Water resource optimal configuration method based on cost performance

Abstract

The invention provides a water resource optimal configuration method based on cost performance, and the method comprises the steps: building a dynamic bidding game model based on the demand characteristics of multiple users, the optimal selection theory of consumers and the demand price elasticity, and enabling the dynamic bidding game model to integrate a performance factor based on water resources and a cost performance evaluation system of a price factor; solving the dynamic bidding game model in different supply and demand states, and determining water distribution results of each water resource for different users; and generating a water resource optimization scheduling scheme based on the water distribution result. According to the invention, the problems of low resource allocation efficiency, unbalanced supply-demand relationship and incapability of meeting diversified demands of users caused by single and solidified pricing and allocation modes based on water supply cost or administrative instructions in the prior art are avoided.

Description

Technical Field

[0001] This invention relates to the field of water resource scheduling technology, and in particular to a method for optimizing water resource allocation based on cost-effectiveness. Background Technology

[0002] Traditional water resource allocation methods often rely on administrative orders or cost-based fixed pricing systems. In the context of complex water networks with multiple water resources, multiple users, and interconnected pipelines, traditional allocation models often lead to problems such as resource misallocation, low allocation efficiency, and low user satisfaction. Summary of the Invention

[0003] This invention provides a cost-effective water resource optimization allocation method to address the shortcomings of existing technologies.

[0004] This invention provides a method for optimizing water resource allocation based on cost-effectiveness, comprising the following steps: Based on the demand characteristics of multiple users, the theory of optimal consumer choice, and the price elasticity of demand, a dynamic bidding game model is established. The dynamic bidding game model integrates a cost-effectiveness evaluation system based on the performance factors and price factors of water resources. Under different supply and demand conditions, the dynamic bidding game model is solved to determine the water allocation results for different users of each water resource. Based on the water allocation results, a water resource optimization scheduling scheme is generated.

[0005] According to the present invention, a water resource optimization allocation method based on cost-effectiveness is provided, wherein a dynamic bidding game model is established based on the demand characteristics of multiple users, the theory of optimal consumer choice, and the price elasticity of demand, including: The demand characteristics of each user are used as input parameters for applying the consumer optimal choice theory to model the pricing strategy of each user to maximize water use utility while meeting water quality requirements. The price elasticity of demand for each user is introduced as a dynamic constraint to adjust the impact of price changes on demand during the bidding process. By combining the aforementioned pricing strategy and dynamic constraints, a dynamic bidding game model is constructed with multiple users as players and multiple water resources as competitors.

[0006] According to the present invention, a water resource optimization allocation method based on cost-effectiveness is provided, wherein the introduction of the demand price elasticity of each user as a dynamic constraint condition for adjusting the impact of price changes on demand during the bidding process includes: Based on the price elasticity of demand for each user, the demand type of each user is determined from preset types, wherein the preset types include at least one of elastic, inelastic, and unit elastic. Based on the demand types of each user, the dynamic constraints are set such that during the bidding process, the demand of flexible users is more sensitive to price changes than that of inelastic users.

[0007] According to the present invention, a water resource optimization allocation method based on cost-effectiveness is provided, wherein the price elasticity of demand is determined based on the following steps: Obtain the relative rate of change in water resource prices; Obtain the relative rate of change in water demand caused by price fluctuations; The ratio of the relative rate of change in water resource demand to the relative rate of change in price is defined as the price elasticity of demand.

[0008] According to the present invention, a water resource optimization allocation method based on cost-effectiveness is provided. The consumer optimal choice theory is that, under a given budget constraint, a user chooses among multiple water resources to form a water resource combination that maximizes the user's water utility function.

[0009] According to the present invention, a water resource optimization allocation method based on cost-effectiveness is provided, wherein the supply and demand state includes at least one of a balanced state, a state of supply exceeding demand, and a state of supply falling short of demand.

[0010] The water resource optimization allocation method based on cost-effectiveness provided by this invention establishes a dynamic bidding game model that integrates a cost-effectiveness evaluation system and simulates the game process under different supply and demand conditions, thereby realizing the dynamic and market-oriented optimal allocation of water resources. Because this model organically combines user demand characteristics (such as water price affordability and water quality requirements), consumer choice theory, and the physical properties of water resources (water quantity, water quality, and guarantee rate), it can reflect the true market value of water resources and the differentiated needs of users for different water qualities and guarantee rates. This avoids the problems of low resource allocation efficiency, supply-demand imbalance, and inability to meet diverse user needs caused by traditional single and fixed pricing and allocation models based on water supply costs or administrative orders. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating the cost-effective water resource optimization allocation method provided by the present invention.

[0013] Figure 2This is a flowchart illustrating another cost-effective water resource optimization allocation method provided by the present invention.

[0014] Figure 3 This is a schematic diagram of the bidding game mechanism provided by the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0016] In the field of water resource allocation, various allocation methods have been explored and applied to balance supply and demand. Traditional water resource allocation methods often rely on administrative directives or cost-based fixed-price systems. Administrative directives typically involve water management departments allocating water quotas to various water-using units from top to bottom based on macro-planning and water consumption quotas. Cost-based pricing systems, on the other hand, establish uniform or tiered fixed water prices based on water supply costs (such as raw water fees, water treatment fees, and pipeline transportation fees).

[0017] However, in the context of complex water networks with multiple water resources, multiple users, and interconnected pipelines, traditional scheduling models often lead to problems such as resource misallocation, low allocation efficiency, and low user satisfaction. Specifically, the administrative command allocation model is relatively rigid and struggles to respond in real time to the dynamic changes in water demand, water preferences, and acceptance of different water qualities (such as conventional tap water, reclaimed water, and recycled water) of different users (e.g., industrial, agricultural, and residential users), lacking flexibility. Meanwhile, cost-based fixed-price systems fail to accurately reflect the scarcity and marginal value of water resources in different times and spaces, and also fail to reflect the differences in price sensitivity among different users. They lack effective economic incentives to guide users to make optimal choices based on their own needs and the value of water resources, thus limiting the improvement of overall water resource allocation efficiency.

[0018] In response, this invention provides a cost-effective water resource optimization allocation method, which aims to introduce a market-based game bidding mechanism into the water resource allocation process. By establishing a dynamic bidding game model that can reflect the differentiated needs of multiple users, consumer choice behavior, and price sensitivity, dynamic, refined, and optimized allocation of water resources in complex water supply networks can be achieved, thereby effectively solving the resource misallocation problem under the traditional scheduling mode and improving the efficiency of water resource allocation and the comprehensive water use utility of users.

[0019] in, Figure 1This is a flowchart illustrating the cost-effective water resource optimization allocation method provided by the present invention, as shown below. Figure 1 As shown, the method includes steps 110, 120 and 130.

[0020] Step 110: Based on the demand characteristics of multiple users, the theory of optimal consumer choice, and the price elasticity of demand, a dynamic bidding game model is established. The dynamic bidding game model integrates a cost-effectiveness evaluation system based on the performance factors and price factors of water resources.

[0021] In this embodiment, the demand characteristics of multiple users can be understood as the specific attributes and requirements of different water users (i.e., users) in terms of water use. These may include, but are not limited to, the total water demand of each user, minimum water quality requirements, water use efficiency, and water price affordability. User types can be domestic water users, industrial water users, service industry water users, etc., and the demand characteristics of each type of user are different. For example, domestic water users have higher water quality requirements but relatively limited water price affordability; industrial water users may have different water quality requirements depending on specific production processes, and their water use efficiency and water price affordability are usually higher.

[0022] Consumer optimal choice theory states that, given a budget constraint, users will choose among different water resources in order to find a combination of water resources that maximizes their total utility.

[0023] As an optional embodiment, the user's budget constraint can be expressed as: in, , These are the prices of two different water resources. , It is the corresponding purchase quantity. This is the user's total budget or total water expenditure. The user will select a set ( , This allows the water utility function U(x, y) to be maximized. This embodiment applies this theory to model how users, when faced with water resources of different prices and performance, will prioritize the combination of water resources that brings them the greatest overall benefit.

[0024] Price elasticity of demand quantifies how sensitive the quantity demanded by users is to changes in price; it can be expressed as the ratio of the percentage change in quantity demanded to the percentage change in price. As an optional implementation, its calculation formula can be: in, The price elasticity of demand is the coefficient. The relative rate of change in demand. This represents the relative rate of change in prices. This theory is used to define the reaction mechanisms of different users to price fluctuations in a model.

[0025] when > At this time, demand is elastic, and price changes have a significant impact on the quantity demanded; when < Demand is inelastic, and price changes have little impact on the quantity demanded; when = At that time, the unit demand elasticity means that changes in quantity demanded are proportional to changes in price.

[0026] The dynamic bidding game model can be understood as a dynamic interactive system with multiple stakeholders and multiple objectives, in which multiple users act as players and various water resources compete for resources. Each player proposes its own price and water demand based on its own demand characteristics and its prediction of the strategies of other players. Through one or more rounds of bidding, an equilibrium resource allocation pattern is ultimately formed.

[0027] Furthermore, the cost-effectiveness evaluation system is the core decision-making basis of the aforementioned game theory model. This system integrates the performance factors and price factors of water resources. Performance factors can be a comprehensive quantitative evaluation of the physical properties and supply reliability of water resources. These performance factors can include water quantity (sufficiency), water quality (what standards it meets, such as Class I, Class II, or Class III water), and water supply guarantee rate (stability of water supply under unfavorable conditions such as dry seasons). For example, local surface water may be Class I, with a guarantee rate significantly affected by rainfall; while imported water may be Class III, but has a higher water supply guarantee rate.

[0028] The price factor is the dynamic price of water resources formed during the competitive bidding process. By quantifying the performance factor into a comprehensive performance score and comparing it with the price factor (e.g., performance score / price), the "cost-effectiveness" of each water resource at a specific moment can be obtained. In the above game model, the basic behavioral principle of users is to prioritize and compete for water resources with higher cost-effectiveness.

[0029] Step 120: Under different supply and demand conditions, solve the dynamic bidding game model to determine the water allocation results for different users.

[0030] In this embodiment, different supply and demand states can be understood as different relationships between the total available water resources and the total demand. These supply and demand states can include: a balanced state, for example, in a year with 75% water inflow, the total available water supply is approximately equal to the total demand; a state of supply exceeding demand, for example, in a year with 50% water inflow (abundant water), the total available water supply is significantly greater than the total demand; and a state of supply falling short of demand, for example, in a year with 95% water inflow (dry water), the total available water supply is far less than the total demand.

[0031] Solving a dynamic bidding game model involves using a specific supply and demand state as the initial boundary condition of the model (e.g., determining the total available water volume for each water resource), and then starting the model for computation. During the computation, each user (the player) will bid for the water resource with the highest cost-effectiveness based on a cost-effectiveness evaluation system. The competitive behavior of users will drive up the price of popular water resources, thereby reducing their cost-effectiveness, until the cost-effectiveness of each water resource in the market tends to reach equilibrium or the resources are allocated. As an optional implementation, to prevent irrational competition, the model can set constraints, such as "a user's bid will not exceed 1.1 times the cost-effectiveness of the weakest competitor (the user with the lowest water price affordability among those competing for the water resource)."

[0032] The water allocation result is the final data output after the model solution. It can be a detailed resource allocation list, clearly indicating how much water of each type of water resource is allocated to each user under the current supply and demand conditions, and the corresponding transaction price. For example, under a balanced state, the model solution might yield the water allocation results shown in Table 1: Table 1

[0033] By repeatedly performing this step under different supply and demand conditions, multiple sets of water allocation results can be obtained to address different macroeconomic scenarios, providing data support for developing flexible and robust scheduling schemes.

[0034] Step 130: Based on the water allocation results, generate a water resource optimization scheduling scheme.

[0035] In this embodiment, the water resource optimization scheduling scheme can be understood as a specific implementation plan for water resource management departments. The water resource optimization scheduling scheme translates the allocation results of "who gets it, how much it gets, and at what price" determined in step 120 into scheduling instructions at the engineering level.

[0036] Specifically, based on the water allocation results (as shown in Table 1), the generated scheduling plan will clearly indicate that after the balance status warning is issued, the scheduling system should open the water supply valve from Reservoir A (local surface water) to the industrial park, ensuring a total supply of 166.9 million m³. Simultaneously, groundwater extraction equipment will be activated to supply a portion (168.73 million m³) of the 208.63 million cubic meters of groundwater to industrial users, and another portion (39.9 million m³) to the commercial area with a high concentration of service industries. Furthermore, coordination with external water transfer agencies such as the South-to-North Water Diversion Project will be conducted to ensure that of the water supplied to the city, 239.44 million m³ enters the residential water supply network and 53.56 million m³ enters the service industry water supply network. At the same time, according to the transaction price in the water resource optimization scheduling plan, corresponding metering and charging will be implemented for each user.

[0037] The cost-effectiveness-based water resource optimization allocation method provided in this embodiment establishes a dynamic bidding game model that integrates a cost-effectiveness evaluation system and simulates the game process under different supply and demand conditions, thereby realizing dynamic and market-oriented optimal allocation of water resources. Because this model organically combines user demand characteristics (such as water price affordability and water quality requirements), consumer choice theory, and the physical properties of water resources (water quantity, water quality, and guarantee rate), it can reflect the true market value of water resources and users' differentiated needs for different water qualities and guarantee rates. This avoids the problems of low resource allocation efficiency, supply-demand imbalance, and inability to meet diverse user needs caused by traditional single and fixed pricing and allocation models based on water supply costs or administrative orders.

[0038] Based on the above embodiments, step 110 establishes a dynamic bidding game model based on the demand characteristics of multiple users, the theory of optimal consumer choice, and the price elasticity of demand, including: Step 111: Use the demand characteristics of each user as input parameters for the consumer optimal choice theory to model the pricing strategy of each user to maximize water use utility while meeting water quality requirements.

[0039] In this step, the aim is to provide each player (i.e., user) in the game model with a rational action guide aimed at maximizing their own interests, namely a bidding strategy.

[0040] The pricing strategy here can be understood as the decision-making scheme of users in the bidding process regarding the specific price and corresponding quantity of water resources they are willing to offer. It is not a fixed value, but a dynamic function that reflects the user's behavioral tendency after comprehensively considering their own needs, water resource performance, and market prices.

[0041] To construct this pricing strategy, this embodiment uses the demand characteristics of each user as the core input parameters. These demand characteristics include the user's total water demand, minimum water quality requirements, water price affordability, and water use efficiency, which together define the user's "preferences" and "capabilities".

[0042] Consumer optimal choice theory plays the role of the "decision engine" here. The core idea of ​​this theory is that users will make choices that maximize their "utility" within their budget and capabilities. Here, "water utility" can be understood as a comprehensive satisfaction index, which is not only related to the amount of water obtained, but also positively or negatively correlated with factors such as water quality and acquisition cost (price).

[0043] In the specific modeling process, a preliminary screening is conducted: water quality requirements are met. That is, for any user, only water resources with a quality level higher than or equal to their minimum requirement will be considered and included in subsequent utility calculations and pricing decisions. For example, for domestic water users requiring Class III or higher water quality, potentially Class IV water resources will not be included in the pricing.

[0044] After water quality screening, the model applies consumer optimal choice theory to assess, for each user: the water use benefits of acquiring a unit of a certain water resource (such as local surface water) (e.g., industrial users can improve product quality and generate higher profits by using high-quality water), and compare it with their own water price affordability and budget constraints.

[0045] Based on the above assessment, a pricing function is formulated with the goal of maximizing water utility. This function determines how much water a user is willing to buy at a given price level, or the maximum price a user is willing to pay to obtain a specific amount of water.

[0046] For example, an industrial user with a water efficiency of 15.0 yuan / m³ and a water price affordability of 11.0 yuan / m³ will have a more aggressive pricing strategy than a residential user with a water efficiency of 8.0 yuan / m³ and a water price affordability of 6.0 yuan / m³, especially when competing for water resources with excellent water quality (high performance factor).

[0047] Step 112: Introduce the price elasticity of demand for each user as a dynamic constraint to adjust the impact of price changes on demand during the bidding process.

[0048] In this step, dynamic constraints can be understood as a rule or function used to dynamically adjust the demand of each user based on real-time prices during multiple rounds of iterative calculations in the game theory model. Dynamic constraints mean that the user's demand is no longer a fixed constant, but a variable that changes with price fluctuations.

[0049] The core basis of dynamic constraints is the price elasticity of demand for each user. Price elasticity of demand measures the sensitivity of the quantity demanded to changes in price, and this elasticity coefficient is different for different users.

[0050] For users with low price elasticity of demand (such as domestic water users, whose basic water demand is rigid), even if prices rise to a certain extent, the decrease in their demand will be relatively small.

[0051] For users with high price elasticity of demand (such as some industrial users who can use high-priced water for core processes and low-priced water for auxiliary processes), price increases may lead them to significantly reduce their demand for high-priced water and instead seek alternative water resources or adjust their production processes.

[0052] Introducing this constraint into the model means that after each round of bidding causes a change Δp in the price p of a certain water resource, the model will automatically adjust the demand of the main competing users of that water resource based on their price elasticity of demand coefficient Ed. Changes .

[0053] Step 113: Integrate the bidding strategy and dynamic constraints to construct a dynamic bidding game model with multiple users as players and multiple water resources as competitors.

[0054] In this step, the bidding strategy drives users to submit their bids at the start of each round of the game, based on the current cost-effectiveness of each water resource. The aggregated bids then form a new market price.

[0055] The dynamic constraint (price elasticity of demand) adjusts the user's demand based on the new price, and this adjustment will affect the user's pricing strategy in the next round.

[0056] Through this iterative cycle of "quoting price - adjusting price - adjusting quantity - requoting price", a complete dynamic bidding game model is eventually constructed.

[0057] In this model, multiple users are the game players, meaning each user is an independent and rational decision-making entity with its own unique pricing strategy and price elasticity of demand. Multiple water resources are the competing objects, meaning each type of water resource (such as local surface water, groundwater, and water transferred from other regions) is a scarce commodity under competition, and its price is dynamically determined by the game behavior of supply and demand.

[0058] The model, which integrates the pricing strategy and dynamic constraints, is tightly coupled with its internal logic. It can reflect both the user's subjective initiative to maximize utility and the objective constraints of the market price mechanism, thus enabling a highly realistic simulation of resource competition and allocation in a complex water network environment.

[0059] This embodiment improves the realism and predictive accuracy of the dynamic bidding game model by modeling the user's bidding strategy as seeking to maximize water utility and using the price elasticity of demand as a dynamic constraint to regulate the supply and demand relationship.

[0060] Based on any of the above embodiments, step 112 introduces the price elasticity of demand for each user as a dynamic constraint to adjust the impact of price changes on demand during the bidding process, including: Step 112a: Based on the price elasticity of demand for each user, determine the demand type of each user from the preset types. The preset types include at least one of elastic, inelastic, and unit elastic.

[0061] In this step, demand type can be understood as a classification label for how sensitive the quantity of user demand is to price fluctuations, which is determined by a quantitative price elasticity coefficient of demand.

[0062] The preset types can include: Elastic (the percentage change in quantity demanded is greater than the percentage change in price); Inelastic (the percentage change in quantity demanded is less than the percentage change in price); and Unit elastic (the percentage change in quantity demanded is equal to the percentage change in price).

[0063] As an alternative embodiment, the user's demand type can be determined based on the absolute value of their demand price elasticity coefficient, |Ed|.

[0064] For example, when |Ed|>1, the user's demand type is determined to be flexible, which typically corresponds to users who have multiple alternatives or whose water costs account for a high proportion of their total costs and are very sensitive to price changes.

[0065] When |Ed|<1, the user's demand type is determined to be inelastic. This usually corresponds to users with rigid demand for water resources, such as domestic water, whose basic demand will not change significantly due to small price fluctuations.

[0066] When |Ed| = 1, the user's demand type is determined to be unit elasticity.

[0067] For example, when analyzing users in City B, domestic water is mainly used to meet basic survival needs, and its substitutability is poor. Therefore, its price elasticity of demand is usually low (|Ed|<1), and it can be identified as a "lacking elasticity" user. For some industrial water users, if their production processes are sensitive to water prices, or if there are multiple options such as water-saving technologies or different grades of water resource substitution, their price elasticity of demand may be high (|Ed|>1), and they can be identified as "elastic" users.

[0068] Step 112b: Based on the demand type of each user, set dynamic constraints so that during the bidding process, the demand of flexible users is more sensitive to price changes than the demand of inelastic users.

[0069] In this embodiment, dynamic constraints can be concretized in the model as a set of parameters or functions associated with demand types. Their core function is to adjust the demand quantity with different "sensitivities" according to the user's demand type in each iteration of the game when the price changes.

[0070] The dynamic constraints can be set by configuring different demand adjustment functions for different demand types.

[0071] For users whose demand type is identified as "inelastic," the price coefficient in their quantity demanded adjustment function will be smaller. This means that even if prices rise significantly, the model's calculation of a decrease in their demand will be relatively limited. For users whose demand type is identified as "elastic," the price coefficient in their quantity demanded adjustment function will be larger. This means that even a slight price increase may lead the model to calculate a significant decrease in their demand.

[0072] In this way, the model can ensure that elastic user demand is more sensitive to price changes than inelastic user demand.

[0073] For example, in one iteration of the model, if the price of imported water increases by 10% due to intense competition, the dynamic constraints built into the model might calculate that the demand of domestic water users identified as "inelastic" decreases by only 2% based on their smaller elasticity coefficient.

[0074] For service users who are also competing for water and are identified as "elastic," the model will calculate, based on their larger elasticity coefficient, that their demand may decrease by 15%, prompting them to seek other water resources (such as local groundwater) that may become more cost-effective.

[0075] This embodiment classifies users based on the price elasticity of demand and sets differentiated dynamic constraints for different types of users, achieving refined and hierarchical management of user behavior simulation in the game theory model. Because this method distinguishes the drastically different reaction patterns of different users (such as users with essential living needs versus cost-sensitive industrial users) when facing price fluctuations, it avoids the coarse simulation of market behavior caused by using a uniform, undifferentiated price-demand response model. This significantly improves the model's prediction accuracy under complex user structures and the practical feasibility of the final scheduling scheme.

[0076] Based on any of the above embodiments, the price elasticity of demand is determined based on the following steps: Obtain the relative rate of change in water resource prices; Obtain the relative rate of change in water demand caused by price fluctuations; The ratio of the relative rate of change in water demand to the relative rate of change in price is defined as the price elasticity of demand.

[0077] In this embodiment, the relative rate of change of water resource prices can be understood as the ratio of the change in price over a certain period of time or after a change due to a specific event (such as a new round of bidding, policy adjustment, etc.) to the original price, usually expressed as a percentage.

[0078] As an alternative embodiment, the relative rate of change in water resource prices can be calculated using the following formula: The relative rate of change in water resource prices = (new price - old price) / old price = Δp / p Here, "old price" refers to the base price before the price change, while "new price" refers to the price after the change.

[0079] The price data mentioned above can be derived from statistical analysis of historical price adjustment records, such as analyzing specific data on several past adjustments to residential or industrial water prices in a city. Alternatively, it can be the change between the transaction price (old price) of a certain water resource in the previous round of bidding and the new price formed in the current round, within the internal iterative calculation of the dynamic bidding game model constructed in the above embodiments.

[0080] For example, suppose that under a state of supply exceeding demand, an industrial user participates in a bidding process for externally diverted water. The initial price is 4.5 yuan / m³. After one round of bidding, the price drops to 4.0 yuan / m³. Then, the relative change rate of the price change is (4.0 - 4.5) / 4.5 ≈ -11.1%.

[0081] Furthermore, the relative rate of change in water demand can be understood as the ratio of the change in user demand for water resources after the aforementioned price change to the original demand.

[0082] As an alternative embodiment, the relative rate of change can be calculated using the following formula: The relative rate of change in water demand = (New demand - Old demand) / Old demand = Here, "old demand" refers to the demand before the price change, while "new demand" refers to the demand under the new price. This demand can be calculated by collecting actual water consumption data of a specific user group before and after each water price adjustment, establishing a functional relationship between price and demand, and thus predicting changes in demand under a specific price change. Alternatively, questionnaires can be distributed to the target user group to inquire about their water usage intentions at different water price levels, thereby statistically determining the expected changes in demand. Furthermore, existing water quota standards and cost-benefit analysis models for specific industries (such as specific types of manufacturing) can be used to simulate possible adjustments in production scale and water consumption when raw material (water) costs change.

[0083] For example, in the case of the aforementioned 11.1% decrease in the price of water transferred from other regions, data analysis shows that the industrial user's water demand increased from 800,000 m³ per month to 900,000 m³ per month. In this case, the relative change rate of water demand is (90 - 80) / 80 = 12.5%.

[0084] Finally, the ratio of the relative rate of change in water demand to the relative rate of change in price is determined as the price elasticity of demand.

[0085] Specifically, the price elasticity of demand ( The formula for calculating ) is: = (Relative rate of change in water demand) / (Relative rate of change in water price) = Continuing with the example above, the price elasticity of demand for the aforementioned industrial users... The calculation is as follows: = 12.5% ​​ / -11.1% ≈ -1.13 Obtain the price elasticity of demand After the value is -1.13, since its absolute value |-1.13|>1, the demand type of this industrial user in this price range can be determined as "elastic".

[0086] Based on any of the above embodiments, the consumer optimal choice theory is that, under a given budget constraint, a user chooses among multiple water resources to form a water resource combination that maximizes the user's water utility function.

[0087] Here, the given budget constraint can be understood as the financial limitation faced by each user in terms of water usage. This not only refers to the total amount of money available for users to pay water bills, but also indirectly reflects the user's ability to afford water prices.

[0088] As an alternative embodiment, when a user faces two types of water resources (e.g., X is local surface water, Y is diverted water), their budget constraint can be represented by the following mathematical equation: in, , These are the prices of two different water resources. , It is the corresponding purchase quantity. This refers to the user's total budget or total water expenditure. The above constraint means that all of the user's choices must fall within this budget boundary.

[0089] The user's choice among multiple water resources reflects their proactive decision-making process. In this application scenario, multiple water resources refer to different water resources available for the user to choose from, such as local surface water, local groundwater, and water transferred from other regions. These water resources are not entirely homogeneous; they differ significantly in performance factors (such as water quality and water supply guarantee rate) and price factors. The user's "choice" involves weighing and selecting among these options with different cost-effectiveness ratios.

[0090] The core objective of this theory is to "form a water resource combination that maximizes the user's water utility function," which is also the fundamental principle for simulating user behavior in the model.

[0091] The water utility function U(x, y, ...) is a mathematical function used to quantify the total satisfaction or total benefit a user obtains from consuming different combinations of water resources. Constructing this function is crucial for modeling, as it requires comprehensive consideration of the user's demand characteristics. For example, a complete water utility function is not only positively correlated with the amount of water obtained, but also with performance factors such as water quality level and water supply guarantee rate, while being negatively correlated with the price paid.

[0092] A water resource combination refers to the overall water use plan ultimately chosen by a user, consisting of various water resources in different quantities. Maximization, on the other hand, means that the model uses an algorithm to find a water resource combination that maximizes the water utility function U for each user, while satisfying budget constraints.

[0093] For example, an industrial user with high water efficiency and strong affordability might assign a very high weight to high-quality water (such as Class I surface water) in their water utility function because it leads to higher product yield and economic benefits. Therefore, even if Class I water is more expensive, this user might still choose to purchase a certain amount of Class I water for core production processes when solving their utility maximization problem, while simultaneously purchasing lower-priced, slightly lower-quality Class III imported water for auxiliary processes such as cooling. This final combination of "high-quality water + low-quality water" represents the "water resource combination" that maximizes their total utility within that budget. For residential water users, their utility function might prioritize water quality compliance (e.g., meeting or exceeding Class III water standards) and low prices; therefore, their optimal choice might be to acquire as much water as possible at the lowest price while meeting basic water quality requirements.

[0094] Based on any of the above embodiments, the supply and demand status includes at least one of a balanced state, a state of supply exceeding demand, and a state of supply falling short of demand.

[0095] Here, supply and demand status can be understood as the macroscopic relationship between the total amount of water resources available for allocation within a specific period (e.g., a hydrological year or a scheduling cycle) and the total demand of all users.

[0096] The supply and demand status can be correlated with the available water volume under different water inflow frequencies. Specifically, the equilibrium state can refer to a situation where the total water supply and total demand in a region are basically equal. As an optional implementation, this state can correspond to a normal water inflow year in a region, where market competition exists but is relatively mild, and users have limited choices. Solving the game theory model can reveal the true preferences of each user based on their water use efficiency and water price affordability when resources are "just right," allowing the value of performance factors (water quality, guarantee rate) to be clearly reflected through price differences.

[0097] A supply exceeding demand situation refers to a condition where the total available water resources in a region significantly exceed the total demand. As an alternative example, this state can correspond to a region's wet year. In this state, water resources are no longer scarce, market competition is less intense, and users have a wider range of choices. Solving the game theory model will show a general downward trend in prices. At this point, users' bidding strategies will tend to lower prices, making the cost-effectiveness of different water resources more consistent. For example, even the best local surface water may have its price driven down due to a lack of intense competition until its cost-effectiveness is comparable to other water resources, thus allowing users with lower affordability to enjoy high-quality water resources.

[0098] A supply-demand imbalance refers to a situation where the total demand for water resources in a region significantly exceeds the total supply. As an alternative example, this state can correspond to a dry year or a drought in a region. Under these conditions, water resources are extremely scarce, and market competition is exceptionally fierce. Solving game theory models can most profoundly reflect the scarcity value of water resources. At this point, users' bidding strategies become very aggressive, and water price affordability and water use efficiency become key to acquiring water rights. The model shows that users with high water price affordability (such as industrial and service users) will continuously raise their bids to ensure water supply, sometimes even exceeding the limits of users with lower affordability (such as residential users), thus winning the competition and obtaining limited water resources.

[0099] By setting at least one of the above-mentioned supply and demand states as initial conditions and solving them in the dynamic bidding game model, a set of water allocation results for different macro scenarios can be obtained, thereby forming a series of scenario-based water resource optimization scheduling schemes.

[0100] in, Figure 2 This is a flowchart illustrating another cost-effective water resource optimization allocation method provided by the present invention, as shown below. Figure 2As shown, firstly, the performance factors and price factors of water resources are determined, and a cost-effectiveness evaluation system is constructed based on these factors. Next, based on the demand characteristics of multiple users, the theory of optimal consumer choice, and the price elasticity of demand, and integrating the cost-effectiveness evaluation system, a dynamic bidding game model is constructed.

[0101] After constructing the model, different supply and demand states are simulated to solve the dynamic bidding game model and obtain the game results. Based on the game handover process, the water allocation results for each water resource to different users are determined, and an optimized water resource scheduling scheme is generated based on the water allocation results. In addition, the factor weights in the cost-effectiveness evaluation system can be adjusted according to the game results of the model, thereby iteratively optimizing the dynamic bidding game model to obtain a more reasonable scheduling scheme.

[0102] Figure 3 This is a schematic diagram of the competitive bidding game mechanism provided by the present invention, such as... Figure 3 As shown, this mechanism establishes a dynamic bidding game model comprising a "user competition layer" and a "water resource allocation layer." In the water resource allocation layer, there are various water resources with different performance factors (such as water quality and quantity) and initial price factors, which together constitute the initial cost-effectiveness of each water resource. In the user competition layer, multiple users (game players) formulate their own bidding strategies based on their respective demand characteristics (such as water consumption, water quality requirements, and benefits) and consumer optimal choice theory. Their goal is to maximize water use utility within their budget, i.e., to prioritize competing for water resources with high initial cost-effectiveness.

[0103] Then, the bidding process begins, with multiple users bidding on water resources that meet their quality requirements. Competition for the same high-quality, cost-effective water resource inevitably leads to higher bids, thus altering the price factor of that water resource. This change is then updated in real-time through a "cost-effectiveness feedback" process, reflecting a decrease in the cost-effectiveness of the water resource as the price increases.

[0104] Next, the entire bidding process is regulated by the "price elasticity of demand constraint." Price elasticity of demand serves as a dynamic constraint, used to adjust the impact of price changes on the quantity demanded. For example, for users with elastic demand, when the price of a certain water resource rises too rapidly due to competition, their demand will decrease significantly, thus suppressing irrational high-price competition. Conversely, users with inelastic demand are willing to pay higher prices to obtain the necessary water resources. Finally, the model solves under different supply and demand conditions to determine the final water allocation results and transaction prices for each water resource to different users. This result will serve as the basis for generating optimal water resource scheduling schemes.

[0105] In one specific embodiment, the main water resources of City A include: local surface water, which is of very good quality and classified as Class I water. The water price is relatively low, approximately 2 yuan / m³, based on water supply prices from water conservancy projects, water resource taxes, and water plant supply costs. 3 The available surface water varies depending on precipitation; under 75% water inflow conditions, it is 166.9 million m³. 3 The average annual inflow is 193 million cubic meters. 3 Under 95% water inflow conditions, the amount is 1.08 trillion cubic meters. 3 .

[0106] The local groundwater is of relatively good quality, classified as Class II. Water prices are low, approximately 2.5 yuan / m³, based on factors such as water supply prices from water conservancy projects, water resource taxes, and water plant costs. 3 The availability of groundwater varies relatively little; the amount of groundwater available for non-agricultural use in Weifang City is approximately 208.63 million m³. 3 The water diverted from elsewhere is of relatively poor quality compared to local surface water and groundwater, classified as Class III. The source water price is relatively high, approximately 4.5 yuan / m³, based on the water supply price of the water diversion project, water resource tax, and water plant supply costs. 3 The recent water supply is 293.25 million cubic meters. 3 .

[0107] In addition, there are three types of users: domestic water users, industrial water users, and service industry water users. The demand characteristics of these users are as follows: domestic water demand is 239.44 million m³. 3 The water price affordability is 6.0 yuan / m³. 3 The water use efficiency is 8.0 yuan / m³. 3 The water quality requirement is Class III or above.

[0108] The industrial water demand is 335.63 million m³. 3 The affordable water price is 11.0 yuan / m³. 3 The water use efficiency is 15.0 yuan / m³. 3 The water quality requirement is Class III or above.

[0109] The water demand of the service industry is 93.46 million cubic meters. 3 The water price affordability is 7.0 yuan / m³. 3 The water use efficiency is 10.0 yuan / m³. 3 The water quality requirement is Class III or above.

[0110] A dynamic competitive game model is constructed by introducing price elasticity of demand as a dynamic constraint to adjust the impact of price changes on demand during the bidding process. For example, domestic water is generally considered inelastic, meaning that price increases have little impact on its demand; however, some industrial water may be elastic. During the game, a rule is set that "the bid cannot exceed 1.1 times the weakest competitor's water price affordability."

[0111] The above model is solved under different supply and demand conditions to determine the water allocation results for different users. This embodiment simulates three typical supply and demand conditions, specifically including: (1) Equilibrium state (75% water supply level): Under this state, the total water supply and total water demand are basically equal. The water allocation results and the final equilibrium price obtained after solving the dynamic bidding game model are shown in Table 2.

[0112] Table 2 The water allocation results in Table 2 are the outcome of a competitive game, which significantly alters the cost-effectiveness of each water source, as shown in Table 3. Industrial users with high water efficiency drive up the price of high-quality water sources through competitive bidding, causing their cost-effectiveness to decrease and converge.

[0113] Table 3 (2) Supply exceeds demand (50% of the inflow level). Under this condition, the total water supply exceeds the total water demand. After solving the model, due to the abundance of water resources and reduced competition, the prices of various water sources generally decrease. The user's selection strategy makes the final cost-effectiveness of different water sources tend to be consistent, and the water allocation results are shown in Table 4.

[0114] Table 4 As shown in Table 5, in a market with sufficient supply, users' rational choices lead to a convergence of the final cost-effectiveness of different water sources, and the initial cost-effectiveness differences are smoothed out by market competition.

[0115] Table 5 (3) Supply falls short of demand (95% water supply level). Under this condition, the total water demand exceeds the total water supply, and water resources are scarce. After solving the model, the competition is extremely fierce, leading to a significant price increase. Industrial users with the strongest water use efficiency and water price affordability obtain the best water source, while users with lower price affordability face the situation of having their water usage squeezed out. The water allocation results are shown in Table 6.

[0116] Table 6 As shown in Table 7, when resources are scarce, fierce competition drives up the prices of all water sources, resulting in a general decline in cost-effectiveness. It is noteworthy that because the competitive bids from industrial and service sectors for water transfers exceed the affordability of domestic water users, the same water source (transferred water) exhibits different post-competition cost-effectiveness for different users.

[0117] Table 7 Finally, based on the water allocation results under the different supply and demand conditions, a corresponding water resource optimization scheduling scheme can be generated.

[0118] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0119] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0120] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing water resource allocation based on cost-effectiveness, characterized in that, include: Based on the demand characteristics of multiple users, the theory of optimal consumer choice, and the price elasticity of demand, a dynamic bidding game model is established. The dynamic bidding game model integrates a cost-effectiveness evaluation system based on the performance factors and price factors of water resources. Under different supply and demand conditions, the dynamic bidding game model is solved to determine the water allocation results for different users of each water resource. Based on the water allocation results, a water resource optimization scheduling scheme is generated.

2. The water resource optimization allocation method based on cost-effectiveness according to claim 1, characterized in that, The dynamic bidding game model, based on the demand characteristics of multiple users, the theory of optimal consumer choice, and the price elasticity of demand, includes: The demand characteristics of each user are used as input parameters for applying the consumer optimal choice theory to model the pricing strategy of each user to maximize water use utility while meeting water quality requirements. The price elasticity of demand for each user is introduced as a dynamic constraint to adjust the impact of price changes on demand during the bidding process. By combining the aforementioned pricing strategy and dynamic constraints, a dynamic bidding game model is constructed with multiple users as players and multiple water resources as competitors.

3. The water resource optimization allocation method based on cost-effectiveness according to claim 2, characterized in that, The introduction of the price elasticity of demand for each user as a dynamic constraint to adjust the impact of price changes on demand during the bidding process includes: Based on the price elasticity of demand for each user, the demand type of each user is determined from preset types, wherein the preset types include at least one of elastic, inelastic, and unit elastic. Based on the demand types of each user, the dynamic constraints are set such that during the bidding process, the demand of flexible users is more sensitive to price changes than that of inelastic users.

4. The water resource optimization allocation method based on cost-effectiveness according to any one of claims 1 to 3, characterized in that, The price elasticity of demand is determined based on the following steps: Obtain the relative rate of change in water resource prices; Obtain the relative rate of change in water demand caused by price fluctuations; The ratio of the relative rate of change in water resource demand to the relative rate of change in price is defined as the price elasticity of demand.

5. The water resource optimization allocation method based on cost-effectiveness according to any one of claims 1 to 3, characterized in that, The consumer optimal choice theory states that, given a budget constraint, users choose among multiple water resources to form a water resource combination that maximizes the user's water utility function.

6. The method for optimal allocation of water resources based on cost-effectiveness according to any one of claims 1 to 3, characterized in that, The supply and demand state includes at least one of the following: a balanced state, a state of supply exceeding demand, and a state of supply falling short of demand.

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