Rural digital twin energy storage management method and system based on internet of things

Abstract

The present application relates to the technical field of digital twinning, especially to a rural digital twinning energy storage management method and system based on the Internet of Things. The method comprises the following steps: collecting real-time operation data through rural distributed energy storage equipment to obtain rural energy storage data; constructing a digital twinning model according to the rural energy storage data to obtain a rural energy storage digital twinning model; detecting the energy storage state according to the rural energy storage digital twinning model to obtain rural energy storage state data; and optimizing scheduling according to the rural energy storage state data to obtain rural energy storage scheduling data for rural energy storage management auxiliary operation. Through the construction of the digital twinning model and the virtual-real synchronous management, the system can realize multi-level state monitoring, load prediction and priority scheduling of the equipment, and ensure the efficient operation of the energy storage equipment in the rural power grid environment.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of digital twinning, in particular to a rural digital twinning energy storage management method and system based on an Internet of Things. BACKGROUND

[0002] With the rapid development of renewable energy and distributed energy systems, especially in rural areas, more and more rural distributed energy storage systems are applied to agricultural production, rural power grid peak shaving and clean energy power supply. However, due to the complex environment of the energy storage system in actual application, the large fluctuation of load demand, and the variety of equipment, the traditional manual management and timing monitoring method is difficult to effectively cope with the dynamic load demand and complex operation of the energy storage equipment. The traditional management method is not only low in efficiency and slow in response speed, but also has a great influence on the service life of the energy storage equipment and the system energy efficiency. SUMMARY

[0003] To solve the above technical problems, the application provides a rural digital twinning energy storage management method and system based on an Internet of Things to solve at least one of the above technical problems.

[0004] The application provides a rural digital twinning energy storage management method based on an Internet of Things, which comprises the following steps:

[0005] S1, collecting real-time operation data of rural distributed energy storage equipment to obtain rural energy storage data;

[0006] S2, constructing a digital twinning model according to the rural energy storage data to obtain a rural energy storage digital twinning model;

[0007] S3, detecting the state of the energy storage according to the rural energy storage digital twinning model to obtain rural energy storage state data;

[0008] S4, optimizing scheduling according to the rural energy storage state data to obtain rural energy storage scheduling data for rural energy storage management auxiliary operation.

[0009] In the application, real-time data is used to quickly respond to changes in the energy storage equipment and ensure stable operation of the system. The digital twinning model can virtually simulate the physical system and provide accurate performance analysis, so that the system can make accurate analysis. By simulating different operation scenarios, the energy storage management strategy is optimized, and the overall efficiency of the system is improved. According to the digital twinning model, the state detection can timely find potential problems (such as equipment failure or performance decline), and reduce maintenance cost. Through the optimization of the scheduling algorithm, the energy storage equipment can be ensured to operate optimally under different load conditions, and the energy utilization rate is improved. Through efficient energy storage management, the integration of renewable energy is supported, and the sustainable development of rural areas is promoted.

[0010] Optionally, S1 comprises:

[0011] S11, collecting real-time operation data through rural distributed energy storage equipment to obtain energy storage real-time operation data;

[0012] S12, performing hierarchical processing according to the energy storage real-time operation data to obtain energy storage hierarchical data;

[0013] S13, performing data synchronization according to the energy storage hierarchical data to obtain rural energy storage data.

[0014] In the present application, real-time operation data is collected through rural distributed energy storage equipment, and the system can obtain the most accurate and up-to-date operation information, which helps to realize rapid response and decision-making. Through hierarchical processing, the original data can be classified as needed, redundant information can be filtered out, data volume can be reduced, and system processing efficiency can be improved. Hierarchical processing divides data into different characteristic categories (such as temperature, load, voltage, etc.), which facilitates model construction and analysis and enhances the operability of data. The results of hierarchical processing can accurately reflect the multi-dimensional state of the energy storage system, providing a clear basis for subsequent precise optimization. Through synchronization processing, hierarchical data from different sources and different times is integrated to form complete rural energy storage data, which is convenient for global optimization management. In the data synchronization process, seamless transmission of data from each device to the central system is ensured, important information is avoided to be lost, and the integrity of the data is guaranteed.

[0015] Optionally, the hierarchical processing comprises:

[0016] performing device type hierarchical processing according to the energy storage real-time operation data to obtain first energy storage hierarchical data;

[0017] performing electrical characteristic hierarchical processing according to the energy storage real-time operation data to obtain second energy storage hierarchical data;

[0018] performing time dimension hierarchical processing according to the energy storage real-time operation data to obtain third energy storage hierarchical data;

[0019] performing hierarchical network construction according to the first energy storage hierarchical data, the second energy storage hierarchical data and the third energy storage hierarchical data to obtain the energy storage hierarchical data.

[0020] This invention employs a stratified approach based on equipment type to manage different types of energy storage devices (such as battery type and energy storage system type), facilitating targeted optimization of operational strategies for each type of device. Different equipment types possess different operating characteristics; stratification by equipment type allows for comparison and analysis of similar devices, facilitating rapid diagnosis of malfunctioning equipment. Stratification by electrical characteristics (such as voltage, current, power, and temperature) enables more intuitive observation and analysis of changes in electrical parameters, especially during overload or abnormal conditions. Stratification based on electrical characteristics allows for rapid identification and location of anomalies in different electrical characteristics, thereby reducing the difficulty and time required for fault detection. Stratification by time dimension (such as minutes, hours, and days) helps understand the changing trends of load and energy storage, providing support for short-term load forecasting and long-term energy storage planning. Time-based stratification can identify the periodic changes and peak load periods of the energy storage system, facilitating scheduling optimization and improving the balance and response speed of the energy storage system.

[0021] Optionally, the device type hierarchical processing includes:

[0022] Based on real-time energy storage operation data, the equipment types are classified to obtain energy storage equipment classification data;

[0023] Subsystem data is extracted based on energy storage device classification data to obtain energy storage subsystem classification data;

[0024] Based on the classification data of energy storage subsystems, the operating characteristics are classified to obtain energy storage operating characteristic data;

[0025] Based on the energy storage operation characteristic data, the equipment characteristics are aggregated to obtain the first energy storage stratification data.

[0026] This invention categorizes energy storage devices by type, allowing for separate management of different devices (such as lithium batteries, lead-acid batteries, and thermal energy storage devices), thus enabling targeted maintenance and optimization. Based on this device classification, subsystem data (such as battery management systems, temperature control systems, and charge / discharge modules) is further extracted, resulting in more refined data and improved system monitoring accuracy. Categorizing subsystem data according to different operating characteristics (such as temperature, voltage, and current) helps to clearly display the real-time status of energy storage devices. Aggregating multi-level characteristic data based on device characteristics significantly reduces data redundancy, facilitating management and storage.

[0027] Optionally, the electrical characteristic layering process includes:

[0028] Based on real-time energy storage operation data, electrical parameters are identified to obtain energy storage electrical parameter classification data;

[0029] Voltage and current characteristics are processed based on the classification data of energy storage electrical parameters to obtain energy storage voltage and current distribution data;

[0030] Based on the real-time operation data of energy storage, the load balancing characteristics are analyzed to obtain energy storage load characteristic data;

[0031] Power characteristic data of energy storage are obtained by analyzing the classification data of energy storage electrical parameters.

[0032] Temperature efficiency data of energy storage is obtained by performing temperature efficiency correlation analysis based on real-time energy storage operation data, energy storage voltage and current distribution data and energy storage power characteristic data.

[0033] Based on the classification data of energy storage electrical parameters, energy storage voltage and current distribution data, energy storage load characteristic data, energy storage power characteristic data, and energy storage temperature and efficiency data, the electrical characteristics are aggregated to obtain the second energy storage stratification data;

[0034] The load balance characteristics include:

[0035] Load characteristics are extracted from real-time energy storage operation data to obtain energy storage load characteristic data;

[0036] Load clustering is performed based on energy storage load characteristic data to obtain energy storage load clustering data;

[0037] Based on the energy storage load clustering data, graph load characteristic processing is performed to obtain energy storage load graph data;

[0038] The mixed load balance index is calculated based on the energy storage load map data to obtain the energy storage load mixed index data;

[0039] Time-sensitivity processing is performed on the energy storage load mixing index data to obtain energy storage load characteristic data.

[0040] This invention categorizes real-time operating data according to different electrical characteristics by identifying electrical parameters (such as voltage, current, and power), laying a structured foundation for data analysis and processing. Distribution analysis of voltage and current data helps to understand the voltage and current conditions at each node, identifying problems such as overvoltage, undervoltage, or abnormal current. Analyzing the load balance characteristics of the energy storage system identifies the system's operating status under different load conditions, facilitating load allocation and management. Power characteristic analysis allows for the evaluation of the energy storage system's power efficiency, identifying high-energy-consuming components to develop energy-saving measures. Temperature-efficiency correlation analysis assesses the impact of temperature changes on equipment efficiency, optimizing temperature control strategies and extending equipment lifespan. Aggregated electrical characteristic data helps optimize the overall scheduling strategy of the energy storage system, ensuring balanced operation across different electrical characteristic dimensions.

[0041] This invention extracts load characteristics from real-time energy storage operation data to capture key load features (such as load fluctuations and peak loads) within the energy storage system. Clustered load data hierarchically displays the load status of the energy storage system, simplifying the scheduling strategy formulation process and improving scheduling accuracy. Graph load characteristic processing establishes relationships between load data using a graph structure, uncovering the interdependence and transmission effects of load nodes. Hybrid index calculation dynamically adjusts the load balance evaluation criteria by combining current load status and historical data, enabling the system to adapt to different load changes. Time-series sensitivity analysis identifies the time sensitivity of the load balance index, predicts load change trends, and improves the system's predictability.

[0042] Optionally, the time-dimension hierarchical processing includes:

[0043] Time-stamped data of energy storage is obtained by processing real-time operation data of energy storage.

[0044] First-time characteristic analysis is performed based on energy storage time tag data to obtain short-term time characteristic data;

[0045] A second time characteristic analysis is performed based on the energy storage time tag data to obtain the medium-term time characteristic data;

[0046] A third time characteristic analysis is performed based on the energy storage time tag data to obtain long-term time characteristic data. The time data corresponding to the first time characteristic analysis is less than or equal to the time data corresponding to the second time characteristic analysis, and the time data corresponding to the second time characteristic analysis is less than or equal to the time data corresponding to the third time characteristic analysis.

[0047] Peak and valley time periods are segmented based on short-term, medium-term, and long-term time characteristic data to obtain peak and valley characteristic data.

[0048] Based on peak and valley characteristic data, time correlation characteristics are extracted from short-term, medium-term, and long-term time characteristic data to obtain the third energy storage stratification data.

[0049] In this invention, time-tagged data can be analyzed hierarchically across short-term, medium-term, and long-term scales, facilitating the identification of characteristics at different time scales. Through short-term, medium-term, and long-term time characteristic analysis, the load and energy storage characteristics of the energy storage system at different time scales can be captured, providing a foundation for optimized scheduling across multiple time scales. By identifying peak and off-peak periods, charging can be performed during off-peak hours and discharging during peak hours, achieving load balancing and energy efficiency optimization for energy storage devices. Peak-off-peak period segmentation helps energy storage systems charge during off-peak hours and discharge during peak hours, thereby saving electricity costs and maximizing the economic benefits of the energy storage system. Through time-related characteristic extraction, correlation analysis of short-term, medium-term, and long-term time characteristics helps identify the relationships between characteristics at different time scales. Time-related characteristic data can reveal the correlations between different time characteristics, providing more accurate data support for load forecasting and scheduling optimization.

[0050] Optionally, S2 includes:

[0051] S21. Construct a digital twin mapping based on rural energy storage data to obtain an initial digital twin model of rural energy storage;

[0052] S22. Based on the initial digital twin model of rural energy storage, extract the features of the energy storage system to obtain the feature data of the energy storage system;

[0053] S23. Construct a real-time response layer based on the characteristic data of the energy storage system to obtain the real-time response layer data of the energy storage system.

[0054] S24. Based on the initial digital twin model of rural energy storage, perform virtual-physical characteristic pairing to obtain virtual-physical characteristic calibration table data;

[0055] S25. Based on the real-time response layer data of the energy storage system and the virtual-real characteristic calibration table data, the initial digital twin model of rural energy storage is verified to obtain the rural energy storage digital twin model.

[0056] This invention creates a virtual mapping of an energy storage system by constructing a digital twin mapping model, providing a foundation for subsequent performance analysis, fault detection, and optimized scheduling. Multidimensional feature data (such as temperature, current, voltage, and load status) of the energy storage system are extracted from the initial digital twin model, facilitating the analysis of key system characteristics. By constructing a real-time response layer, the energy storage system can respond quickly to changes or anomalies, improving the system's dynamic adaptability. Through pairing virtual and physical characteristics, precise calibration between the virtual model and physical devices is achieved, ensuring that the digital twin model accurately reflects the state of the physical devices. Verification of the initial digital twin model ensures its accuracy and reliability, preventing deviations in practical applications.

[0057] Optionally, S3 includes:

[0058] S31. Extract the energy storage status based on the rural energy storage digital twin model to obtain energy storage status data;

[0059] S32. Detect abnormal states based on energy storage status data to obtain abnormal energy storage status data;

[0060] S33. Based on the energy storage status data and energy storage abnormal status data, conduct an energy storage health assessment to obtain energy storage health data;

[0061] S34. Obtain historical energy storage usage data, and make an energy storage life prediction based on the historical energy storage usage data and energy storage health data to obtain energy storage life prediction data.

[0062] S35. Calculate the energy usage efficiency based on the energy storage status data to obtain the energy storage energy usage efficiency data;

[0063] S36. Real-time risk processing is performed on the energy storage life prediction data and energy storage energy utilization efficiency data to obtain rural energy storage status data.

[0064] This invention extracts real-time status data of the energy storage system through a digital twin model, including current, voltage, temperature, and load, enabling real-time monitoring of the equipment's operating status. Through real-time anomaly detection, the system can quickly identify faults or abnormal states in the energy storage equipment, such as excessively high temperatures or abnormal current, allowing for timely countermeasures. Health assessment using status and anomaly data accurately evaluates the health status of the energy storage equipment, including battery life and temperature control effectiveness. Energy storage lifespan prediction based on health data and historical usage data helps predict the remaining lifespan of the equipment, thereby optimizing usage plans. Calculating the energy efficiency of the energy storage system identifies inefficient components, providing guidance for energy efficiency optimization. Based on energy storage lifespan and energy efficiency data, real-time identification of risk factors in the energy storage system facilitates rapid early warning measures to prevent risks from escalating.

[0065] Optionally, S4 includes:

[0066] S41. Based on the rural energy storage status data, perform load forecasting and demand matching to obtain load demand matching table data;

[0067] S42. Set priority strategies based on load demand matching table data to obtain energy storage scheduling priority scheme data;

[0068] S43. Optimize the dynamic charging and discharging window based on the energy storage scheduling priority scheme data to obtain dynamic charging and discharging schedule data;

[0069] S44. Based on the dynamic charge and discharge schedule data, perform multi-device collaborative scheduling to obtain rural energy storage scheduling data for auxiliary operations in rural energy storage management.

[0070] This invention utilizes load forecasting and demand matching to prepare for charging and discharging in advance during peak load periods and to schedule charging during off-peak periods, ensuring energy supply meets demand. Priority strategies are implemented to ensure critical loads (such as agricultural irrigation and important power supply loads) receive priority power support during energy shortages, guaranteeing the stable operation of the system's basic functions. Dynamic charging and discharging window optimization selects the optimal charging and discharging time based on load demand and electricity prices, ensuring charging during off-peak hours and discharging during peak hours, improving the system's economic efficiency. Multi-device collaborative scheduling evenly distributes the load, ensuring each energy storage device operates within a reasonable load range, avoiding overload of individual devices and improving device stability. Collaborative scheduling enables multiple energy storage devices to work together, providing rapid response during high loads or sudden demand surges, improving system scheduling efficiency and power supply reliability.

[0071] Optionally, this application also provides an IoT-based rural digital twin energy storage management system for executing the IoT-based rural digital twin energy storage management method described above, wherein the IoT-based rural digital twin energy storage management system includes:

[0072] The rural energy storage real-time data acquisition module is used to collect real-time operating data from rural distributed energy storage devices to obtain rural energy storage data.

[0073] The digital twin model building module is used to build a digital twin model based on rural energy storage data, thereby obtaining a rural energy storage digital twin model.

[0074] The energy storage status detection module is used to detect the energy storage status based on the rural energy storage digital twin model and obtain rural energy storage status data.

[0075] The rural energy storage scheduling module is used to optimize scheduling based on rural energy storage status data, obtain rural energy storage scheduling data, and carry out auxiliary operations for rural energy storage management.

[0076] The purpose of this invention is:

[0077] 1. By collecting real-time operational data from rural distributed energy storage devices, real-time and accurate information support is provided for modeling, monitoring, and optimized scheduling, significantly improving the system's response speed to changes in device status and environment. The adoption of hierarchical processing and synchronization mechanisms ensures structured, streamlined storage and cross-device consistency of data, saving data processing time and improving data reliability. This enables the system to more efficiently grasp the operational status and load demand of energy storage devices from a global perspective.

[0078] 2. The digital twin model establishes a precise virtual mapping for the physical energy storage system. Through intelligent analysis and feature extraction of energy storage data, it synchronizes various characteristics and operating statuses of the equipment to the virtual environment, providing a global visual view of the system. The model not only simplifies the monitoring of the energy storage system but also enhances its transparency, enabling users to observe and control the performance of the energy storage equipment in real time. By constructing a "real-time response layer" and "virtual-physical characteristic pairing," the digital twin model can maintain high-precision synchronization with the physical equipment. Virtual-physical pairing enables precise system calibration, ensuring that the digital twin model accurately reflects the equipment status at all times, providing highly reliable data support for optimized scheduling and fault detection. Through energy storage status detection using the digital twin model, multi-dimensional monitoring of the equipment (such as temperature, current, voltage, load, etc.) is achieved.

[0079] 3. Abnormal state detection promptly identifies potential operational risks, preventing system downtime due to sudden failures and significantly improving equipment safety. Based on load forecasting and priority strategies, the charging and discharging windows are dynamically adjusted, allowing the system to charge during off-peak hours and discharge during peak hours, improving the utilization efficiency of energy storage resources and reducing energy storage costs. Furthermore, multi-device collaborative scheduling can balance load pressure during peak periods, ensuring that multiple energy storage devices operate efficiently within reasonable load ranges, thereby improving the overall stability of the system. Attached Figure Description

[0080] Other features, objects, and advantages of this application will become more apparent from the following detailed description of the non-limiting embodiments, taken with reference to the accompanying drawings:

[0081] Figure 1 A flowchart illustrating the steps of an IoT-based rural digital twin energy storage management method is shown in one embodiment.

[0082] Figure 2 A flowchart illustrating the steps of a real-time data acquisition method for rural energy storage according to an embodiment is shown.

[0083] Figure 3 A flowchart illustrating the steps of a digital twin model construction method according to one embodiment is shown.

[0084] Figure 4 A flowchart illustrating the steps of an energy storage state detection method according to one embodiment is shown.

[0085] Figure 5 A flowchart illustrating the steps of a rural energy storage dispatching method according to an embodiment is shown.

[0086] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0087] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0088] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. Functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0089] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0090] A distributed energy storage system was installed in a rural area, comprising five energy storage units (batteries), each with a rated capacity of 50kWh. The system's energy storage devices support real-time data collection of voltage, current, and temperature, which is transmitted to a cloud platform via IoT devices for use by a digital twin model. Real-time data collected from each energy storage unit via IoT devices includes: voltage: 240V, current: 120A, temperature: 25℃, charge / discharge status: charging, current stored energy: 40kWh (80% remaining). After hierarchical processing and synchronous transmission, the rural energy storage dataset is obtained in the cloud.

[0091] Based on the collected energy storage data, the system constructs a digital twin model to map the energy storage status to the physical environment and enable real-time response. A digital twin object is created for each energy storage unit in the cloud, with initial parameters set to 240V, 120A, 25℃, and charging status. The digital twin model periodically receives real-time data from each device and updates the virtual device's operating status. The system aggregates and analyzes characteristic data such as voltage, current, and temperature; for example, over the past hour, the temperature has stabilized within the 25-28℃ range, and the voltage has remained between 235-245V. A real-time response layer is built into the model, enabling the system to dynamically adjust based on real-time data. For example, if the current exceeds 130A, an alarm is triggered and the charging rate is reduced. Characteristic pairing is performed between each physical device and its digital twin object to ensure the model accurately reflects the device's status. If the voltage drops below 230V or the temperature exceeds 30℃, an early warning is triggered. By comparing the status of the virtual model and the physical equipment through data feedback from the real-time response layer, the data synchronization rate between the two is ensured to be above 95%. The verified model is used as a digital twin model for rural energy storage for real-time monitoring, charging and discharging optimization, and risk prediction.

[0092] The real-time response layer is the core module of the digital twin model, used to receive, process, and feed back real-time data from energy storage devices. The core architecture of this layer can be divided into four main modules: data acquisition and synchronization module, status monitoring and anomaly detection module, dynamic decision-making and control module, and feedback and adaptive optimization module.

[0093] The data acquisition and synchronization module collects real-time data streams from various energy storage devices (such as battery cells, temperature control systems, charging and discharging modules, etc.) to ensure that the system receives multi-dimensional data inputs, such as voltage, current, temperature, and load status.

[0094] The condition monitoring and anomaly detection module analyzes data such as voltage, current, and temperature to monitor key status parameters of the energy storage system in real time. It incorporates built-in machine learning models (such as time series analysis and anomaly detection algorithms) or rule-based detection mechanisms to identify abnormal equipment behavior (such as voltage over-limit, abnormal current fluctuations, and excessively high temperatures). When an abnormal state is detected, the system immediately triggers an early warning signal, marking and recording relevant information about the malfunctioning equipment.

[0095] The dynamic decision-making and control module incorporates a built-in charging and discharging strategy in its real-time response layer. This strategy dynamically adjusts the charging and discharging behavior of energy storage devices based on load demand and current electricity prices (e.g., reducing excessive discharge current and delaying off-peak charging). Combined with a scheduling priority scheme, critical equipment is prioritized for scheduling during power shortages or periods of high load demand, ensuring stable system operation even under heavy loads. Based on real-time monitoring of current, voltage, and temperature data, the module dynamically adjusts the power output and temperature control strategies of the equipment to ensure optimal operation.

[0096] The feedback and adaptive optimization module feeds adjustment data from the real-time response layer back to the main database of the digital twin model, enabling parameter updates and adjustments within the virtual model. Through analysis and learning from historical data, the system continuously adjusts the response rules and control strategies of the real-time response layer to adapt to changes in load demand. The system can update scheduling and control strategies based on long-term monitoring results, such as updating charging and discharging windows and optimizing priority strategies, to achieve higher energy efficiency.

[0097] The system uses a digital twin model to detect and analyze energy storage status data, including anomaly detection, health assessment, and lifespan prediction. Through feature analysis, the system detected an anomaly: the current in energy storage unit 1 briefly reached 140A. The system immediately triggered an anomaly warning and recorded it in the energy storage anomaly status data. Based on the status and anomaly data, the health of the energy storage units was assessed. Analysis showed that energy storage unit 1 had a health score of 80 / 100, while other energy storage units scored between 85 and 90 / 100. Combining historical and health data, the system predicted the remaining lifespan of energy storage unit 1 to be 4 years, and the average remaining lifespan of other energy storage units to be 5 years. Energy storage lifespan prediction data was generated. The system calculated the energy efficiency of each energy storage unit, finding that energy storage unit 1's energy efficiency was 85% (slightly lower than the 90% of other units), which was recorded in the energy storage energy utilization efficiency data. Based on the lifespan prediction and energy efficiency data, risk management was implemented for energy storage unit 1, recommending preventative maintenance and adjustments to its charging and discharging priority.

[0098] The system analyzes the load demand for the next 24 hours, predicting a peak period from 7 PM to 9 PM, requiring 80 kWh of energy storage. The system prioritizes matching available energy storage devices, generating a load demand matching table. Energy storage unit 1 is prioritized for nighttime demand response, but with a lower charging rate to reduce load pressure. Other units are set to medium priority, dynamically responding to demand during other time periods, generating an energy storage scheduling priority scheme. The energy storage scheduling priority scheme is shown below:

[0099] Energy storage unit 1 is set as high priority to ensure priority power supply during peak nighttime load periods (e.g., 19:00-23:00). Other energy storage units (units 2 to 5) are set as medium priority to dynamically respond to demand during other periods, avoiding excessive system load pressure caused by multiple units discharging simultaneously at night. Energy storage unit 1 responds first during peak nighttime load periods, but its charging rate is set to a lower level (e.g., 20kW) to reduce charging current and lower instantaneous load pressure on the grid. Energy storage units 2 to 5 are prioritized for charging during low-load periods (e.g., 1:00-5:00 AM), with a higher charging rate (e.g., 30-40kW) to ensure they are fully charged and ready for use during the daytime. 1. Energy storage unit 1 (high priority), priority: high, response period: 19:00-23:00 (peak nighttime load demand period). Dispatch strategy: During 19:00-23:00, energy storage unit 1 discharges first to respond to nighttime load demand. The charging rate is set at 20kW, and the nighttime charging rate is controlled to avoid high load on the power grid. Discharge target: To meet peak nighttime electricity demand and ensure stable power supply to major loads. 2. Energy storage units 2 to 5 (medium priority), priority: medium response time: dynamically responding to load demands during other time periods (such as low-to-medium load demand periods during the day). Dispatch strategy: During the low-load period from 1:00 AM to 5:00 AM, priority is given to charging at a rate of 30-40kW to ensure they are fully charged during daytime electricity demand. During off-peak hours during the day and night, they are on standby as needed, prioritizing discharge during low-to-medium load demand periods (such as 10:00 AM to 4:00 PM during the day).

[0100] Rapid charging during low-load periods ensures auxiliary power support during peak daytime electricity consumption, reducing the load pressure on energy storage unit 1 during the day. Based on electricity prices and load demand, the system sets the charging window during the off-peak period of 1-5 AM and the discharging window during the peak period of 7-9 PM, optimizing the charging and discharging schedule to reduce energy consumption. According to the charging and discharging schedule, the system synchronously schedules five energy storage units to discharge collaboratively during the nighttime peak period and charge during the daytime off-peak period. This collaborative scheduling scheme ensures that the 80kWh energy storage demand is met while effectively reducing equipment load, ultimately yielding rural energy storage scheduling data.

[0101] Please see Figures 1 to 5 This application provides a rural digital twin energy storage management method based on the Internet of Things, the method comprising:

[0102] S1. Collect real-time operation data through rural distributed energy storage devices to obtain rural energy storage data;

[0103] Specifically, sensors, smart meters, controllers, and other devices are used to monitor parameters such as voltage, current, temperature, and charging / discharging status of distributed energy storage devices in real time. The collected data is then uploaded to a cloud data platform or a local server via wireless communication modules (such as NB-IoT and LoRa) for subsequent analysis and model building.

[0104] S2. Construct a digital twin model based on rural energy storage data to obtain a rural energy storage digital twin model;

[0105] Specifically, based on collected historical and real-time data, the parameters in the digital twin model are initialized to reflect the current state of the distributed energy storage system. The latest real-time data is continuously input into the model to keep it synchronized with the actual energy storage devices. Based on the characteristics of the actual physical system and the collected data, a virtual scenario consistent with the physical system is created in the digital twin model to simulate operational conditions. The model parameters are periodically calibrated to ensure accuracy, especially when equipment operating conditions or the environment change.

[0106] S3. Detect the energy storage status based on the rural energy storage digital twin model to obtain rural energy storage status data;

[0107] Specifically, real-time data from the digital twin model is used to analyze changes in the state of energy storage devices, such as charging status, discharging status, and battery life prediction. By setting thresholds or rules, abnormal states of energy storage devices are automatically detected to determine whether there are risks such as abnormal temperature, charging overload, or malfunction. The detected energy storage state information is output and saved as a basis for further decision-making and scheduling.

[0108] S4. Optimize scheduling based on rural energy storage status data to obtain rural energy storage scheduling data for auxiliary operations in rural energy storage management.

[0109] Specifically, based on historical energy storage status data and current demand, deep learning algorithms, such as regression models or vector machine algorithms, are used to predict future energy storage demand to optimize the charging and discharging scheduling of the energy storage system. Based on the demand forecast results and real-time status, optimal scheduling strategies are formulated, such as prioritizing charging during periods of low grid load and prioritizing discharging during periods of high load, to achieve efficient utilization of the energy storage system. Scheduling data for controlling energy storage devices is generated, including parameters such as the charging and discharging power and time of each device. The scheduling execution effect is monitored in real time, and scheduling parameters are adjusted promptly based on actual operating conditions to ensure continuous optimization of management support operations.

[0110] Specifically, based on time-series forecasting algorithms, such as regression functions or time-average shifting algorithms, future demand is predicted using historical data and digital twin models. For example, if the State of Charge (SOC) decline rate is 10% / day, and the grid load is expected to be 80% tomorrow, charging plans need to be increased in advance. Scheduling strategies are generated to improve energy storage utilization and stability. For example, during peak daytime electricity demand (e.g., 9 AM to 6 PM), priority is given to discharging. During off-peak nighttime electricity demand (e.g., 11 PM to 6 AM), priority is given to charging. Scheduling data output: The scheduling strategies are converted into data commands. For example: Discharge command: From 9 AM to 6 PM, discharge 10 kWh per hour to ensure daytime electricity demand is met. Charging command: From 11 PM to 6 AM, charge 15 kWh per hour to restore energy reserves. The scheduling effect is monitored in real time. If the battery temperature rises too quickly or the SOC changes abnormally, the discharge or charging rate is adjusted immediately. For example, when the temperature rises to 43°C, the discharge power is reduced to prevent overheating.

[0111] Optionally, S1 includes:

[0112] S11. Collect real-time operation data through rural distributed energy storage devices to obtain real-time energy storage operation data;

[0113] Specifically, multiple sensors are installed on each energy storage device to collect different types of operational data, such as voltage, current, temperature, and charge / discharge status. At fixed time intervals (e.g., every 5 minutes), the sensors collect data and generate a set of real-time operational data samples, for example: Voltage (V): 415V, Current (A): 120A, Temperature (°C): 32°C, SOC (State of Charge): 65%.

[0114] S12. Perform layered processing based on real-time energy storage operation data to obtain layered energy storage data;

[0115] Specifically, data is stratified based on its source, nature, and time dimension. For example, device-level data consists of independent real-time data for each device. Regional-level data is comprehensive data from different devices, aggregated into an average or total value for a specific region. Historical-level data is statistical results of data over a certain period (e.g., 1 hour) for trend analysis. Within the device layer, data for each device is filtered and aggregated to calculate current device status parameters, such as charging or discharging rates. The regional layer integrates data from multiple devices within the same region to obtain the average voltage, current, and SOC for the entire region. For example, the average SOC of 5 devices in the region is 68%. Based on the device and regional layers, the historical layer calculates long-term trend data (e.g., average temperature over the past 24 hours) to facilitate subsequent optimization and scheduling. The stratified data is refreshed periodically (e.g., device-level data is updated every 5 minutes, and regional-level data every hour), and adjustments are made to the stratified structure based on real-time data changes to maintain the accuracy and timeliness of the data hierarchy.

[0116] S13. Synchronize data based on energy storage stratification data to obtain rural energy storage data.

[0117] Specifically, select the data types and ranges to be synchronized, such as device status information, battery level, temperature, and other parameters. Selectively extract key indicators based on the layered data to reduce the amount of data transmitted. For example, extract only SOC and temperature from the device layer, and average SOC and temperature from the regional layer. Ensure consistency of data across all layers at the same point in time. For example, align device layer, regional layer, and historical layer data to the current timestamp to generate accurate rural energy storage data. Before synchronization, compress unimportant details, retaining only core data to improve transmission efficiency. For example, summarize and compress historical layer data into hourly averages instead of minute-by-minute data.

[0118] Optionally, the layered processing includes:

[0119] Based on real-time energy storage operation data, the equipment type is stratified to obtain the first energy storage stratification data;

[0120] Specifically, the data is categorized according to different equipment types (e.g., battery type, installation location, capacity, etc.). For example: Battery type: lithium-ion batteries, lead-acid batteries, etc. Installation location: indoor, outdoor. Capacity: small (≤100kWh), medium (100–500kWh), large (>500kWh). Independent real-time operating data is generated for each equipment type. For example, for lithium-ion battery equipment, real-time SOC, temperature, voltage, etc., are generated. For each equipment category, averages, totals, or trends are calculated. For example, the average SOC for lithium-ion batteries is 66%, and the average SOC for lead-acid batteries is 62%. The categorized data is then combined into the first energy storage stratification data.

[0121] Based on the real-time operation data of energy storage, the electrical characteristics are layered to obtain the second energy storage layer data;

[0122] Specifically, the data is stratified based on electrical characteristics. For example, it is classified by voltage level, power status (charging / discharging), and current type: Voltage level: high voltage (≥400V), medium voltage (200-400V), low voltage (<200V). Power status: whether it is currently charging or discharging. Parameters for different electrical characteristic categories are calculated. For example, for high-voltage level equipment, average temperature, current, and other data are recorded. Parameters are calculated separately for charging and discharging states, such as average voltage during charging and temperature during discharging. The electrical characteristic classification data is combined to form the second layer of energy storage data.

[0123] The third energy storage stratification data is obtained by performing time-dimensional stratification processing on real-time energy storage operation data.

[0124] Specifically, data is processed in layers based on time to reflect temporal changes. For example: Minute-level: Data is collected in real-time and updated every 5 minutes. Hourly-level: Statistical analysis is performed on hourly data to generate hourly averages, maximums, etc. Daily-level: Daily data summaries are generated, such as the SOC trend and charge / discharge cycles throughout the day. Averages or totals are calculated for each time level. For example: averaging voltage and temperature is calculated at the hourly level. Total energy consumption is calculated at the daily level. The third-layer energy storage data is output.

[0125] A hierarchical network is constructed based on the first, second, and third energy storage hierarchical data to obtain energy storage hierarchical data.

[0126] Specifically, data is correlated based on device type, electrical characteristics, and time dimension to construct a multi-layered data network for multi-faceted data analysis. The connection relationships between different layers of data are defined; for example, data from the same device at different points in time are linked together to form a time-series chain. Correlation between device type and electrical characteristics is established: for example, the voltage and current data of lithium batteries are correlated with their state of charge and discharge to obtain the electrical characteristics of each device type under different power states. Correlation between the time dimension and device type is also established: for example, the SOC changes of lithium battery types throughout the day are formed into a time series for trend prediction.

[0127] Hierarchical network table

[0128] Layered types Associated dimensions Real-time data Device type layer Lithium, lead acid batteries Individual device real-time SOC, temperature Electrical characteristics layer High voltage, state of charge Voltage, state of power Time dimension layer Hourly, daily Hourly, daily SOC trends

[0129] Optionally, the device type hierarchical processing includes:

[0130] Based on real-time energy storage operation data, the equipment types are classified to obtain energy storage equipment classification data;

[0131] Specifically, real-time data from energy storage devices is categorized by device type to identify different device types, such as lithium batteries and lead-acid batteries. Device classification can be based on factors such as device model, installation location, and storage capacity. For example: Device A: Lithium battery, indoor installation, 300kWh capacity. Device B: Lead-acid battery, outdoor installation, 500kWh capacity. Real-time data for each device is associated with its device type to generate device classification data. Real-time data for each device type includes voltage, current, temperature, and SOC (State of Charge).

[0132] Subsystem data is extracted based on energy storage device classification data to obtain energy storage subsystem classification data;

[0133] Specifically, based on the equipment's configuration and function, each type of equipment is further divided into different subsystems, such as charging / discharging systems, temperature control systems, and power management systems. For example, if both equipment A and equipment B have temperature control and power management subsystems, the data is further categorized accordingly. Data from each subsystem is then extracted and processed separately. For instance, the temperature control subsystem extracts temperature data, and the power management system extracts voltage and SOC data.

[0134] Based on the classification data of energy storage subsystems, the operating characteristics are classified to obtain energy storage operating characteristic data;

[0135] Specifically, based on the functions and operating states of different subsystems, the subsystem data is further categorized according to operating characteristics. For example: Temperature control system: categorized according to temperature status (e.g., whether it exceeds the limit). Power management system: categorized according to charging / discharging status (e.g., charging, discharging, idle). A subsystem status is generated for each operating characteristic. For example: The temperature status of the temperature control system of device A is normal (<40℃). The power management system of device A is in charging state, with a SOC of 70%.

[0136] Based on the energy storage operation characteristic data, the equipment characteristics are aggregated to obtain the first energy storage stratification data.

[0137] Specifically, data on different operating characteristics are aggregated to form an overview of equipment characteristics. For example, the average voltage, average current, average temperature, and state of charge (SOC) of each type of equipment are aggregated. The number of states of different subsystems is counted, such as the number of devices in charging state and the number of devices in idle state. Operating characteristic data are aggregated by subsystem and equipment type. For example, the average voltage, current, and temperature of lithium battery equipment.

[0138] Optionally, the electrical characteristic layering process includes:

[0139] Based on real-time energy storage operation data, electrical parameters are identified to obtain energy storage electrical parameter classification data;

[0140] Specifically, key electrical parameters, such as voltage, current, power, and frequency, are identified through real-time data from energy storage devices. For different devices, these electrical parameters are associated with device IDs to generate categorized data. The identified data is then classified according to the type of electrical parameter, for example, by voltage, current, and power.

[0141] Voltage and current characteristics are processed based on the classification data of energy storage electrical parameters to obtain energy storage voltage and current distribution data;

[0142] Specifically, the voltage and current data in the electrical parameter classification data undergo characteristic processing, including voltage and current distribution, peak values, and fluctuation ranges. The voltage and current ranges for each device are calculated, identifying typical and outlier values. Voltage and current distribution data are generated, including average values ​​and fluctuation amplitudes. For example, device A has a voltage distribution between 410V and 420V and an average current of 120A.

[0143] Based on the real-time operation data of energy storage, the load balancing characteristics are analyzed to obtain energy storage load characteristic data;

[0144] Specifically, based on real-time equipment data, load characteristics are analyzed, such as whether the current power meets load demand and whether load imbalance exists. The ratio of the load level to the equipment's maximum load is calculated to determine if load imbalance exists. Load balance data is generated, including load ratio and load status. For example, equipment A has a load ratio of 80% and a balanced status.

[0145] Power characteristic data of energy storage are obtained by analyzing the classification data of energy storage electrical parameters.

[0146] Specifically, the power values ​​in the electrical parameter data are analyzed for characteristics, including average power, peak power, and fluctuation range. The trends in power data, such as peak and minimum power, are calculated. Power characteristic data is generated; for example, device A has an average power of 49.8 kW and a fluctuation range between 45 and 55 kW.

[0147] Temperature efficiency data of energy storage is obtained by performing temperature efficiency correlation analysis based on real-time energy storage operation data, energy storage voltage and current distribution data and energy storage power characteristic data.

[0148] Specifically, temperature data is correlated with voltage, current, and power characteristic data to study the impact of temperature changes on equipment efficiency. It determines whether temperature affects power output stability and analyzes the trend of power output changes with temperature. Correlation data between temperature and efficiency is generated. For example, equipment A has stable efficiency at 32℃, but its efficiency decreases at 40℃.

[0149] Based on the classification data of energy storage electrical parameters, energy storage voltage and current distribution data, energy storage load characteristic data, energy storage power characteristic data, and energy storage temperature and efficiency data, the electrical characteristics are aggregated to obtain the second energy storage stratification data;

[0150] Specifically, data from various layers is aggregated, including electrical parameter classifications, voltage and current distributions, load characteristics, power characteristics, and temperature efficiency data, to form an overall overview of electrical characteristics. Overview data of the electrical characteristics of each device is generated to facilitate further scheduling and management. Data on different characteristics, such as the voltage range, current range, load status, and power efficiency of device A, are summarized.

[0151] The load balance characteristics include:

[0152] Load characteristics are extracted from real-time energy storage operation data to obtain energy storage load characteristic data;

[0153] Specifically, load characteristic parameters, such as power demand, load curves, and peak load, are extracted from real-time energy storage operation data. Load changes of the equipment at different time periods are recorded to determine daily and hourly load demand trends. Data tables are generated based on the extracted load characteristic parameters. For example, the peak power demand of equipment A is recorded as 60kW during peak hours (e.g., 6 PM to 10 PM) and 20kW during off-peak hours (e.g., 2 AM to 5 AM).

[0154] Load clustering is performed based on energy storage load characteristic data to obtain energy storage load clustering data;

[0155] Specifically, energy storage load characteristic data is clustered to classify equipment into different load types based on load characteristics, such as high load, medium load, and low load. Equipment is also grouped according to the similarity of load demand to identify similar load patterns. For example, equipment with higher loads during peak hours is grouped together.

[0156] Based on the energy storage load clustering data, graph load characteristic processing is performed to obtain energy storage load graph data;

[0157] Specifically, based on load clustering data, a graph structure is constructed to represent the equipment and their load relationships across different load categories, displaying the load relationships and dependencies between equipment. Graph nodes represent equipment, and edges represent load correlations or load transfer relationships between equipment. For example, high-load equipment A is connected to equipment B to show their similarity during peak load periods. Energy storage load graph data is generated to represent the relationships between equipment of different load categories. For example, equipment A and B are connected as nodes of the same type in the graph, showing their load similarity during peak hours.

[0158] The mixed load balance index is calculated based on the energy storage load map data to obtain the energy storage load mixed index data;

[0159] Specifically, a mixed load balance index is calculated from the load diagram data to measure the degree of balance between different load categories. The load balance index is generated by calculating the load similarity and load volatility between equipment. A higher load similarity and lower load volatility result in a higher balance index. Mixed load balance index data is generated for each equipment or load category. For example, the mixed load balance indices for equipment A and B are 0.85 and 0.80, respectively.

[0160] Specifically, nodes (equipment) and edges (load associations between equipment) are obtained from the energy storage load map. Each edge is marked with the load similarity and load volatility between equipment. For each equipment, the following data is extracted: average load power (unit: kW), load volatility range (e.g., voltage, current), and load similarity (similarity of load patterns between equipment, with values ​​between 0 and 1, where 1 indicates complete similarity). For each pair of equipment, their load similarity score is calculated. Based on the load similarity and load volatility between equipment, the following is calculated: S i,j The load similarity score is calculated between devices i and j. V represents the load similarity between devices i and j. i,j V represents the load fluctuation rate between devices i and j. max Let S be the maximum load fluctuation rate in the system. The load similarity between A and B is 0.8, their load fluctuation rates are 0.2, and the maximum load fluctuation rate in the system is 1. Then S... i,j =0.64. The load balance index for each device is the average of the device similarity scores, reflecting the degree of load correlation between the device and other devices. The formula is: B i Let S be the load balance index of the i-th device, j be the device order term, and S be the load balance index of the i-th device. i,j Let n be the load similarity score between devices i and j, and n be the number of devices. Device A has similarity scores of 0.64, 0.7, and 0.6 with devices B, C, and D, respectively. Therefore, the load balance index of device A is B. A =0.65. The system's mixed load balance index is the average of the load balance indices of all equipment, representing the degree of load coordination of the entire energy storage system. Q represents the mixed load balance index, i represents the equipment sequence number, n represents the number of equipment, and B represents the equipment quantity. i Let Q be the load balance index of the i-th device. The load balance indices for devices A, B, C, and D are 0.65, 0.72, 0.68, and 0.55, respectively, so Q = 0.65. The closer the index value is to 1, the higher the load matching degree among the system devices and the more balanced the load status. The closer the value is to 0, the more unbalanced the system load is, potentially indicating uneven load distribution. A threshold is set; for example, a value greater than 0.8 indicates good load balance, 0.5 to 0.8 indicates moderate load, and a value below 0.5 indicates unbalanced load requiring scheduling optimization.

[0161] Time-sensitivity processing is performed on the energy storage load mixing index data to obtain energy storage load characteristic data.

[0162] Specifically, time-series sensitivity analysis is performed based on load mixing index data to detect the degree of load variation in different time periods. The time-series fluctuations of the load are identified, with higher sensitivity during peak hours marked as high time-series sensitivity for adaptive scheduling. For example, equipment A's load varies significantly during peak hours, indicating high sensitivity. The output is energy storage load characteristic data, including classification results for high, medium, and low time-series sensitivity. For example, equipment A has high time-series sensitivity, while equipment B has medium sensitivity.

[0163] Optionally, the time-dimension hierarchical processing includes:

[0164] Time-stamped data of energy storage is obtained by processing real-time operation data of energy storage.

[0165] Specifically, timestamp information is extracted from real-time operational data to time-stamp the data for feature analysis by time period. Time-labeled data, including date, time, and hour, is generated to distinguish data from different time periods.

[0166] First-time characteristic analysis is performed based on energy storage time tag data to obtain short-term time characteristic data;

[0167] Specifically, time-stamped data is used to analyze short-term temporal characteristics, such as hourly or daily load demand and volatility. Short-term data analysis reflects changes in energy storage data within an hour or day, used to identify instantaneous load demand. Short-term characteristic data is generated, such as load demand fluctuations from 8:00 AM to 9:00 AM on a given day.

[0168] A second time characteristic analysis is performed based on the energy storage time tag data to obtain the medium-term time characteristic data;

[0169] Specifically, mid-term analysis is performed on time-stamped data, such as weekly load patterns and daily fluctuation trends. Mid-term characteristic data reflects load demand patterns over multiple days or weeks, providing a reference for judging weekly trends. Weekly time characteristic data is generated to reflect load change trends. For example, the load difference between weekdays and weekends can be statistically analyzed.

[0170] A third time characteristic analysis is performed based on the energy storage time tag data to obtain long-term time characteristic data. The time data corresponding to the first time characteristic analysis is less than or equal to the time data corresponding to the second time characteristic analysis, and the time data corresponding to the second time characteristic analysis is less than or equal to the time data corresponding to the third time characteristic analysis.

[0171] Specifically, long-term analysis of time-stamped data allows for the study of monthly or quarterly load variation patterns, such as seasonal fluctuations and monthly peak loads. Long-term time characteristic analysis helps identify long-term trends and cyclical fluctuations. Monthly or quarterly time characteristic data is generated. For example, load differences between summer and winter can be recorded.

[0172] Peak and valley time periods are segmented based on short-term, medium-term, and long-term time characteristic data to obtain peak and valley characteristic data.

[0173] Specifically, short-term, medium-term, and long-term time characteristic data are used to segment peak and off-peak periods, dividing the data into peak and off-peak periods. Peak and off-peak feature identification helps optimize load management, such as performing charging operations during off-peak periods. Each period is labeled as a peak or off-peak period. For example, 6 PM to 8 PM is a peak period, and 2 AM to 4 AM is an off-peak period.

[0174] Based on peak and valley characteristic data, time correlation characteristics are extracted from short-term, medium-term, and long-term time characteristic data to obtain the third energy storage stratification data.

[0175] Specifically, peak-valley characteristic data is used to correlate and extract short-term, medium-term, and long-term time characteristic data, integrating characteristics at different time scales to identify the correlation of load changes across multiple time periods. This analysis can reveal characteristic correlations at various time scales, which is helpful for optimizing the scheduling of energy storage devices. By integrating the time correlation characteristic data of short-term, medium-term, long-term, and peak-valley features, a third layer of energy storage data is generated. For example, the changes in morning and evening peak loads in the short and long term can be calculated.

[0176] Optionally, S2 includes:

[0177] S21. Construct a digital twin mapping based on rural energy storage data to obtain an initial digital twin model of rural energy storage;

[0178] Specifically, using rural energy storage data as a foundation, a data mapping is performed on the actual energy storage system to construct a virtual model corresponding to the physical system. The model includes parameters such as voltage, current, temperature, and SOC (state of charge) of the energy storage devices. A virtual digital space is created to synchronize and reflect the data status of the physical energy storage system in real time, ensuring that the initial digital twin model has characteristics consistent with the actual system. Modeling is based on the collected data, such as the fluctuation range of voltage and current, and the changing trends of temperature and SOC. The initial digital twin model includes the main state parameters and real-time changing characteristics of each energy storage device.

[0179] S22. Based on the initial digital twin model of rural energy storage, extract the features of the energy storage system to obtain the feature data of the energy storage system;

[0180] Specifically, key characteristic parameters of the energy storage system are extracted from the digital twin model, including the equipment's operating mode, charging and discharging state, and temperature change rate. Characteristic data of the equipment under different states are analyzed, such as the temperature rise rate under high load and current changes at different voltage levels. The extracted characteristic data is then organized to construct a system characteristic table. For example, the maximum discharge power, charging efficiency, and temperature control characteristics of each device are recorded.

[0181] S23. Construct a real-time response layer based on the characteristic data of the energy storage system to obtain the real-time response layer data of the energy storage system.

[0182] Specifically, based on the characteristic data of the energy storage system, a real-time response layer is constructed, enabling the digital twin model to dynamically respond upon receiving real-time data and simulate the device's performance under different states. Key characteristic triggering conditions are set, such as simulating temperature and current changes under high load conditions and monitoring SOC and voltage trends under low load conditions. Based on the construction of the real-time response layer, corresponding data is output. For example, simulating temperature changes under high load conditions and charging efficiency changes under low load conditions.

[0183] S24. Based on the initial digital twin model of rural energy storage, perform virtual-physical characteristic pairing to obtain virtual-physical characteristic calibration table data;

[0184] Specifically, the virtual characteristic data in the digital twin model is compared and calibrated item by item with the physical characteristic data of the actual energy storage device to ensure the consistency between the virtual model and the physical system. The pairing includes key characteristics such as voltage, current, state of charge (SOC), and temperature, verifying whether the operating modes of the virtual and physical systems are consistent. A pairing table of the virtual model and physical system is generated, displaying the matching status. For example, the deviation between the virtual voltage and the actual voltage of device A is recorded.

[0185] S25. Based on the real-time response layer data of the energy storage system and the virtual-real characteristic calibration table data, the initial digital twin model of rural energy storage is verified to obtain the rural energy storage digital twin model.

[0186] Specifically, real-time response layer data and virtual-real characteristic calibration table data are used to validate the initial digital twin model, ensuring the model's simulation effect and accuracy under different conditions. By comparing real-time response data and actual equipment status data, the model's real-time response capability and virtual-real matching degree are confirmed, and deviations are identified and corrected. Based on the validation results, the parameters and operating characteristics of the digital twin model are adjusted to more closely resemble the behavior of actual energy storage systems, forming an energy storage digital twin model. The digital twin model accurately reflects the characteristics of rural energy storage systems, including response characteristics under different load conditions and dynamic changes in key parameters.

[0187] Optionally, S3 includes:

[0188] S31. Extract the energy storage status based on the rural energy storage digital twin model to obtain energy storage status data;

[0189] Specifically, key energy storage status data, including current voltage, current, temperature, and SOC (state of charge), are extracted from the digital twin model to reflect the real-time operating status of the equipment.

[0190] S32. Detect abnormal states based on energy storage status data to obtain abnormal energy storage status data;

[0191] Specifically, threshold ranges (such as voltage, temperature, and SOC) are set to detect abnormal conditions in energy storage devices. If the data exceeds the set range, it is marked as an abnormal state. Abnormal conditions are detected, such as excessively high voltage, excessively high temperature, and excessively low SOC. The detected abnormal data is recorded in an abnormal state table. For example, if the temperature of device A exceeds 40°C, it is marked as a temperature abnormality.

[0192] S33. Based on the energy storage status data and energy storage abnormal status data, conduct an energy storage health assessment to obtain energy storage health data;

[0193] Specifically, the health of the equipment is assessed based on energy storage status and abnormal status data. The assessment considers factors such as the long-term operational stability of the equipment and the frequency of anomalies. Health is calculated as a percentage based on scores for various indicators. The health assessment results are output; for example, equipment A has a health of 85%, and equipment B has a health of 70%.

[0194] S34. Obtain historical energy storage usage data, and make an energy storage life prediction based on the historical energy storage usage data and energy storage health data to obtain energy storage life prediction data.

[0195] Specifically, historical usage data of the equipment is acquired, including the number of charge / discharge cycles per cycle, temperature change records, and SOC changes. Using historical usage data and health data, the equipment's lifespan trend is analyzed, and lifespan is estimated. Based on historical data and current health status, the remaining lifespan of the equipment is estimated. For example, equipment A has an estimated lifespan of 3 years under its current health status.

[0196] S35. Calculate the energy usage efficiency based on the energy storage status data to obtain the energy storage energy usage efficiency data;

[0197] Specifically, the energy utilization efficiency of the equipment is calculated based on the energy storage status data. Factors such as charge / discharge efficiency, temperature stability, and state of charge (SOC) change rate are used to measure the effective utilization of energy. The energy storage efficiency is calculated by the ratio of the equipment's output power to its input power. The energy utilization efficiency of each device is then output. For example, the energy utilization efficiency of device A is 88%.

[0198] S36. Real-time risk processing is performed on the energy storage life prediction data and energy storage energy utilization efficiency data to obtain rural energy storage status data.

[0199] Specifically, risk management is conducted using energy storage lifetime estimates and energy efficiency data to identify potential risks and short-term failures in equipment. Early warnings or scheduling recommendations are issued for equipment with low lifetime or low efficiency, optimizing their usage patterns to reduce failure risks. Rural energy storage status data is output, including the current status of the equipment, risk level, and recommended operational measures. For example, if equipment B has a low remaining lifetime, it is recommended to reduce high-load operation.

[0200] Optionally, S4 includes:

[0201] S41. Based on the rural energy storage status data, perform load forecasting and demand matching to obtain load demand matching table data;

[0202] Specifically, based on energy storage status data and historical load data, future load demand is predicted, and load trends for different time periods are determined. Load forecasting primarily focuses on load demand during peak and off-peak periods to rationally allocate energy storage resources. Based on the predicted load demand, the capacity and charging / discharging capabilities of energy storage devices are matched with the demand to generate a load demand matching table. Periods with high load demand are identified, and sufficient energy storage devices are ensured to meet those demands.

[0203] S42. Set priority strategies based on load demand matching table data to obtain energy storage scheduling priority scheme data;

[0204] Specifically, scheduling priorities are established based on the load demand matching table. Priority strategies can be set according to the urgency of load demand and equipment availability. Time periods with high load demand and insufficient matching have the highest priority, ensuring that more energy storage devices are scheduled during peak load periods. A priority scheduling scheme is generated, marking each time period with a priority and allocating appropriate energy storage device resources. For example, the peak period from 18:00 to 20:00 is set as the highest priority.

[0205] S43. Optimize the dynamic charging and discharging window based on the energy storage scheduling priority scheme data to obtain dynamic charging and discharging schedule data;

[0206] Specifically, the charging and discharging time periods for each device are optimized based on scheduling priorities. Higher-priority time periods are allocated longer discharging windows, while lower-load demand periods are allocated charging windows, allowing energy storage devices to fully utilize off-peak electricity prices and charging opportunities. A dynamic charging and discharging strategy is set to ensure high State of Charge (SOC) during high-load demand periods and low power consumption during low-load demand periods. A dynamic charging and discharging schedule is generated, specifying the charging and discharging time periods for each energy storage device. For example, device A's discharging time period is 18:00-20:00, and its charging time period is 2:00-4:00 AM.

[0207] S44. Based on the dynamic charge and discharge schedule data, perform multi-device collaborative scheduling to obtain rural energy storage scheduling data for auxiliary operations in rural energy storage management.

[0208] Specifically, based on a dynamic charge / discharge schedule, energy storage devices are coordinated and scheduled across different time periods to achieve load balancing and efficient energy management. The charging and discharging states of each device are coordinated to ensure sufficient power to meet load demands during peak hours. When allocating tasks, device health and remaining lifespan are considered, prioritizing devices with higher health to extend system lifespan. Multi-device coordinated scheduling data is output, marking the specific tasks of each device in each time period, such as discharging during peak hours and charging during off-peak hours.

[0209] Optionally, this application also provides an IoT-based rural digital twin energy storage management system for executing the IoT-based rural digital twin energy storage management method described above, wherein the IoT-based rural digital twin energy storage management system includes:

[0210] The rural energy storage real-time data acquisition module is used to collect real-time operating data from rural distributed energy storage devices to obtain rural energy storage data.

[0211] The digital twin model building module is used to build a digital twin model based on rural energy storage data, thereby obtaining a rural energy storage digital twin model.

[0212] The energy storage status detection module is used to detect the energy storage status based on the rural energy storage digital twin model and obtain rural energy storage status data.

[0213] The rural energy storage scheduling module is used to optimize scheduling based on rural energy storage status data, obtain rural energy storage scheduling data, and carry out auxiliary operations for rural energy storage management.

[0214] Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended application documents rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of the equivalents of the application documents be incorporated into the invention.

[0215] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A rural digital twin energy storage management method based on the Internet of Things, characterized in that, The method includes: S1. Collect real-time operation data through rural distributed energy storage devices to obtain real-time energy storage operation data; perform hierarchical processing based on the real-time energy storage operation data to obtain hierarchical energy storage data; synchronize the data based on the hierarchical energy storage data to obtain rural energy storage data; S2. Construct a digital twin model based on rural energy storage data to obtain a rural energy storage digital twin model; S3. Detect the energy storage status based on the rural energy storage digital twin model to obtain rural energy storage status data; S4. Optimize scheduling based on rural energy storage status data to obtain rural energy storage scheduling data for auxiliary operations in rural energy storage management; The layered processing includes: Based on real-time energy storage operation data, the equipment type is stratified to obtain the first energy storage stratification data; Based on the real-time operation data of energy storage, the electrical characteristics are layered to obtain the second energy storage layer data; The third energy storage stratification data is obtained by performing time-dimensional layering processing on real-time energy storage operation data. A hierarchical network is constructed based on the first, second, and third energy storage hierarchical data to obtain energy storage hierarchical data. The electrical characteristic layering process includes: Based on real-time energy storage operation data, electrical parameters are identified to obtain energy storage electrical parameter classification data; Voltage and current characteristics are processed based on the classification data of energy storage electrical parameters to obtain energy storage voltage and current distribution data; Based on the real-time operation data of energy storage, the load balancing characteristics are analyzed to obtain energy storage load characteristic data; Power characteristic data of energy storage are obtained by analyzing the classification data of energy storage electrical parameters. Temperature efficiency data of energy storage is obtained by performing temperature efficiency correlation analysis based on real-time energy storage operation data, energy storage voltage and current distribution data and energy storage power characteristic data. Based on the classification data of energy storage electrical parameters, energy storage voltage and current distribution data, energy storage load characteristic data, energy storage power characteristic data, and energy storage temperature and efficiency data, the electrical characteristics are aggregated to obtain the second energy storage stratification data; The load balance characteristics include: Load characteristics are extracted from real-time energy storage operation data to obtain energy storage load characteristic data; Load clustering is performed based on energy storage load characteristic data to obtain energy storage load clustering data; Based on the energy storage load clustering data, graph load characteristic processing is performed to obtain energy storage load graph data; The mixed load balance index is calculated based on the energy storage load map data to obtain the energy storage load mixed index data; Time-sensitivity processing is performed on the energy storage load mixing index data to obtain energy storage load characteristic data.

2. The method according to claim 1, characterized in that, The equipment type stratification process includes: Based on real-time energy storage operation data, the equipment types are classified to obtain energy storage equipment classification data; Subsystem data is extracted based on energy storage device classification data to obtain energy storage subsystem classification data; Based on the classification data of energy storage subsystems, the operating characteristics are classified to obtain energy storage operating characteristic data; Based on the energy storage operation characteristic data, the equipment characteristics are aggregated to obtain the first energy storage stratification data.

3. The method according to claim 1, characterized in that, The time dimension layering process includes: Time-stamped data of energy storage is obtained by processing real-time operation data of energy storage. First-time characteristic analysis is performed based on energy storage time tag data to obtain short-term time characteristic data; A second time characteristic analysis is performed based on the energy storage time tag data to obtain the medium-term time characteristic data; A third time characteristic analysis is performed based on the energy storage time tag data to obtain long-term time characteristic data. The time data corresponding to the first time characteristic analysis is less than or equal to the time data corresponding to the second time characteristic analysis, and the time data corresponding to the second time characteristic analysis is less than or equal to the time data corresponding to the third time characteristic analysis. Peak and valley time periods are segmented based on short-term, medium-term, and long-term time characteristic data to obtain peak and valley characteristic data. Based on peak and valley characteristic data, time correlation characteristics are extracted from short-term, medium-term, and long-term time characteristic data to obtain the third energy storage stratification data.

4. The method according to claim 1, characterized in that, S2 include: A digital twin mapping was constructed based on rural energy storage data to obtain an initial digital twin model of rural energy storage; Based on the initial digital twin model of rural energy storage, the characteristics of the energy storage system are extracted to obtain the characteristic data of the energy storage system; A real-time response layer is constructed based on the characteristic data of the energy storage system to obtain the real-time response layer data of the energy storage system. Based on the initial digital twin model of rural energy storage, virtual and physical characteristics are matched to obtain virtual and physical characteristic calibration table data; The initial digital twin model of rural energy storage was validated based on the real-time response layer data of the energy storage system and the virtual-real characteristic calibration table data, resulting in the rural energy storage digital twin model.

5. The method according to claim 1, characterized in that, S3 include: Energy storage status data is obtained by extracting energy storage status based on the rural energy storage digital twin model; Anomaly detection is performed based on energy storage status data to obtain energy storage anomaly status data; Energy storage health is assessed based on energy storage status data and energy storage abnormal status data to obtain energy storage health data; Historical energy storage usage data is obtained, and the energy storage lifespan is estimated based on the historical energy storage usage data and energy storage health data to obtain the energy storage lifespan prediction data. Energy efficiency is calculated based on energy storage status data to obtain energy storage energy efficiency data. Real-time risk processing is performed on energy storage lifespan prediction data and energy storage efficiency data to obtain rural energy storage status data.

6. The method according to claim 1, characterized in that, S4 include: Load forecasting and demand matching are performed based on rural energy storage status data to obtain load demand matching table data; Priority strategies are set based on load demand matching table data to obtain energy storage scheduling priority scheme data; Dynamic charging and discharging window optimization is performed based on energy storage scheduling priority scheme data to obtain dynamic charging and discharging schedule data; Multi-device collaborative scheduling is performed based on dynamic charge and discharge schedule data to obtain rural energy storage scheduling data for auxiliary operations in rural energy storage management.

7. A rural digital twin energy storage management system based on the Internet of Things, characterized in that, For executing the IoT-based rural digital twin energy storage management method as described in claim 1, the IoT-based rural digital twin energy storage management system comprises: The rural energy storage real-time data acquisition module is used to collect real-time operating data from rural distributed energy storage devices to obtain rural energy storage data. The digital twin model building module is used to build a digital twin model based on rural energy storage data, thereby obtaining a rural energy storage digital twin model. The energy storage status detection module is used to detect the energy storage status based on the rural energy storage digital twin model and obtain rural energy storage status data. The rural energy storage scheduling module is used to optimize scheduling based on rural energy storage status data, obtain rural energy storage scheduling data, and carry out auxiliary operations for rural energy storage management.

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