A water resource quantity-quality-efficiency collaborative regulation method and system oriented to three-line constraints

Abstract

This invention relates to the field of resource management technology, and discloses a method and system for coordinated regulation of water resources quantity, quality, and efficiency under three-line constraints. The method includes: constructing a digital spatiotemporal twin of a target physical watershed; coupling a hydrodynamic model, a water quality model, and a water efficiency assessment model through multiple channels; analyzing the simulation results of coordinated regulation of quantity, quality, and efficiency from multi-source water resources data using the water resources coupling model, and determining the coordinated weights based on the simulation results and the driving adjustment coefficients of the multi-source water resources data; analyzing the abrupt change intervals of the target physical watershed using the water resources coupling model for multi-source water resources data and generating three-line constraint thresholds; simulating the quantity, quality, and efficiency regulation strategy of the target physical watershed in the digital spatiotemporal twin; generating quantity, quality, and efficiency regulation instructions for the target physical watershed based on the simulation results, and coordinating the regulation of water resource allocation in the target physical watershed. This invention can improve the accuracy of coordinated regulation of water resources quantity, quality, and efficiency.

Description

Technical Field

[0001] This invention relates to the field of resource management technology, and in particular to a method and system for coordinated regulation of water resources quantity, quality and efficiency oriented to three constraints. Background Technology

[0002] In watershed water resource management, it is necessary to achieve coordinated regulation of water quantity assurance, water quality compliance, and water use efficiency improvement to address the dynamic water resource pressures brought about by climate change and industrial development. The application of technologies such as digital twins and multi-model coupling in the resource and environmental fields is gradually deepening, providing technical support for the refined management of watershed water resources.

[0003] In existing technologies, watershed water resource management often employs single models for water quantity calculation, water quality assessment, or efficiency analysis, lacking an effective dynamic coupling mechanism between models. This results in the inability to exchange data on water quantity, water quality, and water use efficiency in real time. Furthermore, traditional watershed digital models tend to focus on static geometric modeling, failing to integrate historical hydrological sequences with real-time monitoring data to construct a dynamic driving mechanism. This leads to a significant lag between the real-time state of the physical watershed and the simulation results of the digital model, resulting in technical deficiencies in watershed water resource management, including low precision in regulation, poor coordination, and delayed response. Consequently, these technologies cannot meet the demands of coordinated management of quantity, quality, and efficiency in complex watersheds. Summary of the Invention

[0004] This invention provides a method and system for coordinated regulation of water resources quantity, quality and efficiency under three-line constraints. Its main purpose is to solve the problem of low accuracy when carrying out coordinated regulation of water resources quantity, quality and efficiency.

[0005] To achieve the above objectives, this invention provides a method for coordinated regulation of water resource quantity, quality, and efficiency under three-line constraints, comprising: Collect multi-source water resource data of the target physical watershed, and construct a digital spatiotemporal twin of the target physical watershed based on the multi-source water resource data; Based on the digital spatiotemporal twin, the pre-acquired hydrodynamic model, water quality model and water efficiency assessment model are coupled in multiple channels to obtain a water resource coupling model. The quantity-quality-efficiency synergy simulation results of the multi-source water resources data are analyzed using the water resources coupling model, and the synergy weights are determined based on the synergy simulation results and the driving adjustment coefficients of the multi-source water resources data. The water resources coupling model is used to analyze the abrupt change intervals of the target physical watershed based on the multi-source water resources data, and a three-line constraint threshold for the target physical watershed is generated based on the abrupt change intervals. Based on the quantity-quality-efficiency synergistic weight and the three-line constraint threshold, the quantity-quality-efficiency control strategy of the target physical domain is simulated in the digital spatiotemporal twin. Based on the simulation results, quantity, quality, and efficiency control instructions for the target physical watershed are generated, and water resource allocation in the target physical watershed is coordinated and controlled according to the quantity, quality, and efficiency control instructions.

[0006] To address the aforementioned problems, this invention also provides a water resource quantity, quality, and efficiency coordinated regulation system oriented towards three-line constraints, the system comprising: A digital spatiotemporal twin construction module is used to collect multi-source water resource data of a target physical watershed and construct a digital spatiotemporal twin of the target physical watershed based on the multi-source water resource data. The water resources model coupling module is used to perform multi-channel coupling of the pre-acquired hydrodynamic model, water quality model and water efficiency assessment model based on the digital spatiotemporal twin to obtain a water resources coupling model. The quantity-quality-efficiency synergy weight determination module is used to analyze the quantity-quality-efficiency synergy simulation results of the multi-source water resources data using the water resources coupling model, and determine the quantity-quality-efficiency synergy weights based on the quantity-quality-efficiency synergy simulation results and the driving adjustment coefficients of the multi-source water resources data. The three-line constraint threshold generation module is used to analyze the abrupt change interval of the target physical watershed based on the multi-source water resources data using the water resources coupling model, and generate the three-line constraint threshold of the target physical watershed based on the abrupt change interval. The simulation module is used to simulate the quantity, quality, and efficiency control strategy of the target physical watershed in the digital spatiotemporal twin based on the quantity-quality-efficiency synergy weight and the three-line constraint threshold. The water resource allocation module is used to generate quantity, quality, and efficiency control instructions for the target physical watershed based on the simulation results, and to coordinate and control the water resource allocation of the target physical watershed according to the quantity, quality, and efficiency control instructions.

[0007] The advantages of this invention lie in the following aspects: First, it achieves precise digital and dynamic mapping of the physical watershed through multi-source water resource data acquisition and the construction of a digital spatiotemporal twin. Second, it realizes deep synergy between the hydrodynamic model, water quality model, and water efficiency assessment model through multi-channel coupling technology, improving the comprehensiveness and synergy of simulation results. Third, it obtains dynamic synergistic weights adapted to the characteristics of different regions of the watershed through quantity-quality-efficiency synergistic simulation analysis and dynamic weights, providing a scientific priority basis for the formulation of control strategies and improving the targeting of control. Fourth, it solves the problems of ambiguous definition of abrupt change intervals and isolated threshold setting by identifying abrupt change intervals and constructing three-line constraint thresholds, establishing a three-line constraint threshold system that is interconnected and synergistically effective, setting rigid boundaries for control strategies, and improving the safety and stability of control. Fifth, it achieves optimal control strategies that satisfy synergistic priorities without breaking constraint boundaries through control scenario simulation and reverse iterative optimization, improving the feasibility and optimization effect of control strategies. Sixth, it realizes comprehensive synergistic allocation of water quantity, water quality, and water use efficiency through the construction of three types of synergistic control mechanisms and three-dimensional synergistic control, ultimately achieving the goal of synergistic optimization of watershed water resources quantity, quality, and efficiency, and improving the comprehensive benefits of water resource utilization. Therefore, the water resource quantity, quality and efficiency coordinated regulation method and system proposed in this invention can solve the problem of low accuracy when carrying out water resource quantity, quality and efficiency coordinated regulation. Attached Figure Description

[0008] Figure 1 This is a flowchart illustrating a method for coordinated regulation of water resources quantity, quality, and efficiency under three-line constraints, as provided in an embodiment of the present invention.

[0009] Figure 2 This is a functional block diagram of a water resource quantity, quality, and efficiency coordinated regulation system oriented towards three-line constraints, provided in an embodiment of the present invention.

[0010] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0011] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0012] like Figure 1 As shown, the water resource quantity, quality, and efficiency coordinated regulation method oriented towards three-line constraints according to the present invention includes: S1. Collect multi-source water resource data of the target physical watershed, and construct a digital spatiotemporal twin of the target physical watershed based on the multi-source water resource data.

[0013] Multi-source water resources data refers to water resources-related data covering multiple dimensions such as topography, hydrology, ecology, and engineering, obtained from different monitoring channels of the target physical watershed, including but not limited to total water consumption, water quality monitoring data, and water use efficiency data.

[0014] In detail, the target physical watershed is decomposed into multiple monitoring units based on spatiotemporal grid partitioning rules. A heterogeneous sensor array is deployed in each monitoring unit, which includes a flow velocity sensor for collecting water quantity parameters, a multispectral sensor for collecting water quality parameters, and a smart water meter for collecting water use efficiency parameters. The raw data collected by the heterogeneous sensor array is processed by timestamp alignment and spatial coordinate normalization through an edge computing gateway. The processed multi-source water resource data is organized according to the monitoring unit identifier to form a watershed dataset with spatiotemporal correlation. Spatiotemporal alignment is completed at the data collection source to avoid spatiotemporal misalignment problems during multi-source data fusion.

[0015] Constructing a digital spatiotemporal twin of the target physical watershed based on the multi-source water resource data includes: Extract topographic elevation, river cross-section, and water conservancy layout information from the multi-source water resources data, and construct a three-dimensional geometric model of the watershed based on the topographic elevation, river cross-section, and water conservancy layout information; The physical attribute parameters of the watershed three-dimensional geometric model are determined based on the soil type, vegetation cover and land use data in the multi-source water resources data. A dynamic driving mechanism for water cycle in the target physical watershed is constructed based on the historical hydrological sequence and real-time monitoring data in the multi-source water resources data. Based on the aforementioned water cycle dynamic driving mechanism, the real-time monitoring data is fused with the watershed three-dimensional geometric model after adding physical attribute parameters to obtain a digital spatiotemporal twin.

[0016] In detail, terrain elevation information was collected using UAV LiDAR scanning technology.

[0017] River cross-sectional information refers to the shape, size, and related geometric parameters of the river cross-section within the target physical watershed, measured using an acoustic Doppler current profiler.

[0018] Water conservancy layout information refers to the location, scale, and functional configuration data of water conservancy facilities such as reservoirs, pumping stations, sluice gates, and sewage treatment plants within the target physical watershed.

[0019] Data on soil type (sandy soil, loam, clay soil), vegetation cover, and land use type (arable land, forest land, construction land, water area) are collected through ground monitoring stations. Historical hydrological sequences are obtained through hydrological monitoring stations, and real-time monitoring data (including real-time flow velocity, real-time water level, and real-time COD concentration) is obtained through a real-time sensor network.

[0020] The triangular mesh growth algorithm was used to grid the terrain elevation data, with a grid resolution of 10 meters × 10 meters, generating a continuous terrain surface from discrete elevation points. B-spline curve fitting was used to fit the river cross-section data to generate a smooth three-dimensional outline of the river. The water conservancy layout information was superimposed on the terrain surface according to the actual coordinates. Through the collaborative modeling function of CAD and GIS software, an integrated three-dimensional geometric model of the watershed, including terrain, river, and water conservancy projects, was constructed, thereby accurately restoring the spatial geometric characteristics of the watershed and ensuring the spatial consistency between the three-dimensional model and the physical watershed.

[0021] Physical property parameters refer to quantitative indicators that characterize the physical features of a watershed's three-dimensional geometric model. By consulting soil hydraulic properties handbooks, different soil types are mapped to parameters such as saturated hydraulic conductivity and field capacity. Based on vegetation cover, empirical formulas are used to calculate vegetation interception parameters (interception increases by 2-5 mm for every 10% increase in cover). Surface roughness coefficients are determined according to land use types, thereby transforming qualitative land cover data into quantitative physical parameters and improving the physical realism of the model.

[0022] The dynamic water cycle driving mechanism refers to a dynamic response rule built based on historical and real-time data to simulate key water cycle processes such as precipitation, evaporation, runoff generation and confluence, and sewage discharge in a watershed. A Long Short-Term Memory (LSTM) network is used to train historical hydrological sequences to establish a mapping model between rainfall and runoff. The mechanism integrates evaporation calculation formulas (based on the Penman-Monteith formula, calculating potential evaporation using data such as temperature, humidity, and wind speed), runoff generation and confluence models (using the Xin'anjiang model to simulate slope runoff generation and river confluence), and sewage load models (calculating sewage discharge based on industrial, domestic, and agricultural pollution source data), forming a complete water cycle driving chain covering precipitation, evaporation, runoff generation and confluence, and sewage discharge. A real-time data access interface is established to input real-time monitoring data into the driving mechanism, dynamically adjusting the simulation parameters of the water cycle process. This driving mechanism enables dynamic simulation of the water cycle process, solving the simulation lag problem caused by the static driving of existing models and ensuring that the twin can reflect the state changes of the physical watershed in real time.

[0023] The system achieves millisecond-level data interaction between real-time monitoring data and the 3D model through data synchronization protocols (such as MQTT); it maps real-time flow velocity and water level data to the corresponding river positions in the 3D model, dynamically updating the visualization status of river flow; it associates real-time water quality data with the pollutant transport module in the model, updating the spatial distribution of pollutant concentrations within the watershed in real time; and it adjusts the water cycle parameters in the model through a driving mechanism, keeping the evolution process of the 3D model synchronized with the actual water cycle process of the physical watershed. This achieves deep coupling between the geometric model and dynamic data, solving the problem of existing digital models being disconnected from the physical watershed, and ultimately forming a digital spatiotemporal twin that can accurately map the spatiotemporal characteristics of the watershed.

[0024] Furthermore, through a complete process of multi-source data acquisition, 3D geometric modeling, physical parameter determination, dynamic driving mechanism construction, and data-model fusion, the digital and dynamic mapping of the target physical watershed was realized. This solved the problems of incomplete watershed digital model data, low geometric accuracy, and weak dynamic response, providing a high-precision digital carrier for subsequent model coupling and control simulation.

[0025] S2. Based on the digital spatiotemporal twin, the pre-acquired hydrodynamic model, water quality model and water efficiency assessment model are coupled in multiple channels to obtain a water resource coupling model.

[0026] The water resources coupling model is a digital spatiotemporal twin of the target physical watershed, integrating pre-acquired hydrodynamic, water quality, and water efficiency assessment models. It forms a cyclic channel through bidirectional feedback channels, dynamic coupling interfaces, and cross-channel correlations, and is an integrated simulation model obtained after reaching a stable state through equilibrium iteration. Specifically, it includes: A two-way feedback channel is constructed between the hydrodynamic model and the water efficiency evaluation model in the digital spatiotemporal twin.

[0027] A first dynamic coupling interface is constructed between the water quality model and the hydrodynamic model, and a second dynamic coupling interface is constructed between the water quality model and the water efficiency evaluation model.

[0028] The first dynamic coupling interface and the second dynamic coupling interface are cross-channel associated to obtain an associated channel. The bidirectional feedback channel is associated with the associated channel to obtain a loop channel.

[0029] The circulation channel is iterated in a balanced manner until it reaches a balanced state. Then, the coupling model corresponding to the circulation channel is output as a water resource coupling model.

[0030] A hydrodynamic model is a mathematical simulation model that simulates the flow state (including velocity, water level, and flow rate) within a watershed. The hydrodynamic model utilizes the MIKE model. Model 21 is based on a 3D geometric model of a digital spatiotemporal twin, with a computational grid set up. The grid size is consistent with the twin's terrain grid (10m × 10m). Determined physical properties such as surface roughness coefficient and river cross-section parameters are input, and the flow state (initial flow velocity, initial water level) is initialized. The water quality model is a mathematical simulation model that simulates the migration and transformation of pollutants within the watershed (including concentration changes, diffusion range, etc.). The water quality model adopts the QUAL2K model, with parameters such as pollutant degradation coefficient and diffusion coefficient input. The computational domain of the model is set based on the river spatial information of the twin. The water efficiency assessment model is a quantitative analysis model that evaluates the water resource utilization efficiency within the watershed (including industrial water use efficiency, agricultural water use efficiency, domestic water use efficiency, etc.). The water efficiency assessment model adopts the Data Envelopment Analysis (DEA) model, selecting water consumption, water resource output (GDP), and pollutant emissions as assessment indicators. The basic values ​​of the indicators (industrial water use efficiency, agricultural water use efficiency, domestic water use efficiency) are determined based on multi-source data, thereby ensuring the compatibility of each model with the digital spatiotemporal twin and laying the foundation for subsequent coupling.

[0031] The bidirectional feedback channel utilizes a real-time data transmission protocol (such as WebSocket) to establish a channel with a data transmission frequency of once per minute. The output data of the hydrodynamic model (including real-time flow velocity, water level, and flow rate at various river cross-sections) is transmitted to the water efficiency assessment model via the bidirectional feedback channel, serving as the basis for water use efficiency calculations. For example, when the hydrodynamic model outputs that the flow velocity in a certain area increases from 0.5 m / s to 1.0 m / s, the water efficiency assessment model adjusts the water loss coefficient for that area in real time, thereby correcting the water use efficiency calculation results. Conversely, the output data of the water efficiency assessment model (including water use efficiency values ​​and water-saving potential values ​​for various industries) is transmitted back to the hydrodynamic model via the bidirectional feedback channel, serving as the basis for optimizing water flow scheduling. For example, when the water use efficiency of an industrial area is below 0.6, the hydrodynamic model adjusts the water supply flow allocation for that area. This bidirectional feedback channel solves the problem of unidirectional data transmission and lack of interaction between models, achieving dynamic coordination between hydrodynamic status and water use efficiency.

[0032] A dynamic coupling interface is a standardized interface used to connect two models, enabling real-time data conversion and transmission. Based on the pollutant transport equation (convective diffusion equation), the interface data conversion rules are designed to convert the velocity and flow rate data output by the hydrodynamic model into the convective transport parameters required by the water quality model, and to convert the pollutant concentration data output by the water quality model into the additional resistance parameters (such as pollutant concentration and flow resistance coefficient) required by the hydrodynamic model. For example, if the hydrodynamic model outputs a flow rate of 200 m³ / s and a velocity of 0.8 m / s at a certain river cross-section, after conversion through the first dynamic coupling interface, it is input into the water quality model as the dynamic conditions for pollutant diffusion; the water quality model calculates the COD concentration at the cross-section, which is then fed back to the hydrodynamic model after interface conversion, adjusting the flow resistance coefficient for that area. When constructing the second dynamic coupling interface between the water quality model and the water efficiency assessment model, association mapping rules are designed to convert the water use efficiency changes output by the water efficiency assessment model into the wastewater load changes required by the water quality model (e.g., for every 10% increase in water use efficiency, the wastewater load decreases by 8%-12%). Conversely, the pollutant concentration exceeding the standard signal output by the water quality model is converted into the efficiency constraints required by the water efficiency assessment model (e.g., when the COD concentration exceeds 30 mg / L, the water use efficiency assessment weight increases by 20%). For example, if the water efficiency assessment model outputs a 15% increase in industrial water use efficiency, this is converted into a 12% decrease in wastewater load through the second dynamic coupling interface, and the pollutant input is adjusted in the water quality model. If the water quality model detects a COD concentration of 38 mg / L (exceeding the standard) in a certain area, this is fed back to the water efficiency assessment model through the interface, triggering a rigorous assessment of the water use efficiency in that area. These two dynamic coupling interfaces solve the problem of incompatible data formats and inability to directly interact between different types of models, achieving a precise correlation between water quality and hydrodynamics, and between water quality and water efficiency.

[0033] Furthermore, when linking the first and second dynamic coupling interfaces across channels, an interface mapping table is constructed to clarify the correspondence between the data sources, data types, and transmission directions of the two interfaces. For example, the hydrodynamic velocity data of the first interface is linked to the water use efficiency calculation data of the second interface, and the pollutant concentration data of the second interface is linked to the water flow resistance adjustment data of the first interface, forming a linked channel covering three models. When linking the bidirectional feedback channel with the linked channel, a bus architecture design is adopted, connecting all channels to a unified data bus and setting channel priorities (hydrodynamic data transmission with high real-time requirements has the highest priority), forming a cyclic channel of "hydrodynamic model - water quality model - water efficiency assessment model - hydrodynamic model". This channel linking method solves the problem of chaotic data transmission from multiple independent channels and realizes the orderly circulation of data between multiple models. When performing balancing iterations on the cyclic channel, the Newton-Raphson iteration method is used, and the iteration convergence condition is set as the rate of change of the output data of each model in two adjacent iterations being less than 5%. During the iteration process, the hydrodynamic model first outputs flow state data based on real-time data from the twin model, which is then transmitted to the water quality model and the water efficiency assessment model through a circulation channel. The water quality model and the water efficiency assessment model calculate based on the input data and output the results, which are then fed back to the hydrodynamic model and to each other. The hydrodynamic model adjusts its calculation parameters based on the feedback data and outputs new flow state data again. This iterative process continues until the circulation channel reaches equilibrium, at which point the corresponding coupled model is output as the water resource coupled model. This equilibrium iteration method solves the problem of data oscillation and instability after multi-model coupling, ensuring the computational stability and result reliability of the water resource coupled model.

[0034] Furthermore, by constructing a two-way feedback channel, designing a dynamic coupling interface, and implementing cross-channel correlation and balance iteration, deep coupling of the hydrodynamic model, water quality model, and water efficiency assessment model was achieved. This solved the problems of independent operation of multiple models, data interaction lag, and poor coordination, and formed an integrated model that can comprehensively reflect the coordinated relationship between watershed water quantity, water quality, and water use efficiency.

[0035] S3. Analyze the quantity-quality-efficiency synergistic simulation results of the multi-source water resources data using the water resources coupling model, and determine the quantity-quality-efficiency synergistic weights based on the quantity-quality-efficiency synergistic simulation results and the driving adjustment coefficients of the multi-source water resources data.

[0036] The simulation results of quantity-quality-efficiency synergy refer to the simulation data output by the water resources coupling model that can reflect the synergistic relationship between watershed water allocation, water quality, and water use efficiency.

[0037] The simulation results of the quantity, quality, and efficiency synergy of the multi-source water resources data were analyzed using the aforementioned water resources coupling model, including: In the water resource coupling model, a virtual monitoring section is configured, and the spatiotemporal distribution of water quantity allocation, water quality concentration and water use efficiency in the multi-source water resource data is obtained based on the virtual monitoring section. The quantity-quality-efficiency matching degree of different regions in the target physical watershed is calculated based on the spatiotemporal distribution, and the advantageous and disadvantageous areas of water resources in the target physical watershed are identified based on the quantity-quality-efficiency matching degree. Analyze the boundary conditions of the advantageous and disadvantageous regions, and simulate the changing trends of the quantity-quality-efficiency synergy relationship under different scenarios based on the boundary conditions; Based on the changing trend, key influencing factors in the quantity-quality-efficiency synergy relationship are extracted, and the simulation results of the quantity-quality-efficiency synergy of the multi-source water resources data are determined according to the key influencing factors.

[0038] In detail, a virtual monitoring section refers to a virtual monitoring point set up in a water resources coupling model to obtain quantitative, qualitative, and efficiency data at a specific location. Combining the topographic features, water conservancy project distribution, and administrative divisions of the target physical watershed, virtual monitoring sections are set up at key locations such as the confluence of main and tributary rivers, reservoir inlets and outlets, water intakes, and sewage outlets. The monitoring range of each section covers the corresponding river cross-section and the surrounding land area within a 1-kilometer radius. Spatiotemporal distribution refers to the distribution characteristics of water quantity allocation, water quality concentration, and water use efficiency at different spatial locations and time periods within the target physical watershed. Based on these virtual monitoring sections, spatiotemporal distribution data for the past 12 months is obtained: in terms of water quantity allocation, the monthly average allocation of upstream sections and downstream sections is obtained; in terms of water quality concentration, the monthly average COD concentration of the sections is obtained, including the concentration of sections around reservoirs and industrial areas; in terms of water use efficiency, the monthly average water use efficiency of the sections is obtained, including the efficiency of sections in agricultural areas and industrial concentrated areas, thereby accurately capturing quantitative, qualitative, and efficiency data for different regions of the watershed.

[0039] The matching degree of quantity, quality, and efficiency refers to the degree of mutual adaptation among water quantity, water quality, and water use efficiency in a given region. An evaluation system is constructed using the entropy weight-TOPSIS method, with water quantity allocation compliance rate, water quality compliance rate, and water use efficiency compliance rate as evaluation indicators. The objective weights of each indicator (such as water quantity, water quality, and water use efficiency) are determined using the entropy weight method, and then the matching degree of quantity, quality, and efficiency for each administrative region is calculated using the TOPSIS method. For example, the calculated matching degree values ​​for 10 administrative regions range from 0.55 to 0.92. Three administrative regions with a matching degree ≥ 0.8 are identified as advantageous regions, while four administrative regions with a matching degree ≤ 0.65 are identified as disadvantageous regions. Advantageous regions are those with high matching degree of quantity, quality, and efficiency and good water resource utilization; disadvantageous regions are those with low matching degree of quantity, quality, and efficiency and shortcomings in water resource utilization. This approach combines objective weights with relative proximity analysis, solving the problem of strong subjectivity in matching degree calculation and ensuring the accuracy of identifying advantageous and disadvantageous regions.

[0040] The boundary conditions for advantageous areas include: average monthly rainfall ≥ 120 mm, sewage load ≤ 5 tons / month, water conservancy project matching rate ≥ 80%, and an industrial structure dominated by high-tech industries and ecological agriculture. The boundary conditions for disadvantageous areas include: average monthly rainfall ≤ 80 mm or sewage load ≥ 15 tons / month, water conservancy project matching rate ≤ 60%, and an industrial structure dominated by water-intensive industries or traditional agriculture. For example, based on these boundary conditions, three simulation scenarios are set: Scenario 1 is a 20% increase in rainfall, Scenario 2 is a 30% reduction in sewage load, and Scenario 3 is a 15% increase in water conservancy project matching rate. By simulating the changing trends of the quantity-quality-efficiency synergy relationship under each scenario using a water resource coupling model, the matching degree of disadvantageous areas improves by an average of 12% in Scenario 1, 18% in Scenario 2, and 15% in Scenario 3. The matching degree of advantageous areas remains above 0.85 in all scenarios. This scenario simulation method can clearly present the impact of different factors on the synergistic relationship, providing a basis for extracting key influencing factors. Key influencing factors refer to those factors that play a decisive role in the synergistic relationship between quantity, quality, and efficiency. Using grey relational analysis, the correlation between each influencing factor (rainfall, sewage load, water conservancy project matching rate, industrial structure, and water-saving technology application rate) and the matching degree of quantity, quality, and efficiency is calculated. If the correlation degree of all three factors is greater than the target threshold (generally 0.8), they are identified as key influencing factors. The sensitivity of the matching degree of quantity, quality, and efficiency to water quantity (rainfall), water quality (sewage discharge), and water efficiency (matching rate) is calculated (i.e., the ratio of the rate of change of the matching degree to the rate of change of the factors). The simulation result of quantity, quality, and efficiency synergy is obtained as a response vector. For example, the response vector of a weak area is [0.6, 0.9, 0.75], indicating that the synergy level in this area is most sensitive to water quality improvement, followed by water quantity. In subsequent steps, this response vector is weighted and synthesized with the driving adjustment coefficient, which characterizes the difficulty of external regulation, to determine the weight of quantity, quality, and efficiency synergy, achieving a deep binding between weight allocation and the actual risk sensitivity of the watershed.

[0041] The driving adjustment coefficient refers to the adjustment parameter used to correct the synergistic weight, which is determined based on the dynamic change characteristics of multi-source water resources data; the quantity-quality-efficiency synergistic weight refers to the quantitative coefficient used to measure the importance of water quantity, water quality, and water use efficiency in synergistic regulation.

[0042] In this embodiment of the invention, determining the synergistic weight of quantity, quality, and efficiency based on the synergistic simulation results and the driving adjustment coefficients of the multi-source water resources data includes: Based on the distribution of weak areas in the aforementioned quantity-quality-efficiency synergistic simulation results, the basic weights of different regions in the target physical watershed are determined. The stability coefficients of each index in different regions of the target physical watershed are calculated based on the time variation characteristics of the multi-source water resources data. The basic weights and the stability coefficients are weighted and calculated to obtain the preliminary synergistic weights for water resource quantity, quality and efficiency. The initial collaborative weights are dynamically corrected based on the driving adjustment coefficients to obtain the quantity-quality-efficiency collaborative weights.

[0043] In detail, the basic weights refer to the initial values ​​of quantity, quality, and efficiency weights preliminarily determined based on the distribution of weak areas. Using a zonal weighting method, the target physical watershed is divided into three regions: a core region, a transition region, and a peripheral region. The core region, accounting for 20% of the weak area, has basic weights set at 0.3 for water quantity, 0.35 for water quality, and 0.35 for water use efficiency. The transition region, accounting for 35% of the weak area, has basic weights set at 0.35 for water quantity, 0.4 for water quality, and 0.25 for water use efficiency. The peripheral region, accounting for 45% of the weak area, has basic weights set at 0.4 for water quantity, 0.4 for water quality, and 0.2 for water use efficiency. This basic weighting setting incorporates the distribution of weak links in different regions, solving the problem of a one-size-fits-all approach to weighting and improving the regional adaptability of the weights. The stability coefficient is a quantitative indicator that measures the degree of fluctuation of each indicator data over time. Using the coefficient of variation method, the annual coefficients of variation for each indicator (water quantity, water quality, and water use efficiency) over the past 10 years are calculated. ,but ,in For the first The standard deviation of each indicator For the first The mean of each indicator, and the stability coefficient is ,in The variable dynamic perception vector value encodes the current scenario. For example, Context=[1, 0, 0] (dry season), the water volume will be automatically increased to emphasize water conservation; Context=[0, 0.8, 0.2] (water quality improvement period), the weight of water quality will be increased; Context=[0, 1, 0] (sudden pollution event), the mode will be switched to water quality priority mode.

[0044] Specifically, the preliminary collaborative weights refer to the weight values ​​calculated based on the basic weights and stability coefficients without dynamic adjustment, using a weighted product formula: ,in For the first Preliminary coordinated weights for each indicator For the first The basic weight of each indicator For the first The stability coefficient of each indicator The number of indicators is used. The driving adjustment coefficient is determined based on the changing trends of multi-source water resource data over the past three years. When water volume shows an increasing trend, the adjustment coefficient is 1.05; when water quality shows an improving trend, the adjustment coefficient is 1.1; and when water use efficiency shows an increasing trend, the adjustment coefficient is 1.08; conversely, the adjustment coefficient is 0.95-0.98. The correction formula is: ,in For the first The synergistic weights of each indicator For the first The driving adjustment coefficients of each indicator, this dynamic correction method solves the problem that fixed weights cannot adapt to dynamic changes in data, and ensures that the coordinated weights can match the watershed water resources status in real time.

[0045] Furthermore, through weight calculation and correction, accurate simulation results of quantity-quality-efficiency synergy and dynamically adapted synergy weights were obtained, avoiding the problems of insufficient analysis of quantity-quality-efficiency synergy and subjective and rigid weight setting, and providing a quantitative basis for subsequent threshold generation and control simulation.

[0046] S4. Using the water resource coupling model, analyze the abrupt change intervals of the target physical watershed based on the multi-source water resource data, and generate the three-line constraint thresholds of the target physical watershed based on the abrupt change intervals.

[0047] The mutation interval refers to the transition interval in which the water resources state of the target physical watershed changes from a stable state to an unstable state. Within this interval, the synergistic relationship between the quantity, quality, and efficiency of water resources will undergo a qualitative change.

[0048] The water resource coupling model is used to analyze the abrupt change intervals of the target physical watershed based on the multi-source water resource data, including: A progressively changing perturbation sequence is generated in the water resources coupling model, and the state response data of the target physical watershed is analyzed based on the perturbation sequence. The state response data is fitted and analyzed to obtain the critical point at which the state of the target physical watershed undergoes a qualitative change, and the stability characteristics of the target physical watershed within the range of the critical point are analyzed. The stable and unstable states of the target physical watershed are determined based on the stability characteristics described above. The abrupt change range of the target physical watershed is determined based on the transition point between the stable state and the unstable state.

[0049] In detail, a disturbance sequence refers to a progressively changing sequence of disturbance data designed to trigger a state response in the target physical watershed. Three key disturbance factors are selected: rainfall, sewage load, and total water consumption. Ten progressively changing gradients are set for each factor. For example, rainfall gradually increases from 50 mm / month to 200 mm / month (gradient increment 15 mm / month), sewage load gradually increases from 5 tons / month to 50 tons / month (gradient increment 4.5 tons / month), and total water consumption gradually increases from 3 million cubic meters / month to 8 million cubic meters / month (gradient increment 500,000 cubic meters / month). The disturbance gradients of these three factors are combined to generate 90 sets of disturbance sequences to ensure coverage of the main range of factor changes. State response refers to the change in state indicators such as water quantity, water quality, and water use efficiency of a target physical watershed under the action of a disturbance sequence. Each set of disturbance sequences is input into the water resources coupling model to simulate the corresponding state response data. For example, if the water supply and demand gap exceeds 10%, the water supply and demand are basically balanced, or the water supply is sufficient, this state response analysis can clearly show the correlation between factor changes and watershed state, providing data support for critical point identification.

[0050] Using the cusp catastrophe model in catastrophe theory, the state response data is fitted and analyzed. Outlier removal and standardization preprocessing are performed on the state response data. Then, based on the classical cusp catastrophe potential function, a target potential function is constructed by adapting it to the specific application scenario. The undetermined coefficients of the potential function are estimated using the nonlinear least squares method, and the model fit is verified by the coefficient of determination and residual analysis. Finally, the simultaneous equations are solved using the condition that the first and second derivatives of the potential function are both zero, identifying the critical point of state qualitative change for the corresponding scenario, thus completing the entire fitting analysis process. Stability characteristics refer to the property of the target physical watershed to remain stable or tend towards instability within the critical point range. The Lyapunov exponent is used to determine this: when the perturbation factor is below the critical point, the Lyapunov exponent is positive (0.15-0.3), indicating an unstable state; when the perturbation factor is above the critical point, the Lyapunov exponent is negative (-0.3 to -0.15), indicating a stable state. The stable state is defined as a negative Lyapunov index, with a COD concentration ≤30 mg / L, water use efficiency ≥0.7, and a water supply-demand gap ≤5%. The unstable state is defined as a positive Lyapunov index, satisfying any of the following conditions: COD concentration >40 mg / L, water use efficiency <0.6, and water supply-demand gap >10%. Based on the transition point between the stable and unstable states, the fluctuation range (e.g., ±5 units) of each disturbance factor causing the watershed state to transition from stable to unstable is analyzed based on the critical value corresponding to the transition point. The transition numerical range corresponding to each key disturbance factor is determined, and then the transition numerical ranges of all key disturbance factors are integrated to form a comprehensive numerical range that can completely cover the gradual transition of the watershed state from stable to unstable. This comprehensive numerical range is the abrupt change range of the target physical watershed.

[0051] In this embodiment of the invention, the three-line constraint threshold refers to three key thresholds determined based on abrupt change intervals to constrain water allocation, water quality, and water use efficiency. These thresholds include the total water consumption control red line, the water quality compliance baseline, and the water use efficiency upper limit. Based on the nonlinear decay characteristics of the water resource carrying capacity of the target physical watershed, the critical turning point of water allocation is extracted from the abrupt change interval. Identify the boundary conditions under which water quality indicators undergo irreversible deterioration within the aforementioned mutation interval; The inflection point characteristics of water use efficiency from quantitative to qualitative change in the mutation interval were analyzed using a preset regression model. A three-line coupling constraint equation is constructed based on a preset linkage control factor to connect the critical inflection point, the boundary conditions, and the inflection point characteristics. The three-line constraint threshold of the target physical watershed is analyzed using the aforementioned three-line coupling constraint equation.

[0052] In detail, when extracting the critical turning point of water allocation based on the nonlinear decay characteristics of water resource carrying capacity, a Logistic model is used to fit the relationship between water resource carrying capacity and total water consumption. The fitting equation is as follows: ,in Water resource carrying capacity (10,000 cubic meters / year). The total water consumption is 10,000 cubic meters per month. According to the equation, when the total water consumption reaches 6.2 million cubic meters per month, the rate of decline in water resource carrying capacity increases from 50,000 cubic meters per month to 1,000 cubic meters per month. This point is the critical turning point in water allocation. A water quality model was used to simulate the relationship between wastewater discharge load and COD concentration within the abrupt change range. When the wastewater discharge load reaches 38 tons per month, the COD concentration rises to 42 mg / L, and conventional wastewater treatment methods (80% efficiency) cannot reduce it to below 30 mg / L. This COD concentration of 42 mg / L corresponding to this wastewater discharge load represents the boundary condition for irreversible deterioration of water quality indicators. Piecewise linear regression analysis was used to analyze the relationship between total water consumption and water use efficiency. For example, when the total water consumption decreased to 6.3 million cubic meters per month, the water use efficiency jumped from 0.65 to 0.72. Subsequently, as the total water consumption decreased, the rate of increase in water use efficiency increased from 0.001 / 10,000 cubic meters to 0.003 / 10,000 cubic meters. This water use efficiency of 0.72 is the inflection point characteristic value from quantitative change to qualitative change.

[0053] The linkage control factor refers to the quantitative factor set to achieve synergistic linkage between the three constraint thresholds: water quantity threshold linkage factor k1 (water quantity threshold decreases by 5% when water quality threshold is exceeded), water quality threshold linkage factor k2 (water quality threshold tightens by 5% when water quantity threshold is exceeded), and efficiency threshold linkage factor k3 (efficiency threshold increases by 2% when either water quantity or water quality threshold is exceeded). Based on the critical inflection point (Q = 6.2 million cubic meters / month), boundary conditions (C = 42 mg / L), and inflection point eigenvalue (E = 0.72), the three-line coupled constraint equation is constructed as follows: , ( For the number of times the water quality exceeded the standard, (when Q≤620) , ( For the number of times the water volume exceeded the standard, When C≤42), , ( The total number of times the standard was exceeded. When E≥0.7). , (No exceedance), obtain the basic three-line constraint threshold; when (Water quality exceeded standards once), water volume control threshold lowered to [redacted]. Increase the upper limit of water efficiency; when (Water volume exceeds the standard once), the bottom line for water quality compliance is tightened, and the upper limit for water use efficiency is increased. Therefore, this coupled constraint equation realizes the linkage adjustment of the three thresholds, solves the problem of isolated threshold setting and lack of coordination in the existing system, and forms a three-line constraint threshold system that is mutually restrictive and synergistic.

[0054] By constructing a three-line constraint threshold, the range of abrupt changes in the state of water resources in the basin was accurately identified and a linkage threshold system was established. This avoids problems such as inaccurate identification of abrupt change intervals, isolated threshold settings, and lack of linkage mechanisms, and provides rigid constraints for subsequent simulation of regulation strategies.

[0055] S5. Based on the quantity-quality-efficiency synergistic weight and the three-line constraint threshold, the quantity-quality-efficiency control strategy of the target physical domain is simulated in the digital spatiotemporal twin.

[0056] The quantity, quality, and efficiency regulation strategy refers to a combination of regulation measures formulated to achieve the coordinated optimization of water quantity, water quality, and water use efficiency in a target physical watershed.

[0057] Based on the aforementioned quantity-quality-efficiency synergistic weights and the aforementioned three-line constraint thresholds, the quantity-quality-efficiency control strategy for the target physical watershed is simulated in the digital spatiotemporal twin, including: A control scenario is generated based on the engineering scheduling scheme of the target physical watershed, and the control scenario is integrated into the digital spatiotemporal twin. In the digital spatiotemporal twin, the comprehensive control indicators of the control scenario are positively analyzed based on the quantity-quality-efficiency synergistic weights. The comprehensive control indicators are screened based on the three-line constraint thresholds to obtain the target control scenario; The target control scenario is subjected to inverse iterative simulation analysis to obtain the target engineering scheduling scheme, and the quantity, quality and efficiency control strategy of the target physical watershed is generated based on the target engineering scheduling scheme.

[0058] In detail, a control scenario refers to a specific scenario used for simulation based on different engineering scheduling schemes. Combining the types of water conservancy projects within the basin (reservoirs, pumping stations, sluice gates, and wastewater treatment plants), for example, Scheme 1 is reservoir ecological scheduling (adjusting outflow and flood control level), Scheme 2 is pumping station water supply scheduling (adjusting water supply flow and water supply time periods), Scheme 3 is sluice gate joint scheduling (adjusting sluice gate opening and scheduling sequence), and Scheme 4 is wastewater treatment plant enhanced scheduling (adjusting treatment intensity and reclaimed water reuse ratio). Based on these four types of schemes, eight control scenarios are generated. The scheduling parameters of each control scenario are integrated into a digital spatiotemporal twin, and the scenario simulation function of the twin is used to digitally recreate the control scenario. The comprehensive control index refers to a comprehensive evaluation index obtained by weighting water quantity guarantee rate, water quality compliance rate, and water use efficiency value according to the synergistic weights of quantity, quality, and efficiency. ,Right now ,in For the first The control indicators include water quantity guarantee rate, water quality compliance rate, and water use efficiency (each indicator value is standardized to the 0-1 range). This positive analysis method combines quantity, quality, and efficiency synergistic weights to ensure that the comprehensive indicators can reflect the synergistic optimization effect of the control scenario and solves the problem of lack of priority consideration in the comprehensive evaluation.

[0059] The screening criteria were set as follows: water supply guarantee rate corresponding to a total water consumption of ≤6.2 million cubic meters / month (the water supply red line in the three-line threshold), water quality compliance rate corresponding to a COD concentration of ≤42 mg / L (water quality baseline), and water use efficiency value ≥0.72 (efficiency upper limit). The indicators of eight control scenarios were verified, scenarios that did not meet the conditions were eliminated, and the scenario with the highest comprehensive control index was selected as the target control scenario. This ensured that the target control scenario had both optimal synergistic effect and did not exceed the rigid constraint boundary, solving the problem of lack of constraint verification for control scenarios. Reverse iterative simulation analysis refers to a simulation analysis method that starts from the target comprehensive control index and derives the optimal engineering scheduling scheme in reverse. A genetic algorithm was used as the reverse iterative algorithm, setting the target comprehensive control index to 0.9. Water supply guarantee rate, water quality compliance rate, and water use efficiency value were used as optimization targets, and reservoir outflow, pump station water supply flow, gate opening, and sewage treatment intensity were used as optimization variables. The variable constraint range was set, and the number of iterations was set to 50 generations. Through simulation calculations using a digital spatiotemporal twin, and after 38 iterations, an optimal engineering scheduling scheme was obtained, such as a reservoir outflow of 1.05 million cubic meters per month, a pumping station water supply flow of 620,000 cubic meters per month, a gate opening of 65%, and a sewage treatment intensity of 93%. The corresponding comprehensive control indicators at this point meet the target requirements. Based on this target engineering scheduling scheme, a quantity-quality-efficiency control strategy was generated, including: ecological water replenishment to the reservoir at an outflow of 1.05 million cubic meters per month; water supply to the pumping station at a flow of 620,000 cubic meters per month during the peak water consumption period from 8:00 AM to 10:00 PM daily; maintaining a 65% gate opening in the core area to optimize water flow distribution; and increasing the sewage treatment plant's treatment intensity to 93% and the reclaimed water reuse rate to 40%. This achieved precise derivation from the target to the scheme, solving the problem of the control strategy being out of sync with the target, and ensuring the targetedness and feasibility of the control strategy.

[0060] By generating quantitative, qualitative, and effective control strategies through regulatory scenarios, the problems of subjective formulation of control strategies, lack of simulation verification, and failure to meet the requirements of coordination and constraints are avoided, thus providing a scientific and feasible solution for the final coordinated control of water resources.

[0061] S6. Generate quantity, quality, and efficiency control instructions for the target physical watershed based on the simulation results, and coordinate the water resource allocation of the target physical watershed according to the quantity, quality, and efficiency control instructions.

[0062] Quantity, quality, and efficiency control instructions refer to specific operational instructions that transform quantity, quality, and efficiency control strategies into guidance for the operation of water conservancy projects and the allocation of water resources.

[0063] In detail, the optimal control scheme obtained from the simulation is extracted and decomposed into specific engineering operation instructions, including water allocation instructions, water quality control instructions, and water use efficiency improvement instructions. Among them, water allocation instructions are used to adjust the water distribution ratio, water supply flow, and scheduling sequence within the basin; water quality control instructions are used to adjust the sewage treatment intensity, pollutant emission reduction, and reclaimed water reuse ratio; and water use efficiency improvement instructions are used to adjust industry water quotas and water-saving technology application requirements.

[0064] For example, the water allocation instructions include: setting the outflow from the upstream reservoir to 1.05 million cubic meters per month, the water supply flow from the main stream pumping station to 620,000 cubic meters per month, adjusting the tributary gate opening to 65%, and setting the daily water supply period from the midstream and downstream intakes to 8:00 AM to 10:00 PM; the water quality control instructions include: increasing the treatment intensity of the two sewage treatment plants in the basin to 93%, setting the COD removal rate target to 90%, increasing the reclaimed water reuse ratio from 30% to 40%, and adjusting the pollutant discharge concentration limit of industrial sewage outlets to COD ≤ 35 mg / L; the water use efficiency improvement instructions include: adjusting the water use quota for the industrial sector to 1.2 million cubic meters per month, adjusting the water use quota for the agricultural sector to 2.8 million cubic meters per month, keeping the domestic water use quota unchanged at 800,000 cubic meters per month, and requiring industrial enterprises to increase the coverage rate of water-saving technology applications to 90%.

[0065] Based on the water allocation command, a first collaborative control mechanism is established for the gate opening and pump station power of the target physical watershed; based on the water quality control command, a second collaborative control mechanism is established for the sewage treatment intensity and the proportion of reclaimed water reuse; and based on the water use efficiency improvement command, a third collaborative control mechanism is established for the industry water use quota. By coordinating and associating the first collaborative control mechanism, the second collaborative control mechanism, and the third collaborative control mechanism, a three-dimensional collaborative regulation diagram is obtained. The path of the three-dimensional collaborative control map is used to regulate the water resource allocation of the target physical watershed.

[0066] In detail, the first coordinated control mechanism refers to the control logic that coordinates the adjustment of gate opening and pump station power based on water allocation commands, and constructs a correlation model between gate opening and pump station power: Pump station power ,in The synergy coefficient (value 1.2) This refers to the water supply flow rate (cubic meters per second). Water supply head (meters) To achieve a pump station efficiency of 0.85, dynamic matching between gate opening and pump station power was implemented, ensuring that the water allocation process simultaneously met the downstream ecological base flow and the water dilution requirements at the intake. The second collaborative control mechanism refers to the control logic that coordinates the adjustment of wastewater treatment intensity and reclaimed water reuse ratio based on water quality control commands. A linkage equation between wastewater treatment intensity and reclaimed water reuse ratio is established: ,in The proportion of reclaimed water reuse. For wastewater treatment intensity (0.8≤S≤1.0), this mechanism coordinates the treatment intensity of wastewater treatment plants with the proportion of reclaimed water reuse, creating a positive feedback loop between water purification and water use efficiency improvement, thus resolving the disconnect between water quality control and efficiency improvement. The third collaborative control mechanism refers to the control logic that dynamically adjusts industry water use quotas based on water use efficiency improvement commands, employing a dynamic quota adjustment model: Industry Water Use Quota ,in As the benchmark quota, Adjustment coefficients (0.8 for industry, 0.6 for agriculture). For current water efficiency, With target water efficiency (0.85 for industry and 0.75 for agriculture), the water use quotas for each industry are dynamically adjusted based on water use efficiency monitoring data. This optimizes the economic output of water resources while ensuring total quantity control, thus solving the problem of fixed water use quotas and lack of dynamic adjustment.

[0067] Specifically, the three-dimensional collaborative control diagram refers to a visual chart that integrates three collaborative control mechanisms, presenting the control paths of water quantity, water quality, and water use efficiency. When the three collaborative control mechanisms are linked in a coordinated manner, a three-dimensional collaborative control diagram is constructed based on water quantity allocation, with water quality control as a constraint and water use efficiency as the objective. This chart uses water quantity allocation ratio (x-axis), sewage treatment intensity (y-axis), and water use efficiency value (z-axis) as three-dimensional coordinates, mapping the control paths of the three mechanisms onto the coordinate system to form a control path from the current state to the optimal state. When using the path of this three-dimensional collaborative control diagram to regulate water resource allocation, the control operations of the three types of control mechanisms are executed step by step in the order of "first ensuring the water quantity baseline, then strengthening water quality control, and finally optimizing efficiency improvement": the first step is to adjust the gate opening and pump station power to ensure that the water quantity allocation meets the ecological base flow and water supply needs; the second step is to increase the sewage treatment intensity and the proportion of reclaimed water reuse to ensure that water quality meets standards; the third step is to dynamically adjust the industry water use quota to improve water use efficiency, forming a three-dimensional collaborative control pattern in which water quantity supports water quality, water quality constrains water quantity, and efficiency optimizes allocation. This collaborative approach solves the problem of multiple control mechanisms operating independently and causing regulatory conflicts, thus achieving comprehensive collaborative optimization of water resource allocation.

[0068] like Figure 2The diagram shown is a functional block diagram of a water resource quantity, quality and efficiency coordinated regulation system oriented towards three-line constraints provided in an embodiment of the present invention.

[0069] The water resource quantity, quality, and efficiency coordinated regulation system 100 oriented towards three-line constraints described in this invention can be installed in an electronic device. Depending on the functions implemented, the water resource quantity, quality, and efficiency coordinated regulation system 100 may include a digital spatiotemporal twin construction module 101, a water resource model coupling module 102, a quantity, quality, and efficiency coordinated weight determination module 103, a three-line constraint threshold generation module 104, a simulation module 105, and a water resource allocation module 106. The module described in this invention can also be called a unit, referring to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0070] In this embodiment, the functions of each module / unit are as follows: The digital spatiotemporal twin construction module 101 is used to collect multi-source water resource data of the target physical watershed and construct a digital spatiotemporal twin of the target physical watershed based on the multi-source water resource data. The water resources model coupling module 102 is used to perform multi-channel coupling of the pre-acquired hydrodynamic model, water quality model and water efficiency assessment model based on the digital spatiotemporal twin to obtain a water resources coupling model. The quantity-quality-efficiency synergy weight determination module 103 is used to analyze the quantity-quality-efficiency synergy simulation results of the multi-source water resources data using the water resources coupling model, and determine the quantity-quality-efficiency synergy weight based on the quantity-quality-efficiency synergy simulation results and the driving adjustment coefficients of the multi-source water resources data. The three-line constraint threshold generation module 104 is used to analyze the abrupt change interval of the target physical watershed based on the water resource coupling model for the multi-source water resource data, and generate the three-line constraint threshold of the target physical watershed based on the abrupt change interval. The simulation module 105 is used to simulate the quantity, quality, and efficiency control strategy of the target physical domain in the digital spatiotemporal twin based on the quantity-quality-efficiency synergy weight and the three-line constraint threshold. The water resource allocation module 106 is used to generate quantity, quality and efficiency control instructions for the target physical watershed based on the simulation results, and to coordinate and control the water resource allocation of the target physical watershed based on the quantity, quality and efficiency control instructions.

[0071] In detail, the modules in the water resource quantity, quality, and efficiency coordinated regulation system 100 oriented towards three-line constraints described in this embodiment of the invention adopt the same characteristics as described above during use. Figure 1 The method used here is the same as the three-line constraint-oriented water resource quantity, quality and efficiency coordinated regulation method described above, and can produce the same technical effect, so it will not be repeated here.

[0072] In the several embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0073] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0074] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0075] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0076] Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects. The scope of the invention is not limited to the foregoing description, and all variations within the meaning and scope of equivalents falling within the protection scope are intended to be included in the invention.

[0077] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0078] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or systems stated in a system claim may also be implemented by a single unit or system through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for coordinated regulation of water resource quantity, quality, and efficiency under three-line constraints, characterized in that, The method includes: Collect multi-source water resource data of the target physical watershed, and construct a digital spatiotemporal twin of the target physical watershed based on the multi-source water resource data; Based on the digital spatiotemporal twin, the pre-acquired hydrodynamic model, water quality model and water efficiency assessment model are coupled in multiple channels to obtain a water resource coupling model. The quantity-quality-efficiency synergy simulation results of the multi-source water resources data are analyzed using the water resources coupling model, and the synergy weights are determined based on the synergy simulation results and the driving adjustment coefficients of the multi-source water resources data. The water resources coupling model is used to analyze the abrupt change intervals of the target physical watershed based on the multi-source water resources data, and a three-line constraint threshold for the target physical watershed is generated based on the abrupt change intervals. Based on the quantity-quality-efficiency synergistic weight and the three-line constraint threshold, the quantity-quality-efficiency control strategy of the target physical domain is simulated in the digital spatiotemporal twin. Based on the simulation results, quantity, quality, and efficiency control instructions for the target physical watershed are generated, and water resource allocation in the target physical watershed is coordinated and controlled according to the quantity, quality, and efficiency control instructions.

2. The method for coordinated regulation of water resources quantity, quality, and efficiency under three-line constraints as described in claim 1, characterized in that, The construction of a digital spatiotemporal twin of the target physical watershed based on the multi-source water resource data includes: Extract topographic elevation, river cross-section, and water conservancy layout information from the multi-source water resources data, and construct a three-dimensional geometric model of the watershed based on the topographic elevation, river cross-section, and water conservancy layout information; The physical attribute parameters of the watershed three-dimensional geometric model are added based on the soil type, vegetation cover and land use data in the multi-source water resources data; A dynamic driving mechanism for water cycle in the target physical watershed is constructed based on the historical hydrological sequence and real-time monitoring data in the multi-source water resources data. Based on the aforementioned water cycle dynamic driving mechanism, the real-time monitoring data is fused with the watershed three-dimensional geometric model after adding physical attribute parameters to obtain a digital spatiotemporal twin.

3. The method for coordinated regulation of water resources quantity, quality, and efficiency under three-line constraints as described in claim 1, characterized in that, The water resources coupling model is obtained by multi-channel coupling of the pre-acquired hydrodynamic model, water quality model, and water efficiency assessment model based on the digital spatiotemporal twin, including: A two-way feedback channel is established in the digital spatiotemporal twin using a real-time data transmission protocol to realize two-way data interaction between the hydrodynamic model and the water efficiency evaluation model; A first dynamic coupling interface is constructed between the water quality model and the hydrodynamic model, and a second dynamic coupling interface is constructed between the water quality model and the water efficiency assessment model; the first dynamic coupling interface is used to convert the output data of the hydrodynamic model into the input data required by the water quality model; the second dynamic coupling interface is used to convert the output data of the water efficiency assessment model into the input data required by the water quality model. An interface mapping table is constructed to clarify the correspondence between the data source, data type, and transmission direction of the first dynamic coupling interface and the second dynamic coupling interface, forming an associated channel; a bus architecture is adopted to connect the associated channel and the bidirectional feedback channel to a unified data bus, set channel priorities, and form a loop channel; Using the Newton-Raphson iteration method, the iteration convergence condition is set; after the rate of change of the output data of each model in two adjacent iterations meets the preset threshold, the loop channel reaches the equilibrium state, and the corresponding coupled model output is the water resource coupled model.

4. The method for coordinated regulation of water resource quantity, quality, and efficiency under three-line constraints as described in claim 1, characterized in that, The simulation results of the quantity, quality, and efficiency synergy of the multi-source water resources data were analyzed using the aforementioned water resources coupling model, including: Virtual monitoring sections are configured in the water resource coupling model to obtain the spatiotemporal distribution of water allocation, water quality concentration and water use efficiency; Based on the spatiotemporal distribution, the entropy weight-TOPSIS method is used to calculate the quantity-quality-efficiency matching degree of each administrative region in the target physical watershed; based on the quantity-quality-efficiency matching degree, the advantageous and weak areas of water resources in the target physical watershed are identified. Analyze the boundary conditions of the advantageous and disadvantageous regions, and simulate the changing trends of the quantity-quality-efficiency synergy relationship under different scenarios based on the boundary conditions; Based on the grey relational analysis method, the key influencing factors in the quantity-quality-efficiency synergy relationship are extracted from the changing trend. The sensitivity coefficient of the quantity-quality-efficiency matching degree to the key influencing factors is calculated. The response vector composed of the sensitivity coefficients of water quantity, water quality and water efficiency is determined as the quantity-quality-efficiency synergy simulation result of the multi-source water resources data.

5. The method for coordinated regulation of water resource quantity, quality, and efficiency under three-line constraints as described in claim 1, characterized in that, The quantity-quality-efficiency synergy weights are determined based on the simulation results and the driving adjustment coefficients of the multi-source water resources data, including: Based on the proportion of weak areas, the target physical watershed is divided into core area, transition area, and edge area; basic weights are set for core area, transition area, and edge area, including basic weights for water quantity, water quality, and water use efficiency. Using the coefficient of variation method, based on the time variation characteristics of the multi-source water resources data, the stability coefficients of water quantity, water quality, and water use efficiency in the core area, transition area, and edge area of ​​the target physical watershed are calculated. The basic weights and the stability coefficients are weighted and calculated to obtain the preliminary synergistic weights for water resource quantity, quality and efficiency. Based on the changing trends of multi-source water resources data over the past three years, driving adjustment coefficients for water quantity, water quality, and water use efficiency are preset, and the preliminary collaborative weights are dynamically corrected to obtain the quantity-quality-efficiency collaborative weights.

6. The method for coordinated regulation of water resource quantity, quality, and efficiency under three-line constraints as described in claim 1, characterized in that, The water resource coupling model is used to analyze the abrupt change intervals of the target physical watershed based on the multi-source water resource data, including: In the water resource coupling model, a progressively changing perturbation data sequence is generated, and the state response data of water quantity, water quality, and water use efficiency in the target physical watershed are analyzed based on the perturbation data sequence. Using the cusp catastrophe model in catastrophe theory, the state response data is fitted and analyzed to obtain the critical point at which the state of the target physical watershed undergoes a qualitative change. The Lyapunov exponent is then used to determine and analyze the stability characteristics of the target physical watershed within the range of the critical point. The stable and unstable states of the target physical watershed are determined based on the stability characteristics described above. The abrupt change range of the target physical watershed is determined based on the transition point between the stable state and the unstable state.

7. The method for coordinated regulation of water resources quantity, quality, and efficiency under three-line constraints as described in claim 1, characterized in that, The three-line constraint thresholds for the target physical watershed are generated based on the abrupt change interval, including: The relationship between water resource carrying capacity and total water consumption is fitted using a Logistic model, and the critical turning point of water allocation is extracted from the abrupt change interval. The relationship between sewage load and water quality indicators within the abrupt change interval is simulated using a water quality model to identify the boundary conditions under which water quality indicators undergo irreversible deterioration within the abrupt change interval. The relationship between total water consumption and water use efficiency is analyzed using a pre-defined regression model, and the inflection point characteristics of water use efficiency from quantitative to qualitative change in the abrupt change interval are identified. A three-line coupled constraint equation is constructed based on the preset linkage control factors of water quantity, water quality and water use efficiency, the critical inflection point, the boundary conditions and the inflection point characteristics. The three-line constraint threshold of the target physical watershed is analyzed using the aforementioned three-line coupling constraint equation.

8. The method for coordinated regulation of water resources quantity, quality, and efficiency under three-line constraints as described in claim 1, characterized in that, Based on the aforementioned quantity-quality-efficiency synergistic weights and the aforementioned three-line constraint thresholds, the quantity-quality-efficiency control strategy for the target physical watershed is simulated in the digital spatiotemporal twin, including: A control scenario is generated based on the engineering scheduling scheme of the target physical watershed, and the control scenario is integrated into the digital spatiotemporal twin. In the digital spatiotemporal twin, the comprehensive control index of the control scenario is positively analyzed based on the quantity-quality-efficiency synergy weight; the comprehensive control index refers to the weighted average of water quantity guarantee rate, water quality compliance rate, and water use efficiency value based on the quantity-quality-efficiency synergy weight. The comprehensive control indicators are screened based on the three-line constraint thresholds, and the scenario with the highest comprehensive control indicator is selected as the target control scenario. Using a genetic algorithm, the target control scenario is subjected to inverse iterative simulation analysis to obtain the optimal target project scheduling scheme, and the quantity, quality and efficiency control strategy of the target physical watershed is generated based on the target project scheduling scheme.

9. The method for coordinated regulation of water resources quantity, quality, and efficiency under three-line constraints as described in claim 1, characterized in that, The coordinated regulation of water resource allocation in the target physical watershed according to the aforementioned quantity, quality, and efficiency control instructions includes: The quantity, quality and efficiency control instructions are parsed into water quantity allocation instructions, water quality control instructions and water use efficiency improvement instructions. Based on the water allocation instructions, a first collaborative control mechanism is established for the gate opening and pump station power of the target physical watershed. Based on the water quality control instructions, a second collaborative control mechanism is established for the sewage treatment intensity and reclaimed water reuse ratio. Based on the water use efficiency improvement instructions, a third collaborative control mechanism is established for the industry water use quota. The first collaborative control mechanism establishes a correlation model between gate opening and pump station power. The second collaborative control mechanism establishes a linkage equation between sewage treatment intensity and reclaimed water reuse ratio. The third collaborative control mechanism adopts a dynamic adjustment model for the industry water use quota. By coordinating and associating the first collaborative control mechanism, the second collaborative control mechanism, and the third collaborative control mechanism, a three-dimensional collaborative regulation diagram is obtained. The path of the three-dimensional collaborative control map is used to regulate the water resource allocation of the target physical watershed.

10. A water resource quantity, quality, and efficiency coordinated regulation system oriented towards three-line constraints, characterized in that, The system is used to implement the water resource quantity-quality-efficiency coordinated regulation method oriented towards three-line constraints as described in any one of claims 1-9, the system comprising: A digital spatiotemporal twin construction module is used to collect multi-source water resource data of a target physical watershed and construct a digital spatiotemporal twin of the target physical watershed based on the multi-source water resource data. The water resources model coupling module is used to perform multi-channel coupling of the pre-acquired hydrodynamic model, water quality model and water efficiency assessment model based on the digital spatiotemporal twin to obtain a water resources coupling model. The quantity-quality-efficiency synergy weight determination module is used to analyze the quantity-quality-efficiency synergy simulation results of the multi-source water resources data using the water resources coupling model, and determine the quantity-quality-efficiency synergy weights based on the quantity-quality-efficiency synergy simulation results and the driving adjustment coefficients of the multi-source water resources data. The three-line constraint threshold generation module is used to analyze the abrupt change interval of the target physical watershed based on the multi-source water resources data using the water resources coupling model, and generate the three-line constraint threshold of the target physical watershed based on the abrupt change interval. The simulation module is used to simulate the quantity, quality, and efficiency control strategy of the target physical watershed in the digital spatiotemporal twin based on the quantity-quality-efficiency synergy weight and the three-line constraint threshold. The water resource allocation module is used to generate quantity, quality, and efficiency control instructions for the target physical watershed based on the simulation results, and to coordinate and control the water resource allocation of the target physical watershed according to the quantity, quality, and efficiency control instructions.

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