A multi-objective dynamic optimization method and system for a solar seawater desalination process

Abstract

The application provides a multi-objective dynamic optimization method and system for a solar seawater desalination process, belonging to the technical field of solar seawater desalination control. Firstly, the application collects solution conductivity data at the inlet and outlet positions and irradiance trend data above the photovoltaic array. Then, the conductivity difference is calculated to generate gradient data to map the desalination demand vector, and the cloud motion vector is extracted from the irradiance data using the optical flow method to construct the energy supply potential vector. Subsequently, the two are aligned and convolved to generate a state space matrix, which is input into the constructed Kalman filter to predict the target voltage reference trajectory. Finally, the trajectory is substituted into the multi-objective constraint function to calculate the target duty cycle command, and the DC converter switching state is adjusted to change the operating voltage of the desalination device. The application can achieve real-time multi-objective dynamic optimization of the water production efficiency and equipment safety of the solar seawater desalination system under the dual fluctuations of light and salinity.

Description

A multi-objective dynamic optimization method and system for solar-powered seawater desalination process Technical Field

[0001] This application belongs to the field of solar seawater desalination control technology, and in particular relates to a multi-objective dynamic optimization method and system for solar seawater desalination process. Background Technology

[0002] Solar-powered seawater desalination technology utilizes clean and renewable energy to drive the electrochemical desalination process, offering an effective solution to the freshwater shortage problem. Multi-objective dynamic optimization methods, by coordinating energy supply and desalination demand, demonstrate significant application potential in improving water quality and reducing energy consumption.

[0003] Existing solar-powered seawater desalination control methods typically rely solely on maximum power point tracking (MPPT) technology to ensure photovoltaic output, or simple constant voltage or constant current control based on instantaneous conductivity feedback. These methods often assume a relatively stable operating environment and lack mechanisms for predicting and coordinating adjustments to rapid changes in light intensity and fluctuations in influent salinity.

[0004] In actual operation, due to the random fluctuations of photovoltaic energy and the time-varying nature of desalination load, existing methods struggle to achieve precise dynamic matching of energy flow and material flow, easily leading to voltage regulation lag or over-driving. This not only causes significant fluctuations in water production rate but also easily results in electrode surface polarization and scaling due to voltage runaway, thus shortening the device's lifespan. Therefore, existing technologies suffer from insufficient multi-objective dynamic optimization due to the difficulty in balancing water production efficiency and equipment safety under dual source-load fluctuations. Summary of the Invention

[0005] The purpose of this application is to provide a multi-objective dynamic optimization method and system for the solar-powered seawater desalination process, so as to solve the problem of insufficient multi-objective dynamic optimization in the prior art.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a multi-objective dynamic optimization method for a solar-powered seawater desalination process, comprising:

[0007] Acquire the conductivity data of the first solution at the liquid inlet position and the conductivity data of the second solution at the liquid outlet position of the electrochemical desalination device, and simultaneously collect the irradiance trend data of the area above the photovoltaic array;

[0008] By calculating the ratio of the difference between the conductivity data of the first solution and the conductivity data of the second solution, conductivity gradient data is generated. The conductivity gradient data is then nonlinearly mapped to obtain the desalination demand vector.

[0009] The optical flow method is used to extract cloud motion vectors from irradiance trend data to construct an energy supply potential vector. The diluted demand vector and the energy supply potential vector are temporally aligned and convolved to generate a state space matrix.

[0010] The state-space matrix is ​​used as the observed value of the system state, and the target voltage reference trajectory is predicted by the constructed Kalman filter. The Kalman filter is constructed based on the photovoltaic power source internal resistance model and the electrochemical equivalent circuit model.

[0011] The target voltage reference trajectory is input into the preset multi-objective constraint function, and the target duty cycle command of the DC converter in the current switching cycle is calculated by the linear weighted sum method. The optimization objectives of the multi-objective constraint function include maximizing the desalination rate and minimizing the risk of electrode scaling.

[0012] According to the target duty cycle command, the conduction state of the power switch tubes of the DC converter connecting the photovoltaic array and the electrochemical desalination device is adjusted to change the operating voltage across the electrochemical desalination device.

[0013] Optionally, the method further includes:

[0014] The conductivity data of the first solution is input into a preset mapping function to calculate the resistance and capacitance parameters of the electrochemical desalination device at the current moment.

[0015] The coefficients of the circuit differential equations in the electrochemical equivalent circuit model of the Kalman filter are recalculated using resistance and capacitance parameters.

[0016] The state transition matrix in the Kalman filter is updated using the coefficients of the circuit differential equation.

[0017] Optionally, the method further includes:

[0018] The rate of change of energy supply is calculated based on the energy supply potential vector, and the variance of salinity fluctuation is calculated based on the desalination demand vector.

[0019] The energy supply change rate and salinity fluctuation variance are input into a preset fuzzy logic controller. The fuzzy logic controller is used to calculate the first weighting factor and the second weighting factor. The first weighting factor is associated with maximizing the desalination rate, and the second weighting factor is associated with minimizing the risk of electrode scaling.

[0020] The target voltage reference trajectory is input into a preset multi-objective constraint function, and the target duty cycle command of the DC-DC converter in the current switching cycle is calculated using a linear weighted sum method, including:

[0021] The target voltage reference trajectory is input into a preset multi-objective constraint function. Based on the first weighting factor and the second weighting factor, the target duty cycle command of the DC converter in the current switching cycle is calculated by the linear weighted sum method.

[0022] Optionally, the target voltage reference trajectory is input into a preset multi-objective constraint function, and the target duty cycle command of the DC-DC converter in the current switching cycle is calculated using a linear weighted sum method based on the first weighting factor and the second weighting factor, including:

[0023] The target voltage reference trajectory is converted into a base duty cycle using the voltage transfer relationship of the DC-DC converter, and a set of candidate duty cycles is generated within a preset range based on the base duty cycle.

[0024] The first predicted value is obtained by calculating the predicted desalination rate corresponding to each candidate duty cycle using the preset voltage-current relationship, and the second predicted value is obtained by calculating the difference between the predicted voltage corresponding to each candidate duty cycle and the preset safety threshold.

[0025] The first coefficient is obtained by multiplying the first weighting factor and the first predicted value, and the second coefficient is obtained by multiplying the second weighting factor and the second predicted value.

[0026] The composite coefficient corresponding to each candidate duty cycle is obtained by calculating the difference between the first coefficient and the second coefficient. The candidate duty cycle corresponding to the composite coefficient with the largest value is determined as the target duty cycle instruction.

[0027] Optionally, the method further includes:

[0028] Acquire fluid temperature data flowing through the electrochemical desalination unit;

[0029] The correction factor is calculated by inputting fluid temperature data into the preset Nernst equation.

[0030] By calculating the ratio of the difference between the conductivity data of the first solution and the conductivity data of the second solution, conductivity gradient data is generated. This conductivity gradient data is then nonlinearly mapped to obtain a dilution demand vector, including:

[0031] By calculating the ratio of the difference between the conductivity data of the first solution and the conductivity data of the second solution, conductivity gradient data is generated. Based on the correction factor, the conductivity gradient data is nonlinearly mapped to obtain the desalination demand vector.

[0032] Optionally, conductivity gradient data is generated by calculating the ratio of the difference between the conductivity data of the first solution and the conductivity data of the second solution. Based on a correction factor, the conductivity gradient data is then nonlinearly mapped to obtain a desalination demand vector, including:

[0033] The difference between the conductivity data of the first solution and the conductivity data of the second solution is calculated, and the ratio of the difference to the conductivity data of the first solution is calculated to obtain the conductivity gradient data;

[0034] The adjusted nonlinear mapping function is obtained by adjusting the parameters of the preset nonlinear mapping function using a correction factor.

[0035] The conductivity gradient data is input into the adjusted nonlinear mapping function for nonlinear transformation to obtain the de-saturation demand vector.

[0036] Optionally, cloud motion vectors are extracted from irradiance trend data using optical flow to construct an energy supply potential vector. The diluted demand vector and the energy supply potential vector are then temporally aligned and convolved to generate a state-space matrix, including:

[0037] The cloud motion vector is obtained by calculating the pixel brightness difference between adjacent frames in the irradiance trend data.

[0038] The distribution of the area of ​​the photovoltaic array that will be shaded within a future preset time window is determined by using cloud motion vectors. The effective power generation area sequence of the photovoltaic array is calculated based on the area distribution, and the effective power generation area sequence is converted into an energy supply potential vector.

[0039] By performing sliding convolution on the de-emphasis demand vector and the energy supply potential vector, a matching sequence is obtained, and the matching sequence is arranged in chronological order to form a state space matrix.

[0040] Secondly, this application provides a multi-objective dynamic optimization system for a solar-powered seawater desalination process, comprising:

[0041] The acquisition module is used to acquire the conductivity data of the first solution at the liquid inlet position and the conductivity data of the second solution at the liquid outlet position of the electrochemical desalination device, and simultaneously collect the irradiance trend data of the area above the photovoltaic array.

[0042] The generation module is used to generate conductivity gradient data by calculating the ratio of the difference between the conductivity data of the first solution and the conductivity data of the second solution, and to obtain the desalination demand vector by performing nonlinear mapping on the conductivity gradient data.

[0043] The generation module is also used to extract cloud motion vectors from irradiance trend data using optical flow to construct an energy potential vector, and to perform temporal alignment and convolution operations on the diluted demand vector and the energy potential vector to generate a state space matrix.

[0044] The prediction module uses the state space matrix as the observed value of the system state and uses the constructed Kalman filter to predict the target voltage reference trajectory. The Kalman filter is constructed based on the photovoltaic power source internal resistance model and the electrochemical equivalent circuit model.

[0045] The generation module is also used to input the target voltage reference trajectory into a preset multi-objective constraint function, and calculate the target duty cycle command of the DC converter in the current switching cycle through the linear weighted sum method. The optimization objectives of the multi-objective constraint function include maximizing the desalination rate and minimizing the risk of electrode scaling.

[0046] The adjustment module is used to adjust the conduction state of the power switch tubes of the DC converter connecting the photovoltaic array and the electrochemical desalination device according to the target duty cycle command, so as to change the operating voltage across the electrochemical desalination device.

[0047] Thirdly, this application provides an electronic device, comprising:

[0048] Memory, used to store computer programs;

[0049] A processor is configured to execute the computer program to implement the steps of the multi-objective dynamic optimization method for the solar desalination process as described in the first aspect above.

[0050] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the multi-objective dynamic optimization method for the solar-powered seawater desalination process described in the first aspect above.

[0051] The multi-objective dynamic optimization method for solar-powered seawater desalination provided in this application firstly collects irradiance trends and influent / outfluent conductivity data above the photovoltaic array, and constructs energy potential vectors and desalination demand vectors, enabling a comprehensive perception of the real-time status of energy fluctuations at the source and the desalination task at the load end. Secondly, by using a Kalman filter combined with a photovoltaic internal resistance and electrochemical equivalent circuit model for prediction, environmental interference can be effectively filtered out and the target voltage reference trajectory can be predicted in advance, solving the problem of response lag in traditional methods.

[0052] Finally, by utilizing a multi-objective constraint function to calculate the target duty cycle command that balances maximizing the desalination rate and minimizing the scaling risk, precise dynamic adjustment of the DC-DC converter is achieved, avoiding electrode polarization caused by over-driving. Therefore, this application can achieve a dynamic balance between water production efficiency and equipment lifespan under complex operating conditions of fluctuating light and salinity, significantly improving the multi-objective dynamic optimization effect of the solar desalination process.

[0053] Furthermore, this application inputs the conductivity data of the first solution into a preset mapping function to calculate the resistance and capacitance parameters in real time, accurately reflecting the changes in the physical characteristics of the electrochemical desalination device under different salinity concentrations. By recalculating the coefficients of the circuit differential equations and updating the state transition matrix of the Kalman filter using these parameters, it ensures that the filter's prediction model can follow the dynamic time-varying characteristics of the desalination load in real time, effectively solving the prediction mismatch problem caused by fixed model parameters. This online parameter update mechanism significantly improves the accuracy of voltage reference trajectory prediction, enabling the system to maintain precise control even when the influent salinity fluctuates greatly. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 is a flowchart illustrating a multi-objective dynamic optimization method for a solar-powered seawater desalination process provided in an embodiment of this application;

[0056] Figure 2 is a flowchart illustrating a method for generating a state space matrix according to an embodiment of this application;

[0057] Figure 3 is a flowchart illustrating a method for determining a target duty cycle instruction according to an embodiment of this application;

[0058] Figure 4 is a schematic diagram of a multi-objective dynamic optimization system for a solar-powered seawater desalination process provided in an embodiment of this application.

[0059] Figure 5 is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0060] In the field of solar-powered seawater desalination, existing technologies typically rely on simple voltage-current feedback or maximum power point tracking strategies. This static or passive control logic struggles to cope with the dual challenges of random fluctuations in photovoltaic energy and time-varying influent salinity during actual operation. Specifically, traditional methods lack the ability to predict sudden changes in sunlight caused by cloud cover and fluctuations in source water quality, making it impossible to establish a precise spatiotemporal match between energy supply and desalination requirements. This often leads to response lags or improper adjustments in the control system, ultimately resulting in a contradiction between low water production efficiency and shortened equipment lifespan, making it difficult to achieve multi-objective collaborative optimization under complex dynamic environments.

[0061] To address the aforementioned issues, this application proposes a multi-objective dynamic optimization method for the solar-powered seawater desalination process. The core of this method lies in constructing a dynamic sensing and predictive control closed loop at both the source and load ends. This method synchronously collects conductivity gradient and irradiance trend data, utilizes optical flow and convolution operations to generate a spatiotemporal state matrix including energy supply potential and desalination demand, and combines this with a Kalman filter based on a physical model to accurately predict the target voltage reference trajectory. Furthermore, a multi-objective constraint function is introduced to dynamically calculate the target duty cycle command, adjusting the DC-DC converter in real time to change the operating voltage.

[0062] This method abandons the single feedback control mode and, through advanced prediction of future illumination and load conditions and multi-objective trade-offs, ensures that it can adaptively find the optimal balance between maximizing the desalination rate and minimizing the risk of scaling when illumination fluctuates and salinity changes. It avoids forced operation when energy is scarce and eliminates resource waste and equipment damage when energy is abundant. It solves the technical problem that existing technologies cannot balance efficiency and safety under dual fluctuations of source and load, and significantly improves dynamic adaptability and overall operating efficiency.

[0063] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0064] To address the problems of existing technologies, embodiments of this application provide a multi-objective dynamic optimization method, apparatus, device, computer storage medium, and computer program product for a solar-powered seawater desalination process. The multi-objective dynamic optimization method for a solar-powered seawater desalination process provided in this application embodiment will be described first below.

[0065] Figure 1 shows a flowchart illustrating a multi-objective dynamic optimization method for a solar-powered seawater desalination process according to an embodiment of this application. As shown in Figure 1, the method includes:

[0066] S101. Acquire the conductivity data of the first solution at the liquid inlet position and the conductivity data of the second solution at the liquid outlet position of the electrochemical desalination device, and simultaneously collect the irradiance trend data of the area above the photovoltaic array.

[0067] The first solution conductivity data refers to a numerical sequence representing the source water salinity level and desalination load intensity, collected by sensors installed at the inlet of the electrochemical desalination unit. The unit is typically microsiemens per centimeter (μS / cm). The second solution conductivity data refers to a numerical sequence representing the product water quality and current desalination effect, collected by sensors installed at the outlet. The unit is typically microsiemens per centimeter (μS / cm). Irradiance trend data refers to time-series images or numerical matrices, including current radiation intensity and cloud cover trends, acquired by sky imaging equipment or sensor arrays above the photovoltaic array. The unit is watts per square meter (W / m²).

[0068] During the implementation of the scheme, firstly, high-precision conductivity sensors, such as four-electrode conductivity meters, are deployed in the inlet and outlet pipes of the electrochemical desalination device to continuously measure the conductivity values ​​of the inlet and outlet water at a fixed sampling frequency, such as 1Hz. The raw conductivity signal collected by the inlet sensor is converted from analog to digital to generate the first solution conductivity data, while the outlet sensor simultaneously generates the second solution conductivity data. Both are temporarily stored in a local cache in the form of digital sequences.

[0069] Meanwhile, a sky imager or total radiation sensor is installed above the photovoltaic array. By continuously capturing sky images and analyzing cloud movement, irradiance trend data can be extracted. Specifically, the pixel brightness difference between adjacent frames can be calculated using the optical flow method to derive the cloud displacement vector, thereby predicting the short-term trend of irradiance changes.

[0070] For example, suppose that in a photovoltaic-driven electrochemical desalination system on an island, data acquisition is initiated at time point t using capacitive deionization technology. The conductivity sensor at the inlet measures the conductivity of the first solution as a sequence A = [51000, 51200, 50900] μS / cm, and the sensor at the outlet measures the conductivity of the second solution as a sequence B = [1200, 1180, 1210] μS / cm, representing the instantaneous salinity values ​​of the inlet and outlet water, respectively. Simultaneously, a sky imager acquires sky images at a frequency of 10Hz, and by analyzing cloud movement, generates an irradiance trend data sequence C = [800, 750, 700] W / m², representing the decreasing irradiance trend over the next 3 minutes.

[0071] S102. By calculating the difference ratio between the conductivity data of the first solution and the conductivity data of the second solution, conductivity gradient data is generated, and the conductivity gradient data is nonlinearly mapped to obtain the desalination demand vector.

[0072] Conductivity gradient data refers to a numerical sequence reflecting the difference in salinity between influent and effluent water, indicating the current physical desalination effect. The desalination demand vector, after temperature correction and nonlinear transformation, is a feature vector that accurately represents the current urgency of electrochemical load and energy demand.

[0073] Optionally, the method further includes:

[0074] Acquire the temperature data of the fluid flowing through the electrochemical desalination device.

[0075] Fluid temperature data refers to a real-time numerical sequence of data collected by thermistors deployed in the fluid channel, reflecting the thermodynamic state of the liquid within the electrochemical reaction chamber.

[0076] During the implementation of the scheme, firstly, high-precision temperature probes are installed at key nodes in the main inlet pipe or internal flow channel of the electrochemical desalination unit. A sampling frequency consistent with that of the conductivity sensor is set to synchronously monitor changes in seawater temperature flowing through the unit. Secondly, it is assumed that the analog thermal signal collected at sampling time t will be processed by a transmitter, converted into digital format, and timestamped to form a continuous fluid temperature data sequence. The unit is ℃.

[0077] The correction factor is calculated by inputting fluid temperature data into the preset Nernst equation.

[0078] The correction factor is a dimensionless coefficient or proportionality constant calculated based on thermodynamic principles, used to compensate for conductivity drift and electrode potential changes caused by temperature variations. The pre-defined Nernst equation is the classical thermodynamic equation describing the relationship between electrode potential, ion concentration, and temperature.

[0079] During the implementation of the scheme, the real-time fluid temperature data is first read and converted into absolute temperature units. Secondly, the absolute temperature values ​​of the fluid temperature data are substituted into the preset Nernst equation for calculation; the calculation formula is as follows: ,in, This represents the calculated correction factor. Represents the ideal gas constant. This represents the absolute temperature value of the collected fluid temperature data. Represents the number of electrons transferred during the electrode reaction. This represents the Faraday constant. The correction factor can be calculated using this formula. For example, suppose the acquired fluid temperature data sequence... Substituting into the formula, the correction factor sequence is calculated. This numerical sequence reflects the degree to which temperature affects performance at different times.

[0080] Step S102 generates conductivity gradient data by calculating the ratio of the difference between the conductivity data of the first solution and the conductivity data of the second solution. The conductivity gradient data is then nonlinearly mapped to obtain a desalination demand vector, including:

[0081] S1021. By calculating the difference ratio between the conductivity data of the first solution and the conductivity data of the second solution, conductivity gradient data is generated. Based on the correction factor, the conductivity gradient data is nonlinearly mapped to obtain the desalination demand vector.

[0082] During the implementation of the plan, firstly, the first solution conductivity data obtained in the previous steps, i.e., the influent concentration, is used. The conductivity data of the second solution, i.e., the concentration of the effluent. The difference between the two values ​​is calculated, and then this difference is divided by the influent concentration to obtain the conductivity gradient data reflecting the instantaneous desalination efficiency. Next, the calculated correction factor is introduced. This factor is used to adjust the parameters of the nonlinear mapping function, such as adjusting the sensitivity or judgment threshold of the function. Finally, the conductivity gradient data is input into the adjusted nonlinear mapping function, which can be a sigmoid function, and can output a vector of diluted demand. .

[0083] This embodiment incorporates fluid temperature data and a Nernst equation correction mechanism to fully consider the influence of thermodynamic factors on electrochemical reactions when calculating the desalination demand vector. By dynamically adjusting the nonlinear mapping parameters using a correction factor, the load assessment bias caused by temperature fluctuations is eliminated, enabling the generated desalination demand vector to more accurately reflect the actual desalination energy consumption demand. This, in turn, improves control accuracy and energy utilization efficiency under complex environmental temperatures.

[0084] Optionally, step S1021, which generates conductivity gradient data by calculating the ratio of the difference between the conductivity data of the first solution and the conductivity data of the second solution, and then performs nonlinear mapping on the conductivity gradient data to obtain the desalination demand vector according to the correction factor, may specifically include:

[0085] S10211. Calculate the difference between the conductivity data of the first solution and the conductivity data of the second solution, and calculate the ratio of the difference to the conductivity data of the first solution to obtain the conductivity gradient data.

[0086] During the implementation of the scheme, the conductivity data sequences of the first and second solutions are first obtained. Then, for each synchronous sampling point in the sequence, the conductivity difference is obtained by subtracting the conductivity data of the second solution from the conductivity data of the first solution. This conductivity difference is then normalized by dividing the corresponding conductivity data of the first solution to obtain the conductivity gradient data.

[0087] For example, suppose the sequence and sequence For the first point, calculate (51000-1200) / 51000, and so on for subsequent points, generating a conductivity gradient data sequence. .

[0088] S10212. By adjusting the parameters of the preset nonlinear mapping function using a correction factor, the adjusted nonlinear mapping function is obtained.

[0089] The pre-defined nonlinear mapping function refers to a mathematical model that establishes the relationship between the physical desalination rate and energy demand, typically in the form of an exponential or polynomial function. During implementation, the pre-defined nonlinear mapping function is first obtained, and key parameters controlling the curve shape, such as the slope parameter, are identified. Next, correction factors calculated in previous steps are introduced, and these parameters are adjusted in real time through multiplication or weighted operations. The specific parameter adjustment logic can be expressed as follows: ,in These are the adjusted parameters. These are preset parameters. This is a correction factor.

[0090] For example, suppose the slope parameter of the preset function is... Correction factor sequence The parameters are dynamically adjusted at different times to obtain the adjusted parameter sequence, such as the parameters adjusted at the first time. This allows for the construction of an adjusted nonlinear mapping function that adapts to the current temperature conditions.

[0091] S10213. Input the conductivity gradient data into the adjusted nonlinear mapping function for nonlinear transformation to obtain the de-saturation demand vector.

[0092] During the implementation of the scheme, the conductivity gradient data calculated in step S10211 is used as an input variable, and is substituted point by point into the adjusted nonlinear mapping function generated in step S10212 for calculation. For example, the conductivity gradient data sequence... Input parameters Defined Sigmoid function In, that is The final output vector for reducing the need for dilution is calculated. .

[0093] This embodiment not only achieves accurate conversion from physical quantities to control quantities, but also effectively compensates for the influence of temperature changes on electrochemical reaction kinetics, ensuring that the generated desalination demand vector can truly reflect the actual energy demand under complex operating conditions, thereby improving the adaptability and robustness of the control.

[0094] S103. The cloud motion vector is extracted from the irradiance trend data using the optical flow method to construct the energy supply potential vector. The faded demand vector and the energy supply potential vector are time-aligned and convolved to generate the state space matrix.

[0095] Optionally, step S103, which uses optical flow to extract cloud motion vectors from irradiance trend data to construct an energy supply potential vector, and performs temporal alignment and convolution operations on the diluted demand vector and the energy supply potential vector to generate a state space matrix, may specifically include:

[0096] Figure 2 shows a flowchart of a method for generating a state space matrix according to an embodiment of this application. As shown in Figure 2, the method includes:

[0097] S1031. By calculating the pixel brightness difference between adjacent frames in the irradiance trend data, the cloud motion vector is obtained.

[0098] The cloud motion vector is a two-dimensional vector field that describes the direction and speed of cloud movement, obtained through calculation. This vector accurately characterizes the dynamic migration characteristics of cloud blocking incident radiation sources.

[0099] In the implementation process, firstly, two or more frames of sky images at consecutive time points from the irradiance trend data are acquired and converted into grayscale matrices. Secondly, for each pixel in the image, the difference in brightness values ​​between adjacent frames is calculated, and an optical flow constraint equation is established using the assumption of constant brightness. By solving this system of equations, such as using sparse optical flow algorithms like the Lucas-Kanade algorithm, the displacement vector of feature points in the image per unit time is calculated. Finally, the displacement vectors of all feature points are statistically averaged or filtered to obtain the cloud motion vector representing the overall cloud movement trend.

[0100] For example, suppose the selected time Based on the two frames of cloud images from the next moment, it can be calculated that the cloud layer as a whole is moving at a horizontal speed. and vertical velocity Drifting southeast, generating cloud motion vectors This indicates that the cloud layer has moved 3 pixels to the right and 1 pixel down in the image coordinate system.

[0101] S1032. Use cloud motion vectors to determine the area distribution of the photovoltaic array that will be shaded within a future preset time window, calculate the effective power generation area sequence of the photovoltaic array based on the area distribution, and convert the effective power generation area sequence into an energy supply potential vector.

[0102] The effective power generation area sequence refers to the numerical sequence of the area of ​​photovoltaic modules that are not shaded and can normally receive solar radiation within a time window, changing over time. The energy supply potential vector refers to the energy supply characteristic vector formed by mapping the effective area to the theoretical maximum output power.

[0103] In the implementation process, firstly, using the cloud motion vector obtained in step S1031, the current cloud image is extrapolated along the vector direction by a time step, for example, simulating the change in the cloud's position in the sky within a preset future time window, such as the next 5 minutes. Combining the solar altitude angle and the geometric projection relationship of the photovoltaic array on the ground, the size of the area where the cloud shadow falls on the photovoltaic array at each predicted time is calculated. Secondly, the effective light-receiving area is obtained by subtracting the shaded area from the total area of ​​the photovoltaic array, and then divided by the total area to obtain a normalized effective power generation area sequence. Finally, this sequence is input into the photoelectric conversion model and converted into an energy potential vector.

[0104] For example, assuming that the cloud shadows gradually move in over the next three time steps, resulting in effective light reception ratios of 90%, 80%, and 70%, respectively, the generated energy potential vector would be: This indicates that energy supply capacity will show a downward trend.

[0105] S1033. By performing sliding convolution calculation on the de-emphasis demand vector and the energy supply potential vector, a matching sequence is obtained, and the matching sequence is arranged in time order to form a state space matrix.

[0106] The matching sequence refers to the resulting vector of the convolution operation, and its magnitude reflects whether the power supply potential can cover the diluted demand under different time delays. The state space matrix is ​​a two-dimensional matrix formed by arranging the matching sequences in multiple time dimensions in an ordered row or column order.

[0107] During the implementation of the scheme, the faded demand vector output in step S102 and the energy supply potential vector output in step S1032 are first obtained. Next, a discrete sliding convolution operation is performed, i.e., keeping one vector stationary while allowing another vector to slide along the time axis, and calculating the sum of the products of the elements in the overlapping regions. Finally, as time progresses, new matching sequences are continuously generated, and these sequences are stacked in chronological order of generation to construct the state space matrix.

[0108] For example, suppose in continuous At each sampling time, the fade-out requirement vector at the current time is first used. With energy supply potential vector Perform sliding convolution calculations to obtain a matching sequence that reflects the characteristics of supply and demand matching. Among them, vector elements in Represents the characteristic value of the electrochemical load urgency after temperature correction; vector elements in This represents the predicted theoretical maximum output power of the photovoltaic array at different time steps based on cloud motion vector prediction; convolution result. This is the sum of the products of two vectors sliding and overlapping along the time axis; its magnitude reflects the extent to which the energy supply potential covers the dilution demand under different time delays. Subsequently, the continuous... The matching sequences generated at each sampling time point Fill the state space matrix according to the order of their generation time. For each row, construct the state space matrix It is presented in the following form:

[0109]

[0110] In this matrix, each row represents the load-energy matching sequence calculated at a sampling time, which is the system's panoramic perception of the future energy supply and demand matching state at that time; each column represents the intensity component of the matching sequence at different convolutional displacements.

[0111] This embodiment achieves advanced prediction of cloud movement and photovoltaic array shading, not only quantifying the source-load relationship at the current moment but also revealing the energy supply and demand evolution trend within a short-term time window. This spatiotemporal information fusion significantly enhances the ability to predict sudden changes in illumination, thereby effectively avoiding control lag and oscillations caused by energy fluctuations.

[0112] S104. Using the state space matrix as the observed value of the system state, the target voltage reference trajectory is predicted using the constructed Kalman filter. The Kalman filter is constructed based on the photovoltaic power source internal resistance model and the electrochemical equivalent circuit model.

[0113] The target voltage reference trajectory refers to a time-varying sequence of voltage setpoints output after filtering. This sequence is designed to guide how the power supply system applies voltage over a future period to approximate the theoretical optimal operating condition while satisfying physical constraints. A Kalman filter is a recursive optimal estimation algorithm that combines the predicted values ​​from a mathematical model with the observations from external sensors, minimizing the variance of the estimation error through a weighted average, thereby reconstructing the true state from noisy data.

[0114] A photovoltaic power supply internal resistance model is a mathematical expression describing the fluctuation of the equivalent series resistance inside a photovoltaic cell array as light intensity and temperature change. It is used to calculate the output impedance characteristics of the power supply under different operating conditions. An electrochemical equivalent circuit model is a network model constructed using ideal circuit elements such as resistors and capacitors to simulate the dynamic response characteristics of an electrochemical desalination device, such as a second-order RC circuit model.

[0115] Equivalent internal resistance of photovoltaic power source Typically affected by real-time irradiance and ambient temperature The influence of the photovoltaic power source internal resistance model can be expressed by the formula shown in formula (1):

[0116] (1)

[0117] This indicates the rated internal resistance under standard test conditions. Indicates standard irradiation and standard temperature. These are real-time environmental parameters reflected by irradiance trend data and fluid temperature data. This represents the temperature coefficient of resistance. This model is used to calculate the output impedance of the power supply under different operating conditions and serves as a reference for the process noise covariance or control input of the Kalman filter.

[0118] The electrochemical equivalent circuit model abstracts the device as a combination of resistors and capacitors to reflect solution loss, charge transfer, and double-layer charge storage capacity. The electrochemical equivalent circuit model uses a second-order RC circuit model to simulate the dynamic response characteristics of the electrochemical desalination device. This model abstracts the device as a combination of resistors and capacitors to reflect solution loss, charge transfer, and double-layer charge storage capacity. The state-space expression corresponding to the circuit differential equation of the electrochemical equivalent circuit model involves the rate of change of voltage, which can be expressed by the formula shown in equation (2):

[0119] (2)

[0120] The ohmic resistance of the solution varies with the conductivity of the first solution. It is a charge transfer resistor; The double-layer capacitance represents the charge storage capacity at the electrode interface. These represent the double-layer voltage and the diffusion layer voltage, respectively.

[0121] Online update mechanism, utilizing mapping functions and ,in For conductivity, calculate the above in real time. , parameter.

[0122] During the implementation of the scheme, the Kalman filter is first initialized by filling the state transition matrix with the coefficients of the circuit differential equations obtained from the previous steps based on the real-time conductivity. In this process, the internal resistance model of the photovoltaic power source is integrated into the control input matrix or the process noise covariance matrix. Next, the row vector or eigenvalue corresponding to the current time step is extracted from the state space matrix generated in S103 and used as the observation vector. The input is fed into the filter.

[0123] Subsequently, the filter executes a prediction update loop: in the prediction phase, the prior state estimate at the current time is calculated using the optimal posterior estimate and state transition matrix from the previous time step; in the update phase, the Kalman gain is calculated. This gain is determined by the ratio of the prediction covariance to the observation noise covariance. Using the calculated gain, the observation vector is fused into the prior estimate, and the optimal posterior state estimate for the current time step is obtained. Finally, based on this posterior state, the state transition equation is used to recursively predict the state for several future time steps, forming a continuous target voltage reference trajectory.

[0124] Optionally, before step S104, the method further includes:

[0125] The conductivity data of the first solution is input into a preset mapping function to calculate the resistance and capacitance parameters of the electrochemical desalination device at the current moment.

[0126] Resistance parameters refer to the combined physical quantities used in electrochemical equivalent circuit models to represent the ohmic loss and Faraday reaction impedance of the solution. They mainly include equivalent series resistance and charge transfer resistance, with units of [unit missing]. Capacitance parameter refers to the physical quantity used to represent the charge storage capacity of the double-layer structure at the electrode-solution interface, i.e., double-layer capacitance, with units of 1000 kJ / m². The preset mapping function refers to a mathematical model established based on experimental data fitting, which describes the quantitative relationship between the conductivity of the influent solution and the equivalent impedance characteristics of the device.

[0127] During the implementation of the scheme, the conductivity data of the first solution at the current sampling time is first obtained. Then, this conductivity value is substituted into a preset mapping function for calculation. This function typically uses an inverse proportional relationship to calculate resistance and a direct proportional or nonlinear power function relationship to calculate capacitance.

[0128] For example, suppose the first solution conductivity data sequence The value in Substitute into the preset mapping function and ,in , For conductivity data, Using a preset constant, the resistance parameters at the current moment are calculated. Capacitor parameters .

[0129] The coefficients of the circuit differential equations in the electrochemical equivalent circuit model of the Kalman filter are recalculated using resistance and capacitance parameters.

[0130] In the implementation process, firstly, based on the selected second-order equivalent circuit model structure, a set of differential equations describing the time-varying changes of the double-layer voltage and the diffusion layer voltage are established. Secondly, the resistance and capacitance parameters obtained in step S10401 are used in conjunction with the time step of the control cycle. Calculate the key coefficients in the discretized equations The specific calculation formula is shown in formula (3) below:

[0131] (3)

[0132] in, Represents the coefficients of the circuit's differential equation. Represents the sampling time interval and the unit is , Represents resistance parameters and the unit is , Represents capacitance parameters and the unit is For example, assume a time step. , Substituting into the above formula (3), the coefficient corresponding to the fast response is calculated. The coefficient corresponding to the slow response. .

[0133] The state transition matrix in the Kalman filter is updated using the coefficients of the circuit differential equation.

[0134] During the implementation of the scheme, the coefficients of the circuit differential equations calculated in step S10402 will be used. and According to the predefined state-space equation format of the Kalman filter, the calculated coefficients are filled into the diagonal positions of the state transition matrix. For example, for a second-order state variable model, the calculated coefficients are filled into the matrix to obtain the updated state transition matrix. :

[0135]

[0136] Then, using this matrix in conjunction with the optimal estimate from the previous moment, the voltage state vector for the next moment is jointly predicted.

[0137] This embodiment realizes the adaptive tracking of time-varying salinity by the electrochemical equivalent circuit model, effectively eliminating the prediction bias caused by fixed model parameters, and significantly improving the estimation accuracy of the target voltage reference trajectory under the fluctuation of influent water quality, thereby ensuring the robustness of control and the stability of desalination effect.

[0138] S105. Input the target voltage reference trajectory into the preset multi-objective constraint function, and calculate the target duty cycle command of the DC converter in the current switching cycle by the linear weighted sum method. The optimization objectives of the multi-objective constraint function include maximizing the desalination rate and minimizing the risk of electrode scaling.

[0139] A multi-objective constraint function is a mathematical expression that includes a desalination rate benefit term and a scaling risk penalty term, used to evaluate the overall performance score under a given control input. The linear weighted sum method is a method that combines multiple conflicting optimization objectives by assigning different weight coefficients to form a single-objective comprehensive evaluation function. The target duty cycle command is a control signal determined after optimization calculation, used to control the on-time ratio of the power switches in a DC-DC converter, directly determining the voltage applied across the load.

[0140] Optionally, the method further includes:

[0141] Figure 3 shows a flowchart of a method for constructing a desalination demand vector according to an embodiment of this application. When determining the control command of the DC-DC converter, the current power supply status and load demand are first quantitatively evaluated. The power supply change rate is calculated based on the power supply potential vector to reflect the volatility of energy supply, and the salinity fluctuation variance is calculated based on the desalination demand vector to reflect the stability of the object being processed.

[0142] Subsequently, these two characteristic parameters are input into a preset fuzzy logic controller, and the weights of the optimization objectives are dynamically allocated through a fuzzy inference mechanism to calculate the first weight factor associated with maximizing the desalination rate and the second weight factor associated with minimizing the risk of electrode scaling.

[0143] Finally, the target voltage reference trajectory is introduced into the multi-objective constraint function, and the above two weighting factors are used for linear weighted summation to calculate the optimal target duty cycle command of the DC converter in the current switching cycle, so as to achieve adaptive balance control between desalination efficiency and equipment life under different operating conditions. As shown in Figure 3, the method includes:

[0144] The rate of change of energy supply is calculated based on the energy supply potential vector, and the variance of salinity fluctuation is calculated based on the desalination demand vector.

[0145] The rate of change in energy supply refers to the first derivative or difference magnitude of the energy supply potential vector over time, used to quantify the stability or volatility of the output power of a photovoltaic system. Salinity fluctuation variance refers to the statistical variance of the desalination demand vector within a preset time window, used to reflect the dispersion or abrupt changes in influent load.

[0146] During the implementation of the scheme, the energy supply potential vector generated in step S1032 is first extracted. and the fade-out demand vector generated in step S102 Secondly, for the energy supply potential vector, the difference between the values ​​at two adjacent time points is calculated, and this difference is divided by the time step. The absolute value is then used to obtain the energy supply change rate. For the desalination demand vector, a sliding window is formed by selecting data from the current moment and several past moments, such as the past 10 sampling points. The arithmetic mean of the data within this window is calculated, and then the average of the sum of squares of the differences between each data point and the mean is calculated to obtain the salinity fluctuation variance.

[0147] For example, suppose Energy supply potential vector De-emphasis on demand sequence The rate of change is then calculated. ,variance .

[0148] The energy supply change rate and salinity fluctuation variance are input into a preset fuzzy logic controller. The fuzzy logic controller is used to calculate the first weighting factor and the second weighting factor. The first weighting factor is associated with maximizing the desalination rate, and the second weighting factor is associated with minimizing the risk of electrode scaling.

[0149] A fuzzy logic controller is a control algorithm module based on fuzzy set theory. It transforms precise input quantities into fuzzy linguistic variables and performs logical deduction through a preset inference rule base, and then defuzzifies the results into precise control parameters.

[0150] During the implementation of the scheme, the energy supply change rate and salinity fluctuation variance calculated by S10501 are first used as two input variables and fed into a preset fuzzy logic controller. Next, the controller converts these two explicit values ​​into fuzzy linguistic variables using a membership function. Then, fuzzy inference is performed by querying the internal rule base according to Table 1. Finally, the first weight factor is output using defuzzification strategies such as the centroid method. Second weighting factor The rule base for the preset fuzzy logic controller is shown in Table 1 below:

[0151] Table 1: Fuzzy Logic Rule Base

[0152]

[0153] Table 1 shows a fuzzy logic rule base provided in one embodiment of this application. The table defines in detail the first weighting factor that the fuzzy controller should output under different level combinations of energy supply change rate and salinity fluctuation variance, such as low, medium, and high. Second weighting factor The qualitative level.

[0154] For example, assuming an input change rate of 0.1 is classified as medium and a variance of 0.005 is classified as low, after querying the internal rule base according to Table 1, the controller outputs weighting factors. , .

[0155] Step S105 inputs the target voltage reference trajectory into a preset multi-objective constraint function, and calculates the target duty cycle command of the DC converter in the current switching cycle using a linear weighted sum method, including:

[0156] The target voltage reference trajectory is input into a preset multi-objective constraint function. Based on the first weighting factor and the second weighting factor, the target duty cycle command of the DC converter in the current switching cycle is calculated by the linear weighted sum method.

[0157] During the implementation of the scheme, firstly, based on the target voltage reference trajectory and combined with the input-output voltage relationship of the DC-DC converter, a series of candidate duty cycles are set. Secondly, these candidate values ​​are substituted into the multi-objective constraint function, and the first weighting factor obtained in step S10502 is used. Second weighting factor Calculate the combined score by weighted summation of the two target items. Select the candidate duty cycle with the highest score as the target duty cycle. .

[0158] This embodiment realizes adaptive dynamic adjustment of multi-objective optimization weights, effectively solving the problem that traditional fixed weights cannot balance efficiency and safety, and significantly enhancing robustness and equipment protection capabilities.

[0159] Optionally, the process of inputting the target voltage reference trajectory into a preset multi-objective constraint function and calculating the target duty cycle command of the DC-DC converter in the current switching cycle using a linear weighted sum method based on the first and second weighting factors can specifically include:

[0160] The target voltage reference trajectory is converted into a base duty cycle using the voltage transfer relationship of the DC-DC converter, and a set of candidate duty cycles is generated within a preset range based on the base duty cycle.

[0161] The voltage transfer relationship of a DC-DC converter refers to the formula describing the mathematical dependence between the input voltage, output voltage, and duty cycle. The base duty cycle is the theoretical duty cycle value derived from this transfer relationship and the target voltage reference value.

[0162] During the implementation of the scheme, the real-time input voltage on the photovoltaic side is first obtained. and the current voltage value in the target voltage reference trajectory output in step S104 Secondly, reverse calculations are performed using the topology equations of DC-DC converters such as Buck or Boost converters. For example, for a Buck converter, the calculation formula is... ,in Based on duty cycle, For the target voltage, The input voltage is used as the base duty cycle. Next, a search radius is set around this base duty cycle, such as... and step size Generate candidate duty cycle sequence .

[0163] The first predicted value is obtained by calculating the predicted desalination rate corresponding to each candidate duty cycle using a preset voltage-current relationship, and the second predicted value is obtained by calculating the difference between the predicted voltage corresponding to each candidate duty cycle and the preset safety threshold.

[0164] The preset voltage-current relationship refers to a data model describing the current response characteristics of the desalination device under different operating voltages. The preset safety threshold refers to the upper limit of voltage or current density set to prevent electrode scaling or polarization. The preset voltage-current relationship is shown in Table 2 below:

[0165] Table 2: Preset Voltage-Current Relationship Table

[0166]

[0167] Table 2 shows a preset voltage-current relationship table provided in one embodiment of this application. This table describes the average equivalent impedance characteristics and corresponding current response capabilities of the electrochemical desalination device within different voltage ranges. By consulting this table, the operating current in the circuit can be quickly estimated based on the applied voltage, and thus the desalination rate can be derived.

[0168] The preset security thresholds are shown in Table 3 below:

[0169] Table 3: Preset Safety Threshold Comparison Table

[0170]

[0171] Table 3 shows a preset safety threshold table provided in one embodiment of this application. This table defines the maximum safe voltage threshold that the device is allowed to apply under different influent salinity (i.e., conductivity) and temperature conditions. Exceeding this threshold may lead to severe hydrogen evolution reaction or electrode scaling.

[0172] During the implementation of the scheme, for each candidate duty cycle generated in step S1051, its corresponding predicted voltage is first calculated using the voltage transfer formula. Then, using this predicted voltage, the corresponding current response coefficient and base bias current are looked up in Table 2 and substituted into the linear equation. Where α is the current response coefficient, V is the predicted voltage, I_0 is the basic bias current, the predicted current is calculated, and the normalized current value is used as the first predicted value.

[0173] Simultaneously, based on the current conductivity data of the first influent solution and the fluid temperature data, the corresponding safe voltage thresholds are matched in Table 3. The difference between the safe voltage threshold and the predicted voltage is calculated, and the reciprocal or exponentially decaying form of this difference is used as the second predicted value.

[0174] For example, suppose the candidate duty cycle is The predicted voltage is The current can be obtained from Table 2, i.e., the interval [10, 15). After normalization, the first predicted value is obtained. Assume the current conductivity is... From Table 3, the safety threshold is: Predicted voltage at this time The threshold has been exceeded, the difference is calculated to be -0.5, and it is converted into a high-risk second prediction value.

[0175] The first coefficient is obtained by multiplying the first weighting factor and the first predicted value, and the second coefficient is obtained by multiplying the second weighting factor and the second predicted value.

[0176] During the implementation of the scheme, the first weighting factor output in step S10502 is first obtained. Second weighting factor Next, these two factors are multiplied by the first and second predicted values ​​calculated in step S1052, respectively, to obtain the first coefficient. Second coefficient .

[0177] The composite coefficient corresponding to each candidate duty cycle is obtained by calculating the difference between the first coefficient and the second coefficient. The candidate duty cycle corresponding to the composite coefficient with the largest value is determined as the target duty cycle instruction.

[0178] The comprehensive coefficient is the final score evaluating the performance of a duty cycle. A higher score indicates a better desalination effect at that operation point while meeting safety requirements. During implementation, firstly, for each candidate duty cycle, the comprehensive coefficient is obtained by subtracting the second coefficient from its corresponding first coefficient. Secondly, the comprehensive coefficients of all candidate duty cycles are iterated through to find the maximum value, and the duty cycle value corresponding to this maximum value is determined as the optimal instruction for the current moment.

[0179] Specifically, multi-objective constraint functions The mathematical expression of is shown in the following formula (4):

[0180] (4)

[0181] in, Represents the duty cycle of each candidate The corresponding comprehensive coefficient; and These represent the first and second weighting factors, which are dynamically allocated by the fuzzy logic controller, respectively. The first predicted value indicates the candidate duty cycle. Driven by the predicted desalination rate benefit; The second predicted value represents the candidate duty cycle. Predictive electrode fouling risk penalty under driving.

[0182] In this constraint function, the first predicted value Based on the current response coefficient matched by the predicted voltage in the preset voltage-current relationship table With base bias current The predicted current, obtained after linear combination, is used to measure water production efficiency. Second predicted value. Then, based on the predicted voltage and the safe voltage threshold determined by real-time salinity and temperature... The positive deviation between them is obtained; when the predicted voltage exceeds the safety threshold, As a negative benefit term, the overall coefficient is significantly reduced by the linear weighted sum method. The value of this value enables the control algorithm to automatically eliminate high-risk operating points that could lead to electrode polarization or scaling during the optimization process, ultimately achieving a dynamic balance between maximizing the desalination rate and minimizing the scaling risk.

[0183] For example, assume the first coefficient Second coefficient The comprehensive coefficient is If the other candidate duty cycle That is, the corresponding voltage The first coefficient generated is However, because the voltage did not exceed the limit, the second coefficient was only [a certain value]. Then its comprehensive coefficient is After comparison, the high-risk option will be discarded. Choose the one with better overall performance As the target duty cycle instruction.

[0184] This embodiment achieves refined optimization of the target duty cycle command, which not only avoids the problem of solving complex nonlinear equations in real time and improves computational efficiency, but also ensures that the final selected control command strictly complies with safety specifications, effectively preventing equipment damage caused by blindly pursuing high voltage.

[0185] S106. According to the target duty cycle command, adjust the conduction state of the power switch tube of the DC converter connecting the photovoltaic array and the electrochemical desalination device to change the operating voltage across the electrochemical desalination device.

[0186] A DC-DC converter is a power electronic device connected between a photovoltaic power source and an electrochemical load to achieve voltage level conversion and energy transfer control. Common topologies include buck converters, boost converters, or buck-boost converters. Power switching transistors are the core controllable components in the converter circuit, such as insulated-gate bipolar transistors (IGBTs) or metal-oxide-semiconductor field-effect transistors (MOSFETs). Their on / off states directly determine the energy flow of the circuit.

[0187] During implementation, the target duty cycle command output in step S105 is first received. This command is a control quantity with a value between 0 and 1. Next, the pulse width modulation module inside the control unit generates a PWM control signal with a fixed frequency but variable pulse width based on this command. The ratio of the high-level duration to the entire switching cycle strictly corresponds to the target duty cycle value. Then, this PWM signal is amplified by the gate drive circuit. The enhanced signal is then applied to the control gate of the power switch in the DC-DC converter, driving the switch to switch on and off at extremely high frequencies, such as tens of kilohertz.

[0188] During the turn-on phase, the photovoltaic array charges the inductor or capacitor energy storage element and supplies power to the load; during the turn-off phase, the energy storage element releases energy to maintain load operation. By changing the on-time ratio of the switching transistor, the charge-discharge balance state of the energy storage element is altered, thereby establishing a new average voltage level at the output. Ultimately, this adjusted voltage directly acts on the electrochemical desalination device, changing its internal electric field strength.

[0189] For example, suppose the target duty cycle instruction calculated in the preceding steps is... The control unit then generates a duty cycle of The PWM waveform drives the MOSFETs of the Buck converter. When the input voltage is... Under these conditions, the operating voltage across the electrochemical desalination unit is precisely controlled by the converter. about.

[0190] This embodiment achieves precise control of the power switching transistors of the DC-DC converter, ensuring that the operating voltage of the electrochemical desalination device can be adjusted with a millisecond-level response speed. This allows for rapid stabilization of the load condition when the photovoltaic input fluctuates, effectively avoiding the risks of substandard water production and equipment damage caused by voltage overshoot or drop, and ensuring the implementation of the multi-objective dynamic optimization strategy.

[0191] Figure 4 is a schematic diagram of a specific implementation of a multi-objective dynamic optimization system for a solar seawater desalination process provided in this application. Referring to Figure 4, the system may include:

[0192] The acquisition module 410 is used to acquire the first solution conductivity data at the liquid inlet position and the second solution conductivity data at the liquid outlet position of the electrochemical desalination device, and simultaneously collect the irradiance trend data of the area above the photovoltaic array.

[0193] The generation module 420 is used to generate conductivity gradient data by calculating the difference ratio between the conductivity data of the first solution and the conductivity data of the second solution, and to obtain the desalination demand vector by performing nonlinear mapping on the conductivity gradient data.

[0194] The generation module 420 is also used to extract cloud motion vectors from irradiance trend data using optical flow to construct an energy potential vector, and to perform temporal alignment and convolution operations on the diluted demand vector and the energy potential vector to generate a state space matrix.

[0195] The prediction module 430 is used to use the state space matrix as the observed value of the system state and use the constructed Kalman filter to predict the target voltage reference trajectory. The Kalman filter is constructed based on the photovoltaic power source internal resistance model and the electrochemical equivalent circuit model.

[0196] The generation module 420 is also used to input the target voltage reference trajectory into a preset multi-objective constraint function, and calculate the target duty cycle command of the DC converter in the current switching cycle through the linear weighted sum method. The optimization objectives of the multi-objective constraint function include maximizing the desalination rate and minimizing the risk of electrode scaling.

[0197] The adjustment module 440 is used to adjust the conduction state of the power switch tube of the DC converter connecting the photovoltaic array and the electrochemical desalination device according to the target duty cycle command, so as to change the operating voltage across the electrochemical desalination device.

[0198] The multi-objective dynamic optimization system for the solar desalination process in this application is used to implement the aforementioned multi-objective dynamic optimization method for the solar desalination process. Therefore, the specific implementation of the multi-objective dynamic optimization system for the solar desalination process can be found in the embodiment section of the multi-objective dynamic optimization method for the solar desalination process described above. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0199] Figure 5 shows a schematic diagram of the hardware structure of an electronic device provided in one embodiment of this application.

[0200] The electronic device may include a processor 510 and a memory 520 storing computer program instructions.

[0201] Specifically, the processor 510 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0202] Memory 520 may include mass storage for data or instructions. For example, and not limitingly, memory 520 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 520 may include removable or non-removable (or fixed) media. Where appropriate, memory 520 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 520 is non-volatile solid-state memory.

[0203] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to the first aspect of this disclosure.

[0204] The processor 510 reads and executes computer program instructions stored in the memory 520 to implement any of the multi-objective dynamic optimization methods for the solar desalination process in the above embodiments.

[0205] In one example, the electronic device may also include a communication interface 530 and a bus 540. As shown in Figure 5, the processor 510, memory 520, and communication interface 530 are connected via the bus 540 and communicate with each other.

[0206] The communication interface 530 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0207] Bus 540 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 540 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0208] The electronic device can execute the multi-objective dynamic optimization method for the solar desalination process in the embodiments of this application, thereby realizing the multi-objective dynamic optimization method for the solar desalination process described in conjunction with the accompanying drawings.

[0209] Furthermore, in conjunction with the multi-objective dynamic optimization method for the solar-powered seawater desalination process described in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores computer program instructions; when executed by a processor, these computer program instructions implement any of the multi-objective dynamic optimization methods for the solar-powered seawater desalination process described in the above embodiments.

[0210] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0211] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0212] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0213] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0214] The above provides a detailed description of a multi-objective dynamic optimization method and system for a solar-powered seawater desalination process. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of these embodiments are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A multi-objective dynamic optimization method for a solar-powered seawater desalination process, characterized in that, The method includes: acquiring first solution conductivity data at the inlet position and second solution conductivity data at the outlet position of the electrochemical desalination device, and simultaneously collecting irradiance trend data of the area above the photovoltaic array; generating conductivity gradient data by calculating the difference ratio between the first solution conductivity data and the second solution conductivity data, and performing nonlinear mapping on the conductivity gradient data to obtain a desalination demand vector; extracting cloud motion vectors from the irradiance trend data using optical flow to construct an energy supply potential vector, and performing temporal alignment and convolution operations on the desalination demand vector and the energy supply potential vector to generate a state space matrix; and using the state space matrix as the system... The observed values ​​of the state are used to predict the target voltage reference trajectory using a pre-constructed Kalman filter, which is based on the photovoltaic power supply internal resistance model and the electrochemical equivalent circuit model. The target voltage reference trajectory is input into a preset multi-objective constraint function, and the target duty cycle command of the DC converter in the current switching cycle is calculated by a linear weighted sum method. The optimization objectives of the multi-objective constraint function include maximizing the desalination rate and minimizing the risk of electrode scaling. According to the target duty cycle command, the conduction state of the power switch tubes of the DC converter connecting the photovoltaic array and the electrochemical desalination device is adjusted to change the operating voltage across the electrochemical desalination device.

2. The method according to claim 1, characterized in that, The method further includes: inputting the conductivity data of the first solution into a preset mapping function to calculate the resistance and capacitance parameters of the electrochemical desalination device at the current time; recalculating the circuit differential equation coefficients of the electrochemical equivalent circuit model in the Kalman filter using the resistance and capacitance parameters; and updating the state transition matrix in the Kalman filter using the circuit differential equation coefficients.

3. The method according to claim 1, characterized in that, The method further includes: calculating the energy supply change rate based on the energy supply potential vector, and calculating the salinity fluctuation variance based on the desalination demand vector; inputting the energy supply change rate and the salinity fluctuation variance into a preset fuzzy logic controller, and using the fuzzy logic controller to calculate a first weighting factor and a second weighting factor, wherein the first weighting factor is associated with maximizing the desalination rate, and the second weighting factor is associated with minimizing the electrode scaling risk; and inputting the target voltage reference trajectory into a preset multi-objective constraint function, and calculating the target duty cycle command of the DC converter in the current switching cycle using a linear weighted sum method, which includes: inputting the target voltage reference trajectory into a preset multi-objective constraint function, and calculating the target duty cycle command of the DC converter in the current switching cycle using a linear weighted sum method based on the first weighting factor and the second weighting factor.

4. The method according to claim 3, characterized in that, The step of inputting the target voltage reference trajectory into a preset multi-objective constraint function, and calculating the target duty cycle command of the DC-DC converter in the current switching cycle using a linear weighted sum method based on the first weighting factor and the second weighting factor, includes: converting the target voltage reference trajectory into a base duty cycle using the voltage transfer relationship of the DC-DC converter, and generating a set of candidate duty cycles within a preset range based on the base duty cycle; calculating the predicted desalination rate corresponding to each candidate duty cycle using a preset voltage-current relationship to obtain a first predicted value, and obtaining a second predicted value by calculating the difference between the predicted voltage corresponding to each candidate duty cycle and a preset safety threshold; calculating the product of the first weighting factor and the first predicted value to obtain a first coefficient, and calculating the product of the second weighting factor and the second predicted value to obtain a second coefficient; obtaining a comprehensive coefficient corresponding to each candidate duty cycle by calculating the difference between the first coefficient and the second coefficient, and determining the candidate duty cycle corresponding to the comprehensive coefficient with the largest value as the target duty cycle command.

5. The method according to claim 1, characterized in that, The method further includes: acquiring fluid temperature data flowing through the electrochemical desalination device; calculating a correction factor by inputting the fluid temperature data into a preset Nernst equation; generating conductivity gradient data by calculating the difference ratio between the first solution conductivity data and the second solution conductivity data, and performing nonlinear mapping on the conductivity gradient data to obtain a desalination demand vector, including: generating conductivity gradient data by calculating the difference ratio between the first solution conductivity data and the second solution conductivity data, and performing nonlinear mapping on the conductivity gradient data to obtain a desalination demand vector according to the correction factor.

6. The method according to claim 5, characterized in that, The step of generating conductivity gradient data by calculating the ratio of the difference between the conductivity data of the first solution and the conductivity data of the second solution, and then performing nonlinear mapping on the conductivity gradient data according to the correction factor to obtain a desalination demand vector includes: calculating the difference between the conductivity data of the first solution and the conductivity data of the second solution, and calculating the ratio of the difference to the conductivity data of the first solution to obtain the conductivity gradient data; adjusting the parameters of a preset nonlinear mapping function using the correction factor to obtain the adjusted nonlinear mapping function; and inputting the conductivity gradient data into the adjusted nonlinear mapping function for nonlinear transformation to obtain the desalination demand vector.

7. The method according to claim 1, characterized in that, The step of extracting cloud motion vectors from the irradiance trend data using optical flow to construct an energy supply potential vector, and performing temporal alignment and convolution operations on the faded demand vector and the energy supply potential vector to generate a state space matrix, includes: obtaining the cloud motion vector by calculating the pixel brightness difference of adjacent frames in the irradiance trend data; determining the area distribution of the photovoltaic array being shaded within a future preset time window using the cloud motion vector, calculating the effective power generation area sequence of the photovoltaic array based on the area distribution, and converting the effective power generation area sequence into the energy supply potential vector; obtaining a matching sequence by performing sliding convolution calculation on the faded demand vector and the energy supply potential vector, and arranging the matching sequence in chronological order to form the state space matrix.

8. A multi-objective dynamic optimization system for a solar-powered seawater desalination process, characterized in that, include: The acquisition module is used to acquire the conductivity data of the first solution at the liquid inlet position and the conductivity data of the second solution at the liquid outlet position of the electrochemical desalination device, and simultaneously collect the irradiance trend data of the area above the photovoltaic array. The generation module is used to generate conductivity gradient data by calculating the difference ratio between the conductivity data of the first solution and the conductivity data of the second solution, and to perform nonlinear mapping on the conductivity gradient data to obtain a desalination demand vector. The generation module is also used to extract cloud motion vectors from the irradiance trend data using optical flow to construct an energy supply potential vector, and to perform temporal alignment and convolution operations on the desalination demand vector and the energy supply potential vector to generate a state space matrix. The prediction module uses the state space matrix as the observed value of the system state and uses a constructed Kalman filter to predict the target voltage reference orbit. The target voltage reference trajectory is constructed based on the photovoltaic power supply internal resistance model and the electrochemical equivalent circuit model. The generation module is also used to input the target voltage reference trajectory into a preset multi-objective constraint function, and calculate the target duty cycle command of the DC converter in the current switching cycle through the linear weighted sum method. The optimization objectives of the multi-objective constraint function include maximizing the desalination rate and minimizing the risk of electrode scaling. The adjustment module is used to adjust the conduction state of the power switch tube of the DC converter connecting the photovoltaic array and the electrochemical desalination device according to the target duty cycle command, so as to change the operating voltage across the electrochemical desalination device.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the multi-objective dynamic optimization method for the solar desalination process as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the implementation of the multi-objective dynamic optimization method for the solar-powered seawater desalination process as described in any one of claims 1 to 7.

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