Floor type equipment for producing water from air

Abstract

The invention relates to the field of water production systems, and discloses floor type air water production equipment which comprises a multi-stage energy sensing module, an energy budget core module, a dynamic energy distribution module, a water production switching module and an energy optimization closed-loop module. The multi-stage energy sensing module is used for collecting data information of solar input power, battery charge state, environment temperature and environment humidity in real time; the energy budgeting core module calculates current available energy and predicts available energy in a future short period in real time, and deduces an energy trend based on a calculation result; a hierarchical energy management module used for executing a hierarchical energy management strategy according to the budgeting result of the energy budgeting core module; the water production switching module comprises a plurality of water production working modes, and each water production working mode corresponds to different energy consumption and water production efficiency; the energy optimization closed-loop module adjusts the dynamic energy distribution module and the energy budgeting core module; the device can continuously and efficiently work even in a low-light or extreme environment.

Description

Technical Field

[0001] The present invention relates to the technical field of water production systems, and in particular to a floor-standing air-water production device. Background Art

[0002] Many of my country's islands and reefs are in an environment of "three highs"—high temperature, high humidity, and high salinity. Freshwater resources on the islands and reefs are extremely scarce. Currently, many water-scarce areas at home and abroad are adopting seawater desalination technology. Seawater desalination is a process of removing salt and minerals from seawater through special equipment to produce clean drinking water. However, after desalination, the long-term discharge of salt in the seawater and chemicals used to clean the seawater will cause harm to the marine environment. At the same time, seawater desalination has very high requirements for the site.

[0003] Therefore, the solar air water machine is more suitable for the above scenario. The solar air water machine is a device that uses solar panels to convert solar energy into electrical energy and drive the compressor to compress the water molecules in the air into water droplets. Subsequently, the water droplets are rapidly cooled through the condenser and eventually condensed into water. The water is stored in a water tank and collected for use.

[0004] However, existing solar air water production equipment lacks dynamic perception and autonomous decision-making capabilities under conditions of insufficient power supply (such as low light environment, decreased solar energy supply) or harsh environment (such as low temperature and low humidity). It is unable to intelligently switch working modes according to real-time energy status and environmental changes, resulting in low water production efficiency, abnormal increase in energy consumption, and even equipment shutdown or reverse energy consumption, seriously affecting overall performance and reliability. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a floor-standing air-to-water device to solve the problems mentioned in the above background.

[0006] The present invention provides the following technical solution: The present invention discloses a floor-standing air-to-water device, comprising a multi-level energy sensing module, an energy budget core module, a dynamic energy allocation module, a water production switching module, and an energy optimization closed-loop module; A multi-level energy sensing module is used to collect data information on solar input power, battery state of charge, ambient temperature and ambient humidity in real time and output the data information; An energy budget core module, which calculates the current and predicted short-term future available energy in real time based on the data information output by the multi-level energy sensing module, and infers energy trends based on the calculation results; A dynamic energy allocation module, configured to execute a hierarchical energy management strategy based on the budget result of the energy budget core module; A water production switching module includes multiple water production operating modes, each corresponding to different energy consumption and water production efficiency. The water production switching module dynamically selects the optimal operating mode based on real-time energy budget, environmental parameters, and future energy trends; The energy optimization closed-loop module realizes adaptive regulation of system energy consumption by adjusting the dynamic energy allocation module and the energy budget core module based on the real-time data provided by the multi-level energy sensing module and the working status of the water production switching module.

[0007] As a preferred solution, the multi-level energy sensing module includes a sensor array, and the sensor array includes: Solar energy sensor, used to detect current solar energy input power; Battery status sensor, used to monitor the battery charge status; The environmental sensor group is used to measure the ambient temperature and ambient humidity respectively.

[0008] As a preferred solution, the energy budget core module calculates available energy based on the following dynamic energy budget formula: ; Where E(t+Δt) represents the estimated available energy from time t to t+Δt, η solar is the photoelectric conversion efficiency, P solar (t) is the solar input power at time t, C bat (t) is the battery state of charge at the current time t, E batmax The maximum energy storage capacity of the battery.

[0009] As a preferred solution, the hierarchical energy management strategy of the dynamic energy allocation module includes: When the energy budget result indicates that the available energy is sufficient, the core water production module is controlled to operate at the maximum rated power; When the energy budget result indicates that the available energy is insufficient, the core water production module is controlled to reduce the power output, and the auxiliary subsystem is placed in standby or dormant state, and the auxiliary subsystem is dynamically started or shut down according to the real-time remaining energy situation.

[0010] As a preferred solution, the water production working modes include high-efficiency water production mode, energy-saving water production mode, system survival mode and deep sleep mode.

[0011] As a preferred solution, the water production switching module selects the current optimal working mode based on the following mode optimization objective function: ; Among them, U(M i) is the optimal objective function value of the i-th working mode, W(M i ) is the water production per unit time in the i-th working mode, P(M i ) is the system power consumption in the i-th working mode, and α and β are dynamically adjustable weight coefficients.

[0012] As a preferred solution, the intelligent multi-mode water production switching module selects the current working mode according to the following decision rules: ; That is, choose the objective function U(M i ) Take the working mode M(t) with the maximum value as the current operating mode.

[0013] As a preferred solution, the energy optimization closed-loop module includes a fault detection submodule, which identifies system operation failures and generates adjustment signals based on energy acquisition data anomalies, water production efficiency anomalies and battery performance attenuation indicators, and adjusts the dynamic energy allocation strategy and working mode switching logic.

[0014] As a preferred solution, the energy optimization closed-loop module also includes a power consumption recording submodule, which records the power consumption data of each module under different working conditions in real time. The power consumption data serves as the input basis for the energy budget core module to dynamically optimize the energy budget model.

[0015] As a preferred solution, the water production equipment further includes a weight dynamic adjustment module, which is used to dynamically update the weight coefficients α and β based on the environmental energy changes in each cycle Δt, so that the equipment can adapt to environmental changes. The weight update formula is: ; ; Among them, f1 and f2 are weight adjustment functions, and ΔE(t) is the energy change in the current cycle.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention significantly improves water production efficiency and system energy efficiency through mechanisms such as intelligent perception, energy budgeting, dynamic allocation, flexible mode switching and fault detection. The equipment can intelligently switch working modes according to real-time environmental changes, produce water efficiently when there is sufficient light, save energy and reduce consumption when energy is insufficient, and extend operating time. At the same time, real-time monitoring and closed-loop optimization improve the reliability of the equipment, effectively prevent faults, and ensure stable operation. With its adaptive ability, the equipment can continue to work efficiently even in dim light or extreme environments, comprehensively solving the problem of performance degradation of traditional water production systems under harsh conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of the system flow of the self-optimizing water production equipment of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] See also Figure 1 A floor-standing air-to-water device in this embodiment includes a multi-level energy sensing module, an energy budget core module, a dynamic energy allocation module, a water production switching module, and an energy optimization closed-loop module; A multi-level energy sensing module collects and outputs real-time data on solar input power, battery state of charge, ambient temperature, and humidity. This data not only provides the basis for subsequent energy budgeting and allocation, but also provides an important basis for selecting the device's operating mode. The multi-level energy sensing module includes a sensor array, and the sensor array includes: Solar sensor, used to detect the current solar input power. Its main function is to detect the current solar input power to evaluate the external lighting conditions and available solar resources. Its main function is to detect the current solar input power to evaluate the external lighting conditions and available solar resources. The battery status sensor is used to monitor the battery's state of charge. Its main task is to monitor the battery's state of charge in real time and can also be expanded to monitor the battery's health status. This sensor can provide information on battery parameters such as current voltage, current, and temperature, and use this information to estimate the remaining power and predict the usable time. The environmental sensor group is used to measure ambient temperature and ambient humidity respectively, and includes at least two sub-modules: an ambient temperature sensor and an ambient humidity sensor. The ambient temperature sensor is used to measure the current ambient temperature and provide a reference for the thermal management and performance optimization of the energy system (such as reducing the charging rate when the temperature is too high). The ambient humidity sensor monitors the humidity level in the air and can be used to determine the potential risk of condensation when necessary. It is of great protection significance for electronic systems in outdoor or high-humidity environments.

[0020] An energy budget core module, which calculates the current and predicted short-term future available energy in real time based on the data information output by the multi-level energy sensing module, and infers energy trends based on the calculation results; The energy budget core module can optionally use a lightweight neural network or fuzzy logic controller to analyze the current energy status in real time. The core function of this module is to calculate the system's energy budget, including the current available energy, the expected energy change trend, and the possible energy shortage period. Based on this budget result, the system can dynamically adjust the working mode of each module to ensure that the water production task can be completed at any time without exceeding the energy budget. The flexibility of this module enables the system to operate efficiently under different energy conditions.

[0021] A dynamic energy allocation module, configured to execute a hierarchical energy management strategy based on the budget result of the energy budget core module; The main task of this module is to reasonably distribute energy to various devices and modules according to the energy supply situation, ensuring that the system can work stably while minimizing unnecessary energy waste.

[0022] A water production switching module includes multiple water production operating modes, each corresponding to different energy consumption and water production efficiency. The water production switching module dynamically selects the optimal operating mode based on real-time energy budget, environmental parameters, and future energy trends; An energy optimization closed-loop module, based on the real-time data provided by the multi-level energy sensing module and the working status of the water production switching module, realizes adaptive regulation of system energy consumption by adjusting the dynamic energy allocation module and the energy budget core module; Based on the real-time data provided by the multi-level energy sensing module and the working status of the water production switching module, it intelligently adjusts the coordination between the dynamic energy allocation module and the energy budget core module to optimize the overall energy consumption of the system.

[0023] In summary, the core advantage of this water-making equipment lies in its high degree of intelligence and adaptability. Through real-time perception and data acquisition, dynamic energy distribution, intelligent mode switching and optimized closed-loop control, the system can flexibly adjust its working strategy under different environmental and energy conditions to maximize water-making efficiency and extend the service life of the system. Whether in sufficient light or in extremely low-energy environments, the system can ensure stable operation and continuous water supply. In addition, the system's energy management mechanism can significantly reduce energy waste, optimize energy use, reduce operating costs, and extend the effective use time of the system. Overall, this technological breakthrough not only solves the performance problems of traditional water-making systems in complex environments, but also greatly improves the energy efficiency and reliability of the system.

[0024] Furthermore, the energy budget core module calculates available energy based on the following dynamic energy budget formula: ; Among them, E(t+Δt) represents the estimated available energy from time t to t+Δt, which directly determines the sustainable operation capability of the system in the future time period, η solar Photovoltaic conversion efficiency is the efficiency of photovoltaic modules in converting received solar energy into electrical energy. This parameter is usually affected by factors such as lighting conditions, temperature, and equipment aging. solar (t) is the solar input power at time t, reflecting the change of solar irradiance over time. The power change curve can be estimated in advance by predicting future weather conditions, sunshine duration, etc. bat (t) is the battery state of charge at the current time t, usually expressed as a dimensionless value between 0 and 1, C bat (t) = 1 means the battery is fully charged, C bat (t) = 0 means the battery is out of power, E batmax The maximum energy storage capacity of the battery.

[0025] Analysis of the physical meaning of the formula: Solar energy contribution : Calculate the available electrical energy obtained from solar energy in the future Δt time window. The integration operation represents the cumulative energy of solar power changing over time, multiplied by the photoelectric conversion efficiency η solar Finally, the actual electrical energy that can be used for system operation is obtained; Battery energy contribution : It indicates the immediate backup energy that the battery can provide, reflecting the system's ability to rely on stored energy to maintain operation even when there is short-term insufficient sunlight.

[0026] This dynamic energy budget formula integrates environmental perception (solar energy prediction) and system status perception (battery power monitoring) to form a dual synergistic mechanism and achieve real-time adaptive optimization: as the weather changes, the intensity of sunshine, and the energy storage status change, the system can dynamically adjust the water production strategy (such as adjusting the water production rate, selecting energy consumption mode, etc.), and also improve the system's energy utilization and stability. It is particularly suitable for use in remote areas, islands, deserts and other environments with limited electricity resources.

[0027] Furthermore, the hierarchical energy management strategy of the dynamic energy allocation module includes: When the energy budget indicates that sufficient energy is available, the core water production module is controlled to operate at the maximum rated power. The system can fully utilize its water production capacity in high-efficiency mode to ensure the maximum output of water production and meet the high demand for water resources. When the energy budget results indicate that the available energy is insufficient, the core water production module is controlled to reduce the power output, and the auxiliary subsystems (including but not limited to the auxiliary heating module and the wireless communication module) are placed in standby or hibernation mode. The auxiliary subsystems are dynamically started or shut down based on the real-time remaining energy situation. These subsystems usually consume less energy, but they still have a certain impact on the overall energy budget. By temporarily shutting down these unnecessary modules, the system can save valuable electricity.

[0028] To enable timely responses to external events (such as urgent water needs or changes in the external environment), the system also includes an ultra-low-power wake-up mechanism. Even when the system is in a low-energy state, it can still remain in standby mode through low-power consumption and quickly respond to external requests when needed. This mechanism ensures the system's flexibility and adaptability under extreme conditions.

[0029] Furthermore, the water production operating mode includes a high-efficiency water production mode, in which the system operates at maximum water production rate to quickly and fully meet water resource needs. In this mode, the system utilizes all available photovoltaic power and stored battery energy to ensure maximum water production; In energy-saving water production mode, the system moderately reduces the water production rate to reduce energy consumption. The water production rate is dynamically adjusted according to the remaining energy and energy budget to ensure that energy is not exhausted prematurely. System survival mode: The system only maintains the minimum survival conditions and does not produce water. At this time, the system will shut down all non-core functions and only retain the most basic survival support functions to ensure that the system can survive the period of energy shortage; In deep sleep mode, the system shuts down almost all non-essential systems and enters an extremely low-power sleep state, maintaining only the most basic standby state. In this mode, the system's power consumption is reduced to a minimum to maximize the system's survival time, waiting for the external environment or energy supply to recover.

[0030] This hierarchical energy management strategy ensures that the system can respond flexibly under various energy conditions through intelligent scheduling of different working modes, ensuring the execution of water production tasks, while achieving energy conservation and efficient operation of the system. By reasonably managing energy consumption, the system can not only provide high water production when energy is sufficient, but also maximize the use time when energy is short, avoiding system shutdown due to insufficient energy.

[0031] Furthermore, the water production switching module selects the current optimal working mode based on the following mode optimization objective function: ; Among them, U(M i ) is the optimal objective function value of the i-th working mode, which is used to evaluate the optimal performance under different working modes.i ) is the water production per unit time under the i-th working mode, indicating the amount of water produced per unit time under this mode, P(M i ) is the system power consumption in the i-th working mode, which indicates the energy consumed by the system in this mode. α and β are dynamically adjustable weight coefficients. These two weight coefficients are dynamically adjustable and are used to control the contribution of water production and system power consumption to the objective function. α is used to adjust the importance of water production, and β is used to adjust the impact of power consumption on the optimization target. Under different application scenarios and operating requirements, these two parameters can be adjusted according to actual conditions.

[0032] Objective function U(M i ) is a combination of the following factors: Balance between energy efficiency and water production: By combining water production and power consumption, the objective function achieves comprehensive optimization of water production and energy efficiency; Energy availability: By considering the availability of future energy, the objective function ensures that a high water yield can be maintained under limited energy conditions while avoiding system downtime due to excessive energy consumption; The form of the objective function enables the system to weigh the relationship between power consumption and water production according to different working modes, thereby dynamically selecting the optimal working mode.

[0033] The specific working process is as follows: For each candidate working mode M i , calculate the corresponding objective function value U(M i ); Compare the objective function values under different working modes and select U(M i ) The largest working mode is regarded as the optimal mode at the current moment; This selection process can be dynamically adjusted based on real-time demand or energy conditions, ensuring that the system can operate efficiently in different working environments.

[0034] In actual applications, α and β may be dynamically adjusted according to different times or operating conditions. For example, in certain periods, more emphasis may be placed on increasing water production (such as during high-demand periods), in which case α can be increased. In other periods (such as when energy supply is tight), more emphasis may be placed on reducing power consumption, in which case β can be increased. This dynamic adjustment enables the system to respond more flexibly to various actual situations.

[0035] Furthermore, the intelligent multi-mode water production switching module selects the current working mode according to the following decision rules: ; That is, choose the objective function U(M i ) Take the working mode M(t) with the maximum value as the current operating mode.

[0036] Through this switching strategy based on benefit maximization, the intelligent module can adaptively adjust according to environmental changes (such as water source conditions, external water supply demand, changes in energy consumption prices, etc.), thereby ensuring that the entire system is always in the optimal or suboptimal operating state.

[0037] Furthermore, the energy optimization closed-loop module includes a fault detection submodule, which identifies system operation faults and generates adjustment signals based on energy acquisition data anomalies, water production efficiency anomalies and battery performance attenuation indicators, and adjusts the dynamic energy allocation strategy and working mode switching logic.

[0038] Specifically, the fault detection submodule uses the following three core indicators to accurately identify system faults: Energy collection data anomaly detection: Real-time monitoring of energy data collected by the system from the environment (such as photovoltaic output, battery charging and discharging status, etc.). If the energy input data deviates from the normal range (such as sudden drops or abnormal fluctuations), an abnormality alarm is triggered; Water production efficiency anomaly detection: Continuously evaluates efficiency indicators during the water production process, such as water production per unit energy consumption. If the efficiency drops significantly (below a set threshold), it is considered a potential fault or performance degradation. Battery performance degradation detection: Based on battery charge and discharge curves, remaining capacity, internal resistance changes and other parameters, it monitors the battery's health status and identifies performance degradation problems caused by battery aging.

[0039] When the fault detection submodule identifies an anomaly, it immediately generates an adjustment signal and transmits it to the system's dynamic energy management unit. Based on the type and severity of the fault, the system automatically performs the following optimization operations: Dynamically adjust energy allocation strategies (for example, prioritize power supply to core modules and reduce power consumption of non-core modules); Optimize or reconfigure the current operating mode switching logic (for example, switching to low-power water-making mode or entering fault protection mode) to ensure system stability and maximize energy utilization.

[0040] Through this closed-loop adaptive mechanism, the system can continue to maintain efficient and reliable operation in the face of environmental changes, equipment aging or abnormal conditions, significantly improving the system's intelligence and self-repair capabilities.

[0041] Furthermore, the energy optimization closed-loop module also includes a power consumption recording submodule, which records the power consumption data of each module under different working conditions in real time. The power consumption data serves as the input basis for the energy budget core module to dynamically optimize the energy budget model.

[0042] The power consumption recording submodule can record the detailed power consumption data of each module (such as energy collection module, water production module, control module, sensor module, etc.) in real time under different working modes and different working conditions (such as changes in ambient temperature, load, water source conditions, etc.).

[0043] The collected power consumption data includes key indicators such as instantaneous power, average power, and cumulative energy consumption, forming a multi-dimensional power consumption data set classified by time, module, and mode.

[0044] This real-time updated power consumption data is fed into the energy budget core module as input, serving as an important basis for its dynamic optimization of the energy budget model. Specifically, by continuously learning and updating power consumption characteristics, the energy budget model can more accurately predict future energy consumption trends and dynamically adjust energy allocation and budget strategies to achieve the following: Maximize energy utilization; Extended system operating life; Optimizing water production under limited energy conditions; Improved ability to respond quickly to sudden environmental changes.

[0045] The introduction of the power consumption recording sub-module enables the entire self-optimizing water production system to have data-driven self-learning capabilities. It can not only adapt to changes in the performance of the equipment itself, but also adapt to changes in the external environment, realizing truly efficient and intelligent energy management.

[0046] Furthermore, the water production equipment also includes a weight dynamic adjustment module, which is used to dynamically update the weight coefficients α and β based on the environmental energy changes in each cycle Δt, so that the system adapts to environmental changes. Through the dynamic adjustment of the weights, the energy optimization decision can more sensitively reflect the changes in environmental conditions, such as the fluctuation of the available solar power and the change of the remaining battery capacity, thereby continuously optimizing the water production efficiency and energy utilization efficiency. The weight update formula is: ; ; Among them, f1 and f2 are weight adjustment functions generated based on online learning algorithms or lightweight neural networks, and ΔE(t) is the energy change in the current period Δt.

[0047] The two functions f1 and f2 are implemented based on the following technology: Online learning algorithms (such as recursive least squares, incremental gradient descent, and other lightweight adaptive algorithms) can be used to train and adjust in real time based on the historical and current trends of input variables; Alternatively, a lightweight neural network model (such as TinyML or a low-parameter MLP network) can be used to perform real-time inference to obtain optimized weight update values, thereby balancing response speed and computing resource overhead.

[0048] Here is an example of one of the two functions f1 and f2: ; ; Among them, NeuralNet represents a simple feedforward neural network with two input nodes, namely P solar (t) and ΔE(t). The network obtains weight coefficients through training to optimize the function output. The network contains one hidden layer, the activation function uses ReLU, and the output layer is activated by sigmoid to obtain the coefficient α(t+Δt); Similarly, f2 is also a value based on the input battery charge state C bat (t) and energy change ΔE(t). The trained output is used to adjust the weight coefficient β(t+Δt). The network architecture is the same as f1, and the activation function also uses ReLU and sigmoid.

[0049] Through the above dynamic weight adjustment mechanism, we can achieve: When the external environment is energy-rich (e.g., when there is sufficient sunlight), the priority of water production is increased (i.e., α is increased); When the remaining battery energy is low or the energy supply is unstable, the priority of energy conservation or energy efficiency optimization is automatically increased (i.e., β is increased, such as during continuous cloudy days); The system's optimization goal is to flexibly balance water production capacity and energy efficiency, always adapting to the current actual environment and resource conditions.

[0050] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A floor-standing air-to-water device, characterized by: It includes a multi-level energy sensing module, an energy budget core module, a dynamic energy allocation module, a water production switching module, and an energy optimization closed-loop module; A multi-level energy sensing module is used to collect data information on solar input power, battery state of charge, ambient temperature and ambient humidity in real time and output the data information; An energy budget core module, which calculates the current and predicted short-term future available energy in real time based on the data information output by the multi-level energy sensing module, and infers energy trends based on the calculation results; A dynamic energy allocation module, configured to execute a hierarchical energy management strategy based on the budget result of the energy budget core module; A water production switching module includes multiple water production operating modes, each corresponding to different energy consumption and water production efficiency. The water production switching module dynamically selects the optimal operating mode based on real-time energy budget, environmental parameters, and future energy trends; The energy optimization closed-loop module realizes adaptive regulation of system energy consumption by adjusting the dynamic energy allocation module and the energy budget core module based on the real-time data provided by the multi-level energy sensing module and the working status of the water production switching module.

2. The floor-standing air-to-water device according to claim 1, characterized in that: The multi-level energy sensing module includes a sensor array, and the sensor array includes: Solar energy sensor, used to detect current solar energy input power; Battery status sensor, used to monitor the battery charge status; The environmental sensor group is used to measure the ambient temperature and ambient humidity respectively.

3. The floor-standing air-to-water device according to claim 1, characterized in that: The energy budget core module calculates available energy based on the following dynamic energy budget formula: ； Where E(t+Δt) represents the estimated available energy from time t to t+Δt, η solar is the photoelectric conversion efficiency, P solar (t) is the solar input power at time t, C bat (t) is the battery state of charge at the current time t, E batmax The maximum energy storage capacity of the battery.

4. The floor-standing air-to-water device according to claim 1, characterized in that: The hierarchical energy management strategy of the dynamic energy allocation module includes: When the energy budget result indicates that the available energy is sufficient, the core water production module is controlled to operate at the maximum rated power; When the energy budget result indicates that the available energy is insufficient, the core water production module is controlled to reduce the power output, and the auxiliary subsystem is placed in standby or dormant state, and the auxiliary subsystem is dynamically started or shut down according to the real-time remaining energy situation.

5. The floor-standing air-to-water device according to claim 3, characterized in that: The water production working modes include high-efficiency water production mode, energy-saving water production mode, system survival mode and deep sleep mode.

6. The floor-standing air-to-water device according to claim 5, characterized in that: The water production switching module selects the current optimal working mode based on the following mode optimization objective function: ； Among them, U(M i ) is the optimal objective function value of the i-th working mode, W(M i ) is the water production per unit time in the i-th working mode, P(M i ) is the system power consumption in the i-th working mode, and α and β are dynamically adjustable weight coefficients.

7. The floor-standing air-to-water device according to claim 6, characterized in that: The intelligent multi-mode water production switching module selects the current working mode according to the following decision rules: ； That is, choose the objective function U(M i ) Take the working mode M(t) with the maximum value as the current operating mode.

8. The floor-standing air-to-water device according to claim 1, characterized in that: The energy optimization closed-loop module includes a fault detection submodule, which identifies system operation faults and generates adjustment signals based on abnormal energy collection data, abnormal water production efficiency and battery performance attenuation indicators, and adjusts the dynamic energy allocation strategy and working mode switching logic.

9. The floor-standing air-to-water device according to claim 8, characterized in that: The energy optimization closed-loop module also includes a power consumption recording submodule, which records the power consumption data of each module under different working conditions in real time. The power consumption data serves as the input basis for the energy budget core module to dynamically optimize the energy budget model.

10. The floor-standing air-to-water device according to claim 6, characterized in that: The water production equipment further includes a weight dynamic adjustment module, which is used to dynamically update weight coefficients α and β based on environmental energy changes in each cycle Δt, so that the system adapts to environmental changes. The weight update formula is: ； ； Among them, f1 and f2 are weight adjustment functions, and ΔE(t) is the energy change in the current cycle.

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