Water resource utilization regulation method and system based on multi-ring coupling value flow

Abstract

The application provides a water resource utilization regulation method and system based on multi-ring coupling value flow, and belongs to the technical field of water resource optimization scheduling. The method comprises the following steps: constructing a value flow network coupled by a physical hydrology ring, an economic value ring, a feedback ring, a dynamic configuration ring, an implicit cost ring and a symbiotic relationship ring; collecting time series data to calibrate network parameters; simulating and extracting direct value variation, implicit cost variation, reconfiguration value variation and ecological value variation after applying regulation measures; calculating a symbiosis index of the water resource system and determining a symbiosis level; when the effect level is lower than a preset standard, tracing back to a key antagonistic link; and constructing an optimization model with the target of improving the symbiosis index to solve an optimal regulation strategy. The application realizes systematic discrimination of real net benefits of water saving measures and collaborative optimization of regulation strategies.

Description

Technical Field

[0001] This application belongs to the field of water resource optimization and scheduling technology, and in particular relates to a water resource utilization regulation method and system based on multi-ring coupled value stream. Background Technology

[0002] Existing water resource utilization and regulation methods can be mainly categorized as follows: First, evaluation and regulation methods based on single indicators such as water consumption per unit of product, per capita daily water consumption, and the penetration rate of water-saving appliances. These methods assess effectiveness by comparing changes in water consumption before and after the implementation of water-saving measures and formulate regulation plans accordingly. Second, static value assessment techniques based on ecosystem gross product accounting. Third, assessment techniques that consider water quantity and quality correction factors, making partial improvements to traditional accounting methods. Fourth, techniques for analyzing the fairness of cross-regional water resource allocation and designing water rights trading mechanisms, focusing on the rationality of water allocation and the formulation of trading rules. In practical operation, these regulation methods largely rely on the collection of water quantity and quality data at key monitoring nodes. Statistical analysis or model simulation is used to verify the effectiveness of specific intervention measures, and water use plans and water-saving strategies are adjusted accordingly.

[0003] However, none of the aforementioned existing technologies have solved a fundamental technical problem: the lack of quantification capabilities for the system-level feedback effects triggered by water-saving measures. This makes it impossible to distinguish between genuine water-saving benefits and pseudo-water-saving phenomena characterized by "nominal water savings but actual losses," thus hindering the development of effective system-wide coordinated control strategies. Specifically, multiple feedback effects actually overlap, cancel each other out, or amplify each other, causing a systematic deviation between control strategies based on traditional assessment results and the actual net system benefits. This can even lead to incorrect control directions, where measures nominally promoting water conservation actually result in a decline in the overall water resource utilization efficiency of the system. Summary of the Invention

[0004] This application provides a water resource utilization regulation method and system based on multi-loop coupled value streams. By constructing a multi-loop coupled value stream network, it quantifies all system-level feedback effects triggered by water-saving measures, and calculates a symbiosis index to characterize the degree of system synergy to identify value loss links. Then, it performs closed-loop optimization regulation with the goal of improving the symbiosis index. This solves the problem that existing methods cannot distinguish between real water saving and pseudo water saving due to the inability to quantify the system-level feedback effects of water-saving measures, resulting in a deviation between the regulation strategy and the system's net benefits.

[0005] Firstly, this application provides a water resource utilization regulation method based on multi-loop coupled value streams, including: Construct a multi-ring coupled value stream network within the target area to determine the value changes and the true net benefits of water-saving measures throughout the entire water resource circulation process; Collect multi-period time-series data of the target area to calibrate the undetermined parameters of the value stream network, and obtain the calibrated value stream network; Water-saving measures are applied to the calibrated value stream network to extract the various value changes caused by the water-saving measures; these value changes include direct value changes, implicit cost changes, water reallocation value changes, and ecological value changes. Based on the changes in various values, a symbiosis index is calculated to characterize the degree of synergy between water-saving measures and water resources systems, so as to determine the effectiveness level of water-saving measures based on the symbiosis index. If the effect level is lower than the preset standard, the value loss link that caused the low symbiosis index is determined based on the components of the obtained symbiosis index. Optimization variables and constraints are set for the control parameters corresponding to the value loss link, so as to construct and solve the optimization model with the goal of improving the symbiosis index, and obtain the optimal combination of decision variables as the control strategy to improve the synergy of the water resource system.

[0006] Optionally, a multi-ring coupled value stream network is constructed within the target area to determine the value changes throughout the water resource circulation process and the true net benefits of water-saving measures, including: Using the location of each water-using unit, the transmission path between water-using units, and the treatment facilities as nodes and edges, a physical hydrological loop is constructed to describe the material migration path of water resources. Multiple independent value transformation functions are configured on each water-using unit to map the value transformation functions to each node of the physical hydrological loop, thereby obtaining an economic value loop that characterizes the transmission and transformation of value streams. We construct a price-behavior feedback chain based on the relationship between changes in water price or water cost and changes in water demand; a technology-resource feedback chain based on the relationship between changes in water-saving technology operating parameters and changes in related resource consumption; and a water-ecology feedback chain based on the relationship between changes in water consumption and dynamic changes in ecological capital stock. This results in a feedback loop consisting of three feedback chains. Determine the amount of available water released by water-saving measures along each transmission path and the change in marginal value after the release of water is reconfigured, in order to construct a dynamic configuration loop for tracking the reconfiguration process of water-saving releases. An implicit cost loop is constructed, consisting of the environmental costs of the entire life cycle of related resources caused by the technology-resource feedback chain and the ecological restoration costs caused by the water-ecology feedback chain. The symbiotic relationship loop is represented by an influence coefficient matrix, which is used to record the direction and intensity of the interaction between the elements of the value stream network. In the influence coefficient matrix, positive values ​​indicate that the interaction between elements is promoting, while negative values ​​indicate that the interaction between elements is inhibiting.

[0007] Optionally, multi-period time-series data of the target area are collected to calibrate the undetermined parameters of the value stream network, resulting in a calibrated value stream network, including: The data on water consumption, economic output, related resource consumption, ecological monitoring indicators, and water price changes of each water-using node in the target area over multiple periods were collected and divided into test datasets and validation datasets. The coefficients of the feedback loop, the value transformation function of the economic value loop, and the elements of the symbiotic relationship loop in the value stream network are jointly calibrated based on the test dataset. The Nash efficiency coefficient is calculated based on the validation dataset; when the Nash efficiency coefficient reaches a preset threshold, the calibration of the undetermined parameters of the value stream network is deemed satisfactory, and the calibrated value stream network is obtained.

[0008] Optionally, water-saving measures are applied to the calibrated value stream network to extract the various value changes caused by the water-saving measures, including: The difference between the sum of the value output of each water-using node before and after the implementation of water-saving measures is taken as the direct value change; The sum of the shadow cost of related resources and the cost of ecological restoration is taken as the implicit cost change; whereby the shadow cost of related resources is the sum of the product of the consumption change of each related resource and the unit price of the environmental cost of each related resource throughout its entire life cycle; the cost of ecological restoration is calculated based on the amount of ecological capital loss, the unit restoration cost and the risk premium of irreversible loss. Based on the available water released by water-saving measures along each transmission path, the difference between the marginal value product after the water release reallocation and the marginal value product before the implementation of water-saving measures is calculated, and then summed after being weighted by path priority to obtain the change in water reallocation value. Extract the set of ecosystem service elements and construct a value assessment function for each ecosystem service element with the coverage area, average annual service value coefficient and unit value of the ecosystem service element as independent variables. The net change in ecosystem service value is obtained by summing the value changes of each ecosystem service element obtained from the value assessment function before and after the implementation of water-saving measures and by using the symbiotic relationship loop to correct for positive and negative values.

[0009] Optionally, based on changes in various values, a symbiosis index is calculated to characterize the degree of synergy in improving the water resource system through water-saving measures. The effectiveness level of the water-saving measures is then determined based on the symbiosis index, including: The changes in direct value and the changes in the value of water reallocation are considered as positive effects, while the changes in implicit costs are considered as negative effects, in order to calculate the overall net effect of the water resource system. The total disturbance intensity of the water resources system is calculated by summing the absolute values ​​of changes in direct value, changes in implicit costs, and changes in the value of water reallocation, and then combining the weighted terms of the absolute value of the net change in the value of ecosystem services. The ratio of the overall net effect to the total disturbance intensity is used as the coexistence index; When the symbiosis index is greater than 1, it is judged as a synergistic enhancement type, indicating that water-saving measures improve the overall synergy of the water resource system; When the symbiosis index is greater than 0 and does not exceed 1, it is judged as an inefficient synergy type, indicating that water-saving measures generally produce positive benefits but the improvement of the synergy of the water resource system is limited; When the symbiosis index does not exceed 0, it is judged as a system loss type, indicating that water-saving measures have led to the deterioration of the symbiotic relationship of the water resource system.

[0010] Optionally, optimization variables and constraints are set for the control parameters corresponding to the value loss stage to construct and solve an optimization model aimed at improving the symbiosis index, obtaining the optimal combination of decision variables as a control strategy to improve the synergy of the water resource system, including: The optimization objective is to maximize the symbiosis index, and the decision variables are the water-saving technology operation parameters of each water-using unit, the comprehensive unit water cost and the benchmark value of the comprehensive unit water cost of each water-using unit, the ecological capital stock and the total investment budget. The constraints on decision variables include the lower and upper limits of the operating parameters of water-saving technologies, the maximum allowable threshold for price adjustments, the minimum guarantee line for ecological capital stock, and the sum of the investment allocation for each technology not exceeding the total investment budget. Construct an optimization model based on the optimization objective and constraints; The optimization model is solved using a genetic algorithm to obtain a set of candidate values ​​for the decision variables; Each candidate value is substituted into the calibrated value stream network for simulation to calculate the co-occurrence index corresponding to each candidate value. The candidate values ​​that satisfy all constraints and maximize the co-occurrence index are taken as the optimal combination of decision variables. The optimal water-saving technology parameters, optimal price level, optimal ecological stock, and optimal investment allocation in the optimal decision variable combination are compared with the actual water-saving technology parameters, actual price level, actual ecological stock, and actual investment allocation monitored in real time. When the actual water-saving technology parameters are lower than the optimal value, a control instruction to improve the operating efficiency of the water-saving technology is generated; when the actual water-saving technology parameters are higher than the allowable upper limit, a control instruction to reduce the operating efficiency of the water-saving technology is generated. When the actual price level deviates from the optimal value, a price pullback instruction is generated; When the actual ecological stock is lower than the optimal value, a control command to initiate ecological water replenishment is generated. When the actual investment allocation deviates from the optimal budget, an investment structure adjustment instruction is generated.

[0011] Secondly, this application provides a water resource utilization regulation system based on multi-loop coupled value stream, comprising: The first construction module is used to construct a multi-ring coupled value stream network within the target area to determine the value changes and the true net benefits of water-saving measures throughout the entire water resource circulation process. The calibration module is used to collect multi-period time-series data of the target area to calibrate the parameters to be determined in the value stream network, thereby obtaining the calibrated value stream network. The extraction module is used to apply water-saving measures to the calibrated value stream network to extract the various value changes caused by the water-saving measures; among which, the various value changes include direct value changes, implicit cost changes, water reallocation value changes, and ecological value changes; The calculation module is used to calculate the symbiosis index, which characterizes the degree of synergy between water-saving measures and water resources system based on the changes in various values, so as to determine the effectiveness level of water-saving measures based on the symbiosis index. The determination module is used to identify the value loss links that cause the low coexistence index when the effect level is lower than the preset standard, based on the components of the obtained coexistence index. The second construction module is used to set optimization variables and constraints for the control parameters corresponding to the value loss link, so as to construct and solve the optimization model with the goal of improving the symbiosis index, and obtain the optimal combination of decision variables as the control strategy to improve the synergy of the water resource system.

[0012] Thirdly, this application provides a computer device, including a processor and a memory; wherein, when the processor executes a computer program stored in the memory, it implements the steps of the water resource utilization regulation method based on multi-ring coupled value stream described in the first aspect.

[0013] Fourthly, this application 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 utilization regulation method based on multi-ring coupling value stream described in the first aspect.

[0014] Fifthly, this application provides a computer program product, including computer-executable instructions or a computer program, which, when executed by a processor, implements the steps of the water resource utilization regulation method based on multi-ring coupling value stream described in the first aspect.

[0015] Beneficial effects: By constructing a multi-ring coupled value stream network and extracting four value changes—direct value change, implicit cost change, water reallocation value change, and ecological value change—caused by water-saving measures, this approach specifically addresses the technical deficiency pointed out in the background section: existing methods cannot quantify the system-level multiple feedback effects caused by water-saving measures. This allows the true net benefits of water-saving measures to be fully identified, rather than merely remaining at the level of direct water volume changes. Furthermore, by calculating a symbiosis index characterizing the degree of system synergy and determining the effect level, the evaluation dimension is elevated from a single water volume to the overall synergy of the system. This avoids the problems caused by the different directions, mutual cancellation, or amplification of various feedback effects. To address the issue of inaccurate evaluation, when the effect level falls below the preset standard, further analysis of the components of the symbiosis index is used to accurately pinpoint the value loss link. An optimization model is then constructed targeting the control parameters of the value loss link, aiming to improve the symbiosis index, to solve for the optimal control strategy. This provides managers with a clear causal diagnostic tool and quantifiable optimization direction when water resource utilization effects fall short of expectations. This transforms water resource utilization control from static open-loop control to dynamic closed-loop continuous optimization, ultimately systematically solving the dilemma of identifying pseudo-water saving ("nominal water saving, actual loss") and the problem of the control strategy deviating from the system's net benefits, as pointed out in the background technology. Attached Figure Description

[0016] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a water resource utilization regulation method based on multi-loop coupled value stream, provided for an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a target area water resource utilization system provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a six-ring coupled value stream network provided in an embodiment of this application; Figure 4 This is a schematic diagram of a water resource utilization regulation system based on multi-ring coupled value stream, provided as an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0019] Example 1 like Figure 1 As shown, this embodiment provides a water resource utilization regulation method based on multi-loop coupled value stream, characterized by including: Step 101: Construct a multi-ring coupled value stream network within the target area to determine the value changes and the true net benefits of water-saving measures throughout the entire water resource circulation process.

[0020] For example, a physical hydrological loop Φ is constructed to describe the material migration paths of water resources, using the location of each water-using unit (as nodes), the transmission path between water-using units (as edges), and the treatment facility (as nodes) as nodes and edges.

[0021] Specifically, the location information μ of water-using unit i in the water resource system is collected. i The water resource transmission path τ between water user unit i and water user unit j ij Water purification or recycling facilities at water-using unit i i Construct a physical hydrological loop Φ that describes the spatiotemporal migration paths of water resources; Φ = (μ1, μ2, ..., μ i ,…,μ N ,θ1,θ2,…,θ i ,…,θ N ,τ 12 ,τ 23 ,…,τ ij ,…,τ N-1N N represents the total number of water-using units within the target area.

[0022] Structural model of the water resource system in the target area, such as Figure 2 As shown, the water use unit structure in the target area is chain-like, with the discharge units connected to the river outside the city. Each water use unit ( Figure 2 (1 to 6) Data acquisition device C3 is installed on C1 and the water resource transmission path C2. Water use unit C1 includes water source, water users, water purification facilities, and discharge outlets. The line between water use units is the water resource transmission path C2. Data acquisition device C3 integrates a data transmission module C4, mainly used for real-time collection and monitoring of data such as capacity and operating status at the water use units, as well as monitoring data such as transmission capacity and efficiency of the water resource path. The collected monitoring data is then transmitted to the data analysis module C5 through the data transmission module C4.

[0023] 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 analysis of the monitoring data and couples it with the multi-loop value stream network. The data center then stores the data and generates the water resource utilization optimization strategy for water use unit C1.

[0024] Multiple independent value transformation functions are configured on each water-using unit to map the value transformation functions to each node of the physical hydrological loop, thus obtaining the economic value loop Ψ used to characterize the transmission and transformation of value streams.

[0025] Specifically, mapped onto the physical hydrological loop Φ, each water-using unit is associated with a value transformation function. Based on the physical hydrological loop Φ and the value transformation function, an economic value loop Ψ is constructed to represent the transmission, value-added, and dissipation of value along the industrial chain and supply and demand chain. The value transformation function is as follows: Ψ i =φ i (W i Q i E i M i ).

[0026] Among them, W i Q represents the water consumption of water-using unit i; i The cleanliness of water resources in water-using unit i; E i M represents the power consumption of water-using unit i; i The dosage of water purification agent for water-using unit i; Let i be the value transformation function for water-using unit i.

[0027] For example, for an industrial water unit, the value transformation function can be expressed as: .

[0028] Where p is the unit price of the product; w0 is the water consumption quota per unit of product; K' is the upper limit of product production capacity; Q min This is the minimum water quality requirement; In Q i >Q min Take 1 in the case of Q i <Q min In the case of c, take 0; E and c M These are the unit prices of energy and materials, respectively; E i and M i These represent the total energy consumption and total material consumption of water-using unit i, respectively. For water-using units such as agriculture, domestic use, and wastewater treatment, a similar output function based on water consumption, water quality, energy consumption, and materials can be established.

[0029] Construct price-behavior feedback chains Γ based on the relationship between changes in water price or water cost and changes in water demand. PB To construct a technology-resource feedback chain based on the relationship between changes in water-saving technology operating parameters and related resource consumption. TR The water-ecological feedback chain Γ is constructed based on the relationship between changes in water consumption and the dynamic changes in ecological capital stock. WE Thus, a multidimensional feedback loop Γ consisting of three feedback chains is obtained.

[0030] Specifically, a price-behavior feedback chain Γ is constructed to characterize the impact of changes in water prices or water usage costs on user water usage behavior. PB The expression is as follows: .

[0031] in, Let η be the change in water demand of water-using unit i at time t due to changes in water cost; i P represents the price elasticity of demand for water user unit i, obtained by calibration from historical data. It is typically less than 0, and its absolute value reflects the sensitivity of water usage behavior to price changes. i (t) represents the comprehensive unit water consumption cost of water-using unit i at time t after the implementation of water-saving (intervention) measures; P i,base B is the benchmark unit water consumption cost for water-using unit i, i.e., the unit water consumption cost before the implementation of water-saving measures; i,base (t) represents the baseline water demand of water-using unit i at time t before the implementation of water-saving measures.

[0032] Constructing a technology-resource feedback chain Γ to assess the impact of changes in water-saving technology parameters on associated resource demand vectors. TR The expression is as follows: .

[0033] in, Let θ be the change in the consumption of the k-th related resource by water-using unit i at time t due to changes in water-saving technology; i,k The rate of change in the consumption of the kth associated resource caused by a change in a unit technical parameter at water-using unit i can be obtained by calibration using equipment performance curves or historical data. T i (t) represents the change in key operating parameters (such as pressure, temperature, etc.) of the water-saving technology implemented by water-using unit i at time t relative to the baseline state (no water-saving measures implemented).

[0034] Constructing a water-ecological feedback chain Γ to characterize the dynamic impact of water-saving behaviors on ecological capital stock E (such as groundwater storage and river ecological baseflow). WE The expression is as follows: .

[0035] Where d represents the differentiation; γ is the natural replenishment efficiency coefficient; W nat For the rate of replenishment of ecological natural water resources; W abs ξ is the rate of artificial water intake; μ is the ecological dissipation coefficient; W is the effective coefficient of return water. ret The return water rate is defined as the amount of water returned to the water source after use.

[0036] To determine the amount of available water released by water-saving measures along each transmission path and the change in marginal value after the release of water is reconfigured, a dynamic configuration loop is constructed to track the reconfiguration process of water-saving releases.

[0037] Specifically, a dynamic configuration ring Λ is constructed to characterize the amount of disposable water resources ΔW generated by water-saving measures. free The system's reconfiguration potential. The dynamic configuration loop Λ tracks and quantifies the water reduction achieved by water-saving measures at a specific stage of the system, and after internal system adjustments, the water is redistributed to other water-using units or ecological nodes. The construction steps are as follows: Calculate the marginal product value (VMP) generated by each water resource transmission path. For economic uses, VMP is the marginal output value per unit of water; for ecological uses, VMP is the ecosystem service value supported by per unit of water; and for storage uses, VMP is the discounted value of the expected marginal value for future water use.

[0038] Assign a priority weight ω to each water resource transmission path. m , where ω m Let m be the priority weight of the m-th water resource transmission path.

[0039] Based on the constraints of available water volume and water resource transmission path volume, an optimal water allocation scheme κ is formulated with the goal of maximizing the total allocation value.

[0040] Organize the above parameters and construct a dynamic configuration ring: Λ=({ΔW free},{VMP target_1 VMP target_m},{VMP baseline_1 VMP baseline_m},{ω1,…,ω m},κ).

[0041] Among them, VMP target_m VMP is the marginal product value of the water volume actually allocated to the target use along the m-th water resource transmission path. baseline_m Let be the marginal product value of the water volume along the m-th water resource transmission path under the baseline condition.

[0042] An implicit cost loop is constructed, consisting of the environmental costs of the entire life cycle of related resources caused by the technology-resource feedback chain and the ecological restoration costs caused by the water-ecology feedback chain. The implicit cost loop Σ includes the shadow costs of related resources σ. link and potential costs of ecological restoration σ eco .

[0043] Specifically, the shadow cost of related resources σ link Technology-Resource Feedback Chain TR The additional energy and chemical consumption incurred, calculated based on their total life-cycle environmental cost, is expressed as follows: .

[0044] Where K is the total number of associated resource types; R k (t) represents the change in the consumption of the kth related resource caused by the change in water-saving technology at time t; Let be the unit price of the total life cycle environmental cost of the k-th resource; t represents the time interval for collecting data on changes in associated resource consumption.

[0045] Potential costs of ecological restoration σ eco Water-Ecological Feedback Chain WE The resulting ecological capital loss is calculated based on the cost required to restore the baseline state in the future, as shown in the following expression: .

[0046] in, E loss The amount of ecological capital loss accumulated in the water resource system; C restore ρ is the unit ecological capital restoration cost of the water resource system; r is the social discount rate; t is the irreversible loss risk premium coefficient. a The estimated implementation time for the repair is as follows.

[0047] The symbiotic relationship loop is represented by an influence coefficient matrix, which is used to record the direction and intensity of the interaction between the elements of the value stream network. In the influence coefficient matrix, positive values ​​indicate that the interaction between elements is promoting, while negative values ​​indicate that the interaction between elements is inhibiting.

[0048] Specifically, the strength and direction of the interaction relationships among various elements (i.e., water-using units) within the water resource system are quantified, and a symbiotic relationship loop Π is constructed. The symbiotic relationship loop Π is represented by an influence coefficient matrix, where the matrix element π ij This represents the influence coefficient of water resource system element i on water resource system element j.

[0049] The influence coefficient matrix is ​​as follows: .

[0050] Where N is the total number of water-using units in the water resources system, that is, the total number of water-using units in the target area.

[0051] like Figure 3 As shown, this application finally obtains a six-ring coupled value stream network Ω=(Φ,Ψ,Γ,Λ,Σ,Π).

[0052] Step 102: Collect multi-period time-series data of the target area to calibrate the undetermined parameters of the value stream network and obtain the calibrated value stream network.

[0053] For example, water consumption, economic output, related resource consumption, ecological monitoring indicators, and water price change records of each water-using node in the target area over multiple periods are collected and divided into test datasets and validation datasets.

[0054] Specifically, high-resolution panel data on water use and economic output across multiple industries, associated resource input-output tables, ecological monitoring time series, and water price change records are collected. The first 80% of the time series data is used as the test dataset, and the last 20% is used as the validation dataset.

[0055] The coefficients of the feedback loop, the value transformation function of the economic value loop, and the elements of the symbiotic relationship loop in the value stream network are jointly calibrated based on the test dataset.

[0056] The Nash efficiency coefficient is calculated based on the validation dataset; when the Nash efficiency coefficient reaches a preset threshold, the calibration of the undetermined parameters of the value stream network is deemed satisfactory, and the calibrated value stream network is obtained.

[0057] Specifically, based on the test dataset, machine learning methods were used to evaluate the demand price elasticity coefficient, technology-resource conversion coefficient, natural replenishment efficiency coefficient, ecological dissipation coefficient, rate of change of resource consumption, irreversible loss risk premium coefficient, value transformation function of ring Ψ, and influence coefficient of ring Π. ij Perform joint calibration. Calculate the Nash efficiency coefficient (NSE) of the water resource system using the validation dataset. If NSE ≥ 0.85, the value stream network calibration is considered successful, and the calibrated value stream network Ω is output. cal .

[0058] The joint calibration calculation method is as follows: Set initialization parameter vector The parameter vector contains all the parameters to be calibrated.

[0059] Set the parameter update iteration formula: .

[0060] in, Let be the parameter to be calibrated in the b-th iteration; α is the learning rate, used to control the magnitude of each update of the parameter to be calibrated. The weighted mean square error L is related to the parameter to be calibrated. The gradient vector.

[0061] The iteration stops when the weighted mean square error L of the validation set no longer decreases, and the calibrated parameters are output.

[0062] The Nash efficiency coefficient is calculated as follows: .

[0063] Among them, Q obs (t) represents the real historical data of the validation dataset at time t; Q sim (t) represents the simulated data of the validation dataset at time t; To verify the mean of the dataset's real historical data.

[0064] The Nash efficiency coefficients of each time series data in the validation dataset are obtained, and the average value of the Nash efficiency coefficients of each time series data is the Nash efficiency coefficient of the water resource system.

[0065] Step 103: Apply water-saving measures to the calibrated value stream network to extract the various value changes caused by the water-saving measures; among which, the various value changes include direct value changes, implicit cost changes, water reallocation value changes, and ecological value changes.

[0066] For example, the difference between the sum of the value output of each water-using node before and after the implementation of water-saving measures is taken as the direct value change.

[0067] Specifically, the change in direct value ΔΨ is calculated according to the following formula. α : .

[0068] in, After implementing water-saving measures, the first The value output of each water-using unit; Before implementing water-saving measures The value output of each water-using unit.

[0069] The shadow cost of related resources σ link With ecological restoration (potential) costs σ eco The sum of these as implicit cost changes ΔΣ β Among them, the shadow cost of related resources is the sum of the product of the consumption change of each related resource and the unit price of the environmental cost of each related resource throughout its entire life cycle; the ecological restoration cost is calculated based on the amount of ecological capital loss, the unit restoration cost and the risk premium of irreversible loss.

[0070] Based on the amount of available water released by water-saving measures along each transmission path, the difference between the marginal value product after the water release reallocation and the marginal value product before the implementation of water-saving measures is calculated, and then summed after being weighted by path priority weights to obtain the change in water reallocation value.

[0071] Specifically, tracking ΔW free Calculate the change in the value of water reallocation ΔΛ along the water resource transport path in the Λ ring. γ The calculation formula is as follows: .

[0072] in, The amount of disposable water resources generated by water-saving measures along the m-th water resource transmission path.

[0073] Extract the set of ecosystem service elements and construct a value assessment function for each ecosystem service element with the coverage area, average annual service value coefficient, and unit value of the ecosystem service element as independent variables.

[0074] Specifically, ecosystem service elements (such as lake water level, groundwater depth, etc.) are extracted to establish an ecosystem service element set E. coset ={ε1,ε2,ε3,…,ε h};ε h Let h be the h-th ecosystem service element; h is the total number of ecosystem service elements.

[0075] Construct a value assessment function for each ecological element: .

[0076] Where V(h) is ε h Value assessment function; A h For ε h The coverage area of ​​ecosystem service elements; D h The average annual service value coefficient of ecosystem service elements; P unit The unit value of ecological service elements.

[0077] The changes in the value of each ecosystem service element obtained from the value assessment function before and after the implementation of water-saving measures are summed, and the positive and negative values ​​are corrected using the symbiotic relationship loop to obtain the net change in ecosystem service value ΔΠ. δ .

[0078] Step 104: Calculate the symbiosis index, which characterizes the degree of synergy between water-saving measures and water resources system based on the changes in various values, and determine the effectiveness level of water-saving measures based on the symbiosis index.

[0079] For example, changes in direct value and changes in the value of water reallocation are considered as positive effects, while changes in implicit costs are considered as negative effects, in order to calculate the overall net effect (SCE) of the water resource system: .

[0080] Where η is the ecological value enhancement coefficient; η≥0; η=1 indicates that ecological value is regarded as ordinary economic commodity; η>1 indicates that ecological protection has a higher weight.

[0081] The total disturbance intensity (SDI) of the water resources system is calculated by summing the absolute values ​​of changes in direct value, implicit costs, and water reallocation value, and then incorporating a weighted term based on the absolute value of the net change in ecosystem service value. .

[0082] Where ζ is the ecological disturbance weight, i.e., the weighting term, which is usually set to 1 and can be adjusted according to regional sensitivity.

[0083] The ratio of the overall net effect to the total disturbance intensity (SCE / SDI) is used as the coexistence index.

[0084] When the symbiosis index is greater than 1, it is judged as a synergistic enhancement type, that is, a high-order symbiosis state, indicating that water-saving measures improve the overall synergy of the water resource system.

[0085] When the symbiosis index is greater than 0 and does not exceed 1, it is judged as an inefficient synergy type, that is, a low degree of symbiosis, indicating that water-saving measures generally produce positive benefits but the improvement of the synergy of the water resource system is limited.

[0086] When the symbiosis index does not exceed 0, it is judged as a system loss type, that is, an antagonistic state, indicating that water-saving measures have led to the deterioration of the symbiotic relationship of the water resource system, and the overall development mode is unsustainable.

[0087] Step 105: If the effect level is lower than the preset standard, determine the value loss link that caused the low coexistence index based on the components of the obtained coexistence index.

[0088] For example, the symbiosis index is decomposed to extract the values ​​of each component in the comprehensive net effect of the water resource system.

[0089] Based on the numerical values ​​of each component, key antagonistic links leading to a low symbiosis index are identified. Specifically, when the implicit cost item makes a negative contribution, the link where the associated resource consumption increases or the ecological damage is lost is identified as the key antagonistic link. When the contribution of the water reallocation value item is insufficient, the path where the released water is allocated to low marginal value uses is identified as the key antagonistic link. When the ecological value change item makes a negative contribution, the link where the net loss of ecosystem service value is lost is identified as the key antagonistic link.

[0090] The optimization direction is determined based on the identified key antagonistic links, and optimization variables and constraint boundaries are set for the control parameters corresponding to the key antagonistic links.

[0091] Step 106: Set optimization variables and constraints for the control parameters corresponding to the value loss link, so as to construct and solve the optimization model with the goal of improving the symbiosis index, and obtain the optimal combination of decision variables as the control strategy to improve the synergy of the water resources system.

[0092] For example, maximizing the symbiosis index is used as the optimization objective, and the water-saving technology operation parameters of each water-using unit, the comprehensive unit water cost and the benchmark value of the comprehensive unit water cost of each water-using unit, the ecological capital stock and the total investment budget are used as decision variables.

[0093] Specifically, construct the objective function f(x) that maximizes the co-occurrence index of the system: .

[0094] Where, a larger f(x) indicates a higher system symbiosis index and better water resource system synergy; x=[T i (t),P i (t),P i,base ,E,I total ] T Let T be the vector of decision variables; T represents the matrix transpose; T i (t) represents the water-saving technical parameters implemented by water-using unit i at time t (such as high-pressure cleaning pressure, process water reuse rate, rated flow rate of water-saving appliances, etc.); P i (t) represents the comprehensive unit water consumption cost of water-using unit i at time t after implementing water-saving measures; P i,base The benchmark unit water consumption cost for water-using unit i; P i (t) and P i,base Together, they constitute the complete price structure decision variables, where E represents the ecological capital stock, and I... total This represents the total investment budget.

[0095] The constraints for decision-making variables include the lower and upper limits of the operating parameters of water-saving technologies, the maximum allowable threshold for price adjustments, the minimum guarantee line for ecological capital stock, and the fact that the sum of the investment allocations for each technology does not exceed the total investment budget.

[0096] Specifically, decision-making variable constraints are set to ensure that the regulation strategy is within the feasible domain and meets the basic water-saving target; that is, to construct the first constraint (water-saving technology parameter constraint), the second constraint (price adjustment range constraint), the third constraint (ecological red line constraint), and the fourth constraint (investment budget constraint): .

[0097] Among them, T b,min (t) and T b,max (t) represents the water-saving technology parameter T for the b-th type at time t. b The lower and upper bounds of (t); δ i,max E represents the maximum allowed price adjustment threshold for water-using unit i; min This is the minimum guarantee requirement for ecological capital stock; I b The investment allocation amount is for Category b water-saving technologies.

[0098] Build an optimization model based on the optimization objective and constraints.

[0099] The optimization model is solved using a genetic algorithm to obtain a set of candidate values ​​for decision variables.

[0100] For example, a set of candidate values ​​[x1, x2, ..., x] for the decision variable x is generated using a genetic optimization algorithm. y ]; y is the total number of candidate values, which is generally 50 to 80.

[0101] The corresponding optimization objective function values ​​[f1, f2, ..., f] are obtained by calculating the candidate values ​​of the decision variables. y ], namely the water resource system symbiosis index [CSI1, CSI2, ..., CSI y The candidate value x that satisfies all constraints and maximizes the objective function is selected from the candidate values. best The corresponding decision variables are taken as the optimal control strategy, best=1,2,…,y.

[0102] Where, x best =[T i,z (t),P i,z (t),P i,z,base E z ,I total,z ] T ;T i,z (t) represents the z-th candidate value of the water-saving technology parameter implemented by water-using unit i at time t; P i,z (t) represents the z-th candidate value of the comprehensive unit water cost of water user unit i at time t after the implementation of water-saving measures; P i,z,base The z-th candidate value for the benchmark unit water consumption comprehensive cost of water use unit i; E z The z-th candidate value for ecological capital stock; I total,z This is the z-th candidate value for the total investment budget.

[0103] Each candidate value is substituted into the calibrated value stream network for simulation to calculate the co-occurrence index corresponding to each candidate value.

[0104] The candidate values ​​that satisfy all constraints and maximize the co-occurrence index are selected as the optimal combination of decision variables.

[0105] The optimal water-saving technology parameters, optimal price level, optimal ecological stock, and optimal investment allocation in the optimal decision variable combination are compared with the actual water-saving technology parameters, actual price level, actual ecological stock, and actual investment allocation monitored in real time.

[0106] When the actual water-saving technology parameters are lower than the optimal value, control instructions are generated to improve the operating efficiency of the water-saving technology; when the actual water-saving technology parameters are higher than the allowable upper limit, control instructions are generated to reduce the operating efficiency of the water-saving technology.

[0107] When the actual price level deviates from the optimal value, a price pullback instruction is generated.

[0108] When the actual ecological stock is lower than the optimal value, a control command to initiate ecological water replenishment is generated.

[0109] When the actual investment allocation deviates from the optimal budget, an investment structure adjustment instruction is generated.

[0110] Specifically, the optimal control strategy x best The corresponding decision variables are compared with the constraint boundaries, the degree of constraint violation is calculated and fed back to the optimization algorithm.

[0111] Construct the augmented objective function with quadratic penalty: .

[0112] Where μ' is the penalty coefficient; I b,z The z-th candidate value for allocating investment in the b-th type of water-saving technology; max(0,T) i,min (t) T i,z (t)), max(0,T) i,z (t) T i,max (t)), max(0,E) min E z ), This represents the degree of violation of each constraint; if no constraint is violated, the corresponding term is 0; T i,min (t) represents the lower limit of the water-saving technology parameters implemented by water-using unit i at time t; T i,max (t) represents the upper limit of the water-saving technology parameters implemented by water-using unit i at time t.

[0113] By setting μ'=100, the water-saving technology parameters in the candidate values ​​are strictly limited to the upper and lower limits, the price adjustment range is limited to the maximum allowed price adjustment range, the ecological capital stock is limited to the minimum red line, and the investment allocation is limited to the total budget, so as to avoid generating infeasible control strategies.

[0114] Using the augmented objective function Φ(x) as the new objective function and re-applying the genetic algorithm to solve the optimization model, a set of candidate values ​​for decision variables is obtained until the preset number of updates is reached.

[0115] After repeated iterations, the optimal decision variable x that satisfies all constraints and maximizes the objective function is found. best Its corresponding water resource system symbiosis index (CSI), also known as the optimal water-saving technology parameter T, is... best Optimal price structure P best Optimal ecological red line E best With the optimal investment allocation scheme I best .

[0116] Based on the optimal combination of decision variables, and compared with the actual operating parameters monitored in real time by the water-using unit, water resource utilization is regulated in real time. The optimal water-saving technology parameter T best Optimal price structure P best Optimal ecological red line E best With the optimal investment allocation scheme I best They were respectively compared with the water-saving technology parameter T under real-time monitoring. actual Actual unit water consumption comprehensive cost P actual Actual ecological capital stock E actual and actual investment allocation I actual Comparison: If T actual <T best If the actual operating efficiency of the water-saving technology is lower than the optimal value, then control commands are generated to optimize the operation and scheduling of the water-saving technology facilities, adjust the operating load of the water-saving equipment, and optimize process parameters, so that the operating efficiency of the water-saving technology gradually increases to the optimal parameter T. best .

[0117] If T actual >T best If the actual water-saving technology operates more efficiently than the optimal value, then two cases are handled: if T actual ≤T max If the Coexistence Index (CSI) meets the standard, the current operating status of the water-saving equipment will be maintained; if T actual >T max If excessive operation leads to a surge in related resource consumption and an increase in hidden costs, then control instructions will be generated to appropriately reduce the intensity of technical operation and restore the operating parameters to the optimal range [T].best ,T max [Inside; T] max This represents the threshold for the actual operating efficiency of water-saving technologies.

[0118] If P actual <P best If this is done, a control command will be generated to appropriately increase the actual unit water price, ensuring that the water-saving incentive intensity reaches the optimal level and improving the Coexistence Index (CSI).

[0119] If P actual >P best If the water price is too high, a control command will be generated to appropriately reduce the actual unit water price, strictly control the water price below the optimal water price, and prevent the behavioral rebound effect caused by excessively high water prices.

[0120] If E actual <E best If so, a control command is generated to initiate the ecological water diversion plan, prioritizing the allocation of flow to areas with high ecological value and reducing inefficient water use projects.

[0121] If E actual >E best If the actual ecological capital stock meets the standard, the current ecological scheduling strategy will be maintained, the trend of ecological capital stock changes will be monitored, and the symbiosis index (CSI) will be kept stable.

[0122] If I actual <I best If the actual investment does not reach the optimal budget, then control instructions are generated to optimize the investment strategy and give full play to the systemic synergistic incentive effect of funds.

[0123] If I actual >I best If the actual investment exceeds the optimal budget, a control instruction is generated to adjust the investment structure, prioritizing investment in projects that contribute highly to the Coexistence Index (CSI) and reducing inefficient investment projects.

[0124] This embodiment further discloses specific examples: The target area includes water sources (reservoir G1, groundwater well D1), water-using units (industrial area I1, agricultural area A1, residential area R1), water purification facilities (waterworks W1), treatment facilities (sewage treatment plant T1), and discharge outlets (river discharge point O1). All nodes are connected by physical pipelines.

[0125] Construct a physical hydrological loop Φ. Collect data on the location, capacity, and operational status of each water-using unit. Examples of data from some water-using units are shown in Table 1. Table 1. Information on Partial Water Use Units

[0126] Construct an economic value loop Ψ. Define a value transformation function for each water-using unit.

[0127] Taking Industrial Zone I1 as an example: Ψ I1 =P product ·Y(W I1 Q I1 )-P E ·E I1 -P M ·M I1 .

[0128] Among them, Ψ I1 Y is the economic value of industrial zone I1; Y(·) is the output function; W I1 Water consumption of industrial zone I1; Q I1 Water quality for industrial zone I1; P product For product price; P E For energy prices; P M For material prices; E I1 Energy consumption for industrial zone I1; M I1 Material consumption for industrial zone I1.

[0129] Construct the feedback loop Γ. Define the price-behavior feedback chain Γ. PB For residential area R1, the demand elasticity model is used: ΔD R1 (t)=η R1 ·[(C unit,R1(t) -C unit,R1,base ) / C unit,R1,base ]·D R1,base(t) .

[0130] Where, ΔD R1 (t) represents the change in water demand in residential area R1 at time t due to changes in water cost; η R1 The demand price coefficient (η) R1 <0); C unit,R1(t) The comprehensive unit water consumption cost of residential area R1 at time t after implementing water-saving measures; C unit,R1,base The comprehensive cost of water use per unit of residential area R1; D R1,base(t) The baseline water demand of residential area R1 at time t before the implementation of water-saving measures.

[0131] Define the technology-resource feedback chain Γ TR For wastewater treatment plant T1, the change in power consumption ΔE after adopting water-saving technology. T1(t) : ΔE T1(t) =θ T1,E ·ΔT T1(t) .

[0132] Where, θ T1,E For the technology-to-electricity conversion factor; ΔT T1(t) Let t be the change in operating parameters of the water-saving technology implemented by the wastewater treatment plant at time t relative to the baseline state.

[0133] Define the water-ecological feedback chain Γ WE For river ecological flow E river : dE river / dt=α'·(W in -W out )-β'·E river .

[0134] Where α' and β' are the first and second ecological coefficients, respectively, calibrated from ecological monitoring data; W in For river ecological flow replenishment; W out The amount of water taken for the ecological flow of the river.

[0135] Construct a dynamic configuration loop Λ. For example, the water released by drip irrigation in agricultural areas can be allocated to ecological lakes (λ1, priority ω1=0.6) or industrial expansion (λ2, priority ω2=0.4).

[0136] Construct a hidden cost loop Σ. For example, plant T1 increases its electricity consumption ΔE. T1 Its shadow cost is ΔE T1 ·C electricity_shadow C electricity_shadow A green energy environmental premium of 0.2 yuan / kWh will be added to the local electricity supply.

[0137] Construct a symbiotic relationship cycle Π. Construct a symbiotic relationship matrix Π, for example, π. I1T1 This represents the influence coefficient of industrial wastewater quality on the treatment efficiency of wastewater treatment plants.

[0138] The system is calibrated by collecting time-series data from the target region over the past 36 months. The calibration yields the demand price coefficient η. R1 =-0.19, Technology-to-Electricity Conversion Factor θ T1,E =120kWh / unit technology change, coexistence coefficient π I1T1 =-0.05.

[0139] Implement water-saving measure I. Measure I includes: The I1 Industrial Zone will promote a high-efficiency circulating water system (an investment of 2 million yuan, expected to reduce water consumption by 15%).

[0140] The residential area R1 will implement tiered water pricing (the base water price will increase by 15%).

[0141] Drip irrigation technology will be promoted in agricultural zone A1 (coverage area increased by 30%, and water saving is expected to be 20%).

[0142] The dynamic simulation was run over a period of 12 months.

[0143] The calculation yielded: The industrial zone saw an increase of 1.5 million yuan in output value due to water conservation and efficiency improvement; the residential area experienced an increase of 200,000 yuan in living costs due to water price adjustments; and the agricultural zone saw an increase of 500,000 yuan in production due to water conservation. Therefore, the direct explicit value increment ΔΨ α =150-20+50=1.8 million yuan.

[0144] Due to changes in treated water quality, Plant T1 increased its electricity consumption by 80 MWh (shadow price 0.2 yuan / kWh) and added 0.5 tons of chemicals (shadow price 8000 yuan / ton); the river's ecological flow decreased by 50,000 m³. 3 (Restoration cost: 1 yuan / m) 3 Then the associated implicit cost increment ΔΣ β =(80×1000×0.2 / 10000+0.5×0.8)+5=(1.6+0.4)+5=70,000 yuan.

[0145] Water release ΔW in agricultural areas free =100,000 m 3 60,000 m 3 Configuration for ecological lakes (VMP) target =8 yuan / m 3 VMP baseline =2 yuan / m 3 ), 40,000 m 3 Configured for industrial expansion (VMP) target =15 yuan / m 3 VMP baseline =2 yuan / m 3 The priority weights are all 1. The value effect ΔΛ is then reconfigured. γ =6×(8-2)+4×(15-2)=36+52=880,000 yuan.

[0146] The assessment measures have a positive impact on river ecological baseflow (due to reduced agricultural runoff), with a net change in ecosystem service value ΔΠ. δ =120,000 yuan.

[0147] The ecological value enhancement coefficient η = 1.5 (with a higher weight for regional ecological protection) and the ecological disturbance weight ζ = 1 are set. The calculation yields: The comprehensive net effect of the water resources system, SCE, is 261 + 1.5 × 12 = 261 + 18 = 2.79 million yuan.

[0148] Total disturbance intensity SDI of the water resource system = |180|+|7|+|88|+1×|12|=180+7+88+12=2.87 million yuan.

[0149] The symbiosis degree index CSI of the water resource system = 279 / 287 ≈ 0.97.

[0150] CSI ≈ 0.97 (0 < CSI ≤ 1), which is determined as "low-degree symbiosis state". The intervention measures are mainly focused on economic contribution, and the improvement of the synergy of the water resource system is limited. Although the net created value is relatively high, the hidden cost and disturbance intensity are also relatively large, resulting in the symbiosis degree index not exceeding 1.

[0151] Where ΔΣ β the ecological restoration cost (50,000 yuan) accounts for a relatively high proportion, which mainly stems from the impact of industrial drainage water quality fluctuations on rivers, and this is the key antagonistic link.

[0152] An optimization algorithm is used for optimization and a balanced solution is selected therefrom to obtain: an investment amount of 2.5 million yuan, a 12% increase in water price, and the ecological water replenishment red line is set as 95% of the base flow demand. Re-simulation gives CSI = 1.12, achieving a high-order symbiosis state.

[0153] In summary, the water resource utilization regulation method based on multi-ring coupled value flow provided by the present application constructs a multi-ring value flow network formed by coupling a physical hydrological ring, an economic value ring, a feedback ring, a dynamic allocation ring, a hidden cost ring and a symbiotic relationship ring, performs value tracking and decoupling quantification on the whole process of water resources from water intake through each water use link to ecological discharge, can calculate the direct explicit value increment, and simultaneously quantify the hidden costs such as the associated resource consumption increase and ecological capital loss caused by regulation measures, as well as the marginal value gain and ecological service value change created by the reallocation of water volume released by water saving between different uses, calculates the system symbiosis degree index based on the above multi-dimensional value changes and determines the symbiosis level, realizes accurate identification of the true net benefit of water saving regulation measures, determines it as pseudo water saving when the nominal water saving has a low or even negative actual symbiosis degree index, and breaks through the technical limitation of traditional evaluation that only focuses on a single water volume change index and cannot identify "nominal water saving with actual loss"; on this basis, when the determined effect level is lower than the preset standard, the key antagonistic links are traced by decomposing the composition of the symbiosis degree index, and an optimization model aiming at improving the symbiosis degree index is constructed, with technical operation parameters, price leverage, ecological red line and investment allocation as decision variables to solve for the optimal regulation strategy, so that the system evolves to a high-order symbiosis state, realizing the leap from "post-event evaluation" to "pre-deduction and active optimization", and providing systematic collaborative decision support for regional water resource management that takes into account resource efficiency, economic benefits and ecological security.

[0154] Embodiment 2 Based on the same inventive concept as Embodiment 1, this embodiment provides a water resource utilization regulation system based on multi-loop coupled value stream. Since the principle of this system in solving the problem is similar to the aforementioned water resource utilization regulation method based on multi-loop coupled value stream, the implementation of this system can refer to the implementation of the water resource utilization regulation method based on multi-loop coupled value stream.

[0155] like Figure 4 As shown, a water resource utilization regulation system based on multi-loop coupled value stream includes: The first construction module 10 is used to construct a multi-ring coupled value flow network within the target area for determining the value changes and the true net benefits of water-saving measures throughout the entire water resource circulation process.

[0156] The calibration module 20 is used to collect multi-period time-series data of the target area to calibrate the undetermined parameters of the value stream network and obtain the calibrated value stream network.

[0157] Extraction module 30 is used to apply water-saving measures on the calibrated value stream network to extract the various value changes caused by the water-saving measures; among which, the various value changes include direct value changes, implicit cost changes, water reallocation value changes and ecological value changes.

[0158] The calculation module 40 is used to calculate the symbiosis index, which characterizes the degree of synergy between water-saving measures and water resources system based on the changes in various values, so as to determine the effectiveness level of water-saving measures based on the symbiosis index.

[0159] Module 50 is used to determine the value loss link that causes the low coexistence index when the effect level is lower than the preset standard, based on the components of the obtained coexistence index.

[0160] The second construction module 60 is used to set optimization variables and constraints for the control parameters corresponding to the value loss link, so as to construct and solve the optimization model with the goal of improving the symbiosis index, and obtain the optimal combination of decision variables as the control strategy to improve the coordination of the water resources system.

[0161] 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.

[0162] 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 utilization regulation method based on multi-ring coupling value stream described in Embodiment 1.

[0163] 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.

[0164] 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 utilization regulation method based on multi-ring coupling value stream described in Embodiment 1.

[0165] 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.

[0166] 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 utilization regulation method based on multi-ring coupling value stream described in Embodiment 1.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] The present application 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 application. Those skilled in the art will understand that various equivalent substitutions, modifications, or improvements can be made to the technical solutions and implementation methods of the present application without departing from the spirit and scope of the present application, and all such modifications and improvements fall within the scope of the present application. The scope of protection of the present application is determined by the appended claims.

Claims

1. A water resource utilization regulation method based on multi-loop coupled value stream, characterized in that, include: Construct a multi-ring coupled value stream network within the target area to determine the value changes and the true net benefits of water-saving measures throughout the entire water resource circulation process; Collect multi-period time-series data of the target area to calibrate the undetermined parameters of the value stream network, and obtain the calibrated value stream network; Apply water-saving measures to the calibrated value stream network to extract the various value changes caused by the water-saving measures, including: The difference between the sum of the value output of each water-using node before and after the implementation of water-saving measures is taken as the direct value change; The sum of the shadow cost of related resources and the cost of ecological restoration is taken as the implicit cost change; whereby the shadow cost of related resources is the sum of the product of the consumption change of each related resource and the unit price of the environmental cost of each related resource throughout its entire life cycle; the cost of ecological restoration is calculated based on the amount of ecological capital loss, the unit restoration cost and the risk premium of irreversible loss. Based on the available water released by water-saving measures along each transmission path, the difference between the marginal value product after the water release reallocation and the marginal value product before the implementation of water-saving measures is calculated, and then summed after being weighted by path priority to obtain the change in water reallocation value. Extract the set of ecosystem service elements and construct a value assessment function for each ecosystem service element with the coverage area, average annual service value coefficient and unit value of the ecosystem service element as independent variables. The net change in ecosystem service value is obtained by summing the value changes of each ecosystem service element obtained from the value assessment function before and after the implementation of water-saving measures and by using the symbiotic relationship loop to correct for positive and negative values. Based on changes in various values, a symbiosis index is calculated to characterize the degree of synergy between water-saving measures and the water resource system. The effectiveness level of water-saving measures is then determined based on the symbiosis index, including: The changes in direct value and the changes in the value of water reallocation are considered as positive effects, while the changes in implicit costs are considered as negative effects, in order to calculate the overall net effect of the water resource system. The total disturbance intensity of the water resources system is calculated by summing the absolute values ​​of changes in direct value, changes in implicit costs, and changes in the value of water reallocation, and then combining the weighted terms of the absolute value of the net change in the value of ecosystem services. The ratio of the overall net effect to the total disturbance intensity is used as the coexistence index; When the symbiosis index is greater than 1, it is judged as a synergistic enhancement type, indicating that water-saving measures improve the overall synergy of the water resource system; When the symbiosis index is greater than 0 and does not exceed 1, it is judged as an inefficient synergy type, indicating that water-saving measures generally produce positive benefits but the improvement of the synergy of the water resource system is limited; When the symbiosis index does not exceed 0, it is judged as a system loss type, indicating that water-saving measures lead to the deterioration of the symbiotic relationship of the water resource system. If the effect level is lower than the preset standard, the value loss link that caused the low symbiosis index is determined based on the components of the obtained symbiosis index. Optimization variables and constraints are set for the control parameters corresponding to the value loss link, so as to construct and solve the optimization model with the goal of improving the symbiosis index, and obtain the optimal combination of decision variables as the control strategy to improve the synergy of the water resource system.

2. The water resource utilization regulation method according to claim 1, characterized in that, Construct a multi-ring coupled value stream network within the target area to determine the value changes throughout the entire water resource circulation process and the true net benefits of water-saving measures, including: Using the location of each water-using unit, the transmission path between water-using units, and the treatment facilities as nodes and edges, a physical hydrological loop is constructed to describe the material migration path of water resources. Multiple independent value transformation functions are configured on each water-using unit to map the value transformation functions to each node of the physical hydrological loop, thereby obtaining an economic value loop that characterizes the transmission and transformation of value streams. We construct a price-behavior feedback chain based on the relationship between changes in water price or water cost and changes in water demand; a technology-resource feedback chain based on the relationship between changes in water-saving technology operating parameters and changes in related resource consumption; and a water-ecology feedback chain based on the relationship between changes in water consumption and dynamic changes in ecological capital stock. This results in a feedback loop consisting of three feedback chains. Determine the amount of available water released by water-saving measures along each transmission path and the change in marginal value after the release of water is reconfigured, in order to construct a dynamic configuration loop for tracking the reconfiguration process of water-saving releases. An implicit cost loop is constructed, consisting of the environmental costs of the entire life cycle of related resources caused by the technology-resource feedback chain and the ecological restoration costs caused by the water-ecology feedback chain. The symbiotic relationship loop is represented by an influence coefficient matrix, which is used to record the direction and intensity of the interaction between the elements of the value stream network. In the influence coefficient matrix, positive values ​​indicate that the interaction between elements is promoting, while negative values ​​indicate that the interaction between elements is inhibiting.

3. The water resource utilization regulation method according to claim 2, characterized in that, Multi-period time-series data of the target region are collected to calibrate the undetermined parameters of the value stream network, resulting in a calibrated value stream network, including: The data on water consumption, economic output, related resource consumption, ecological monitoring indicators, and water price changes of each water-using node in the target area over multiple periods were collected and divided into test datasets and validation datasets. The coefficients of the feedback loop, the value transformation function of the economic value loop, and the elements of the symbiotic relationship loop in the value stream network are jointly calibrated based on the test dataset. The Nash efficiency coefficient is calculated based on the validation dataset; when the Nash efficiency coefficient reaches a preset threshold, the calibration of the undetermined parameters of the value stream network is deemed satisfactory, and the calibrated value stream network is obtained.

4. The water resource utilization regulation method according to claim 1, characterized in that, Optimization variables and constraints are set for the control parameters corresponding to the value loss stage to construct and solve an optimization model aimed at improving the symbiosis index. The optimal combination of decision variables is obtained as a control strategy to improve the synergy of the water resource system, including: The optimization objective is to maximize the symbiosis index, and the decision variables are the water-saving technology operation parameters of each water-using unit, the comprehensive unit water cost and the benchmark value of the comprehensive unit water cost of each water-using unit, the ecological capital stock and the total investment budget. The constraints on decision variables include the lower and upper limits of the operating parameters of water-saving technologies, the maximum allowable threshold for price adjustments, the minimum guarantee line for ecological capital stock, and the sum of the investment allocation for each technology not exceeding the total investment budget. Construct an optimization model based on the optimization objective and constraints; The optimization model is solved using a genetic algorithm to obtain a set of candidate values ​​for the decision variables; Each candidate value is substituted into the calibrated value stream network for simulation to calculate the co-occurrence index corresponding to each candidate value. The candidate values ​​that satisfy all constraints and maximize the co-occurrence index are taken as the optimal combination of decision variables. The optimal water-saving technology parameters, optimal price level, optimal ecological stock, and optimal investment allocation in the optimal decision variable combination are compared with the actual water-saving technology parameters, actual price level, actual ecological stock, and actual investment allocation monitored in real time. When the actual water-saving technology parameters are lower than the optimal value, a control instruction to improve the operating efficiency of the water-saving technology is generated; when the actual water-saving technology parameters are higher than the allowable upper limit, a control instruction to reduce the operating efficiency of the water-saving technology is generated. When the actual price level deviates from the optimal value, a price pullback instruction is generated; When the actual ecological stock is lower than the optimal value, a control command to initiate ecological water replenishment is generated. When the actual investment allocation deviates from the optimal budget, an investment structure adjustment instruction is generated.

5. A water resource utilization regulation system based on multi-loop coupled value stream, used to execute the water resource utilization regulation method based on multi-loop coupled value stream as described in claim 1, characterized in that, include: The first construction module is used to construct a multi-ring coupled value stream network within the target area to determine the value changes and the true net benefits of water-saving measures throughout the entire water resource circulation process. The calibration module is used to collect multi-period time-series data of the target area to calibrate the parameters to be determined in the value stream network, thereby obtaining the calibrated value stream network. The extraction module is used to apply water-saving measures to the calibrated value stream network to extract the various value changes caused by the water-saving measures; among which, the various value changes include direct value changes, implicit cost changes, water reallocation value changes, and ecological value changes; The calculation module is used to calculate the symbiosis index, which characterizes the degree of synergy between water-saving measures and water resources system based on the changes in various values, so as to determine the effectiveness level of water-saving measures based on the symbiosis index. The determination module is used to identify the value loss links that cause the low coexistence index when the effect level is lower than the preset standard, based on the components of the obtained coexistence index. The second construction module is used to set optimization variables and constraints for the control parameters corresponding to the value loss link, so as to construct and solve the optimization model with the goal of improving the symbiosis index, and obtain the optimal combination of decision variables as the control strategy to improve the synergy of the water resource system.

6. A computer device, characterized in that, It includes 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 utilization regulation method based on multi-ring coupling value stream as described in any one of claims 1-4.

7. 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 utilization regulation method based on multi-ring coupling value stream as described in any one of claims 1-4.

8. A computer program product, characterized in that, It includes computer-executable instructions or computer programs, which, when executed by a processor, implement the steps of the water resource utilization regulation method based on multi-ring coupling value stream as described in any one of claims 1-4.

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