Water source supply and demand collaborative optimization method and system for drought scenario

Abstract

This invention discloses a method and system for coordinated optimization of water supply and demand in drought scenarios, belonging to the field of smart water conservancy technology. The method includes: constructing a multi-source water network model containing atmospheric water, surface water, and groundwater within a target drought area; collecting real-time information on available water volume and water demand from each source; calling a matching water production efficiency model to predict the dynamic water production forecast value of virtual atmospheric water source nodes; constructing a multi-objective function and constraints to generate a comprehensive water allocation scheme; and triggering an extreme drought mode to prioritize atmospheric water and generate extreme scheduling instructions when the drought index exceeds the limit. This invention solves the technical problems of traditional drought areas relying solely on surface water and groundwater for water allocation, the fragmented and independent nature of atmospheric water extraction equipment making overall planning difficult, and the inability to predict water extraction capacity in conjunction with meteorological changes. It achieves integrated coordinated water allocation of atmospheric water extraction and conventional water sources, accurately predicts the scale of atmospheric water production, and ensures the priority use of atmospheric water extraction during severe droughts.

Description

Technical Field

[0001] This invention relates to the field of smart water conservancy technology, and in particular to a method and system for coordinated optimization of water supply and demand in drought scenarios. Background Technology

[0002] Arid and water-scarce areas are widely distributed in northern China. The capacity to guarantee water supply from multiple sources directly impacts regional domestic water use, agricultural irrigation, and the ecological protection of groundwater resources. MOF (Metal-on-Air) water capture, as a novel water extraction technology, is gradually being applied to drought-affected water replenishment scenarios. Current water resource management in arid areas primarily revolves around surface water and groundwater. Traditional management methods lack a comprehensive sensor network, making it difficult to collect integrated data on meteorology, water reserves, and water demand. They cannot quantify and predict the dynamic production capacity of atmospheric water, hindering the coordinated optimization of water allocation between surface and groundwater sources. Furthermore, they cannot automatically prioritize atmospheric water supply during severe droughts, resulting in fragmented water allocation data and insufficient precision in scheduling plans, thus failing to achieve refined water resource management in drought scenarios. Summary of the Invention

[0003] This application provides a method and system for coordinating water supply and demand optimization in arid scenarios, which solves the technical problems of traditional arid areas relying solely on surface water and groundwater for water allocation, the fragmented and independent air water collection equipment that cannot be planned in a coordinated manner, and the difficulty in predicting water extraction capacity in conjunction with meteorological changes.

[0004] The first aspect of this application provides a method for coordinated optimization of water supply and demand in drought scenarios. The method includes: deploying MOF atmospheric water harvesting units within a target drought area and defining them as virtual atmospheric water source nodes in a regional water supply network; simultaneously defining surface water source nodes and groundwater source nodes to construct a multi-source network model; acquiring real-time information on the available water volume of each source node in the multi-source network model, as well as water demand information for the target drought area; and, based on meteorological forecast data for a preset future period, invoking a water production efficiency model that matches the material properties of the MOF atmospheric water harvesting units to generate water. The system generates a dynamic water production forecast value for the virtual atmospheric water source node within a preset future time period. Using the water demand information as a constraint, and based on the dynamic water production forecast value of the virtual atmospheric water source node, the available water information of the surface water source node, and the available water information of the groundwater source node, it optimizes and generates a comprehensive water allocation scheme. When the drought index of the target arid region exceeds a preset threshold, it enters an extreme drought mode, increases the water supply priority of the virtual atmospheric water source node in the multi-source network model, updates the comprehensive water allocation scheme, and generates an extreme dispatch instruction.

[0005] The second aspect of this application provides a water supply and demand collaborative optimization system for drought scenarios. The system includes: a multi-source network model construction module, used to deploy MOF atmospheric water harvesting units within a target arid region and define them as virtual atmospheric water source nodes in a regional water supply network, while simultaneously defining surface water source nodes and groundwater source nodes to construct a multi-source network model; a water quantity and usage information acquisition module, used to acquire in real time the available water quantity information of each water source node in the multi-source network model, as well as the water demand information of the target arid region; and a water production prediction value generation module, which, based on meteorological forecast data for a preset future period, calls a value matching the material properties of the MOF atmospheric water harvesting unit. A water production efficiency model generates a dynamic water production prediction value for the virtual atmospheric water source node within a preset future time period. A water allocation scheme generation module is used to generate a comprehensive water allocation scheme based on the water demand information, the dynamic water production prediction value of the virtual atmospheric water source node, the available water information of the surface water source node, and the available water information of the groundwater source node, using these as the supply basis. An extreme dispatch instruction generation module is used to enter an extreme drought mode when the drought index of the target drought area exceeds a preset threshold, increase the water supply priority of the virtual atmospheric water source node in the multi-source network model, update the comprehensive water allocation scheme, and generate an extreme dispatch instruction.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] This application deploys air-source water extraction devices in arid regions and incorporates them into a water source network model. By combining hydrological, water usage, and meteorological data, it predicts the air-source water extraction capacity and optimizes the multi-source water allocation scheme with the dual objectives of supply and demand and cost. After drought exceeds the standard, it adjusts the water supply weight, reschedules, and reverses the control of water extraction equipment. This achieves dynamic and coordinated water supply from multiple water sources, improving the scientific nature and stability of water allocation in arid regions. It also achieves integrated coordinated water distribution of air-source water extraction and conventional water sources, accurately predicts the scale of air-source water production, and ensures the technical effect of prioritizing the use of air-source water extraction during severe droughts. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating the water supply and demand coordinating optimization method for drought scenarios provided in this application embodiment.

[0010] Figure 2 This is a schematic diagram of the structure of the water supply and demand collaborative optimization system for drought scenarios provided in the embodiments of this application.

[0011] Figure labeling: Module 1 for constructing a multi-source network model, Module 2 for acquiring water quantity and usage information, Module 3 for generating predicted water production, Module 4 for generating water allocation schemes, and Module 5 for generating extreme scheduling instructions. Detailed Implementation

[0012] This application provides a method and system for coordinating water supply and demand optimization in arid scenarios, which solves the technical problems of traditional arid areas relying solely on surface water and groundwater for water allocation, the fragmented and independent air water collection equipment that cannot be planned in a coordinated manner, and the difficulty in predicting water extraction capacity in conjunction with meteorological changes.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0015] Example 1, as Figure 1 As shown, a method for coordinated optimization of water supply and demand in drought scenarios is described, wherein the method includes:

[0016] Within the target arid region, MOF atmospheric water collection units are deployed and defined as virtual atmospheric water source nodes in the regional water supply network. At the same time, surface water source nodes and groundwater source nodes are defined to construct a multi-source network model.

[0017] In this embodiment, MOF (Metal-Organic Framework) is a metal-organic framework adsorbent material with porous adsorption properties, serving as the core adsorbent material for air-based water extraction. The MOF atmospheric water collection unit is a complete physical water extraction device that fills with MOF material and extracts fresh water from the air through adsorption and desorption. The virtual atmospheric water source node is a water source data unit that can be uniformly allocated, formed by digitally abstracting the physical MOF water extraction device within the scheduling system. The multi-source water network model is a digital computational model that integrates atmospheric, surface, and groundwater source parameters with water conveyance topology for coordinated water allocation.

[0018] Specifically, within the target arid region, the locations and number of MOF atmospheric water collection units are determined based on the distribution data of water-using units in the existing water supply plan. The number of units is calculated using the formula: Number of units = (Daily basic water demand of the target arid region ÷ Rated water production of a single MOF atmospheric water collection unit) × 1.2, where 1.2 is a meteorological fluctuation redundancy coefficient, and the result is rounded up; Daily basic water demand = Daily total water demand of the target arid region – Conventionally available surface water and groundwater. The MOF atmospheric water collection unit adopts a multi-layer adsorption-desorption rotating structure. The MOF atmospheric water collection units are installed in open locations with good ventilation and no building obstructions within 500 meters of the water-using units, at a height of 2 to 3 meters above the ground. Each MOF atmospheric water collection unit is assigned a unique IoT identification code and is equipped with temperature and humidity sensors, electromagnetic water production sensors, and operational status sensors. Sensor data is uploaded to the regional management platform in real time via 4G / 5G network, with a data collection frequency of once every 5 minutes.

[0019] After completing the deployment of MOF atmospheric water collection units, three types of water source nodes are defined in the regional water supply network. Surface water source nodes are defined, corresponding to surface water intakes such as reservoirs and rivers within the target arid region. The geographical location, maximum water supply capacity, real-time water storage, and unit water supply cost parameters of each surface water source node are recorded. The unit water supply cost parameter is the comprehensive cost actually incurred in producing and transporting a unit volume of water from various water sources. Groundwater source nodes are defined, corresponding to groundwater intake facilities such as drinking water wells and irrigation wells within the target arid region. The geographical location, maximum allowable extraction volume, real-time water output, and unit water supply cost parameters of each groundwater source node are recorded. Virtual atmospheric water source nodes are defined, abstracting each deployed MOF atmospheric water collection unit as an independent virtual atmospheric water source node. The topology of these virtual atmospheric water source nodes in the multi-source network model is configured to be close to the water-using units indicated by the water demand information. For each virtual atmospheric water source node, associate the corresponding MOF atmospheric water collection unit with the device identifier, effective MOF material loading mass, and cumulative effective runtime parameters.

[0020] Next, a multi-source water network model is constructed based on the three defined types of water source nodes. Specifically: First, a set of water source nodes is established, including all surface water source nodes, groundwater source nodes, and atmospheric water virtual source nodes, with each node assigned a unique node number. Then, a set of water conveyance edges is established, constructed solely based on existing actual water conveyance pipelines in the target arid region, recording the pipeline length, diameter, maximum water conveyance capacity, and unit water conveyance cost parameters for each edge. Next, a node attribute matrix is ​​constructed, where rows correspond to each water source node, and columns correspond to the node's geographical location, maximum water supply capacity, real-time available water volume, and unit water supply cost attribute parameters. The corresponding parameters for each node are filled into the matrix, with the initial real-time available water volume for atmospheric water virtual source nodes set to 0. Finally, a water conveyance topology matrix is ​​constructed, where rows and columns correspond to water source nodes and water-using units. A matrix element value of 1 indicates the existence of an actual water conveyance pipeline between the two corresponding nodes, while a value of 0 indicates the absence of a water conveyance pipeline. The parameters of the water conveyance edges are also associated with the corresponding matrix elements.

[0021] Finally, the set of water source nodes, the set of water conveyance edges, the node attribute matrix, and the water conveyance topology matrix are integrated to obtain a complete multi-source network model.

[0022] By deploying MOF atmospheric water collection units equipped with sensing and acquisition functions nearby and dividing them into three types of water source nodes, and building a standardized multi-water source network model based on existing water transmission pipelines, the digital grid-connected modeling of the air water collection device is realized, providing complete data and model support for subsequent multi-water source coordinated optimization and allocation.

[0023] The system acquires in real time the available water quantity information of each water source node in the multi-water source network model, as well as the water demand information of the target arid area.

[0024] Optionally, the first step is to collect information on the available water volume at each water source node. For surface water source nodes, level transmitters and flow meters are installed at the corresponding water intake facilities. The meters are set to collect real-time reservoir water level and instantaneous flow rate at the outlet every 15 minutes. The remaining available surface water volume is calculated using a water level-reservoir capacity conversion formula. The collected data is transmitted to the regional management platform via wired communication. For groundwater source nodes, downhole water level sensors and pipeline flow meters are installed inside each water intake well. The equipment also uploads monitoring data every 15 minutes. Combined with the local approved annual maximum extraction quota for a single well, the remaining extractable water volume, i.e., the real-time available water volume, is obtained after deducting the extracted water volume. For virtual atmospheric water source nodes, relying on the electromagnetic water production sensor mounted on the corresponding MOF atmospheric water collection unit, the actual water volume collected and temporarily stored in the storage container every 15 minutes is recorded. This water volume represents the real-time available water volume of the virtual atmospheric water source node at the current moment, which differs from the dynamic water production prediction value obtained through the water production efficiency model in subsequent steps. The water volume data from these three types of water source nodes are then uniformly transmitted to the regional management platform for aggregation and storage.

[0025] Next, the collection of water demand information for the target arid region will be carried out. Smart water meters will be installed at the inlet or main water supply point of various water-using units within the region. Domestic water-using units will rely on smart water meters to collect hourly actual water consumption, while simultaneously retrieving local water supply regulations to determine the average daily water quota for residents. Agricultural irrigation water-using units will collect real-time water intake data using field water sampling devices, and calculate theoretical water requirements based on crop physiological water requirements. Industrial water-using units will rely on the main water supply meter in the factory area to collect water consumption data and refer to the enterprise's declared production water use plan. All metering instruments will upload data every 30 minutes. When water demand changes relatively smoothly, a 30-minute sampling interval is sufficient to reflect actual water consumption fluctuations while reducing data transmission and storage pressure. The specific sampling interval can be adjusted by those skilled in the art according to the actual situation of the target arid region. The control platform will periodically capture the metering data of all water-using units, and after integrating and summarizing various water quotas, it will form the overall real-time water demand information for the target arid region.

[0026] Based on meteorological forecast data for a future preset period, a water production efficiency model that matches the material properties of the MOF atmospheric water collection unit is invoked to generate a dynamic water production forecast value for the virtual atmospheric water source node within the future preset period.

[0027] In this embodiment, the water production efficiency model is a mathematical calculation model obtained by fitting temperature and humidity data with measured water production data, used to predict the water production value per unit mass of MOF material based on environmental parameters.

[0028] In one embodiment of this application, meteorological forecast data for a future preset time period containing temperature and humidity sequences is obtained. This data is then input into a water production efficiency model adapted to MOF materials to obtain a theoretical water production sequence per unit time. Finally, the dynamic water production prediction value of the virtual water source node of atmospheric water is obtained by combining the effective adsorption material mass of the unit with the time period calculation. The specific implementation process of this step will be described in detail later.

[0029] Based on the water demand information, and using the dynamic water production prediction of the virtual atmospheric water source node, the available water information of the surface water source node, and the available water information of the groundwater source node as the supply basis, a comprehensive water allocation scheme is generated.

[0030] Specifically, constrained by the water demand information of the target arid region, and relying on the available water quantity information of three types of water source nodes—atmospheric water, surface water, and groundwater—a multi-objective function is constructed and solved to minimize the total water supply deficit and the total water supply cost. This generates a comprehensive water quantity allocation scheme for each water source node corresponding to the water supply of each water demand point. The specific implementation process of this step will be described in detail later.

[0031] When the drought index of the target arid region exceeds a preset threshold, the extreme drought mode is entered, the water supply priority of the atmospheric water virtual water source node in the multi-water source network model is increased, the comprehensive water volume configuration scheme is updated, and extreme scheduling instructions are generated.

[0032] Specifically, when the regional drought index exceeds the preset threshold, the extreme drought mode is activated. The water supply priority is increased by lowering the unit water supply cost coefficient of atmospheric water or raising its water supply security weight. The water allocation plan is re-optimized and extreme dispatch instructions are generated to prioritize the use of atmospheric water and restrict groundwater extraction. The specific implementation process of this step will also be described in detail later.

[0033] Furthermore, the method provided in this application embodiment includes:

[0034] The MOF atmospheric water collection unit adopts a multi-layer adsorption-desorption rotating structure, and the topology of the atmospheric water virtual water source node in the multi-water source network model is configured to be close to the water-using unit indicated by the water demand information.

[0035] Specifically, the MOF atmospheric water collection unit adopts a multi-layer adsorption-desorption rotary structure. The core of this structure is a cylindrical rotor, which is composed of multiple layers of porous aluminum foil substrates with a thickness of 0.1 mm to 0.2 mm. Each layer of aluminum foil substrate is uniformly impregnated with MOF adsorbent material, and a 2 mm to 3 mm airflow gap is reserved between adjacent aluminum foil substrates. The rotor is connected to an AC geared motor via a central shaft, which drives it to rotate at a constant speed, with the motor output speed adjustable to 1 to 2 revolutions per hour. The rotor is divided into two independent regions along its circumference: an adsorption zone and a desorption zone. The adsorption zone occupies 70% of the rotor's circumferential area, and the desorption zone occupies 30%. The two regions are sealed and isolated using a silicone sealing strip.

[0036] The basic operating logic of this structure is as follows: Ambient air, driven by an axial fan, passes vertically through the gap between the rotors in the adsorption zone at a wind speed of 1.5 m / s to 2 m / s. Water vapor in the air is captured by the MOF adsorption material, and the dry air is directly discharged. The adsorption-saturated MOF material enters the desorption zone as the rotor rotates. The desorption zone is equipped with an electric heating coil and a temperature controller. The temperature controller monitors the air temperature in the desorption zone in real time through a thermocouple and controls the heating power of the electric heating coil to maintain the air temperature flowing through the desorption zone within the set range of 80°C to 100°C. The hot air passes through the gap between the rotors, causing the water vapor in the MOF material to desorb, forming high-temperature and high-humidity air. The high-temperature and high-humidity air enters the condenser heat exchanger, where water vapor is converted into liquid water through air-cooled condensation. The liquid water is collected by the water collection pan and transported to the water storage tank, completing one adsorption-desorption water production cycle.

[0037] In the construction of the multi-source water network model, the topology of the virtual atmospheric water source nodes is configured to be close to the water-using units indicated by the water demand information. Specifically, in the node attribute matrix, the geographical location parameter of each virtual atmospheric water source node is set to the actual installation location of the corresponding MOF atmospheric water collection unit. The straight-line distance between this installation location and the nearest water-using unit does not exceed 500 meters. In the water transmission topology matrix, priority is given to establishing water transmission connections between the virtual atmospheric water source nodes and the corresponding nearest water-using units. Water transmission paths that cross long distances are not set up, thereby shortening the water transmission distance and reducing water loss and energy consumption during the water transmission process.

[0038] Furthermore, the method provided in this application embodiment includes:

[0039] Under multiple sets of set environmental temperature and humidity combinations, water production tests were conducted on the MOF atmospheric water collection unit to obtain multiple sets of measured water production efficiency data. The measured water production efficiency data included at least the environmental temperature, environmental humidity, and the corresponding water production per unit mass of MOF material per unit time. Using the environmental temperature and environmental humidity from the multiple sets of measured water production efficiency data as input features and the corresponding water production per unit time as output labels, a model was trained through regression fitting to obtain a water production efficiency model that matches the material characteristics of the MOF atmospheric water collection unit.

[0040] Optionally, the water production of the MOF atmospheric water collection unit is tested under multiple sets of set environmental temperature and humidity combinations. The tests are conducted in a constant temperature and humidity chamber under standard atmospheric pressure. For example, the ambient temperature range is set to 10°C to 40°C with a temperature gradient of 5°C, and the relative humidity range is set to 20% to 80% with a humidity gradient of 10%, resulting in 49 different temperature and humidity combinations. During the test, the operating parameters of the MOF atmospheric water collection unit are fixed: the impeller speed is set to 1.5 revolutions per hour, the desorption zone temperature is set to 90°C, and the fan speed is set to 1.8 meters per second. The MOF atmospheric water collection unit, filled with a fixed mass of MOF material, is placed in the chamber and operated continuously for 24 hours under each set of temperature and humidity conditions. The actual temperature, actual humidity, and cumulative water production of the MOF atmospheric water collection unit are collected every 15 minutes. After each test, the water production per unit time of a unit mass of MOF material under that condition is calculated. The calculation method is to divide the total water production of the test by the effective loading mass of the MOF material and then divide by the test duration. The ambient temperature, ambient humidity and water production per unit time corresponding to each group are used as a set of measured water production efficiency data. After completing all groups of tests, a complete set of measured water production efficiency data is obtained.

[0041] Next, the obtained measured water production efficiency dataset was preprocessed to remove outlier data points where the water production was negative, or where the water production deviation exceeded 20% under adjacent temperature and humidity conditions with the same temperature but a different humidity gradient, or the same humidity but a different temperature gradient. The preprocessed dataset was then randomly divided into training and testing sets in an 8:2 ratio. The training set was used to solve for the model parameters, and the testing set was used to verify the model accuracy. The water production efficiency model specifically adopts a bivariate quadratic polynomial regression form: , where Q is the water production per unit mass of MOF material per unit time, in liters per kilogram per hour; T is the ambient temperature, in degrees Celsius; RH is the ambient relative humidity, in percentage; and a, b, c, d, e, and f are the regression coefficients to be solved.

[0042] Subsequently, the least squares method was used to perform regression fitting on the training set data to solve for the regression coefficients. Specifically, the temperature and humidity data of all samples in the training set were substituted into the aforementioned bivariate quadratic polynomial to construct a system of linear equations concerning the regression coefficients. By solving this system of equations, the optimal solutions for each coefficient were obtained, which minimized the sum of the squared errors between the predicted and measured water production. The obtained coefficients were then substituted into the model to obtain the preliminary water production efficiency model. The accuracy of the preliminary model was verified using test set data, and the coefficient of determination between the model's predicted and measured water production was calculated. ,like If the model accuracy meets the requirements, then the model is considered to be accurate. Then, supplement the original test range with 5 to 10 sets of water production test data with different temperature and humidity gradients, and repeat the above data preprocessing and model training steps until the model's determination coefficient is reached. The requirements have been met.

[0043] Measured data were obtained through standardized temperature and humidity gradient water production tests with fixed equipment operating parameters. The model parameters were solved using a combination of bivariate quadratic polynomial regression and least squares method, and the accuracy was verified. A water production efficiency model that accurately matches the material properties and operating parameters of the MOF atmospheric water collection unit was obtained, providing a reliable calculation basis for the dynamic water production prediction of the subsequent atmospheric water virtual water source node.

[0044] Furthermore, the method provided in this application embodiment includes:

[0045] Obtain meteorological forecast data for the future preset time period, including temperature and humidity sequences; input the temperature and humidity sequences into the water production efficiency model that matches the material characteristics of the MOF atmospheric water collection unit to obtain the theoretical water production sequence per unit time of the MOF atmospheric water collection unit within the future preset time period; calculate the theoretical water production sequence per unit time based on the effective adsorption material mass of the MOF atmospheric water collection unit to generate the dynamic water production prediction value of the virtual atmospheric water source node for each sub-period within the future preset time period.

[0046] Specifically, relying on the standardized meteorological forecast public interface of the target arid area, meteorological forecast data for a future preset time period is retrieved. This future preset time period is fixed to the next 24 hours, which is divided into continuous sub-time periods according to the one-hour time interval. The interface output data adopts the industry-standard CSV format. The management and control platform automatically completes the data retrieval through the preset interface protocol, extracts the temperature and relative humidity values ​​corresponding to each sub-time period from the received data, and arranges them into temperature series and humidity series according to the time sequence. The two sets of time series data are synchronously stored in the built-in database of the management and control platform.

[0047] Next, retrieve the water production efficiency model whose parameters have been calibrated in the previous steps. The model expression is written as: First, all sequence data are traversed. If a single set of temperature and humidity values ​​exceeds the range of model training parameters, the interval boundary values ​​are directly substituted into the calculation. The remaining compliant data are calculated by batch sequential substitution. The temperature and humidity parameters at the same time position are filled into the calculation formula in turn. The theoretical water production per unit time corresponding to the unit mass of MOF material is solved for each time period. After all time series calculations are completed, they are integrated to generate a continuous theoretical water production per unit time sequence.

[0048] Finally, the pre-archived and stored MOF effective adsorbent mass is retrieved from the archive of the virtual water source node. The remaining weight of the material is measured and the archive parameters are updated synchronously after each periodic maintenance of the equipment. The water production per unit mass in a single time period is multiplied by the corresponding effective adsorbent mass. All data in the sequence are traversed in turn, and the node water production corresponding to each sub-time period is calculated item by item. All results are integrated to generate a dynamic water production prediction value.

[0049] By standardizing the format and time period division of meteorological data retrieval, adding calculation rules for parameter over-limit replacements, and binding a dynamic update mechanism for material quality, the time-based water production prediction results are obtained by segmented calculation based on the water production efficiency model, providing accurate atmospheric water source prediction data for multi-source collaborative optimization of water allocation.

[0050] Furthermore, the method provided in this application embodiment includes:

[0051] Multiply the theoretical water production per unit time output by the water production efficiency model by the total loading mass of effective MOF material in the MOF atmospheric water collection unit, and then multiply by the water production efficiency decay coefficient to obtain the dynamic water production prediction value at each time point within a future preset period; wherein, the water production efficiency decay coefficient is inversely proportional to the cumulative effective operating time of the MOF atmospheric water collection unit since it has been put into use, and the value of the water production efficiency decay coefficient is between 0 and 1.

[0052] Specifically, the calibration of the water production efficiency attenuation coefficient was first carried out. MOF adsorbent material from the same batch used on-site was selected and filled into a test unit with a structure identical to the on-site equipment. Long-term operation tests were conducted under standard test conditions of standard atmospheric pressure, temperature 25 degrees Celsius, and relative humidity 60%. The fixed operating parameters of the test unit were: rotor speed 1.5 revolutions per hour, desorption zone temperature 90 degrees Celsius, and fan speed 1.8 meters per second. Water production was continuously tested for 24 hours at cumulative operating hours of 0, 1000, 2000, 3000, 4000, and 5000 hours. Using the initial water production at hour 0 as a baseline, the ratio of water production at each time point to the initial water production was calculated. This ratio is the water production efficiency attenuation coefficient for the corresponding cumulative operating time. Multiple sets of cumulative operating time and corresponding attenuation coefficient data were used as samples, and the least squares method was used for fitting to obtain the functional relationship between the water production efficiency attenuation coefficient and the cumulative effective operating time. , where k is the water production efficiency decay coefficient, with a value between 0 and 1, and is inversely proportional to the cumulative effective operating time t; t is the decay rate constant; t is the cumulative effective operating time of the MOF atmospheric water collection unit since it was put into use, in hours. This formula is applicable when the cumulative effective operating time does not exceed 5000 hours, and the fitted result is... The values ​​are pre-stored in the regional management platform.

[0053] Subsequently, a real-time statistical mechanism for the cumulative effective runtime of the MOF atmospheric water collection units was established. A timing module is built into the local controller of each MOF atmospheric water collection unit. When the equipment simultaneously meets three conditions—normal rotation of the impeller, normal operation of the desorption zone heating, and normal operation of the fan—it is determined to be in effective operation, and the timing module begins accumulating runtime. When the equipment is in standby, faulty, or stopped state, the timing module pauses accumulation. Every 15 minutes, the local controller uploads the current cumulative effective runtime data to the regional management platform, which updates and stores the cumulative effective runtime corresponding to each atmospheric water virtual source node in real time.

[0054] Furthermore, when the MOF adsorbent material is replaced, the local controller automatically resets the accumulated effective runtime and uploads it to the platform. Every 1000 hours of accumulated operation, an on-site water production calibration test is conducted under standard temperature and humidity conditions of 25 degrees Celsius and 60% relative humidity. The water production is continuously tested for 24 hours, and the ratio of the actual water production to the initial water production under the same conditions is calculated to obtain the actual attenuation coefficient. If the deviation between the actual value and the fitted value exceeds 5%, 5000 to 6000 hours of test data are added, and the attenuation rate constant is refitted and updated. .

[0055] Finally, the predicted dynamic water production of the virtual atmospheric water source node is calculated. Specifically: the theoretical water production sequence per unit time output by the water production efficiency model is retrieved; the total effective MOF material loading mass of the corresponding MOF atmospheric water collection unit is retrieved from the node parameter file; the theoretical water production per unit time is multiplied by the total effective MOF material loading mass to obtain the theoretical total water production per unit time. Then, the current cumulative effective runtime of the node is retrieved from the control platform, and the attenuation coefficient function is substituted to calculate the current water production efficiency attenuation coefficient. The theoretical total water production per unit time is multiplied by the water production efficiency attenuation coefficient to obtain the predicted actual dynamic water production at that time point. The above calculations are performed on all data in the theoretical water production sequence per unit time in chronological order, ultimately generating a complete sequence of predicted dynamic water production values ​​for each time point within a preset future time period.

[0056] The quantitative relationship between the water production efficiency attenuation coefficient and the operating time was obtained through laboratory calibration. Combined with real-time statistics of the effective operating time of the equipment, the material replacement and zeroing mechanism, and the periodic calibration process, the theoretical water production was attenuated and corrected. This effectively eliminated the impact of MOF material aging on the accuracy of water production prediction and improved the long-term accuracy of water production prediction for the atmospheric water virtual water source node.

[0057] Furthermore, the method provided in this application embodiment includes:

[0058] A multi-objective function is established with the first objective of minimizing the total water shortage in the target arid region and the second objective of minimizing the total water supply cost. The dynamic water production prediction of the virtual atmospheric water source node, the available water information of the surface water source node, the available water information of the groundwater source node, and the water demand information are used as constraints on the multi-objective function. The multi-objective function is solved under the constraints to generate a comprehensive water allocation scheme that includes the water supply from the virtual atmospheric water source node, the surface water source node, and the groundwater source node to each water demand point.

[0059] Specifically, a multi-objective optimization function is established for drought scenarios. The first objective function is to minimize the total water supply deficit in the target drought area, and its expression is: Where n is the total number of water demand points and m is the total number of water source nodes. Let i be the water demand at the i-th water demand point. Let J be the water supply from the j-th water source node to the i-th water demand node; the second objective function is to minimize the total water supply cost, expressed as: ,in Let be the unit water supply cost coefficient for the j-th water source node. Different types of water source nodes correspond to different unit water supply cost coefficients. The specific method for determining the value will be explained in detail in the corresponding embodiments below.

[0060] Next, the two objective functions are normalized to eliminate dimensional differences. The normalized value of the first objective function is: ,in The maximum water supply deficit when the total available water volume at all water source nodes is 0 is the sum of water demand at all water demand points; the normalized value of the second objective function is: ,in The total water supply cost is the cost of supplying all water demand when it is supplied solely by the water source node with the highest unit water supply cost.

[0061] Then, constraints are set for the multi-objective function. The first type of constraint is the upper limit constraint on the water supply from the water source, expressed as: ,in Let be the real-time available water volume of the j-th water source node. For surface water source nodes and groundwater source nodes, The aforementioned real-time collected measured available water information is used, such as reservoir storage and remaining exploitable groundwater. For virtual atmospheric water source nodes, the available water consists of two parts: the current water storage in the tank plus the predicted dynamic water production for this sub-period, the specific expression of which is shown in the dynamic inventory constraint in the rolling optimization strategy below; the second type of constraint is the water supply constraint at the demand point, the expression of which is: The third type of constraint is the water conveyance capacity constraint, and its expression is: ,in Let be the maximum water transport capacity of the pipeline from the j-th water source node to the i-th water demand point. If there is no water transport pipeline between the two nodes, then... .

[0062] Since the dynamic water production prediction value of the virtual water source node of atmospheric water is a sequence value of each sub-period within a future preset period, such as a prediction value for the next 24 hours or one per hour, and the water storage tank has a water storage and regulation function, the available water volume information of surface water and groundwater sources and the water demand information of each water demand point may also change over time, in order to achieve accurate supply and demand matching, this embodiment adopts a rolling optimization strategy and explicitly considers the dynamic changes of the water storage tank inventory.

[0063] Divide a future time period, such as the next 24 hours, into K sub-time periods at fixed 1-hour intervals. For each sub-time period k=1,2,…,K, establish and solve the aforementioned multi-objective function and its constraints to obtain the water supply from each water source node to each water demand point within each sub-time period. .in:

[0064] For surface water source nodes and groundwater source nodes, the available water volume in sub-time period k If the real-time collected available water volume is the cumulative remaining total, then the water is allocated to different time periods based on the extraction plan or water inflow process within the future time period. If there is no specific process information, it is evenly allocated to each sub-time period according to time. For the virtual water source node of atmospheric water, let its water tank storage volume at the beginning of sub-time period k be... The predicted dynamic water production value for this sub-period is Specifically, the water supply that this node can provide to the water demand point within sub-time period k is calculated using the aforementioned water production efficiency model combined with the attenuation coefficient. Must meet: .

[0065] At the same time, the water supply cannot cause the water level in the storage tank to become negative. The water level update equation for the storage tank is: Initial water volume The actual water volume in the storage tank is collected in real time. (If the storage tank has a maximum capacity limit...) Then it also needs to meet the following requirements. To simplify implementation, we can assume that the water tank capacity is large enough and not subject to an upper limit. In this case, we only need to ensure that the water volume is non-negative.

[0066] For the water demand of each water demand point i in sub-time period k It can be obtained by time allocation based on historical water usage patterns or real-time monitoring data.

[0067] After independently solving the above optimization problem with dynamic inventory constraints for each sub-period k, the optimization results of all sub-periods are integrated in chronological order to generate a complete comprehensive water allocation scheme. This scheme includes the water supply instructions from each water source node to each water demand point within each sub-period.

[0068] Next, a weighted summation method is used to transform the normalized multi-objective optimization into a single-objective optimization. For example, the weight of the first objective function is set to 0.7, and the weight of the second objective function is set to 0.3. The transformed single-objective function is as follows: The transformed single objective function and all constraints (including the aforementioned dynamic inventory constraints) are substituted into a professional linear programming solver. The simplex method is used to solve for each sub-period, obtaining the optimal water supply from each water source node to each water demand point within each sub-period. The solution results for all sub-periods are then organized according to the water source node and the water demand point to finally generate a complete time-series integrated water allocation scheme.

[0069] By constructing a dual-objective optimization function that prioritizes water supply shortage and secondarily costs, and eliminating dimensional differences through normalization, a rolling optimization strategy is combined to process the dynamic water production prediction sequence and the dynamic inventory of water storage tanks. Furthermore, constraints on the supply capacity of three types of water sources, water demand, and water transmission capacity are introduced to perform time-series linear programming solutions, thereby generating a reasonable multi-source time-series integrated water allocation scheme to achieve efficient and coordinated utilization of water resources in arid regions.

[0070] Furthermore, the method provided in this application embodiment includes:

[0071] In the multi-objective function, different unit water supply cost coefficients are assigned to the atmospheric water virtual water source node, the surface water source node, and the groundwater source node, respectively, wherein the unit water supply cost coefficient of the groundwater source node is higher than that of the surface water source node.

[0072] In one embodiment, the unit water supply cost coefficient for the three types of water source nodes is calculated separately. The unit water supply cost coefficient only includes the actual physical costs generated during the operation of the water source and does not involve any market transaction prices. All cost items are uniformly converted to a benchmark of per cubic meter of water volume. The unit water supply cost coefficient for surface water source nodes consists of three parts: water intake energy consumption cost, water transmission pipeline depreciation cost, and daily maintenance cost. The water intake energy consumption cost is calculated based on the rated power of the water pump at the water intake, the water intake volume per unit time, and the local industrial electricity price. The water transmission pipeline depreciation cost is converted to per cubic meter of water volume based on the pipeline's design service life and total investment. The daily maintenance cost is calculated by dividing the total annual maintenance cost by the total annual water supply volume.

[0073] The unit water supply cost coefficient of groundwater source nodes consists of three parts: pumping energy consumption cost, pump equipment depreciation cost, and well maintenance cost. Pumping energy consumption cost is calculated based on the rated power of deep well pumps, pumping volume per unit time, and local industrial electricity prices. Since groundwater extraction requires overcoming greater head pressure, its unit pumping energy consumption is significantly higher than that of surface water extraction. Therefore, the unit water supply cost coefficient of groundwater source nodes is higher than that of surface water source nodes.

[0074] The unit water supply cost coefficient of the virtual water source node consists of four parts: fan operating energy consumption, de-heating energy consumption, rotor drive energy consumption, and MOF material depreciation cost. All energy consumption is calculated based on the rated power of the equipment, the water production per unit time, and the local industrial electricity price. The MOF material depreciation cost is calculated based on the rated total water production of the material and the purchase cost to the water production per cubic meter.

[0075] Finally, the calculated unit water supply cost coefficients for the three types of water source nodes are pre-stored in the parameter database of the regional management and control platform, with the coefficients retained to two decimal places. At the end of each year, based on the actual operating energy consumption, maintenance costs, and total water production data of each type of water source, the unit water supply cost coefficients for each water source node are recalculated and updated. After the update, a historical data backtest is performed for verification. If the cost calculation deviation exceeds 5%, the source of the cost data is checked and recalculated. During the multi-objective function solution process, the management and control platform automatically retrieves the latest unit water supply cost coefficients and substitutes them into the calculation to ensure the cost optimization of the comprehensive water allocation scheme.

[0076] By calculating the actual physical operating costs of various water sources separately, differentiated unit water supply cost coefficients are determined, and an annual dynamic update and verification mechanism is established. This makes the cost calculation of the multi-objective optimization function more realistic, effectively reducing the overall regional water supply operating cost while ensuring water supply.

[0077] Furthermore, the method provided in this application embodiment includes:

[0078] The drought index of the target arid region is continuously monitored. When the drought index exceeds a preset severe drought threshold, an extreme drought mode is triggered and entered. Under the extreme drought mode, the unit water supply cost coefficient of the atmospheric water virtual water source node is reduced, and / or the weight of the water supply guarantee rate of the atmospheric water virtual water source node in the objective function is increased to improve the water supply priority of the atmospheric water virtual water source node. Based on the improved water supply priority, combined with the dynamic water production prediction value of the atmospheric water virtual water source node, the available water information of the surface water source node, and the available water information of the groundwater source node, an updated comprehensive water allocation scheme is re-optimized and generated. Based on the updated comprehensive water allocation scheme, an extreme scheduling instruction is generated that prioritizes the use of the atmospheric water virtual water source node for water supply and restricts the extraction volume of the groundwater source node.

[0079] Optionally, the standardized precipitation index (SCI) of the target drought area is continuously monitored. Daily precipitation data from all meteorological stations within the area are used to calculate the SCI on a 30-day timescale. The calculation method involves fitting the precipitation data to a gamma distribution and then converting it to a standard normal distribution. When multiple meteorological stations exist within the target drought area, the arithmetic mean method is used to calculate the overall SCI for the area. The precipitation data from the previous 30 days is automatically updated daily at midnight, and the drought index is recalculated. The calculated result is compared with a preset severe drought threshold, which is a SCI less than or equal to -1.5. When the drought index calculated for three consecutive days is less than or equal to this threshold, the extreme drought mode is automatically triggered, and a mode switching alarm is generated on the regional control platform. When the drought index calculated for seven consecutive days is greater than -1.2, the extreme drought mode is automatically exited, and normal water supply parameters are restored.

[0080] After entering the extreme drought mode, two methods are combined to enhance the water supply priority of atmospheric water virtual source nodes. The first method is to reduce the unit water supply cost coefficient of atmospheric water virtual source nodes. For example, the original unit water supply cost coefficient is multiplied by a discount factor of 0.5 to obtain the temporary unit water supply cost coefficient under the extreme drought mode. The second method is to adjust the overall weight allocation of the multi-objective function. For example, the weight of the first objective function in the original single objective function is increased from 0.7 to 0.9, and the weight of the second objective function is decreased from 0.3 to 0.1. By strengthening the priority of the water supply guarantee objective, the water supply proportion of atmospheric water virtual source nodes with stable water production capacity is indirectly increased. These two methods can be used individually or in combination by those skilled in the art based on the actual drought severity.

[0081] Next, based on the adjusted unit water supply cost coefficient and objective function weights, a new multi-objective optimization function is constructed. The original constraints on the upper limit of water source supply, water demand point supply, and water conveyance capacity remain unchanged. A new constraint on groundwater extraction is added, stipulating that the total water supply of a single groundwater source node must not exceed 50% of its real-time available water volume. This percentage can be dynamically adjusted between 30% and 70% depending on the severity of the drought. The updated objective function and all constraints are then substituted into a professional linear programming solver, and the simplex method is used to solve the problem. This yields the optimal water supply value from each water source node to each water demand point under the extreme drought model, and an updated comprehensive water allocation scheme is generated.

[0082] Based on the updated integrated water allocation scheme, standardized extreme dispatch instructions are generated. These instructions include full-load operation instructions for each MOF atmospheric water collection unit, water supply adjustment instructions for each surface water intake, and extraction volume restriction instructions for each groundwater intake well. The dispatch instructions are sent to the local controllers at each water source node via a dedicated regional wireless communication network. Upon receiving the instructions, the local controllers immediately execute the corresponding water supply adjustment operations and report the execution status back to the regional control platform within 10 minutes. The control platform retransmits the instructions every 5 minutes to nodes that have not reported an execution status, up to a maximum of 3 retransmissions. If no feedback is received after 3 retransmissions, a device communication anomaly alarm is generated.

[0083] By continuously monitoring drought levels through standardized precipitation indices and establishing automatic triggering and exit mechanisms, a combination of cost coefficient discounts and objective function weight adjustments is adopted to prioritize atmospheric water supply. Combined with dynamic groundwater extraction constraints, the water allocation scheme is re-optimized and dispatch instructions with feedback mechanisms are generated. Under extreme drought conditions, renewable atmospheric water resources are prioritized for use, effectively protecting groundwater resources and ensuring basic water supply security in the region.

[0084] Furthermore, the method provided in this application embodiment includes:

[0085] Based on the water supply allocated to the virtual water source node in the updated integrated water allocation scheme, the target water production rate of the MOF atmospheric water collection unit is calculated; based on the target water production rate, a water capture control command is generated and issued to increase the adsorption-desorption cycle frequency of the MOF atmospheric water collection unit.

[0086] In one embodiment, the total water supply for the scheduling cycle allocated to the corresponding atmospheric water virtual water source node is extracted from the updated integrated water allocation scheme, with the scheduling cycle uniformly set to 1 hour. Simultaneously, the actual water level in the storage tank is obtained from the level sensor of the node's storage tank, and the actual water production required is calculated. The actual water production required is equal to the total water supply for the scheduling cycle minus the current water level in the storage tank. If the calculated actual water production required is negative, it is set to 0. The actual water production required is divided by the scheduling cycle duration to obtain the target water production rate of the MOF atmospheric water collection unit within the current scheduling cycle, in liters per hour. The target water production rate is limited to the range between the equipment's rated minimum water production rate and rated maximum water production rate; if the calculated value exceeds this range, the corresponding boundary value is used.

[0087] Next, a pre-calibrated table showing the relationship between rotor speed and water production rate was retrieved. This table was calibrated under standard test conditions of standard atmospheric pressure, temperature 25 degrees Celsius, and relative humidity 60%. During calibration, the desorption zone temperature was fixed at 90 degrees Celsius and the fan speed at 1.8 meters per second. The entire operating range of rotor speed, from 1 revolution per hour to 3 revolutions per hour, was covered, with each speed gradient representing 0.2 revolutions per hour. Each speed point was continuously tested for 24 hours, and the average water production was taken as the standard water production rate at that speed. The real-time temperature and humidity of the current environment were obtained and substituted into the water production efficiency model to calculate the water production efficiency correction factor under the current environment. The water production efficiency correction factor is equal to the water production per unit mass of MOF material per unit time under the current environment divided by the water production per unit mass of MOF material per unit time under the standard environment. The target water production rate was divided by the water production efficiency correction factor to obtain the equivalent target water production rate under the standard environment. The corresponding target rotor speed was then obtained by looking up the table.

[0088] Finally, a water capture control command containing the target impeller speed is generated and sent to the local controller of the MOF atmospheric water collection unit via a dedicated regional wireless communication network. Upon receiving the command, the local controller adjusts the output frequency of the AC geared motor to adjust the impeller speed to the target speed, thereby increasing the adsorption-desorption cycle frequency. The local controller collects the actual water production rate every 15 minutes and calculates the deviation between the actual and target water production rates. If the deviation exceeds 5%, the impeller speed is fine-tuned for closed-loop correction, with each fine-tuning increment being 0.1 revolutions per hour. If the deviation still exceeds 5% after three consecutive fine-tunings, fine-tuning is stopped and an abnormal water production alarm is generated, while the current speed is maintained.

[0089] By calculating the target water production rate based on the water distribution plan, and combining it with the environmental temperature and humidity correction to obtain the target rotor speed and issue control commands, the water production rate of the MOF atmospheric water collection unit can be dynamically adjusted, so that the atmospheric water production can be accurately matched with the scheduling needs, thereby improving the water resource utilization efficiency under extreme drought conditions.

[0090] In summary, the water supply and demand co-optimization method for drought scenarios provided in this application has the following technical effects:

[0091] This application collects data on available water from multiple water sources and water demand in the target arid region. Based on a fitted water production efficiency model, it dynamically predicts the dynamic water production forecast and constructs a multi-objective function to generate a comprehensive water allocation scheme. It monitors the drought index of the target arid region and triggers an extreme drought mode when the index exceeds the limit. This dynamically prioritizes atmospheric water supply, regulates the target water production rate of the MOF atmospheric water collection unit, and collaboratively optimizes the allocation of multiple water sources. This effectively improves the accuracy of regional water supply and demand matching and the stability of water supply security under drought scenarios. It achieves integrated and coordinated water allocation of atmospheric water intake and conventional water sources, accurately predicts the scale of atmospheric water production, and ensures the technical effect of prioritizing atmospheric water intake during severe drought.

[0092] Example 2, as Figure 2 As shown, based on the same inventive concept as in Embodiment 1 above, this application provides a water supply and demand collaborative optimization system for drought scenarios, the system comprising:

[0093] Multi-source water network model construction module 1 is used to deploy MOF atmospheric water collection units in the target arid region and define them as virtual atmospheric water source nodes in the regional water supply network. At the same time, it defines surface water source nodes and groundwater source nodes to construct a multi-source water network model.

[0094] Water quantity and water usage information acquisition module 2 is used to acquire in real time the available water quantity information of each water source node in the multi-water source network model, as well as the water demand information of the target arid area.

[0095] The water production prediction value generation module 3 generates dynamic water production prediction values ​​for the atmospheric water virtual water source node within a future preset period based on meteorological forecast data for a future preset period and by calling a water production efficiency model that matches the material characteristics of the MOF atmospheric water collection unit.

[0096] Water allocation scheme generation 4 is used to generate a comprehensive water allocation scheme by taking the water demand information as a constraint and taking the dynamic water production prediction value of the virtual atmospheric water source node, the available water information of the surface water source node and the available water information of the groundwater source node as the supply basis.

[0097] The extreme scheduling instruction generation module 5 is used to enter extreme drought mode when the drought index of the target drought area exceeds a preset threshold, increase the water supply priority of the atmospheric water virtual water source node in the multi-water source network model, update the comprehensive water volume configuration scheme, and generate extreme scheduling instructions.

[0098] Furthermore, the multi-source network model construction module 1 is used to perform the following steps:

[0099] The MOF atmospheric water collection unit adopts a multi-layer adsorption-desorption rotating structure, and the topology of the atmospheric water virtual water source node in the multi-water source network model is configured to be close to the water-using unit indicated by the water demand information.

[0100] Furthermore, the water production prediction generation module 3 is used to perform the following steps:

[0101] Under multiple sets of set environmental temperature and humidity combinations, water production tests were conducted on the MOF atmospheric water collection unit to obtain multiple sets of measured water production efficiency data. The measured water production efficiency data included at least the environmental temperature, environmental humidity, and the corresponding water production per unit mass of MOF material per unit time. Using the environmental temperature and environmental humidity from the multiple sets of measured water production efficiency data as input features and the corresponding water production per unit time as output labels, a model was trained through regression fitting to obtain a water production efficiency model that matches the material characteristics of the MOF atmospheric water collection unit.

[0102] Furthermore, the water production prediction generation module 3 is used to perform the following steps:

[0103] Obtain meteorological forecast data for the future preset time period, including temperature and humidity sequences; input the temperature and humidity sequences into the water production efficiency model that matches the material characteristics of the MOF atmospheric water collection unit to obtain the theoretical water production sequence per unit time of the MOF atmospheric water collection unit within the future preset time period; calculate the theoretical water production sequence per unit time based on the effective adsorption material mass of the MOF atmospheric water collection unit to generate the dynamic water production prediction value of the virtual atmospheric water source node for each sub-period within the future preset time period.

[0104] Furthermore, the water production prediction generation module 3 is used to perform the following steps:

[0105] Multiply the theoretical water production per unit time output by the water production efficiency model by the total loading mass of effective MOF material in the MOF atmospheric water collection unit, and then multiply by the water production efficiency decay coefficient to obtain the dynamic water production prediction value at each time point within a future preset period; wherein, the water production efficiency decay coefficient is inversely proportional to the cumulative effective operating time of the MOF atmospheric water collection unit since it has been put into use, and the value of the water production efficiency decay coefficient is between 0 and 1.

[0106] Furthermore, the water allocation scheme 4 is used to perform the following steps:

[0107] A multi-objective function is established with the first objective of minimizing the total water shortage in the target arid region and the second objective of minimizing the total water supply cost. The dynamic water production prediction of the virtual atmospheric water source node, the available water information of the surface water source node, the available water information of the groundwater source node, and the water demand information are used as constraints on the multi-objective function. The multi-objective function is solved under the constraints to generate a comprehensive water allocation scheme that includes the water supply from the virtual atmospheric water source node, the surface water source node, and the groundwater source node to each water demand point.

[0108] Furthermore, the water allocation scheme 4 is used to perform the following steps:

[0109] In the multi-objective function, different unit water supply cost coefficients are assigned to the atmospheric water virtual water source node, the surface water source node, and the groundwater source node, respectively, wherein the unit water supply cost coefficient of the groundwater source node is higher than that of the surface water source node.

[0110] Furthermore, the extreme scheduling instruction generation module 5 is used to perform the following steps:

[0111] The drought index of the target arid region is continuously monitored. When the drought index exceeds a preset severe drought threshold, an extreme drought mode is triggered and entered. Under the extreme drought mode, the unit water supply cost coefficient of the atmospheric water virtual water source node is reduced, and / or the weight of the water supply guarantee rate of the atmospheric water virtual water source node in the objective function is increased to improve the water supply priority of the atmospheric water virtual water source node. Based on the improved water supply priority, combined with the dynamic water production prediction value of the atmospheric water virtual water source node, the available water information of the surface water source node, and the available water information of the groundwater source node, an updated comprehensive water allocation scheme is re-optimized and generated. Based on the updated comprehensive water allocation scheme, an extreme scheduling instruction is generated that prioritizes the use of the atmospheric water virtual water source node for water supply and restricts the extraction volume of the groundwater source node.

[0112] Furthermore, the extreme scheduling instruction generation module 5 is used to perform the following steps:

[0113] Based on the water supply allocated to the virtual water source node in the updated integrated water allocation scheme, the target water production rate of the MOF atmospheric water collection unit is calculated; based on the target water production rate, a water capture control command is generated and issued to increase the adsorption-desorption cycle frequency of the MOF atmospheric water collection unit.

[0114] The water supply and demand collaborative optimization system for drought scenarios provided in the embodiments of the present invention can execute the water supply and demand collaborative optimization method for drought scenarios provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0115] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0116] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for coordinated optimization of water supply and demand in drought scenarios, characterized in that, The method includes: Within the target arid region, MOF atmospheric water collection units are deployed and defined as virtual atmospheric water source nodes in the regional water supply network. At the same time, surface water source nodes and groundwater source nodes are defined to construct a multi-source network model. Real-time acquisition of available water information for each water source node in the multi-water source network model, as well as water demand information for the target arid region; Based on meteorological forecast data for a future preset period, a water production efficiency model that matches the material properties of the MOF atmospheric water collection unit is invoked to generate a dynamic water production forecast value for the virtual atmospheric water source node within the future preset period. Based on the water demand information, and using the dynamic water production prediction of the virtual atmospheric water source node, the available water information of the surface water source node, and the available water information of the groundwater source node as the supply basis, a comprehensive water allocation scheme is generated in the best possible way. When the drought index of the target arid region exceeds a preset threshold, the extreme drought mode is entered, the water supply priority of the atmospheric water virtual water source node in the multi-water source network model is increased, the comprehensive water volume configuration scheme is updated, and extreme scheduling instructions are generated.

2. The water supply and demand co-optimization method for drought scenarios as described in claim 1, characterized in that, The MOF atmospheric water collection unit adopts a multi-layer adsorption-desorption rotating structure, and the topology of the atmospheric water virtual water source node in the multi-water source network model is configured to be close to the water-using unit indicated by the water demand information.

3. The water supply and demand co-optimization method for drought scenarios as described in claim 1, characterized in that, Constructing the water production efficiency model includes: Under multiple sets of set ambient temperature and humidity combinations, the MOF atmospheric water collection unit was tested for water production, and multiple sets of measured water production efficiency data were obtained. The measured water production efficiency data included at least ambient temperature, ambient humidity and the corresponding water production per unit mass of MOF material per unit time. Using the ambient temperature and humidity from the multiple sets of measured water production efficiency data as input features and the corresponding water production per unit time as output labels, a water production efficiency model matching the material properties of the MOF atmospheric water collection unit is obtained through regression fitting for model training.

4. The water supply and demand coordinating optimization method for drought scenarios as described in claim 3, characterized in that, Based on meteorological forecast data for a future preset period, a water production efficiency model matching the material properties of the MOF atmospheric water harvesting unit is invoked to generate a dynamic water production forecast value for the virtual atmospheric water source node within the future preset period, including: Obtain meteorological forecast data for the future preset time period, the meteorological forecast data including temperature series and humidity series; The temperature and humidity sequences are input into the water production efficiency model that matches the material properties of the MOF atmospheric water collection unit to obtain the theoretical water production per unit time sequence of the MOF atmospheric water collection unit in a future preset period. Based on the effective adsorption material mass of the MOF atmospheric water collection unit, the theoretical water production sequence per unit time is calculated to generate the dynamic water production prediction value of the virtual atmospheric water source node for each sub-period within a future preset time period.

5. The water supply and demand co-optimization method for drought scenarios as described in claim 4, characterized in that, The calculation of the theoretical water production sequence per unit time based on the effective adsorbent mass of the MOF atmospheric water collection unit also includes: Multiply the theoretical water production per unit time output by the water production efficiency model by the total loading mass of effective MOF material in the MOF atmospheric water collection unit, and then multiply by the water production efficiency decay coefficient to obtain the dynamic water production prediction value at each time point in the future preset period. The water production efficiency attenuation coefficient is inversely proportional to the cumulative effective operating time of the MOF atmospheric water collection unit since it has been put into use, and the value of the water production efficiency attenuation coefficient is between 0 and 1.

6. The water supply and demand co-optimization method for drought scenarios as described in claim 1, characterized in that, Based on the water demand information, and using the dynamic water production prediction of the virtual atmospheric water source node, the available water information of the surface water source node, and the available water information of the groundwater source node as the supply basis, a comprehensive water allocation scheme is optimally generated, including: Establish a multi-objective function with the first objective of minimizing the total water shortage in the target arid region and the second objective of minimizing the total water supply cost. The predicted dynamic water production of the virtual atmospheric water source node, the available water information of the surface water source node, the available water information of the groundwater source node, and the water demand information are used as constraints for the multi-objective function. The multi-objective function is solved under the constraints to generate a comprehensive water allocation scheme that includes the water supply from the atmospheric water virtual water source node, the surface water source node, and the groundwater source node to each water demand point.

7. The water supply and demand co-optimization method for drought scenarios as described in claim 6, characterized in that, In the multi-objective function, different unit water supply cost coefficients are assigned to the atmospheric water virtual water source node, the surface water source node, and the groundwater source node, respectively, wherein the unit water supply cost coefficient of the groundwater source node is higher than that of the surface water source node.

8. The water supply and demand co-optimization method for drought scenarios as described in claim 1, characterized in that, When the drought index of the target arid region exceeds a preset threshold, the system enters extreme drought mode, increases the water supply priority of the atmospheric water virtual water source node in the multi-source water network model, updates the comprehensive water allocation scheme, and generates extreme scheduling instructions, including: The drought index of the target drought area is continuously monitored. When the drought index is determined to exceed the preset severe drought threshold, the extreme drought mode is triggered and entered. Under the extreme drought mode, the unit water supply cost coefficient of the virtual water source node is reduced, and / or the weight of the water supply guarantee rate of the virtual water source node in the objective function is increased, so as to improve the water supply priority of the virtual water source node. Based on the improved water supply priority, and combined with the dynamic water production prediction of the atmospheric water virtual water source node, the available water information of the surface water source node and the available water information of the groundwater source node, the updated comprehensive water allocation scheme is re-optimized and generated. Based on the updated integrated water volume configuration scheme, a limit scheduling instruction is generated that prioritizes the use of the atmospheric water virtual water source node for water supply and limits the extraction volume of the groundwater source node.

9. The water supply and demand co-optimization method for drought scenarios as described in claim 8, characterized in that, The method further includes: The target water production rate of the MOF atmospheric water collection unit is calculated based on the water supply allocated to the atmospheric water virtual water source node in the updated integrated water allocation scheme. Based on the target water production rate, a water capture control command is generated and issued to increase the adsorption-desorption cycle frequency of the MOF atmospheric water collection unit.

10. A water supply and demand collaborative optimization system for drought scenarios, characterized in that, The system is used to implement the water supply and demand coordinating optimization method for drought scenarios according to any one of claims 1-9, the system comprising: The multi-source water network model construction module is used to deploy MOF atmospheric water collection units in the target arid region and define them as virtual atmospheric water source nodes in the regional water supply network. At the same time, it defines surface water source nodes and groundwater source nodes to construct a multi-source water network model. The water quantity and water usage information acquisition module is used to acquire in real time the available water quantity information of each water source node in the multi-water source network model, as well as the water demand information of the target arid area. The water production prediction generation module, based on meteorological forecast data for a future preset period, calls a water production efficiency model that matches the material characteristics of the MOF atmospheric water collection unit to generate the dynamic water production prediction value of the atmospheric water virtual water source node for the future preset period. A water allocation scheme is generated to optimize and generate a comprehensive water allocation scheme based on the water demand information, the dynamic water production prediction value of the virtual atmospheric water source node, the available water information of the surface water source node, and the available water information of the groundwater source node. The extreme scheduling instruction generation module is used to enter extreme drought mode when the drought index of the target drought area exceeds a preset threshold, increase the water supply priority of the atmospheric water virtual water source node in the multi-water source network model, update the comprehensive water volume configuration scheme, and generate extreme scheduling instructions.

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