Full-automatic water condensation and collection system and method in desert

Abstract

The invention discloses a full-automatic condensation water collection system and method in desert, and the system comprises an environment pretreatment layer: a bionic honeycomb filter screen is combined with an electrostatic adsorption technology to filter sand and dust, a spiral pre-cooling channel is used for pre-cooling, and back flushing cleaning is automatically started every 2 hours to maintain the performance of the filter screen; in the energy supply layer, the optimal combination of photovoltaic-wind energy-heat energy three-mode power generation is dynamically calculated through an AI algorithm; photovoltaic power supply is preferentially used in the daytime, and residual energy is stored in the nitrate phase change energy storage material; wind power generation is started when the wind speed is high at night, and phase-change energy storage is used for complementing when the wind speed is insufficient; condensing the core layer: when the environment humidity is relatively high, the modified MOF material preferentially adsorbs water vapor, and switching to a condensation mode every 4 hours; the thermoelectric refrigeration sheet is started to trigger phase change condensation; the intelligent control layer is used for collecting data through a sensor, predicting future environmental parameters through an LSTM neural network and adjusting condensation power in advance; and fault early warning is carried out by combining vibration analysis and temperature gradient detection.

Description

Technical Field

[0001] This invention relates to the field of condensate collection technology, and in particular to a fully automated condensate collection system and method for desert environments. Background Technology

[0002] Desert regions such as the Sahara and Taklamakan cover one-third of the Earth's land area. They are extremely arid, with annual precipitation of less than 250 mm, daily temperature differences of more than 30°C, and frequent sandstorms, with more than 100 sandstorm days per year. This makes traditional water resource acquisition methods costly and unsustainable. According to statistics, the cost of water for residents in desert regions can be 5 to 10 times that of urban areas, while industrial / ecological water use is almost entirely dependent on external input. Against this backdrop, air condensation water extraction technology has become an important breakthrough in solving the water problem for residents in desert regions because it directly utilizes the scarce water vapor in the desert air.

[0003] However, traditional desert condensation water extraction systems and methods are inefficient and cannot effectively solve water problems. For example, WaterSeer is a device that extracts water through temperature difference condensation, claiming to produce 37 liters of water per day and suitable for desert environments. However, since the average humidity in deserts is often below 30%, the condensation efficiency drops sharply as humidity decreases. Moreover, the device relies on natural temperature differences, but although the diurnal temperature range in the desert is large, the lack of air circulation leads to low condensation efficiency. Actual test data shows that in the Mojave Desert (humidity of about 20%-25%), the device can only produce 0.7 liters of water per kilogram of material per day (700 ml / kg·day). When the relative humidity further drops below 15%, the maximum daily water production drops sharply to 300 ml / kg·day, far below the theoretical value, and cannot meet basic needs. Therefore, a fully automatic condensation water collection system and method for deserts is proposed. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a fully automated condensate collection system and method for desert environments.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A fully automated condensate collection system for desert environments includes: Environmental pretreatment layer: Air first enters the biomimetic honeycomb filter (pore size <50μm), which filters sand and dust using electrostatic adsorption technology; then it passes through a spiral pre-cooling channel, which uses the low temperature air at night to pre-cool the intake air (temperature reduction of 5-8℃), providing a high-quality air source with low dust and low temperature for the condensation core layer. It automatically starts back-flushing cleaning (air pressure 0.6MPa) every 2 hours to maintain the filter performance. Energy supply layer: Powers the condensation core layer and monitors photovoltaic radiation, wind speed, and temperature data in real time. AI algorithms dynamically calculate the optimal combination of photovoltaic-wind-thermal power generation (aiming to maximize water production / energy consumption ratio); photovoltaic power is used first during the day, and the remaining energy is stored in nitrate phase change energy storage material (energy storage density 150kJ / kg); wind power generation is started when the wind speed is greater than 1.5m / s at night, and the energy is supplemented by phase change energy storage when insufficient. Condensation core layer: A gradient condensation structure is adopted: When the ambient humidity is greater than 25%, the modified MOF material (loaded with hydrophobic groups) preferentially adsorbs water vapor and switches to condensation mode every 4 hours; at this time, the thermoelectric cooling chip is activated, which lowers the condensation temperature to -3℃, triggering phase change condensation. The generated water droplets converge along the nano-silica hydrophilic coating to the water collection tank (flow rate > 0.5L / min). An aerogel insulation layer (thermal conductivity < 0.015W / m·K) is used to reduce cold loss, and the water production, condensation efficiency, and material state (such as MOF adsorption capacity) are uploaded to the intelligent control layer optimization algorithm model in real time. Intelligent control layer: Sends cleaning commands to the environmental pretreatment layer, power generation-storage switching commands to the energy supply layer, and power adjustment commands to the condensation core layer; collects data at a frequency of 1Hz through sensors (temperature, humidity, PM10, wind speed, etc.), and uses an LSTM neural network to predict environmental parameters for the next 24 hours, adjusting the condensation power in advance; combines vibration analysis and temperature gradient detection for fault warning, automatically starting the high-pressure air gun to clean the filter when the PM10 concentration is greater than 0.5mg / m³ or the pressure difference is greater than 200Pa; users can view the water production and energy consumption in real time through the 4G / LoRa remote control interface, and remotely adjust parameters for fully automatic operation with minimal human intervention.

[0006] The above technical solution further includes: Furthermore, the air first enters a biomimetic honeycomb filter, which, combined with electrostatic adsorption technology, filters out dust, including the following steps: Bionic honeycomb filter for physical filtration: Air enters the biomimetic honeycomb filter (pore size <50μm) at a flow rate of 1-2m / s. The hexagonal honeycomb structure captures coarse dust particles with a diameter >50μm through inertial collision and interception. The filter is made of lightweight aluminum with a single-sided thickness <0.2mm, resulting in a ventilation rate >95% and a wind resistance <50Pa, thus reducing energy consumption. Electrostatic adsorption of ionized dust particles: The filter screen integrates an electrostatic adsorption module at the rear end. A high-voltage power supply (output voltage ±60kV, current 20mA) generates corona discharge at the discharge electrode, which ionizes the air to generate positive and negative ions. When dust particles (PM10) pass through the electric field, they collide with the ions, become charged on the surface, and migrate towards the dust collection electrode (filter screen skeleton) under the action of the electric field force and are adsorbed. Backflush cleaning maintains performance: The back-flushing cleaning system is activated every 2 hours. Compressed air (0.6MPa pressure) is blown in the opposite direction through the nozzles on the back of the filter to remove attached sand and dust. The cleaning time is 30 seconds, ensuring that the pressure difference of the filter is less than 200Pa. The electrostatic module stops working during cleaning to avoid high pressure risks. The adsorption function is automatically restored after cleaning.

[0007] Furthermore, the process of pre-cooling the intake air using low-temperature nighttime air through a spiral pre-cooling channel includes the following steps: Introducing cold air at night: When the ambient temperature drops to 10-15℃ at night (22:00-5:00), switch to pre-cooling mode, and the fan will introduce the low-temperature outside air into the spiral pre-cooling channel; Spiral channel heat exchange: Air enters a spiral precooling channel (channel length 2m, spiral diameter 0.5m), the inner wall of which is coated with a nano-silica coating to enhance thermal conductivity; the low-temperature air flows along the spiral path and comes into full contact with the channel wall, reducing the intake air temperature by 5-8℃ through convection heat transfer. Flow field optimization: The spiral structure creates a three-dimensional vortex in the air, prolonging the heat exchange time. At the same time, the spaced columns (50mm spacing) maintain channel stability and reduce flow resistance (pressure drop <30Pa). The pre-cooled air is evenly distributed to the condensation core layer by the guide plate, improving the stability of the intake temperature. Daytime pre-cooling supplement: If the ambient temperature is below 25°C during the day, the pre-cooling channel will be selectively activated to further cool the air using ambient air. However, the main pre-cooling function relies on the low-temperature period at night to save energy.

[0008] Furthermore, the AI ​​algorithm dynamically calculates the optimal combination of photovoltaic-wind-thermal power generation, including the following steps: Real-time data acquisition and preprocessing: The system collects real-time data (photovoltaic output power, wind speed, and ambient temperature) at a frequency of 1Hz through photovoltaic radiation sensors, anemometers, and temperature sensors deployed in the energy supply layer; it reads the current energy storage capacity of the nitrate phase change energy storage material (PCM) (indirectly calculated through temperature sensors, with an energy storage density of 150kJ / kg); and it receives the current water production demand (based on LSTM predictions of water consumption in the next 24 hours) and real-time energy consumption data of the condensation core layer from the intelligent control layer. Short-term energy forecast (10 minutes): Based on historical photovoltaic data (past 24 hours) and real-time radiation values, an LSTM neural network is used to predict the photovoltaic power generation in the next 10 minutes (error < 5%). Combined with real-time wind speed data and historical wind speed-power curves (wind turbine parameters), the wind power generation in the next 10 minutes is calculated (starting when wind speed > 1.5 m / s). Based on the current operating status of the condensation core layer (thermoelectric cooling fin power, waste heat temperature), the recoverable electrical energy of the thermoelectric generator (TEG) is estimated (starting when waste heat temperature > 50℃). Construct a multi-objective optimization model: Objective function: Maximize the water production / energy consumption ratio (L / kWh); Constraints: Energy storage capacity limit: PCM remaining energy is greater than 0 (to avoid over-discharge); Condensing power requirement: The power of the condensing core layer must be greater than the minimum operating power (0.5kW). Energy priority: During the daytime (8:00-18:00), the proportion of photovoltaic power supply should be greater than 80%; Execute the dynamic combination algorithm: Daytime mode (mainly solar power): If the predicted photovoltaic power output is greater than the required condensing power output, photovoltaic power will be used first, and the remaining energy will be stored in the PCM (storage efficiency > 90%). If the predicted photovoltaic power output is less than the required power output, the PCM will be activated to discharge and make up the shortfall, while the condensing power output will be adjusted to the range that the photovoltaic power output can support. Night mode (mainly wind power + PCM): If the wind speed is greater than 1.5 m / s, wind power generation will be started, and wind power will be used as the priority for power supply. If the power supply is insufficient, it will be supplemented by PCM. If the wind speed is less than 1.5 m / s and the PCM energy storage is greater than 30%, only PCM power supply will be used. If the PCM energy storage is less than 30%, the thermoelectric generator will be started to generate electricity using condensation waste heat. If the weather changes abruptly (such as a sandstorm causing a sudden drop in photovoltaic output), switch to "water production protection" mode within 30 seconds: reduce the condensing power to the lowest operating value and prioritize ensuring the basic water production (2L / hour). Issue execution instructions: The optimized power supply combination (e.g., "70% photovoltaic + 20% wind power + 10% PCM") is sent to the energy supply layer, and the inverter output voltage / current is adjusted. If charging is required, photovoltaic / wind power is activated to store energy in the PCM. If discharging is required, the power of the PCM heating element is adjusted (discharge rate is controllable). When the waste heat temperature is greater than 50°C, the TEG is activated and its load resistance is adjusted to maximize power generation efficiency (TEG output voltage is linked to condensation waste heat temperature). Feedback optimization: Record the energy distribution results (actual power supply of photovoltaic / wind / PCM / TEG), water production, and energy consumption ratio data every 10 minutes; upload the local data to the cloud after anonymization, aggregate it with data from other devices to train the global model, and update the local AI algorithm parameters every 24 hours to improve prediction accuracy.

[0009] Furthermore, the gradient condensation structure includes the following steps: Activate adsorption mode: When the intelligent control layer detects that the ambient humidity is greater than 25% through the temperature and humidity sensor, the adsorption mode is activated. A modified MOF (metal-organic framework) material is used, which has a pore size of 2-3 nm, a surface area greater than 2000 m² / g, and is loaded with hydrophobic groups to prevent water vapor from condensing inside the material and maintain adsorption capacity. The adsorption capacity is 1.2 g water vapor / g material (at a relative humidity of 30%), and it can continuously adsorb for 4 hours. Air enters the adsorption zone after being purified by the pretreatment layer. Water vapor molecules are combined with the MOF channels through van der Waals forces and are efficiently captured. During the adsorption process, the thermoelectric cooling chip remains in standby mode, and the temperature of the adsorption zone is maintained below 35°C only through passive heat dissipation to avoid premature condensation. Switch to adsorption-condensation mode: The adsorption time reaches 4 hours (system preset cycle), and the adsorption capacity of MOF material is greater than 90% (mass change is monitored by weight sensor); the intelligent control layer sends a command to close the air inlet valve of the adsorption zone and start the thermoelectric cooling chip; after the thermoelectric cooling chip is powered on, the cold end temperature drops to -3℃ within 30 seconds, and the hot end is isolated from the condensation zone by the aerogel insulation layer. Phase change condensation: After air enters the condensation zone, the temperature drops sharply to -3°C. Water vapor exceeds the saturated vapor pressure under the current pressure, undergoing a phase change and condensing into micron-sized water droplets. An aerogel insulation layer (thermal conductivity less than 0.015 W / m·K) envelops the condensation zone, reducing external heat penetration and maintaining a low-temperature environment. The surface of the condensation zone is covered with a nano-silica hydrophilic coating (contact angle less than 5°). Water droplets spread rapidly on the coating surface and converge along the guide channel to the water collection tank. The bottom of the water collection tank is tilted at 5°, and the water droplets flow into the water storage tank at a flow rate greater than 0.5 L / min under the action of gravity. Material recycling: After the condensation mode is activated, the temperature in the adsorption zone rises to 50°C due to heat dissipation from the hot end of the thermoelectric cooling chip. Water molecules in the pores of the MOF material desorb, restoring more than 90% of the adsorption capacity. The regeneration process requires no additional energy and is completed using the residual heat of condensation, with a cycle of less than 10 minutes. The condensation core layer uploads data such as water production, condensation efficiency, and remaining MOF capacity to the intelligent control layer in real time via a 4G / LoRa interface to optimize the adsorption-condensation switching cycle (e.g., from 4 hours to 3.5 hours).

[0010] Furthermore, sending cleaning commands to the environmental pretreatment layer, sending power generation-storage switching commands to the energy supply layer, and sending power adjustment commands to the condensation core layer includes the following steps: Send a cleaning command to the environmental pretreatment layer (dust filter maintenance): The intelligent control layer monitors the filter status in real time using PM10 and differential pressure sensors, with a data acquisition frequency of 1Hz (once per second) to respond promptly to sudden sandstorm changes. Triggering conditions are a PM10 concentration greater than 0.5 mg / m³ (sandstorm warning) or a filter differential pressure greater than 200 Pa (severe clogging). If either condition is met, the control layer immediately generates a cleaning command (priority: high-pressure cleaning > backflushing cleaning). If both conditions are met simultaneously, high-pressure cleaning (0.8 MPa, 45 seconds) is performed first, followed by backflushing cleaning (0.6 MPa, 30 seconds). The cleaning command is sent to the environmental pretreatment layer PLC via the LoRa communication module, closing the air intake valve and starting the high-pressure air gun / backflushing. During cleaning, the differential pressure sensor provides real-time data feedback. If the differential pressure remains greater than 150 Pa after cleaning, a secondary cleaning is automatically triggered. After cleaning, the filter status is marked as "maintained," and the cleaning time and energy consumption are recorded in the blockchain log. Sending a power generation-storage switching command to the energy supply layer (dynamic energy allocation): Data on photovoltaic output power (via current and voltage sensors), wind speed (via ultrasonic anemometer), and PCM energy storage capacity (indirectly calculated using temperature sensors) are collected. LSTM neural networks are used to predict photovoltaic / wind energy output for the next 10 minutes. Combined with the real-time energy consumption demand of the condensation core layer (monitored by power sensors), the optimal energy combination is calculated (objective: maximizing water production / energy consumption ratio). Instruction type: Daytime mode (8:00-18:00): Prioritize photovoltaic power supply (ratio greater than 80%), with remaining energy stored in the PCM; if photovoltaic power is insufficient, the PCM is activated to discharge and supplement the power. Nighttime mode (18:00-8:00): Wind power generation is activated when the wind speed is greater than 1.5m / s, and the PCM is used to supplement the power if insufficient; if the wind speed is less than 1.5m / s and the PCM energy storage is less than 30%, the thermoelectric generator is activated; PWM signals are sent to the inverter / controller of the energy supply layer via the 4G communication module (CAT-M1) to adjust the output ratio of photovoltaic / wind power / energy storage; the energy supply layer feeds back the actual power supply (photovoltaic, wind power, energy storage) to the intelligent control layer every 10 seconds. If the actual power supply deviates from the prediction by more than 10%, the control layer recalculates the optimal combination and issues a correction command (e.g., adjusting the photovoltaic ratio from 70% to 65%). Send a power adjustment command (condensation efficiency optimization) to the condenser core layer: The system collects data on ambient temperature and humidity, condenser core layer temperature, water production, and MOF material status. Combined with historical data, it predicts environmental parameters (such as humidity and temperature) for the next 24 hours and plans condensation power in advance. If the predicted ambient humidity is greater than 30% in the next 2 hours, the condensation power is increased to 1.5kW (maximum water production mode). If the predicted humidity is less than 20% or energy is insufficient, the power is reduced to 0.5kW (maintain water production mode). Real-time adjustments are made based on the MOF adsorption capacity: if the remaining capacity is less than 20%, adsorption mode is activated and condensation power is reduced; if the capacity is greater than 80%, condensation mode is switched to and power is increased. Power adjustment commands are sent to the condenser core layer inverter via the CAN bus. The inverter adjusts the input voltage of the thermoelectric cooling element, changing the cooling power. The condenser core layer uploads water production and energy consumption data to the control layer every minute for optimizing subsequent commands (e.g., further fine-tuning the power if water production does not meet expectations). The cleaning command has a higher priority than the energy switching command, which in turn has a higher priority than the power adjustment command. If the command fails to be issued (e.g., communication is interrupted), the intelligent control layer will start a local backup strategy (e.g., run according to preset parameters). If the status does not meet the standard after execution (e.g., the pressure difference is still greater than 200Pa after cleaning), an alarm will be triggered and pushed to the user terminal (4G / LoRa remote notification).

[0011] Furthermore, the user can view the water production and energy consumption in real time through the 4G / LoRa remote control interface, and remotely adjust parameters to achieve fully automatic operation with minimal human intervention, including the following steps: Process demonstration: The intelligent control layer encapsulates data in JSON format, including timestamps, device IDs, parameter types (such as "water production"), numerical values, and unit fields. Users monitor in real time via a mobile app / webpage, using TCP protocol to transmit real-time data, ensuring high reliability (packet loss rate <0.1%). In remote, low-power scenarios, LoRaWAN protocol is used to transmit low-frequency critical data (such as daily water production and fault codes) at 30-minute intervals. The mobile app is developed based on the Flutter framework, supporting iOS / Android, and includes: a real-time data dashboard (water production, energy consumption, PM10 concentration), historical data graphs (trends over the past 24 hours / 7 days), and status indicator lights (green for normal / red for fault). The web-based management platform is developed based on the Vue.js framework, supporting centralized management of multiple devices, and includes functions such as: device map positioning (GPS module integration), batch parameter adjustment, and alarm log query (filtering conditions: time, device ID, fault type). Command execution: User commands are input for backflushing cleaning cycle (2 hours → 3 hours), high-pressure cleaning air pressure (0.6MPa → 0.8MPa), photovoltaic power supply priority (80% → 90%), PCM discharge threshold (30% → 40%), condensing power (1.0kW → 1.5kW), and adsorption-condensation switching cycle (4 hours → 3.5 hours). Input methods include a slider (continuous parameters, such as air pressure 0.6-1.0MPa), a drop-down menu (discrete parameters, such as cleaning cycle 2 / 3 / 4 hours), and a power button (start / stop control, such as "emergency stop"). Command data is encrypted using AES-128-CBC; the key is negotiated and generated during the initial connection between the user equipment and the control layer. The instruction packet contains a CRC checksum. The receiving end verifies the data integrity; if the verification fails, it requests a retransmission. The intelligent control layer decrypts the instruction packet, extracts the parameter type, target device ID, and new value, and verifies the parameter range (e.g., air pressure not exceeding 1.0 MPa). If the limit is exceeded, the instruction is discarded and an error message is pushed. The layer sends adjustment instructions to the target device (e.g., the environmental pretreatment layer PLC) via a 4G / LoRa module. After receiving the instructions, the PLC / inverter adjusts the corresponding parameters (e.g., modifying the backflushing cleaning cycle register value), and returns a confirmation packet (including execution result and timestamp) to the control layer. The intelligent control layer pushes the execution result (e.g., "Backflushing cycle has been adjusted to 3 hours") to the user equipment via 4G / LoRa. All remote operations are recorded in the blockchain log (timestamp, user ID, operation content, and result). Balancing fully automated operation with human intervention: By default, it operates in fully automatic mode, with all parameters dynamically adjusted by AI algorithms (such as cleaning cycle and condensing power). When users remotely adjust parameters, they can choose "temporary modification" or "permanent modification". Temporary modifications take effect within 24 hours, after which AI control is restored. Permanent modifications are written to the device's EEPROM, overwriting the original AI strategy (requiring secondary confirmation). When a sandstorm causes a sudden increase in PM10 concentration (greater than 1 mg / m³) or equipment malfunctions (such as overheating of the thermoelectric cooling element), emergency manual intervention is triggered. Users can send an "emergency stop" command via the APP / Web terminal, and the control layer will immediately shut down all equipment (condenser, generator, and intake valve) and initiate fault diagnosis. After the fault is resolved, users can manually restart or select "restore automatic mode".

[0012] A fully automated method for collecting condensate water in the desert includes: Environmental pretreatment: When the user triggers the device, air first enters the bionic honeycomb filter, which uses electrostatic adsorption technology to filter sand and dust. At night (22:00-5:00), the spiral pre-cooling channel is automatically activated to pre-lower the intake air temperature by 5-8°C using low-temperature air, reducing subsequent condensation energy consumption. Backflushing cleaning (0.6MPa air pressure, lasting 30 seconds) is started every 2 hours to remove sand and dust from the filter surface. Data is collected at a frequency of 1Hz using temperature, humidity, PM10, and wind speed sensors and uploaded to the intelligent control layer; if a sandstorm is detected (PM10 > 1mg / m³), it automatically switches to high-intensity filtration mode (backflushing frequency increases to once every 1 hour). Dynamic energy supply: When the photovoltaic panels provide more than 80% of the power during the day, photovoltaic power is used first, and the remaining energy is stored in nitrate phase change energy storage materials; when the wind speed is greater than 1.5m / s at night, micro wind turbines are started, and the energy is supplemented by phase change energy storage when the wind speed is insufficient; the thermoelectric generator generates electricity using the waste heat of the condenser. AI-driven dynamic allocation: the intelligent control layer calculates the optimal energy consumption ratio every 10 minutes and adjusts the usage ratio of photovoltaic / wind power / energy storage; if the forecast is cloudy the next day, wind power charging is strengthened at night. Condensation collection: When the ambient humidity is greater than 25%, the modified MOF material adsorbs water vapor and switches to condensation mode every 4 hours. After the phase change condensation and water droplet collection condensation mode is activated, the thermoelectric cooling chip lowers the temperature to -3℃, triggering the phase change of water vapor; the generated water droplets converge along the nano-silica hydrophilic coating to the water collection tank; the aerogel insulation layer reduces the loss of cooling capacity. The water production and energy consumption data of the condensation layer are uploaded to the intelligent control layer in real time. Intelligent maintenance closed-loop optimization: When the PM10 concentration is greater than 0.5 mg / m³ or the pressure difference is greater than 200 Pa, the high-pressure air gun is automatically activated to clean the filter screen; the MOF material is regenerated every 7 days by solar heating, restoring the adsorption capacity to more than 90%, and the regeneration process data is recorded to the blockchain; By combining vibration analysis and temperature gradient detection, problems such as filter blockage and aging of cooling elements can be identified in advance. Users can remotely view water production and energy consumption and adjust parameters via 4G / LoRa interface; abnormal logs are automatically uploaded to the cloud and maintained through blockchain traceability. Continuous optimization of the data loop: Data from multiple devices is aggregated through federated learning to optimize AI models (such as improving humidity prediction accuracy), while local data is encrypted and stored; blockchain records all operation and maintenance data. By accumulating long-term data, we can learn about desert hydrological cycles and dynamically adjust pre-cooling and condensation strategies.

[0013] The present invention has the following beneficial effects: This invention employs a biomimetic honeycomb filter combined with electrostatic adsorption technology to improve dust filtration efficiency. A spiral pre-cooling channel utilizes the low nighttime air temperature to pre-cool the air by 5-8°C, significantly reducing subsequent condensation energy consumption. Then, an AI algorithm dynamically calculates the optimal combination of photovoltaic-wind-thermal power generation for stable 24-hour power supply. A gradient condensation structure is used; when the ambient humidity is greater than 25%, the modified MOF material preferentially adsorbs water vapor, switching to condensation mode every 4 hours. A thermoelectric cooling element lowers the temperature to -3°C, triggering phase change condensation, and water droplets flow along the hydrophilic nano-silica coating. The water is collected in a collection tank, and the aerogel insulation layer reduces heat loss. Data is collected at a frequency of 1Hz, and LSTM neural network is used to predict environmental parameters for the next 24 hours, adjusting the condensation power in advance. Fault warning is provided by combining vibration analysis and temperature gradient detection. When the PM10 concentration is greater than 0.5mg / m³ or the pressure difference is greater than 200Pa, the high-pressure air gun is automatically activated to clean the filter screen. Users can remotely monitor and adjust parameters through 4G / LoRa interface to achieve fully automatic operation with low human intervention, effectively solving the problem of low efficiency of desert condensation water collection systems and methods. Attached Figure Description

[0014] Figure 1 This is a system block diagram of a fully automated condensate collection system for deserts proposed in this invention; Figure 2 This is a flowchart of a fully automated condensation and water collection method in the desert proposed in this invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Please see Figure 1 As shown, this invention relates to a fully automated condensate collection system and method for desert environments, comprising: A fully automated condensate collection system for desert environments includes: Environmental pretreatment layer: Air first enters the biomimetic honeycomb filter (pore size <50μm), which filters sand and dust using electrostatic adsorption technology; then it passes through a spiral pre-cooling channel, which uses the low temperature air at night to pre-cool the intake air (temperature reduction of 5-8℃), providing a high-quality air source with low dust and low temperature for the condensation core layer. It automatically starts back-flushing cleaning (air pressure 0.6MPa) every 2 hours to maintain the filter performance. Energy supply layer: Powers the condensation core layer and monitors photovoltaic radiation, wind speed, and temperature data in real time. AI algorithms dynamically calculate the optimal combination of photovoltaic-wind-thermal power generation (aiming to maximize water production / energy consumption ratio); photovoltaic power is used first during the day, and the remaining energy is stored in nitrate phase change energy storage material (energy storage density 150kJ / kg); wind power generation is started when the wind speed is greater than 1.5m / s at night, and the energy is supplemented by phase change energy storage when insufficient. Condensation core layer: A gradient condensation structure is adopted: When the ambient humidity is greater than 25%, the modified MOF material (loaded with hydrophobic groups) preferentially adsorbs water vapor and switches to condensation mode every 4 hours; at this time, the thermoelectric cooling chip is activated, which lowers the condensation temperature to -3℃, triggering phase change condensation. The generated water droplets converge along the nano-silica hydrophilic coating to the water collection tank (flow rate > 0.5L / min). An aerogel insulation layer (thermal conductivity < 0.015W / m·K) is used to reduce cold loss, and the water production, condensation efficiency, and material state (such as MOF adsorption capacity) are uploaded to the intelligent control layer optimization algorithm model in real time. Intelligent control layer: Sends cleaning commands to the environmental pretreatment layer, power generation-storage switching commands to the energy supply layer, and power adjustment commands to the condensation core layer; collects data at a frequency of 1Hz through sensors (temperature, humidity, PM10, wind speed, etc.), and uses an LSTM neural network to predict environmental parameters for the next 24 hours, adjusting the condensation power in advance; combines vibration analysis and temperature gradient detection for fault warning, automatically starting the high-pressure air gun to clean the filter when the PM10 concentration is greater than 0.5mg / m³ or the pressure difference is greater than 200Pa; users can view the water production and energy consumption in real time through the 4G / LoRa remote control interface, and remotely adjust parameters for fully automatic operation with minimal human intervention.

[0017] In one embodiment, the air first enters a biomimetic honeycomb filter, which uses electrostatic adsorption technology to filter dust, including the following steps: Bionic honeycomb filter for physical filtration: Air enters the biomimetic honeycomb filter (pore size <50μm) at a flow rate of 1-2m / s. The hexagonal honeycomb structure captures coarse dust particles with a diameter >50μm through inertial collision and interception. The filter is made of lightweight aluminum with a single-sided thickness <0.2mm, resulting in a ventilation rate >95% and a wind resistance <50Pa, thus reducing energy consumption. Electrostatic adsorption of ionized dust particles: The filter screen integrates an electrostatic adsorption module at the rear end. A high-voltage power supply (output voltage ±60kV, current 20mA) generates corona discharge at the discharge electrode, which ionizes the air to generate positive and negative ions. When dust particles (PM10) pass through the electric field, they collide with the ions, become charged on the surface, and migrate towards the dust collection electrode (filter screen skeleton) under the action of the electric field force and are adsorbed. Backflush cleaning maintains performance: The back-flushing cleaning system is activated every 2 hours. Compressed air (0.6MPa pressure) is blown in the opposite direction through the nozzles on the back of the filter to remove attached sand and dust. The cleaning time is 30 seconds, ensuring that the pressure difference of the filter is less than 200Pa. The electrostatic module stops working during cleaning to avoid high pressure risks. The adsorption function is automatically restored after cleaning.

[0018] In one embodiment, the pre-cooling of the intake air using low-temperature nighttime air through a spiral pre-cooling channel includes the following steps: Introducing cold air at night: When the ambient temperature drops to 10-15℃ at night (22:00-5:00), switch to pre-cooling mode, and the fan will introduce the low-temperature outside air into the spiral pre-cooling channel; Spiral channel heat exchange: Air enters a spiral precooling channel (channel length 2m, spiral diameter 0.5m), the inner wall of which is coated with a nano-silica coating to enhance thermal conductivity; the low-temperature air flows along the spiral path and comes into full contact with the channel wall, reducing the intake air temperature by 5-8℃ through convection heat transfer. Flow field optimization: The spiral structure creates a three-dimensional vortex in the air, prolonging the heat exchange time. At the same time, the spaced columns (50mm spacing) maintain channel stability and reduce flow resistance (pressure drop <30Pa). The pre-cooled air is evenly distributed to the condensation core layer by the guide plate, improving the stability of the intake temperature. Daytime pre-cooling supplement: If the ambient temperature is below 25°C during the day, the pre-cooling channel will be selectively activated to further cool the air using ambient air. However, the main pre-cooling function relies on the low-temperature period at night to save energy.

[0019] In one embodiment, the AI ​​algorithm dynamically calculates the optimal combination of photovoltaic-wind-thermal power generation, including the following steps: Real-time data acquisition and preprocessing: The system collects real-time data (photovoltaic output power, wind speed, and ambient temperature) at a frequency of 1Hz through photovoltaic radiation sensors, anemometers, and temperature sensors deployed in the energy supply layer; it reads the current energy storage capacity of the nitrate phase change energy storage material (PCM) (indirectly calculated through temperature sensors, with an energy storage density of 150kJ / kg); and it receives the current water production demand (based on LSTM predictions of water consumption in the next 24 hours) and real-time energy consumption data of the condensation core layer from the intelligent control layer. Short-term energy forecast (10 minutes): Based on historical photovoltaic data (past 24 hours) and real-time radiation values, an LSTM neural network is used to predict the photovoltaic power generation in the next 10 minutes (error < 5%). Combined with real-time wind speed data and historical wind speed-power curves (wind turbine parameters), the wind power generation in the next 10 minutes is calculated (starting when wind speed > 1.5 m / s). Based on the current operating status of the condensation core layer (thermoelectric cooling fin power, waste heat temperature), the recoverable electrical energy of the thermoelectric generator (TEG) is estimated (starting when waste heat temperature > 50℃). Construct a multi-objective optimization model: Objective function: Maximize the water production / energy consumption ratio (L / kWh); Constraints: Energy storage capacity limit: PCM remaining energy is greater than 0 (to avoid over-discharge); Condensing power requirement: The power of the condensing core layer must be greater than the minimum operating power (0.5kW). Energy priority: During the daytime (8:00-18:00), the proportion of photovoltaic power supply should be greater than 80%; Execute the dynamic combination algorithm: Daytime mode (mainly solar power): If the predicted photovoltaic power output is greater than the required condensing power output, photovoltaic power will be used first, and the remaining energy will be stored in the PCM (storage efficiency > 90%). If the predicted photovoltaic power output is less than the required power output, the PCM will be activated to discharge and make up the shortfall, while the condensing power output will be adjusted to the range that the photovoltaic power output can support. Night mode (mainly wind power + PCM): If the wind speed is greater than 1.5 m / s, wind power generation will be started, and wind power will be used as the priority for power supply. If the power supply is insufficient, it will be supplemented by PCM. If the wind speed is less than 1.5 m / s and the PCM energy storage is greater than 30%, only PCM power supply will be used. If the PCM energy storage is less than 30%, the thermoelectric generator will be started to generate electricity using condensation waste heat. If the weather changes abruptly (such as a sandstorm causing a sudden drop in photovoltaic output), switch to "water production protection" mode within 30 seconds: reduce the condensing power to the lowest operating value and prioritize ensuring the basic water production (2L / hour). Issue execution instructions: The optimized power supply combination (e.g., "70% photovoltaic + 20% wind power + 10% PCM") is sent to the energy supply layer, and the inverter output voltage / current is adjusted. If charging is required, photovoltaic / wind power is activated to store energy in the PCM. If discharging is required, the power of the PCM heating element is adjusted (discharge rate is controllable). When the waste heat temperature is greater than 50°C, the TEG is activated and its load resistance is adjusted to maximize power generation efficiency (TEG output voltage is linked to condensation waste heat temperature). Feedback optimization: Record the energy distribution results (actual power supply of photovoltaic / wind / PCM / TEG), water production, and energy consumption ratio data every 10 minutes; upload the local data to the cloud after anonymization, aggregate it with data from other devices to train the global model, and update the local AI algorithm parameters every 24 hours to improve prediction accuracy.

[0020] In one embodiment, the gradient condensation structure includes the following steps: Activate adsorption mode: When the intelligent control layer detects that the ambient humidity is greater than 25% through the temperature and humidity sensor, the adsorption mode is activated. A modified MOF (metal-organic framework) material is used, which has a pore size of 2-3 nm, a surface area greater than 2000 m² / g, and is loaded with hydrophobic groups to prevent water vapor from condensing inside the material and maintain adsorption capacity. The adsorption capacity is 1.2 g water vapor / g material (at a relative humidity of 30%), and it can continuously adsorb for 4 hours. Air enters the adsorption zone after being purified by the pretreatment layer. Water vapor molecules are combined with the MOF channels through van der Waals forces and are efficiently captured. During the adsorption process, the thermoelectric cooling chip remains in standby mode, and the temperature of the adsorption zone is maintained below 35°C only through passive heat dissipation to avoid premature condensation. Switch to adsorption-condensation mode: The adsorption time reaches 4 hours (system preset cycle), and the adsorption capacity of MOF material is greater than 90% (mass change is monitored by weight sensor); the intelligent control layer sends a command to close the air inlet valve of the adsorption zone and start the thermoelectric cooling chip; after the thermoelectric cooling chip is powered on, the cold end temperature drops to -3℃ within 30 seconds, and the hot end is isolated from the condensation zone by the aerogel insulation layer. Phase change condensation: After air enters the condensation zone, the temperature drops sharply to -3°C. Water vapor exceeds the saturated vapor pressure under the current pressure, undergoing a phase change and condensing into micron-sized water droplets. An aerogel insulation layer (thermal conductivity less than 0.015 W / m·K) envelops the condensation zone, reducing external heat penetration and maintaining a low-temperature environment. The surface of the condensation zone is covered with a nano-silica hydrophilic coating (contact angle less than 5°). Water droplets spread rapidly on the coating surface and converge along the guide channel to the water collection tank. The bottom of the water collection tank is tilted at 5°, and the water droplets flow into the water storage tank at a flow rate greater than 0.5 L / min under the action of gravity. Material recycling: After the condensation mode is activated, the temperature in the adsorption zone rises to 50°C due to heat dissipation from the hot end of the thermoelectric cooling chip. Water molecules in the pores of the MOF material desorb, restoring more than 90% of the adsorption capacity. The regeneration process requires no additional energy and is completed using the residual heat of condensation, with a cycle of less than 10 minutes. The condensation core layer uploads data such as water production, condensation efficiency, and remaining MOF capacity to the intelligent control layer in real time via a 4G / LoRa interface to optimize the adsorption-condensation switching cycle (e.g., from 4 hours to 3.5 hours).

[0021] In one embodiment, sending a cleaning command to the environmental pretreatment layer, a power generation-storage switching command to the energy supply layer, and a power adjustment command to the condensation core layer includes the following steps: Send a cleaning command to the environmental pretreatment layer (dust filter maintenance): The intelligent control layer monitors the filter status in real time using PM10 and differential pressure sensors, with a data acquisition frequency of 1Hz (once per second) to respond promptly to sudden sandstorm changes. Triggering conditions are a PM10 concentration greater than 0.5 mg / m³ (sandstorm warning) or a filter differential pressure greater than 200 Pa (severe clogging). If either condition is met, the control layer immediately generates a cleaning command (priority: high-pressure cleaning > backflushing cleaning). If both conditions are met simultaneously, high-pressure cleaning (0.8 MPa, 45 seconds) is performed first, followed by backflushing cleaning (0.6 MPa, 30 seconds). The cleaning command is sent to the environmental pretreatment layer PLC via the LoRa communication module, closing the air intake valve and starting the high-pressure air gun / backflushing. During cleaning, the differential pressure sensor provides real-time data feedback. If the differential pressure remains greater than 150 Pa after cleaning, a secondary cleaning is automatically triggered. After cleaning, the filter status is marked as "maintained," and the cleaning time and energy consumption are recorded in the blockchain log. Sending a power generation-storage switching command to the energy supply layer (dynamic energy allocation): Data on photovoltaic output power (via current and voltage sensors), wind speed (via ultrasonic anemometer), and PCM energy storage capacity (indirectly calculated using temperature sensors) are collected. LSTM neural networks are used to predict photovoltaic / wind energy output for the next 10 minutes. Combined with the real-time energy consumption demand of the condensation core layer (monitored by power sensors), the optimal energy combination is calculated (objective: maximizing water production / energy consumption ratio). Instruction type: Daytime mode (8:00-18:00): Prioritize photovoltaic power supply (ratio greater than 80%), with remaining energy stored in the PCM; if photovoltaic power is insufficient, the PCM is activated to discharge and supplement the power. Nighttime mode (18:00-8:00): Wind power generation is activated when the wind speed is greater than 1.5m / s, and the PCM is used to supplement the power if insufficient; if the wind speed is less than 1.5m / s and the PCM energy storage is less than 30%, the thermoelectric generator is activated; PWM signals are sent to the inverter / controller of the energy supply layer via the 4G communication module (CAT-M1) to adjust the output ratio of photovoltaic / wind power / energy storage; the energy supply layer feeds back the actual power supply (photovoltaic, wind power, energy storage) to the intelligent control layer every 10 seconds. If the actual power supply deviates from the prediction by more than 10%, the control layer recalculates the optimal combination and issues a correction command (e.g., adjusting the photovoltaic ratio from 70% to 65%). Send a power adjustment command (condensation efficiency optimization) to the condenser core layer: The system collects data on ambient temperature and humidity, condenser core layer temperature, water production, and MOF material status. Combined with historical data, it predicts environmental parameters (such as humidity and temperature) for the next 24 hours and plans condensation power in advance. If the predicted ambient humidity is greater than 30% in the next 2 hours, the condensation power is increased to 1.5kW (maximum water production mode). If the predicted humidity is less than 20% or energy is insufficient, the power is reduced to 0.5kW (maintain water production mode). Real-time adjustments are made based on the MOF adsorption capacity: if the remaining capacity is less than 20%, adsorption mode is activated and condensation power is reduced; if the capacity is greater than 80%, condensation mode is switched to and power is increased. Power adjustment commands are sent to the condenser core layer inverter via the CAN bus. The inverter adjusts the input voltage of the thermoelectric cooling element, changing the cooling power. The condenser core layer uploads water production and energy consumption data to the control layer every minute for optimizing subsequent commands (e.g., further fine-tuning the power if water production does not meet expectations). The cleaning command has a higher priority than the energy switching command, which in turn has a higher priority than the power adjustment command. If the command fails to be issued (e.g., communication is interrupted), the intelligent control layer will start a local backup strategy (e.g., run according to preset parameters). If the status does not meet the standard after execution (e.g., the pressure difference is still greater than 200Pa after cleaning), an alarm will be triggered and pushed to the user terminal (4G / LoRa remote notification).

[0022] In one embodiment, the user can view the water production and energy consumption in real time through a 4G / LoRa remote control interface, and remotely adjust parameters to achieve fully automatic operation with minimal human intervention, including the following steps: Process demonstration: The intelligent control layer encapsulates data in JSON format, including timestamps, device IDs, parameter types (such as "water production"), numerical values, and unit fields. Users monitor in real time via a mobile app / webpage, using TCP protocol to transmit real-time data, ensuring high reliability (packet loss rate <0.1%). In remote, low-power scenarios, LoRaWAN protocol is used to transmit low-frequency critical data (such as daily water production and fault codes) at 30-minute intervals. The mobile app is developed based on the Flutter framework, supporting iOS / Android, and includes: a real-time data dashboard (water production, energy consumption, PM10 concentration), historical data graphs (trends over the past 24 hours / 7 days), and status indicator lights (green for normal / red for fault). The web-based management platform is developed based on the Vue.js framework, supporting centralized management of multiple devices, and includes functions such as: device map positioning (GPS module integration), batch parameter adjustment, and alarm log query (filtering conditions: time, device ID, fault type). Command execution: User commands are input for backflushing cleaning cycle (2 hours → 3 hours), high-pressure cleaning air pressure (0.6MPa → 0.8MPa), photovoltaic power supply priority (80% → 90%), PCM discharge threshold (30% → 40%), condensing power (1.0kW → 1.5kW), and adsorption-condensation switching cycle (4 hours → 3.5 hours). Input methods include a slider (continuous parameters, such as air pressure 0.6-1.0MPa), a drop-down menu (discrete parameters, such as cleaning cycle 2 / 3 / 4 hours), and a power button (start / stop control, such as "emergency stop"). Command data is encrypted using AES-128-CBC; the key is negotiated and generated during the initial connection between the user equipment and the control layer. The instruction packet contains a CRC checksum. The receiving end verifies the data integrity; if the verification fails, it requests a retransmission. The intelligent control layer decrypts the instruction packet, extracts the parameter type, target device ID, and new value, and verifies the parameter range (e.g., air pressure not exceeding 1.0 MPa). If the limit is exceeded, the instruction is discarded and an error message is pushed. The layer sends adjustment instructions to the target device (e.g., the environmental pretreatment layer PLC) via a 4G / LoRa module. After receiving the instructions, the PLC / inverter adjusts the corresponding parameters (e.g., modifying the backflushing cleaning cycle register value), and returns a confirmation packet (including execution result and timestamp) to the control layer. The intelligent control layer pushes the execution result (e.g., "Backflushing cycle has been adjusted to 3 hours") to the user equipment via 4G / LoRa. All remote operations are recorded in the blockchain log (timestamp, user ID, operation content, and result). Balancing fully automated operation with human intervention: By default, it operates in fully automatic mode, with all parameters dynamically adjusted by AI algorithms (such as cleaning cycle and condensing power). When users remotely adjust parameters, they can choose "temporary modification" or "permanent modification". Temporary modifications take effect within 24 hours, after which AI control is restored. Permanent modifications are written to the device's EEPROM, overwriting the original AI strategy (requiring secondary confirmation). When a sandstorm causes a sudden increase in PM10 concentration (greater than 1 mg / m³) or equipment malfunctions (such as overheating of the thermoelectric cooling element), emergency manual intervention is triggered. Users can send an "emergency stop" command via the APP / Web terminal, and the control layer will immediately shut down all equipment (condenser, generator, and intake valve) and initiate fault diagnosis. After the fault is resolved, users can manually restart or select "restore automatic mode".

[0023] A fully automated method for collecting condensate water in the desert includes: Environmental pretreatment: When the user triggers the device, air first enters the bionic honeycomb filter, which uses electrostatic adsorption technology to filter sand and dust. At night (22:00-5:00), the spiral pre-cooling channel is automatically activated to pre-lower the intake air temperature by 5-8°C using low-temperature air, reducing subsequent condensation energy consumption. Backflushing cleaning (0.6MPa air pressure, lasting 30 seconds) is started every 2 hours to remove sand and dust from the filter surface. Data is collected at a frequency of 1Hz using temperature, humidity, PM10, and wind speed sensors and uploaded to the intelligent control layer; if a sandstorm is detected (PM10 > 1mg / m³), it automatically switches to high-intensity filtration mode (backflushing frequency increases to once every 1 hour). Dynamic energy supply: When the photovoltaic panels provide more than 80% of the power during the day, photovoltaic power is used first, and the remaining energy is stored in nitrate phase change energy storage materials; when the wind speed is greater than 1.5m / s at night, micro wind turbines are started, and the energy is supplemented by phase change energy storage when the wind speed is insufficient; the thermoelectric generator generates electricity using the waste heat of the condenser. AI-driven dynamic allocation: the intelligent control layer calculates the optimal energy consumption ratio every 10 minutes and adjusts the usage ratio of photovoltaic / wind power / energy storage; if the forecast is cloudy the next day, wind power charging is strengthened at night. Condensation collection: When the ambient humidity is greater than 25%, the modified MOF material adsorbs water vapor and switches to condensation mode every 4 hours. After the phase change condensation and water droplet collection condensation mode is activated, the thermoelectric cooling chip lowers the temperature to -3℃, triggering the phase change of water vapor; the generated water droplets converge along the nano-silica hydrophilic coating to the water collection tank; the aerogel insulation layer reduces the loss of cooling capacity. The water production and energy consumption data of the condensation layer are uploaded to the intelligent control layer in real time. Intelligent maintenance closed-loop optimization: When the PM10 concentration is greater than 0.5 mg / m³ or the pressure difference is greater than 200 Pa, the high-pressure air gun is automatically activated to clean the filter screen; the MOF material is regenerated every 7 days by solar heating, restoring the adsorption capacity to more than 90%, and the regeneration process data is recorded to the blockchain; By combining vibration analysis and temperature gradient detection, problems such as filter blockage and aging of cooling elements can be identified in advance. Users can remotely view water production and energy consumption and adjust parameters via 4G / LoRa interface; abnormal logs are automatically uploaded to the cloud and maintained through blockchain traceability. Continuous optimization of the data loop: Data from multiple devices is aggregated through federated learning to optimize AI models (such as improving humidity prediction accuracy), while local data is encrypted and stored; blockchain records all operation and maintenance data. By accumulating long-term data, we can learn about desert hydrological cycles and dynamically adjust pre-cooling and condensation strategies.

[0024] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A fully automated condensate collection system for desert environments, characterized in that: include: Environmental pretreatment layer: Air first enters the biomimetic honeycomb filter, which filters sand and dust using electrostatic adsorption technology; then it passes through a spiral pre-cooling channel, which uses the low temperature of the night air to pre-cool the intake air, providing a high-quality air source with low dust and low temperature for the condensation core layer. The filter is automatically cleaned every 2 hours to maintain its performance. Energy supply layer: It supplies power to the condensation core layer, monitors photovoltaic radiation, wind speed, and temperature data in real time, and uses AI algorithms to dynamically calculate the optimal combination of photovoltaic-wind-thermal power generation. During the day, photovoltaic power is used first, and the remaining energy is stored in nitrate phase change energy storage materials. At night, when the wind speed is greater than 1.5m / s, wind power generation is started, and if it is insufficient, it is supplemented by phase change energy storage. Condensation core layer: A gradient condensation structure is adopted: When the ambient humidity is greater than 25%, the modified MOF material preferentially adsorbs water vapor and switches to condensation mode every 4 hours; at this time, the thermoelectric cooler is activated, which lowers the condensation temperature to -3℃, triggering phase change condensation. The generated water droplets are collected along the nano-silica hydrophilic coating into the water collection tank. When the ambient humidity is less than 25%, the MOF adsorption stage is skipped, and the thermoelectric cooler is directly activated for active condensation. At the same time, the cooling power is optimized through vibration analysis, an aerogel insulation layer is used to reduce cold loss, and the water production, condensation efficiency, and material status are uploaded to the intelligent control layer optimization algorithm model in real time. Intelligent control layer: Sends cleaning commands to the environmental pretreatment layer, power generation-storage switching commands to the energy supply layer, and power adjustment commands to the condensation core layer; collects data at a frequency of 1Hz through sensors, and uses an LSTM neural network to predict environmental parameters for the next 24 hours, adjusting condensation power in advance; combines vibration analysis and temperature gradient detection for fault warning, automatically starting high-pressure air gun to clean the filter when PM10 concentration is greater than 0.5mg / m³ or pressure difference is greater than 200Pa; users can view water production and energy consumption in real time through a 4G / LoRa remote control interface, and remotely adjust parameters for fully automatic operation with minimal human intervention.

2. The fully automated condensate collection system for desert environments according to claim 1, characterized in that, The air first enters a biomimetic honeycomb filter, which uses electrostatic adsorption technology to filter dust, including the following steps: Bionic honeycomb filter for physical filtration: Air enters the biomimetic honeycomb filter at a flow rate of 1-2 m / s. The hexagonal honeycomb structure captures coarse dust particles larger than 50 μm in diameter through inertial collision and interception. The filter is made of lightweight aluminum with a thickness of less than 0.2 mm on each side, resulting in a ventilation rate of more than 95% and a wind resistance of less than 50 Pa, thus reducing energy consumption. Electrostatic adsorption of ionized dust particles: The filter screen integrates an electrostatic adsorption module at the rear end. The high-voltage power supply generates corona discharge at the discharge electrode, which ionizes the air to generate positive and negative ions. When dust particles (PM10) pass through the electric field, they collide with the ions, become charged on the surface, and migrate towards the dust collection electrode under the action of the electric field force and are adsorbed. Backflush cleaning maintains performance: The back-flushing cleaning module is activated every 2 hours. Compressed air is blown in the opposite direction through the nozzles on the back of the filter to remove attached sand and dust. The cleaning time is 30 seconds, so that the pressure difference of the filter is less than 200Pa. The electrostatic module stops working during cleaning and automatically resumes adsorption function after cleaning.

3. The fully automated condensate collection system for desert environments according to claim 1, characterized in that, The process of pre-cooling the intake air using low-temperature nighttime air through a spiral pre-cooling channel includes the following steps: Introducing cold air at night: When the ambient temperature drops to 10-15℃ at night, switch to pre-cooling mode, and the fan will introduce low-temperature outside air into the spiral pre-cooling channel; Spiral channel heat exchange: The air inlet channel is a spiral pre-cooling channel with a length of 2m and a spiral diameter of 0.5m. The inner wall of the channel is coated with a nano-silica coating to enhance thermal conductivity. The low-temperature air flows along the spiral path and comes into full contact with the channel wall, reducing the inlet air temperature by 5-8℃ through convection heat transfer. Flow field optimization: The spiral structure creates a three-dimensional vortex in the air, prolonging the heat exchange time. At the same time, the spaced columns maintain channel stability and reduce flow resistance. The pre-cooled air is evenly distributed to the condensation core layer by the guide plate, improving the stability of the intake temperature. Daytime pre-cooling supplement: If the ambient temperature is below 25°C during the day, the pre-cooling channel will be selectively activated to further cool the air using ambient air. However, the main pre-cooling function relies on the low-temperature period at night to save energy.

4. The fully automatic condensate collection system for desert environments according to claim 1, characterized in that, The AI ​​algorithm dynamically calculates the optimal combination of photovoltaic-wind-thermal power generation, including the following steps: Real-time data acquisition and preprocessing: The system uses photovoltaic radiation sensors, anemometers, and temperature sensors deployed in the energy supply layer to collect real-time data including photovoltaic output power, wind speed, and ambient temperature at a frequency of 1Hz; it also reads the current energy storage capacity of the nitrate phase change energy storage material PCM; and receives current water production demand and real-time energy consumption data of the condensation core layer from the intelligent control layer. Short-term energy forecast: Based on historical photovoltaic data and real-time radiation values ​​over the past 24 hours, an LSTM neural network is used to predict the photovoltaic power generation in the next 10 minutes. Combined with real-time wind speed data and historical wind speed-power curves, the wind power generation in the next 10 minutes is calculated. Based on the current operating status of the condensation core layer, the recoverable electrical energy of the thermoelectric generator (TEG) is estimated. Construct a multi-objective optimization model: Objective function: Maximize the water production / energy consumption ratio; Constraints: Energy storage capacity limitation: PCM remaining energy is greater than 0; Condensation power requirement: The power of the condensation core layer must be greater than the minimum operating power; Energy priority: Daytime photovoltaic power generation accounts for more than 80%; Execute the dynamic combination algorithm: Daytime mode: If the predicted photovoltaic power output is greater than the condensing power demand, photovoltaic power will be used first, and the remaining energy will be stored in the PCM. If the predicted photovoltaic power output is less than the demand, the PCM will be activated to discharge and make up the gap, while the condensing power output will be adjusted to the range that the photovoltaic power output can support. Night mode: If the wind speed is greater than 1.5 m / s, wind power generation will be started, and wind power will be used as the priority for power supply. If the power supply is insufficient, it will be supplemented by PCM. If the wind speed is less than 1.5 m / s and the PCM energy storage is greater than 30%, only PCM power supply will be used. If the PCM energy storage is less than 30%, the thermoelectric generator will be started to generate electricity using condensation waste heat. If the weather changes abruptly, switch to "Water Production Protection" mode within 30 seconds: reduce condensing power to the lowest operating value and prioritize ensuring basic water production. Issue execution instructions: The optimized power supply combination is sent to the energy supply layer, and the inverter output voltage / current is adjusted. If charging is required, photovoltaic / wind power is activated to store energy in the PCM. If discharging is required, the power of the PCM heating element is adjusted. When the waste heat temperature is greater than 50°C, the TEG is activated and its load resistance is adjusted to maximize power generation efficiency. The energy distribution results, water production, and energy consumption ratio data are recorded every 10 minutes. The local data is anonymized and uploaded to the cloud, and aggregated with data from other devices to train the global model. The local AI algorithm parameters are updated every 24 hours to improve prediction accuracy.

5. A fully automated condensate collection system for desert environments according to claim 1, characterized in that, The gradient condensation structure includes the following steps: Activate adsorption mode: When the intelligent control layer detects that the ambient humidity is greater than 25% through the temperature and humidity sensor, the adsorption mode is activated. A modified MOF material is used, with a pore size of 2-3 nm, a surface area greater than 2000 m² / g, and hydrophobic groups to prevent water vapor condensation inside the material and maintain adsorption capacity. The adsorption capacity is 1.2 g water vapor / g material, allowing for continuous adsorption for 4 hours. Air enters the adsorption zone after being purified by the pretreatment layer, and water vapor molecules are efficiently captured by combining with the MOF channels through van der Waals forces. During adsorption, the thermoelectric cooling chip remains in standby mode, maintaining the adsorption zone temperature below 35°C only through passive heat dissipation to prevent premature condensation. Switch to adsorption-condensation mode: The adsorption time reaches 4 hours, and the adsorption capacity of MOF material is greater than 90%. The intelligent control layer sends a command to close the air inlet valve of the adsorption zone and start the thermoelectric cooling chip. After the thermoelectric cooling chip is powered on, the cold end temperature drops to -3℃ within 30 seconds, and the hot end is isolated from the condensation zone by the aerogel insulation layer. Phase change condensation: After air enters the condensation zone, the temperature drops sharply to -3°C. Water vapor exceeds the saturated vapor pressure under the current pressure, undergoes a phase change and condenses, forming micron-sized water droplets. An aerogel insulation layer wraps around the condensation zone, reducing external heat penetration and maintaining a low-temperature environment. The surface of the condensation zone is covered with a nano-silica hydrophilic coating, and water droplets spread rapidly on the coating surface, converging along the guide channel to the water collection tank. The bottom of the water collection tank is tilted at 5°, and the water droplets flow into the water storage tank at a flow rate greater than 0.5 L / min under the action of gravity. Material recycling: After the condensation mode is activated, the temperature in the adsorption zone rises to 50°C due to heat dissipation from the hot end of the thermoelectric cooling chip. Water molecules in the pores of the MOF material desorb, restoring more than 90% of the adsorption capacity. The regeneration process requires no additional energy and is completed using the residual heat of condensation, with a cycle of less than 10 minutes. The condensation core layer uploads data such as water production, condensation efficiency, and remaining MOF capacity to the intelligent control layer in real time via a 4G / LoRa interface to optimize the adsorption-condensation switching cycle.

6. The fully automated condensate collection system for desert environments according to claim 1, characterized in that, Sending cleaning commands to the environmental pretreatment layer, power generation-storage switching commands to the energy supply layer, and power adjustment commands to the condensation core layer includes the following steps: Send cleaning instructions to the environmental pretreatment layer: The intelligent control layer monitors the filter status in real time through PM10 and differential pressure sensors, with a data acquisition frequency of 1Hz, and responds promptly to sudden changes in sand and dust conditions. The triggering conditions are a PM10 concentration greater than 0.5mg / m³ or a filter differential pressure greater than 200Pa. If either condition is met, the control layer immediately generates a cleaning command. If both conditions are met, high-pressure cleaning is performed first, followed by backflushing cleaning. The cleaning command is sent to the environmental pretreatment layer PLC via the LoRa communication module, the air intake valve is closed, and the high-pressure air gun / backflushing is started. During the cleaning process, the differential pressure sensor provides real-time data feedback. If the differential pressure is still greater than 150Pa after cleaning, a second cleaning is automatically triggered. After cleaning, the filter status is marked as "maintained," and the cleaning time and energy consumption are recorded in the blockchain log. Send a power generation-storage switching command to the energy supply layer: Collect data on photovoltaic output power, wind speed, and PCM energy storage capacity; use LSTM neural network to predict photovoltaic / wind energy output for the next 10 minutes, and calculate the optimal energy combination by combining the real-time energy consumption demand of the condensation core layer. Instruction type: Daytime mode: Prioritize photovoltaic power supply, with surplus energy stored in the PCM; if photovoltaic power is insufficient, the PCM is activated to discharge and supplement the power. Nighttime mode: When the wind speed is greater than 1.5 m / s, wind power generation is activated, and if insufficient, the PCM is used to supplement the power. If the wind speed is less than 1.5 m / s and the PCM energy storage is less than 30%, the thermoelectric generator is activated. A PWM signal is sent to the inverter / controller of the energy supply layer via the 4G communication module to adjust the output ratio of photovoltaic / wind power / energy storage. The energy supply layer feeds back the actual power supply to the intelligent control layer every 10 seconds. If the actual power supply deviates from the prediction by more than 10%, the control layer recalculates the optimal combination and issues a correction command. Send power adjustment commands to the condenser core layer: The system collects data on ambient temperature and humidity, condenser core layer temperature, water production, and MOF material status. Combined with historical data, it predicts environmental parameters for the next 24 hours and plans condensation power in advance. If the predicted ambient humidity is greater than 30% in the next 2 hours, the condensation power is increased to 1.5kW; if the predicted humidity is less than 20% or energy is insufficient, the power is reduced to 0.5kW. Real-time adjustments are made based on MOF adsorption capacity: if the remaining capacity is less than 20%, adsorption mode is activated and condensation power is reduced; if the capacity is greater than 80%, condensation mode is switched and power is increased. Power adjustment commands are sent to the condenser core layer inverter via the CAN bus; the inverter adjusts the input voltage of the thermoelectric cooling element, changing the cooling power. The condenser core layer uploads water production and energy consumption data to the control layer every minute for optimizing subsequent commands. The cleaning command has a higher priority than the energy switching command, which in turn has a higher priority than the power adjustment command. If the command fails to be issued, the intelligent control layer will activate the local backup strategy. If the status does not meet the requirements after execution, an alarm will be triggered and pushed to the user terminal.

7. The fully automated condensate collection system for desert environments according to claim 1, characterized in that, The user can view the water production and energy consumption in real time through the 4G / LoRa remote control interface, and remotely adjust parameters to achieve fully automatic operation with minimal human intervention, including the following steps: Process demonstration: The intelligent control layer encapsulates data in JSON format, including timestamp, device ID, parameter type, value, and unit fields. Users monitor in real time via a mobile app / webpage, using TCP protocol to transmit real-time data for improved reliability. In remote, low-power scenarios, LoRaWAN protocol is used to transmit low-frequency critical data with a 30-minute transmission interval. The mobile app is developed based on the Flutter framework, supporting iOS / Android, and includes a real-time data dashboard, historical data graphs, and status indicator lights. The web-based management platform is developed based on the Vue.js framework, supporting centralized management of multiple devices, and features include device map positioning, batch parameter adjustment, and alarm log querying. Command execution: User commands input the backflush cleaning cycle, high-pressure cleaning air pressure, photovoltaic power supply priority, PCM discharge threshold, condensation power, and adsorption-condensation switching cycle; input methods include sliders, drop-down menus, and power buttons; command data is encrypted using AES-128-CBC, with the key negotiated and generated during the initial connection between the user equipment and the control layer; the command packet includes a CRC checksum, and the receiving end verifies data integrity, requesting retransmission if verification fails; the intelligent control layer decrypts the command packet, extracts the parameter type, target device ID, and new value, verifies the parameter range, discards the command and pushes an error message if the range is exceeded; adjustment commands are sent to the target device via a 4G / LoRa module; after receiving the command, the PLC / inverter adjusts the corresponding parameters, executes the command, and returns an acknowledgment packet containing the execution result and timestamp to the control layer; the intelligent control layer pushes the execution result to the user equipment via 4G / LoRa, and all remote operations are recorded in the blockchain log; Balancing fully automated operation with human intervention: It runs in fully automatic mode by default, with all parameters dynamically adjusted by AI algorithms. When users remotely adjust parameters, they can choose "temporary modification" or "permanent modification". Temporary modifications take effect within 24 hours, after which AI control is restored. Permanent modifications are written to the device's EEPROM, overwriting the original AI strategy. When a sandstorm causes a sudden increase in PM10 concentration and the device malfunctions, emergency manual intervention is triggered. Users can send an "emergency stop" command via the APP / Web terminal. The control layer immediately shuts down all devices and initiates fault diagnosis. After the fault is resolved, users can manually restart or choose "restore automatic mode".

8. A fully automatic condensate collection system for deserts and a corresponding fully automatic condensate collection method for deserts, as described in claim 1, characterized in that, include: Environmental pretreatment: After the user triggers the device, air first enters the bionic honeycomb filter, which uses electrostatic adsorption technology to filter sand and dust; at night, the spiral pre-cooling channel is automatically activated to pre-cool the intake air temperature by 5-8°C using low-temperature air, reducing subsequent condensation energy consumption; every 2 hours, back-flushing cleaning is started to remove sand and dust from the filter surface. Data is collected at a frequency of 1Hz using temperature, humidity, PM10, and wind speed sensors and uploaded to the intelligent control layer; if a sandstorm is detected, it automatically switches to a high-intensity filtration mode. Dynamic energy supply: When the photovoltaic panels provide more than 80% of the power during the day, photovoltaic power is used first, and the remaining energy is stored in nitrate phase change energy storage materials; when the wind speed is greater than 1.5m / s at night, micro wind turbines are started, and the energy is supplemented by phase change energy storage when the wind speed is insufficient; the thermoelectric generator generates electricity using the waste heat of the condenser. AI-driven dynamic allocation: the intelligent control layer calculates the optimal energy consumption ratio every 10 minutes and adjusts the usage ratio of photovoltaic / wind power / energy storage; if the forecast is cloudy the next day, wind power charging is strengthened at night. Condensation collection: When the ambient humidity is greater than 25%, the modified MOF material adsorbs water vapor and switches to condensation mode every 4 hours. After the phase change condensation and water droplet collection condensation mode is activated, the thermoelectric cooling chip lowers the temperature to -3℃, triggering the phase change of water vapor; the generated water droplets converge along the nano-silica hydrophilic coating to the water collection tank; the aerogel insulation layer reduces the loss of cooling capacity. The water production and energy consumption data of the condensation layer are uploaded to the intelligent control layer in real time. Intelligent maintenance closed-loop optimization: When the PM10 concentration is greater than 0.5 mg / m³ or the pressure difference is greater than 200 Pa, the high-pressure air gun is automatically activated to clean the filter screen; the MOF material is regenerated every 7 days by solar heating, restoring the adsorption capacity to more than 90%, and the regeneration process data is recorded to the blockchain; By combining vibration analysis and temperature gradient detection, problems such as filter blockage and aging of cooling elements can be identified in advance. Users can remotely view water production and energy consumption and adjust parameters via 4G / LoRa interface; abnormal logs are automatically uploaded to the cloud and maintained through blockchain traceability. Continuous optimization of the data loop: Data from multiple devices is aggregated through federated learning to optimize the AI ​​model, while local data is encrypted and stored; the blockchain records all operation and maintenance data. By accumulating long-term data, we can learn about desert hydrological cycles and dynamically adjust pre-cooling and condensation strategies.

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