Water resource regulation method based on actual utilization value evaluation of water resource and storage medium

Abstract

The application provides a water resource regulation method based on actual utilization value evaluation of water resources and a storage medium, and belongs to the technical field of water resource optimal scheduling. In the method, historical data of all water use nodes in a target region are used to calibrate a water saving feedback model; under the intervention of water saving measures, direct water saving effect, behavior rebound effect and associated transfer effect are calculated; according to the three effects, real water saving amount and collaborative deficit index of all water use nodes are calculated; and water resource utilization is regulated with the minimum collaborative deficit index and the maximum real water saving amount as optimization targets. The application introduces a behavior rebound and associated resource transfer feedback link, can quantify the nonlinear feedback effect caused by water saving measures, overcomes the problem that the traditional method is easy to overestimate the net water saving benefit, and provides a scientific decision basis for regional water resource regulation.

Description

Technical Field

[0001] This invention belongs to the field of water resource optimization and scheduling technology, and in particular relates to a water resource regulation method and storage medium based on the assessment of the actual utilization value of water resources. Background Technology

[0002] Traditional water-saving effect assessments typically focus only on the direct reduction in water consumption at water-using nodes, neglecting the complex systemic feedback effects that water-saving measures may trigger. For example, improved water use efficiency may stimulate increased water demand due to lower water costs, a phenomenon known as the "rebound effect." The operation of water-saving technologies (such as membrane treatment and high-efficiency pumps) may increase energy or chemical consumption, thereby indirectly increasing the consumption of "virtual water" or related resources, a phenomenon known as the "transfer effect." These effects may partially or even completely offset the direct water-saving benefits, leading to distorted assessment results and even causing the negative synergistic consequence of "water saving but increased energy consumption and emissions."

[0003] Existing assessment methods often rely on simple comparisons of water consumption data from water meters or employ Life Cycle Assessment (LCA) to categorize and quantify different resources into uniform dimensionless characteristic values ​​for comparative analysis of resource and environmental footprint. However, nonlinear feedback often stems from cross-media coupling effects. While the former method confines water-saving effects to the water meter and fails to capture nonlinear feedback, the latter, though capable of assessing multiple environmental impacts, typically doesn't deeply integrate dynamic behavioral feedback and economic mechanisms into the system model. Furthermore, standard LCA calculates environmental impacts through linear summation, making it difficult to decouple the various effects of water-saving interventions and to proactively, quantitatively, and dynamically assess the true net benefits and synergies of specific water-saving interventions within a particular water system. Therefore, there is an urgent need for a method that can systematically decouple the various effects of water-saving measures, scientifically assess their true water-saving contributions and system synergies, and guide the development of optimization strategies. Summary of the Invention

[0004] This invention provides a water resource regulation method and storage medium based on the assessment of the actual utilization value of water resources. In a first aspect, the present invention provides a water resource regulation method based on an assessment of the actual utilization value of water resources, comprising: The water-saving feedback model of all water-using nodes in the target area is calibrated using historical data from all water-using nodes over multiple consecutive periods. The direct water-saving effect, behavioral rebound effect, and correlation transfer effect of the calibrated water-saving feedback model were calculated under the intervention of water-saving measures. The actual water savings and synergistic deficit index of all water-using nodes are calculated based on the direct water-saving effect, behavioral rebound effect, and associated transfer effect. The water resource utilization of all water-using nodes in the target area is regulated with the goal of minimizing the collaborative deficit index of all water-using nodes and maximizing the actual water saving.

[0005] Optionally, calibrating the water-saving feedback model of all water-using nodes in the target area using historical data over multiple consecutive periods includes: The expression for constructing the set of water-using nodes V: ; in, Let I be the position vector of the I-th water-using node; Let be the attribute vector of the i-th water-using node; I is the total number of water-using nodes in the target area; The expression for constructing the physical path set E: ; Among them, e I-1,I This is the physical water flow path vector between the (I-1)th water-using node and the 1st water-using node; This is the attribute vector of the physical water flow path between the (I-1)th water-using node and the 1st water-using node; The expression for constructing the feedback link set F: F=[F behavior F transfer F reliability ]; Among them, F behavior This is a behavioral feedback loop used to quantify the change in water consumption at each water-using node due to changes in water usage costs; F transfer This is used to establish a resource transfer feedback chain to quantify the non-water resource consumption resulting from water-saving measures implemented at each water-using node; F reliability This serves as a model reliability feedback link, used to quantify the maintenance economic costs incurred by implementing water-saving measures at each water-using node; Construct the expression for the water-saving feedback model M based on the set of water-using nodes, the set of physical paths, and the set of feedback links: M = (V, E, F); Historical data from all water-using nodes over multiple consecutive periods are acquired to calibrate the water-saving feedback model.

[0006] Optionally, quantifying the change in water consumption at each water-using node due to changes in water usage costs includes: The change in water consumption at each water-using node due to changes in water usage costs at the target time is calculated using the following formula: ; in, Let be the change in water consumption of the i-th water-using node at time t due to changes in water usage costs; C represents the price elasticity of demand for the i-th water-using node; unit,i (t) represents the comprehensive water consumption cost of the i-th water-using node at time t after implementing water-saving measures; C unit,i,base D represents the comprehensive unit water consumption cost of the i-th water-using node under the baseline condition before implementing water-saving measures. i,base (t) represents the water consumption of the i-th water-using node at time t under the baseline state before the implementation of water-saving measures.

[0007] Optionally, quantifying the non-water resource consumption resulting from implementing water-saving measures at each water-using node includes: The consumption of the k-th associated resource at each water-using node due to water-saving measures at the target time is calculated using the following formula: ; in, Let be the amount of the k-th associated resource consumed as a result of implementing water-saving measures at the i-th water-using node at time t; Let t be the change in operating parameters of the i-th water-using node before and after the implementation of water-saving measures; for The conversion coefficient between the kth associated resource and the kth associated resource.

[0008] Optionally, the quantification of the maintenance economic costs incurred by implementing water-saving measures at each water-using node includes: The maintenance cost of implementing water-saving measures at each water-using node at the target time can be calculated using the following formula: ; in, The maintenance cost incurred by implementing water-saving measures at the i-th water-using node at time t; Let be the estimated failure rate of the i-th water-using node at time t; The estimated failure rate of the i-th water-using node under the baseline state before the implementation of water-saving measures; Let be the average repair time for the i-th water-using node; The labor cost incurred per unit of maintenance time; The mechanical cost incurred per unit of maintenance time; Let be the complexity impact coefficient of the i-th water-using node; This represents the percentage of complexity of the water-saving measures implemented at the i-th water-using node.

[0009] Optionally, the calculation of the direct water-saving effect, behavioral rebound effect, and correlation transfer effect of the calibrated water-saving feedback model under water-saving intervention includes: W baseand The difference is taken as the direct water-saving effect of the calibrated water-saving feedback model; where W base The baseline water consumption at all water-using nodes before the implementation of water-saving measures; This is the baseline water consumption of all water-using nodes under the condition of a rebound feedback link in the case of prohibited behavior after the implementation of water-saving measures; W sim and The difference is used as the behavioral rebound effect of the calibrated water-saving feedback model; where W sim This represents the water consumption at all water-using points after the implementation of water-saving measures. The correlation transfer effect of the calibrated water-saving feedback model is calculated using the following formula. : ; in, Let t be the change in the k-th associated resource caused by the operation of water-saving measures at the i-th water-using node at time t; I is the total number of water-using nodes in the target area; K is the total number of associated resources; and T' is the total simulation time of the water-saving feedback model.

[0010] Optionally, the step of calculating the actual water savings and synergistic deficit index of all water-using nodes based on direct water-saving effects, behavioral rebound effects, and associated transfer effects includes: The actual water saving W at all water usage nodes is calculated using the following formula. real : ; in, The direct water-saving effect of the calibrated water-saving feedback model; This is to describe the behavioral rebound effect of the calibrated water-saving feedback model; The correlation transfer effect of the calibrated water-saving feedback model; Indicates taking the absolute value; The collaborative deficit index for all water-using nodes is calculated using the following formula. : ; in, This is a negative benefit item; It is a positive benefit item; The shadow price of water resources; P k The price of the k-th associated resource; Costs or benefits resulting from changes in the reliability of water usage nodes.

[0011] Optionally, the regulation of water resource utilization at all water-using nodes in the target area, with the optimization objective of minimizing the collaborative deficit index of all water-using nodes and maximizing the actual water saving, includes: Construct the expression for the first objective function f(x): ; in, The collaborative deficit index for all water-using nodes; ; The synergy-water-saving trade-off coefficient; The actual water savings of all water-using nodes under the decision variable x; ; The shadow price of water resources; S L R represents the subsidy amount for water-saving equipment of category L; L represents the total number of water-saving equipment categories; R represents the operating parameters of the water-saving equipment; T represents matrix transpose; W min W represents the minimum actual water savings across all water usage points during the historical statistical period. max This represents the maximum actual water savings across all water usage nodes during the historical statistical period. Genetic optimization algorithm is used to generate a set of candidate values ​​[x1, x2, ..., x] for decision variable x. m ,…,x y ];x y Let x be the y-th candidate value of the decision variable; y is the total number of candidate values; ; The shadow price of water resources in the m-th candidate value; R represents the subsidy amount for the L-th type of water-saving equipment in the m-th candidate value; m The operating parameters of the water-saving equipment in the m-th candidate value; The decision variable corresponding to the candidate value that satisfies the constraints and minimizes the first objective function f(x) is taken as the optimal control strategy; where the constraints satisfy: ; in, This is the lower limit for water prices; This is the upper limit for water prices; For the first Subsidy amount for water-saving equipment; S total For the total subsidy budget; R min R represents the lower limit of water-saving equipment performance. max This represents the upper limit of the performance of water-saving equipment.

[0012] Optionally, the method of regulating water resource utilization at all water-using nodes in the target area with the optimization objectives of minimizing the collaborative deficit index of all water-using nodes and maximizing the actual water saving also includes: Construct a penalty function The expression: ; in, Let be the first objective function for the m-th candidate value of the decision variable x; μ is the penalty coefficient. The decision variable corresponding to the candidate value that satisfies the constraints and minimizes the second objective function among all candidate values ​​is taken as the optimal control strategy; where the penalty function is taken as the second objective function.

[0013] In a second aspect, the present invention provides a computer-readable storage medium for storing a computer program; when the computer program is executed by a processor, it implements the steps of the water resource regulation method based on the actual utilization value assessment of water resources as described in the first aspect.

[0014] This invention provides a water resource regulation method and storage medium based on the assessment of the actual utilization value of water resources. Existing methods typically only focus on the reduction of direct water consumption, making it difficult to quantify the "behavioral rebound effect" caused by changes in water costs, and also difficult to assess the "transfer effect" of water-saving measures on related resources (such as energy and chemicals). This invention, by constructing and calibrating a water-saving feedback model, introduces behavioral rebound feedback links and related resource transfer feedback links, which can simulate the nonlinear feedback process triggered by water-saving measures to a certain extent. Based on this, calculating the behavioral rebound effect and related transfer effect separately helps to overcome the shortcomings of traditional methods that only assess direct water-saving effects, providing a feasible quantitative path for systematically assessing the comprehensive impact of water-saving measures.

[0015] Existing assessment methods may overestimate water-saving benefits by ignoring implicit feedback, or even lead to negative synergistic consequences such as "water saving but increased energy consumption and emissions." This invention, after obtaining the three effects, further calculates the actual water savings (the absolute value of the direct water-saving effect minus the behavioral rebound effect and the associated transfer effect) and the synergistic deficit index (quantifying the net value of the comprehensive positive and negative benefits brought by water-saving measures). These two indicators assess the actual effectiveness of water-saving measures from the perspectives of "water saving" and "system synergistic benefits," respectively, helping to more objectively determine whether water-saving measures involve implicit resource consumption or cross-media burdens, and reducing the risk of assessment distortion.

[0016] Existing methods struggle to provide forward-looking, dynamic optimization guidance for water-saving interventions. This invention uses the minimization of the collaborative deficit index and the maximization of actual water savings as dual optimization objectives to uniformly regulate water resource utilization at water-using nodes within a target area. This optimization mechanism provides a reference framework for water-saving strategy formulation based on system feedback models and multi-objective optimization, helping to predict the potential effects of different regulation schemes during the planning stage and improving the scientific rigor and adaptability of water-saving strategy formulation. Attached Figure Description

[0017] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a water resource regulation method based on the assessment of the actual utilization value of water resources, provided as an embodiment of the present invention; Figure 2 This is a schematic diagram of the structural model of a target area water resource utilization system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a water resource regulation system based on the assessment of the actual utilization value of water resources, provided as an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1 like Figure 1 As shown, this embodiment provides a water resource regulation method based on the assessment of the actual utilization value of water resources, including: Step 101: Use historical data from all water-using nodes in the target area over multiple consecutive periods to calibrate the water-saving feedback model for all water-using nodes.

[0021] In this step, the structural model of the water resource utilization system in the target area is as follows: Figure 2 As shown, the water usage node structure in the target area is chain-like, with the outlet nodes connecting to the river outside the city. Data acquisition devices C3 are installed on each water usage node C1 and the physical path C2. Water usage node C1 includes water sources, water users, water purification facilities, and discharge outlets. The lines between water usage nodes form the physical path C2 for water resource transmission. Furthermore, the data acquisition device C3 integrates a data transmission module C4, primarily used for real-time collection and monitoring of data at the water usage nodes, including capacity, operating status (including energy consumption, power, temperature, flow rate, etc.), economic parameters (including water price, electricity price, water purification cost, etc.), and the transmission capacity, transmission efficiency, and economic parameters of the physical path. The collected monitoring data is then transmitted to the data analysis module C5 via the data transmission module C4.

[0022] The data analysis module C5 also communicates with the data center. Based on the received monitoring data, the data analysis module C5 performs calculations and analyses on the monitoring data, and the data center stores the data and dynamically schedules the water resource utilization optimization and control scheme for water-using node C1.

[0023] For example, construct the expression for the set of water-using nodes V: .

[0024] in, Let be the location vector of the I-th water-using node; the location vector contains three data points: longitude, latitude, and altitude of the I-th water-using node. Let be the attribute vector of the i-th water-using node. The attributes of the water-using node include capacity, operating status, economic parameters, etc.; I is the total number of water-using nodes in the target area.

[0025] The expression for constructing the physical path set E: .

[0026] Among them, e I-1,I This is the physical water flow path vector between the (I-1)th water-using node and the 1st water-using node; This is the attribute vector of the physical water flow path between the (I-1)th water user node and the Ith water user node. The attributes of the physical water flow path include transmission capacity, transmission efficiency, economic parameters, etc.

[0027] The expression for constructing the feedback link set F: F=[F behavior F transfer F reliability ].

[0028] Among them, F behavior This is a behavioral feedback loop used to quantify the change in water consumption at each water-using node due to changes in water usage costs; F transfer This is used to establish a resource transfer feedback chain to quantify the non-water resource consumption resulting from water-saving measures implemented at each water-using node; F reliability This serves as a model reliability feedback link, used to quantify the maintenance economic costs incurred by implementing water-saving measures at each water-using node.

[0029] Specifically, the change in water consumption at each water-using node due to changes in water usage costs is calculated using the following formula at the target time: .

[0030] in, η represents the change in water consumption at time t due to changes in water usage costs at the i-th water-using node; iLet η be the price elasticity of demand for the i-th water-using node. Price elasticity of demand is an economic indicator that measures the responsiveness of quantity demanded to changes in price. It is obtained by calibration using historical data, where η i <0, η i The absolute value represents the magnitude of the rebound strength; C unit,i (t) represents the comprehensive water consumption cost of the i-th water-using node at time t after implementing water-saving measures; C unit,i,base D represents the comprehensive unit water consumption cost of the i-th water-using node under the baseline condition before implementing water-saving measures. i,base (t) represents the water consumption of the i-th water-using node at time t under the baseline state before the implementation of water-saving measures.

[0031] The consumption of the k-th associated resource at each water-using node due to water-saving measures at the target time is calculated using the following formula: .

[0032] in, Let t be the amount of the k-th associated resource consumed as a result of implementing water-saving measures at the i-th water-using node, where associated resources include electricity, water purification agents, etc. Let t be the change in operating parameters (such as energy consumption, durability, working pressure, etc.) of the i-th water-using node before and after the implementation of water-saving measures; for The conversion coefficient between the kth associated resource and the water-saving equipment is determined by the performance curve of the water-saving equipment.

[0033] The maintenance cost of implementing water-saving measures at each water-using node at the target time can be calculated using the following formula: .

[0034] in, The maintenance cost incurred by implementing water-saving measures at the i-th water-using node at time t; Let be the estimated failure rate of the i-th water-using node at time t; The estimated failure rate of the i-th water-using node under the baseline state before the implementation of water-saving measures; Let be the average repair time for the i-th water-using node; The labor cost incurred per unit of maintenance time; The mechanical costs incurred per unit of time for maintenance (including the energy consumption costs of maintenance machinery, replacement costs of spare parts and consumable parts, equipment costs such as maintenance equipment rental fees). γ is the complexity impact coefficient of the i-th water-using node. i >0; The percentage of complexity (complexity index) of the water-saving measures implemented for the i-th water-using node.

[0035] The expression for the water-saving feedback model M (of the water resource utilization system) is constructed based on the set of water-using nodes, the set of physical paths, and the set of feedback links: M = (V, E, F).

[0036] Historical data from all water-using nodes over multiple consecutive periods are acquired to calibrate the water-saving feedback model.

[0037] Specifically, historical data on water consumption, water quality, resource consumption, economic output, and water price at water-using nodes are collected over multiple consecutive periods to form a calibration dataset D. base ; where D base The expression is as follows: .

[0038] Among them, Q h Z represents the historical water consumption sequence of water usage nodes. h Water quality sequence of historical water use nodes; W h This is a historical sequence of resource consumption at water usage nodes; E h For historical economic output series; J h This is historical water price data.

[0039] The water-saving feedback model was calibrated using the maximum likelihood estimation algorithm. The first 80 percent of the calibration dataset was used as the training set, and the last 20 percent of the calibration dataset was used as the validation set.

[0040] The maximum likelihood estimation method is used to calibrate the parameters of the training set to obtain the complexity influence coefficient γ of the i-th water-using node. i The complexity index CI of implementing water-saving measures at the i-th water-using node i and the price elasticity of demand η for the i-th water-using node i .

[0041] Specifically, with η i For example, based on economic experience, η i Constructing an empirical linear demand model based on water price: lnQ i,t =β i +η i lnJ i,t .

[0042] Among them, Q i,t J represents the water consumption of the i-th water-using node at time t; i,t Let β be the water price of the i-th water-using node at time t; i This is the intercept term for the i-th water-using node, reflecting the influence of factors other than water price.

[0043] Constructing the likelihood function yields: .

[0044] Where T' is the total duration of the training set sequence and the total simulation duration of the water-saving feedback model.

[0045] The η that maximizes the likelihood function is obtained using numerical analytical methods. i That is, the price elasticity of demand coefficient η for the i-th water-using node. i .

[0046] The above parameters are input into the water-saving feedback model of the water resource utilization system. The water-saving feedback model is run using the validation set. The simulation results are compared with the measured results. The Nash efficiency coefficient NSE between the simulation results and the measured results is calculated. The closer the Nash efficiency coefficient NSE is to 1, the better the simulation results are.

[0047] If the Nash coefficient NSE ≥ 0.87, the simulation effect is deemed satisfactory, and the calibrated water-saving feedback model is obtained; if NSE < 0.87, the simulation result is deemed unsatisfactory, and the empirical model of the maximum likelihood estimation algorithm (such as the Weibull regression empirical model, the multiple linear empirical model, etc.) is modified and the solution is repeated until the Nash coefficient NSE ≥ 0.87.

[0048] Step 102: Calculate the direct water-saving effect, behavioral rebound effect, and correlation transfer effect of the calibrated water-saving feedback model under the intervention of water-saving measures.

[0049] For example, W base and The difference is taken as the direct water-saving effect of the calibrated water-saving feedback model, that is, the behavioral bounce feedback link is disabled in the water-saving feedback model, and only the water-saving effect directly caused by the water-saving measures is calculated to obtain the direct water-saving effect; where W base The baseline water consumption at all water-using nodes before the implementation of water-saving measures; This is the baseline water consumption of all water-using nodes under the condition of a rebound feedback link in the event of prohibited behavior after the implementation of water-saving measures.

[0050] W sim and The difference is taken as the behavioral rebound effect of the calibrated water-saving feedback model, that is, the simulation results of enabling and disabling the behavioral rebound feedback link to obtain the behavioral rebound effect caused by the change in water cost at the water-using node; where W sim This represents the water consumption at all water-using nodes after the implementation of water-saving measures.

[0051] The correlation transfer effect of the calibrated water-saving feedback model is calculated using the following formula. : .

[0052] in, Let t be the change in the k-th related resource (disinfectant cost, water-saving management labor cost, water-saving equipment installation cost, etc.) caused by the operation of water-saving measures at the i-th water-using node at time t; I is the total number of water-using nodes in the target area; K is the total number of related resources; and T' is the total simulation time of the water-saving feedback model.

[0053] Step 103: Calculate the actual water saving amount and collaborative deficit index of all water-using nodes based on the direct water-saving effect, behavioral rebound effect, and associated transfer effect.

[0054] For example, the actual water saving W at all water usage nodes is calculated according to the following formula. real : .

[0055] in, The direct water-saving effect of the calibrated water-saving feedback model; This is to describe the behavioral rebound effect of the calibrated water-saving feedback model; The correlation transfer effect of the calibrated water-saving feedback model; This indicates taking the absolute value.

[0056] The collaborative deficit index for all water-using nodes is calculated using the following formula. : .

[0057] in, This is a negative benefit item; It is a positive benefit item; The shadow price of water resources; P k The price of the k-th associated resource; Costs or benefits resulting from changes in the reliability of water usage nodes.

[0058] Based on actual water saving W real With the collaborative deficit index A water resources utilization assessment system will be established, and the specific assessment system is as follows: Establish a multi-dimensional synergistic benefit judgment criterion, and classify the systemic impact level of water-saving behaviors by using a systemic synergistic deficit index threshold, so as to achieve quantitative diagnosis and graded evaluation of the real utility and systemic synergistic effect of water-saving behaviors: When the collaborative deficit index >0 and actual water saving W real When the value is greater than 0, it is determined that the water-saving measures are effective but there is a deficit in the overall water-saving measures. The associated resource consumption of the water-saving measures is too large, the overall water-saving measures have a negative effect, and the overall resource utilization efficiency is lost.

[0059] When the collaborative deficit index >0 and actual water saving W real When the value is less than 0, it is determined that the water-saving measures are ineffective and there is a deficit in water conservation efforts. The water-saving measures have hidden excessive water consumption, resulting in a negative overall effect and an overall loss of resource utilization efficiency.

[0060] When the collaborative deficit index <0 and actual water saving W real When the value is greater than 0, it is determined to be a state of effective synergistic surplus of water-saving measures, indicating the comprehensive positive benefits of water-saving measures and an overall improvement in resource utilization efficiency.

[0061] When the collaborative deficit index <0 and actual water saving W real When the value is less than 0, it is judged as an ineffective water-saving measure but a state of synergistic surplus. The water-saving measures have hidden water resource consumption that is too large, but the overall benefits of the water-saving measures are positive, and the overall resource utilization efficiency is improved.

[0062] Step 104: Regulate water resource utilization at all water-using nodes in the target area with the optimization objectives of minimizing the collaborative deficit index of all water-using nodes and maximizing the actual water saving.

[0063] This step, based on the composition analysis of the collaborative deficit index, identifies the dominant sources of negative effects. With the optimization objectives of minimizing the collaborative deficit index and maximizing actual water savings within an optimization cycle, and using water prices, subsidies, and operating parameters of water-saving equipment as decision variables, a water resource utilization optimization model is constructed. A genetic optimization algorithm is then used to solve for and generate the optimal control strategy.

[0064] For example, the expression for the first objective function f(x) is constructed as follows: .

[0065] in, The collaborative deficit index for all water-using nodes; ; The synergy-water-saving trade-off coefficient characterizes the relative importance of the two objectives (minimizing the synergy deficit index of all water-using nodes and maximizing the actual water saving), and is determined by the regional water resources management stage. The actual water savings of all water-using nodes under the decision variable x; ; The shadow price of water resources; S L R represents the subsidy amount for water-saving equipment of category L; L represents the total number of water-saving equipment categories; R represents the operating parameters of the water-saving equipment; T represents matrix transpose; W min W represents the minimum actual water savings across all water usage points during the historical statistical period. maxThis represents the maximum actual water savings across all water-using nodes during the historical statistical period.

[0066] Genetic optimization algorithm is used to generate a set of candidate values ​​[x1, x2, ..., x] for decision variable x. m ,…,x y ];x y Let x be the y-th candidate value of the decision variable x; y is the total number of candidate values ​​(50-80 in this example). ; The shadow price of water resources in the m-th candidate value; R represents the subsidy amount for the L-th type of water-saving equipment in the m-th candidate value; m The m-th candidate value represents the operating parameters of the water-saving equipment.

[0067] The decision variable corresponding to the candidate value that satisfies the constraints and minimizes the first objective function f(x) is taken as the optimal control strategy; where the constraints (from top to bottom are water price boundary constraints, subsidy budget constraints, and equipment performance constraints) satisfy: .

[0068] in, This is the lower limit for water prices; This is the upper limit for water prices; For the first Subsidy amount for water-saving equipment; S total For the total subsidy budget; R min R represents the lower limit of water-saving equipment performance. max This represents the upper limit of the performance of water-saving equipment.

[0069] The optimal control strategy is compared with the constraint boundary, the degree of constraint violation is calculated, and, for example, a penalty function is constructed. The expression: .

[0070] in, Let x be the first objective function for the m-th candidate value of the decision variable x; μ is the penalty coefficient; max(0,P) water,min P water,m ), max(0,P water,m P water,max ), max(0,R) min R m ) and max(0,R m R max The values ​​represent the degree of violation of each constraint. If no constraint is violated, the corresponding value is 0.

[0071] In this embodiment, μ=100, strictly limiting the water price in the candidate values ​​to the upper and lower limits of the water price, the total amount of subsidies to the budget, and the equipment parameters to the technical performance range, so as to avoid generating infeasible control strategies.

[0072] Use penalty function The operation is used as a new objective function (second objective function) and returned to generate the optimal control strategy until the preset number of updates is reached.

[0073] After repeated iterations, the optimal decision variable x that satisfies the constraints and minimizes the composite objective function is found. best The corresponding minimum collaborative deficit index and maximum actual water saving amount are the optimal water price P. water,best Optimal subsidy plan With optimal equipment operating parameters R best .

[0074] Based on the optimal combination of decision variables, and compared with the actual operating parameters monitored in real time at water-using nodes, water resource utilization is regulated in real time. The optimal water price P water,best Optimal subsidy plan With optimal equipment parameters R best Compared with the actual water price P monitored in real time actual Actual subsidy disbursement S actual and actual equipment operating status R actual Comparison: If P actual >P water,best If the water price is too high, a control command will be generated to appropriately reduce the actual water price and strictly control the water price below the optimal water price to prevent the behavioral rebound effect caused by excessively high water prices from expanding.

[0075] If P actual <P water,best If this is done, a control command will be generated to appropriately increase the actual water price, ensuring that the water-saving incentive reaches the optimal level and increasing the actual water saving.

[0076] If S actual > If the actual subsidies exceed the optimal budget, a control instruction is generated to adjust the subsidy distribution structure, prioritizing subsidies for high water-saving equipment and reducing inefficient subsidy items.

[0077] If S actual < If the actual subsidies do not reach the optimal budget, control instructions will be generated to optimize the subsidy distribution strategy, including: increasing the subsidy standards for high water-saving equipment, expanding the coverage of subsidies for water-saving equipment or technologies, or extending the subsidy period, so as to give full play to the water-saving incentive role of fiscal funds.

[0078] If R actual <R best If the actual operating efficiency of the water-saving equipment is lower than the optimal value, then control commands are generated to optimize the equipment operation scheduling, including: adjusting equipment load distribution, optimizing operating periods, or activating auxiliary facilities, so that the equipment operating efficiency gradually approaches the optimal parameter R. best .

[0079] If R actual >R best If the actual operating efficiency of the water-using equipment is higher than the optimal value, then two cases should be handled: if R actual ≤R max If the actual water saving meets the standard, then maintain the current operating status; if R actual >R max If excessive operation leads to a surge in energy costs and accelerated equipment wear, control commands will be generated to appropriately reduce the equipment's operating intensity and restore the operating parameters to the optimal range. best ,R max Within this scope, we must avoid the expansion of the related resource transfer effect caused by excessive operation.

[0080] If the real-time monitored collaborative deficit index exceeds the preset threshold or the actual water saving is lower than the guaranteed threshold, the re-optimization process is triggered. The water resource utilization system readjusts the water price and subsidy combination based on the current water use structure, water price response coefficient and related resource price data, and dynamically optimizes water resource allocation.

[0081] The water resource utilization system repeats the "monitoring-simulation-optimization-control" process every 10 minutes to achieve rolling optimization and real-time regulation of water resource utilization. Use the sliding window method to create an initialization window and set the window size to 10 minutes.

[0082] The window is placed within the monitoring sequence of the water resource utilization system (including water consumption, water price response, equipment status, and collaborative deficit index data), and a bi-objective composite nonlinear programming model is used to solve the problem based on the data within the window.

[0083] When new data is added to the monitoring sequence of the water resource utilization system, the new data is placed at the end of the window, and the old data at the front of the window is removed. The collaborative deficit index and the actual water saving are recalculated for the data in the window. If the collaborative deficit index continues to deviate from the optimal value or the actual water saving fluctuates beyond the allowable range, the genetic optimization algorithm is used again, and the objective function is calculated according to constraints such as water price boundary, subsidy budget, and equipment performance to obtain the updated collaborative deficit index and the actual water saving, so as to update the optimal combination of decision variables and thus achieve rolling real-time updates.

[0084] To make the solution in this embodiment clearer, specific examples are further disclosed in this embodiment: The target area includes the following water-using nodes: Water sources: groundwater wells (G1), reservoirs (S1); Water purification facilities: Waterworks (W1), Wastewater treatment plant (T1); Water users: Industrial area (I1), Agricultural area (A1), Residential area (R1); Discharge point: River discharge point (O1); The set of water-using nodes is shown in Table 1: Table 1. Information on the set of water-using nodes

[0085] The physical path set is shown in Table 2: Table 2 Physical Path Set Information Table

[0086] The price elasticity coefficient of demand in the industrial zone was obtained after calibration using a water-saving feedback model. I1 =-0.32; Price elasticity of demand in residential areas η R1 =-0.19; Price elasticity of demand coefficient η in agricultural areas A1 =-0.12; Complexity Index (CI) of Water Conservation Measures in Industrial Zones I1 =2.6; Complexity index γ of water-saving measures in industrial areas I1 =0.0021; Complexity Index (CI) of Water Conservation Measures in Residential Areas R1 =1.7; Complexity index γ of water-saving measures in residential areas R1 =0.0032; Complexity Index (CI) of Water Conservation Measures in Industrial Zones A1 =2.2; Complexity index γ of water-saving measures in industrial areas A1 =0.0019.

[0087] After implementing water-saving measures: The industrial park will promote a high-efficiency circulating water system (investment of 3 million yuan). tiered water pricing will be implemented in residential areas (the base water price will be increased by 10%). Drip irrigation technology will be promoted in agricultural areas (coverage area increased by 30%). Run the model to obtain simulation results W base =1.65 million tons; =1.48 million tons; W sim =1.52 million tons; The direct water-saving effect ΔW was calculated. direct =165-148=170,000 tons; Behavioral rebound effect ΔW rebound =152-148=40,000 tons; Related transfer effect ΔW transfer =1200 MWh of electricity × (2.5 × 10 -4 (10,000 tons / MWh) + 800 kg of reagent × (2.5 × 10) -4 (10,000 tons / kg) = 5,000 tons. Of which 2.5 × 10 -4 The power conversion factor is 10,000 tons / MWh; 2.5×10 -4 The conversion factor is 10,000 tons / kg.

[0088] The actual water saving W was calculated. real =17-4-0.5=125,000 tons.

[0089] The shadow water price P was collected. water =5 yuan / ton, electricity price P 电力 =0.6 yuan / kWh, reagent price P 药剂 =10 yuan / kg; = -20,000 yuan (a negative value indicates increased maintenance costs).

[0090] Further calculation of the collaborative deficit index yields: ΔV positive =850,000 yuan; ΔV negative =168,000 yuan; ΔV net =(16.8 / 85)-1=-0.802; ΔV net <0 and W real A value greater than 0 indicates "effective water-saving measures with a synergistic surplus," signifying a comprehensive positive benefit from water-saving measures and an overall improvement in resource utilization efficiency.

[0091] In summary, this embodiment provides a water resource regulation method based on the assessment of the actual utilization value of water resources. By calibrating the water-saving feedback model using historical data of all water-using nodes in the target area over multiple consecutive periods, and calculating the direct water-saving effect, behavioral rebound effect, and correlation transfer effect under the intervention of water-saving measures, the actual water-saving volume and collaborative deficit index of all water-using nodes are calculated based on these three effects. Finally, water resource utilization is regulated with the minimum collaborative deficit index and the maximum actual water-saving volume as the optimization objectives. This overcomes or alleviates to some extent the problems in the prior art, such as the difficulty in quantifying the behavioral rebound effect and correlation transfer effect caused by water-saving measures, the tendency to overestimate the net water-saving benefits, and the lack of forward-looking dynamic optimization guidance.

[0092] Building upon this foundation, this embodiment constructs a water-saving feedback model that includes a set of water-using nodes, a set of physical paths, and three types of feedback links: behavioral rebound, correlation transfer, and reliability. This provides a clear structured expression for the model, improving its interpretability and calibration accuracy. By introducing a demand price elasticity coefficient to quantify the behavioral rebound effect, the reverse stimulus effect of changes in water costs on water resource consumption can be predicted more reasonably. By calculating the associated resource consumption through changes in operating parameters and conversion coefficients, the indirect impact of water-saving measures on non-water resources such as electricity and chemicals can be quantified to some extent. By introducing failure rate, mean time to repair, complexity impact coefficient, and complexity index, the maintenance economic cost generated by water-saving measures is quantified, which helps to more comprehensively assess the long-term economic efficiency and reliability of operation.

[0093] Furthermore, this embodiment provides quantifiable input parameters for calculating the actual water saving volume and the collaborative deficit index by separately calculating the direct water-saving effect, the behavioral rebound effect, and the correlation transfer effect. It also incorporates water conservation and system synergy benefits into a unified evaluation framework, providing quantitative criteria for the synergistic state of water-saving behavior (such as synergistic surplus or synergistic deficit). Further, this embodiment constructs a bi-objective optimization model with water price, subsidies, and water-saving equipment operating parameters as decision variables, and combines a penalty function with a genetic optimization algorithm. This allows for seeking a feasible balance strategy between maximizing actual water saving volume and minimizing the collaborative deficit index, while avoiding the generation of infeasible control schemes.

[0094] Example 2 Based on the same inventive concept as Embodiment 1, this embodiment also provides a water resource regulation system based on the assessment of the actual utilization value of water resources. Since the principle of this system in solving problems is similar to the aforementioned water resource regulation method based on the assessment of the actual utilization value of water resources, the implementation of this system can refer to the implementation of the water resource regulation method based on the assessment of the actual utilization value of water resources.

[0095] like Figure 3As shown, a water resource regulation system based on the assessment of the actual utilization value of water resources includes: The calibration module 10 is used to calibrate the water-saving feedback model of all water-using nodes in the target area using historical data from all water-using nodes over multiple consecutive periods.

[0096] The first calculation module 20 is used to calculate the direct water-saving effect, behavioral rebound effect, and correlation transfer effect of the calibrated water-saving feedback model under the intervention of water-saving measures.

[0097] The second calculation module 30 is used to calculate the actual water saving and collaborative deficit index of all water-using nodes based on the direct water-saving effect, behavioral rebound effect and related transfer effect.

[0098] The optimization and control module 40 is used to regulate the water resource utilization of all water-using nodes in the target area with the optimization objectives of minimizing the collaborative deficit index of all water-using nodes and maximizing the actual water saving.

[0099] For more detailed information on the working process of each of the above modules, please refer to the relevant content disclosed in Example 1, which will not be repeated here.

[0100] Example 3 This embodiment provides a computer device, including a processor and a memory; wherein, when the processor executes the computer program stored in the memory, it implements the steps of the water resource regulation method based on the actual utilization value assessment of water resources as described in Embodiment 1.

[0101] For a more detailed explanation of the above method, please refer to the relevant content disclosed in Example 1, which will not be repeated here.

[0102] Example 4 This embodiment provides a computer-readable storage medium for storing a computer program; when the computer program is executed by a processor, it implements the steps of the water resource regulation method based on the actual utilization value assessment of water resources described in Embodiment 1.

[0103] For a more detailed explanation of the above method, please refer to the relevant content disclosed in Example 1, which will not be repeated here.

[0104] Example 5 This embodiment provides a computer program product, including computer-executable instructions or computer programs. When the computer-executable instructions or computer programs are executed by a processor, they implement the steps of the water resource regulation method based on the actual utilization value assessment of water resources described in Embodiment 1.

[0105] For a more detailed explanation of the above method, please refer to the relevant content disclosed in Example 1, which will not be repeated here.

[0106] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems, devices, storage media, and computer program products disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and relevant parts can be referred to the method section.

[0107] Those skilled in the art will clearly understand that the techniques in the embodiments of the present invention can be implemented using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a 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 various embodiments or certain parts of the embodiments of the present invention.

[0108] In some embodiments, computer-executable instructions may take the form of programs, software, software modules, scripts, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as stand-alone programs or as modules, components, subroutines, or other units suitable for use in a computing environment.

[0109] As an example, computer-executable instructions may, but do not necessarily, correspond to files in a file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple co-located files (e.g., files that store one or more modules, subroutines, or code sections).

[0110] As an example, computer-executable instructions can be deployed to execute on a single electronic device, or on multiple electronic devices located at one location, or on multiple electronic devices distributed across multiple locations and interconnected via a communication network.

[0111] The present invention has been described in detail above with reference to specific embodiments and exemplary examples; however, these descriptions should not be construed as limiting the present invention. Those skilled in the art will understand that various equivalent substitutions, modifications, or improvements can be made to the technical solutions and embodiments of the present invention without departing from the spirit and scope of the invention, and all such modifications and improvements fall within the scope of the present invention. The scope of protection of the present invention is defined by the appended claims.

[0112] The present invention has been described in detail above with reference to specific embodiments and exemplary examples; however, these descriptions should not be construed as limiting the present invention. Those skilled in the art will understand that various equivalent substitutions, modifications, or improvements can be made to the technical solutions and embodiments of the present invention without departing from the spirit and scope of the invention, and all such modifications and improvements fall within the scope of the present invention. The scope of protection of the present invention is defined by the appended claims.

Claims

1. A water resource regulation method based on the assessment of the actual utilization value of water resources, characterized in that, include: The water-saving feedback model of all water-using nodes in the target area is calibrated using historical data from all water-using nodes over multiple consecutive periods. The direct water-saving effect, behavioral rebound effect, and correlation transfer effect of the calibrated water-saving feedback model were calculated under the intervention of water-saving measures. The actual water savings and synergistic deficit index of all water-using nodes are calculated based on the direct water-saving effect, behavioral rebound effect, and associated transfer effect. The water resource utilization of all water-using nodes in the target area is regulated with the goal of minimizing the collaborative deficit index of all water-using nodes and maximizing the actual water saving.

2. The water resource regulation method according to claim 1, characterized in that, The process of calibrating the water-saving feedback model for all water-using nodes in the target area using historical data from all water-using nodes over multiple consecutive periods includes: The expression for constructing the set of water-using nodes V: ； in, Let I be the position vector of the I-th water-using node; Let be the attribute vector of the i-th water-using node; I is the total number of water-using nodes in the target area; The expression for constructing the physical path set E: ； Among them, e I-1,I This is the physical water flow path vector between the (I-1)th water-using node and the 1st water-using node; This is the attribute vector of the physical water flow path between the (I-1)th water-using node and the 1st water-using node; The expression for constructing the feedback link set F: F=[F behavior ,F transfer ,F reliability ]; Among them, F behavior This is a behavioral feedback loop used to quantify the change in water consumption at each water-using node due to changes in water usage costs; F transfer This is used to establish a resource transfer feedback chain to quantify the non-water resource consumption resulting from water-saving measures implemented at each water-using node; F reliability This serves as a model reliability feedback link, used to quantify the maintenance economic costs incurred by implementing water-saving measures at each water-using node; Construct the expression for the water-saving feedback model M based on the set of water-using nodes, the set of physical paths, and the set of feedback links: M = (V, E, F); Historical data from all water-using nodes over multiple consecutive periods are acquired to calibrate the water-saving feedback model.

3. The water resource regulation method according to claim 2, characterized in that, The quantification of the change in water consumption at each water-using node due to changes in water usage costs includes: The change in water consumption at each water-using node due to changes in water usage costs at the target time is calculated using the following formula: ； in, Let be the change in water consumption of the i-th water-using node at time t due to changes in water usage costs; C represents the price elasticity of demand for the i-th water-using node; unit,i (t) represents the comprehensive water consumption cost of the i-th water-using node at time t after implementing water-saving measures; C unit,i,base D represents the comprehensive unit water consumption cost of the i-th water-using node under the baseline condition before implementing water-saving measures. i,base (t) represents the water consumption of the i-th water-using node at time t under the baseline state before the implementation of water-saving measures.

4. The water resource regulation method according to claim 2, characterized in that, The quantification of non-water resource consumption resulting from implementing water-saving measures at each water-using node includes: The consumption of the k-th associated resource at each water-using node due to water-saving measures at the target time is calculated using the following formula: ； in, Let be the amount of the k-th associated resource consumed as a result of implementing water-saving measures at the i-th water-using node at time t; Let t be the change in operating parameters of the i-th water-using node before and after the implementation of water-saving measures; for The conversion coefficient between the kth associated resource and the kth associated resource.

5. The water resource regulation method according to claim 2, characterized in that, The quantitative maintenance costs incurred by implementing water-saving measures at each water-using node include: The maintenance cost of implementing water-saving measures at each water-using node at the target time can be calculated using the following formula: ； in, The maintenance cost incurred by implementing water-saving measures at the i-th water-using node at time t; Let be the estimated failure rate of the i-th water-using node at time t; The estimated failure rate of the i-th water-using node under the baseline state before the implementation of water-saving measures; Let be the average repair time for the i-th water-using node; The labor cost incurred per unit of maintenance time; The mechanical cost incurred per unit of maintenance time; Let be the complexity impact coefficient of the i-th water-using node; This represents the percentage of complexity of the water-saving measures implemented at the i-th water-using node.

6. The water resource regulation method according to claim 1, characterized in that, The direct water-saving effect, behavioral rebound effect, and correlation transfer effect of the calibrated water-saving feedback model calculated under water-saving intervention measures include: W base and The difference is taken as the direct water-saving effect of the calibrated water-saving feedback model; where W base The baseline water consumption at all water-using nodes before the implementation of water-saving measures; This is the baseline water consumption of all water-using nodes under the condition of a rebound feedback link in the case of prohibited behavior after the implementation of water-saving measures; W sim and The difference is used as the behavioral rebound effect of the calibrated water-saving feedback model; where W sim This represents the water consumption at all water-using points after the implementation of water-saving measures. The correlation transfer effect of the calibrated water-saving feedback model is calculated using the following formula. : ； in, Let t be the change in the k-th associated resource caused by the operation of water-saving measures at the i-th water-using node at time t; I is the total number of water-using nodes in the target area; K is the total number of associated resources; and T' is the total simulation time of the water-saving feedback model.

7. The water resource regulation method according to claim 1, characterized in that, The calculation of the actual water savings and synergistic deficit index for all water-using nodes based on direct water-saving effects, behavioral rebound effects, and associated transfer effects includes: The actual water saving W at all water usage nodes is calculated using the following formula. real : ； in, The direct water-saving effect of the calibrated water-saving feedback model; This is to describe the behavioral rebound effect of the calibrated water-saving feedback model; The correlation transfer effect of the calibrated water-saving feedback model; Indicates taking the absolute value; The collaborative deficit index for all water-using nodes is calculated using the following formula. : ； in, This is a negative benefit item; It is a positive benefit item; The shadow price of water resources; P k The price of the k-th associated resource; Costs or benefits resulting from changes in the reliability of water usage nodes.

8. The water resource regulation method according to claim 1, characterized in that, The regulation of water resource utilization at all water-using nodes in the target area, with the optimization objectives of minimizing the collaborative deficit index and maximizing the actual water saving, includes: Construct the expression for the first objective function f(x): ； in, The collaborative deficit index for all water-using nodes; ; The synergy-water-saving trade-off coefficient; The actual water savings of all water-using nodes under the decision variable x; ; The shadow price of water resources; S L R represents the subsidy amount for water-saving equipment of category L; L represents the total number of water-saving equipment categories; R represents the operating parameters of the water-saving equipment; T represents matrix transpose; W min W represents the minimum actual water savings across all water usage points during the historical statistical period. max This represents the maximum actual water savings across all water usage nodes during the historical statistical period. Genetic optimization algorithm is used to generate a set of candidate values ​​[x1, x2, ..., x] for decision variable x. m ,…,x y ];x y Let x be the y-th candidate value of the decision variable; y is the total number of candidate values; ; The shadow price of water resources in the m-th candidate value; R represents the subsidy amount for the L-th type of water-saving equipment in the m-th candidate value; m The operating parameters of the water-saving equipment in the m-th candidate value; The decision variable corresponding to the candidate value that satisfies the constraints and minimizes the first objective function f(x) is taken as the optimal control strategy; where the constraints satisfy: ； in, This is the lower limit for water prices; This is the upper limit for water prices; For the first Subsidy amount for water-saving equipment; S total For the total subsidy budget; R min R represents the lower limit of water-saving equipment performance. max This represents the upper limit of the performance of water-saving equipment.

9. The water resource regulation method according to claim 8, characterized in that, The method of regulating water resource utilization at all water-using nodes in the target area, with the optimization objectives of minimizing the collaborative deficit index of all water-using nodes and maximizing the actual water saving, also includes: Construct a penalty function The expression: ； in, Let be the first objective function for the m-th candidate value of the decision variable x; μ is the penalty coefficient. The decision variable corresponding to the candidate value that satisfies the constraints and minimizes the second objective function among all candidate values ​​is taken as the optimal control strategy; where the penalty function is taken as the second objective function.

10. A computer-readable storage medium, characterized in that, Used to store computer programs; when the computer programs are executed by a processor, they implement the steps of the water resource regulation method based on the actual utilization value assessment of water resources as described in any one of claims 1-9.

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