Wind energy and solar energy hybrid power supply method and system for portable shelter

Abstract

The invention provides a wind energy and solar energy hybrid power supply method and system for a portable shelter. Wherein the solar panel array and the wind generating set are installed on the top and the side face of the square cabin, and the angle and the height are automatically adjusted to maximize the energy collection efficiency; based on the preliminary collection configuration, predicting future illumination intensity and wind speed trend, and generating and executing an optimal adjustment scheme; establishing an energy consumption prediction model, monitoring the operation state and the power demand of the equipment in real time, intelligently switching working modes, and generating an optimized energy distribution strategy; when external energy supply fluctuates or is lower than a safety threshold value, an emergency response mechanism is activated, operation of key equipment is guaranteed preferentially, an energy-saving mode is started to reduce power consumption of unnecessary equipment, the discharge rate of a standby battery pack is optimized, and basic functions of the shelter are maintained. Excess power is stored in the lithium ion battery pack or converted into standard alternating current to be supplied to an external power grid. According to the technical scheme provided by the invention, the energy capture efficiency and utilization efficiency are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of renewable energy technology, and more particularly to a wind and solar hybrid power supply method and system for a portable cabin. Background Art

[0002] Portable shelters are widely used in field operations, emergency rescue, temporary housing, and mobile medical care. These application scenarios are usually located in remote areas or areas with incomplete infrastructure, and have high requirements for the stability and reliability of power supply. Traditional power supply methods, such as diesel generators, not only rely on external fuel supply, but also have environmental pollution and noise problems.

[0003] Currently, there are some hybrid power supply systems based on wind and solar energy on the market to provide electricity for portable cabins. These systems usually include fixed solar panels and small wind turbines. They manage energy collection and distribution through simple control systems and use battery packs to store excess power. In addition, some advanced systems are also equipped with basic energy management systems that can adjust the working status of the equipment according to real-time light intensity and wind speed to improve energy capture efficiency.

[0004] However, most existing systems use fixed solar panels and wind turbines, which cannot automatically adjust their positions and angles according to changes in environmental conditions, resulting in low energy capture efficiency. Especially when light intensity and wind speed vary greatly, this fixed setting makes it difficult to maximize the use of natural resources. Existing energy management systems are usually simple and lack prediction and optimization functions. For example, they cannot effectively predict future light intensity and wind speed trends, nor can they intelligently switch working modes based on historical energy consumption data and real-time monitoring data, resulting in energy waste and unnecessary power consumption. When the external energy supply fluctuates or falls below the safety threshold, existing systems often do not have an efficient emergency response mechanism. This may affect the operation of critical equipment or even cause system downtime. In addition, the discharge rate of the backup battery pack has not been optimized, affecting the battery life and the overall stability of the system. Summary of the invention

[0005] The embodiments of the present application provide a wind and solar hybrid power supply method and system for a portable cabin, which is used to solve the problems of low energy capture efficiency and utilization efficiency in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a wind and solar hybrid power supply method for a portable shelter, comprising:

[0007] Installing solar panel arrays with adaptive adjustment functions and retractable micro wind turbines on the top and sides of the portable cabin, automatically adjusting the angle of the solar panel array and the height of the wind turbine to maximize energy capture efficiency and generate a preliminary energy collection configuration;

[0008] Based on the preliminary energy collection configuration, the future light intensity and wind speed trends are predicted, the best adjustment plan is generated, and the plan is executed through the control unit to ensure that the solar panels and wind turbines are in the best working state;

[0009] Using the generated optimal adjustment plan and based on historical energy consumption data, the intelligent energy management system uses a deep learning algorithm to establish an energy consumption prediction model, monitors the operating status and power requirements of each device in the cabin in real time, and intelligently switches the working modes of solar panels and wind turbines according to the energy consumption prediction model and real-time monitoring data, generating an optimized energy allocation strategy to ensure immediate energy needs while minimizing energy waste;

[0010] When the system detects that the external energy supply fluctuates or is below the safety threshold, the emergency response mechanism is immediately activated based on the optimized energy allocation strategy to prioritize the operation of key equipment. At the same time, the energy-saving mode is started to reduce the power consumption of non-essential equipment. The discharge rate of the backup battery pack is optimized through the adaptive control algorithm, and an emergency energy management plan is generated to ensure that the basic functions of the shelter are maintained under adverse conditions.

[0011] For excess electricity that exceeds immediate demand, based on optimized energy distribution strategies and emergency energy management plans, energy storage technology is used to store the excess electricity that exceeds immediate demand in high-performance lithium-ion battery packs, or the excess electricity is converted into standard AC power through high-efficiency inverters to supply power to the external power grid, generating stable power output and ensuring the overall stability and reliability of the system.

[0012] Optionally, the optimal adjustment scheme generated is used to establish an energy consumption prediction model based on historical energy consumption data by using a deep learning algorithm in the intelligent energy management system, and monitor the operating status and power demand of each device in the cabin in real time. According to the energy consumption prediction model and real-time monitoring data, the working modes of solar panels and wind turbines are intelligently switched to generate an optimized energy allocation strategy to ensure immediate energy demand while minimizing energy waste, including:

[0013] Using the generated optimal adjustment plan, combined with historical energy consumption data, a deep learning algorithm is used to train and process the historical energy consumption data to obtain an energy consumption prediction model;

[0014] Based on the energy consumption prediction model, the intelligent energy management system monitors the operating status and power demand of each device in the cabin in real time, analyzes and processes the monitoring data, and generates a real-time energy consumption assessment report;

[0015] According to the real-time energy consumption assessment report, the working modes of the solar panels and wind turbines are intelligently switched to generate optimized working mode instructions;

[0016] Using the optimized working mode instructions, the intelligent energy management system adjusts the working states of the solar panels and wind turbines to generate an optimized energy distribution strategy.

[0017] Optionally, the optimal adjustment scheme generated is combined with historical energy consumption data, and a deep learning algorithm is used to train and process the historical energy consumption data to obtain an energy consumption prediction model, including:

[0018] Using the optimal adjustment scheme and combining the historical energy consumption data of the shelter, the generated optimal adjustment scheme and the historical energy consumption data of the shelter are fused to obtain a training set;

[0019] Based on the training set, a deep learning algorithm is used for training processing, so that the deep learning algorithm learns the mapping relationship between environmental conditions and energy consumption of equipment inside the cabin, and generates a trained energy consumption prediction model.

[0020] Optionally, based on the obtained energy consumption prediction model, the intelligent energy management system monitors the operating status and power demand of each device in the cabin in real time, analyzes and processes the monitoring data, and generates a real-time energy consumption assessment report, including:

[0021] Using the energy consumption prediction model obtained, the intelligent energy management system monitors the operating status and power requirements of each device in the cabin in real time and collects real-time monitoring data;

[0022] Based on the real-time monitoring data, the intelligent energy management system analyzes and processes the real-time monitoring data, identifies existing energy consumption anomalies, and generates analysis and processing results;

[0023] The results of the analysis and processing are used to generate a real-time energy consumption assessment report, which records in detail the energy consumption of each device in the cabin, as well as any abnormal energy consumption and optimization suggestions.

[0024] Optionally, when the system detects that the external energy supply fluctuates or is below a safety threshold, the emergency response mechanism is immediately activated based on the optimized energy allocation strategy to prioritize the operation of key equipment, while starting the energy-saving mode to reduce the power consumption of non-essential equipment, optimizing the discharge rate of the backup battery pack through an adaptive control algorithm, and generating an emergency energy management plan to ensure that the basic functions of the shelter are maintained under adverse conditions, including:

[0025] When the system detects that the external energy supply fluctuates or is below the safety threshold, the emergency response mechanism is activated based on the optimized energy allocation strategy and an emergency response instruction is generated;

[0026] Using the emergency response instructions, the intelligent energy management system prioritizes the operation of key equipment, ensures the power supply of important equipment, and generates a key equipment protection list;

[0027] Based on the key equipment protection list, start the energy-saving mode, reduce the power consumption of non-essential equipment, save electricity by shutting down or reducing the power requirements of non-critical equipment, and generate an energy-saving mode execution command;

[0028] Utilizing the energy-saving mode execution command, the discharge rate of the backup battery pack is optimized through an adaptive control algorithm, the discharge speed is intelligently determined according to the current energy demand and the remaining power, and an optimized discharge rate setting is generated;

[0029] Based on the emergency response instructions, key equipment protection list, energy-saving mode execution command and optimized discharge rate setting, the intelligent energy management system generates an emergency energy management plan to ensure that the basic functions of the shelter are maintained under adverse conditions and that the shelter smoothly transitions back to normal working conditions.

[0030] Optionally, the energy-saving mode execution command generated is used to optimize the discharge rate of the backup battery pack through an adaptive control algorithm, intelligently determine the discharge speed according to the current energy demand and the remaining power, and generate an optimized discharge rate setting, including:

[0031] Using the generated energy-saving mode execution command, the intelligent energy management system starts the energy-saving mode, reduces the power consumption of non-essential equipment, and generates the energy-saving mode execution result;

[0032] Based on the execution result of the energy-saving mode, the intelligent energy management system monitors the current energy consumption of each device in the cabin and the remaining power of the backup battery pack in real time, and collects real-time energy consumption data and remaining power data;

[0033] Utilizing the real-time energy consumption data and the remaining power data, the discharge rate of the backup battery pack is optimized through an adaptive control algorithm, the discharge speed is intelligently determined, and an optimized discharge rate setting is generated;

[0034] Using the optimized discharge rate setting, the intelligent energy management system adjusts the discharge rate of the backup battery pack to ensure that the basic functions of the cabin are maintained under adverse conditions and to extend the service life of the backup battery pack.

[0035] Optionally, based on the preliminary energy collection configuration, the future light intensity and wind speed trends are predicted, an optimal adjustment plan is generated, and the plan is executed by the control unit to ensure that the solar panels and wind turbines are in the optimal working state, including:

[0036] Using the preliminary energy collection configuration, the intelligent energy management system combines the environmental perception algorithm and weather forecast data to predict the future light intensity and wind speed trends and obtain the light and wind speed forecast results;

[0037] Based on the light and wind speed prediction results, the intelligent energy management system generates an optimal adjustment plan, which specifies in detail the angle to which the solar panel array should be adjusted and the height to which the wind turbine generator set should be adjusted in different time periods;

[0038] By utilizing the optimal adjustment scheme, the control unit executes the scheme to automatically adjust the angle of the solar panel array and the height of the wind turbine generator set, ensuring that the solar panels and wind turbine generators are always in the optimal working state.

[0039] In a second aspect, an embodiment of the present application provides a wind and solar hybrid power supply system for a portable shelter, comprising:

[0040] An adjustment module is used to install a solar panel array with adaptive adjustment function and a retractable micro wind turbine generator set on the top and side of the portable cabin, automatically adjust the angle of the solar panel array and the height of the wind turbine generator set to maximize the energy capture efficiency and generate a preliminary energy collection configuration;

[0041] A prediction module is used to predict the future light intensity and wind speed trends based on the preliminary energy collection configuration, generate the best adjustment plan, and execute the plan through the control unit to ensure that the solar panels and wind turbines are in the best working state;

[0042] The switching module is used to utilize the generated optimal adjustment plan. Based on historical energy consumption data, the intelligent energy management system uses a deep learning algorithm to establish an energy consumption prediction model, monitor the operating status and power requirements of each device in the cabin in real time, and intelligently switch the working modes of solar panels and wind turbines according to the energy consumption prediction model and real-time monitoring data to generate an optimized energy allocation strategy to ensure immediate energy demand while minimizing energy waste;

[0043] The optimization module is used to immediately activate the emergency response mechanism based on the optimized energy allocation strategy when the system detects that the external energy supply fluctuates or falls below the safety threshold, giving priority to the operation of key equipment, while starting the energy-saving mode to reduce the power consumption of non-essential equipment, optimizing the discharge rate of the backup battery pack through the adaptive control algorithm, and generating an emergency energy management plan to ensure that the basic functions of the shelter are maintained under adverse conditions;

[0044] The storage and conversion module is used to store excess power that exceeds immediate demand in high-performance lithium-ion battery packs based on optimized energy distribution strategies and emergency energy management plans, or to convert excess power into standard AC power through high-efficiency inverters to supply power to the external power grid, thereby generating stable power output and ensuring the overall stability and reliability of the system.

[0045] In the embodiment of the present application, a solar panel array with adaptive adjustment function and a retractable micro wind turbine generator set are installed on the top and side of the portable cabin, and the angle of the solar panel array and the height of the wind turbine generator set are automatically adjusted to maximize the energy capture efficiency and generate a preliminary energy collection configuration; based on the preliminary energy collection configuration, the future light intensity and wind speed trends are predicted, and the optimal adjustment plan is generated, and the plan is executed by the control unit to ensure that the solar panels and wind turbines are in the optimal working state; using the generated optimal adjustment plan, based on the historical energy consumption data, the intelligent energy management system adopts a deep learning algorithm to establish an energy consumption prediction model, monitors the operating status and power requirements of each device in the cabin in real time, and intelligently switches the working modes of the solar panels and wind turbines according to the energy consumption prediction model and real-time monitoring data to generate The optimized energy allocation strategy ensures immediate energy demand while minimizing energy waste. When the system detects that the external energy supply fluctuates or is below the safety threshold, the emergency response mechanism is immediately activated based on the optimized energy allocation strategy to prioritize the operation of key equipment. At the same time, the energy-saving mode is started to reduce the power consumption of non-essential equipment. The discharge rate of the backup battery pack is optimized through an adaptive control algorithm, and an emergency energy management plan is generated to ensure that the basic functions of the cabin are maintained under adverse conditions. For excess power that exceeds immediate demand, based on the optimized energy allocation strategy and emergency energy management plan, energy storage technology is used to store the excess power that exceeds immediate demand in high-performance lithium-ion battery packs, or the excess power is converted into standard AC power through a high-efficiency inverter to supply power to the external power grid, generating stable power output and ensuring the overall stability and reliability of the system.

[0046] The technical solution of this application has the following beneficial effects:

[0047] This application can optimize energy collection according to real-time environmental conditions by adaptively adjusting the angle of the solar panel array and the height of the wind turbine generator set, thereby improving the overall energy utilization efficiency. Based on the preliminary energy collection configuration, the system can predict future light intensity and wind speed trends, and generate the best adjustment plan to ensure that the equipment is always in the optimal working state, thereby improving the responsiveness and adaptability of the system. The energy consumption prediction model is established using a deep learning algorithm. Combined with real-time monitoring data, the working modes of solar panels and wind turbines can be intelligently switched to achieve dynamic energy distribution, meet immediate energy needs and minimize energy waste. When the external energy supply fluctuates or falls below the safety threshold, the system can immediately activate the emergency response mechanism, give priority to the operation of key equipment, start the energy-saving mode to reduce the power consumption of non-essential equipment, and ensure that the basic functions of the cabin are not affected. For excess electricity beyond immediate needs, the system can be stored in a high-performance lithium-ion battery pack through energy storage technology, or converted into standard AC power to supply power to the external power grid, enhancing the stability and reliability of the system.

[0048] Furthermore, the present application improves the accuracy of energy consumption prediction by combining historical energy consumption data and the best adjustment plan, and using deep learning algorithm training to obtain an energy consumption prediction model. The intelligent energy management system can monitor the operating status and power requirements of each device in the cabin in real time, analyze and process the monitoring data, and generate a real-time energy consumption assessment report, which helps to discover and solve problems in a timely manner. According to the real-time energy consumption assessment report, the system can intelligently switch the working mode of solar panels and wind turbines to ensure that the equipment always operates in the most efficient manner. By intelligently adjusting the working status of solar panels and wind turbines, an optimized energy allocation strategy is generated to ensure that immediate energy needs are met while minimizing energy waste.

[0049] Furthermore, when the application detects that the external energy supply fluctuates or is below the safety threshold, the system can quickly activate the emergency response mechanism, generate emergency response instructions, and ensure the power supply of important equipment. By generating a key equipment protection list, the system can prioritize the operation of key equipment to ensure that the basic functions of the shelter are not affected. Based on the key equipment protection list, the system starts the energy-saving mode, reduces the power consumption of non-essential equipment, and saves electricity by shutting down or reducing the power requirements of non-critical equipment. Through the adaptive control algorithm, the system can intelligently determine the discharge speed of the backup battery pack, generate optimized discharge rate settings based on the current energy demand and remaining power, and extend the battery life. The system generates an emergency energy management plan to ensure that the basic functions of the shelter are maintained under adverse conditions, and help the shelter smoothly transition back to normal working conditions, enhancing the robustness and stability of the system.

[0050] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 A flow chart of a wind and solar hybrid power supply method for a portable shelter provided in an embodiment of the present application;

[0053] Figure 2 A schematic diagram of the structure of a wind and solar hybrid power supply system for a portable shelter provided in an embodiment of the present application;

[0054] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0056] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0057] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0058] Figure 1 A flow chart of a wind and solar hybrid power supply method for a portable shelter is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0059] 101. Install a solar panel array with adaptive adjustment function and a retractable micro wind turbine generator set on the top and side of the portable cabin, automatically adjust the angle of the solar panel array and the height of the wind turbine generator set to maximize energy capture efficiency and generate a preliminary energy collection configuration;

[0060] In this step, the solar panel array is a photovoltaic panel installed on the top and sides of the cabin to convert sunlight into electricity. The retractable micro wind turbine is a small wind turbine installed on the top of the cabin, which can adjust its height according to the wind speed changes to optimize wind energy capture. The adaptive adjustment function is that the system can automatically adjust the angle of the solar panel and the height of the wind turbine according to environmental conditions to maximize energy capture efficiency. The initial energy collection configuration is through the initial installation and adaptive adjustment, the system generates a basic energy collection plan, which provides a basis for subsequent optimization.

[0061] First, install the solar panel array and wind turbine.

[0062] Secondly, light intensity and wind speed are monitored through sensors.

[0063] Furthermore, the angle of the solar panels and the height of the wind turbines are automatically adjusted.

[0064] Finally, a preliminary energy harvesting configuration is generated.

[0065] In this application example, it is assumed that in a field operation scenario, a portable cabin is equipped with an adaptive solar panel array and a retractable micro wind turbine. The system monitors the light intensity and wind speed through sensors, and automatically adjusts the angle of the solar panels and the height of the wind turbine to ensure optimal energy output under different times and weather conditions. For example, on sunny days, the solar panels will automatically adjust to the optimal angle to maximize light absorption; when the wind speed is high, the wind turbine will rise to capture more wind energy.

[0066] 102. Based on the preliminary energy collection configuration, the future light intensity and wind speed trends are predicted, the best adjustment plan is generated, and the plan is executed by the control unit to ensure that the solar panels and wind turbines are in the best working state;

[0067] In this step, the trend prediction of light intensity and wind speed uses environmental perception algorithms and weather forecast data to predict the changes in light intensity and wind speed in the future. The best adjustment plan is based on the prediction results to generate a specific adjustment plan, including the angle of the solar panel and the height of the wind turbine. The control unit is responsible for executing the adjustment plan to ensure that the equipment is always in the best working state.

[0068] First, predictions are made using environmental perception algorithms and weather forecast data.

[0069] Second, generate the best adjustment plan.

[0070] Finally, the adjustment plan is implemented by the control unit.

[0071] In this application example, assume that at the emergency rescue site, the intelligent energy management system combines the environmental perception algorithm and weather forecast data to predict the changes in light intensity and wind speed in the next 24 hours. The system generates the best adjustment plan and automatically adjusts the angle of the solar panels and the height of the wind turbine through the control unit. For example, if the forecast shows that there will be strong winds in the afternoon, the system will adjust the height of the wind turbine in advance in the morning to prepare for the upcoming wind peak.

[0072] Optionally, the method in step 102 predicts future trends of light intensity and wind speed based on the preliminary energy collection configuration, generates an optimal adjustment plan, and executes the plan through the control unit to ensure that the solar panels and wind turbines are in the optimal working state, including: using the preliminary energy collection configuration, the intelligent energy management system combines the environmental perception algorithm and weather forecast data to predict and process future trends of light intensity and wind speed to obtain light and wind speed prediction results; based on the light and wind speed prediction results, the intelligent energy management system generates an optimal adjustment plan, which details the angle to which the solar panel array should be adjusted and the height to which the wind turbine should be adjusted in different time periods; using the optimal adjustment plan, the control unit executes the plan to automatically adjust the angle of the solar panel array and the height of the wind turbine to ensure that the solar panels and wind turbines are always in the optimal working state.

[0073] In this step, the preliminary energy collection configuration refers to the initial settings of the solar panel array and wind turbine generator set generated by the adaptive adjustment function to maximize the energy capture efficiency. The environmental perception algorithm combines sensor data and weather forecast information to predict the changes in light intensity and wind speed in the future. Weather forecast data is the forecast data of future weather conditions from meteorological stations or third-party service providers, including light intensity, wind speed, etc. The light and wind speed forecast results are specific values ​​of future light intensity and wind speed trends generated based on the environmental perception algorithm and weather forecast data. The optimal adjustment plan is a detailed adjustment plan generated based on the light and wind speed forecast results, indicating the angle to which the solar panel array should be adjusted and the height to which the wind turbine generator set should be adjusted in different time periods. The control unit is the hardware and software system responsible for executing the optimal adjustment plan to ensure that the equipment is always in the best working state.

[0074] First, the intelligent energy management system uses the preliminary energy collection configuration, combined with the environmental perception algorithm and weather forecast data, to predict the future light intensity and wind speed trends to obtain light and wind speed prediction results.

[0075] Secondly, based on the light and wind speed forecast results, the intelligent energy management system generates the best adjustment plan, which details the angle to which the solar panel array should be adjusted and the height to which the wind turbine should be adjusted during different time periods.

[0076] Finally, using the optimal adjustment plan, the control unit automatically adjusts the angle of the solar panel array and the height of the wind turbine generator set to ensure that the solar panels and wind turbines are always in the best working condition.

[0077] In an embodiment of the present application, at a field operation site, a portable cabin is equipped with an adaptively adjustable solar panel array and a retractable micro wind turbine. The intelligent energy management system first uses the preliminary energy collection configuration, combined with the environmental perception algorithm and weather forecast data, to predict the changes in light intensity and wind speed in the next 24 hours. For example, the system predicts that the light intensity is high between 8 and 10 a.m., and the wind speed is high between 2 and 4 p.m. Based on these prediction results, the system generates the best adjustment plan, which specifically points out the angle to which the solar panel array should be adjusted and the height to which the wind turbine should be adjusted in each time period. According to this plan, the control unit adjusts the solar panel to the maximum light absorption angle between 8 and 10 a.m., and raises the wind turbine to capture more wind energy between 2 and 4 p.m. In this way, the system ensures that the solar panels and wind turbines are always in the best working state and maximizes the energy capture efficiency.

[0078] 103. Using the generated optimal adjustment plan and based on historical energy consumption data, the intelligent energy management system uses a deep learning algorithm to establish an energy consumption prediction model, monitors the operating status and power requirements of each device in the cabin in real time, and intelligently switches the working modes of solar panels and wind turbines according to the energy consumption prediction model and real-time monitoring data, generating an optimized energy allocation strategy to ensure immediate energy needs while minimizing energy waste;

[0079] In this step, the historical energy consumption data records the historical energy consumption of the equipment inside the shelter.

[0080] The energy consumption prediction model is a model trained by deep learning algorithms to predict future energy consumption needs. Real-time monitoring is to continuously monitor the operating status and power requirements of each device in the cabin. Intelligent switching is to dynamically adjust the working mode of solar panels and wind turbines based on the prediction model and real-time data. The optimized energy allocation strategy is to ensure that the immediate energy needs are met while minimizing energy waste.

[0081] First, the energy consumption prediction model is trained using historical energy consumption data and the best adjustment plan.

[0082] Secondly, monitor the operating status and power requirements of each device in the cabin in real time.

[0083] Furthermore, the working modes of solar panels and wind turbines are switched intelligently based on the prediction model and real-time data.

[0084] Finally, an optimized energy allocation strategy is generated.

[0085] In this application example, it is assumed that in a mobile medical cabin, the intelligent energy management system uses historical energy consumption data and the best adjustment plan to establish an energy consumption prediction model through a deep learning algorithm. The system monitors the operating status and power requirements of medical equipment in real time, and intelligently switches the working modes of solar panels and wind turbines based on the prediction model and real-time data. For example, when it is predicted that the power consumption will be lower at night, the system will reduce the output of solar panels to save electricity; and during the peak power consumption period during the day, the output of solar panels will be increased to ensure the stable operation of medical equipment.

[0086] Optionally, the step 103 uses the generated optimal adjustment scheme in combination with historical energy consumption data, and uses a deep learning algorithm to train and process the historical energy consumption data to obtain an energy consumption prediction model, including: using the optimal adjustment scheme in combination with the historical energy consumption data of the cabin, fusing the generated optimal adjustment scheme and the historical energy consumption data of the cabin to obtain a training set; based on the training set, using a deep learning algorithm to perform training processing, so that the deep learning algorithm learns the mapping relationship between environmental conditions and the energy consumption of equipment inside the cabin, and generates a trained energy consumption prediction model.

[0087] Optionally, in step 103, based on the obtained energy consumption prediction model, the intelligent energy management system monitors the operating status and power requirements of each device in the cabin in real time, analyzes and processes the monitoring data, and generates a real-time energy consumption assessment report, including: using the obtained energy consumption prediction model, the intelligent energy management system monitors the operating status and power requirements of each device in the cabin in real time, and collects real-time monitoring data; based on the real-time monitoring data, the intelligent energy management system analyzes and processes the real-time monitoring data, identifies existing energy consumption anomalies, and generates analysis and processing results; using the analysis and processing results, a real-time energy consumption assessment report is generated, which records in detail the energy consumption of each device in the cabin, as well as existing energy consumption anomalies and optimization suggestions.

[0088] Optionally, the best adjustment scheme generated in step 103 is used, based on historical energy consumption data, the intelligent energy management system uses a deep learning algorithm to establish an energy consumption prediction model, monitors the operating status and power requirements of each device in the cabin in real time, and intelligently switches the working modes of solar panels and wind turbines according to the energy consumption prediction model and real-time monitoring data, and generates an optimized energy allocation strategy to ensure immediate energy demand while minimizing energy waste, including: using the best adjustment scheme generated, combined with historical energy consumption data, and using a deep learning algorithm to train and process the historical energy consumption data to obtain an energy consumption prediction model; based on the energy consumption prediction model, the intelligent energy management system monitors the operating status and power requirements of each device in the cabin in real time, analyzes and processes the monitoring data, and generates a real-time energy consumption evaluation report; according to the real-time energy consumption evaluation report, the working modes of solar panels and wind turbines are intelligently switched to generate optimized working mode instructions; using the optimized working mode instructions, the intelligent energy management system adjusts the working status of solar panels and wind turbines to generate an optimized energy allocation strategy.

[0089] In this step, the optimal adjustment plan is a detailed adjustment plan generated based on the results of light and wind speed prediction, which is used to guide the angle adjustment of the solar panel array and the height adjustment of the wind turbine generator set. The historical energy consumption data is data that records the energy consumption of the equipment inside the shelter in the past period of time. The deep learning algorithm is a machine learning method that automatically learns feature representation from a large amount of data through a multi-layer neural network, and is used to establish an energy consumption prediction model. The training set is a data set obtained by fusion processing of the optimal adjustment plan and historical energy consumption data, which is used to train the deep learning algorithm. The energy consumption prediction model is a model trained by the deep learning algorithm, which can predict the energy consumption of the equipment inside the shelter in the future. The real-time monitoring data is the operating status and power demand data of each device in the shelter collected in real time by the intelligent energy management system. The real-time energy consumption evaluation report is a report generated based on the real-time monitoring data, which records the energy consumption of each device in the shelter, the existing energy consumption anomalies and optimization suggestions. The optimized working mode instruction is an instruction generated based on the real-time energy consumption evaluation report, which is used to intelligently switch the working mode of the solar panels and wind turbines. The optimized energy allocation strategy is a strategy that ensures immediate energy demand while minimizing energy waste.

[0090] Firstly, the optimal adjustment scheme and the historical energy consumption data of the shelter are fused to obtain the training set.

[0091] Secondly, based on the training set, a deep learning algorithm is used for training processing, so that the algorithm can learn the mapping relationship between environmental conditions and the energy consumption of equipment inside the cabin, and generate a trained energy consumption prediction model.

[0092] Next, using the energy consumption prediction model, the intelligent energy management system monitors the operating status and power requirements of each device in the cabin in real time, collects real-time monitoring data, analyzes and processes the data, identifies existing energy consumption anomalies, and generates real-time energy consumption assessment reports.

[0093] Furthermore, based on the real-time energy consumption assessment report, the intelligent energy management system intelligently switches the working modes of the solar panels and wind turbines and generates optimized working mode instructions.

[0094] Finally, using the optimized working mode instructions, the intelligent energy management system adjusts the working status of solar panels and wind turbines to generate an optimized energy allocation strategy.

[0095] In the embodiment of the present application, assuming that in an application scenario of a mobile medical shelter, the intelligent energy management system first generates a training set by combining the optimal adjustment plan generated in the early stage and the historical energy consumption data of the shelter. Then, the system uses a deep learning algorithm to train the training set and generate an energy consumption prediction model. The model can accurately predict the energy consumption of medical equipment in the shelter in different time periods in the future.

[0096] In actual operation, the intelligent energy management system monitors the operating status and power requirements of each medical device in the cabin in real time and collects real-time monitoring data. For example, the system detects that the energy consumption of a certain medical device increases abnormally during a specific period of time. By analyzing and processing these data, the system identifies the cause of the abnormal energy consumption (such as equipment failure or improper use) and generates a real-time energy consumption assessment report, which records in detail the energy consumption of each device, the existing energy consumption anomalies, and optimization suggestions.

[0097] Based on the real-time energy consumption assessment report, the intelligent energy management system intelligently switches the working modes of solar panels and wind turbines. For example, when electricity consumption is low at night, the system will reduce the output of solar panels; and during the peak period of electricity consumption during the day, the output of solar panels will be increased. At the same time, the system will dynamically adjust the height of wind turbines according to real-time energy consumption needs to capture more wind energy. In this way, the system generates an optimized energy distribution strategy to ensure immediate energy needs while minimizing energy waste.

[0098] Long short-term memory network is a special type of recurrent neural network, which is particularly suitable for processing and predicting time series data. Long short-term memory network effectively solves the gradient vanishing or exploding problem in traditional recurrent neural networks by introducing gating mechanisms (input gate, forget gate and output gate) to control the flow of information, and can better capture long-term dependencies. In the energy consumption prediction of portable cabins, long short-term memory networks can learn the complex mapping relationship between environmental conditions and the energy consumption of equipment inside the cabins, and generate accurate energy consumption prediction models.

[0099] Optionally, the generated optimal adjustment scheme in step 103 is combined with historical energy consumption data, and a deep learning algorithm is used to train and process the historical energy consumption data to obtain an energy consumption prediction model, including:

[0100] Based on the training set, a long short-term memory network is used for training processing, so that the long short-term memory network learns the mapping relationship between the environmental conditions and the energy consumption of the equipment inside the shelter, and generates a trained energy consumption prediction model;

[0101] The training process of the long short-term memory network is expressed as follows through the following calculation formula:

[0102] y t =f(x t ,h t-1 ;θ)

[0103] Among them, the forward propagation function f of the long short-term memory network includes the following steps:

[0104] Input gate: i t =σ(W ix x t +W ih h t-1 +b i )

[0105] Forget gate: f t =σ(W fx x t +W fh h t-1 +b f )

[0106] Cell status update:

[0107] Output gate: o t =σ(W ox x t +W oh h t-1 +b o )

[0108] Hide status update:h t =o t ⊙tanh(c t )

[0109] Output: y t =W hy h t +b y

[0110] Among them, x trepresents the input feature vector at time step t, including environmental conditions and historical energy consumption data of equipment inside the shelter; h t-1 represents the hidden state at time step t-1; y t represents the output at time step t, that is, the predicted energy consumption value; σ represents the Sigmoid activation function; tanh represents the hyperbolic tangent activation function; ⊙ represents element-by-element multiplication; W ix , W ih , W fx , W fh , W cx , W ch , W ox , W oh , W hy represents the weight matrix; b i , b f , b c , b o , b y represents the bias term; θ represents the set of all learnable parameters, including all weight matrices W and bias terms b; c t represents the cell state at time step t; c t-1 represents the cell state at time step t-1.

[0111] The core of the LSTM network lies in its forward propagation process, which controls the flow of information through a series of gating mechanisms. These gating mechanisms allow the LSTM network to selectively remember or forget information, thereby better handling long-term dependencies in time series data. Each part of the overall formula is designed very carefully to ensure that the model can effectively learn and predict future energy consumption.

[0112] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0113] Input Gate i t =σ(W ix x t +W ih h t-1 +b i ): determines how much new information is added to the cell state; forget gate f t =σ(W fx x t +W fh h t-1 +b f ): determines how much old information to discard from the cell state; cell state update and c t : Through element-by-element multiplication operation, the new information is combined with the old information to form a new cell state; the output gate o t=σ(W ox x t +W oh h t-1 +b o ): Determine how much information to output from the cell state to the hidden state; the hidden state updates h t =o t ⊙tanh(c t ): Generate a new hidden state through activation function and element-by-element multiplication operation; output y t =W hy h t +b y : Generate the final prediction value through linear transformation and bias term.

[0114] The following is a brief introduction to how to obtain the parameters of the formula:

[0115] Among them, x t is the input feature vector, including environmental conditions and historical energy consumption data of equipment inside the shelter, which can be obtained through sensors and historical records; (t-1) is the hidden state of the previous time step, and is the memory state of the long short-term memory network at the previous time step; W ix , W ih , W fx , W fh , W cx , W ch , W ox , W oh , W hy is the weight matrix, which is optimized by the back propagation algorithm during training; b i , b f , b c , b o , b y is the bias term, which is also optimized by the back propagation algorithm during training; θ is the set of all learnable parameters, including all weight matrices and bias terms; c t and c (t-1) is the cell state at the current and previous time steps, which is automatically generated through the computational process of the long short-term memory network.

[0116] Consider a simple example with the following data:

[0117] Input feature vector x t : Includes light intensity, wind speed and historical energy consumption data.

[0118] Light intensity: 500 lux

[0119] Wind speed: 10m / s

[0120] Historical energy consumption: 1000Wh

[0121] The hidden state h at the previous time step (t-1) : [0.2, 0.3, 0.4, 0.5]

[0122] Weight matrix W and bias term b: For simplicity, assume that they have been obtained through training and are randomly initialized.

[0123] Calculation process

[0124] Input Gate i t :

[0125] i t =σ(W ix x t +W ih h (t-1) +b i )

[0126] Forget Gate t :

[0127] f t =σ(W fx x t +W fh h (t-1) +b f )

[0128] Cell status update

[0129]

[0130] Cell state c t :

[0131]

[0132] Output gate o t :

[0133] o t =σ(W ox x t +W oh h (t-1) +b o )

[0134] Hidden state h t :

[0135] h t =o t ⊙tanh(c t )

[0136] Output y t :

[0137] y t =W hy h t +b y

[0138] Substitute the values ​​to calculate:

[0139] Assume that the specific values ​​of the weight matrix W and the bias term b are as follows (for simplicity, small matrices and vectors are used):

[0140] W ix =[0.1, 0.2, 0.3, 0.4]

[0141]

[0142] W fx =[0.15, 0.25, 0.35, 0.45]

[0143]

[0144] W cx =[0.2, 0.3, 0.4, 0.5]

[0145]

[0146] W ox =[0.25, 0.35, 0.45, 0.55]

[0147]

[0148] W hy =[0.1, 0.2, 0.3, 0.4]

[0149] b i =[0.1, 0.1, 0.1, 0.1]

[0150] b f =[0.2, 0.2, 0.2, 0.2]

[0151] b c =[0.3, 0.3, 0.3, 0.3]

[0152] b o =[0.4, 0.4, 0.4, 0.4]

[0153] b y =0.5

[0154] c (t-1) =[0.1, 0.2, 0.3, 0.4]

[0155] Calculate input gate i t :

[0156]

[0157] Assume that after calculation, we get:

[0158] i t =[0.8, 0.7, 0.6, 0.5]

[0159] Calculate the forget gate f t :

[0160]

[0161] Assume that after calculation, we get:

[0162] f t =[0.9, 0.8, 0.7, 0.6]

[0163] Compute cell state updates

[0164]

[0165] Assume that after calculation, we get:

[0166]

[0167] Calculate the cell state c t :

[0168] c t =[0.9, 0.8, 0.7, 0.6]⊙[0.1, 0.2, 0.3, 0.4]+[0.8, 0.7, 0.6, 0.5]⊙[0.5, 0.6, 0.7, 0.8]

[0169] Assume that after calculation, we get:

[0170] c t =[0.5, 0.6, 0.7, 0.8]

[0171] Calculate the output gate o t :

[0172]

[0173] Assume that after calculation, we get:

[0174] o t =[0.8, 0.7, 0.6, 0.5]

[0175] Calculate the hidden state h t :

[0176] h t=[0.8, 0.7, 0.6, 0.5]⊙tanh([0.5, 0.6, 0.7, 0.8])

[0177] Assume that after calculation, we get:

[0178] h t =[0.6, 0.5, 0.4, 0.3]

[0179] Calculate the output y t :

[0180] y t =[0.1, 0.2, 0.3, 0.4]·[0.6, 0.5, 0.4, 0.3]+0.5

[0181] Assume that after calculation, we get:

[0182] y t =1.0

[0183] In this example, the energy consumption forecast value y at a certain point in the future is calculated by the long short-term memory network model. t =1.0. This predicted value indicates that at a certain point in the future, the expected energy consumption of the equipment inside the shelter is 1.0 unit (for example, 1000Wh). This prediction result can help the smart energy management system to prepare energy allocation strategies in advance, ensuring immediate energy demand while minimizing energy waste.

[0184] Through a large amount of data training in actual applications, the long short-term memory network model can continuously optimize its weights and bias terms, thereby improving prediction accuracy. In actual deployment, the system will dynamically adjust the working mode of solar panels and wind turbines based on real-time monitoring data and prediction results to ensure that the shelter is always in the best working condition.

[0185] 104. When the system detects that the external energy supply fluctuates or is below the safety threshold, the emergency response mechanism is immediately activated based on the optimized energy allocation strategy to prioritize the operation of key equipment, while starting the energy-saving mode to reduce the power consumption of non-essential equipment, optimizing the discharge rate of the backup battery pack through the adaptive control algorithm, and generating an emergency energy management plan to ensure that the basic functions of the shelter are maintained under adverse conditions;

[0186] In this step, external energy supply fluctuation refers to the situation where the supply of solar energy and wind energy is unstable or insufficient. The emergency response mechanism is a series of measures automatically activated by the system when energy supply fluctuations or below the safety threshold are detected. Key equipment protection is to prioritize the power supply of key equipment. Energy-saving mode is to reduce the power consumption of non-essential equipment and save electricity. The adaptive control algorithm is to intelligently adjust the discharge rate of the backup battery pack according to the current energy demand and the remaining power. The emergency energy management plan is to ensure the basic functions of the shelter under adverse conditions.

[0187] First, it is detected that the external energy supply fluctuates or falls below a safety threshold.

[0188] Second, activate the emergency response mechanism.

[0189] Next, give priority to ensuring the operation of key equipment.

[0190] Furthermore, the energy saving mode is activated to reduce the power consumption of non-essential devices.

[0191] Furthermore, the discharge rate of the backup battery pack is optimized through an adaptive control algorithm.

[0192] Finally, an emergency energy management plan is generated.

[0193] In the example of this application, it is assumed that in a portable cabin in a temporary residential area, when the system detects that the external energy supply fluctuates or falls below the safety threshold, the emergency response mechanism is immediately activated. The system gives priority to the operation of key equipment such as lighting and heating, and at the same time starts the energy-saving mode to shut down or reduce the power demand of non-critical equipment. The discharge rate of the backup battery pack is optimized through an adaptive control algorithm to generate an emergency energy management plan. For example, when a sudden strong wind at night causes a wind turbine to fail, the system will immediately enable the backup battery pack and give priority to the operation of lighting and heating equipment to ensure that the basic lives of residents are not affected.

[0194] Optionally, in step 104, when the system detects that the external energy supply fluctuates or is below a safety threshold, based on the optimized energy allocation strategy, the emergency response mechanism is immediately activated to prioritize the operation of key equipment, and the energy-saving mode is started to reduce the power consumption of non-essential equipment. The discharge rate of the backup battery pack is optimized through an adaptive control algorithm, and an emergency energy management plan is generated to ensure that the basic functions of the cabin are maintained under adverse conditions, including: when the system detects that the external energy supply fluctuates or is below a safety threshold, based on the optimized energy allocation strategy, the emergency response mechanism is activated and an emergency response instruction is generated; using the emergency response instruction, the intelligent energy management system prioritizes the operation of key equipment and ensures the power supply of important equipment , generate a key equipment protection list; based on the key equipment protection list, start the energy-saving mode, reduce the power consumption of non-essential equipment, save electricity by shutting down or reducing the power requirements of non-critical equipment, and generate an energy-saving mode execution command; using the energy-saving mode execution command, optimize the discharge rate of the backup battery pack through an adaptive control algorithm, intelligently determine the discharge speed according to the current energy demand and the remaining power, and generate an optimized discharge rate setting; based on the emergency response instructions, the key equipment protection list, the energy-saving mode execution command and the optimized discharge rate setting, the intelligent energy management system generates an emergency energy management plan to ensure that the basic functions of the shelter are maintained under adverse conditions and that the shelter smoothly transitions back to normal working conditions.

[0195] Optionally, the energy-saving mode execution command generated in step 104 is used to optimize the discharge rate of the backup battery pack through an adaptive control algorithm, and the discharge speed is intelligently determined according to the current energy demand and the remaining power, and an optimized discharge rate setting is generated, including: using the generated energy-saving mode execution command, the intelligent energy management system starts the energy-saving mode, reduces the power consumption of non-essential equipment, and generates an energy-saving mode execution result; based on the energy-saving mode execution result, the intelligent energy management system monitors the current energy consumption of each device in the cabin and the remaining power of the backup battery pack in real time, and collects real-time energy consumption data and remaining power data; using the real-time energy consumption data and the remaining power data, the discharge rate of the backup battery pack is optimized through an adaptive control algorithm, the discharge speed is intelligently determined, and an optimized discharge rate setting is generated; using the optimized discharge rate setting, the intelligent energy management system adjusts the discharge rate of the backup battery pack to ensure that the basic functions of the cabin are maintained under adverse conditions and the service life of the backup battery pack is extended. In the wind and solar hybrid power supply system of the portable cabin, when the external energy supply fluctuates or is lower than the safety threshold, the backup battery pack becomes the key to maintaining the basic functions of the cabin. In order to ensure that the energy of the battery pack can be effectively utilized under adverse conditions while extending the battery life, the discharge rate of the battery pack needs to be optimized through an adaptive control algorithm. This formula is designed to intelligently determine the discharge speed based on the current energy demand and the remaining power, generating an optimized discharge rate setting.

[0196] In this step, the fluctuation of external energy supply or the power below the safety threshold means that the power provided by solar panels and wind turbines is unstable or insufficient to meet the immediate needs of the shelter. The optimized energy allocation strategy is to intelligently decide how to allocate energy to ensure the operation of key equipment based on the current energy demand and the remaining power. The emergency response mechanism is a series of measures automatically activated by the system when insufficient external energy supply is detected, including giving priority to the operation of key equipment and starting the energy-saving mode. The key equipment guarantee list is a list of key equipment that keeps running and the power required. The energy-saving mode saves power by shutting down or reducing the power demand of non-critical equipment. The adaptive control algorithm dynamically adjusts the discharge rate of the backup battery pack based on the real-time energy consumption data and the remaining power data to extend the battery life and ensure the basic functions of the shelter. The emergency energy management plan is a comprehensive energy management plan generated under adverse conditions to ensure a smooth transition of the shelter back to normal working conditions.

[0197] First, when the system detects that the external energy supply fluctuates or falls below the safety threshold, the emergency response mechanism is immediately activated and emergency response instructions are generated based on the optimized energy allocation strategy.

[0198] Secondly, using emergency response instructions, the intelligent energy management system prioritizes the operation of key equipment and generates a key equipment protection list.

[0199] Next, based on the key equipment protection list, the energy-saving mode is started to reduce the power consumption of non-essential equipment and generate an energy-saving mode execution command.

[0200] Furthermore, the energy-saving mode execution command is utilized to optimize the discharge rate of the backup battery pack through an adaptive control algorithm, and the discharge speed is intelligently determined according to the current energy demand and the remaining power to generate an optimized discharge rate setting.

[0201] Finally, based on emergency response instructions, key equipment protection lists, energy-saving mode execution commands, and optimized discharge rate settings, the intelligent energy management system generates an emergency energy management plan to ensure that the basic functions of the shelter are maintained under adverse conditions and that the shelter smoothly transitions back to normal working conditions.

[0202] In the embodiment of the present application, it is assumed that at a field operation site, a portable shelter is equipped with a wind and solar hybrid power supply system. It is assumed that a sudden strong wind one night causes the wind turbine to fail, and due to weather reasons, the power generation of the solar panel also drops significantly. The system detects that the external energy supply fluctuates and is below the safety threshold.

[0203] First, when the system detects that the external energy supply fluctuates or falls below the safety threshold, it immediately activates the emergency response mechanism, generates an emergency response instruction, and notifies the system to enter the emergency management mode.

[0204] Secondly, according to the emergency response instructions, the intelligent energy management system identifies key equipment that needs priority protection, such as medical equipment, communication equipment, etc. A key equipment protection list is generated to ensure that the power supply of these equipment is prioritized.

[0205] Next, based on the critical equipment protection list, the system starts the energy-saving mode. Turn off or reduce the power requirements of non-critical equipment to save electricity. Generate energy-saving mode execution commands to ensure that the power consumption of non-critical equipment is minimized.

[0206] Furthermore, using the energy-saving mode execution command, the intelligent energy management system optimizes the discharge rate of the backup battery pack through an adaptive control algorithm. The system monitors the current energy consumption of each device in the cabin and the remaining power of the backup battery pack in real time. It collects real-time energy consumption data and remaining power data, intelligently determines the discharge speed through an adaptive control algorithm, and generates an optimized discharge rate setting. This optimized setting ensures that while meeting the needs of key equipment, the battery energy is reasonably distributed and the battery life is extended.

[0207] Finally, based on the emergency response instructions, key equipment protection list, energy-saving mode execution command and optimized discharge rate settings, the intelligent energy management system generates an emergency energy management plan, which details how to maintain the basic functions of the shelter under adverse conditions and ensure that the shelter can smoothly transition back to normal working conditions.

[0208] Through the above steps, the system not only ensures the normal operation of key equipment, but also reduces the power consumption of non-essential equipment through energy-saving mode, prolongs the service life of the backup battery pack, and thus improves the overall reliability and economy of the system. This comprehensive emergency energy management solution effectively copes with the fluctuation of external energy supply and ensures the continuous operation of the shelter under adverse conditions.

[0209] Optionally, the energy-saving mode execution command generated in step 104 is used to optimize the discharge rate of the backup battery pack through an adaptive control algorithm, intelligently determine the discharge speed according to the current energy demand and the remaining power, and generate an optimized discharge rate setting, including:

[0210] The optimized discharge rate setting D(t) is calculated using the following formula:

[0211]

[0212] Among them, P req (t) represents the total power required by the key equipment and the equipment that keeps running at time T; W req Indicates the weight coefficient of current demand; P hist (t) represents the average power demand in the future period predicted based on historical data; W hist Represents the weight coefficient of historical demand; C bat Indicates the total capacity of the backup battery pack; E used (t) represents the amount of electricity used up to time t; α represents the sensitivity index of the remaining electricity; K is the adjustment coefficient used to balance the relationship between the discharge rate and the remaining electricity.

[0213] The formula takes into account the actual power demand of the current device, the average power demand in the future predicted based on historical data, the total capacity of the battery pack, and the amount of power already used, and balances the impact of these factors through weight coefficients and sensitivity indexes. This ensures that while meeting immediate energy needs, battery energy is reasonably allocated to avoid shortening battery life due to over-discharge.

[0214] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0215] In the optimized discharge rate setting formula D(t), P req (t)·Wreq This part reflects the total power actually required by key equipment and equipment that keeps running at the current moment, and is calculated through the weight coefficient W req To emphasize its importance. In an emergency, the operation of critical equipment is the top priority, so this section ensures that the immediate energy needs of these equipment are met first; P hist (t)·W hist This part predicts the average power demand in the future based on historical data and uses the weight coefficient W hist To adjust the focus on future needs. Historical data can help the system make more reasonable long-term plans and better manage battery energy; (C bat -E used (t)) α This part reflects the remaining power of the battery pack and adjusts the effect of the remaining power on the discharge rate through the sensitivity index α. When the remaining power is low, the discharge rate can be slowed down by increasing α, thereby extending the battery life; K is an adjustment factor used to balance the relationship between the discharge rate and the remaining power. It can be set according to the system strategy and actual test results to ensure that there is enough power left to cope with emergencies even in high demand situations.

[0216] The following is a brief introduction to how to obtain the parameters of the formula:

[0217] Among them, P req (t) is obtained by real-time monitoring of the current energy consumption of each device in the cabin; W req and W hist： Set according to actual needs and historical data analysis; hist (t) Predicted by energy consumption prediction model (such as LSTM); C bat Given in the technical specification provided by the battery manufacturer; E used (t) is obtained by real-time monitoring of the status of the battery pack; α is set according to battery characteristics and system requirements; K is set according to system strategy and actual test results.

[0218] Assume a specific example with the following data:

[0219] P req (t): 500W; W req :0.8;P hist (t): 400W; W hist： 0.2;C bat : 1000Ah;

[0220] E used (t): 600Ah; α: 1.5; K: 0.8;

[0221] Calculate the numerator part:

[0222] P req (t)·W req +P hist (t)·W hist =500W·0.8+400W·0.2=400W+80W

[0223] =480W

[0224] Calculate the denominator:

[0225]

[0226] Calculate the discharge rate setting D(t):

[0227]

[0228] In this example, the calculated optimized discharge rate setting is D(t) = 0.048 A. This means that the discharge rate of the backup battery pack is 0.048 amperes under the current energy demand and remaining power conditions.

[0229] This result shows that a balance has been found between current energy demand and remaining power. By setting the discharge rate to 0.048 amps, it is possible to ensure that the energy consumption needs of current key devices are met while retaining enough power to cope with future energy consumption needs. In addition, this low discharge rate helps extend the battery life, as excessive discharge rates accelerate battery aging.

[0230] Through dynamic adjustments in actual applications, the system can continuously optimize the discharge rate settings according to changing energy needs and battery status, ensuring that the cabin is always in the best working condition.

[0231] 105. For the excess electricity exceeding the immediate demand, based on the optimized energy distribution strategy and emergency energy management plan, energy storage technology is used to store the excess electricity exceeding the immediate demand in high-performance lithium-ion battery packs, or the excess electricity is converted into standard AC power through high-efficiency inverters to supply power to the external power grid, thereby generating stable power output and ensuring the overall stability and reliability of the system.

[0232] In this step, excess power is power in excess of immediate demand.

[0233] Energy storage technology uses high-performance lithium-ion battery packs to store excess electricity.

[0234] High-efficiency inverters convert direct current into standard alternating current for use by the external power grid.

[0235] Stable power output is to ensure that the system can provide stable power supply under various circumstances.

[0236] First, excess power beyond immediate demand is detected.

[0237] Secondly, energy storage technology is used to store excess electricity in high-performance lithium-ion battery packs.

[0238] Furthermore, the excess electricity can be converted into standard AC power through a high-efficiency inverter to supply power to the external power grid.

[0239] Finally, a stable electrical energy output is generated.

[0240] In the example of this application, it is assumed that at a field operation site, when the electricity generated by solar panels and wind turbines exceeds the immediate demand, the system stores the excess electricity in a high-performance lithium-ion battery pack through energy storage technology. In addition, the system can also convert the excess electricity into standard AC power through a high-efficiency inverter and supply power to the external power grid. For example, during the day when there is sufficient sunlight and strong winds, the system will generate a large amount of excess electricity, which will be stored in the battery pack or transmitted to the external power grid through an inverter for use by other equipment or facilities. This not only improves the overall stability and reliability of the system, but also enhances the flexibility and sustainability of energy.

[0241] Figure 2 A schematic diagram of a wind and solar hybrid power supply system for a portable shelter is provided in the embodiment of the present application. Figure 2 As shown, the system includes:

[0242] An adjustment module 21 is used to install a solar panel array with an adaptive adjustment function and a retractable micro wind turbine generator set on the top and side of the portable cabin, automatically adjust the angle of the solar panel array and the height of the wind turbine generator set to maximize the energy capture efficiency and generate a preliminary energy collection configuration;

[0243] A prediction module 22, for predicting future light intensity and wind speed trends based on preliminary energy collection configuration, generating an optimal adjustment plan, and executing the plan through a control unit to ensure that the solar panels and wind turbines are in optimal working conditions;

[0244] The switching module 23 is used to utilize the generated optimal adjustment plan. Based on the historical energy consumption data, the intelligent energy management system uses a deep learning algorithm to establish an energy consumption prediction model, monitor the operating status and power demand of each device in the cabin in real time, and intelligently switch the working modes of the solar panels and wind turbines according to the energy consumption prediction model and real-time monitoring data to generate an optimized energy allocation strategy to ensure immediate energy demand while minimizing energy waste;

[0245] The optimization module 24 is used to immediately activate the emergency response mechanism based on the optimized energy allocation strategy when the system detects that the external energy supply fluctuates or is below the safety threshold, give priority to the operation of key equipment, start the energy-saving mode to reduce the power consumption of non-essential equipment, optimize the discharge rate of the backup battery pack through the adaptive control algorithm, and generate an emergency energy management plan to ensure that the basic functions of the shelter are maintained under adverse conditions;

[0246] The storage conversion module 25 is used to store the excess power exceeding the immediate demand in a high-performance lithium-ion battery pack based on the optimized energy allocation strategy and emergency energy management plan, or to convert the excess power into standard AC power through a high-efficiency inverter to supply power to the external power grid, thereby generating stable power output and ensuring the overall stability and reliability of the system.

[0247] Figure 2 The wind and solar hybrid power supply system of the portable shelter can be implemented Figure 1 The implementation principle and technical effect of the wind and solar hybrid power supply method for the portable shelter described in the embodiment are not described in detail. The specific way in which each module and unit performs the operation of the wind and solar hybrid power supply system for the portable shelter in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0248] In one possible design, Figure 2 The wind and solar hybrid power supply system of the portable shelter in the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0249] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0250] The processing component 32 is used to: install a solar panel array with adaptive adjustment function and a retractable micro wind turbine generator set on the top and side of the portable cabin, automatically adjust the angle of the solar panel array and the height of the wind turbine generator set to maximize the energy capture efficiency, and generate a preliminary energy collection configuration; based on the preliminary energy collection configuration, predict the future light intensity and wind speed trend, generate an optimal adjustment plan, and execute the plan through the control unit to ensure that the solar panels and wind turbines are in the optimal working state; using the generated optimal adjustment plan, based on historical energy consumption data, the intelligent energy management system uses a deep learning algorithm to establish an energy consumption prediction model, monitors the operating status and power requirements of each device in the cabin in real time, and intelligently switches the working mode of the solar panels and wind turbines according to the energy consumption prediction model and real-time monitoring data to generate an optimal adjustment plan. An optimized energy allocation strategy is formed to ensure immediate energy demand while minimizing energy waste; when the system detects that the external energy supply fluctuates or is below the safety threshold, the emergency response mechanism is immediately activated based on the optimized energy allocation strategy to prioritize the operation of key equipment, while starting the energy-saving mode to reduce the power consumption of non-essential equipment, and optimizing the discharge rate of the backup battery pack through an adaptive control algorithm to generate an emergency energy management plan to ensure that the basic functions of the cabin are maintained under adverse conditions; for excess power exceeding immediate demand, based on the optimized energy allocation strategy and emergency energy management plan, energy storage technology is used to store the excess power exceeding immediate demand in high-performance lithium-ion battery packs, or the excess power is converted into standard AC power through a high-efficiency inverter to supply power to the external power grid, generating stable power output and ensuring the overall stability and reliability of the system.

[0251] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0252] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0253] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0254] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0255] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0256] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0257] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The portable shelter of the illustrated embodiment has a hybrid wind and solar power supply method.

[0258] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0259] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0260] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0261] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A wind and solar hybrid power supply method for a portable shelter, characterized in that: include: Installing solar panel arrays with adaptive adjustment functions and retractable micro wind turbines on the top and sides of the portable cabin, automatically adjusting the angle of the solar panel array and the height of the wind turbine to maximize energy capture efficiency and generate a preliminary energy collection configuration; Based on the preliminary energy collection configuration, the future light intensity and wind speed trends are predicted, the best adjustment plan is generated, and the plan is executed through the control unit to ensure that the solar panels and wind turbines are in the best working state; Using the generated optimal adjustment plan and based on historical energy consumption data, the intelligent energy management system uses a deep learning algorithm to establish an energy consumption prediction model, monitors the operating status and power requirements of each device in the cabin in real time, and intelligently switches the working modes of solar panels and wind turbines according to the energy consumption prediction model and real-time monitoring data, generating an optimized energy allocation strategy to ensure immediate energy needs while minimizing energy waste; When the system detects that the external energy supply fluctuates or is below the safety threshold, the emergency response mechanism is immediately activated based on the optimized energy allocation strategy to prioritize the operation of key equipment. At the same time, the energy-saving mode is started to reduce the power consumption of non-essential equipment. The discharge rate of the backup battery pack is optimized through the adaptive control algorithm, and an emergency energy management plan is generated to ensure that the basic functions of the shelter are maintained under adverse conditions. For excess electricity that exceeds immediate demand, based on optimized energy distribution strategies and emergency energy management plans, energy storage technology is used to store the excess electricity that exceeds immediate demand in high-performance lithium-ion battery packs, or the excess electricity is converted into standard AC power through high-efficiency inverters to supply power to the external power grid, generating stable power output and ensuring the overall stability and reliability of the system.

2. The wind and solar hybrid power supply method for a portable shelter according to claim 1 is characterized in that: The optimal adjustment plan generated by the above-mentioned utilization is based on historical energy consumption data. The intelligent energy management system uses a deep learning algorithm to establish an energy consumption prediction model, monitors the operating status and power requirements of each device in the shelter in real time, and intelligently switches the working modes of solar panels and wind turbines according to the energy consumption prediction model and real-time monitoring data to generate an optimized energy allocation strategy to ensure immediate energy demand while minimizing energy waste, including: Using the generated optimal adjustment plan, combined with historical energy consumption data, a deep learning algorithm is used to train and process the historical energy consumption data to obtain an energy consumption prediction model; Based on the energy consumption prediction model, the intelligent energy management system monitors the operating status and power demand of each device in the cabin in real time, analyzes and processes the monitoring data, and generates a real-time energy consumption assessment report; According to the real-time energy consumption assessment report, the working modes of the solar panels and wind turbines are intelligently switched to generate optimized working mode instructions; Using the optimized working mode instructions, the intelligent energy management system adjusts the working states of the solar panels and wind turbines to generate an optimized energy distribution strategy.

3. The wind and solar hybrid power supply method for a portable shelter according to claim 2 is characterized in that: The optimal adjustment scheme generated is combined with historical energy consumption data, and a deep learning algorithm is used to train and process the historical energy consumption data to obtain an energy consumption prediction model, including: Using the optimal adjustment scheme and combining the historical energy consumption data of the shelter, the generated optimal adjustment scheme and the historical energy consumption data of the shelter are fused to obtain a training set; Based on the training set, a deep learning algorithm is used for training processing, so that the deep learning algorithm learns the mapping relationship between environmental conditions and energy consumption of equipment inside the cabin, and generates a trained energy consumption prediction model.

4. The wind and solar hybrid power supply method for a portable shelter according to claim 3 is characterized in that: The optimal adjustment scheme generated is combined with historical energy consumption data, and a deep learning algorithm is used to train and process the historical energy consumption data to obtain an energy consumption prediction model, including: Based on the training set, a long short-term memory network is used for training processing, so that the long short-term memory network learns the mapping relationship between the environmental conditions and the energy consumption of the equipment inside the shelter, and generates a trained energy consumption prediction model; The training process of the long short-term memory network is expressed as follows through the following calculation formula: y t =f(x t ,h t-1 ;θ) Among them, the forward propagation function f of the long short-term memory network includes the following steps: Input gate: i t =σ(W ix x t +W ih h t-1 +b i ) Forget gate: f t =σ(W fx x t +W fh h t-1 +b f ) Cell status update: Output gate: o t =σ(W ox x t +W oh h t-1 +b o ) Hide status update:h t =o t ⊙tanh(c t ) Output: y t =w hy h t +b y Among them, x t represents the input feature vector at time step t, including environmental conditions and historical energy consumption data of equipment inside the shelter; h t-1 represents the hidden state at time step t-1; y t represents the output at time step t, that is, the predicted energy consumption value; σ represents the Sigmoid activation function; tanh represents the hyperbolic tangent activation function; ⊙ represents element-by-element multiplication; W ix , W ih , W fx , W fh , W cx , W ch , W ox , W oh , W hy represents the weight matrix; b i , b f , b c , b o , b y represents the bias term; θ represents the set of all learnable parameters, including all weight matrices W and bias terms b; c t represents the cell state at time step t; c t-1 represents the cell state at time step t-1.

5. The wind and solar hybrid power supply method for a portable shelter according to claim 2 is characterized in that: Based on the energy consumption prediction model obtained, the intelligent energy management system monitors the operating status and power demand of each device in the cabin in real time, analyzes and processes the monitoring data, and generates a real-time energy consumption assessment report, including: Using the energy consumption prediction model obtained, the intelligent energy management system monitors the operating status and power requirements of each device in the cabin in real time and collects real-time monitoring data; Based on the real-time monitoring data, the intelligent energy management system analyzes and processes the real-time monitoring data, identifies existing energy consumption anomalies, and generates analysis and processing results; The results of the analysis and processing are used to generate a real-time energy consumption assessment report, which records in detail the energy consumption of each device in the cabin, as well as any abnormal energy consumption and optimization suggestions.

6. The wind and solar hybrid power supply method for a portable shelter according to claim 1 is characterized in that: When the system detects that the external energy supply fluctuates or is below the safety threshold, the emergency response mechanism is immediately activated based on the optimized energy allocation strategy to prioritize the operation of key equipment, while starting the energy-saving mode to reduce the power consumption of non-essential equipment, optimizing the discharge rate of the backup battery pack through the adaptive control algorithm, and generating an emergency energy management plan to ensure that the basic functions of the shelter are maintained under adverse conditions, including: When the system detects that the external energy supply fluctuates or is below the safety threshold, the emergency response mechanism is activated based on the optimized energy allocation strategy and an emergency response instruction is generated; Using the emergency response instructions, the intelligent energy management system prioritizes the operation of key equipment, ensures the power supply of important equipment, and generates a key equipment protection list; Based on the key equipment protection list, start the energy-saving mode, reduce the power consumption of non-essential equipment, save electricity by shutting down or reducing the power requirements of non-critical equipment, and generate an energy-saving mode execution command; Utilizing the energy-saving mode execution command, the discharge rate of the backup battery pack is optimized through an adaptive control algorithm, the discharge speed is intelligently determined according to the current energy demand and the remaining power, and an optimized discharge rate setting is generated; Based on the emergency response instructions, key equipment protection list, energy-saving mode execution command and optimized discharge rate setting, the intelligent energy management system generates an emergency energy management plan to ensure that the basic functions of the shelter are maintained under adverse conditions and that the shelter smoothly transitions back to normal working conditions.

7. The wind and solar hybrid power supply method for a portable shelter according to claim 6 is characterized in that: The generated energy-saving mode execution command is used to optimize the discharge rate of the backup battery pack through an adaptive control algorithm, and the discharge speed is intelligently determined according to the current energy demand and the remaining power, and an optimized discharge rate setting is generated, including: Using the generated energy-saving mode execution command, the intelligent energy management system starts the energy-saving mode, reduces the power consumption of non-essential equipment, and generates the energy-saving mode execution result; Based on the execution result of the energy-saving mode, the intelligent energy management system monitors the current energy consumption of each device in the cabin and the remaining power of the backup battery pack in real time, and collects real-time energy consumption data and remaining power data; Utilizing the real-time energy consumption data and the remaining power data, the discharge rate of the backup battery pack is optimized through an adaptive control algorithm, the discharge speed is intelligently determined, and an optimized discharge rate setting is generated; Using the optimized discharge rate setting, the intelligent energy management system adjusts the discharge rate of the backup battery pack to ensure that the basic functions of the cabin are maintained under adverse conditions and to extend the service life of the backup battery pack.

8. The wind and solar hybrid power supply method for a portable shelter according to claim 7 is characterized in that: The generated energy-saving mode execution command is used to optimize the discharge rate of the backup battery pack through an adaptive control algorithm, and the discharge speed is intelligently determined according to the current energy demand and the remaining power, and an optimized discharge rate setting is generated, including: The optimized discharge rate setting D(t) is calculated using the following formula: Among them, P req (t) represents the total power required by the key equipment and the equipment that keeps running at time t; W req Indicates the weight coefficient of current demand; P hist (t) represents the average power demand in the future period predicted based on historical data; W hist Represents the weight coefficient of historical demand; C bat Indicates the total capacity of the backup battery pack; E used (t) represents the amount of electricity used up to time t; α represents the sensitivity index of the remaining electricity; K is the adjustment coefficient used to balance the relationship between the discharge rate and the remaining electricity.

9. The wind and solar hybrid power supply method for a portable shelter according to claim 1, characterized in that: Based on the preliminary energy collection configuration, the future light intensity and wind speed trends are predicted, the best adjustment plan is generated, and the plan is executed by the control unit to ensure that the solar panels and wind turbines are in the best working state, including: Using the preliminary energy collection configuration, the intelligent energy management system combines the environmental perception algorithm and weather forecast data to predict the future light intensity and wind speed trends and obtain the light and wind speed forecast results; Based on the light and wind speed prediction results, the intelligent energy management system generates an optimal adjustment plan, which specifies in detail the angle to which the solar panel array should be adjusted and the height to which the wind turbine generator set should be adjusted in different time periods; By utilizing the optimal adjustment scheme, the control unit executes the scheme to automatically adjust the angle of the solar panel array and the height of the wind turbine generator set, ensuring that the solar panels and wind turbine generators are always in the optimal working state.

10. A wind and solar hybrid power supply system for a portable shelter, characterized in that: include: An adjustment module is used to install a solar panel array with adaptive adjustment function and a retractable micro wind turbine generator set on the top and side of the portable cabin, automatically adjust the angle of the solar panel array and the height of the wind turbine generator set to maximize the energy capture efficiency and generate a preliminary energy collection configuration; A prediction module is used to predict the future light intensity and wind speed trends based on the preliminary energy collection configuration, generate the best adjustment plan, and execute the plan through the control unit to ensure that the solar panels and wind turbines are in the best working state; The switching module is used to utilize the generated optimal adjustment plan. Based on historical energy consumption data, the intelligent energy management system uses a deep learning algorithm to establish an energy consumption prediction model, monitor the operating status and power requirements of each device in the cabin in real time, and intelligently switch the working modes of solar panels and wind turbines according to the energy consumption prediction model and real-time monitoring data to generate an optimized energy allocation strategy to ensure immediate energy demand while minimizing energy waste; The optimization module is used to immediately activate the emergency response mechanism based on the optimized energy allocation strategy when the system detects that the external energy supply fluctuates or falls below the safety threshold, giving priority to the operation of key equipment, while starting the energy-saving mode to reduce the power consumption of non-essential equipment, optimizing the discharge rate of the backup battery pack through the adaptive control algorithm, and generating an emergency energy management plan to ensure that the basic functions of the shelter are maintained under adverse conditions; The storage and conversion module is used to store excess power that exceeds immediate demand in high-performance lithium-ion battery packs based on optimized energy distribution strategies and emergency energy management plans, or to convert excess power into standard AC power through high-efficiency inverters to supply power to the external power grid, thereby generating stable power output and ensuring the overall stability and reliability of the system.

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