Roof type air water taking system based on solar power supply

Abstract

The invention relates to the field of intelligent control, and particularly discloses a roof-type air water taking system based on solar power supply, which monitors environment, energy and water production states in real time, processes the information, particularly innovatively introduces quantitative evaluation on a water-energy conversion efficiency index, and provides a roof-type air water taking system based on solar power supply on the basis of the efficiency index. And in combination with key parameters such as solar input power and battery charge state, intelligent operation mode decision is performed. Through the intelligent scheduling, the system puts the limited solar energy resources into the water production task at the most appropriate time, so that the negative correlation between the instantaneous energy availability and the instantaneous water production efficiency is decoupled, and the maximization of the total water yield in the complete period of 24 hours is finally realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent control, and more particularly, to a roof type air water taking system based on solar power supply. BACKGROUND

[0002] Under the background of global water resources becoming increasingly scarce, especially in arid and desert regions with weak power infrastructure, direct water taking from air is considered as a highly potential distributed water resource solution. Combining air water taking technology with solar photovoltaic power generation technology to build a roof type air water taking system based on solar power supply can utilize the abundant solar energy resources in these regions to achieve energy self-sufficiency and on-site water production, providing a feasible technical path to solve the drinking water problem in off-grid environments.

[0003] However, the existing solar air water taking system generally faces the core problem of water production not meeting expectations in actual operation. This is mainly due to the relatively simple control strategy, which usually adopts an intuitive threshold control logic: when the solar power is sufficient during the day, the water taking equipment is preferentially operated, and the excess power is stored in the battery; when the solar power is insufficient, the battery is powered. This control method ensures the operation of the equipment, but ignores the dynamic change relationship between system operation efficiency and environmental conditions, resulting in inefficient use of energy.

[0004] The root of the problem lies in that the instantaneous energy availability of the system and the instantaneous water-energy conversion efficiency present a significant negative correlation characteristic within a 24-hour period. Specifically, during the day, especially at noon, the solar power generation power reaches the peak, and the energy supply is most abundant; but at this time, the environmental temperature is usually the highest, and the relative humidity is the lowest. Under such high-temperature and low-humidity conditions, the dew point temperature of air is extremely low, and the refrigeration system needs to consume a huge amount of energy to cool the air below the dew point to condense water, resulting in extremely low water-energy conversion efficiency. On the contrary, at night, although there is no direct solar energy supply, the environmental temperature decreases and the relative humidity increases, and the efficiency of the refrigeration system for water taking is much higher than that during the day. The traditional control strategy exactly uses valuable solar power for inefficient water taking during the day when the efficiency is the lowest, and fails to store sufficient energy to support the highest efficiency of water taking at night, resulting in a serious mismatch between energy and efficiency, which ultimately limits the total water production of the system in the whole period. SUMMARY

[0005] To solve the above technical problems, the present application is proposed. According to a roof type air water taking system based on solar power supply according to the present application, comprising:

[0006] a data parameter acquisition module for acquiring environmental temperature, environmental relative humidity, solar input power, battery voltage, battery current and instantaneous water production flow rate;

[0007] a system comprehensive state evaluation module configured to perform system comprehensive state evaluation based on the ambient temperature, the ambient relative humidity, the battery voltage, the battery current, and the instantaneous water production flow to obtain a battery state of charge, a water extraction system load power, a current water-to-energy conversion efficiency index, and an operation time partition;

[0008] a control mode generation module configured to perform intelligent operation mode decision-making on the solar input power, the battery state of charge, the current water-to-energy conversion efficiency index, and the operation time partition to obtain a control mode;

[0009] a device control parameter generation module configured to generate device control parameters based on the control mode, the device control parameters including a compressor state and a fan rotation speed.

[0010] Compared with the prior art, the roof type air water extraction system powered by solar energy provided in the present application constructs a control system capable of dynamic evaluation and intelligent decision-making to replace the conventional simple threshold control logic. The scheme monitors the environment, energy, and water production state in real time, processes these information, and particularly innovatively introduces quantitative evaluation of the water-to-energy conversion efficiency index, and based on the efficiency index, intelligent operation mode decision-making is performed in combination with key parameters such as the solar input power and the battery state of charge. This method solves the energy and efficiency mismatch problem pointed out in the background art: when the sunlight is sufficient during the day but the water extraction efficiency is low, the system can decide to charge the battery first and store the energy; and at night when the environment is suitable and the water extraction efficiency is high, the stored energy is called to efficiently produce water. Through this intelligent scheduling, the system will invest the limited solar energy resources into the water production task at the most appropriate time, thereby decoupling the negative correlation between the instantaneous energy availability and the instantaneous water production efficiency, and finally maximizing the total water production in a 24-hour complete cycle. BRIEF DESCRIPTION OF DRAWINGS

[0011] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description of embodiments of the present application taken in conjunction with the accompanying drawings. The drawings provided in the present application are used to provide further understanding of the embodiments of the present application, and constitute a part of the specification, and are used to explain the present application together with the embodiments of the present application, and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally indicate the same components or steps.

[0012] Figure 1 FIG. 1 is a block diagram of a roof type air water extraction system powered by solar energy according to an embodiment of the present application.

[0013] Figure 2 FIG. 1 is a data flow diagram of a roof type air water extraction system powered by solar energy according to an embodiment of the present application.

[0014] Figure 3A block diagram of a system comprehensive state evaluation module in a solar-powered roof-mounted air water extraction system according to an embodiment of the present application.

[0015] Figure 4 A data flow diagram of a system comprehensive state evaluation module in a solar-powered roof-mounted air water extraction system according to an embodiment of the present application. DETAILED DESCRIPTION

[0016] Embodiments of the present disclosure will be described in more detail with reference to the drawings. Although certain embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms and should not be interpreted as being limited to the embodiments set forth herein, but rather the embodiments are provided so that the present disclosure can be more thoroughly and completely understood. It is understood that the drawings and embodiments of the present disclosure are for exemplary purposes only and are not intended to limit the scope of protection of the present disclosure.

[0017] The present application is proposed in view of the problems in the prior art. Figure 1 A block diagram of a solar-powered roof-mounted air water extraction system according to an embodiment of the present application. Figure 2 A data flow diagram of a solar-powered roof-mounted air water extraction system according to an embodiment of the present application. Specifically, as shown in Figure 1 and Figure 2 A solar-powered roof-mounted air water extraction system 100 according to an embodiment of the present application includes a data parameter acquisition module 110 configured to acquire environmental temperature, environmental relative humidity, solar input power, battery voltage, battery current, and instantaneous water production flow rate; a system comprehensive state evaluation module 120 configured to perform system comprehensive state evaluation based on the environmental temperature, the environmental relative humidity, the battery voltage, the battery current, and the instantaneous water production flow rate to obtain a battery state of charge, a water extraction system load power, a current water-energy conversion efficiency index, and a running time partition; a control mode generation module 130 configured to perform intelligent running mode decision on the solar input power, the battery state of charge, the current water-energy conversion efficiency index, and the running time partition to obtain a control mode; and a device control parameter generation module 140 configured to generate device control parameters based on the control mode, the device control parameters including a compressor state and a fan rotating speed.

[0018] Specifically, the data parameter acquisition module 110 is configured to acquire the ambient temperature, the ambient relative humidity, the solar input power, the battery voltage, the battery current and the instantaneous water production flow. It can be understood that, in order to overcome the mismatch between energy and efficiency in the existing solar air water taking equipment, that is, the water taking efficiency is low in the daytime when the energy is abundant, and the water taking efficiency is high at night when the energy is scarce, the control logic needs to change from a simple threshold judgment to an intelligent decision-making mode that can understand the overall situation and dynamically optimize. The basis of this decision-making mode is to comprehensively and accurately master the key dynamic variables that affect energy supply, energy storage, water taking efficiency and equipment state. Therefore, before any complex evaluation and decision-making, the first step is to build a front-end perception layer that can capture multi-dimensional running parameters in real time and synchronously, to provide complete and reliable real-time data input for subsequent system comprehensive state evaluation and intelligent control mode generation, so as to ensure the scientificity and effectiveness of the decision-making. Therefore, in the present application, the first step is to acquire the ambient temperature, the ambient relative humidity, the solar input power, the battery voltage, the battery current and the instantaneous water production flow.

[0019] In a feasible technical solution, the data parameter acquisition module 110 is processed as follows: the core of the module is a microcontroller unit integrated with multiple interfaces, which is responsible for coordinating and collecting data from different sensors, and integrating these data into a structured data frame for subsequent module calling.

[0020] The acquisition process of the ambient temperature and the ambient relative humidity is as follows: the ambient temperature refers to the macro temperature of the surrounding air at the location of the device, and the ambient relative humidity represents the saturation degree of water vapor in the air. These two parameters together determine the dew point temperature of the air, which is the primary basis for judging the current water taking efficiency. Therefore, at the air inlet of the device, a high-precision digital temperature and humidity sensor is installed. The sensor is placed in a radiation-proof and rain-proof louver box to ensure that its measurement value can truly reflect the state of the surrounding air and avoid the interference of direct sunlight or device self-heating. The microcontroller unit sends a read instruction to the sensor through the I2C serial bus protocol at a preset time interval, for example, every 10 seconds. The analog-to-digital converter inside the sensor converts the sensed temperature and humidity physical quantities into digital signals and transmits them back to the microcontroller unit through the I2C bus. For example, at a certain sampling time, the microcontroller unit receives digital quantities representing an ambient temperature of 45.0 degrees Celsius and an ambient relative humidity of 15.0%.

[0021] The process of obtaining the solar input power is as follows: the solar input power refers to the real-time electrical power generated by the PV array under the current lighting conditions, which represents the immediate energy supply available to the device at the moment. This parameter is obtained indirectly through the maximum power point tracking (MPPT) solar charge controller connected to the PV array. The controller itself continuously monitors the output voltage and output current of the PV array. The microcontroller unit establishes communication with the charge controller through an industrial bus interface such as RS485 or CAN. At each sampling period, the microcontroller unit queries the charge controller for the PV voltage and current values stored in its internal registers. After obtaining these two values, the microcontroller unit immediately performs a multiplication operation, i.e. power equals voltage multiplied by current, to calculate the current solar input power. For example, at the same sampling time as above, the microcontroller unit reads from the charge controller that the PV array output voltage is 36.0 volts and the output current is 27.8 amperes, and immediately calculates the solar input power to be 1000.8 watts.

[0022] The process of obtaining the battery voltage and battery current is as follows: the battery voltage is an important reference indicator for determining the remaining capacity of the energy storage battery pack, while the battery current clearly indicates whether the battery is in a charging or discharging state and its rate, and the combination of the two is the basis for accurately assessing the battery state of charge (SoC). For this purpose, a Hall effect current sensor is connected in series in the main loop of the battery pack, and a precision voltage dividing resistor network is used to monitor the voltage across the battery pack. The analog output terminals of these two sensors are connected to the analog-to-digital conversion (ADC) channels of the microcontroller unit. The microcontroller unit periodically starts the ADC conversion to convert the received analog voltage signals into digital values. For the current, a positive value can be predefined as the charging current, and a negative value as the discharging current. For example, at the same sampling time, the microcontroller unit measures that the voltage after voltage division corresponds to the actual battery voltage of 25.5 volts, and at the same time measures that the signal output by the Hall sensor corresponds to the current flowing into the battery of 10.0 amperes, which indicates that at this moment the solar power generation not only drives the device, but also charges the battery.

[0023] The acquisition process of the instantaneous water production flow rate is as follows: the instantaneous water production flow rate is a direct reflection of the actual output capacity of the device under the current working condition, and is a key parameter for calculating the real water-energy conversion efficiency index. At the end of the condensate water collection pipeline of the device, a high-sensitivity liquid flow meter is installed, such as a tipping bucket rain gauge or a micro-turbine flow meter. When a certain amount of condensate water flows through, the flow meter will generate one or more electrical pulse signals, and the frequency of the pulse signal is proportional to the flow rate. An external interrupt pin of the microcontroller unit is configured to capture these pulses. By counting the number of pulses in a fixed time window, such as 60 seconds, and multiplying by the pre-calibrated pulse equivalent of the flow meter, such as one pulse per milliliter of water, the average water production rate in the time window can be accurately calculated. For example, at the above-mentioned sampling time, due to high temperature and low humidity, the water production efficiency is extremely low, and the microcontroller unit only captures 33 pulses in the past 60 seconds. According to the calibration value, the corresponding instantaneous water production flow rate is calculated to be 0.2 liters per hour.

[0024] Finally, at the end of each sampling period, the data parameter acquisition module 110 will package and calculate all the parameters, i.e. the environmental temperature 45.0 degrees Celsius, the environmental relative humidity 15.0%, the solar input power 1000.8 watts, the battery voltage 25.5 volts, the battery current 10.0 amperes, and the instantaneous water production flow rate 0.2 liters per hour into a unified data structure.

[0025] Specifically, the system comprehensive state evaluation module 120 is configured to evaluate the system comprehensive state based on the environmental temperature, the environmental relative humidity, the battery voltage, the battery current, and the instantaneous water production flow rate to obtain the battery state of charge, the water taking system load power, the current water-energy conversion efficiency index, and the operation time partition. As shown in Figure 3 and Figure 4 As shown in Figure 3 is a block diagram of the system comprehensive state evaluation module in the solar-powered roof-mounted air water taking system according to an embodiment of the present application. Figure 4A data flow diagram of the system comprehensive state evaluation module in the solar-powered rooftop air water extraction system according to the embodiments of the present application. Accordingly, the data acquisition in the previous stage only provides discrete and original physical quantities, which cannot be directly used to guide complex control decisions. In order to solve the mismatch between energy availability and water production efficiency in the time axis mentioned in the background art, the control core must be able to understand the deep meaning behind these raw data. The simple ambient temperature or battery voltage value cannot directly reveal whether energy storage or water production should be prioritized at the moment. Therefore, a comprehensive evaluation link needs to be established to process, refine and integrate the multi-dimensional data flow collected at the front end, and to convert it into a set of core state indicators that can intuitively reflect the current energy reserve state, power consumption level, operation efficiency and macroscopic time background of the device. This process is the key bridge connecting raw perception and advanced decision-making, and the evaluation results generated by it provide direct, clear and quantified decision-making basis for the generation of subsequent intelligent control modes.

[0026] The processing of the system comprehensive state evaluation module 120 is as follows: first, the running time partition is performed. Considering the core problem that the present application aims to solve, i.e. the negative correlation between solar energy availability and water-energy conversion efficiency within a day, the control strategy must be able to adaptively adjust according to the typical characteristics of different time periods in a day. The energy is abundant but the efficiency is low during the day, and vice versa at night. Therefore, in order to provide a macroscopic and basic basis for subsequent intelligent decision-making, it is necessary to define the macroscopic time interval to which the current time belongs. By dividing the whole day into running time partitions with different typical characteristics, it can lay the foundation for taking differentiated running modes (such as charging priority or water extraction priority) in different time periods. For this purpose, in a feasible technical solution, the system comprehensive state evaluation module 120 comprises: a current time reading unit 121 for the controller to read the internal real-time clock to obtain the current time; a running time partition generation unit 122 for obtaining the running time partition based on the comparison between the current time and the preset time threshold.

[0027] In particular, the function of the current time reading unit 121 is implemented by relying on a high-precision real-time clock (RTC) circuit integrated within the main controller of the device. This real-time clock is powered by a separate button cell battery to ensure that the clock continues to keep accurate time even in the event of a power outage or failure of the main power supply, thereby ensuring the long-term reliability of the time information. At the beginning of each control cycle, the firmware program of the main controller executes a pre-set function that accesses specific register addresses of the real-time clock via an internal bus. These registers store the current precise time information in binary coded form, including the year, month, day, hour, minute, and second. The operation performed by the current time reading unit 121 is to read the data from these registers and decode and convert it into a standard time format. For example, at the start of an evaluation cycle, the unit obtains a series of values from the real-time clock and combines them to determine that the current time is 14:00. After receiving the current time, such as 14:00, from the current time reading unit 121, the running time zone generation unit 122 labels the current time with a classification label that has a clear physical meaning, i.e., the running time zone, based on the time information. This process is accomplished by comparing the current time with a set of pre-set time thresholds. These thresholds include the start time of the peak period, the end time of the peak period, the start time of the night period, and the end time of the night period. These thresholds are determined based on statistical analysis of long-term meteorological data and solar radiation data at the installation site of the device. For example, by analyzing historical data, it can be determined that the period of highest solar power generation in this region is usually between 11:00 AM and 15:00 PM, so the start time of the peak period can be set to 11:00 and the end time of the peak period to 15:00. Similarly, the period of highest water production efficiency due to low temperature and high relative humidity usually occurs at night to early morning, so the start time of the night period can be set to 22:00 and the end time of the night period to 06:00 the next day. These thresholds are fixed in the non-volatile memory of the controller. The internal logic of this unit is implemented as a conditional judgment structure. When receiving the current time of 14:00, it performs the following comparisons: first, it determines whether the current time is greater than or equal to the start time of the peak period, 11:00, and less than the end time of the peak period, 15:00. Since 14:00 satisfies this condition, the unit determines the current running time zone as the peak period. If the current time does not satisfy this condition, it continues to determine whether it is greater than or equal to the start time of the night period, 22:00, or less than the end time of the night period, 06:00. If so, it is classified as the night period. If neither of the above two conditions is met, the time point is classified as the transition period. In this example, the final output of the running time zone generation unit 122 is a label: peak period.

[0028] Then the battery state of charge is calculated. Considering that the raw electrical parameters such as real-time voltage and current of the battery reflect the instantaneous state, but cannot directly quantify the total energy reserve available for dispatch in the energy storage unit. In order to make the decision to preferentially store energy in the period of low water production efficiency, and evaluate whether there is enough reserve energy to support long-term operation in the period of high water production efficiency, the control core needs to master a stable and accurate energy inventory index. Therefore, the battery state of charge needs to be calculated. To this end, in a feasible technical solution, the system comprehensive state evaluation module comprises: a battery state of charge analysis unit 123, configured to calculate the battery state of charge based on the battery current, using the following formula:

[0029]

[0030] wherein, is the battery state of charge of the last sampling period, is the battery current, is the sampling time interval, is the battery rated capacity, is the current battery state of charge.

[0031] Specifically, the battery state of charge analysis unit 123 executes an algorithm called ampere-hour integration method, and is assisted by an open-circuit voltage calibration mechanism to accurately track the remaining power of the energy storage battery. The unit first receives real-time battery current data from the data parameter acquisition module, and reads three key parameters from its own non-volatile memory: the battery state of charge of the last sampling period, the preset sampling time interval, and the preset battery rated capacity. Among them, the battery rated capacity is an inherent physical property of the battery, which represents the total charge amount that can be completely discharged under standard conditions. This value, for example, 100 ampere-hours, is obtained from the battery specification book and is set when the device is first configured. The sampling time interval is the time length for the controller to perform a complete calculation cycle, for example, it is set to 10 seconds. The unit then calculates according to the following formula: This formula calculates the amount of charge flowing into or out of the battery in a very short sampling time interval , and compares it with the total capacity of the battery , so as to obtain the small change amount of the state of charge, and then add this change amount to the state value of the last time. Taking the data in the preceding embodiment as an example, the unit receives the input: the battery current is 10.0 ampere (defined as positive value for charging). At the same time, it reads that the battery state of charge of the last period is 65.0%, i.e. 0.65; the sampling time interval is 10 seconds; and the battery rated capacity 100 ampere-hours. In calculation, the units need to be unified, and the sampling time interval needs to be converted from seconds to hours, that is, 10 seconds is equal to 10 / 3600 hours. First, the incremental charge is calculated: Then the percentage change of the state of charge is calculated: That is, 0.0278%. Finally, the current state of charge of the battery is updated: The newly calculated value will be stored as the state of charge of the battery of the last sampling period of the next calculation period. To solve the cumulative error that may be generated by the ampere-hour integration method after a long time, the unit also contains a calibration mechanism. The controller continuously monitors the battery current, and when it detects that the absolute value of the battery current is continuously below a minimum threshold such as 0.1 ampere and the duration exceeds a preset time such as 1 hour, the controller determines that the battery is in a stationary state. At this time, the unit triggers a calibration program: it first obtains the current battery voltage from the data parameter acquisition module, which can be approximately regarded as the open-circuit voltage of the battery. Subsequently, the unit queries a state of charge-open-circuit voltage relationship table pre-stored in the memory. The relationship table is calibrated by a large number of charge and discharge experiments on the same type of battery during the design stage of the device, which accurately maps different open-circuit voltage values to the corresponding true state of charge. For example, the query result may show that the measured open-circuit voltage of 25.8 volts corresponds to a state of charge of 68.0%. At this time, regardless of the value calculated by the ampere-hour integration method, the unit will forcibly update the stored state of charge of the battery to this more accurate value obtained from the table, for example, 68.0%, thereby completing an error elimination. Finally, the battery state of charge analysis unit takes the calibrated current state of charge of the battery as its output. That is, the output value of the unit is dynamically determined: in most running time, the output is the value continuously calculated by the ampere-hour integration method; only when the long stationary condition is met, the output value will be forcibly updated by the result of the open-circuit voltage table lookup method. Therefore, in the above example scenario of the present application, since the battery current is 10.0 amperes, which is much greater than the threshold of 0.1 amperes for triggering calibration, the calibration program is not executed, and the final output of the battery state of charge by the unit is 65.0278%, which is calculated by the ampere-hour integration method.

[0032] Then, before any efficiency evaluation is performed, it is necessary to determine the power consumption of the device in different operating states, that is, the load power of the water taking system. Without accurate knowledge of the total load, the control logic cannot determine whether the current high power consumption brings corresponding high output, nor can it perform effective cost-benefit analysis. Therefore, in a feasible technical solution, the system comprehensive state evaluation module comprises a water taking system load power calculation unit 124 for adding the real-time power of the compressor, the real-time power of the fan, and the power of the control system and sensors to obtain the load power of the water taking system.

[0033] Specifically, first, the unit needs to obtain the compressor real-time power. The compressor is the core of the refrigeration cycle and the largest energy-consuming component, and its power changes dynamically with the refrigeration demand. The compressor real-time power refers to the electric power consumed by the compressor in the current operating state. The value is obtained through communication between the main controller and the compressor driver. The compressor driver is a variable frequency controller that can accurately control the speed of the compressor and monitor the operating voltage and current in real time. The main controller sends a query instruction to the driver through a serial communication interface, such as an RS485 bus. The internal program of the driver calculates the real-time power based on the monitored electrical parameters and returns the data to the main controller. Taking the severe working condition of 14:00 in the afternoon and an environmental temperature of 45 degrees Celsius in the previous embodiment as an example, in order to achieve the dew point temperature, the compressor needs to run at high load, and the real-time power value obtained by the unit from the compressor driver is 700 watts. Second, the unit obtains the fan real-time power. The fan is used to force air to flow through the evaporator and condenser, and is a key component of the heat exchange process, and its power is also variable. The fan real-time power refers to the electric power consumed by the fan motor at the current speed. Similar to the compressor, the fan is driven by a dedicated brushless DC motor controller. The main controller sends a speed instruction to the fan controller according to the heat dissipation and heat exchange demand, and at the same time can query its operating power. In a high temperature environment, in order to ensure effective heat dissipation of the condenser, the fan also needs to run at high speed. In this embodiment, the unit obtains the real-time power of the fan as 75 watts through communication with the fan controller. Third, the unit needs to take into account the control system and sensor power, which is a relatively fixed background power consumption, including the total power consumption of the main controller, various sensors (temperature and humidity, flow, etc.), display panel and communication module and all electronic components. The power value is measured by a precision power analyzer during the design and calibration of the device in standby and standard operating state, and a stable and representative average value is taken. This value is fixed as a constant in the program of the main controller. For example, the constant is preset to 5 watts. Finally, the unit performs a summation calculation. It adds the three power values obtained by arithmetic: compressor real-time power 700 watts + fan real-time power 75 watts + control system and sensor power 5 watts, to get the water taking system load power 780 watts. This calculation result accurately reflects the total electric energy rate that the device needs to consume in order to perform the water taking task at the current time.

[0034] Next, the estimated water-energy conversion efficiency index is calculated. In order to achieve intelligent scheduling of energy, the control logic can predict the efficiency level that can be achieved by starting the water taking function at any time, even in a state where the function is not started. If the efficiency can only be evaluated by measuring the actual water production after the device is running, the ability to make proactive decisions is lost, and the optimal strategy of only storing energy without producing water cannot be actively selected during the period of low efficiency. This passive evaluation method is one of the fundamental reasons for the energy mismatch in the prior art. Therefore, in a feasible technical solution, the system comprehensive state evaluation module includes: an air dew point temperature calculation unit 125 for calculating the air dew point temperature based on the ambient temperature and the ambient relative humidity according to the following formula:

[0035]

[0036]

[0037] wherein, is the ambient relative humidity, is the ambient temperature, and are common coefficients, and are two empirical coefficients determined by curve fitting of the formula with a large number of accurate water vapor saturation pressure experimental data to obtain the best parameter value with the smallest error in a specific temperature range, is the air dew point temperature; and an estimated water-energy conversion efficiency calculation unit 126 for interpolating the air dew point temperature and the ambient temperature in a pre-marked two-dimensional lookup table to obtain the estimated water-energy conversion efficiency index.

[0038] Specifically, the air dew point temperature calculation unit 125 first receives the ambient temperature and the ambient relative humidity from the data parameter acquisition module. Taking the data collected at 14:00 in the afternoon in the previous embodiment as an example, the inputs received by the unit are: the ambient temperature is 45.0 degrees Celsius, and the ambient relative humidity is 15.0%. The core task of this unit is to calculate the current air dew point temperature using a recognized physical formula, i.e., a variant of the Magnus formula. The calculation process consists of two steps. First, an intermediate variable is calculated. The calculation formula of the variable is: . Substituting the input data: first, convert the relative humidity from a percentage to a decimal, i.e., 15.0 / 100 = 0.15. Then calculate the two parts of the formula: ; . Therefore, . Second, use the calculated value to solve the final air dew point temperature The formula is: The value obtained in the previous step and the preset coefficient are substituted into: This calculation result accurately quantifies the critical temperature at which the current air is cooled to saturation and begins to condense water. After receiving the air dew point temperature of 12.4 degrees Celsius passed from the previous unit, the estimated water-energy conversion efficiency calculation unit 126 also retrieves the ambient temperature of 45.0 degrees Celsius from the data parameter acquisition module. The core of this unit is to access a two-dimensional lookup table pre-stored in the controller's non-volatile memory. This lookup table is calibrated during the development of the device by conducting a large number of tests on the water extraction equipment in an environmental simulation laboratory. The two dimensions of this table are the ambient temperature and the air dew point temperature, and each value in the table is the actual water-energy conversion efficiency index (for example, in liters / kilowatt-hour) measured when the device is running stably under the corresponding environmental conditions. Since the input values received by this unit (ambient temperature 45.0 degrees Celsius, dew point temperature 12.4 degrees Celsius) are unlikely to directly correspond to the grid points in the lookup table, this unit will perform a two-dimensional interpolation algorithm, namely bilinear interpolation. This algorithm will first locate the four grid points closest to the input coordinates in the lookup table, for example, (ambient temperature 45, dew point 12), (ambient temperature 45, dew point 14), (ambient temperature 50, dew point 12), and (ambient temperature 50, dew point 14) and their corresponding efficiency index values. Then, by weighted averaging the efficiency values of these four points, the accurate efficiency index at the input coordinate point (45.0, 12.4) is calculated. The size of the weight depends on the distance between the input coordinate point and the four grid points. In this high-temperature and low-humidity harsh working condition, the estimated water-energy conversion efficiency index calculated by interpolation may be a relatively low value, for example, 0.27. This index represents an accurate quantitative prediction of the potential water production performance under the current environment even when the water extraction function is not started.

[0039] Finally, the current water-to-energy conversion efficiency index is calculated. The previous steps provide a predicted value of future efficiency and a measured value of current energy consumption, but for the final intelligent decision, a single, clear and valid at any time efficiency index is needed. The control logic cannot rely on two values that can conflict or be valid only under different conditions when deciding whether to store energy, produce water or shut down. In particular, when the device is not started, the measured efficiency is not available, and after starting, the measured efficiency reflects the real performance better than the predicted value. For this reason, in a feasible technical solution, the system comprehensive state evaluation module further comprises: a conversion efficiency index calculation unit 127, in response to the system running state flag being true, dividing the instantaneous water production flow rate by the water taking system load power to obtain the current water-to-energy conversion efficiency index; and a predicted water-to-energy conversion efficiency index assignment unit 128, in response to the system running state flag being false, assigning the predicted water-to-energy conversion efficiency index to the current water-to-energy conversion efficiency index.

[0040] Specifically, the flag is a Boolean variable in the main controller memory, which is set to true when the main controller issues a command to start the compressor and the fan for water taking operation, and is set to false when the command stops the water taking operation. In one scenario, for example, at 14:00 in the afternoon, the control module decides to start the water taking operation, and the running state flag is set to true. At this time, the conversion efficiency index calculation unit 127 is activated. The unit first receives the instantaneous water production flow rate from the data parameter acquisition module, which is 0.2 liters per hour. At the same time, it receives the water taking system load power from the aforementioned load calculation unit, which is 780 watts. In order to calculate the efficiency index with standard physical meaning, the unit first needs to unify the units, converting the load power from watts (W) to kilowatts (kW), i.e. 780 watts is equal to 0.78 kilowatts. Subsequently, the unit performs the core division operation, i.e. dividing the instantaneous water production flow rate by the water taking system load power after unit conversion. The calculation process is: 0.2 (L / h) / 0.78 (kW) ≈ 0.256 (L / kWh). This calculation result 0.256 reflects the actual water output per kilowatt-hour of electricity consumed under the current harsh working conditions. Therefore, the output of the conversion efficiency index calculation unit 127 is 0.256.

[0041] In another scenario, the control module decides not to start the water production operation under the same environmental conditions, but due to a lower battery state of charge or other strategies, the system running status flag is set to false. In this case, the estimated water-energy conversion efficiency index assignment unit 128 is activated. The logic of this unit is very simple, it does not perform complex calculations. It first receives the estimated water-energy conversion efficiency index from the aforementioned estimated efficiency calculation unit, which is 0.27. Since the device is not running at this time, the instantaneous water production flow rate is zero, and it is impossible to calculate a meaningful actual efficiency, so the only indicator of the water production potential under the current environment is this estimated value. The task of this unit is to directly output the received estimated value (0.27) as its output, which is equivalent to an assignment operation that directly assigns the prediction result to the final efficiency index.

[0042] In particular, during the actual operation of the device, if the instantaneous water production flow rate is simply divided by the water production system load power to calculate the efficiency, two serious practical problems will be encountered, thereby seriously affecting the accuracy and stability of subsequent intelligent decision-making. The first is the start-up inertia problem, that is, after the compressor is started, due to the thermal inertia required for the evaporator coil to cool down, there is a significant time delay before the first drop of water is produced. During this period, the water production system load power is already high and the instantaneous water production flow rate is zero, and a misleading zero efficiency will be obtained by direct calculation, which may lead to incorrect shutdown decisions by the control logic before the device enters a stable operating condition. The second is the measurement noise and volatility problem, the water production process itself may be in the form of discontinuous dripping, combined with the inherent noise of the sensor readings, which will cause the directly calculated instantaneous efficiency value to fluctuate dramatically and chaotically. This unstable input will cause the subsequent decision-making module to swing, leading to frequent start-stop of the equipment, damaging the service life of the components and reducing the total water production. Therefore, the present application considers introducing a more precise processing, which fuses the estimated water-energy conversion efficiency index calculated based on the physical environment and having a priori property with the real-time measured efficiency full of noise through a dynamic weighting method to generate a current water-energy conversion efficiency index that is both quickly responsive to the real efficiency change trend and effectively filters out the start-up delay and random noise interference, thereby providing a highly reliable and representative core basis for the subsequent intelligent operation mode decision.

[0043] Based on this, in a feasible preferred technical solution, the conversion efficiency index calculation unit 127, in response to the system running status flag being true, performs adaptive smoothing filtering on the instantaneous water production flow rate and the water production system load power for system stability-oriented water-energy conversion efficiency index to obtain the current water-energy conversion efficiency index, including:

[0044] The instantaneous water production flow rate is divided by the water intake system load power to obtain the original measurement efficiency. First, the original measurement efficiency is calculated, the instantaneous, original feedback of the device performance at the current time is taken as new evidence for correcting and updating the efficiency estimate value, to obtain an unprocessed efficiency value that can reflect the instantaneous working condition but may contain significant noise and bias. Specifically, first, the instantaneous water production flow rate is received from the data parameter acquisition module, and the water intake system load power is received from the load calculation unit. To avoid division by zero error when the load is very low or zero, the program first judges whether the water intake system load power is greater than a preset minimum threshold such as 10 watts, which is determined by measuring the power consumption of the measuring device when only the control system is running in standby state and adding a safety margin on this basis. If the condition is met, the division operation is performed to obtain the original measurement efficiency; otherwise, the original measurement efficiency is set to zero. Since the water intake system load power of the present application is 780 watts, i.e. 0.78 kilowatts, which is much greater than the minimum threshold at 14:00 in the afternoon, the instantaneous water production flow rate is divided by the water intake system load power to obtain the original measurement efficiency, i.e. , the original measurement efficiency is 0.256.

[0045] A dynamic smoothing factor is determined. That is, a dynamically changing weight is provided for the subsequent filtering algorithm, which can be adaptively adjusted according to the running state of the device, and is the key to solving the starting inertia problem. In this way, a smoothing factor is generated which is very small at the beginning of starting, then linearly increases and finally stabilizes at a preset value, so as to realize the dynamic adjustment of the reliability of the original measurement efficiency over time. Specifically, the dynamic smoothing factor is calculated according to the continuous running time of the device after this start . At the beginning of starting , the dynamic smoothing factor is very small, because of thermal inertia, the confidence of the original measurement efficiency is very low, so the smoothing factor should be very small, so that the filter largely ignores this measurement value; when the device runs to a stable state, for example , the original measurement efficiency becomes a reliable indicator, the smoothing factor should be increased to a stable value to give greater weight to the new measurement value. This process is achieved through the following linear growth function:

[0046]

[0047] wherein is a preset initial smoothing factor, which is a small value set by experience, such as 0.01, is a normal smoothing factor, a normal smoothing factor value, for example, 0.1, which is selected by experiment to trade off between response speed and filtering smoothness, is an estimated stabilization time, which is determined by physical testing of the hardware during the device development stage to calibrate the average time required from start-up to output stable water flow, such as 180 seconds, is the cumulative running time since the last start-up. For example, if the device has been running continuously for 60 seconds, then .

[0048] The original measured efficiency is updated using an exponential moving average filter to obtain the current water-energy conversion efficiency index. Finally, the historical state (memory) of the system and the current new evidence (original measured efficiency) are smoothly fused to filter out noise and fluctuations, generating the final stable efficiency index, outputting a smooth changing efficiency curve, effectively suppressing the impact of instantaneous noise and skillfully handling the special working condition of the start-up phase. Specifically, an exponential moving average filter is used to update the final current water-energy conversion efficiency index . In particular, in the first calculation period of the first start-up of the device, since there is no effective historical record, the estimated water-energy conversion efficiency index obtained by the estimated water-energy conversion efficiency calculation unit, such as 0.27, is taken as its initial value. For all subsequent calculation steps, the following filtering formula is applied: wherein, is the water-energy conversion efficiency index of the last period, is the current water-energy conversion efficiency index. That is, since the true efficiency does not jump from one value to another instantaneously, but changes smoothly, the term in the formula provides the memory of the system state. When the measurement value contains noise, since the smoothing factor is much smaller than 1, it can effectively suppress the impact of any single noisy original measured efficiency reading. Moreover, considering that start-up is a special working condition, by initializing using the estimated water-energy conversion efficiency index and using a dynamically changing from low to high, the stage of direct measurement meaningless in the early stage of system start-up can be intelligently handled, thereby preventing false shutdown decisions. In the above example, if the current water-energy conversion efficiency index of the last period is 0.27, then the value of the current period is . In this way, since the output current water-energy conversion efficiency index is a smooth and stable signal that can accurately reflect the true running efficiency of the device and is not affected by instantaneous spikes and drops, enabling smooth and consistent decision-making in subsequent intelligent running mode decision-making, preventing device oscillation and enhancing stability. Moreover, the device will no longer incorrectly calculate zero efficiency at startup, but will rely on the estimated water-energy conversion efficiency index value based on physical principles to give the hardware sufficient time to reach its stable working state, thereby improving the reliability of starting a water production cycle through robust startup behavior. As a result, by avoiding premature shutdown and unstable running behavior, the total running time of the device in the effective working condition is increased, thereby achieving higher total water production in a 24-hour cycle.

[0049] Specifically, the control mode generation module 130 is configured to make intelligent running mode decisions on the solar input power, the battery state of charge, the current water-energy conversion efficiency index, and the running time partition to obtain the control mode. It should be understood that the previous comprehensive state evaluation module has successfully refined and converted the discrete raw data stream into a set of core indicators that can represent the current state of the device. However, these indicators, such as sufficient battery power, strong solar power, but low water production efficiency, may themselves point to contradictory operation instructions, which is the core difficulty that traditional control logic cannot solve. Without a final decision hub, these evaluation results cannot be effectively utilized. Therefore, the present application establishes a top-level intelligent decision-making link that weighs and arbitrates all key state indicators based on a set of pre-set complex logic aimed at maximizing total cycle water production, ultimately outputs an optimal and unique macroscopic running instruction under the current comprehensive conditions, and thus converts the evaluation results into a specific and executable control strategy.

[0050] In one possible technical solution, the control mode generation module 130 is configured to input the solar input power, the battery state of charge, the current water-energy conversion efficiency index, and the running time partition into a decision tree model to obtain the control mode, wherein the control mode includes a charging priority mode, a water taking priority mode, a balanced mode, and a protective shutdown mode.

[0051] In the above technical solution, the processing of the control mode generation module 130 is as follows: this module is a rule-based decision tree model, which is solidified in the program code of the main controller, and its structure is essentially a series of nested conditional judgment statements, which are used to select one of the four predefined running modes according to the input real-time state combination. The decision tree model constructs its judgment rules through a series of preset threshold parameters. The setting of these thresholds is the key to achieving intelligent control, and they are determined comprehensively during the device development stage through a large number of laboratory environment simulation tests and statistical analysis of historical meteorological data of the target deployment area, combined with the calibration of the device energy efficiency characteristic curve. For example: the protection shutdown state of charge can be set to 20%, which is a hard safety boundary set to prevent battery over-discharge and protect its service life. The charging start state of charge can be set to 85%, which means that when the battery power is below this value, if there is available solar energy, charging should be prioritized. The water taking start state of charge can be set to 50%, ensuring that the battery has at least enough reserve energy to support a meaningful water production process when entering the efficient night period. The high power state of charge can be set to 95%, indicating that the battery is basically full. The high efficiency threshold can be set to 1.2 liters / kilowatt-hour, representing that the device is running in a very efficient interval. The low efficiency threshold can be set to 0.8 liters / kilowatt-hour, representing an acceptable but non-optimal efficiency level. The minimum charging power can be set to 50 watts, representing that the photovoltaic power generation power must exceed this value to effectively charge the battery.

[0052] The four control modes and their core judgment rules built into the decision tree model are defined as follows: first, the protection shutdown mode, which has the highest priority, its trigger condition is that the battery state of charge is lower than the protection shutdown state of charge, which aims to prioritize battery safety in any situation. Second, the charging priority mode, whose trigger condition is that the running time partition is the peak period or the battery state of charge is lower than the charging start state of charge, and the solar input power is greater than the minimum charging power, this mode aims to store energy when energy is abundant but water production efficiency is low, or when the battery power is insufficient. Third, the water taking priority mode, whose trigger condition is that the running time partition is the night period and the battery state of charge is greater than the water taking start state of charge, and the current water-energy conversion efficiency index is greater than the high efficiency threshold, this mode focuses on maximizing water production using stored energy in the most efficient period. Finally, the balanced mode, whose trigger condition is that the running time partition is the transition period and the battery state of charge is greater than the high power state of charge, and the current water-energy conversion efficiency index is greater than the low efficiency threshold, this mode is an opportunistic strategy that uses surplus solar energy to directly drive water production in the transition period when the battery is close to full and the efficiency is acceptable, avoiding energy waste.

[0053] Based on the above rule set, the system comprehensive state evaluation module receives its complete output as its own input. Taking the 2:00 PM operating condition in the previous embodiment as an example, the specific input values received by the module are: solar input power of 1000.8 watts; battery state of charge of 65.0278%; current water-to-energy conversion efficiency index of 0.256 (liter / kWh); and operating time partition of peak period.

[0054] Upon receiving the input data, the decision tree logic within the module begins to make judgments layer by layer. The order of judgment starts from the most important and critical rules, such as the safety protection rule. In the first step, the condition of the protective shutdown mode is evaluated: whether the battery state of charge is lower than the protective shutdown state of charge. In this example, 65.0278% is not lower than 20%, so this rule is not triggered, and the program continues to execute downward. If it is lower than 20%, regardless of other conditions, the module will immediately output the protective shutdown mode to protect the battery. In the second step, the condition of the charge priority mode is evaluated: the condition of this rule is to determine whether it is in a period of energy abundance but low efficiency, or the battery power itself needs to be supplemented. Its logic can be expressed as: if (operating time partition is peak period or battery state of charge is lower than the charge start state of charge) and solar input power is greater than the minimum charge power, then select the charge priority mode. Substituting the data in this example: the current operating time partition is the peak period, satisfying the first half of the or logic; at the same time, the solar input power of 1000.8 watts is much greater than the minimum charge power of 50 watts. Both conditions are met, so this rule is triggered. Once the decision tree has successfully matched a rule, it will immediately output the result and terminate this round of judgment. Therefore, in this embodiment, the final output of the control mode generation module 130 is a clear instruction: charge priority mode. This decision-making process clearly reflects how the module solves the core contradiction in the background technology. Although the solar input power is as high as 1000.8 watts at this time, the energy is extremely abundant, but the decision tree correctly judges that it is a great waste of energy to take water at this moment by combining the operating time partition of peak period and the extremely low current water-to-energy conversion efficiency index of 0.256 calculated by the previous module. Therefore, it does not choose to produce water, but outputs the charge priority mode, instructing the downstream module to prioritize all available solar power to charge the battery, storing efficient electrical energy for use in the night or transition period when the environmental conditions are more suitable.

[0055] If in another scenario, for example, at 04:00 AM, the module receives the input: solar input power is 0 W, battery state of charge is 88%, current water-to-energy conversion efficiency index is 1.5, and the operation time partition is night period. At this time, the decision tree will skip the charging rule and instead evaluate the conditions of the water-priority mode, for example: if (operation time partition is night period and battery state of charge is greater than water-priority start state of charge) and current water-to-energy conversion efficiency index is greater than high-efficiency threshold, then select water-priority mode. Since all conditions are met, the module will output water-priority mode.

[0056] Specifically, the device control parameter generation module 140 is configured to generate device control parameters based on the control mode, wherein the device control parameters include compressor state and fan speed. That is, the previous stage of intelligent decision outputs a macroscopic and conceptual operation mode, for example, the charging priority mode. However, this is only a strategy label and cannot be directly recognized and executed by the underlying execution components such as the compressor or the fan. What these hardware devices need are specific and explicit instructions such as switch signals, voltage levels or digitalized speed setting values. In order to convert this high-level strategy intention into real and accurate device actions in the physical world, the present application finally sets up a final instruction analysis and generation link to map the received control mode into a set of specific and quantifiable device control parameters, thereby completing the closed loop from abstract decision to hardware execution and ensuring that the top-level strategy can be implemented.

[0057] In one possible technical solution, the device control parameter generation module 140 is processed as follows: the essence of this module is an instruction mapper, which internally solidifies a set of rules for translating the abstract control mode passed from the upstream into the underlying "device control parameters" that can be directly executed by the downstream hardware drivers.

[0058] When the module receives the control mode of the present application as the charging priority mode, the internal logic of the module is as follows: the core goal of this mode is to use all available solar energy for storage while minimizing unnecessary energy consumption. Therefore, for the compressor state, the module will generate a stop instruction. This instruction may be a signal that physically sets the general-purpose input-output pin controlling the compressor relay to low, or a specific stop command frame sent to the compressor driver through the serial bus. For the fan speed, considering that the battery and control circuit board will still generate some heat during charging, basic heat dissipation is needed to ensure safety, so the module will not completely shut it down, but will generate a preset low-speed running parameter for it. This parameter, for example, 300 revolutions per minute, is determined through thermal simulation and experiments during the design stage of the device, which is sufficient to maintain the normal operating temperature of the key components without generating significant energy consumption. This speed parameter will be converted into a pulse width modulation signal with a specific duty cycle and sent to the fan speed control pin. Therefore, in this mode, the final output of the module is: {compressor state: stop; fan speed: 300 revolutions per minute}.

[0059] In contrast, if the module receives the control mode as the water priority mode in another scenario, its internal logic will generate completely different parameters. For the compressor state, the module will generate a start instruction to activate the refrigeration cycle. For the fan speed, it will not be a fixed low-speed value, but an optimal value dynamically calculated according to the current working conditions. For example, the module will simultaneously obtain the ambient temperature from the data parameter acquisition module and determine the fan speed according to a preset fan speed-ambient temperature mapping table. This mapping table is also calibrated through experiments, aiming to ensure that the fan can provide sufficient air volume to achieve the most efficient heat exchange under different ambient temperatures. For example, when the ambient temperature is 20 degrees Celsius, the fan speed is set to 1500 revolutions per minute; when the ambient temperature rises to 30 degrees Celsius, the speed is increased to 2500 revolutions per minute.

[0060] For the other two modes, the equalization mode may generate a compressor start instruction, but at the same time instruct the compressor driver to run at a lower power level such as 50%, and the fan speed is set at a medium level; while the protective shutdown mode will generate the simplest parameter combination: {compressor state: stop; fan speed: 0 revolutions per minute}.

[0061] Finally, the specific and executable device control parameters output by the device control parameter generation module 140 will be directly sent to the corresponding hardware drive circuit, accurately controlling the physical behavior of the compressor and fan, and finally landing the top-level decision into the actual running state of the device.

[0062] In summary, the solar-powered rooftop air-to-water system 100 based on the embodiments of the present application is illustrated to build a control system that can dynamically evaluate and make intelligent decisions, replacing the traditional simple threshold control logic. The scheme monitors the environment, energy and water production status in real time, and processes these information, especially innovatively introduces the quantitative evaluation of water-energy conversion efficiency index, and based on this efficiency index, combined with the key parameters such as solar input power and battery state of charge, makes intelligent operation mode decision. This method solves the energy and efficiency mismatch problem pointed out in the background art: when the sunlight is sufficient during the day but the water production efficiency is low, the system can decide to charge the battery first and store the energy; while at night when the environment is suitable and the water production efficiency is high, the stored energy is used to produce water efficiently. Through this intelligent scheduling, the system will invest the limited solar energy resources into water production tasks at the most appropriate time, thus decoupling the negative correlation between instantaneous energy availability and instantaneous water production efficiency, and ultimately maximizing the total water production in a 24-hour complete cycle.

[0063] The implementations of the disclosure have been described above with the aid of functional and structural descriptions of specific implementations and instrumentalities. Characterizations are exemplary and not exhaustive. Also, the implementations disclosed are not limited to those described, but many modifications and variations are possible without departing from the scope and spirit of the described implementations.

Claims

1. A solar powered rooftop air to water system, characterized in that, The system comprises: a data parameter acquisition module configured to acquire an ambient temperature, an ambient relative humidity, a solar input power, a battery voltage, a battery current, and an instantaneous water production flow rate; a system comprehensive state evaluation module configured to perform system comprehensive state evaluation based on the ambient temperature, the ambient relative humidity, the battery voltage, the battery current, and the instantaneous water production flow rate to obtain a battery state of charge, a water extraction system load power, a current water-energy conversion efficiency index, and a running time partition; a control mode generation module configured to perform intelligent operation mode decision-making on the solar input power, the battery state of charge, the current water-energy conversion efficiency index, and the running time partition to obtain a control mode; a device control parameter generation module configured to generate device control parameters based on the control mode, the device control parameters comprising a compressor state and a fan rotating speed.

2. The solar powered rooftop air water system of claim 1, wherein, The system comprehensive state evaluation module comprises: a current time reading unit configured to read an internal real-time clock by a controller to obtain a current time; a running time partition generation unit configured to obtain the running time partition based on a comparison between the current time and a preset time threshold.

3. The solar powered rooftop air water system of claim 1, wherein, The system comprehensive state evaluation module comprises a battery state of charge analysis unit configured to calculate the battery state of charge based on the battery current according to the following formula: ； wherein, is the battery state of charge of the previous sampling period, is the battery current, is the sampling time interval, is the battery rated capacity, is the current battery state of charge.

4. The solar powered rooftop air water system of claim 1, wherein, The system comprehensive state evaluation module comprises a water extraction system load power calculation unit configured to add a compressor real-time power, a fan real-time power, and a control system and sensor power to obtain the water extraction system load power.

5. The solar powered rooftop air water system of claim 1, wherein, The system comprehensive state evaluation module comprises: an air dew point temperature calculation unit configured to calculate an air dew point temperature based on the ambient temperature and the ambient relative humidity according to the following formula: ； ； wherein is the relative humidity of the environment, is the temperature of the environment, and is a common factor, and , is the dew point temperature of the air; a predicted water-energy conversion efficiency calculation unit configured to perform interpolation calculation on the air dew point temperature and the ambient temperature in a pre-marked two-dimensional lookup table to obtain a predicted water-energy conversion efficiency index.

6. The solar powered rooftop air water system of claim 5, wherein, The system comprehensive state evaluation module further comprises: a conversion efficiency index calculation unit configured to divide the instantaneous water production flow rate by the water extraction system load power to obtain the current water-energy conversion efficiency index in response to a system running state flag being true; a predicted water-energy conversion efficiency index assignment unit configured to assign the predicted water-energy conversion efficiency index to the current water-energy conversion efficiency index in response to the system running state flag being false.

7. The solar power based rooftop air water system of claim 1, wherein, The control mode generation module is configured to input the solar input power, the battery state of charge, the current water-energy conversion efficiency index, and the running time partition into a decision tree model to obtain the control mode, the control mode comprising a charging priority mode, a water extraction priority mode, a balanced mode, and a protective shutdown mode.

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