Method, device, equipment and medium for collaborative configuration optimization of brackish water and fresh water

Abstract

The invention discloses a brackish water and fresh water collaborative configuration optimization method, device, equipment and medium. The method comprises the steps of determining basic water source data corresponding to each calculation time period in a target planning year based on a selected target area; comparing the salinity value of each water supply object in each calculation time period in the target planning year with the salinity tolerance threshold value of each water supply object in each calculation time period, and determining the water supply mode of each water supply object in each calculation time period; according to the basic water source data and the water supply mode, constructing constraint conditions for a pre-constructed multi-objective optimization model; and adopting an improved genetic algorithm, carrying out iterative solution on the multi-objective optimization model based on constraint conditions, and outputting a brackish water and fresh water collaborative configuration scheme of each calculation time period in a target planning year. Precise collaborative configuration of brackish water and fresh water is realized, the value of the brackish water for replacing fresh water is maximized while the water demand of a water supply object is guaranteed, and the pressure of shortage of fresh water resources is relieved.

Description

Technical Field

[0001] This invention relates to the field of water conservancy engineering technology, and in particular to a method, apparatus, equipment and medium for the coordinated optimization of brackish water and fresh water. Background Technology

[0002] Brackish water, referring to unconventional water sources with a mineralization of 2-5 g / L, serves as an important supplement to freshwater resources. Its development and utilization in agriculture has become a crucial way to alleviate agricultural water shortages. Currently, research on the synergistic allocation of brackish water and freshwater has been gradually carried out both domestically and internationally. The relevant technical approaches are typically based on traditional freshwater source allocation models, incorporating brackish water as a new source into the allocation system. The allocation process primarily considers the differences in the availability of brackish water among different water users, thus forming a preliminary research and application framework for the synergistic allocation of brackish water and freshwater.

[0003] However, existing technical solutions still have significant shortcomings, making it difficult to fully guarantee the safety of brackish water utilization and the adaptability of the configuration model: First, existing configuration models only add brackish water as an additional water source to the traditional freshwater configuration system, only considering the differences in the number of users who can use brackish water, and lack sufficient consideration of the potential safety hazards that brackish water may bring to users due to its special mineralization. They cannot scientifically constrain the configuration scope and usage of brackish water from the perspective of water quality safety. Second, the time step design of existing configuration models is consistent with traditional freshwater source configuration models, without taking into account the supply characteristics of brackish water and the time-dependent needs of agricultural water use. This results in a mismatch between the model's time resolution and the actual needs of brackish water collaborative configuration, affecting the accuracy and practicality of the configuration scheme. Summary of the Invention

[0004] Based on this, the present invention provides a method, apparatus, equipment and medium for the coordinated configuration optimization of brackish water and fresh water, in order to solve the problems of insufficient consideration of the safety of brackish water, low configuration accuracy and difficulty in balancing water supply security and fresh water substitution in the coordinated configuration of brackish water and fresh water.

[0005] In a first aspect, embodiments of the present invention provide a method for optimizing the synergistic configuration of brackish water and fresh water, comprising: Based on the selected target area, the basic water source data corresponding to each calculation period is determined within the target planning year; the basic water source data includes: the salinity value of brackish water source, the set of available supply periods within the target planning year, the planned supply volume of brackish water source, the planned supply volume of various freshwater sources, the planned water demand of each water supply object, and the corresponding salinity tolerance threshold. The mineralization value of each water supply object in each calculation period of the target planning year is compared with the salinity threshold of each water supply object in each calculation period to determine the water supply mode of each water supply object in each calculation period. Based on the aforementioned basic water source data and water supply mode, constraints are constructed for the pre-built multi-objective optimization model; wherein, the multi-objective optimization model aims to minimize the total water shortage of the system and maximize the total utilization of brackish water. An improved genetic algorithm is used to iteratively solve the pre-built multi-objective optimization model based on the constraints, and output the brackish water and freshwater co-configuration scheme for each calculation period of the target year.

[0006] Secondly, embodiments of the present invention also provide a device for the coordinated configuration and optimization of brackish water and fresh water, comprising: The basic water source data determination module is used to determine the basic water source data corresponding to each calculation period within the target planning year based on the selected target area. The basic water source data includes: the salinity value of brackish water source, the set of available supply periods within the target planning year, the planned supply volume of brackish water source, the planned supply volume of various freshwater sources, the planned water demand of each water supply object, and the corresponding salinity tolerance threshold. The water supply mode determination module is used to compare the mineralization value of each water supply object in each calculation period of the target planning year with the salinity threshold of each water supply object in each calculation period to determine the water supply mode of each water supply object in each calculation period. The constraint construction module is used to construct constraints for the pre-built multi-objective optimization model based on the basic water source data and the water supply mode; wherein, the multi-objective optimization model aims to minimize the total water shortage of the system and maximize the total utilization of brackish water. The multi-source coordinated configuration scheme output module is used to iteratively solve the pre-built multi-objective optimization model based on the constraints using an improved genetic algorithm, and output the brackish water and freshwater coordinated configuration scheme for each calculation period of the target year.

[0007] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a brackish water and freshwater co-configuration optimization method according to any embodiment of the present invention.

[0008] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions, which are used to cause a processor to execute and implement the brackish water and fresh water co-configuration optimization method described in any embodiment of the present invention.

[0009] Fifthly, embodiments of the present invention also provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements a brackish water and freshwater synergistic configuration optimization method as described in any embodiment of the present invention.

[0010] This invention, through a time-period and object-period comparison of the salinity value of brackish water sources with the salinity threshold of the water supply targets, determines the water supply mode, thereby avoiding water safety hazards caused by the water quality characteristics of brackish water from the source and solving the problem of insufficient consideration of brackish water safety in traditional configurations. Using "target area - target planning year - calculation period" as a hierarchical boundary, combined with full-time matching of basic water source data, the configuration scheme can accurately adapt to the different needs of different time periods and different water supply targets, avoiding the extensive configuration of traditional fixed modes. The multi-objective optimization model simultaneously considers minimizing the total water shortage of the system and maximizing the total utilization of brackish water, maximizing the substitution value of brackish water for freshwater while ensuring water demand, and alleviating the pressure of freshwater resource shortage. Through improved genetic algorithm iterative solution, a structured collaborative configuration scheme for each calculation period is output, which can be directly used for actual water source scheduling, reducing the difficulty of implementation.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a method for optimizing the synergistic configuration of brackish water and fresh water according to Embodiment 1 of the present invention; Figure 2 This is a reference diagram of a coordinated configuration scheme for the use of brackish water and fresh water for agricultural irrigation in a target area within a target planning year, applicable to an embodiment of the present invention. Figure 3 This is a flowchart of another method for co-configuring and optimizing brackish water and fresh water according to Embodiment 2 of the present invention; Figure 4 This is a schematic diagram of a brackish water and fresh water co-configuration optimization device according to Embodiment 3 of the present invention; Figure 5This is a schematic diagram of the structure of an electronic device that implements a method for optimizing the synergistic configuration of brackish water and fresh water according to an embodiment of the present invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] Example 1 Figure 1 This is a flowchart of a method for co-configuration optimization of brackish water and freshwater according to Embodiment 1 of the present invention. This embodiment is applicable to the situation of co-configuration optimization of brackish water and freshwater in agricultural irrigation scenarios within a target area. This method can be executed by a brackish water and freshwater co-configuration optimization device, which can be implemented in hardware and / or software and can be configured in a smart water management terminal. Figure 1 As shown, the method includes: S110. Based on the selected target area, determine the basic water source data corresponding to each calculation period within the target planning year; the basic water source data includes: the salinity value of brackish water source, the set of available supply periods within the target planning year, the planned supply volume of brackish water source, the planned supply volume of various freshwater sources, the planned water demand of each water supply object, and the corresponding salinity tolerance threshold.

[0017] The target area is the specific geographical region (such as an agricultural irrigation area or a county-level farmland distribution area) selected in this embodiment that requires optimization of the coordinated allocation of brackish and freshwater. The target planning year is a pre-set time period for optimizing the coordinated allocation of brackish and freshwater. The calculation period refers to the smallest time unit within the target planning year used for water source allocation calculations, ensuring that the allocation scheme can adapt to water demand at different times. Basic water source data refers to the core set of basic data related to water supply and water demand required for coordinated allocation optimization. Specifically: the salinity value of brackish water refers to the measured salinity value of usable brackish water within the target area, a key water quality indicator for determining whether brackish water is suitable for the water supply target. The set of available supply periods for brackish water refers to the set of all periods within the target planning year where brackish water can be stably supplied within the target area, clarifying the usable time range of brackish water. The planned available water volume value of brackish water refers to the planned water volume that can be supplied within the target area during each calculation period within the target planning year, serving as the basis for constraining the upper limit of brackish water allocation. The planned available water volume for various freshwater sources refers to the planned supply of freshwater (excluding brackish water) within the target area for each calculation period within the target planning year. This serves as the upper limit for freshwater allocation. The planned water demand for each water user refers to the planned water demand of each water user (such as agricultural water users like wheat-growing areas and corn-growing areas) within the target area for each calculation period within the target planning year. The salinity tolerance threshold for each water user refers to the maximum salinity that each water user (such as different crops) within the target area can tolerate within the corresponding calculation period. This is a core safety indicator for determining whether brackish water can be used for that water user.

[0018] Optionally, before determining the basic water source data for each calculation period within the target planning year, the following may also be included: The target planning year is divided into 12 natural months as the basic time period. Based on the set of available time periods for slightly saline water sources in the basic water source data and the type of water supply object corresponding to each natural month, the calculation time period division method within each natural month is determined. If the current natural month falls within the set of available water supply periods, and the corresponding water supply object includes agricultural irrigation objects, then the current natural month will be divided into 3 ten-day periods, with each ten-day period being an independent calculation period. If the current calendar month does not fall into the available time period set, or does not include agricultural irrigation objects, it will not be split, and the entire calendar month will be treated as an independent calculation period. The set of all calculation periods within each calendar month is used as a time step set.

[0019] The basic time period refers to the 12 time units formed by dividing the target planning year into natural months. A natural month refers to a standard time unit divided by months in the Gregorian calendar. The water supply object type refers to the category of each water supply object according to its water use attributes. The core category can be agricultural irrigation objects, such as wheat and corn planting areas; other categories can include domestic water users, industrial water users, etc., which is the key basis for determining whether a more refined division of the calculation time period is needed.

[0020] The current calendar month falls within the period when brackish water is available, meaning it is usable. The water supply targets for the current calendar month include agricultural irrigation crops, indicating that there is a demand for water in agriculture. This suggests that the current calculation period is a core period for the coordinated allocation of brackish and freshwater. Agricultural irrigation crops exhibit strong periodicity in their water needs during their growth cycle (e.g., concentrated and sensitive water requirements during the jointing and heading stages), and the use of brackish water must strictly match the salt tolerance characteristics of crops. Therefore, this calendar month is divided into three ten-day periods (approximately 10 days each), making each ten-day period an independent calculation period. This achieves refined allocation at the "ten-day level," avoiding supply-demand mismatches caused by uniform allocation throughout the month.

[0021] The current calendar month does not fall within the period when brackish water is available, meaning there is no brackish water to allocate. Furthermore, the water supply targets for the current calendar month do not include agricultural irrigation targets, indicating no need for refined water use by crops. This suggests that this period is not a core focus of the coordinated allocation. If there is no brackish water supply, the conventional freshwater allocation logic should be followed without further refinement. If there are no non-agricultural irrigation targets, their water demand is typically relatively stable with no significant periodic fluctuations; therefore, no splitting is performed, and the entire calendar month is directly treated as a single calculation period.

[0022] The time step set refers to the set of all calculation periods formed by dividing the current natural month for each natural month. For example, the time step set of an agricultural irrigation month contains three calculation periods: "the first ten days of February, the second ten days, and the third ten days"; the time step set of a non-agricultural irrigation month contains one calculation period: "May".

[0023] S120. Compare the mineralization value of each water supply object in each calculation period of the target planning year with the salinity threshold of each water supply object in each calculation period to determine the water supply mode of each water supply object in each calculation period.

[0024] The water supply mode refers to the combination of brackish and freshwater supply methods determined based on the comparison between the salinity value of the brackish water source and the salinity threshold of the water supply object, which is suitable for the water supply object in the current calculation period. It is the core execution rule for subsequently constructing constraints. This embodiment adopts a refined comparison approach of "each object and each time period". Instead of using a globally unified water supply mode, it compares the salinity value of the brackish water with the salinity threshold of the water supply object for each calculation period in the target planning year (such as early April when wheat is heading and July when corn is jointing). Based on the comparison results, the uniquely suitable water supply mode for this scenario is determined, ensuring that the water supply method for each water supply object meets the salinity safety requirements in each time period, thus avoiding water safety hazards caused by the misuse of brackish water from the source.

[0025] It should be noted that in existing technologies, a crude method of setting a uniform salinity tolerance threshold is usually adopted (such as using only the highest salinity tolerance limit of the water supply object throughout its entire growth cycle as the sole threshold). This method has significant limitations: if the threshold is set too low, it will lead to excessive restriction of brackish water in scenarios where its mineralization meets the salinity tolerance capacity for certain periods; if the threshold is set too high, it will cause water safety risks in periods when the salinity tolerance capacity of the water supply object is weak, as the salinity of the brackish water exceeds the actual tolerance level for that period. Ultimately, this results in the dual contradiction of insufficient utilization of brackish water resources and difficulty in ensuring water safety.

[0026] This embodiment breaks through the limitations of a uniform threshold by dynamically matching thresholds in a two-dimensional "time period-object" framework, achieving precise adaptation between the salinity characteristics of brackish water and the salt tolerance of water supply objects at different times. On the one hand, it can fully explore the salt tolerance potential of water supply objects at different times, maximizing the application scenarios of brackish water within a safe range and increasing the substitution ratio of brackish water for freshwater resources. On the other hand, it can avoid safety risks or resource waste caused by unreasonable setting of a uniform threshold, achieving efficient synergistic configuration of brackish water and freshwater while ensuring water safety.

[0027] S130. Based on the basic water source data and the water supply mode, construct constraints for the pre-built multi-objective optimization model; wherein, the multi-objective optimization model aims to minimize the total water shortage of the system and maximize the total utilization of brackish water.

[0028] The pre-built multi-objective optimization model refers to the mathematical model constructed before the execution of this embodiment, used to solve the coordinated allocation scheme of brackish water and freshwater. Its core feature is that it simultaneously considers two optimization objectives and is the core tool for solving the allocation scheme. Constraints refer to the set of rules formulated based on basic water source data and water supply patterns, limiting the solution scope of the multi-objective optimization model. Minimizing the total water shortage of the system refers to the first optimization objective of the multi-objective optimization model, which minimizes the sum of the differences between the planned water demand and the actual water supply for all water-supplying objects in the target area within the target planning year. The core is to ensure that water demand is met to the greatest extent. Maximizing the total utilization of brackish water refers to the second optimization objective of the multi-objective optimization model, which maximizes the total allocation and utilization of brackish water in the target area within the target planning year. The core is to maximize the substitution of freshwater and alleviate the pressure of freshwater shortage.

[0029] Furthermore, the pre-built multi-objective optimization model aims to minimize the total water shortage of the system and maximize the total utilization of brackish water, and its mathematical expression is defined as follows: ; The total water shortage of the system within the year is planned to meet the target. The total allocation of brackish water supply within the year is planned to meet the target. in, Let be the planned water demand value, representing the demand of the j-th water supply object in the m-th natural month at the p-th time step during the i-th calculation period; For the decision variables of the multi-objective optimization model, it represents the planned water supply of the fifth type of water source (slightly brackish water) to the i-th calculation period, the j-th water supply object, the m-th natural month, and the p-th time step; This represents the sum of planned water supply from five types of water sources under the following conditions: the i-th calculation period, the j-th water supply target, the m-th natural month, and the p-th time step. , respectively representing five types of water sources: local surface water, groundwater, diverted water, unconventional water, and brackish water; j represents the category of water supply object. Based on the actual classification of water supply objects according to specific application scenarios in this embodiment, the values ​​of s and j can be adjusted according to the actual scenario; The set of time periods during which brackish water is available represents the set of natural months in which brackish water can be allocated. Let p be the set of time steps, representing the set of all computational periods within the m-th natural month, and p be a single computational period within the set of time steps, with values ​​ranging from 1 to 10. .

[0030] The method used in this embodiment The target optimization logic is not a simple aggregation of targets, but rather forms a technological closed loop with differentiated water supply models: when the water supply model is type I, the formula... It will automatically incorporate the ratio of brackish water to various types of fresh water, through... and The coordinated optimization ensures that the mixed water supply meets the water demand plan while maximizing the proportion of brackish water within the current salinity tolerance threshold. When the water supply mode is Class II, the formula will prioritize increasing the efficiency of the mixture. The proportion of brackish water is maintained until the planned available water volume is reached, at which point fresh water is added to maximize the utilization of brackish water under safe conditions. Meanwhile, The design aims to only count the actual shortfall between water supply and demand, avoiding interference from redundant supply with the optimization objective. The results accurately reflect the actual level of water security, providing data accuracy support for the practicality of subsequent configuration schemes. The four-level summation structure of i, j, m, and p in the formula transforms the hierarchical time division of "natural month → calculation period" into a mathematically iterable dimension: through... By linking the inclusion relationship between "natural month (m) and calculation period (p)", the model can calculate the refined period of "agricultural irrigation month divided into ten-day periods" separately, while merging the period of "non-agricultural irrigation month as whole month" for calculation, thus achieving the technical goal of "precise configuration of core period and efficient calculation of non-core period".

[0031] S140. Using an improved genetic algorithm, the pre-built multi-objective optimization model is iteratively solved based on the constraints, and the brackish water and freshwater co-configuration scheme for each calculation period within the year of the target planning is output.

[0032] This embodiment employs a Pareto optimal solution set solution strategy using an improved genetic algorithm to achieve the synergistic satisfaction of dual objectives. The algorithm normalizes the two objectives, using minimizing the total water shortage and maximizing the total utilization of brackish water as dual fitness indices. Iteratively filters out a set of non-dominated solutions that simultaneously satisfy both objectives, and finally selects the comprehensive optimal solution from this set, achieving a balance and consideration of the two optimization objectives. In the formula… and This is a mathematical expression of a dual objective, and its collaborative realization depends on the Pareto solution set solution logic of the improved genetic algorithm.

[0033] The improved genetic algorithm refers to an optimization algorithm used to solve multi-objective optimization models. It selects the optimal solution by simulating the biological genetic evolution process, adapting to the needs of solving multi-objective optimization problems. Iterative solution refers to the computational process of the improved genetic algorithm, which gradually approximates the optimal solution of the multi-objective optimization model through multiple rounds of repeated calculations, ensuring that the final solution obtained is the optimal solution that meets the constraints. The brackish water and freshwater co-allocation scheme refers to the final output result of this embodiment, including the brackish water allocation and various types of freshwater allocation for each calculation period and each water supply object within the target planning year. It is an execution scheme that can be directly used for actual water source scheduling.

[0034] First, a pre-configured improved genetic algorithm is invoked, with the constructed constraints and pre-built multi-objective optimization model as input. The algorithm solves the problem through multiple rounds of iteration, with each round selecting effective solutions based on the constraints, and gradually optimizing to obtain the optimal solution that simultaneously minimizes the total water shortage of the system and maximizes the total utilization of brackish water. Finally, the optimal solution is split into hierarchical segments according to "target planning year - calculation period", and the brackish water and fresh water allocation data of each water supply object in each calculation period are extracted and organized into a directly executable collaborative configuration scheme, completing the transformation from basic data to a practical scheme.

[0035] Intuitively, Figure 2 This is a reference diagram for a coordinated allocation scheme of brackish water and fresh water for agricultural irrigation in a target area within a target year; where m is the natural month. Since the salt tolerance of crops varies at different growth stages, the salt tolerance threshold will change in different months. For example, in the calculation period corresponding to February, the crops are in a growth stage with weak salt tolerance, and the salt tolerance threshold is 3g / L. During the calculation period in April, the salt tolerance of crops increases during their growth stage, and the salt tolerance threshold rises to 4 g / L.

[0036] Optionally, after outputting the brackish water and freshwater co-location schemes for each calculation period within the target planning year, the following may also be included: From the output brackish water and freshwater coordinated allocation scheme, the decision variable data corresponding to each calculation period and each water supply object within the target planning year are extracted; wherein, the decision variable data includes: the brackish water source allocation amount corresponding to the i-th calculation period, the j-th water supply object, the m-th natural month, and the p-th time step. And the total allocation of all water sources under the corresponding calculation period and water supply object. ; Based on the extracted decision variable data, a calculation model for the brackish water replacement rate in the target area during the target planning year is constructed, and its mathematical expression is defined as: Where R is the brackish water replacement rate of the target area within the target planning year. Characterizing the distribution of slightly saline water sources, Characterizes the total allocation of all water sources; The extracted decision variable data are substituted into the brackish water replacement rate calculation model to obtain the brackish water replacement rate of the target area in the target planning year, so as to reflect the actual replacement effect of brackish water on freshwater resources.

[0037] From the output structured collaborative configuration scheme, two types of data required for calculating the brackish water replacement rate were selected: one is the allocation amount of brackish water alone. Secondly, the total water supply from all water sources in this scenario. The aim is to establish a correlation between the output of the configuration scheme and the quantitative substitution rate, and to solve the problem that the configuration scheme in the existing technology only outputs the water supply volume and cannot directly reflect the substitution value of brackish water.

[0038] The brackish water substitution rate calculation model is a mathematical model that quantifies the degree to which brackish water substitutes for freshwater resources, using brackish water allocation and total water supply as core parameters. It serves as a quantitative tool that intuitively reflects the utilization value of brackish water resources. The brackish water substitution rate refers to the proportion of brackish water allocation to the total water supply from all sources within the target planning year. A higher proportion indicates a more significant substitution effect of brackish water on freshwater resources. This model transforms the brackish water substitution effect from a qualitative description into a quantitative indicator, solving the problem of a lack of intuitive quantitative evidence for brackish water utilization in existing technologies. The substitution rate value can serve as a core reference for optimizing allocation schemes. If the substitution rate is too low, the timing of brackish water allocation and the water supply mode can be adjusted accordingly, enabling scheme upgrades driven by quantitative results and further improving the utilization efficiency of brackish water. Simultaneously, the substitution rate can serve as key data for water resource management and agricultural irrigation scheme evaluation, helping water conservancy departments, agricultural production units, and other stakeholders to intuitively judge the resource-saving benefits of the scheme and providing data support for the formulation of subsequent water resource allocation strategies.

[0039] This invention, through a time-period and object-period comparison of the salinity value of brackish water sources with the salinity threshold of the water supply targets, determines the water supply mode, thereby avoiding water safety hazards caused by the water quality characteristics of brackish water from the source and solving the problem of insufficient consideration of brackish water safety in traditional configurations. Using "target area - target planning year - calculation period" as a hierarchical boundary, combined with full-time matching of basic water source data, the configuration scheme can accurately adapt to the different needs of different time periods and different water supply targets, avoiding the extensive configuration of traditional fixed modes. The multi-objective optimization model simultaneously considers minimizing the total water shortage of the system and maximizing the total utilization of brackish water, maximizing the substitution value of brackish water for freshwater while ensuring water demand, and alleviating the pressure of freshwater resource shortage. Through improved genetic algorithm iterative solution, a structured collaborative configuration scheme for each calculation period is output, which can be directly used for actual water source scheduling, reducing the difficulty of implementation.

[0040] Example 2 Figure 3 This is a flowchart of another method for co-configuring and optimizing brackish water and fresh water according to Embodiment 2 of the present invention. This embodiment is a refinement based on Embodiment 1. Specifically, as follows... Figure 3 As shown, the method includes: S310. Based on the selected target area, determine the basic water source data corresponding to each calculation period within the target planning year; the basic water source data includes: the salinity value of brackish water source, the set of available supply periods within the target planning year, the planned supply volume of brackish water source, the planned supply volume of various freshwater sources, the planned water demand of each water supply object, and the corresponding salinity tolerance threshold.

[0041] S320. Obtain the mineralization value of the current water supply object in the current calculation period within the target planning year, and the current salinity threshold of the current water supply object in the current calculation period, and compare the data size between the mineralization value and the current salinity threshold.

[0042] The current salinity tolerance threshold refers to the specific value of the salinity tolerance threshold corresponding to the current water supply object within the current calculation period. For example, "the salinity tolerance threshold for the wheat-growing area (the current water supply object) in early May 2024 (the current calculation period) is 2.5 g / L," which is the direct basis for judging salinity safety in the current scenario. By comparing the data, the compatibility between the slightly brackish water quality and the salinity tolerance capacity of the water supply object is clarified, determining whether it is "salinity exceeding the standard" or "salinity suitable," providing a direct judgment standard for the next step of selecting the water supply mode.

[0043] S330. If the current salinity threshold is lower than the mineralization value, then the water supply mode of the current water supply object in the current calculation period is determined to be the first type of water supply mode; wherein, the first type of water supply mode is to mix slightly saline water source with various fresh water sources in an agreed proportion.

[0044] The first type of water supply mode refers to a collaborative water supply method designed for scenarios where "the current salinity tolerance threshold is less than the mineralization value." Its core feature is the mixing of brackish water and fresh water. The fresh water dilutes the mixed water, reducing its salinity and ensuring the water quality meets the safety requirements of the current water recipient. The agreed-upon ratio is a pre-set mixing ratio of brackish water and various types of fresh water, adapted to the current water recipient and the current calculation period. This ratio needs to be calculated and determined based on the difference between the mineralization value and the current salinity tolerance threshold.

[0045] When the comparison result is "the current salinity threshold is lower than the mineralization value", it means that the direct supply of pure brackish water will exceed the salinity tolerance limit of the current water supply object. However, if brackish water is directly banned at this time, it will waste water resources, and if it is directly supplied, there will be safety risks. Therefore, the first type of water supply mode of "mixing according to the agreed ratio" is designed. The brackish water is diluted with fresh water so that the salinity of the mixed water source is reduced to within the current salinity threshold. This not only ensures water supply safety, but also realizes the partial substitution of fresh water by brackish water.

[0046] S340. If the current salinity threshold data is not lower than the mineralization value, then the water supply mode for the current water supply object in the current calculation period is determined to be the second type of water supply mode; wherein, the second type of water supply mode prioritizes the allocation of slightly saline water sources to the current water supply object, and when the planned supply of slightly saline water sources in the current calculation period is less than the planned demand of water for the current water supply object in the current calculation period, the insufficient part is supplemented by various freshwater sources.

[0047] The second type of water supply mode refers to a collaborative water supply method designed for scenarios where "current salinity threshold ≥ mineralization value". Its core feature is "priority supply of brackish water". Under the premise of ensuring water supply security, it maximizes the use of brackish water to replace fresh water and alleviate the pressure of fresh water shortage. When the comparison result is "current salinity threshold not lower than mineralization value", it means that the direct supply of pure brackish water will not exceed the salinity tolerance limit of the current water supply object. At this time, there is no need for mixing and dilution, and brackish water can be used directly with priority. Therefore, the second type of water supply mode of "brackish water priority + fresh water supplement" is designed. The execution logic is divided into two steps: First, brackish water is allocated to the current water supply object in full according to the planned amount of available brackish water in the current calculation period. Second, the allocated amount of brackish water is compared with the planned amount of water demand of the current water supply object. If the supply of brackish water is insufficient, the remaining gap is supplemented with various types of fresh water, which not only ensures water demand but also maximizes the use of brackish water.

[0048] S350. Based on the basic water source data and the water supply mode, construct constraints for the pre-built multi-objective optimization model; wherein, the multi-objective optimization model aims to minimize the total water shortage of the system and maximize the total utilization of brackish water.

[0049] Optionally, before constructing the constraints for the pre-built multi-objective optimization model based on the basic water source data and the water supply mode, the following may also be included: Based on the set of available time periods for brackish water sources in the basic water source data, a supply prerequisite constraint for brackish water sources is constructed. Brackish water sources are allocated to water supply recipients only when the current natural month falls into the set of available time periods; otherwise, the allocation amount of brackish water sources is set to 0. Based on the planned available water volume of brackish water sources in the basic water source data, an upper limit constraint on the water supply of brackish water sources is constructed. In each calculation period, the allocation of brackish water sources shall not exceed the planned available water volume of brackish water sources in the corresponding calculation period, and the allocation of various types of freshwater sources shall not exceed the planned available water volume of each type of freshwater source in their respective calculation periods. Based on the planned water demand values ​​of each water supply object in the basic water source data, a water demand adaptation constraint is constructed for the water supply object. In each calculation period, the total water supply allocated to each water supply object shall not exceed the planned water demand value of the current water supply object in the current calculation period.

[0050] Supply precondition constraints refer to the constraint rules formulated around the available supply time of brackish water, limiting the time range for brackish water allocation. They act as an "entry threshold" for brackish water allocation, ensuring that brackish water is allocated only during the actual available supply period, avoiding ineffective allocation detached from actual supply. Its mathematical expression can be defined as: when... When, for any i, j, p, This embodiment directly connects to the set of available periods for brackish water supply, transforming the time range defined by the set (such as a specific natural month) into a constraint condition. This ensures that the time boundary of the constraint is completely consistent with the time division rules, avoiding the logical contradiction of "the calculation period is within the available supply set, but the natural month is not." Through the mandatory rule of "otherwise, the brackish water allocation amount is set to 0," the invalid solution space of "allocating brackish water during unsuppliable periods" is eliminated before the model is solved. Even if the subsequent water supply mode judgment is biased, the salinity risk can be avoided by "prohibiting the allocation of brackish water during non-supply periods."

[0051] Water supply ceiling constraints refer to constraints that limit the upper limit of the allocation of a single type of water source, based on the planned available water supply of various water sources. Their mathematical expression can be defined as: ;in, The upper limit constraint of water supply is represented by summing the water supply of all water supply objects of type j for the i-th calculation period, the s-th type of water source, the m-th natural month, and the p-th time step. This represents the planned available water volume for the i-th calculation period, the s-th type of water source, the m-th natural month, and the p-th time step. The core logic is: the total water volume supplied by the same water source to all water recipients within the same calculation period cannot exceed the planned available water volume of that water source in the current period. Upper limits are set separately for brackish water sources and various types of freshwater sources, rather than a uniform total water supply upper limit. This is because the available water volume of different water sources is independent, and individual constraints can prevent supply imbalances caused by over-allocation of a certain type of water source. If the calculation period is a ten-day period, the planned available water volume is constrained according to the ten-day period's planned value; if it is a whole month, the planned available water volume is constrained according to the whole month's planned value, ensuring that the constraint precision matches the time division precision.

[0052] Water demand matching constraints refer to the constraint rules that limit the upper limit of the total water supply for a single water-supplying object, based on the planned water demand value of the water-supplying object. The mathematical expression can be designed as follows: This is represented as the water supply volume from five types of water sources to the specific water supply object in the context of "the i-th calculation period, the j-th type of water supply object, the m-th natural month, and the p-th time step". In total, the total water supply cannot exceed the planned water demand of the recipient under the current circumstances. The total water supply constraint is the "total water supply allocated to each water recipient" (i.e., the sum of brackish water and various types of fresh water), rather than the water supply from a single water source. Regardless of whether "mixed supply" or "priority supply" is adopted, the total water supply will not exceed the planned water demand value, thus avoiding oversupply due to the superposition of multiple water sources.

[0053] Furthermore, based on the aforementioned basic water source data and the aforementioned water supply mode, constraints are constructed for the pre-built multi-objective optimization model, which may include: The supply prerequisite constraints, water supply upper limit constraints, and water demand adaptation constraints constructed based on the basic water source are used as the basic boundary constraints of the multi-objective optimization model. For each water supply object within each calculation period, if the water supply mode is the first type of water supply mode, a mixed ratio constraint is constructed to constrain the allocation of slightly saline water source and the sum of the allocation of various freshwater sources within the current calculation period to meet the preset ratio relationship. If the water supply mode is the second type of water supply mode, then an allocation order constraint is added to constrain the allocation order. During the current calculation period, the brackish water source is first allocated to the current water supply object according to the upper limit of the planned water supply volume of the corresponding calculation period. Only when the allocation volume of brackish water reaches the upper limit and still cannot meet the planned water demand of the current water supply object, then the allocation is supplemented from various freshwater sources. By integrating the basic boundary constraints, mixed proportional constraints, and allocation order constraints, a complete set of constraints is formed for the pre-built multi-objective optimization model.

[0054] The constraints of supply premise, water supply upper limit, and water demand adaptation are invoked and collectively defined as basic boundary constraints. Their bottom-line status in the model constraint system is clarified, that is, no matter what specific constraints are added for different water supply modes in the future, the limitations of these three types of constraints cannot be exceeded.

[0055] The mixing ratio constraint refers to a constraint rule specifically constructed for the first type of water supply mode, limiting the allocation ratio of brackish water and fresh water. The preset ratio relationship refers to the ratio of brackish water to various types of fresh water, calculated based on the salinity value of the brackish water source and the salinity threshold of the water supply object, ensuring that the salinity of the mixed water source does not exceed the salinity threshold. The allocation order constraint is a specific constraint adapted to the second type of water supply mode. Its core is to ensure the maximum utilization of brackish water under safe conditions by clearly defining the allocation order of brackish water and fresh water. The complete constraint set refers to a unified constraint set formed by integrating the basic boundary constraints, mixing ratio constraints, and allocation order constraints. It is the sole constraint basis for solving the multi-objective optimization model, covering the constraint requirements of all configuration scenarios.

[0056] S360. Using an improved genetic algorithm, the pre-built multi-objective optimization model is iteratively solved based on the constraints, and the brackish water and freshwater co-configuration scheme for each calculation period of the target year is output.

[0057] Optionally, an improved genetic algorithm is used to iteratively solve the pre-built multi-objective optimization model based on the constraints, outputting a brackish water and freshwater co-configuration scheme for each calculation period within the target planning year, which may include: The pre-configured improved genetic algorithm is invoked, and the multi-objective optimization model and the complete set of constraints are input into the improved genetic algorithm. Based on the pre-configured real number encoding rules of the algorithm, an initial population corresponding to the decision variables is generated, and for each individual in the initial population, it is verified whether it satisfies the complete set of constraint conditions. Valid populations that pass the verification are then selected from the initial population. Using the total water shortage of the system and the total distribution of brackish water as fitness indicators, the overall fitness of each effective individual in the effective population is calculated. Perform the genetic operations pre-configured by the algorithm to generate a population of offspring. For each generation of offspring, repeat the operation to verify whether it satisfies the complete set of constraints until the number of iterations reaches the maximum number of iterations pre-configured by the algorithm, then terminate the iteration and form the final effective population. The target individuals with the best overall fitness are selected from the final effective population. The target decision variables corresponding to the target individuals are obtained. The allocation of brackish water and the allocation of various freshwater sources for each water supply object in each calculation period are extracted from the target decision variables. The brackish water and freshwater co-configuration schemes for each calculation period in the target planning year are compiled and output.

[0058] The first step is to encode the decision variables according to the pre-configured real number encoding rules, transforming them into gene fragments of individuals to ensure that each individual corresponds to a specific configuration scheme. The second step is to randomly generate multiple such individuals to form an initial population (i.e., a set of potential configuration schemes). The third step is to perform constraint verification on each individual in the initial population, checking whether the configuration scheme corresponding to that individual satisfies the complete set of constraints. The fourth step is to select all verified individuals to form an effective population, directly eliminating invalid individuals that have not passed verification to prevent invalid schemes from entering subsequent iterations and improve solution efficiency.

[0059] First, the evaluation criteria for the two core fitness indicators are clearly defined: the smaller the total water shortage of the system, the better; and the larger the total distribution of brackish water, the better. Then, for each individual in the effective population, the corresponding values ​​of the two fitness indicators are calculated. Finally, through the preset integration rules, the values ​​of the two indicators are converted into a single comprehensive fitness value, so that the merits of each potential solution have clear quantitative results, which facilitates subsequent comparison and screening.

[0060] In multiple iterations, high-fitness individuals are retained through selection, genes from these individuals are fused through crossover, and a small amount of random variation is introduced through mutation, ultimately forming a progeny population. Constraint verification is repeated on the progeny population, checking each individual to ensure it meets the complete set of constraints, thus selecting the effective progeny population. The iteration process is then terminated. If the current iteration count has not reached the pre-configured maximum, the "genetic operation → constraint verification" process is repeated based on the effective progeny population. If the maximum iteration count has been reached, the iteration stops, and the current effective population becomes the final effective population. Throughout this process, multiple iterations continuously eliminate inferior solutions and optimize superior ones, ensuring that the individuals (potential solutions) in the final effective population have superior overall performance.

[0061] The target individual refers to the individual with the highest overall fitness value in the final effective population, corresponding to the optimal potential configuration scheme of this plan. The target decision variable refers to a complete set of decision variable values ​​corresponding to the target individual, containing data on the allocation of brackish water and various types of freshwater for all calculation periods and all water supply objects. It is the core data carrier of the optimal configuration scheme. When selecting the optimal scheme, the overall fitness of all individuals in the final effective population is compared, and the individual with the highest overall fitness is selected as the target individual. The target decision variable corresponding to the target individual is extracted, which contains all core data related to the configuration. The data in the target decision variable is split and extracted according to the hierarchy of "target planning year → calendar month → calculation period → water supply object," extracting the brackish water allocation and various types of freshwater allocation for each calculation period and each water supply object. The extracted data is organized into a structured configuration scheme (such as tables or lists) to ensure that the scheme is clear and can be directly used for actual water source scheduling. Finally, the collaborative configuration scheme is output, completing the entire solution process.

[0062] This invention focuses on a detailed explanation of the determination of differentiated water supply modes, the construction of multi-level constraints, the implementation of improved genetic algorithms, and the collaborative optimization of the entire configuration process. Specifically: the water supply mode is determined by matching salinity and salt tolerance thresholds on a time-period and object-by-object basis, achieving precise adaptation between water supply methods and safety requirements; basic constraints are constructed to avoid ineffective configuration problems from the dimensions of time, supply, and demand; a hierarchical and complete constraint set ensures precise matching between constraints and water supply modes and full coverage of scenarios; and the improved genetic algorithm iterative solution ensures solution efficiency and the practicality of the solution, forming a technical closed loop.

[0063] Example 3 Figure 4 This is a schematic diagram of a brackish water and fresh water co-configuration optimization device provided in Embodiment 3 of the present invention. Figure 4 As shown, the device includes: The basic water source data determination module 410 is used to determine the basic water source data corresponding to each calculation period within the target planning year based on the selected target area. The basic water source data includes: the salinity value of brackish water source, the set of available supply periods within the target planning year, the planned supply volume of brackish water source, the planned supply volume of various freshwater sources, the planned water demand of each water supply object, and the corresponding salinity tolerance threshold. The water supply mode determination module 420 is used to compare the mineralization value of each water supply object in each calculation period of the target planning year with the salinity threshold of each water supply object in each calculation period to determine the water supply mode of each water supply object in each calculation period. The constraint construction module 430 is used to construct constraints for the pre-built multi-objective optimization model based on the basic water source data and the water supply mode; wherein, the multi-objective optimization model aims to minimize the total water shortage of the system and maximize the total utilization of brackish water. The multi-source coordinated configuration scheme output module 440 is used to use an improved genetic algorithm to iteratively solve the pre-built multi-objective optimization model based on the constraints, and output the brackish water and fresh water coordinated configuration scheme for each calculation period of the target year.

[0064] This invention, through a time-period and object-period comparison of the salinity value of brackish water sources with the salinity threshold of the water supply targets, determines the water supply mode, thereby avoiding water safety hazards caused by the water quality characteristics of brackish water from the source and solving the problem of insufficient consideration of brackish water safety in traditional configurations. Using "target area - target planning year - calculation period" as a hierarchical boundary, combined with full-time matching of basic water source data, the configuration scheme can accurately adapt to the different needs of different time periods and different water supply targets, avoiding the extensive configuration of traditional fixed modes. The multi-objective optimization model simultaneously considers minimizing the total water shortage of the system and maximizing the total utilization of brackish water, maximizing the substitution value of brackish water for freshwater while ensuring water demand, and alleviating the pressure of freshwater resource shortage. Through improved genetic algorithm iterative solution, a structured collaborative configuration scheme for each calculation period is output, which can be directly used for actual water source scheduling, reducing the difficulty of implementation.

[0065] Optionally, based on the above embodiments, the water supply mode determination module 420 may include: The salinity comparison unit is used to obtain the mineralization value of the current water supply object in the current calculation period of the target planning year, and the current salinity tolerance threshold of the current water supply object in the current calculation period, and compare the data size between the mineralization value and the current salinity tolerance threshold. The first type of water supply mode determination unit is used to determine the water supply mode of the current water supply object in the current calculation period as the first type of water supply mode if the current salinity threshold is lower than the mineralization value; wherein, the first type of water supply mode is to mix brackish water source with various fresh water sources in an agreed proportion. The second type of water supply mode determination unit is used to determine the water supply mode of the current water supply object in the current calculation period as the second type of water supply mode if the current salinity threshold data is not lower than the mineralization value; wherein, the second type of water supply mode prioritizes the allocation of slightly saline water source to the current water supply object, and when the planned water supply volume of slightly saline water source in the current calculation period is less than the planned water demand of the current water supply object in the current calculation period, the insufficient part is supplemented by various freshwater sources.

[0066] Optionally, based on the above embodiments, it may also include: a calculation period division unit, used to divide the target planning year into 12 natural months before determining the basic water source data corresponding to each calculation period in the target planning year, and to determine the calculation period division method within each natural month based on the set of available supply periods of brackish water sources in the basic water source data and the type of water supply object corresponding to each natural month. If the current natural month falls within the set of available water supply periods, and the corresponding water supply object includes agricultural irrigation objects, then the current natural month will be divided into 3 ten-day periods, with each ten-day period being an independent calculation period. If the current calendar month does not fall into the available time period set, or does not include agricultural irrigation objects, it will not be split, and the entire calendar month will be treated as an independent calculation period. The set of all calculation periods within each calendar month is used as a time step set.

[0067] Optionally, based on the above embodiments, it may further include: a basic constraint construction unit, used to construct a supply prerequisite constraint for brackish water sources based on the available time period set of brackish water sources in the basic water source data before constructing the constraint conditions for the pre-built multi-objective optimization model according to the basic water source data and the water supply mode, and to allocate brackish water sources to the water supply object only when the current natural month falls into the available time period set; otherwise, the allocation amount of brackish water sources is set to 0. Based on the planned available water volume of brackish water sources in the basic water source data, an upper limit constraint on the water supply of brackish water sources is constructed. In each calculation period, the allocation of brackish water sources shall not exceed the planned available water volume of brackish water sources in the corresponding calculation period, and the allocation of various types of freshwater sources shall not exceed the planned available water volume of each type of freshwater source in their respective calculation periods. Based on the planned water demand values ​​of each water supply object in the basic water source data, a water demand adaptation constraint is constructed for the water supply object. In each calculation period, the total water supply allocated to each water supply object shall not exceed the planned water demand value of the current water supply object in the current calculation period.

[0068] Optionally, based on the above embodiments, the constraint construction module 430 may include: The basic constraint invocation unit is used to invoke the supply premise constraints, water supply upper limit constraints, and water demand adaptation constraints constructed based on the basic water source, as the basic boundary constraints of the multi-objective optimization model. The mixed ratio constraint supplementary unit is used to construct a mixed ratio constraint for each water supply object in each calculation period. If the water supply mode is the first type of water supply mode, the mixed ratio constraint is constructed to ensure that the allocation of brackish water source and the sum of the allocation of various fresh water sources in the current calculation period meet the preset ratio relationship. The allocation order constraint supplementary unit is used to supplement the allocation order constraint if the water supply mode is the second type of water supply mode. The constraint is that within the current calculation period, the brackish water source is first allocated to the current water supply object according to the upper limit of the planned water supply volume of the corresponding calculation period. Only when the brackish water allocation volume reaches the upper limit and still cannot meet the planned water demand of the current water supply object, the allocation is supplemented from various types of fresh water sources. The complete constraint set generation unit is used to integrate the basic boundary constraints, mixed proportional constraints, and allocation order constraints to form a complete constraint set for the pre-built multi-objective optimization model.

[0069] Optionally, based on the above embodiments, the pre-built multi-objective optimization model aims to minimize the total water shortage of the system and maximize the total utilization of brackish water, and its mathematical expression is defined as follows: ; The total water shortage of the system within the year is planned to meet the target. The total allocation of brackish water supply within the year is planned to meet the target. in, Let be the planned water demand value, representing the demand of the j-th water supply object in the m-th natural month at the p-th time step during the i-th calculation period; For the decision variables of the multi-objective optimization model, it represents the planned water supply of the fifth type of water source (slightly brackish water) to the i-th calculation period, the j-th water supply object, the m-th natural month, and the p-th time step; This represents the sum of planned water supply from five types of water sources under the following conditions: the i-th calculation period, the j-th water supply target, the m-th natural month, and the p-th time step. , respectively representing five types of water sources: local surface water, groundwater, diverted water, unconventional water, and brackish water; j represents the category of water supply object. Based on the actual classification of water supply objects according to specific application scenarios in this embodiment, the values ​​of s and j can be adjusted according to the actual scenario; The set of time periods during which brackish water is available represents the set of natural months in which brackish water can be allocated. Let be the set of time steps, representing the set of all computational time periods within the m-th natural month. For a single calculation period in the time step set, the value range is: .

[0070] Optionally, based on the above embodiments, the multi-water source collaborative configuration scheme output module 440 may further include: The genetic algorithm calling unit is used to call the pre-configured improved genetic algorithm and input the multi-objective optimization model and the complete set of constraints into the improved genetic algorithm. The effective population screening unit is used to generate an initial population corresponding to the decision variables based on the real number encoding rules pre-configured by the algorithm, and to verify whether each individual in the initial population satisfies the complete set of constraint conditions, and to screen out the effective population that passes the verification from the initial population. The fitness calculation unit is used to calculate the overall fitness of each effective individual in the effective population using the total water shortage of the system and the total distribution of brackish water as fitness indicators. The population iteration unit is used to execute the genetic operations pre-configured by the algorithm to generate offspring populations. For each generation of offspring populations, the operation of verifying whether it satisfies the complete set of constraints is repeated until the number of iterations reaches the maximum number of iterations pre-configured by the algorithm, at which point the iteration terminates and the final effective population is formed. The optimal individual selection unit is used to select the target individual with the best overall fitness from the final effective population, obtain the target decision variables corresponding to the target individual, and extract the allocation of brackish water source and the allocation of various fresh water sources for each water supply object in each calculation period from the target decision variables. The unit then compiles and outputs the brackish water and fresh water co-configuration scheme for each calculation period in the target planning year.

[0071] Optionally, based on the above embodiments, it may further include: a replacement rate calculation unit, used to extract decision variable data corresponding to each calculation period and each water supply object within the target planning year from the output brackish water and freshwater coordinated configuration scheme for each calculation period within the target planning year after outputting the brackish water and freshwater coordinated configuration scheme; wherein, the decision variable data includes: the brackish water source allocation amount corresponding to the i-th calculation period, the j-th water supply object, the m-th natural month, and the p-th time step. And the total allocation of all water sources under the corresponding calculation period and water supply object. ; Based on the extracted decision variable data, a calculation model for the brackish water replacement rate in the target area during the target planning year is constructed, and its mathematical expression is defined as: Where R is the brackish water replacement rate of the target area within the target planning year. Characterizing the distribution of slightly saline water sources, Characterizes the total allocation of all water sources; The extracted decision variable data are substituted into the brackish water replacement rate calculation model to obtain the brackish water replacement rate of the target area in the target planning year, so as to reflect the actual replacement effect of brackish water on freshwater resources.

[0072] The brackish water and fresh water co-configuration optimization device provided in this embodiment of the invention can execute the brackish water and fresh water co-configuration optimization method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0073] Example 4 Figure 5A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0074] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0075] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0076] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a brackish water and freshwater co-configuration optimization method.

[0077] That is: based on the selected target area, determine the basic water source data corresponding to each calculation period within the target planning year; the basic water source data includes: the salinity value of brackish water source, the set of available supply periods within the target planning year, the planned supply volume of brackish water source, the planned supply volume of various freshwater sources, the planned water demand of each water supply object, and the corresponding salinity tolerance threshold. The mineralization value of each water supply object in each calculation period of the target planning year is compared with the salinity threshold of each water supply object in each calculation period to determine the water supply mode of each water supply object in each calculation period. Based on the aforementioned basic water source data and water supply mode, constraints are constructed for the pre-built multi-objective optimization model; wherein, the multi-objective optimization model aims to minimize the total water shortage of the system and maximize the total utilization of brackish water. An improved genetic algorithm is used to iteratively solve the pre-built multi-objective optimization model based on the constraints, and output the brackish water and freshwater co-configuration scheme for each calculation period of the target year.

[0078] In some embodiments, a brackish water and freshwater co-configuration optimization method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the brackish water and freshwater co-configuration optimization method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform a brackish water and freshwater co-configuration optimization method by any other suitable means (e.g., by means of firmware).

[0079] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0080] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0081] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0082] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0083] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0084] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0085] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0086] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for optimizing the synergistic configuration of brackish water and fresh water, characterized in that, include; Based on the selected target area, determine the basic water source data for each calculation period within the target planning year; The basic water source data includes: the salinity value of brackish water sources, the set of available supply periods in the target planning year, the planned supply volume of brackish water sources, the planned supply volume of various freshwater sources, the planned water demand of each water supply object, and the corresponding salinity tolerance threshold. The mineralization value of each water supply object in each calculation period of the target planning year is compared with the salinity threshold of each water supply object in each calculation period to determine the water supply mode of each water supply object in each calculation period. Based on the aforementioned basic water source data and water supply mode, constraints are constructed for the pre-built multi-objective optimization model; wherein, the multi-objective optimization model aims to minimize the total water shortage of the system and maximize the total utilization of brackish water. An improved genetic algorithm is used to iteratively solve the pre-built multi-objective optimization model based on the constraints, and output the brackish water and freshwater co-configuration scheme for each calculation period of the target year.

2. The method according to claim 1, characterized in that, The mineralization value of each water supply object in each calculation period of the target planning year is compared with the salinity threshold of each water supply object in each calculation period to determine the water supply mode of each water supply object in each calculation period, including: Obtain the mineralization value of the current water supply object in the current calculation period within the target planning year, and the current salinity threshold of the current water supply object in the current calculation period, and compare the data size between the mineralization value and the current salinity threshold; If the current salinity threshold is lower than the mineralization value, then the water supply mode of the current water supply object in the current calculation period is determined to be the first type of water supply mode; wherein, the first type of water supply mode is to mix slightly saline water source with various fresh water sources in an agreed proportion; If the current salinity threshold data is not lower than the mineralization value, then the water supply mode for the current water supply object in the current calculation period is determined to be the second type of water supply mode; wherein, the second type of water supply mode prioritizes the allocation of slightly saline water sources to the current water supply object, and when the planned water supply volume of slightly saline water sources in the current calculation period is less than the planned water demand of the current water supply object in the current calculation period, the insufficient part is supplemented by various freshwater sources.

3. The method according to claim 1, characterized in that, Before determining the basic water source data for each calculation period within the target planning year, the following is also included: The target planning year is divided into 12 natural months as the basic time period. Based on the set of available time periods for slightly saline water sources in the basic water source data and the type of water supply object corresponding to each natural month, the calculation time period division method within each natural month is determined. If the current natural month falls within the set of available water supply periods, and the corresponding water supply object includes agricultural irrigation objects, then the current natural month will be divided into 3 ten-day periods, with each ten-day period being an independent calculation period. If the current calendar month does not fall into the available time period set, or does not include agricultural irrigation objects, it will not be split, and the entire calendar month will be treated as an independent calculation period. The set of all calculation periods within each calendar month is used as a time step set.

4. The method according to claim 1, characterized in that, Before constructing the constraints for the pre-built multi-objective optimization model based on the aforementioned basic water source data and water supply mode, the following steps are also included: Based on the set of available time periods for brackish water sources in the basic water source data, a supply prerequisite constraint for brackish water sources is constructed: brackish water sources are allocated to water supply recipients only when the current natural month falls into the set of available time periods; otherwise, the allocation amount of brackish water sources is set to 0. Based on the planned available water volume of brackish water sources in the basic water source data, an upper limit constraint on the water supply of brackish water sources is constructed. In each calculation period, the allocation of brackish water sources shall not exceed the planned available water volume of brackish water sources in the corresponding calculation period, and the allocation of various types of freshwater sources shall not exceed the planned available water volume of each type of freshwater source in their respective calculation periods. Based on the planned water demand values ​​of each water supply object in the basic water source data, a water demand adaptation constraint is constructed for the water supply object. In each calculation period, the total water supply allocated to each water supply object shall not exceed the planned water demand value of the current water supply object in the current calculation period.

5. The method according to claim 4, characterized in that, Based on the aforementioned basic water source data and water supply mode, constraints are constructed for the pre-built multi-objective optimization model, including: The supply prerequisite constraints, water supply upper limit constraints, and water demand adaptation constraints constructed based on the basic water source are used as the basic boundary constraints of the multi-objective optimization model. For each water supply object within each calculation period, if the water supply mode is the first type of water supply mode, a mixed ratio constraint is constructed to constrain the sum of the allocation of slightly saline water source and the allocation of various freshwater sources within the current calculation period to meet the preset ratio relationship. If the water supply mode is the second type of water supply mode, then an allocation order constraint is added. The constraint is that within the current calculation period, the brackish water source is first allocated to the current water supply object according to the upper limit of the planned water supply value of the corresponding calculation period. Only when the brackish water allocation reaches the upper limit and still cannot meet the planned water demand value of the current water supply object, then the allocation is supplemented from various freshwater sources. By integrating the basic boundary constraints, mixed proportional constraints, and allocation order constraints, a complete set of constraints is formed for the pre-built multi-objective optimization model.

6. The method according to claim 3, characterized in that, The pre-built multi-objective optimization model aims to minimize the total water shortage of the system and maximize the total utilization of brackish water. Its mathematical expression is defined as follows: ; The total water shortage of the system within the year is planned to meet the target. The total allocation of brackish water supply within the year is planned to meet the target. in, Let be the planned water demand value, representing the demand of the j-th water supply object in the m-th natural month at the p-th time step during the i-th calculation period; For the decision variables of the multi-objective optimization model, it represents the planned water supply of the fifth type of water source (slightly brackish water) to the i-th calculation period, the j-th water supply object, the m-th natural month, and the p-th time step; This represents the sum of planned water supply volumes for the five water sources under the following conditions: the i-th calculation period, the j-th water supply target, the m-th natural month, and the p-th time step. , respectively representing five types of water sources: local surface water, groundwater, diverted water, unconventional water, and brackish water; j represents the category of the water supply object. Based on the actual classification of water supply objects according to specific application scenarios in this embodiment, the values ​​of s and j can be adjusted according to the actual scenario; The set of time periods during which brackish water is available represents the set of natural months in which brackish water can be allocated. Let p be the set of time steps, representing the set of all computational periods within the m-th natural month, and p be a single computational period within the set of time steps, with values ​​ranging from 1 to 10. .

7. The method according to claim 5, characterized in that, An improved genetic algorithm is used to iteratively solve the pre-built multi-objective optimization model based on the constraints, outputting a brackish water and freshwater co-location scheme for each calculation period within the year, including: The pre-configured improved genetic algorithm is invoked, and the multi-objective optimization model and the complete set of constraints are input into the improved genetic algorithm. Based on the pre-configured real number encoding rules of the algorithm, an initial population corresponding to the decision variables is generated, and for each individual in the initial population, it is verified whether it satisfies the complete set of constraint conditions. Valid populations that pass the verification are then selected from the initial population. Using the total water shortage of the system and the total distribution of brackish water as fitness indicators, the overall fitness of each effective individual in the effective population is calculated. Perform the genetic operations pre-configured by the algorithm to generate a population of offspring. For each generation of offspring, repeat the operation to verify whether it satisfies the complete set of constraints until the number of iterations reaches the maximum number of iterations pre-configured by the algorithm, then terminate the iteration and form the final effective population. The target individuals with the best overall fitness are selected from the final effective population. The target decision variables corresponding to the target individuals are obtained. The allocation of brackish water and the allocation of various freshwater sources for each water supply object in each calculation period are extracted from the target decision variables. The brackish water and freshwater co-configuration schemes for each calculation period in the target planning year are compiled and output.

8. The method according to claim 6, characterized in that, After outputting the brackish water and freshwater co-location schemes for each calculation period within the target planning year, it also includes: From the output brackish water and freshwater coordinated allocation scheme, the decision variable data corresponding to each calculation period and each water supply object within the target planning year are extracted; wherein, the decision variable data includes: the brackish water source allocation amount corresponding to the i-th calculation period, the j-th water supply object, the m-th natural month, and the p-th time step. And the total allocation of all water sources under the corresponding calculation period and water supply object. ; Based on the extracted decision variable data, a calculation model for the brackish water replacement rate in the target area during the target planning year is constructed, and its mathematical expression is defined as: Where R is the brackish water replacement rate of the target area within the target planning year. Characterizing the distribution of slightly saline water sources, Characterizes the total allocation of all water sources; The extracted decision variable data are substituted into the brackish water replacement rate calculation model to obtain the brackish water replacement rate of the target area in the target planning year, so as to reflect the actual replacement effect of brackish water on freshwater resources.

9. A device for the synergistic configuration and optimization of brackish water and fresh water, characterized in that, The device includes: The basic water source data determination module is used to determine the basic water source data corresponding to each calculation period within the target planning year based on the selected target area. The basic water source data includes: the salinity value of brackish water source, the set of available supply periods within the target planning year, the planned supply volume of brackish water source, the planned supply volume of various freshwater sources, the planned water demand of each water supply object, and the corresponding salinity tolerance threshold. The water supply mode determination module is used to compare the mineralization value of each water supply object in each calculation period of the target planning year with the salinity threshold of each water supply object in each calculation period to determine the water supply mode of each water supply object in each calculation period. The constraint construction module is used to construct constraints for the pre-built multi-objective optimization model based on the basic water source data and the water supply mode; wherein, the multi-objective optimization model aims to minimize the total water shortage of the system and maximize the total utilization of brackish water. The multi-source coordinated configuration scheme output module is used to iteratively solve the pre-built multi-objective optimization model based on the constraints using an improved genetic algorithm, and output the brackish water and freshwater coordinated configuration scheme for each calculation period of the target year.

10. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a brackish water and freshwater co-configuration optimization method according to any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method for co-configuration optimization of brackish water and fresh water as described in any one of claims 1-8.

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