Solar hybrid energy supply system for unmanned area monitoring and dynamic adjustment strategy

Abstract

The invention discloses a solar hybrid energy supply system for unmanned area monitoring and a dynamic adjustment strategy method. The system integrates a solar energy collection module, an energy storage module, a wind power / fuel oil auxiliary energy supply module and a monitoring load module, and realizes power supply guarantee through a multi-energy collaborative architecture. The dynamic adjustment strategy is based on real-time data acquisition, solar energy and wind energy are preferentially scheduled by utilizing a machine learning prediction model and a multi-objective optimization algorithm, and a'prediction-scheduling-fault tolerance 'closed-loop management is formed in combination with an energy storage battery buffer and fuel generator standby mechanism. The problems of unstable energy and high operation and maintenance cost in a traditional energy supply mode in a depopulated area are solved, 7 * 24-hour continuous power supply is realized, the utilization rate of renewable energy sources is remarkably improved, and the system has fault self-repairing and remote monitoring functions and is suitable for long-term monitoring scenes in an extreme environment.

Description

Technical Field

[0001] The present invention relates to the technical field of solar energy supply, and in particular to a solar hybrid energy supply system and a dynamic adjustment strategy for monitoring uninhabited areas. Background Art

[0002] Uninhabited areas are characterized by remote locations, harsh environments, and a lack of infrastructure. Traditional grid-based power supply or single-energy sources are unable to meet the long-term stable operation requirements of monitoring equipment. For example, solar power alone is significantly affected by weather, prone to power outages on rainy days or during periods of insufficient sunlight. Single energy storage systems carry the risk of power depletion. While traditional fuel generators can provide continuous power, they are expensive to transport, generate significant pollution, and require frequent maintenance. Existing technologies have significant drawbacks in energy utilization, supply stability, and operation and maintenance costs. Therefore, a solar hybrid power supply system and dynamic adjustment strategy for monitoring uninhabited areas are proposed. Summary of the Invention

[0003] The present invention aims to solve the problems raised in the background technology and provides a solar hybrid energy supply system and a dynamic adjustment strategy for monitoring uninhabited areas.

[0004] The specific technical solutions are as follows:

[0005] A solar hybrid energy supply system for monitoring uninhabited areas, comprising:

[0006] The solar energy collection module includes a solar panel array that can automatically adjust its angle according to the position of the sun. The energy storage module is composed of multiple groups of energy storage batteries controlled by an intelligent battery management system, which has real-time battery status monitoring and adaptive charge and discharge regulation functions.

[0007] Auxiliary energy supply module, which can be started when solar energy is insufficient, includes a small wind turbine and a backup fuel generator, and can intelligently switch operating modes according to load demand;

[0008] The monitoring equipment load module includes various sensors and data transmission equipment used for monitoring unmanned areas, and is electrically connected to the solar energy collection module, energy storage module and auxiliary energy supply module.

[0009] The above-mentioned solar hybrid energy supply system for monitoring uninhabited areas, wherein the solar energy collection module also includes an intelligent shading and self-cleaning device, which automatically shades and cools the solar panels during high temperature periods, and can automatically clean the surface of the solar panels after sandstorms, to ensure stable and efficient operation of the solar panels.

[0010] In the above-mentioned solar hybrid energy supply system for monitoring uninhabited areas, the battery of the energy storage module adopts a new type of nano-composite electrode material and has overcharge, over-discharge and short-circuit protection functions.

[0011] The above-mentioned solar hybrid energy supply system for monitoring uninhabited areas, wherein the small wind turbine in the auxiliary energy supply module adopts a vertical axis design, can operate stably in a low wind speed environment, and can automatically adjust the blade angle according to the wind direction to improve the wind energy capture efficiency.

[0012] The above-mentioned solar hybrid energy supply system for monitoring uninhabited areas, wherein the backup fuel generator in the auxiliary energy supply module has an intelligent start-stop function. When the power of the energy storage module is lower than the set threshold and neither solar energy nor wind energy can meet the load demand, it will automatically start, and during operation, the output power can be adjusted in real time according to load changes.

[0013] The present invention also provides a dynamic adjustment strategy method applicable to a solar hybrid energy supply system, comprising the following steps:

[0014] Real-time collection of solar panel output power, energy storage battery power, wind turbine output power, load power and environmental parameters (light intensity, wind speed, etc.);

[0015] Based on the collected data, a machine learning-based prediction model is used to predict the changing trends of solar energy, wind energy, and load in the future.

[0016] Based on the prediction results and the set optimization goals (such as maximizing the utilization of renewable energy, minimizing operating costs, etc.), the intelligent energy management algorithm dynamically adjusts the solar panel angle, energy storage battery charge and discharge status, wind turbine operating parameters and the start and stop of the auxiliary energy supply module to achieve efficient and stable operation of the system.

[0017] The above-mentioned dynamic adjustment strategy method is applicable to the solar hybrid energy supply system, wherein the prediction model based on machine learning adopts a deep neural network architecture.

[0018] The above-mentioned dynamic adjustment strategy method is applicable to the solar hybrid energy supply system, wherein the intelligent energy management algorithm is a multi-objective optimization algorithm, which comprehensively considers energy cost, equipment life, and energy supply reliability factors, and obtains the optimal system operation strategy by solving the optimization problem.

[0019] The above-mentioned dynamic adjustment strategy method is applicable to the solar hybrid energy supply system. In the adjustment process, when the solar energy is sufficient and the energy storage battery is not full, the energy storage battery is charged first and the load demand of the monitoring equipment is met; when the solar energy is insufficient and the energy storage battery power is higher than the set threshold, the energy storage battery is used to supply power; when the energy storage battery power is lower than the set threshold and neither solar energy nor wind energy can meet the load demand, the auxiliary energy supply module is started.

[0020] The above-mentioned dynamic adjustment strategy method for solar hybrid energy supply system also includes system fault diagnosis and self-repair functions. When a component in the system is detected to have a fault, it automatically switches to the backup component and sends the fault information to the operation and maintenance personnel through the remote communication module. At the same time, the adaptive control algorithm is used to adjust the system operation strategy to maintain the basic monitoring function of the system.

[0021] The present invention has the following beneficial effects:

[0022] 1. High-reliability energy supply: Through a hybrid energy supply mode of solar energy, wind energy and fuel power generation, combined with intelligent dynamic adjustment strategies, it effectively solves the problem of unstable energy supply in uninhabited areas and ensures the continuous operation of monitoring equipment 24 hours a day, 7 days a week.

[0023] 2. Efficient energy utilization: Automatic tracking solar panels, low-wind-speed wind turbines, and intelligent energy management algorithms maximize the efficiency of renewable energy capture and utilization, reduce dependence on fossil energy, and reduce carbon emissions.

[0024] 3. Reduced operation and maintenance costs: The long-life design of the energy storage battery, intelligent fault diagnosis and self-repair functions, and the intelligent start-stop mechanism of the fuel generator significantly reduce the frequency of equipment maintenance and consumables consumption, and reduce the difficulty and cost of operation and maintenance in unmanned areas.

[0025] 4. Strong environmental adaptability: Intelligent sunshade and self-cleaning device, battery protection function, and equipment resistant to harsh environment design enable the system to adapt to complex uninhabited environments such as high temperature, dust, and low wind speed, thereby improving system stability and service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A schematic diagram of the composition of a solar hybrid energy supply system for monitoring uninhabited areas provided by an embodiment of the present invention;

[0027] Figure 2 A flow chart of a dynamic adjustment strategy for a solar hybrid energy supply system for monitoring uninhabited areas provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The technical solution of the present invention will be further described below with reference to the accompanying drawings and through specific implementation methods.

[0029] Among them, the drawings are only used for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting this patent; in order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0030] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "inside", "outside" and the like indicate an orientation or position relationship based on the orientation or position relationship shown in the drawings, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting this patent. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0031] In the description of the present invention, unless otherwise expressly specified or limited, when the term "connection" or the like appears to indicate a connection relationship between components, such term should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be internal communication between two components or an interaction between two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood in specific circumstances.

[0032] Example 1

[0033] Reference Figure 1 This embodiment provides a solar hybrid energy supply system for monitoring uninhabited areas, including: a solar energy collection module, an auxiliary energy supply module, and a monitoring equipment load module, wherein:

[0034] The solar energy collection module includes a solar panel array that can automatically adjust its angle according to the position of the sun; the energy storage module is composed of multiple groups of energy storage batteries controlled by an intelligent battery management system, with real-time battery status monitoring and adaptive charge and discharge regulation functions;

[0035] The auxiliary energy supply module can be started when solar energy is insufficient, including a small wind turbine and a backup fuel generator, and can intelligently switch the operating mode according to load demand;

[0036] The monitoring equipment load module covers various sensors and data transmission equipment used for monitoring unmanned areas, and is electrically connected to the solar energy collection module, energy storage module and auxiliary energy supply module.

[0037] By adopting the above technical solution, the solar energy collection module, intelligent energy storage module, wind / fuel auxiliary energy supply module and monitoring load module are integrated. Through the multi-energy collaborative energy supply architecture, the efficient utilization of renewable energy in uninhabited area monitoring scenarios can be achieved. Combined with the auxiliary energy backup mechanism, the continuous power supply capability in extreme environments is guaranteed to meet the long-term stable operation requirements of the monitoring equipment.

[0038] Specifically, in this embodiment, the solar energy collection module also includes intelligent shading and self-cleaning devices, which automatically shade and cool the solar panels during high temperatures and automatically clean the panel surface after dusty weather, ensuring stable and efficient operation of the solar panels. By adding intelligent shading and self-cleaning devices to the solar energy collection module, it can adaptively cope with harsh environments such as high temperatures and dust. Through dynamic temperature control and surface cleaning, it prevents solar panel efficiency degradation caused by excessive temperature or dust accumulation, ensuring the stability of photoelectric conversion efficiency.

[0039] Specifically, in this embodiment, the energy storage module's batteries utilize a novel nanocomposite electrode material and feature overcharge, over-discharge, and short-circuit protection. This energy storage module, utilizing novel nanocomposite electrode materials and equipped with overcharge and over-discharge protection, effectively increases battery cycle life and reduces energy storage system maintenance costs. Multiple protection mechanisms enhance battery safety, prevent equipment failures caused by abnormal charging and discharging, and extend the overall service life of the energy storage module.

[0040] Among them, the specific scheme of the composition and structural design of the nanocomposite electrode material is as follows:

[0041] 1. Core active materials: Transition metal oxides (such as copper oxide CuO, cobalt oxide Co3O4, etc.) are selected as basic active ingredients. Taking copper oxide as an example, it has the advantages of low price, good electrochemical activity and large specific surface area, and can effectively promote electron transfer under low overpotential conditions. However, copper oxide itself has low electrical conductivity, and nanoparticles are prone to agglomeration during the preparation process, which will seriously affect the overall capacitance performance. Therefore, it needs to be modified.

[0042] 2. Conductivity-enhancing phase: Introducing highly conductive carbon materials, such as graphene or carbon nanotubes. Graphene, a 3D network of carbon atoms, boasts excellent mechanical properties, a large surface area, and strong electron transport capabilities, along with a narrow band gap and good biocompatibility. Carbon nanotubes also possess excellent electrical conductivity, effectively enhancing the overall conductivity of the electrode material, improving the electron conduction path, and reducing resistance, thereby increasing charge and discharge efficiency.

[0043] 3. Doping Modification Elements: Precious metals such as silver (Ag) are selected as doping elements. Silver exhibits electrophilicity, nucleophilicity, and exceptional selectivity for reactions. It is also one of the most conductive and relatively low-cost precious metals. Doping silver into transition metal oxides such as copper oxide creates a strong coupling effect due to their different properties, thereby revealing new functionalities, effectively overcoming the limitations of copper oxide alone and improving the overall performance of the material.

[0044] Preparation process

[0045] 1. Solution preparation:

[0046] 1.1. Weigh the corresponding amounts of silver nitrate (AgNO₃) and copper nitrate trihydrate (Cu(NO₃)₂·3H₂O) in a specific molar ratio (e.g., Cu:Ag = 1:1-3). For example, if the Cu:Ag molar ratio is set to 1:2, accurately calculate and weigh the corresponding masses of the two compounds based on the total amount of material required.

[0047] 1.2. Weigh a certain mass of single-layer graphene (e.g., 0.1 g / group) and place it in a 150 ml beaker containing 20 ml of deionized water. Ultrasonicate at 50°C for 1.5 h to uniformly disperse the graphene and incorporate functional groups to obtain a graphene dispersion.

[0048] 1.3. Weigh silver nitrate and copper nitrate trihydrate separately in 50ml beakers. Add 10ml of deionized water to each solution and stir for 5 minutes. Then, mix the two solutions and stir again for 5 minutes. Next, slowly add an appropriate amount of cetyltrimethylammonium bromide (CTAB, e.g., 0.2g / component) as a surfactant. Ultrasonic stirring is performed at 40°C for 10 minutes to ensure uniform dispersion of the surfactant and the mixed solution, thereby obtaining a first mixed solution.

[0049] 2. Composite reaction: The prepared graphene dispersion was added dropwise to the first mixed solution, and ultrasonic stirring was performed at 20-25°C for 2 hours to allow the components to fully mix and react to obtain a second mixed solution.

[0050] 3. Precipitation reaction: Weigh a certain amount of NaOH (e.g., 6.0g / pack) into a 100ml beaker, add 50ml of deionized water, and magnetically stir for 10 minutes to completely dissolve it to obtain a NaOH solution. Add this NaOH solution dropwise to the second mixed solution and magnetically stir for 1 hour to ensure complete precipitation reaction, thus obtaining a third mixed solution.

[0051] 4. Hydrothermal Synthesis: Transfer the third mixed solution to an autoclave at 180°C and react for 10 hours. After the hydrothermal reaction, allow it to cool naturally to 20-25°C. The product is then washed repeatedly with anhydrous ethanol and then deionized water to remove impurities. After washing, the product is dried in a vacuum drying oven at 80°C for 24 hours to obtain the precursor.

[0052] 5. High-temperature calcination: The dried precursor is placed in a tube furnace and heated to 400°C at a rate of 4°C / min. The mixture is then held at this temperature for 4 hours and then cooled naturally. This high-temperature calcination process ultimately yields ternary nanocomposite electrode materials, such as Ag@CuO@rGO, with specific structures and properties.

[0053] Through the above-mentioned material composition design and preparation process, the new nanocomposite electrode material obtained is applied to the battery of the energy storage module, which can effectively improve the battery's charge and discharge cycle life, increase energy density, and enhance electrode stability. During the charge and discharge process, the unique structural design can shorten the lithium ion transmission path, alleviate the mechanical stress generated by volume expansion, prevent the agglomeration of active material nanoparticles, limit the excessive formation of solid electrolyte interface (SEI) film, and ensure the stability of the electrode material's microstructure during long-term cycling. At the same time, the outer layer of carbon material and doped precious metals significantly improve the material's conductivity, promote the rapid conduction of electrons and ions, and thus provide efficient and reliable energy storage support for the solar hybrid energy supply system for monitoring unmanned areas.

[0054] Specifically, in this embodiment, the small wind turbine in the auxiliary energy supply module adopts a vertical axis design, which enables stable operation in low wind speed environments and can automatically adjust the blade angle according to wind direction to improve wind energy capture efficiency. The wind turbine in the auxiliary energy supply module adopts a vertical axis design with automatically adjustable blades according to wind direction, which can overcome the wind speed limitations of traditional horizontal axis wind turbines and achieve stable power generation in low wind speed environments. Through adaptive wind direction adjustment, wind energy capture efficiency is improved, solar power supply blind spots are supplemented, and the energy acquisition capability of the hybrid energy supply system is enhanced.

[0055] Specifically, in this embodiment, the backup fuel generator in the auxiliary energy supply module has an intelligent start-stop function. When the energy storage module's power level falls below a set threshold and neither solar nor wind power can meet the load demand, it automatically starts and, during operation, can adjust its output power in real time based on load changes. The backup fuel generator has intelligent start-stop and dynamic load power adjustment functions, automatically starting only when renewable energy is insufficient, thus avoiding fuel waste. It also adjusts its output power based on real-time load demand, optimizing fuel consumption efficiency and reducing the operating cost and maintenance frequency of the energy supply system in the uninhabited area.

[0056] Example 2

[0057] Reference Figure 2 The difference between this embodiment and embodiment 1 is that a dynamic adjustment strategy method applicable to the solar hybrid energy supply system in embodiment 1 is provided, comprising the following steps:

[0058] S1: Real-time collection of solar panel output power, energy storage battery power, wind turbine output power, load power and environmental parameters (light intensity, wind speed, etc.);

[0059] S2: Based on the collected data, a machine learning-based prediction model is used to predict the changing trends of solar energy, wind energy, and load in the future.

[0060] S3: Based on the prediction results and the set optimization goals (such as maximizing the utilization of renewable energy, minimizing operating costs, etc.), the intelligent energy management algorithm dynamically adjusts the solar panel angle, energy storage battery charge and discharge status, wind turbine operating parameters and the start and stop of the auxiliary energy supply module to achieve efficient and stable operation of the system.

[0061] By adopting the above technical solution, based on the dynamic adjustment strategy of real-time data collection, machine learning prediction model and intelligent energy management algorithm, through data-driven prediction and optimization mechanism, energy distribution strategy can be planned in advance, and the coordinated scheduling of solar energy, wind energy and energy storage can be achieved. The system's adaptability to the complex environment of uninhabited areas is improved, and the energy supply reliability and energy utilization rate are guaranteed.

[0062] Specifically, in this embodiment, the prediction model based on machine learning adopts a deep neural network architecture. After training with a large amount of historical data, the prediction accuracy of solar energy, wind energy and load change trends can be greatly improved. The prediction model using a deep neural network architecture utilizes the powerful nonlinear fitting ability of machine learning to accurately predict energy output and load fluctuation trends, provide data support for energy management strategies, and reduce energy supply interruptions or waste caused by deviations in energy supply and demand forecasts.

[0063] Specifically, in this embodiment, the intelligent energy management algorithm is a multi-objective optimization algorithm that comprehensively considers energy costs, equipment lifespan, and energy supply reliability, and solves the optimization problem to obtain the optimal system operation strategy. This multi-objective optimization algorithm comprehensively considers energy costs, equipment lifespan, and energy supply reliability, and through global optimization, it balances the contradictions between economy, durability, and stability in the system, avoiding system shortcomings caused by single-objective optimization and achieving long-term, efficient operation of the hybrid energy supply system.

[0064] Specifically, in this embodiment, during the regulation process, when solar energy is sufficient and the energy storage battery is not full, the energy storage battery is charged first to meet the load requirements of the monitoring equipment; when solar energy is insufficient and the energy storage battery charge is above a set threshold, the energy storage battery is used for power supply; when the energy storage battery charge is below a set threshold and neither solar energy nor wind energy can meet the load requirements, the auxiliary energy supply module is activated. Through the prioritized energy scheduling logic (solar energy → energy storage → auxiliary energy), the proportion of renewable energy utilization can be maximized, fossil energy consumption can be reduced, and the system's carbon emissions and operating costs can be lowered; through the buffering effect of the energy storage battery, power supply fluctuations can be smoothed, and the power supply quality of the monitoring equipment can be improved.

[0065] Specifically, in this embodiment, it also includes system fault diagnosis and self-repair functions. When a component in the system is detected to have a fault, it automatically switches to the backup component and sends fault information to the operation and maintenance personnel through the remote communication module. At the same time, the adaptive control algorithm is used to adjust the system operation strategy to maintain the basic monitoring function of the system. By setting up fault diagnosis and self-repair functions (backup component switching + remote alarm + adaptive strategy adjustment), the fault tolerance of the system in an unmanned environment can be improved. The basic monitoring function is maintained through automatic fault handling and backup mechanisms, reducing dependence on manual operation and maintenance, and adapting to the remote operation and maintenance needs of unmanned areas.

[0066] In summary, the solar hybrid energy supply system and dynamic adjustment strategy for uninhabited area monitoring provided in this embodiment have the following advantages:

[0067] 1. High-reliability energy supply: Through a hybrid energy supply mode of solar energy, wind energy and fuel power generation, combined with intelligent dynamic adjustment strategies, it effectively solves the problem of unstable energy supply in uninhabited areas and ensures the continuous operation of monitoring equipment 24 hours a day, 7 days a week.

[0068] 2. Efficient energy utilization: Automatic tracking solar panels, low-wind-speed wind turbines, and intelligent energy management algorithms maximize the efficiency of renewable energy capture and utilization, reduce dependence on fossil energy, and reduce carbon emissions.

[0069] 3. Reduced operation and maintenance costs: The long-life design of the energy storage battery, intelligent fault diagnosis and self-repair functions, and the intelligent start-stop mechanism of the fuel generator significantly reduce the frequency of equipment maintenance and consumables consumption, and reduce the difficulty and cost of operation and maintenance in unmanned areas.

[0070] 4. Strong environmental adaptability: Intelligent sunshade and self-cleaning devices, battery protection function, and equipment design that can withstand harsh environments enable the system to adapt to complex uninhabited environments such as high temperature, dust, and low wind speed, thereby improving system stability and service life.

[0071] How it works

[0072] 1. Energy collection and conversion: The solar energy collection module converts solar energy into electrical energy through an array of automatically tracking solar panels, using new high-efficiency photovoltaic materials to improve photoelectric conversion efficiency. The small wind turbine in the auxiliary energy supply module uses a vertical axis design and automatically adjusts blades in the wind direction to capture wind energy for power generation. The backup fuel generator generates electricity by burning fuel when necessary.

[0073] 2. Energy storage and management: The energy storage module's intelligent battery management system controls the charging and discharging of high-performance energy storage batteries, monitors battery status in real time, uses new nano-composite electrode materials to extend battery life, and ensures battery safety through overcharge, over-discharge, and short-circuit protection functions.

[0074] 3. Dynamic Adjustment and Energy Supply: The system collects real-time data on solar panel output power, energy storage battery capacity, wind turbine output power, load power, and environmental parameters. It uses a machine learning-based prediction model to predict energy and load trends, and dynamically adjusts the operating status of each module through intelligent energy management algorithms. Solar power is prioritized, with excess energy stored in the energy storage battery. When solar power is insufficient, the energy storage battery provides power. When the energy storage battery capacity falls below a threshold and renewable energy is insufficient, a backup fuel generator is activated to ensure continuous and stable operation of the monitoring equipment load.

[0075] How to use

[0076] 1. System deployment: Install solar panel arrays, wind turbines, energy storage battery packs, backup fuel generators, and monitoring equipment at monitoring points in uninhabited areas, and complete system hardware connection and debugging.

[0077] 2. Parameter setting: Set the energy storage battery charge and discharge thresholds, auxiliary energy supply module startup conditions, and dynamic adjustment strategy optimization goals (such as maximizing renewable energy utilization, minimizing operating costs, etc.) through the system control terminal.

[0078] 3. Operation monitoring: The system runs automatically, collects and analyzes energy and environmental data in real time, and autonomously allocates energy based on dynamic adjustment strategies. Operation and maintenance personnel can view the system operation status in real time and receive fault alarm information through the remote communication module.

[0079] 4. Maintenance management: When the system detects a component failure, it automatically switches to the backup component to maintain basic functions. Operation and maintenance personnel perform targeted maintenance based on the fault information; regularly check the status of equipment such as energy storage batteries and fuel generators, and replenish consumables such as fuel.

[0080] The above are only preferred embodiments of the present invention and do not limit the implementation mode and protection scope of the present invention. For those skilled in the art, it should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the description and illustrations of the present invention should be included in the protection scope of the present invention.

Claims

1. A solar hybrid energy supply system for monitoring uninhabited areas, characterized by: include: A solar energy collection module, comprising a solar panel array that can automatically adjust its angle according to the position of the sun; The energy storage module is composed of multiple groups of energy storage batteries controlled by an intelligent battery management system, with real-time battery status monitoring and adaptive charge and discharge regulation functions; Auxiliary energy supply module, which can be started when solar energy is insufficient, includes a small wind turbine and a backup fuel generator, and can intelligently switch operating modes according to load demand; The monitoring equipment load module includes various sensors and data transmission equipment used for monitoring unmanned areas, and is electrically connected to the solar energy collection module, energy storage module and auxiliary energy supply module.

2. The solar hybrid energy supply system for monitoring uninhabited areas according to claim 1 is characterized in that: The solar energy collection module also includes an intelligent shading and self-cleaning device, which automatically shades and cools the solar panels during high temperature periods, and can automatically clean the surface of the solar panels after sandstorms, ensuring stable and efficient operation of the solar panels.

3. The solar hybrid energy supply system for monitoring uninhabited areas according to claim 1 is characterized in that: The battery of the energy storage module adopts a new type of nano-composite electrode material and has overcharge, over-discharge and short-circuit protection functions.

4. The solar hybrid energy supply system for monitoring uninhabited areas according to claim 1 is characterized in that: The small wind turbine in the auxiliary energy supply module adopts a vertical axis design, can operate stably in a low wind speed environment, and can automatically adjust the blade angle according to the wind direction to improve the wind energy capture efficiency.

5. The solar hybrid energy supply system for monitoring uninhabited areas according to claim 1 is characterized in that: The backup fuel generator in the auxiliary energy supply module has an intelligent start-stop function. When the power of the energy storage module is lower than the set threshold and neither solar energy nor wind energy can meet the load demand, it will start automatically, and during operation, the output power can be adjusted in real time according to load changes.

6. A dynamic adjustment strategy method applicable to the solar hybrid energy supply system according to any one of claims 1 to 5, characterized in that: The following steps are involved: Real-time collection of solar panel output power, energy storage battery power, wind turbine output power, load power and environmental parameters; Based on the collected data, a machine learning-based prediction model is used to predict the changing trends of solar energy, wind energy, and load in the future. According to the prediction results and the set optimization goals, the intelligent energy management algorithm dynamically adjusts the solar panel angle, energy storage battery charge and discharge status, wind turbine operating parameters and the start and stop of the auxiliary energy supply module to achieve efficient and stable operation of the system.

7. The dynamic adjustment strategy method for a solar hybrid energy supply system according to claim 6, characterized in that: The machine learning-based prediction model uses a deep neural network architecture.

8. The dynamic adjustment strategy method for a solar hybrid energy supply system according to claim 6, characterized in that: The intelligent energy management algorithm is a multi-objective optimization algorithm that comprehensively considers energy cost, equipment life, and energy supply reliability factors, and obtains the optimal system operation strategy by solving the optimization problem.

9. The dynamic adjustment strategy method for a solar hybrid energy supply system according to claim 6, characterized in that: During the adjustment process, when there is sufficient solar energy and the energy storage battery is not full, the energy storage battery will be charged first to meet the load demand of the monitoring equipment; when there is insufficient solar energy and the energy storage battery power is higher than the set threshold, the energy storage battery will supply power; when the energy storage battery power is lower than the set threshold and neither solar energy nor wind energy can meet the load demand, the auxiliary energy supply module will be started.

10. The dynamic adjustment strategy method applicable to a solar hybrid energy supply system according to claim 6, characterized in that: It also includes system fault diagnosis and self-repair functions. When a component in the system is detected to have a fault, it automatically switches to the backup component and sends fault information to the operation and maintenance personnel through the remote communication module. At the same time, it uses adaptive control algorithms to adjust the system operation strategy to maintain the basic monitoring function of the system.

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