing a reliable data foundation for enterprises. The addition of timeline and multi-dimensional query and traceability functions enables enterprises to quickly locate and analyze data at any point in time, thereby rapidly responding to verification and auditing needs and providing accurate data support. Data visualization and trend analysis functions help enterprises identify data trends and anomalies through intuitive charts and reports, providing strong support for decision-making. Real-time monitoring and feedback mechanisms ensure real-time monitoring of business data and processes, timely detection and feedback of anomalies, helping enterprises respond quickly to problems and prevent escalation of losses. Intelligent analysis and prediction functions utilize advanced machine learning and artificial intelligence technologies to deeply mine and intelligently analyze historical data, predicting future trends and potential... In terms of risk management, it helps enterprises make more scientific decisions and plans. Process optimization and automation functions identify bottlenecks and problems by analyzing historical data and business processes, and propose optimization suggestions to automate parts of the process, improving work efficiency and accuracy. Cost control and budget management functions help enterprises establish cost control systems and budget management mechanisms, discover potential cost savings through data analysis, and optimize resource allocation. Supply chain optimization and collaboration functions, through integration with enterprise ERP, MES, SCM, and other systems, achieve data traceability and collaborative management throughout the supply chain, improving supply chain transparency and efficiency, and reducing operating costs. Decision support and performance evaluation functions, based on historical and real-time data, provide enterprises with decision support and performance evaluation, helping them formulate more scientific strategies and goals, and improve overall competitiveness.

[0034] The edge-cloud collaborative computing architecture also includes the following functions: Resource optimization and dynamic scheduling: The edge-cloud collaborative computing architecture achieves resource optimization through a layered design; edge nodes act as the first layer to process real-time data, while the cloud acts as the second layer for global analysis and storage. This layered mechanism can significantly reduce cloud load and shorten edge response time. The architecture also includes dynamic load balancing and intelligent scheduling algorithms, which can dynamically adjust task allocation based on real-time resource usage and task priority to ensure efficient system operation. Data preprocessing and compression: In the edge-cloud collaborative computing architecture, edge devices are responsible for data preprocessing and compression. By processing and analyzing data locally, edge devices can filter out invalid information and upload only key data to the cloud. This not only reduces the latency and bandwidth consumption of data transmission to the cloud, but also reduces the storage costs of the cloud. Data preprocessing at the edge can also improve data security and privacy protection. Task offloading and collaborative processing: When the computing resources of edge devices are insufficient, the edge-cloud collaborative computing architecture supports offloading some tasks to the cloud computing platform for processing; this task offloading mechanism can be dynamically adjusted according to real-time resource usage and task priority to ensure the efficient operation of the system. Edge devices and the cloud can also collaboratively process complex tasks, achieving rapid response and efficient completion of tasks through division of labor and cooperation. Model training and inference collaboration: In the edge-cloud collaborative computing architecture, edge devices can collect real-time data and perform preliminary processing, and then transmit the data to the cloud computing platform for model training; the trained model can be deployed to the edge device for real-time inference, thereby achieving rapid response. This collaborative mechanism of model training and inference can make full use of the real-time data processing capabilities of edge devices and the powerful computing capabilities of the cloud, thereby improving the overall performance and efficiency of the system. Highly efficient data synchronization and consistency protocol: It has a data synchronization mechanism and consistency protocol to ensure that data between edge devices and the cloud can be synchronized in real time and accurately; this not only improves the reliability and stability of the system, but also provides users with more consistent and accurate data services. Security and privacy protection: Ensuring the security and privacy of data during transmission and storage, including various security measures and technologies such as data encryption, access control, and identity authentication.

[0035] The above solution ensures efficient utilization of computing resources through resource optimization and dynamic scheduling. Layered design and intelligent scheduling algorithms reduce cloud load and shorten edge response time, thereby improving overall system performance and efficiency. Data preprocessing and compression are performed at the edge, effectively reducing invalid data transmission, lowering bandwidth consumption and cloud storage costs. Edge-side data processing enhances data security and privacy protection because sensitive data does not need to be transmitted to the cloud. Task offloading and collaborative processing mechanisms allow some tasks to be transferred to the cloud when edge device resources are limited. This dynamic adjustment ensures efficient task completion, and the collaborative work between edge devices and the cloud enables rapid response. For complex tasks, improving processing speed and efficiency, the collaborative mechanism of model training and inference fully leverages the real-time data processing capabilities of edge devices and the powerful computing capabilities of the cloud. By training models in the cloud and deploying the trained models to edge devices for real-time inference, rapid response and efficient data processing are achieved. Efficient data synchronization and consistency protocols ensure real-time and accurate data synchronization between edge devices and the cloud, improving system reliability and stability and providing users with consistent and accurate data services. Security and privacy protection measures ensure the security and privacy of data during transmission and storage, employing various security methods and technologies, including data encryption, access control, and identity authentication.

[0036] Working principle of the invention: The sensing and data acquisition module monitors soil moisture, salinity, pH, and meteorological data such as light and rainfall in real time using various sensors deployed in the farmland, such as temperature, humidity, and light sensors. The data collected by these sensors provides the foundation for subsequent system operations. The digital twin modeling and simulation module utilizes the real-time data collected by the sensing and data acquisition module to construct a digital twin model of the farmland. This module not only performs 3D modeling and simulation but also data acquisition and integration, data analysis and insight, and 3D visualization and interaction. Through these functions, the module can simulate the soil environment, crop growth status, and water and fertilizer requirements of the farmland in real time, thereby predicting the effects of different irrigation and fertilization schemes and identifying the optimal water and fertilizer ratio. The intelligent decision-making and control module, based on simulation results from the digital twin modeling and simulation module and considering crop growth requirements and water and fertilizer utilization rates, automatically generates irrigation and fertilization decision plans. When sensors detect soil moisture or nutrient content below a set threshold, the system automatically calculates the amount of water and fertilizer to be applied and adjusts these amounts via variable frequency pumps and fertilization devices to ensure precise water and fertilizer supply. The intelligent decision-making and control module also includes sub-modules for intelligent decision-making, intelligent monitoring, intelligent execution, and intelligent interaction to support rapid analysis and processing of power grid events. The execution and feedback module translates the decision plans generated by the intelligent decision-making and control module into actual irrigation and fertilization operations. This module controls the irrigation water pumps and fertilization devices... This module, equipped with actuators, enables precise irrigation and fertilization of farmland. It also collects real-time data during the process, such as irrigation and fertilization amounts, and feeds this data back to the digital twin modeling and simulation module. This allows the system to continuously optimize its decision-making. The historical data tracing and system optimization module records various historical data during system operation and analyzes and mines this data using machine learning algorithms to predict future water and fertilizer demand trends, providing a reference for future irrigation and fertilization decisions. This module can also continuously optimize the system's decision-making algorithms and control strategies based on historical data feedback, improving the system's intelligence level and operational efficiency. The expansion module allows the connection of new sensors and actuators, thereby expanding the system's functionality and application scope. With its flexible interface and modular design, the system can easily adapt to future changes in farmland environment and crop planting needs. The networking module transmits sensor-collected data to the cloud server in real time, allowing users to remotely access and monitor the data via mobile apps, computer clients, and other smart terminals. This enables users to view real-time environmental data, crop growth status, and irrigation and fertilization records anytime, anywhere, achieving comprehensive management and control of the farmland. The edge-cloud collaborative computing architecture deploys edge computing nodes on-site to perform preliminary processing and outlier removal of sensor data, thereby reducing the burden on the cloud server. Edge computing nodes also enable real-time data synchronization and backup, ensuring data security and reliability.Cloud servers are responsible for in-depth data analysis and mining, providing strong support for intelligent decision-making and regulation. Through the application of an edge-cloud collaborative computing architecture, the system can achieve real-time monitoring and precise management of the farmland environment, improving water and fertilizer utilization and crop yield.

[0037] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0038] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A digital twin-driven real-time dynamic water and fertilizer balance adjustment system, characterized in that: include: The sensing and data acquisition module uses various sensors deployed in farmland to monitor soil moisture, salinity, pH, as well as sunlight and rainfall meteorological data in real time. The digital twin modeling and simulation module, based on real-time data collected by the sensing and data acquisition module, constructs a digital twin model of farmland, simulates the soil environment, crop growth status, and water and fertilizer requirements of farmland in real time, and finds the optimal water and fertilizer ratio strategy by simulating the effects of different irrigation and fertilization schemes. The intelligent decision-making and control module automatically generates irrigation and fertilization decision schemes based on the simulation results of the digital twin modeling and simulation module, combined with crop growth requirements and water and fertilizer utilization factors. The execution and feedback module transforms the decision-making schemes generated by the intelligent decision-making and control module into actual irrigation and fertilization operations; The historical data tracing and system optimization module records various historical data during system operation, analyzes and mines this data through machine learning algorithms, predicts future water and fertilizer demand trends, and provides a reference for future irrigation and fertilization decisions. Expansion modules are used to connect new sensors and actuators, expanding the system's functionality and application scope. The networking module transmits the data collected by the sensors to the cloud server in real time, and enables remote access and monitoring via mobile APP, computer client and smart terminal. The edge-cloud collaborative computing architecture deploys edge computing nodes in farmland to perform preliminary processing and outlier removal of sensor data, reducing the burden on cloud servers. Edge computing nodes can also achieve real-time data synchronization and backup.

2. The digital twin-driven real-time dynamic balance adjustment system for water and fertilizer as described in claim 1, characterized in that: The sensors in the sensing and data acquisition module include one or more combinations of temperature sensors, humidity sensors, and light sensors.

3. The digital twin-driven real-time dynamic balance adjustment system for water and fertilizer as described in claim 1, characterized in that: The digital twin modeling and simulation module also includes the following functions: Data acquisition and integration is used to collect, integrate, and process data. 3D modeling and simulation uses 3D modeling tools to accurately model physical objects, forming a visualized virtual twin. Based on real physical parameters, it performs high-precision simulation of the system's motion, mechanics, and thermodynamics. Data analysis and insights involve in-depth mining and processing of collected data, analyzing historical data through machine learning algorithms, predicting future development trends, and providing data for system optimization. 3D visualization and interaction: A virtual 3D graphical interface presents the state and changes of physical objects, providing multi-view viewing functionality; Platform management and collaboration: manages various resources throughout the system and supports collaborative work between different roles; The API interface is integrated with the system, providing standard API interfaces and integration middleware, enabling third-party applications to easily call platform functions for data interaction.

4. The digital twin-driven real-time dynamic balance adjustment system for water and fertilizer as described in claim 1, characterized in that: The intelligent decision-making and control module also includes the following functions: The intelligent decision-making module is used for task decomposition and orchestration. It adopts an auxiliary decision-making mode to quickly and accurately locate and identify disturbance sources, and generates control instructions based on wide-area information, which are then directly sent to the intelligent execution module. The intelligent monitoring module is responsible for data acquisition, processing, and sensing of the power grid's operating status. The intelligent execution module interacts with the automatic generation control (AGC), automatic voltage control (AVC), equipment operation, information release, and management system modules to complete the corresponding operation tasks; The intelligent interaction module acquires events sensed by the intelligent monitoring module, decision suggestions generated by the intelligent decision-making module, and result information output by the intelligent execution module, and interacts with control personnel and other systems. The database and database management module store and manage various data related to decision-making. The model library and model library management module store and manage various decision-making models; The module manages the method library, experts, and knowledge base, storing and managing expert knowledge, experience, and various decision-making methods.

5. The digital twin-driven real-time dynamic balance adjustment system for water and fertilizer as described in claim 1, characterized in that: The execution and feedback module also includes the following functions: The execution module receives instructions from the intelligent decision-making and control module and controls physical devices or virtual entities to perform corresponding operations. The specific functions of the execution module include: action generation, action control, and device interaction. The feedback module is responsible for monitoring the execution results and system status, and feeding back relevant information to the decision-making module or execution module. The specific functions of the feedback module include: status monitoring, information collection, data analysis, and feedback signal generation.

6. The digital twin-driven real-time dynamic balance adjustment system for water and fertilizer as described in claim 1, characterized in that: The historical data tracing and system optimization module also includes the following functions: Data preservation and integrity: Automatically saves all relevant data records to ensure data integrity and security; Timeline and Quick Location: Use the timeline function to quickly locate data at any point in time for detailed viewing and analysis; Multi-dimensional query and traceability: Supports querying and tracing by different dimensions; Data visualization and trend analysis: Visualize historical data intuitively through charts, reports, and other means to help enterprises discover data trends and anomalies, providing support for decision-making; Real-time monitoring and feedback: Monitor business data and processes in real time, promptly detect anomalies and provide feedback to users; Intelligent Analysis and Prediction: Utilizing machine learning and artificial intelligence technologies, this technology deeply mines and intelligently analyzes historical data to predict future trends and potential risks. Process optimization and automation: By analyzing historical data and business processes, the system can identify bottlenecks and problems and propose optimization suggestions; Cost control and budget management: Used by enterprises to establish a cost control system and budget management mechanism, and to discover potential cost savings through data analysis and optimize resource allocation; Supply chain optimization and collaboration: Achieving data traceability and collaborative management throughout the entire supply chain process; Decision support and performance evaluation: Based on historical and real-time data, it provides enterprises with decision support and performance evaluation functions.

7. The digital twin-driven real-time dynamic balance adjustment system for water and fertilizer as described in claim 1, characterized in that: The edge-cloud collaborative computing architecture also includes the following functions: Resource optimization and dynamic scheduling: The edge-cloud collaborative computing architecture achieves resource optimization through layered design; Data preprocessing and compression: In the edge-cloud collaborative computing architecture, edge devices are responsible for data preprocessing and compression; Task offloading and collaborative processing: When edge devices lack computing resources, the edge-cloud collaborative computing architecture supports offloading some tasks to the cloud computing platform for processing. Model training and inference collaboration: In the edge-cloud collaborative computing architecture, edge devices can collect real-time data and perform preliminary processing, and then transmit the data to the cloud computing platform for model training; Highly efficient data synchronization and consistency protocol: It has a data synchronization mechanism and consistency protocol to ensure that data between edge devices and the cloud can be synchronized in real time and accurately; Security and privacy protection: Ensuring the security and privacy of data during transmission and storage.

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