Terminal energy management method and system integrating solar energy and electrochemical capacitor

Abstract

This invention discloses a terminal energy management method and system integrating solar energy and electrochemical capacitors, belonging to the field of low-power IoT device technology. The invention collects multi-source operating status data from a solar energy conversion unit, a three-level energy storage unit, and a load module. Combining historical operating data with weather forecast data, it generates predicted energy consumption demand and power generation for a future set time period. Based on the predicted results and the remaining energy status of the energy storage, it generates charging and discharging control, load operation, and communication parameter matching rules, simultaneously executing coordinated charging and discharging control of the energy storage and dynamic adjustment of load and communication parameters. This invention achieves dynamic matching and refined management of energy supply and demand for outdoor low-power terminals, adapts to the differentiated load characteristics of terminals, improves energy utilization efficiency, extends the maintenance-free operation cycle of terminals, and broadens the environmental adaptability of terminals in areas without grid coverage.

Description

Technical Field

[0001] This invention relates to the field of low-power Internet of Things (IoT) device technology, and in particular to a terminal energy management method and system that integrates solar energy and electrochemical capacitors. Background Technology

[0002] With the large-scale deployment and continuous expansion of IoT technology and its application scenarios, the deployment scale of low-power IoT terminals in outdoor areas without grid coverage continues to grow. They are widely used in environmental monitoring, smart agriculture, emergency security, and information services in remote areas, becoming a core basic unit for the digital and intelligent construction of outdoor scenarios. To meet the long-term operational needs of terminals in grid-free environments, renewable energy power supply technology, with solar power as its core, has become the mainstream power supply solution for outdoor low-power terminals, and related technology research and industrial applications continue to deepen. At the same time, electrochemical energy storage device technology continues to develop, and the performance of different types of energy storage devices in terms of power density, energy density, cycle life, and environmental adaptability is continuously optimized, providing diversified technology options for the design of energy storage systems for outdoor terminals. The integrated application of edge computing and artificial intelligence technologies has also provided new technical paths for energy management of outdoor terminals. The maturity and popularization of low-power wide-area communication technology further realizes remote control and collaborative operation of outdoor terminal clusters. Related technology systems are constantly improving, and the industrial ecosystem continues to mature.

[0003] Current energy management technologies for outdoor low-power terminals still suffer from several technical shortcomings in practical applications, making it difficult to fully adapt to complex outdoor environments and the differentiated operational needs of terminals. Existing energy management solutions often employ single-dimensional status monitoring methods, failing to achieve simultaneous and complete acquisition of multi-source data on solar energy conversion status, energy storage unit operation status, and load module operation status. This hinders the construction of a comprehensive terminal energy operation status perception system and fails to provide accurate and complete data support for subsequent energy management. Furthermore, existing solutions often employ passive charge / discharge control and load regulation modes based on fixed thresholds, lacking forward-looking predictions of future energy consumption demands and solar power generation. This prevents advance matching and dynamic scheduling of energy supply and demand, and makes it difficult to cope with the intermittent and fluctuating changes in outdoor sunlight conditions. In addition, the charge / discharge control of energy storage units, the operation regulation of load modules, and the parameter optimization of communication systems in existing technologies are mostly independent designs, lacking an integrated collaborative management mechanism. This prevents the formation of a complete energy management closed loop, hindering the refined and efficient utilization of terminal energy and failing to guarantee long-term, stable, and maintenance-free operation of outdoor low-power terminals in areas without grid coverage. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a terminal energy management method and system that integrates solar energy and electrochemical capacitors.

[0005] The objective of this invention is achieved through the following technical solution: A method for end-point energy management integrating solar energy and electrochemical capacitors is provided, the method comprising the following steps: S1. Collect output status data of the solar energy conversion unit, voltage status data of the three-level energy storage unit, and operating current data of the load module; S2. Using the collected output status data, voltage status data, and operating current data, combined with historical operating data and weather forecast data, generate energy consumption demand prediction results and power generation prediction results for a future set period; S3. Using the energy consumption demand forecast results and power generation forecast results, combined with the remaining energy status of the three-level energy storage unit, generate the charging and discharging control rules for the three-level energy storage unit, the load module operating parameter adjustment rules, and the communication protocol stack communication parameter matching rules. S4. Perform coordinated charging and discharging control of the three-level energy storage unit according to the charging and discharging control rules of the three-level energy storage unit, and synchronously adjust the operating parameters of the load module and the communication parameters of the communication protocol stack according to the load module operating parameter adjustment rules and the communication protocol stack communication parameter matching rules.

[0006] Furthermore, the data acquisition process in step S1 includes the following sub-steps: S1.1. A fixed acquisition period matching the minimum operating cycle of the load module is adopted to continuously acquire the output voltage and output current of the solar conversion unit, obtain the output status data of the solar conversion unit, and use the moving average filtering method to smooth the acquired raw data and remove abnormal data that exceed the preset fluctuation range. S1.2. The voltages at both ends of the primary electrochemical capacitor, the secondary lithium-ion capacitor, and the tertiary lithium iron phosphate battery in the three-level energy storage unit are synchronously collected to obtain the voltage state data of the three-level energy storage unit. The collection action is synchronized with the collection action of the solar energy conversion unit. S1.3. Real-time acquisition of the working circuit current of the load module, distinguishing between the standby and working current data of the load module, obtaining the operating current data of the load module, and synchronously storing all acquired data to the local storage unit.

[0007] Furthermore, the prediction result generation process in step S2 includes the following sub-steps: S2.1. Extract features from the output status data of the solar energy conversion unit, the voltage status data of the three-level energy storage unit, the operating current data of the load module, historical operating data and weather forecast data. Extract time period features, environmental change features and load operation features from the data to generate a standardized model input feature set. S2.2. Input the model input feature set into the load prediction model that has been pre-trained using a time-series prediction algorithm, and output the energy consumption demand prediction results for a future set period of time. The energy consumption demand prediction results cover the entire operating scenario of the load module. S2.3. Combining the solar irradiance data, temperature data and historical conversion efficiency data of solar energy conversion units from the meteorological forecast data, generate the power generation forecast results for the future set period. The power generation forecast results are completely consistent with the energy consumption demand forecast results in terms of time dimension.

[0008] Furthermore, the rule generation process in step S3 includes the following sub-steps: S3.1. Using the power generation forecast results and the current remaining capacity of the three-level energy storage unit, determine the total dispatchable energy for a future set period. S3.2. Using the total dispatchable energy, energy demand prediction results, and the remaining energy state of the three-level energy storage unit, determine the charging and discharging sequence, charging and discharging current distribution rules, and switch on / off control rules of the first-level electrochemical capacitor, the second-level lithium-ion capacitor, and the third-level lithium iron phosphate battery in the three-level energy storage unit, and generate the charging and discharging control rules of the three-level energy storage unit. S3.3. Using the remaining energy state and energy demand prediction results of the three-level energy storage unit, match multiple preset working modes of the load module, determine the working mode that matches the current energy state, and generate the load module operating parameter adjustment rules and communication protocol stack communication parameter matching rules.

[0009] Furthermore, the rule execution process in step S4 includes the following sub-steps: S4.1. According to the charging and discharging control rules of the three-level energy storage unit, control the on / off state of the charging and discharging switches and the magnitude of the charging and discharging current of the first-level electrochemical capacitor, the second-level lithium-ion capacitor and the third-level lithium iron phosphate battery in the three-level energy storage unit, so as to realize the coordinated charging and discharging control of each level of energy storage unit. S4.2. According to the load module operating parameter adjustment rules, adjust the data acquisition cycle of the load module, the running sequence of the functional modules, and the on / off status of the non-core functional modules to match the load module operating rules in the corresponding working mode; S4.3. Synchronously read the communication parameter configuration corresponding to the communication parameter matching rules of the communication protocol stack, adjust the communication operation parameters of the communication protocol stack, and after the communication parameters are adjusted, report the adjustment result to the communication gateway to complete a complete scheduling loop.

[0010] Furthermore, in step S1, the three-level energy storage unit includes a primary electrochemical capacitor, a secondary lithium-ion capacitor, and a tertiary lithium iron phosphate battery. During the acquisition process, the voltage state data of the primary electrochemical capacitor, the secondary lithium-ion capacitor, and the tertiary lithium iron phosphate battery are acquired separately. The voltage acquisition channels of each energy storage unit are independent of each other. During the acquisition process, the voltage data of each energy storage unit is continuously sampled at multiple points. The sampling frequency is kept completely consistent with the output state data acquisition frequency of the solar energy conversion unit. The voltage data of each level of energy storage unit are respectively stored in the independent data partition of the local storage unit for subsequent calculation of the remaining energy state of the three-level energy storage unit and generation of charge and discharge control rules.

[0011] Furthermore, in step S2, the historical operating data includes historical power consumption data of the load module, historical power generation data of the solar conversion unit, and historical charge and discharge data of the three-level energy storage unit. The weather forecast data includes light intensity data, temperature data, and rainfall data. During the feature extraction process, different types of input data are normalized and standardized to eliminate the differences in the dimensions of different data dimensions. After processing, the data is spliced ​​according to the time series dimension to generate a model input feature set that meets the input format requirements of the load prediction model. This set is used for the calculation and generation of subsequent energy demand prediction results and power generation prediction results.

[0012] Furthermore, in step S3, the three-level energy storage unit charge and discharge control rules prioritize allocating the first-level electrochemical capacitor to bear the transient peak current of the load module, prioritize allocating the second-level lithium-ion capacitor to bear the daily basic power supply of the load module, and allocate the third-level lithium iron phosphate battery to bear the long-term energy supply in scenarios without effective lighting. The three-level energy storage unit charge and discharge control rules specify the charge and discharge trigger conditions and charge and discharge cutoff conditions of the first-level electrochemical capacitor, the second-level lithium-ion capacitor, and the third-level lithium iron phosphate battery. The charge and discharge trigger conditions and charge and discharge cutoff conditions are set based on the voltage state data and remaining energy state of each level of energy storage unit to ensure that the charge and discharge process of each level of energy storage unit conforms to the preset operating rules.

[0013] Furthermore, in step S4, the communication parameters of the communication protocol stack include the spreading factor, wake-up period, and transmit power. The adjustment of the communication parameters is synchronized with the adjustment of the load module's operating mode. Each set of preset load module operating modes corresponds to a unique communication parameter configuration. When the load module's operating parameter adjustment rules are issued, the communication parameter configuration corresponding to the communication protocol stack's communication parameter matching rules is issued simultaneously. After the communication parameters are adjusted, the communication protocol stack completes the communication interaction with the communication gateway according to the adjusted parameters, and at the same time, it synchronously feeds back the adjusted operating status to the energy management unit.

[0014] An integrated solar energy and electrochemical capacitor terminal energy management system is provided. The system includes a solar energy conversion unit, a three-level energy storage unit, an energy management unit, a load module, and a communication protocol stack. The solar energy conversion unit is connected to the three-level energy storage unit to convert solar energy into electrical energy and transmit it to the three-level energy storage unit. The three-level energy storage unit is connected to the energy management unit to store electrical energy and provide power support for the load module. The energy management unit is bidirectionally connected to both the load module and the communication protocol stack to collect various status data, generate prediction results and operation control rules, execute charge and discharge coordinated control, and adjust operating parameters. The load module is connected to the communication protocol stack to complete corresponding business functions according to the adjusted operating parameters.

[0015] The beneficial effects of this invention are: (1) Through multi-source operation status data acquisition, future energy consumption and power generation prediction, multi-dimensional operation control rule generation and collaborative execution, dynamic matching of energy supply and demand of outdoor low power terminals and refined management and control of the whole process can be realized. (2) In view of the different load characteristics of outdoor terminals, the coordinated charging and discharging scheduling of graded energy storage units can adapt to the different power supply requirements of transient and steady state, reduce the operating loss of energy storage devices, and extend the overall maintenance-free operation cycle of the terminal. (3) Through deep collaboration between energy management logic and communication protocol stack, the operating and communication parameters are dynamically matched according to the real-time energy status of the terminal, thereby improving energy utilization efficiency and expanding the environmental adaptability of the terminal in areas without power grid coverage. Attached Figure Description

[0016] Figure 1 A flowchart illustrating the steps of a terminal energy management method integrating solar energy and electrochemical capacitors; Figure 2 The following is a flowchart illustrating the specific steps of a terminal energy management method integrating solar energy and electrochemical capacitors, provided as an example. Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1 See Figure 1 This embodiment provides a terminal energy management method integrating solar energy and electrochemical capacitors, which includes the following steps: S1. Collect output status data of the solar energy conversion unit, voltage status data of the three-level energy storage unit, and operating current data of the load module; S2. Using the collected output status data, voltage status data, and operating current data, combined with historical operating data and weather forecast data, generate energy consumption demand prediction results and power generation prediction results for a future set period; S3. Using the energy consumption demand forecast results and power generation forecast results, combined with the remaining energy status of the three-level energy storage unit, generate the charging and discharging control rules for the three-level energy storage unit, the load module operating parameter adjustment rules, and the communication protocol stack communication parameter matching rules. S4. Perform coordinated charging and discharging control of the three-level energy storage unit according to the charging and discharging control rules of the three-level energy storage unit, and synchronously adjust the operating parameters of the load module and the communication parameters of the communication protocol stack according to the load module operating parameter adjustment rules and the communication protocol stack communication parameter matching rules.

[0019] In some embodiments, the data acquisition process in step S1 includes the following sub-steps: S1.1. A fixed acquisition period matching the minimum operating cycle of the load module is adopted to continuously acquire the output voltage and output current of the solar conversion unit, obtain the output status data of the solar conversion unit, and use the moving average filtering method to smooth the acquired raw data and remove abnormal data that exceed the preset fluctuation range. S1.2. The voltages at both ends of the primary electrochemical capacitor, the secondary lithium-ion capacitor, and the tertiary lithium iron phosphate battery in the three-level energy storage unit are synchronously collected to obtain the voltage state data of the three-level energy storage unit. The collection action is synchronized with the collection action of the solar energy conversion unit. S1.3. Real-time acquisition of the working circuit current of the load module, distinguishing between the standby and working current data of the load module, obtaining the operating current data of the load module, and synchronously storing all acquired data to the local storage unit.

[0020] In some embodiments, the prediction result generation process in step S2 includes the following sub-steps: S2.1. Extract features from the output status data of the solar energy conversion unit, the voltage status data of the three-level energy storage unit, the operating current data of the load module, historical operating data and weather forecast data. Extract time period features, environmental change features and load operation features from the data to generate a standardized model input feature set. S2.2. Input the model input feature set into the load prediction model that has been pre-trained using a time-series prediction algorithm, and output the energy consumption demand prediction results for a future set period of time. The energy consumption demand prediction results cover the entire operating scenario of the load module. S2.3. Combining the solar irradiance data, temperature data and historical conversion efficiency data of solar energy conversion units from the meteorological forecast data, generate the power generation forecast results for the future set period. The power generation forecast results are completely consistent with the energy consumption demand forecast results in terms of time dimension.

[0021] In some embodiments, the rule generation process in step S3 includes the following sub-steps: S3.1. Using the power generation forecast results and the current remaining capacity of the three-level energy storage unit, determine the total dispatchable energy for a future set period. S3.2. Using the total dispatchable energy, energy demand prediction results, and the remaining energy state of the three-level energy storage unit, determine the charging and discharging sequence, charging and discharging current distribution rules, and switch on / off control rules of the first-level electrochemical capacitor, the second-level lithium-ion capacitor, and the third-level lithium iron phosphate battery in the three-level energy storage unit, and generate the charging and discharging control rules of the three-level energy storage unit. S3.3. Using the remaining energy state and energy demand prediction results of the three-level energy storage unit, match multiple preset working modes of the load module, determine the working mode that matches the current energy state, and generate the load module operating parameter adjustment rules and communication protocol stack communication parameter matching rules.

[0022] In some embodiments, the rule execution process in step S4 includes the following sub-steps: S4.1. According to the charging and discharging control rules of the three-level energy storage unit, control the on / off state of the charging and discharging switches and the magnitude of the charging and discharging current of the first-level electrochemical capacitor, the second-level lithium-ion capacitor and the third-level lithium iron phosphate battery in the three-level energy storage unit, so as to realize the coordinated charging and discharging control of each level of energy storage unit. S4.2. According to the load module operating parameter adjustment rules, adjust the data acquisition cycle of the load module, the running sequence of the functional modules, and the on / off status of the non-core functional modules to match the load module operating rules in the corresponding working mode; S4.3. Synchronously read the communication parameter configuration corresponding to the communication parameter matching rules of the communication protocol stack, adjust the communication operation parameters of the communication protocol stack, and after the communication parameters are adjusted, report the adjustment result to the communication gateway to complete a complete scheduling loop.

[0023] In some embodiments, in step S1, the three-level energy storage unit includes a primary electrochemical capacitor, a secondary lithium-ion capacitor, and a tertiary lithium iron phosphate battery. During the acquisition process, the voltage state data of the primary electrochemical capacitor, the secondary lithium-ion capacitor, and the tertiary lithium iron phosphate battery are acquired separately. The voltage acquisition channels of each energy storage unit are independent of each other. During the acquisition process, the voltage data of each energy storage unit is continuously sampled at multiple points. The sampling frequency is kept completely consistent with the output state data acquisition frequency of the solar energy conversion unit. The voltage data of each level of energy storage unit are respectively stored in the independent data partition of the local storage unit for subsequent calculation of the remaining energy state of the three-level energy storage unit and generation of charge and discharge control rules.

[0024] In some embodiments, in step S2, the historical operating data includes historical power consumption data of the load module, historical power generation data of the solar conversion unit, and historical charge and discharge data of the three-level energy storage unit. The weather forecast data includes light intensity data, temperature data, and rainfall data. During the feature extraction process, different types of input data are normalized and standardized to eliminate the dimensional differences between different data dimensions. After processing, the data is spliced ​​according to the time series dimension to generate a model input feature set that meets the input format requirements of the load prediction model, which is used for the calculation and generation of subsequent energy demand prediction results and power generation prediction results.

[0025] In some embodiments, in step S3, the three-level energy storage unit charge and discharge control rules prioritize allocating the first-level electrochemical capacitor to bear the transient peak current of the load module, prioritize allocating the second-level lithium-ion capacitor to bear the daily basic power supply of the load module, and allocate the third-level lithium iron phosphate battery to bear the long-term energy supply in scenarios without effective lighting. The three-level energy storage unit charge and discharge control rules specify the charge and discharge trigger conditions and charge and discharge cutoff conditions of the first-level electrochemical capacitor, the second-level lithium-ion capacitor, and the third-level lithium iron phosphate battery. The charge and discharge trigger conditions and charge and discharge cutoff conditions are set based on the voltage state data and remaining energy state of each level of energy storage unit to ensure that the charge and discharge process of each level of energy storage unit conforms to the preset operating rules.

[0026] In some embodiments, in step S4, the communication parameters of the communication protocol stack include the spreading factor, wake-up period, and transmit power. The adjustment of the communication parameters is synchronized with the adjustment of the load module's operating mode. Each set of preset load module operating modes corresponds to a unique communication parameter configuration. When the load module's operating parameter adjustment rules are issued, the communication parameter configuration corresponding to the communication protocol stack's communication parameter matching rules is issued simultaneously. After the communication parameters are adjusted, the communication protocol stack completes the communication interaction with the communication gateway according to the adjusted parameters, and simultaneously feeds back the adjusted operating status to the energy management unit.

[0027] An integrated solar energy and electrochemical capacitor terminal energy management system is provided. The system includes a solar energy conversion unit, a three-level energy storage unit, an energy management unit, a load module, and a communication protocol stack. The solar energy conversion unit is connected to the three-level energy storage unit to convert solar energy into electrical energy and transmit it to the three-level energy storage unit. The three-level energy storage unit is connected to the energy management unit to store electrical energy and provide power support for the load module. The energy management unit is bidirectionally connected to both the load module and the communication protocol stack to collect various status data, generate prediction results and operation control rules, execute charge and discharge coordinated control, and adjust operating parameters. The load module is connected to the communication protocol stack to complete corresponding business functions according to the adjusted operating parameters.

[0028] Example 2 This embodiment provides a specific implementation process of a terminal energy management method that integrates solar energy and electrochemical capacitors. This method is applied to outdoor low-power IoT terminal devices to achieve long-term stable power supply and operation control of the terminal devices in areas without power grid coverage.

[0029] The low-power IoT terminals applicable to this embodiment include sensing terminals, display terminals, control terminals, and data acquisition terminals. Sensing terminals are used for collecting and sensing environmental physical and chemical quantities, possessing load characteristics of periodic data acquisition and intermittent communication. Display terminals are used for static information display and on-demand dynamic refresh, possessing load characteristics of static low-power maintenance and instantaneous high-power refresh. Control terminals are used for action control and status feedback of field actuators, possessing load characteristics of long-term low-power standby and instantaneous high-power execution. Data acquisition terminals are used for continuous acquisition and periodic aggregation and reporting of multi-source data in the field, possessing load characteristics of stable continuous power consumption and intermittent communication power consumption. This embodiment, through a unified energy management framework, adapts to the differentiated load characteristics of the above-mentioned terminals, enabling long-term maintenance-free operation of terminal devices in areas without power grid or public network coverage. Figure 2 As shown, the specific implementation process is as follows: S1. Operational status data acquisition: S1.1. Solar energy conversion unit output status data acquisition: The solar energy conversion unit is a device that converts light energy into electrical energy. In this embodiment, a monocrystalline silicon solar panel with maximum power point tracking (MPPT) is used. MPPT is an existing control technology that adjusts the working state of the electrical module to enable the solar panel to output maximum power under different light and temperature conditions. In this embodiment, this technology is used to maximize the conversion efficiency of solar energy and reduce the impact of changes in light conditions on power generation efficiency.

[0030] This embodiment employs a fixed acquisition period that matches the minimum operating cycle of the load module to continuously acquire the output voltage and current of the solar energy conversion unit, thereby obtaining the output status data of the solar energy conversion unit. During the acquisition process, the output electrical signal of the solar energy conversion unit is first rectified and filtered to remove high-frequency interference signals, and then analog-to-digital conversion is performed through a high-precision sampling circuit to obtain the raw acquired data.

[0031] This embodiment employs a combination of moving average filtering, median filtering, and amplitude limiting filtering to smooth the acquired raw data. Moving average filtering is an existing data processing method that replaces the current sample value with the average of multiple consecutive sampling points to suppress random fluctuation interference. Median filtering is an existing data processing method that replaces the current sample value with the median value of multiple consecutive sampling points to eliminate sudden pulse interference. Amplitude limiting filtering is an existing processing method that eliminates abnormal data that exceeds a preset fluctuation range by limiting the maximum variation of adjacent sampling points. The combination of these three filtering methods ensures the accuracy and stability of the acquired data.

[0032] S1.2. Voltage status data acquisition of the three-level energy storage unit: The three-level energy storage unit includes a primary electrochemical capacitor, a secondary lithium-ion capacitor, and a tertiary lithium iron phosphate battery. The primary electrochemical capacitor is an electrochemical energy storage device based on the double-layer principle, featuring high power density, ultra-long cycle life, and a wide operating temperature range. In this embodiment, it is used to handle the transient peak current of the load module, preventing large current surges from causing damage to other energy storage units. The secondary lithium-ion capacitor is an existing hybrid energy storage device that combines the energy storage characteristics of double-layer capacitors and lithium-ion batteries, balancing power density and energy density, while also possessing a wide operating temperature range and a long cycle life. In this embodiment, it is used to provide basic daily power supply for the load module, serving as the core carrier for daily energy turnover. The tertiary lithium iron phosphate battery is a lithium-ion energy storage battery using lithium iron phosphate as the positive electrode material, featuring high energy density, stable charge and discharge performance, and a long cycle life. In this embodiment, it is used for long-term energy storage to meet the continuous power supply needs in scenarios without effective sunlight.

[0033] In this embodiment, the voltages at both ends of the primary electrochemical capacitor, the secondary lithium-ion capacitor, and the tertiary lithium iron phosphate battery in the three-level energy storage unit are collected synchronously to obtain the voltage state data of the three-level energy storage unit. The collection action is synchronized with the collection action of the solar energy conversion unit in hardware to ensure that the data timestamps of different collection objects are completely aligned.

[0034] In this embodiment, the voltage state data of the primary electrochemical capacitor, the secondary lithium-ion capacitor, and the tertiary lithium iron phosphate battery are collected separately. The voltage acquisition channels of each energy storage unit are independent of each other, and a differential sampling circuit is used for voltage acquisition. The differential sampling circuit is an existing sampling circuit that suppresses common-mode interference signals by acquiring the voltage difference between two measurement points. It is suitable for high-precision voltage acquisition in complex outdoor electromagnetic environments.

[0035] During the data acquisition process, voltage data of each energy storage unit is continuously sampled at multiple points. The sampling frequency is kept completely consistent with the data acquisition frequency of the output status data of the solar conversion unit. The voltage data of each energy storage unit is stored in an independent data partition of the local storage unit. At the same time, the integrity of the stored data is verified by Cyclic Redundancy Check (CRC), an existing verification algorithm used to verify the integrity of data transmission and storage, ensuring the integrity and reliability of the acquired data. The acquired voltage data is used for the calculation of the remaining energy status of the three-stage energy storage units and the generation of charging and discharging control rules.

[0036] S1.3. Load module operating current data acquisition: The load module is a low-power IoT terminal, including a sensing terminal, a display terminal, a control terminal, and a data acquisition terminal. Different types of terminals have different load characteristics and corresponding current change patterns. During the data acquisition process, the current change characteristics of different load characteristics are synchronously identified and processed differently. This embodiment acquires the working circuit current of the load module in real time, using a multi-range adaptive switching current sampling circuit. This circuit is an existing sampling circuit that automatically switches the sampling range according to the magnitude of the measured current, balancing the accuracy of small current measurements with the range of large current measurements, and adapting to the wide range of current acquisition needs under different load conditions. This embodiment distinguishes between the standby and operating current data of the load module. By combining the current threshold and duration, the current operating state of the load module is identified, and the operating current data of the load module is obtained. All acquired data is synchronously stored in the local storage unit, maintaining timestamp synchronization with the data acquired by the solar energy conversion unit and the tertiary energy storage unit, providing complete basic data support for subsequent energy consumption decomposition, energy consumption prediction, and energy dispatch.

[0037] S2. Energy Consumption and Power Generation Forecast: S2.1. Predicting Input Feature Set Generation: Feature extraction is an existing data processing process that extracts characteristic parameters from raw and associated data that can reflect the patterns of energy consumption and power generation capacity changes. In this embodiment, feature extraction, feature filtering, and standardization are performed on multi-source data to generate a feature set that meets the input requirements of the prediction model.

[0038] This embodiment extracts features from the output status data of the solar energy conversion unit, the voltage status data of the three-level energy storage unit, the operating current data of the load module, historical operating data, and weather forecast data. It extracts time-period features, environmental change features, and load operation features from the data to generate a standardized model input feature set. Historical operating data includes historical power consumption data of the load module, historical power generation data of the solar energy conversion unit, and historical charge / discharge data of the three-level energy storage unit. Weather forecast data includes solar irradiance data, temperature data, and rainfall data.

[0039] During feature extraction, time-period features include daily, weekly, and seasonal features, which are used to capture the periodic variation patterns of load power consumption and solar power generation; environmental change features include light intensity variation, temperature variation, and rainfall features, which are used to capture the impact of environmental factors on power generation efficiency and load power consumption; and load operation features include load status features, current variation features, and functional module operation sequence features, which are used to capture the impact of load operation status on energy consumption.

[0040] This embodiment uses the Pearson correlation coefficient method to screen the extracted initial features. The Pearson correlation coefficient method is an existing statistical method for measuring the degree of linear correlation between two variables. By calculating the correlation coefficient between each feature and energy consumption and power generation, effective features with correlation higher than a preset threshold are screened out, redundant features are eliminated, and the computational complexity of the model is reduced.

[0041] After feature extraction and filtering, this embodiment performs min-max normalization on different types of input data. Min-max normalization is an existing data processing method that linearly maps data to a fixed interval to eliminate the dimensional differences between different data dimensions. After processing, the data is spliced ​​according to the time series dimension to generate a model input feature set that meets the input format requirements of the load forecasting model, which is used for the calculation and generation of subsequent energy demand forecasting results and power generation forecasting results.

[0042] S2.2. Generation of Energy Demand Forecast Results: Time series forecasting algorithm is an existing algorithm that predicts the trend and value of data changes in future periods based on historical time series data. In this embodiment, a load forecasting model is constructed using a time series forecasting algorithm to accurately predict the energy consumption demand of terminal devices in a future set period. The model is deployed on the edge computing unit of the communication gateway to realize distributed forecasting and collaborative management of the terminal group.

[0043] In this embodiment, the model input feature set is input into a load prediction model that has been pre-trained using a time-series prediction algorithm. The model outputs the energy consumption demand prediction results for a future set time period. The energy consumption demand prediction results cover the entire operating scenario of the load module. At the same time, the confidence interval of the prediction results is output to measure the reliability of the prediction results.

[0044] The load forecasting model employs a network structure combining multi-layer temporal convolutional networks and gated recurrent units. The network structure includes an input layer, a feature encoding layer, a temporal feature extraction layer, a confidence output layer, and a result output layer. The input layer and the feature encoding layer are fully connected, the feature encoding layer and the temporal feature extraction layer are cascaded, and the temporal feature extraction layer is fully connected to both the confidence output layer and the result output layer. The input layer receives the standardized model input feature set. The feature encoding layer performs nonlinear transformation and dimensionality compression on the input features to extract deep correlations. The temporal feature extraction layer captures the long-period and short-period time dependencies in the input features. The confidence output layer outputs the confidence interval of the prediction result, and the result output layer outputs the predicted energy consumption demand for a specified future period.

[0045] The training steps for the model include: The first step is to collect multi-source data from historical operation processes to construct a training dataset. The training dataset includes input feature data and corresponding actual energy consumption label data, and the data covers full-scenario samples under different seasons, different weather conditions, and different load operation states. The second step is to perform data cleaning, missing value imputation and standardization on the training dataset, and divide the training set and validation set according to a preset ratio. The third step is to set the key parameters for model training, including the number of iterations, initial learning rate, learning rate decay coefficient, batch size, and loss function. The loss function used is the mean squared error loss function. The fourth step is to iteratively train the model using the training set. After each iteration, the model's prediction accuracy is verified using the validation set. Training is stopped when the model's prediction accuracy reaches a preset threshold or all iterations are completed, and the pre-trained load prediction model is obtained. The fifth step is to perform lightweight processing on the pre-trained model. Existing model compression techniques such as model quantization and structured pruning are used to reduce the computational load and storage consumption of the model, making it suitable for the deployment requirements of edge computing units.

[0046] The formula for calculating the loss function is: Loss=MSE(y pred ,y true ) Where Loss is the loss value for model training, MSE is the mean squared error calculation function, and y pred y represents the predicted energy consumption output by the model.true This corresponds to the actual energy consumption label value.

[0047] In this embodiment, the load prediction model supports online incremental learning. Incremental learning is an existing model optimization method that locally updates the model based on new data without retraining the entire model. By using the actual energy consumption data reported by the terminal device, the model is continuously iterated and optimized to improve its adaptability to environmental and load changes.

[0048] S2.3. Generation of power generation prediction results: Solar energy conversion efficiency refers to the proportion of received light energy converted into electrical energy by a solar energy conversion unit. It is affected by various factors such as light intensity, ambient temperature, cleanliness of the solar panel surface, and the aging degree of the components. In this embodiment, historical conversion efficiency data and weather forecast data are combined to predict power generation for future periods, providing a data basis for subsequent energy dispatch. This embodiment combines light intensity data and temperature data from weather forecast data with historical conversion efficiency data of the solar energy conversion unit to generate a power generation prediction result for a set future period. The power generation prediction result is completely consistent with the energy consumption demand prediction result in terms of time dimension.

[0049] During the prediction process, the solar energy conversion efficiency is corrected for temperature effects using a temperature coefficient correction model. This model is an existing model that calculates the conversion efficiency degradation ratio under different ambient temperatures based on the temperature coefficient parameters of the solar panel. The light intensity is corrected for rainfall effects using a rainfall attenuation coefficient, with different rainfall intensities corresponding to different light attenuation coefficients. The cleanliness effects of dust accumulation are corrected for conversion efficiency using a dust accumulation attenuation coefficient, which is calculated by the ratio of the historical actual power generation to the theoretical power generation under standard operating conditions. Through multi-factor correction, the accuracy of the power generation prediction results is improved.

[0050] S3. Generation of charging / discharging and operation control rules: S3.1. Determination of the total dispatchable energy: The total dispatchable energy refers to all available energy that terminal devices can safely utilize within a future set time period. This includes the currently stored available energy in the tertiary energy storage unit and the predicted power generation that can be obtained through the solar power conversion unit within the future time period. In this embodiment, this parameter is used to determine the boundary conditions for energy dispatch, ensuring the rationality of energy dispatch and the continuity of terminal operation. This embodiment uses the predicted power generation results, combined with the current remaining capacity of the tertiary energy storage unit, to determine the total dispatchable energy for the future set time period.

[0051] In the calculation process, based on the voltage state data of the three-level energy storage units, the current remaining capacity of each energy storage unit is calculated using the open-circuit voltage method combined with the ampere-hour integration method. State of charge (SOC) measures the proportion of remaining energy to the rated capacity of an energy storage unit. The open-circuit voltage method is an existing method for calculating remaining capacity based on the correspondence between the open-circuit voltage and the SOC of the energy storage unit. The ampere-hour integration method is an existing method for calculating the capacity change of the energy storage unit based on the time integration of charging and discharging current. Combining these two methods improves the accuracy of remaining capacity calculation. After deducting line transmission losses, charging and discharging conversion losses, and the reserved amount for static power consumption, the total available dispatchable energy is obtained. Simultaneously, based on the confidence interval of the power generation prediction results, a corresponding energy redundancy is set. The lower the confidence interval, the higher the energy redundancy setting, to address the energy gap risk caused by prediction deviations.

[0052] S3.2. Generation of charging and discharging control rules for the three-level energy storage unit: This embodiment uses the total dispatchable energy, energy demand prediction results, and the remaining energy state of the three-level energy storage unit to determine the charging and discharging sequence, charging and discharging current distribution rules, and switch on / off control rules of the primary electrochemical capacitor, secondary lithium-ion capacitor, and tertiary lithium iron phosphate battery in the three-level energy storage unit, and generates the charging and discharging control rules of the three-level energy storage unit.

[0053] The three-level energy storage unit's charge and discharge control rules prioritize allocating the first-level electrochemical capacitor to handle the transient peak current of the load module, prioritize allocating the second-level lithium-ion capacitor to handle the daily basic power supply of the load module, and allocate the third-level lithium iron phosphate battery to handle the long-term energy supply in scenarios without effective sunlight. The three-level energy storage unit's charge and discharge control rules clearly define the charge and discharge trigger conditions and charge and discharge cutoff conditions for the first-level electrochemical capacitor, the second-level lithium-ion capacitor, and the third-level lithium iron phosphate battery. The charge and discharge trigger conditions and charge and discharge cutoff conditions are set based on the voltage state data and remaining energy state of each level of energy storage unit to ensure that the charge and discharge process of each level of energy storage unit conforms to the preset operating rules.

[0054] During transient load events, the primary electrochemical capacitor independently handles the peak current, preventing high-current discharge from impacting other energy storage units and accelerating aging. After the load ends, the voltage drop of the primary electrochemical capacitor determines whether recharging is necessary. If the voltage drops below a preset threshold and no transient load is predicted to occur again within a set timeframe, the primary electrochemical capacitor is immediately recharged from the secondary lithium-ion capacitor with a small current. If a transient load is predicted to occur again within a set timeframe, recharging is delayed until after the transient load ends to avoid frequent charging and discharging reducing device lifespan. During daily load power supply, the state of charge (SOC) of the secondary lithium-ion capacitor is prioritized to be maintained above a preset range, with the secondary lithium-ion capacitor handling basic daily power supply. All daily energy turnover is completed through the secondary lithium-ion capacitor, reducing the number of charge / discharge cycles of the tertiary lithium iron phosphate battery. When the SOC of the secondary lithium-ion capacitor falls below a preset lower threshold, it is determined whether there will be sufficient sunlight within a set timeframe. If so, solar energy replenishment is awaited; otherwise, the secondary lithium-ion capacitor is recharged from the tertiary lithium iron phosphate battery with a constant current until its SOC returns to the preset range.

[0055] During periods of continuous absence of effective sunlight, power is supplied first through the secondary lithium-ion capacitor. When the state of charge of the secondary lithium-ion capacitor drops to a preset emergency threshold, the tertiary lithium iron phosphate battery is then activated to replenish power. The tertiary lithium iron phosphate battery is used only as a backup energy storage unit to avoid frequent deep charging and discharging, thus extending its cycle life.

[0056] S3.3. Generation of load and communication parameter matching rules: The communication protocol stack refers to the set of communication protocols used for data interaction between the terminal device and the communication gateway. In this embodiment, the LoRa communication protocol stack is used. LoRa is a low-power wide-area network communication technology based on spread spectrum technology, which is suitable for long-distance, low-power IoT terminal communication scenarios. In this embodiment, the communication power consumption and the available energy status of the terminal are dynamically matched by adjusting the operating parameters of the communication protocol stack.

[0057] This embodiment uses the remaining energy state and energy consumption demand prediction results of the three-level energy storage unit to match multiple preset working modes of the load module, determine the working mode that matches the current energy state, and generate the load module operating parameter adjustment rules and communication protocol stack communication parameter matching rules.

[0058] The preset operating modes include high-speed mode, standard mode, energy-saving mode, and life support mode. Each operating mode corresponds to a unique load operation parameter configuration and communication parameter configuration. The matching of operating modes is based on the remaining energy state of the three-level energy storage units and the predicted energy consumption. At the same time, a hysteresis interval for mode switching is set to avoid frequent mode switching caused by energy state fluctuations and ensure the stability of system operation. High-speed mode corresponds to a state with sufficient power, standard mode corresponds to a state with moderate power, energy-saving mode corresponds to a state with low power, and life support mode corresponds to a state with critical power. The load operation strategy and communication strategy of different modes are adapted to the available energy state step by step to achieve fine-grained power consumption control.

[0059] S4. Rule Enforcement and Collaborative Control: S4.1. Three-level energy storage unit charge and discharge coordinated control: This embodiment follows the charging and discharging control rules of a three-level energy storage unit to control the on / off states of the charging and discharging switches and the magnitude of the charging and discharging currents of the primary electrochemical capacitor, the secondary lithium-ion capacitor, and the tertiary lithium iron phosphate battery, thereby achieving coordinated charging and discharging control of each level of the energy storage unit. During the charging and discharging control process, the voltage, current, and temperature status of each level of the energy storage unit are monitored in real time. When abnormal states such as overvoltage, undervoltage, overcurrent, or overtemperature occur, protection actions are immediately triggered to disconnect the charging and discharging switches of the corresponding energy storage unit, ensuring the safe operation of the devices and the system.

[0060] In some embodiments, when the remaining power of the three-stage energy storage unit is completely depleted, the micro-energy harvesting circuit is activated to achieve a cold start of the system. The micro-energy harvesting circuit uses a dedicated boost chip to collect and convert weak light energy. When the output voltage of the solar energy conversion unit drops to a preset threshold, energy harvesting can be activated. First, the first-stage electrochemical capacitor is charged. When the voltage of the first-stage electrochemical capacitor reaches the preset start-up threshold, the main control unit is activated to enter the ultra-low power consumption mode. The main control unit gradually wakes up the management circuits of the second-stage lithium-ion capacitor and the third-stage lithium iron phosphate battery, completing the autonomous cold start of the system without manual on-site intervention.

[0061] In some embodiments, the three-level energy storage units adopt a redundant design with mutual backup. When the three-level lithium iron phosphate battery fails and cannot charge or discharge normally, the two-level lithium-ion capacitor, together with the one-level electrochemical capacitor, can still maintain the basic operation of the terminal device. When the one-level electrochemical capacitor or the two-level lithium-ion capacitor fails, the three-level lithium iron phosphate battery, together with the remaining normal energy storage units, can still ensure the basic operation of the terminal device. The energy management unit adopts an independent power supply design. When the main control unit fails, it automatically switches to the hardware watchdog circuit to reset the system. The hardware watchdog is an existing circuit that monitors the system's operating status at regular intervals and triggers a reset when the system fails, ensuring the reliability of the system operation.

[0062] In some embodiments, the casing of the terminal device adopts a structural design with passive thermal management function, including an inner sealed air jacket and an outer biomimetic heat dissipation groove. The sealed air jacket is used for thermal insulation in low-temperature environments to reduce heat exchange between the internal energy storage unit and the external low-temperature environment. The biomimetic heat dissipation groove is used for auxiliary heat dissipation in high-temperature environments. By increasing the heat dissipation area of ​​the casing, the heat dissipation of the internal components is accelerated. The tertiary lithium iron phosphate battery is arranged in the inner region of the air jacket to reduce the impact of ambient temperature changes on battery performance and ensure the stable operation of the internal energy storage unit and electronic devices under extreme ambient temperatures.

[0063] In some embodiments, during communication idle periods, a micro vibration motor is controlled to perform a self-cleaning action on the solar panel. The vibration removes dust from the surface of the solar panel, reducing the impact of dust on power generation efficiency. The self-cleaning action is triggered based on the deviation between the predicted power generation value and the actual power generation. When the deviation continues to exceed a preset threshold, the self-cleaning action is initiated when the power is sufficient. The power consumption of the self-cleaning action is included in the energy consumption prediction range to avoid affecting the system's energy balance.

[0064] S4.2. Load module operating parameter adjustment control: This embodiment adjusts the data acquisition cycle, functional module execution sequence, and on / off status of non-core functional modules of the load module according to the load module operating parameter adjustment rules, matching the load module operating rules in the corresponding working mode. In high-speed mode, the sensor terminal performs high-frequency acquisition, the display terminal performs frequent refreshes, the control terminal maintains a full response state, and the acquisition terminal performs continuous acquisition and high-frequency reporting. In standard mode, the sensor terminal performs standard-frequency acquisition, the display terminal performs regular refreshes, the control terminal maintains a regular response state, and the acquisition terminal performs standard-frequency reporting. In energy-saving mode, the sensor terminal performs low-frequency acquisition, the display terminal stops refreshing, non-core functional modules are shut down, and the acquisition terminal extends the reporting cycle. In life support mode, only core sensing and emergency communication functions are retained, and all other functional modules are powered down. During load parameter adjustment, non-core functions are shut down sequentially according to functional priority from low to high, prioritizing the power supply and operation of high-priority functions. After parameter adjustment, the actual operating power consumption of the load module is monitored in real time and compared with the target power consumption. If the deviation exceeds the preset range, the parameters are fine-tuned again, forming a closed loop for load power consumption control.

[0065] S4.3. Communication Protocol Stack Parameter Matching and Closed-Loop Feedback: Communication parameters include spreading factor, wake-up period, and transmit power. The spreading factor is a parameter used in LoRa communication to adjust the spreading modulation multiple. The larger the spreading factor, the higher the communication reception sensitivity, the longer the communication distance, and the higher the corresponding communication power consumption. The wake-up period refers to the intermittent wake-up interval of the terminal device's communication module. The shorter the wake-up period, the higher the communication real-time performance, and the higher the corresponding communication power consumption. The transmit power refers to the wireless signal transmission power of the terminal device's communication module. The larger the transmit power, the higher the communication link margin, and the higher the corresponding communication power consumption.

[0066] In this embodiment, the communication parameter configuration corresponding to the communication parameter matching rules of the communication protocol stack is read synchronously, the communication operation parameters of the communication protocol stack are adjusted, and the adjustment result is fed back to the communication gateway after the communication parameters are adjusted, thus completing a complete scheduling loop.

[0067] The adjustment of communication parameters is synchronized with the adjustment of the load module's operating mode. Each preset load module operating mode corresponds to a unique communication parameter configuration. Simultaneously with the issuance of load module operating parameter adjustment rules, the corresponding communication parameter configuration based on the communication protocol stack's communication parameter matching rules is also issued. During the communication parameter adjustment process, optimization is performed based on the current communication link's signal-to-noise ratio, selecting the parameter combination with the lowest power consumption while ensuring communication reliability. After the communication parameters are adjusted, the communication protocol stack completes communication interaction with the communication gateway according to the adjusted parameters, and simultaneously feeds back the adjusted operating status to the energy management unit.

[0068] The terminal device periodically reports the energy state vector to the communication gateway through the communication protocol stack. The formula for calculating the energy state vector is: E=[E remaining ,T endurance ,S charge E predict ] Where E is the energy state vector of the terminal device, E remaining T represents the remaining battery power of the terminal device. endurance S represents the expected battery life of the terminal device. charge E represents the charging status of the terminal device. predict The predicted power generation results for a future time period for terminal equipment.

[0069] The communication gateway collects the energy status of all terminal devices within the area, forming regional energy distribution data. This data is used for subsequent group energy collaborative scheduling and load prediction model calibration and optimization, forming a complete cross-layer collaborative closed loop. The closed loop process includes: the communication gateway queries the energy status of terminal devices within the area, selects a matching operating mode for the terminal based on the energy status and prediction results, and sends operating and communication parameter configurations to the terminal; after receiving the configuration, the terminal adjusts the parameters and monitors the energy consumption status in real time during operation. If a sudden drop in energy occurs, the terminal proactively reports the status information to the communication gateway and automatically downgrades to the matching operating mode; the terminal periodically reports actual operating energy consumption data and power generation data to the communication gateway, which feeds the reported data back to the load prediction model for incremental learning and iterative optimization, continuously improving prediction accuracy.

[0070] This embodiment achieves refined energy management and stable operation control of outdoor low-power terminal devices through the continuous implementation of four steps: multi-source operating status data acquisition, energy consumption and power generation prediction, charging / discharging and operation control rule generation, and coordinated rule execution. Through the coordinated charging / discharging control of three-level energy storage units, this embodiment can adapt to the differentiated load characteristics of different types of terminals, reduce the impact of transient high currents on energy storage units, extend the cycle life of energy storage units, and reduce the on-site maintenance requirements of terminal devices. By predicting energy consumption and power generation in advance, this embodiment can adjust the terminal's operating mode and communication parameters in advance based on environmental changes, improving the continuous operation capability of terminal devices in scenarios without effective sunlight and broadening the environmental adaptability of terminal devices. Through the coordinated optimization of energy management and communication protocols, this embodiment can dynamically match communication parameters according to the terminal's available energy status, achieving adaptation between communication power consumption and available energy, improving energy utilization efficiency, and ensuring long-term maintenance-free operation of terminal devices in areas without grid coverage. The unified energy management framework adopted in this embodiment can adapt to various types of low-power IoT terminals, possessing strong scenario adaptability and versatility.

[0071] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A terminal energy management method integrating solar energy and electrochemical capacitors, characterized in that, Includes the following steps: S1. Collect output status data of the solar energy conversion unit, voltage status data of the three-level energy storage unit, and operating current data of the load module; S2. Using the collected output status data, voltage status data, and operating current data, combined with historical operating data and weather forecast data, generate energy consumption demand prediction results and power generation prediction results for a future set period; S3. Using the energy consumption demand forecast results and power generation forecast results, combined with the remaining energy status of the three-level energy storage unit, generate the charging and discharging control rules for the three-level energy storage unit, the load module operating parameter adjustment rules, and the communication protocol stack communication parameter matching rules. S4. Perform coordinated charging and discharging control of the three-level energy storage unit according to the charging and discharging control rules of the three-level energy storage unit, and synchronously adjust the operating parameters of the load module and the communication parameters of the communication protocol stack according to the load module operating parameter adjustment rules and the communication protocol stack communication parameter matching rules.

2. The method according to claim 1, characterized in that, The data acquisition process in step S1 includes the following sub-steps: S1.

1. A fixed acquisition period matching the minimum operating cycle of the load module is adopted to continuously acquire the output voltage and output current of the solar conversion unit, obtain the output status data of the solar conversion unit, and use the moving average filtering method to smooth the acquired raw data and remove abnormal data that exceed the preset fluctuation range. S1.

2. The voltages at both ends of the primary electrochemical capacitor, the secondary lithium-ion capacitor, and the tertiary lithium iron phosphate battery in the three-level energy storage unit are synchronously collected to obtain the voltage state data of the three-level energy storage unit. The collection action is synchronized with the collection action of the solar energy conversion unit. S1.

3. Real-time acquisition of the working circuit current of the load module, distinguishing between the standby and working current data of the load module, obtaining the operating current data of the load module, and synchronously storing all acquired data to the local storage unit.

3. The method according to claim 1, characterized in that, The prediction result generation process in step S2 includes the following sub-steps: S2.

1. Extract features from the output status data of the solar energy conversion unit, the voltage status data of the three-level energy storage unit, the operating current data of the load module, historical operating data and weather forecast data. Extract time period features, environmental change features and load operation features from the data to generate a standardized model input feature set. S2.

2. Input the model input feature set into the load prediction model that has been pre-trained using a time-series prediction algorithm, and output the energy consumption demand prediction results for a future set period of time. The energy consumption demand prediction results cover the entire operating scenario of the load module. S2.

3. Combining the solar irradiance data, temperature data and historical conversion efficiency data of solar energy conversion units from the meteorological forecast data, generate the power generation forecast results for the future set period. The power generation forecast results are completely consistent with the energy consumption demand forecast results in terms of time dimension.

4. The method according to claim 1, characterized in that, The rule generation process in step S3 includes the following sub-steps: S3.

1. Using the power generation forecast results and the current remaining capacity of the three-level energy storage unit, determine the total dispatchable energy for a future set period. S3.

2. Using the total dispatchable energy, energy demand prediction results, and the remaining energy state of the three-level energy storage unit, determine the charging and discharging sequence, charging and discharging current distribution rules, and switch on / off control rules of the first-level electrochemical capacitor, the second-level lithium-ion capacitor, and the third-level lithium iron phosphate battery in the three-level energy storage unit, and generate the charging and discharging control rules of the three-level energy storage unit. S3.

3. Using the remaining energy state and energy demand prediction results of the three-level energy storage unit, match multiple preset working modes of the load module, determine the working mode that matches the current energy state, and generate the load module operating parameter adjustment rules and communication protocol stack communication parameter matching rules.

5. The method according to claim 1, characterized in that, The rule execution process in step S4 includes the following sub-steps: S4.

1. According to the charging and discharging control rules of the three-level energy storage unit, control the on / off state of the charging and discharging switches and the magnitude of the charging and discharging current of the first-level electrochemical capacitor, the second-level lithium-ion capacitor and the third-level lithium iron phosphate battery in the three-level energy storage unit, so as to realize the coordinated charging and discharging control of each level of energy storage unit. S4.

2. According to the load module operating parameter adjustment rules, adjust the data acquisition cycle of the load module, the running sequence of the functional modules, and the on / off status of the non-core functional modules to match the load module operating rules in the corresponding working mode; S4.

3. Synchronously read the communication parameter configuration corresponding to the communication parameter matching rules of the communication protocol stack, adjust the communication operation parameters of the communication protocol stack, and after the communication parameters are adjusted, report the adjustment result to the communication gateway to complete a complete scheduling loop.

6. The method according to claim 2, characterized in that, In step S1, the three-level energy storage unit includes a primary electrochemical capacitor, a secondary lithium-ion capacitor, and a tertiary lithium iron phosphate battery. During the data acquisition process, the voltage state data of the primary electrochemical capacitor, the secondary lithium-ion capacitor, and the tertiary lithium iron phosphate battery are acquired separately. The voltage acquisition channels of each energy storage unit are independent of each other. During the acquisition process, the voltage data of each energy storage unit is continuously sampled at multiple points. The sampling frequency is kept completely consistent with the output state data acquisition frequency of the solar energy conversion unit. The voltage data of each level of energy storage unit are stored in the independent data partition of the local storage unit for subsequent calculation of the remaining energy state of the three-level energy storage unit and generation of charge and discharge control rules.

7. The method according to claim 3, characterized in that, In step S2, the historical operating data includes the historical power consumption data of the load module, the historical power generation data of the solar conversion unit, and the historical charge and discharge data of the three-level energy storage unit. The weather forecast data includes light intensity data, temperature data, and rainfall data. During the feature extraction process, different types of input data are normalized and standardized to eliminate the differences in the dimensions of different data dimensions. After processing, the data is spliced ​​according to the time series dimension to generate a model input feature set that meets the input format requirements of the load prediction model. This set is used for the calculation and generation of subsequent energy demand prediction results and power generation prediction results.

8. The method according to claim 4, characterized in that, In step S3, the three-level energy storage unit charge and discharge control rules prioritize allocating the first-level electrochemical capacitor to bear the transient peak current of the load module, prioritize allocating the second-level lithium-ion capacitor to bear the daily basic power supply of the load module, and allocate the third-level lithium iron phosphate battery to bear the long-term energy supply in the absence of effective light. The three-level energy storage unit charge and discharge control rules specify the charge and discharge trigger conditions and charge and discharge cutoff conditions of the first-level electrochemical capacitor, the second-level lithium-ion capacitor, and the third-level lithium iron phosphate battery. The charge and discharge trigger conditions and charge and discharge cutoff conditions are set based on the voltage state data and remaining energy state of each level of energy storage unit to ensure that the charge and discharge process of each level of energy storage unit conforms to the preset operating rules.

9. The method according to claim 5, characterized in that, In step S4, the communication parameters of the communication protocol stack include the spreading factor, wake-up period, and transmit power. The adjustment of the communication parameters is synchronized with the adjustment of the load module's operating mode. Each set of preset load module operating modes corresponds to a unique communication parameter configuration. At the same time as the load module's operating parameter adjustment rules are issued, the communication parameter configuration corresponding to the communication protocol stack's communication parameter matching rules is also issued. After the communication parameters are adjusted, the communication protocol stack completes the communication interaction with the communication gateway according to the adjusted parameters, and at the same time, it synchronously feeds back the adjusted operating status to the energy management unit.

10. A terminal energy management system integrating solar energy and electrochemical capacitors, for performing the method as described in any one of claims 1-9, characterized in that, It includes a solar energy conversion unit, a three-level energy storage unit, an energy management unit, a load module, and a communication protocol stack. The solar energy conversion unit is connected to the three-level energy storage unit to convert solar energy into electrical energy and transmit it to the three-level energy storage unit. The three-level energy storage unit is connected to the energy management unit to store electrical energy and provide power support for the load module. The energy management unit is bidirectionally connected to both the load module and the communication protocol stack to collect various status data, generate prediction results and operation control rules, execute charge and discharge coordinated control, and adjust operating parameters. The load module is connected to the communication protocol stack to complete corresponding business functions according to the adjusted operating parameters.

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