Cold region water resource reasonable distribution method and system based on ecological protection

Abstract

The invention discloses a cold region water resource reasonable distribution method and system based on ecological protection, and the method comprises the steps: obtaining ecological water demand spatial and temporal distribution data of different periods, building a life and production water demand prediction model through a gray prediction and regression analysis method, and determining life and production water demand prediction values; constructing a multi-objective optimization model of water resource distribution by taking the ecological water demand as a constraint condition and the life and production water demand predicted value as an optimization objective; solving the multi-objective optimization model by adopting a simulated annealing algorithm and a genetic algorithm to obtain an initial water resource distribution scheme; by defining weight coefficients of different water users, a multi-party benefit balanced water resource allocation coordination mechanism is established, and according to a coordination result of stakeholders, an initial water resource allocation scheme is adjusted and optimized to obtain a final water resource allocation scheme. According to the invention, efficient configuration and utilization of water resources can be realized on the premise of guaranteeing ecological water use.

Description

Technical Field

[0001] The present invention belongs to the field of water resource regulation and allocation, and in particular relates to a method and system for rationally allocating water resources in cold regions based on ecological protection. Background Art

[0002] In the process of water resource allocation in cold regions, it is necessary to take into account the needs of ecological protection, domestic water and production water. However, there is a competitive relationship between different types of water use. How to reasonably allocate limited water resources to meet the needs of life and production while ensuring ecological water use is a complex decision-making problem. Traditional water resource allocation methods are usually based on experience or simple priority rules, lack of systematicity and scientificity, and it is difficult to achieve efficient use of water resources.

[0003] Specifically, water resource allocation in cold regions faces the following technical difficulties: First, quantitative assessment of ecological water demand. The demand for water resources of different ecosystems varies greatly, and the water demand is subject to dynamic changes due to factors such as climate and geography. Accurate assessment of ecological water demand is the basis and prerequisite for allocation. Second, reasonable prediction of water demand for life and production. The urbanization process and economic development will lead to a continuous increase in water demand, but due to resource endowment constraints, it is impossible to meet the demand growth indefinitely. Therefore, it is necessary to determine a reasonable water demand based on development forecasts. Third, conflict coordination and balanced allocation of multiple water use types. When the total water volume is constant, different types of water use have a relationship of increase and decrease, and a balance mechanism needs to be established to achieve multi-objective comprehensive decision-making optimization. Fourth, the temporal and spatial allocation of water resources. Affected by the seasonal changes in precipitation and the uneven regional distribution, the supply of water resources varies greatly in time and space, and the temporal and spatial distribution requirements of different water uses are inconsistent, and the contradiction between supply and demand is prominent. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method and system for rationally allocating water resources in cold regions based on ecological protection. Among them, a method for rationally allocating water resources in cold regions based on ecological protection includes:

[0005] Obtain the spatiotemporal distribution data of ecological water demand in different periods, and based on the spatiotemporal distribution data of ecological water demand, establish a forecast model for domestic and production water demand through grey forecasting and regression analysis methods, and determine the forecast values ​​of domestic and production water demand under high-growth, medium-growth and low-growth development scenarios;

[0006] Taking ecological water demand as a constraint condition and the predicted value of living and production water demand as the optimization target, a multi-objective optimization model for water resource allocation is constructed;

[0007] Using simulated annealing algorithm and genetic algorithm to solve the multi-objective optimization model to obtain an initial water resource allocation plan;

[0008] According to the initial water resource allocation plan, the weight coefficients of different water users are defined, and a water resource allocation coordination mechanism with balanced interests of multiple parties is established. According to the coordination results of stakeholders, the initial water resource allocation plan is adjusted and optimized to obtain the final water resource allocation plan.

[0009] Preferably, the process of obtaining the spatiotemporal distribution data of ecological water demand in different periods includes:

[0010] According to the characteristics of different ecosystems, the water demand law model of ecological functional zones is established by using ecohydrology methods;

[0011] The land use type and vegetation coverage information of the ecological functional zone are obtained through remote sensing technology as input parameters of the model;

[0012] Using geographic information system technology, the ecological functional areas are spatially divided and assessment units are determined;

[0013] Calculate the ecological water demand of the assessment unit at different periods according to the characteristics of the ecosystem and the water demand law model of the assessment unit;

[0014] The calculation results of ecological water demand in different periods are spatially interpolated to obtain the spatiotemporal distribution data of ecological water demand in the entire study area;

[0015] The support vector machine is used to establish an ecological water demand prediction model, and the ecological water demand prediction under different scenarios is carried out to obtain the ecological water demand prediction value.

[0016] Preferably, the process of establishing a forecasting model for domestic and production water demand includes:

[0017] Based on the spatiotemporal distribution data of ecological water demand, obtain the population, economic and water use efficiency data in the study area;

[0018] After cleaning and normalizing the population, economic and water use efficiency data, a prediction model for domestic water demand and production water demand is constructed based on a grey prediction model and regression analysis method, and the processed data is used to train and optimize the model;

[0019] By analyzing the water-saving potential and water-use efficiency improvement space in the study area, the water-saving rate and water-use efficiency improvement coefficient under different development scenarios are determined;

[0020] The water saving rate and water use efficiency improvement coefficient are introduced as parameters into the prediction model to simulate the living and production water demand under three development scenarios of high growth, medium growth and low growth;

[0021] Conduct statistical analysis and visual display of simulation results to generate forecasts of domestic and production water demand under different development scenarios;

[0022] The prediction results are compared and verified with the actual water demand data, the accuracy and reliability of the prediction model are evaluated, and the model is optimized and adjusted based on the feedback results.

[0023] Preferably, the process of constructing a multi-objective optimization model for water resource allocation includes:

[0024] According to the constraints and optimization objectives in the water resources allocation model, obtain the data on ecological water demand, domestic water demand forecast and production water demand forecast;

[0025] Through the water supply cost accounting model, the unit water supply cost under different water supply plans is calculated to obtain the water supply cost data;

[0026] Through the water use economic benefit evaluation model, the unit water use economic benefits of different water use plans are calculated to obtain water use economic benefit data;

[0027] A multi-objective optimization model was constructed by taking ecological water demand as the constraint condition, the predicted values ​​of living and production water demand as the optimization objectives, and the unit water supply cost and unit water use economic benefit as the evaluation indicators.

[0028] Preferably, the process of solving the multi-objective optimization model using a simulated annealing algorithm or a genetic algorithm to obtain an initial water resource allocation plan includes:

[0029] According to the multi-objective optimization model of water resources allocation, determine the optimization objective function and constraints, including ecological protection indicators, social and economic development indicators, water supply and water supply security level;

[0030] When using the simulated annealing algorithm to solve the problem, the initial temperature and cooling rate are set, and a random search is performed in the solution space. The acceptance probability is used to determine whether to accept the new solution until the termination condition is reached and the global optimal solution is obtained.

[0031] The optimal solution is further optimized by using a genetic algorithm. Through selection, crossover and mutation operations, the population is continuously evolved iteratively to optimize individual fitness and obtain an initial water resource allocation plan that meets multiple objectives.

[0032] Preferably, after obtaining the initial water resource allocation plan, the method further includes:

[0033] According to the initial water resource allocation plan, combined with the water resource engineering system and scheduling rules, the water balance method is used to calculate the water revenue and expenditure balance of each region, and the linear programming optimization scheduling technology is used to optimize the allocation of water resources in time and space.

[0034] Preferably, the process of optimizing the allocation of water resources in time and space includes:

[0035] According to the water resource allocation plan and engineering system, the water supply capacity and water demand data of each region are obtained, and the water balance of each region is calculated by the water balance method to obtain the supply and demand difference data;

[0036] Based on the supply-demand gap data, a linear programming optimization algorithm is used to establish an optimization model with the objective function of alleviating the contradiction between supply and demand to the greatest extent possible and the optimal allocation of water resources in time and space as the decision variable.

[0037] According to the scheduling rules, the constraints of the optimization model are set, the optimization model is solved, the optimal configuration plan of water resources in different times and regions is obtained, and the optimized scheduling plan is obtained.

[0038] Preferably, the linear programming optimization algorithm is used, with maximizing the alleviation of the contradiction between supply and demand as the objective function and the optimal allocation of water resources in time and space as the decision variable, and the process of establishing the optimization model includes:

[0039] Based on water resources supply and demand data, a supply and demand contradiction assessment model is established to quantify the supply and demand gap;

[0040] Use spatiotemporal analysis methods to obtain the distribution characteristics of water resources in time and space dimensions;

[0041] Through the optimization algorithm, the optimal water resource allocation plan is determined to minimize the contradiction between supply and demand. If the gap between supply and demand exceeds the preset threshold, the early warning mechanism is triggered and emergency dispatch measures are taken;

[0042] Based on the optimization allocation plan, a water resources scheduling plan is formulated to guide the optimal allocation of water resources, and a reinforcement learning algorithm is used to continuously optimize the water resources allocation strategy.

[0043] Preferably, a water resource allocation coordination mechanism with balanced interests among multiple parties is established, and the initial water resource allocation plan is adjusted and optimized according to the coordination results of stakeholders. The process of obtaining the final water resource allocation plan includes:

[0044] According to the coordination results of stakeholders, the optimization objectives and constraints of water resource allocation are obtained, and a water resource allocation optimization model is established;

[0045] Use heuristic algorithms to solve the water resource allocation optimization model and obtain the optimized water resource allocation plan; and convert it into executable scheduling rules and management policies;

[0046] Incorporate the optimized dispatching rules and management policies into the basin water resources management information system, and exchange data with the dispatching control system and monitoring system through the system interface;

[0047] Obtain real-time monitoring data, dynamically evaluate the water resource allocation process, and determine whether the optimization objectives and constraints are met. If not, re-optimization of the water resource allocation plan is triggered;

[0048] According to the optimized water resource allocation plan, the dispatching rules and management policies are dynamically adjusted and sent to the dispatching control system through the basin water resource management information system to guide water resource dispatching;

[0049] Continuously obtain data on water resource utilization efficiency and benefits, use machine learning algorithms to establish efficiency and benefit evaluation models, and conduct quantitative evaluation of the comprehensive utilization efficiency and benefits of water resources;

[0050] The results of comprehensive water resource utilization efficiency and benefit evaluation are fed back to the water resource allocation optimization model as new optimization objectives or constraints to continuously optimize water resource allocation.

[0051] The present invention also provides a cold region water resource rational distribution system based on ecological protection, comprising:

[0052] Ecological water demand assessment module, used to obtain the spatiotemporal distribution data of ecological water demand in different periods;

[0053] A living and production water demand prediction module is used to establish a living and production water demand prediction model based on the spatiotemporal distribution data of ecological water demand through grey prediction and regression analysis methods, and determine the predicted values ​​of living and production water demand under high growth, medium growth and low growth development scenarios;

[0054] A water resource allocation multi-objective optimization model building module is used to take ecological water demand as a constraint condition and the predicted value of living and production water demand as an optimization target to build a multi-objective optimization model for water resource allocation;

[0055] A water resource allocation solution module is used to solve the multi-objective optimization model using a simulated annealing algorithm and a genetic algorithm to obtain an initial water resource allocation solution;

[0056] A water resource optimization configuration module is used to calculate the water revenue and expenditure balance of each region according to the initial water resource allocation plan, in combination with the water resource engineering system and scheduling rules, and to optimize the configuration of water resources in time and space by using linear programming optimization scheduling technology;

[0057] A water resource allocation coordination mechanism establishment module is used to define the weight coefficients of different water users according to the initial water resource allocation plan and establish a water resource allocation coordination mechanism with balanced interests among multiple parties;

[0058] The water resource allocation plan adjustment and implementation module is used to adjust and optimize the initial water resource allocation plan according to the coordination results of stakeholders to obtain the final water resource allocation plan.

[0059] Compared with the prior art, the present invention has the following advantages and technical effects:

[0060] The method for rationally allocating water resources in cold regions of the present invention firstly adopts ecohydrological methods to evaluate ecological water demand and predict water demand for life and production. Then, the ecological water demand is taken as a constraint condition, and the water demand for life and production is taken as an optimization target, a multi-objective optimization model is constructed, and an intelligent algorithm is used to solve and obtain a water resource allocation plan. Then, in combination with the water resource engineering system, the water balance method and linear programming optimization scheduling technology are adopted to realize the spatiotemporal optimization configuration of water resources. In view of conflicts of interest, a coordination mechanism for balancing the interests of multiple parties is established. Finally, the optimization plan is converted into executable scheduling rules and management policies, and the allocation process is continuously optimized through real-time monitoring and dynamic adjustment. The present invention can realize the efficient configuration and utilization of water resources, effectively alleviate the contradiction between supply and demand, coordinate the interests of all parties, and improve the comprehensive utilization efficiency and benefits of water resources under the premise of ensuring ecological water use. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0062] Figure 1 A schematic diagram of a method flow chart of an embodiment of the present invention;

[0063] Figure 2 Schematic diagram of the system structure of an embodiment of the present invention. DETAILED DESCRIPTION

[0064] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0065] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0066] like Figure 1 As shown, this embodiment provides a method for rationally allocating water resources in cold regions based on ecological protection, comprising the following steps:

[0067] Obtain the spatiotemporal distribution data of ecological water demand in different periods, and based on the spatiotemporal distribution data of ecological water demand, establish a forecast model for domestic and production water demand through grey forecasting and regression analysis methods, and determine the forecast values ​​of domestic and production water demand under high-growth, medium-growth and low-growth development scenarios;

[0068] Taking ecological water demand as a constraint and the predicted value of living and production water demand as the optimization target, a multi-objective optimization model for water resource allocation is constructed.

[0069] The simulated annealing algorithm and genetic algorithm are used to solve the multi-objective optimization model and obtain the initial water resource allocation plan;

[0070] According to the initial water resource allocation plan, the weight coefficients of different water users are defined, and a water resource allocation coordination mechanism with balanced interests of multiple parties is established. According to the coordination results of stakeholders, the initial water resource allocation plan is adjusted and optimized to obtain the final water resource allocation plan.

[0071] Furthermore, the process of obtaining the spatiotemporal distribution data of ecological water demand in different periods includes:

[0072] According to the characteristics of different ecosystems, the water demand law model of ecological functional zones is established by using ecohydrology methods;

[0073] The land use type and vegetation coverage information of ecological functional areas are obtained through remote sensing technology as input parameters of the model;

[0074] Use geographic information system technology to spatially divide ecological functional areas and determine assessment units;

[0075] According to the characteristics of the ecosystem of the assessment unit and the water demand law model, calculate the ecological water demand of the assessment unit in different periods;

[0076] The calculation results of ecological water demand in different periods are spatially interpolated to obtain the spatiotemporal distribution data of ecological water demand in the entire study area;

[0077] The support vector machine is used to establish an ecological water demand prediction model, and the ecological water demand prediction under different scenarios is carried out to obtain the ecological water demand prediction value.

[0078] Specifically, taking the forest ecosystem as an example, by analyzing the relationship between hydrological elements such as rainfall, evapotranspiration, and runoff and forest vegetation, a water demand model for the forest ecosystem is established. This model can reflect the changes in water demand for the forest ecosystem in different seasons and different forest stand structures.

[0079] Taking satellite remote sensing as an example, the Normalized Difference Vegetation Index (NDVI) is calculated using multispectral remote sensing images to estimate vegetation coverage. For forest ecosystems, parameters such as forest stand type and canopy density can be interpreted through high-resolution remote sensing images. This information can be used as model input to improve the accuracy and applicability of water demand models.

[0080] Geographic Information System (GIS) technology has advantages in spatial analysis and visualization. Taking the watershed scale as an example, sub-watersheds can be extracted based on the digital elevation model (DEM) as evaluation units. At the same time, GIS can also integrate spatial data such as land use and vegetation coverage to provide attribute information for each evaluation unit. This spatial division method can fully consider the impact of factors such as terrain and vegetation on ecological water demand.

[0081] When calculating the ecological water demand for each assessment unit, the characteristics of different ecosystem types need to be considered. Taking wetland ecosystems as an example, their ecological water demand mainly includes maintaining wetland area, maintaining water level, and meeting biological water needs. The ecological water demand for different periods can be calculated based on parameters such as wetland type, area, target water level, and combined with hydrological elements such as evaporation and infiltration.

[0082] The spatial interpolation method of this embodiment is specifically the Kriging interpolation method. Taking spatial autocorrelation into account, the calculation results of the ecological water demand of each assessment unit are interpolated to obtain the estimated value of the ecological water demand at any location in the study area, thereby generating continuous spatial distribution data.

[0083] Support vector machines (SVMs) can use historical meteorological data, land use changes and other multidimensional features as input to train an ecological water demand forecasting model. The model can predict ecological water demand in different periods based on scenarios such as future climate change and land use planning. The advantage of this method is that it can capture complex nonlinear relationships and improve forecasting accuracy.

[0084] Through the above technical means, the ecological water demand of the study area can be comprehensively evaluated. Taking a certain watershed as an example, it is assumed that the study area contains multiple ecosystems such as forests, grasslands, and wetlands. First, the land use type map is obtained based on the interpretation of remote sensing images, and the vegetation coverage is calculated to be 60%. Then, the watershed is divided into 50 sub-basins as evaluation units using GIS technology. For each evaluation unit, the corresponding water demand law model is selected according to its dominant ecosystem type. For example, for forest ecosystems, considering factors such as forest stand structure and soil characteristics, the ecological water demand is calculated to be 500 cubic meters / hectare·year. The calculation results of each evaluation unit are kriging interpolated to obtain the spatial distribution of ecological water demand in the entire watershed. The results show that the ecological water demand in mountain forest areas is higher, averaging 600 cubic meters / hectare·year, while that in grassland areas is lower, averaging 300 cubic meters / hectare·year. Using the support vector machine algorithm, an ecological water demand prediction model is established. Under the climate change scenario, it is predicted that the ecological water demand in the watershed will increase by 15% in the next 10 years.

[0085] Furthermore, the process of establishing a forecasting model for domestic and production water demand includes:

[0086] Based on the spatiotemporal distribution data of ecological water demand, obtain the population, economic and water use efficiency data in the study area;

[0087] After cleaning and normalizing the population, economic and water use efficiency data, a prediction model for domestic water demand and production water demand was constructed based on the grey prediction model and regression analysis method, and the processed data was used to train and optimize the model;

[0088] By analyzing the water-saving potential and water-use efficiency improvement space in the study area, the water-saving rate and water-use efficiency improvement coefficient under different development scenarios are determined;

[0089] The water saving rate and water use efficiency improvement coefficient are introduced as parameters into the forecasting model to simulate the living and production water demand under three development scenarios: high growth, medium growth and low growth.

[0090] Conduct statistical analysis and visual display of simulation results to generate forecasts of domestic and production water demand under different development scenarios;

[0091] The prediction results are compared and verified with the actual water demand data, the accuracy and reliability of the prediction model are evaluated, and the model is optimized and adjusted based on the feedback results.

[0092] Specifically, obtaining relevant data on population, economy, and water use efficiency in the study area is the basis for predicting water demand for living and production. For example, indicators such as population, GDP, industrial output, and agricultural irrigation area can be collected from statistical yearbooks, government reports, and professional databases.

[0093] Data preprocessing is a key step to improve the prediction accuracy of the model. For population data, it may be necessary to remove outliers, such as a sudden increase or decrease in population in a certain year; for economic data, it may be necessary to adjust for inflation; for water efficiency data, it may be necessary to unify the measurement units of different industries.

[0094] The grey prediction model is suitable for systems with small samples and incomplete information, and is particularly suitable for complex systems such as water demand that are affected by multiple factors. For example, the GM (1,1) model can be used to predict future population growth trends. The regression analysis method can explore the relationship between water demand and influencing factors. For example, this embodiment establishes a multivariate linear regression model to analyze the impact of factors such as GDP, population, and temperature on domestic water consumption.

[0095] The analysis of water-saving potential and water-use efficiency improvement space is an important basis for formulating reasonable water use policies. For example, by comparing the water quotas of different industries in the study area, it can be found that some industries have a large space for water saving. Assuming that the water consumption per unit output value of a chemical plant in industrial water use is 1.5 times the industry average, then a reasonable water-saving target can be set through technical transformation and management optimization. When simulating different development scenarios, the high-growth scenario may assume an annual GDP growth rate of 8%, an annual population growth rate of 1%, and a water-saving rate of 10%; the medium-growth scenario may assume an annual GDP growth rate of 6%, an annual population growth rate of 0.5%, and a water-saving rate of 15%; the low-growth scenario may assume an annual GDP growth rate of 4%, an annual population growth rate of 0.2%, and a water-saving rate of 20%. These parameters will directly affect the prediction results of the model.

[0096] Model validation is an important step to ensure the reliability of prediction results. This embodiment adopts the holdout method, that is, historical data is divided into a training set and a test set, the training set is used to build the model, and the test set is used to verify the prediction accuracy of the model. If the prediction error of the model on the test set exceeds 10%, it is necessary to readjust the model parameters or consider introducing new influencing factors. Through continuous optimization, a prediction model that can accurately reflect the changing law of water demand in the study area is finally obtained.

[0097] Furthermore, the process of constructing a multi-objective optimization model for water resource allocation includes:

[0098] According to the constraints and optimization objectives in the water resources allocation model, obtain the data on ecological water demand, domestic water demand forecast and production water demand forecast;

[0099] Through the water supply cost accounting model, the unit water supply cost under different water supply plans is calculated to obtain the water supply cost data;

[0100] Through the water use economic benefit evaluation model, the unit water use economic benefits of different water use plans are calculated to obtain water use economic benefit data;

[0101] A multi-objective optimization model was constructed by taking ecological water demand as the constraint condition, the predicted values ​​of living and production water demand as the optimization objectives, and the unit water supply cost and unit water use economic benefit as the evaluation indicators.

[0102] Furthermore, the simulated annealing algorithm and genetic algorithm are used to solve the multi-objective optimization model, and the process of obtaining the initial water resource allocation plan includes:

[0103] According to the multi-objective optimization model of water resources allocation, determine the optimization objective function and constraints, including ecological protection indicators, social and economic development indicators, water supply and water supply security level;

[0104] When using the simulated annealing algorithm to solve the problem, the initial temperature and cooling rate are set, and a random search is performed in the solution space. The acceptance probability is used to determine whether to accept the new solution until the termination condition is reached and the global optimal solution is obtained.

[0105] The optimal solution is further optimized using a genetic algorithm. Through selection, crossover and mutation operations, the population is continuously iterated and evolved, individual fitness is optimized, and an initial water resource allocation plan that meets multiple objectives is obtained.

[0106] Specifically, the water resource allocation model is a key tool for optimizing water resource utilization. First, it is necessary to obtain basic data such as ecological water demand, life and production water demand forecast values. For example, the ecological water demand of a river basin is 1 billion cubic meters per year, the life water demand forecast value is 500 million cubic meters per year, and the industrial and agricultural production water demand forecast value is 1.5 billion cubic meters per year. The water supply cost accounting model takes into account the costs of water source development, water transmission and distribution, etc. Taking surface water as an example, it may include reservoir construction costs, pipeline laying costs, water treatment costs, etc. Assume that the unit water supply cost of a certain scheme is 2 yuan / cubic meter. The water use economic benefit evaluation model measures the economic value generated by water resource utilization, such as agricultural irrigation yield increase benefits, industrial water output value, etc. The unit water use economic benefit of a water use scheme may be 10 yuan / cubic meter. When constructing a multi-objective optimization model, the ecological water demand is used as a hard constraint to ensure that the water use of the ecosystem is not encroached. The life and production water demand forecast values ​​are used as optimization targets to pursue maximum efficiency while meeting basic needs. Unit water supply cost and unit water use economic benefit are used as evaluation indicators to balance economic benefits and cost control.

[0107] Genetic algorithm is an effective method for solving multi-objective optimization problems. It continuously iterates and optimizes the solution set by simulating the biological evolution process. For example, the initial population may contain 100 different water resource allocation schemes. After selection, crossover, mutation and other operations, it gradually converges to the optimal solution. The optimal solution may be: 1 billion cubic meters of ecological water, 480 million cubic meters of domestic water, and 1.45 billion cubic meters of production water. Conduct a feasibility analysis on the optimal solution, considering factors such as the difficulty of project implementation and social impact. If it is found that the allocation of some water users is too low, which may cause conflicts, it is necessary to adjust the model parameters, such as adding water fairness constraints and re-optimization. The final water resource allocation plan should take into account both efficiency and fairness. Based on the final plan, a water resource scheduling plan is formulated, such as water storage in the flood season and reasonable allocation in the dry season. Through precise regulation, the efficiency of water resource utilization can be improved. For example, the water supply can be appropriately increased during the peak period of agricultural water use to avoid crop yield reduction; the water supply can be appropriately reduced during the off-season of industrial production to reduce energy consumption. This dynamic regulation method can maximize the benefits of limited water resources. The significance of optimizing water resource allocation and precise regulation is to achieve sustainable use of water resources. Through scientific demand forecasting, multi-objective optimization and dynamic regulation, it not only meets the water demand in ecology, life, production and other aspects, but also improves the efficiency of water resource utilization and economic benefits, providing important support for regional sustainable development.

[0108] Specifically, obtaining ecological water demand data is the primary task of optimizing water resource allocation. Ecological water demand includes river ecological base flow, wetland water replenishment, etc., which are calculated through field surveys and ecohydrological models. For example, the ecological base flow of a river is 10 cubic meters per second, and the wetland water replenishment demand is 50 million cubic meters per year. These data will be used as hard constraints for the optimization model. The forecast of domestic water and production water demand is the key to optimizing allocation. Time series analysis methods, such as ARIMA model, are used to predict factors such as population growth and economic development. Assume that the average annual growth rate of domestic water demand in a city in the next five years is 2%, the average annual growth rate of industrial water demand is 3%, and the agricultural water demand remains stable. These forecast results will be used as the objective function of the optimization model. Unit water supply cost and water use economic benefits are important indicators for evaluating water resource allocation plans. Water supply costs include the construction and operation costs of water source projects and water transmission and distribution facilities. For example, the cost of surface water supply is 1 yuan per cubic meter, groundwater is 1.5 yuan, and recycled water is 2 yuan. The economic benefits of water use can be measured by the water use coefficient of output value, such as 100 yuan per cubic meter of industrial water and 10 yuan per cubic meter of agricultural water. These data will be used to construct the evaluation function of the multi-objective optimization model. The goal of the multi-objective optimization model is to minimize the water supply cost and maximize the economic benefits of water use under the premise of meeting the ecological water demand. The decision variable of the model is the water supply of each water-using department, and the constraints include the balance of total water resources and ecological water supply guarantee. The Pareto optimal solution set, that is, a series of water resource allocation plans that weigh costs and benefits, can be obtained by solving the model using genetic algorithms. A water supply plan is formulated based on the optimization results to clarify the water supply and water supply time of each water-using department. For example, ecological water and domestic water are given priority in the dry season, and agricultural water use is appropriately increased in the flood season. The water supply plan should have a certain degree of flexibility to cope with fluctuations in actual water demand. A dynamic monitoring and adjustment mechanism is implemented to ensure the effective implementation of the water supply plan. Water use can be monitored in real time through technologies such as smart water meters and remote sensing monitoring. If it is found that the actual water demand deviates from the predicted value by more than 10%, it is necessary to re-forecast the demand and optimize the water supply plan. This dynamic adjustment mechanism can improve the accuracy and adaptability of water resource allocation. Through the above steps, scientific allocation and efficient use of water resources can be achieved. This method can not only meet the water needs of ecology, life and production, but also strike a balance between economic benefits and environmental protection, and promote the sustainable use of water resources. At the same time, the dynamic adjustment mechanism can cope with the uncertainties brought about by climate change and socio-economic development, and improve the resilience of water resource management.

[0109] Specifically, the multi-objective optimization model of water resource allocation aims to balance the needs of ecological protection and socio-economic development. Taking a certain river basin as an example, the optimization objectives of the model may include maximizing the ecological water use guarantee rate, maximizing socio-economic benefits, minimizing water supply costs, etc. Constraints include the total amount of available water resources, ecological base flow requirements, and minimum water user requirements. For example, the average annual water resources in the river basin are 1 billion cubic meters, the ecological base flow is not less than 100 million cubic meters / year, and the urban domestic water use is not less than 200 million cubic meters / year. The simulated annealing algorithm solves the optimization problem by simulating the metal annealing process. The initial temperature can be set to 1000 and the cooling rate is 0.95. In each iteration, the algorithm randomly generates a new water resource allocation plan, such as 120 million cubic meters of ecological water use and 250 million cubic meters of urban water use. If the new plan is better than the current plan, it is accepted; if it is worse than the current plan, it is accepted with a certain probability, and the probability decreases as the temperature decreases. This mechanism enables the algorithm to jump out of the local optimal solution. The algorithm terminates when the temperature drops to 0.01 or there is no improvement after 100 consecutive iterations. Genetic algorithms further optimize solutions by simulating the biological evolution process. First, a set of initial populations is generated, and each individual represents a water resource allocation plan. A new generation of populations is generated through operations such as roulette selection, single-point crossover, and random mutation. For example, after the crossover of two parent plans, a new plan may be generated: 110 million cubic meters of ecological water use and 230 million cubic meters of urban water use. The fitness function can be set as the weighted sum of each objective function. After multiple generations of evolution, the individuals in the population gradually move closer to the optimal solution. The optimized water resource allocation plan needs to balance the interests of all parties. For example, the final plan may be: 150 million cubic meters of ecological water use (guarantee rate 95%), 2.2 million cubic meters of urban domestic water use (guarantee rate 100%), 580 million cubic meters of agricultural irrigation water (guarantee rate 85%), and 50 million cubic meters of industrial water use (guarantee rate 90%). This plan not only meets the requirements of ecological base flow, but also guarantees urban domestic water use, while taking into account the needs of agricultural and industrial development. Plan evaluation and adjustment are key steps to ensure the rationality of water resource allocation. The importance of different goals can be balanced by setting weight coefficients. For example, if the weight of ecological protection is 0.4 and the weight of economic development is 0.6, the optimization process will be more inclined to meet the needs of economic development. If the assessment finds that the water supply security of some water users is too low, the allocation plan can be adjusted appropriately. For example, the industrial water consumption is increased to 60 million cubic meters, and the agricultural water consumption is reduced accordingly. The time and spatial distribution need to be considered to convert the optimized water resource allocation plan into an executable water supply plan. For example, a monthly water supply plan can be formulated: the ecological water consumption in the dry season (December to February of the following year) is not less than 10 million cubic meters / month, and the agricultural water consumption in the flood season (June to August) can reach 250 million cubic meters / month. In terms of space, the maximum water intake and minimum ecological flow of each water intake can be clarified. This refined water supply plan provides specific guidance for water resource management. The dynamic adjustment mechanism of water resource allocation is an effective means to deal with uncertainty.A hydrological-socio-economic coupling model can be established to monitor factors such as precipitation, river runoff, and water demand in real time. When extreme drought events occur, emergency plans can be activated, such as temporarily reducing agricultural water use and increasing ecological water use. If there are major changes in the economic structure, such as the commissioning of an industrial park, it is necessary to re-evaluate water demand and adjust the allocation plan. This dynamic adjustment ensures the adaptability and sustainability of water resource allocation. Through multi-objective optimization and dynamic adjustment, the water resource allocation plan can strike a balance between protecting the ecological environment, promoting economic development, and ensuring people's livelihood, and achieve efficient use of water resources and sustainable development.

[0110] Furthermore, after obtaining the initial water resource allocation plan, the following steps are also included:

[0111] According to the initial water resources allocation plan, combined with the water resources engineering system and scheduling rules, the water balance method is used to calculate the water revenue and expenditure balance of each region, and the linear programming optimization scheduling technology is used to optimize the allocation of water resources in time and space.

[0112] Furthermore, the process of optimizing the allocation of water resources in time and space includes:

[0113] According to the water resource allocation plan and engineering system, the water supply capacity and water demand data of each region are obtained, and the water balance of each region is calculated by the water balance method to obtain the supply and demand difference data;

[0114] Based on the supply-demand gap data, a linear programming optimization algorithm is used to establish an optimization model with the objective function of alleviating the contradiction between supply and demand to the greatest extent possible and the optimal allocation of water resources in time and space as the decision variable.

[0115] According to the scheduling rules, the constraints of the optimization model are set, the optimization model is solved, the optimal configuration plan of water resources in different times and regions is obtained, and the optimized scheduling plan is obtained.

[0116] Furthermore, a linear programming optimization algorithm is used, with the maximum alleviation of the contradiction between supply and demand as the objective function and the optimal allocation of water resources in time and space as the decision variable. The process of establishing the optimization model includes:

[0117] Based on water resources supply and demand data, a supply and demand contradiction assessment model is established to quantify the supply and demand gap;

[0118] Use spatiotemporal analysis methods to obtain the distribution characteristics of water resources in time and space dimensions;

[0119] Through the optimization algorithm, the optimal allocation plan of water resources is determined to alleviate the contradiction between supply and demand to the greatest extent possible. If the gap between supply and demand exceeds the preset threshold, the early warning mechanism is triggered and emergency dispatch measures are taken;

[0120] Based on the optimization allocation plan, a water resources scheduling plan is formulated to guide the optimal allocation of water resources, and a reinforcement learning algorithm is used to continuously optimize the water resources allocation strategy.

[0121] Specifically, first, by collecting the water supply capacity and water demand data of each region in a specific river basin, the water balance method is used to calculate the water revenue and expenditure of each region, and then the supply and demand difference is obtained. For example, a river basin contains three regions A, B, and C. Region A is mainly an agricultural area, region B is mainly an industrial area, and region C is mainly an urban living area. By analyzing the local hydrological data and the water use data of each region, it can be concluded that the water supply capacity of region A is 1 billion cubic meters per year, the water demand is 1.2 billion cubic meters per year, and the supply and demand difference is a gap of 200 million cubic meters; the water supply capacity of region B is 800 million cubic meters per year, the water demand is 700 million cubic meters per year, and there is a surplus of 100 million cubic meters; the water supply capacity of region C is 600 million cubic meters per year, the water demand is 900 million cubic meters per year, and there is a gap of 300 million cubic meters. According to the principle of water balance, the water supply and demand of each region can be analyzed, and the contradiction between water supply and demand in each region can be clearly seen. This can clarify the goals and directions of water resource scheduling. Then, in order to alleviate the contradiction between supply and demand to the greatest extent, a linear programming optimization algorithm is used to establish an optimization model with the optimal configuration of water resources in time and space as the decision variable. Here, the objective function is to minimize the sum of the absolute values ​​of the supply and demand differences in all regions. The decision variables include the amount of water transferred from one region to another and the amount of water transferred in different time periods. Through the linear programming method, the optimal water resource allocation scheme can be found under the premise of meeting various constraints, thereby alleviating the contradiction between supply and demand to the greatest extent. When setting the constraints of the optimization model, it is necessary to consider the temporal and spatial distribution characteristics of water resources, the connectivity of the engineering system and the scheduling capacity. For example, there is a water canal connecting area A and area B, with a maximum water transfer capacity of 150 million cubic meters per year, and water can only be transferred from area B to area A in one direction; there is a reservoir connecting area B and area C, and the water storage capacity of the reservoir can be adjusted to achieve cross-time scheduling of water resources. The maximum water storage capacity of the reservoir is 200 million cubic meters, and the maximum annual discharge is 100 million cubic meters. The connectivity and dispatching capacity of these engineering facilities will affect the allocation of water resources, so they must be taken into account when building the model to ensure the feasibility and operability of the optimization results. Adding these business associations can form a thinking chain between technical steps. After solving the optimization model, the optimal allocation plan for water resources in different times and regions can be obtained. For example, the optimization results show that 100 million cubic meters of water should be transferred from area B to area A every year, and 50 million cubic meters of water should be transferred from area B to area C every year; at the same time, the water storage capacity of the reservoir is used to store the surplus water in area B during the flood season, and the stored water is released to area C during the dry season. The annual water storage capacity is 80 million cubic meters and the release volume is 80 million cubic meters. In this way, through the optimal configuration in space and time, the water supply pressure in areas A and C can be alleviated to a large extent. The optimized dispatching plan is converted into specific dispatching instructions, and the optimal dispatching of water resources is achieved through the control facilities of the engineering system.For example, by controlling the gate opening of the water transfer channel and the operating power of the water pump, accurate water supply from area B to area A and area C can be achieved; by controlling the outflow flow of the reservoir, cross-time water supply from area B to area C can be achieved. These dispatching instructions need to be accurately formulated according to the optimization results and strictly implemented to ensure that the optimal allocation of water resources is implemented. In the process of optimized dispatching, monitoring and feedback are required to evaluate whether the dispatching effect has achieved the expected goal. For example, by monitoring the actual water supply and demand of areas A, B, and C, and re-analyzing the water balance, the new supply and demand difference can be calculated. If the new supply and demand difference is significantly smaller than before the dispatch, it means that the dispatching plan is effective; if the supply and demand difference does not change significantly or even increases, it means that the dispatching plan needs to be adjusted. For example, if the actual water demand of area A is found to be higher than expected during the dispatching process, it can be considered to increase the water transfer volume from area B to area A, or take other water-saving measures to reduce the water demand of area A. Finally, according to the actual effect of optimized dispatching, the rationality and reliability of the water resource allocation plan and engineering system are evaluated. For example, by analyzing indicators such as water loss, energy consumption, and economic costs during the scheduling process, the economy and efficiency of the water resource allocation plan can be evaluated; by analyzing whether there are engineering failures and deviations in the execution of scheduling instructions during the scheduling process, the reliability and stability of the engineering system can be evaluated. These evaluation results can provide decision support for subsequent water resource planning and engineering construction. For example, if it is found that the water transfer capacity of the water transfer channel is insufficient, it can be considered to expand it or build a new water transfer channel; if it is found that the regulation capacity of the reservoir is limited, it can be considered to build a new reservoir or reinforce and expand the existing reservoir. By continuously optimizing scheduling, evaluation and improvement, the level of water resource management and guarantee capabilities can be gradually improved.

[0122] Specifically, the assessment of the contradiction between water supply and demand is the basis for optimal allocation. Taking a certain city as an example, a supply and demand contradiction assessment model was established by collecting water supply data such as precipitation, surface water, and groundwater in the past five years, as well as water demand data such as industrial and agricultural water use and domestic water use. The model uses a multi-factor weighted method to consider factors such as water quantity, water quality, and spatiotemporal distribution to quantify the supply and demand gap. The results show that the supply and demand gap in the city reaches 20% during the dry season, exceeding the warning threshold of 15%. Spatiotemporal analysis methods help to deeply understand the distribution characteristics of water resources. Using GIS technology and remote sensing data, a water resource distribution map was drawn, and it was found that the northern mountainous area of ​​the city is rich in water resources, while the southern plain area is relatively scarce. Through time series analysis, obvious differences between the dry season (December to February of the following year) and the flood season (June to August) were identified. These analysis results provide an important basis for optimal allocation. The optimization algorithm is the key to determining the water resource allocation plan. A multi-objective genetic algorithm is used to maximize water use efficiency and minimize the gap between supply and demand, taking into account constraints such as the spatial and temporal distribution of water resources and engineering scheduling capacity, and generate an optimal configuration plan. The plan recommends diverting water from the northern mountainous area to the southern plain area during the dry season, with a daily water diversion volume of 100,000 cubic meters, which can reduce the gap between supply and demand to less than 10%. Early warning mechanisms and emergency dispatch are the guarantee for dealing with extreme situations. When the gap between supply and demand exceeds 15%, an orange warning is triggered and an emergency dispatch plan is initiated. The plan includes measures such as increasing inter-basin water diversion efforts and restricting water use by high-water-consuming industries. In an extreme drought event, the mechanism successfully controlled the gap between supply and demand to 12%, avoiding a serious water shortage crisis. The water resource scheduling plan is a specific implementation plan for optimal configuration. Based on the results of the optimal configuration, annual, quarterly and monthly scheduling plans are formulated. The plan specifies in detail the water supply, water diversion path and time schedule of each water source. For example, a reservoir in the north supplies 80,000 cubic meters of water to the south every day during the dry season, which is transported through pump station A and pipeline B, taking 6 hours. This refined scheduling significantly improves the efficiency of water resource utilization. The reinforcement learning algorithm can continuously optimize the configuration strategy. A reinforcement learning model for water resource configuration is constructed, with the balance between supply and demand as the reward function, and the optimal strategy is continuously learned through interaction with the environment. After one year of practice, the algorithm successfully reduced the annual average supply-demand gap from 15% to 8%, demonstrating its adaptive optimization capabilities.

[0123] Furthermore, a water resource allocation coordination mechanism with balanced interests among multiple parties is established. Based on the coordination results of stakeholders, the initial water resource allocation plan is adjusted and optimized. The process of obtaining the final water resource allocation plan includes:

[0124] According to the coordination results of stakeholders, the optimization objectives and constraints of water resource allocation are obtained, and a water resource allocation optimization model is established;

[0125] Use heuristic algorithms to solve the water resource allocation optimization model and obtain the optimized water resource allocation plan; and convert it into executable scheduling rules and management policies;

[0126] Incorporate the optimized dispatching rules and management policies into the basin water resources management information system, and exchange data with the dispatching control system and monitoring system through the system interface;

[0127] Obtain real-time monitoring data, dynamically evaluate the water resource allocation process, and determine whether the optimization objectives and constraints are met. If not, re-optimization of the water resource allocation plan is triggered;

[0128] According to the optimized water resource allocation plan, the dispatching rules and management policies are dynamically adjusted and sent to the dispatching control system through the basin water resource management information system to guide water resource dispatching;

[0129] Continuously obtain data on water resource utilization efficiency and benefits, use machine learning algorithms to establish efficiency and benefit evaluation models, and conduct quantitative evaluation of the comprehensive utilization efficiency and benefits of water resources;

[0130] The results of comprehensive water resource utilization efficiency and benefit evaluation are fed back to the water resource allocation optimization model as new optimization objectives or constraints to continuously optimize water resource allocation.

[0131] Specifically, the establishment of a water resources allocation optimization model is the key to achieving scientific scheduling. Taking a certain river basin as an example, the objective function of the model can be set as the weighted sum of maximizing economic benefits and ecological benefits, and the constraints include available water, water demand, water quality requirements, etc. Heuristic algorithms such as genetic algorithms can be used to solve this complex nonlinear model, and the approximate optimal solution can be obtained through multiple iterations. The optimization results can be converted into specific scheduling rules, such as the ecological water consumption in the dry season should not be less than 80% of the minimum ecological flow of the river, and the agricultural irrigation water consumption can be appropriately increased in the flood season. These rules are incorporated into the water resources management information system of the river basin, and data sharing and interaction are achieved with subsystems such as hydrological monitoring and water metering. The system evaluates the effect of water resources allocation through real-time monitoring data. For example, if the actual water inflow in a certain month is 20% lower than the predicted value, the system will automatically trigger the re-optimization process and adjust the water allocation of each water user. The optimized new plan is sent to the reservoir scheduling control system through the system interface to guide the actual operation. Water resources utilization efficiency and benefit evaluation are the basis for continuous optimization. Data such as water consumption and output value can be collected, and an evaluation model can be constructed using machine learning algorithms such as support vector machines. Taking agricultural water use as an example, the model input includes irrigation area, crop type, irrigation method, etc., and the output is the output value per unit of water. The evaluation results are fed back to the optimization model, such as increasing agricultural water use efficiency by 10% as a new constraint. This closed-loop optimization mechanism can adapt to the dynamic changes of the water resources system. As climate change intensifies, the interannual changes in water inflow may become more drastic. Through continuous optimization, the system can better cope with extreme hydrological years, while ensuring basic water demand and improving water resource utilization efficiency. At the same time, with the adjustment of industrial structure and the advancement of water-saving technology, water demand is also changing dynamically. Closed-loop optimization can capture these changes in time, adjust water resource allocation strategies, and achieve sustainable use of water resources. Overall, this water resource allocation optimization method based on informatization and intelligence transforms traditional empirical management into data-driven scientific decision-making. It can not only improve the efficiency of water resource utilization, but also seek a balance between the interests of all parties, providing new ideas and tools for solving complex water resource management problems.

[0132] like Figure 2 As shown, based on the same inventive concept, this embodiment also provides a cold region water resource rational allocation system based on ecological protection, including:

[0133] Ecological water demand assessment module, used to obtain the spatiotemporal distribution data of ecological water demand in different periods;

[0134] The living and production water demand forecasting module is used to establish a living and production water demand forecasting model based on the spatiotemporal distribution data of ecological water demand through grey forecasting and regression analysis methods, and to determine the predicted values ​​of living and production water demand under high-growth, medium-growth and low-growth development scenarios;

[0135] The module for constructing a multi-objective optimization model for water resource allocation is used to construct a multi-objective optimization model for water resource allocation by taking ecological water demand as a constraint and the predicted value of living and production water demand as the optimization target;

[0136] The water resource allocation solution module is used to solve the multi-objective optimization model using simulated annealing algorithm and genetic algorithm to obtain the initial water resource allocation solution;

[0137] The water resources optimization configuration module is used to calculate the water revenue and expenditure balance of each region based on the initial water resources allocation plan, combined with the water resources engineering system and scheduling rules, and use linear programming optimization scheduling technology to optimize the configuration of water resources in time and space;

[0138] The water resources allocation coordination mechanism establishment module is used to define the weight coefficients of different water users according to the initial water resources allocation plan and establish a water resources allocation coordination mechanism with balanced interests among multiple parties;

[0139] The water resource allocation plan adjustment and implementation module is used to adjust and optimize the initial water resource allocation plan based on the results of stakeholder coordination to obtain the final water resource allocation plan.

[0140] The cold region water resources rational allocation system based on ecological protection provided in this embodiment has all the advantages of the cold region water resources rational allocation method based on ecological protection.

[0141] This embodiment further discloses a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the first embodiment.

[0142] This embodiment further discloses a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in the first embodiment are implemented.

[0143] This embodiment also discloses a computer program product, including a computer program, which implements the steps of the method described in the first embodiment when executed by a processor.

[0144] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for rationally allocating water resources in cold regions based on ecological protection, characterized in that: include: Obtain the spatiotemporal distribution data of ecological water demand in different periods, and based on the spatiotemporal distribution data of ecological water demand, establish a forecast model for domestic and production water demand through grey forecasting and regression analysis methods, and determine the forecast values ​​of domestic and production water demand under high-growth, medium-growth and low-growth development scenarios; Taking ecological water demand as a constraint condition and the predicted value of living and production water demand as the optimization target, a multi-objective optimization model for water resource allocation is constructed; Using simulated annealing algorithm and genetic algorithm to solve the multi-objective optimization model to obtain an initial water resource allocation plan; According to the initial water resource allocation plan, the weight coefficients of different water users are defined, and a water resource allocation coordination mechanism with balanced interests of multiple parties is established. According to the coordination results of stakeholders, the initial water resource allocation plan is adjusted and optimized to obtain the final water resource allocation plan.

2. The method according to claim 1, characterized in that: The process of obtaining the spatiotemporal distribution data of ecological water demand in different periods includes: According to the characteristics of different ecosystems, the water demand law model of ecological functional zones is established by using ecohydrology methods; The land use type and vegetation coverage information of the ecological functional zone are obtained through remote sensing technology as input parameters of the model; Using geographic information system technology, the ecological functional areas are spatially divided and the assessment units are determined; Calculate the ecological water demand of the assessment unit at different periods according to the characteristics of the ecosystem and the water demand law model of the assessment unit; The calculation results of ecological water demand in different periods are spatially interpolated to obtain the spatiotemporal distribution data of ecological water demand in the entire study area; The support vector machine is used to establish an ecological water demand prediction model, and the ecological water demand prediction under different scenarios is carried out to obtain the ecological water demand prediction value.

3. The method according to claim 1, characterized in that The process of establishing a forecast model for domestic and production water demand includes: Based on the spatiotemporal distribution data of ecological water demand, obtain the population, economic and water use efficiency data in the study area; After cleaning and normalizing the population, economic and water use efficiency data, a prediction model for domestic water demand and production water demand is constructed based on a grey prediction model and regression analysis method, and the processed data is used to train and optimize the model; By analyzing the water-saving potential and water-use efficiency improvement space in the study area, the water-saving rate and water-use efficiency improvement coefficient under different development scenarios are determined; The water saving rate and water use efficiency improvement coefficient are introduced as parameters into the prediction model to simulate the living and production water demand under three development scenarios of high growth, medium growth and low growth; Conduct statistical analysis and visual display of simulation results to generate forecasts of domestic and production water demand under different development scenarios; The prediction results are compared and verified with the actual water demand data, the accuracy and reliability of the prediction model are evaluated, and the model is optimized and adjusted based on the feedback results.

4. The method according to claim 1, characterized in that: The process of building a multi-objective optimization model for water resource allocation includes: According to the constraints and optimization objectives in the water resources allocation model, obtain the data on ecological water demand, domestic water demand forecast and production water demand forecast; Through the water supply cost accounting model, the unit water supply cost under different water supply plans is calculated to obtain the water supply cost data; Through the water use economic benefit evaluation model, the unit water use economic benefits of different water use plans are calculated to obtain water use economic benefit data; A multi-objective optimization model was constructed by taking ecological water demand as the constraint condition, the predicted values ​​of living and production water demand as the optimization objectives, and the unit water supply cost and unit water use economic benefit as the evaluation indicators.

5. The method according to claim 1, characterized in that The process of solving the multi-objective optimization model by using simulated annealing algorithm and genetic algorithm to obtain the initial water resource allocation plan includes: According to the multi-objective optimization model of water resources allocation, determine the optimization objective function and constraints, including ecological protection indicators, social and economic development indicators, water supply and water supply security level; When using the simulated annealing algorithm to solve the problem, the initial temperature and cooling rate are set, and a random search is performed in the solution space. The acceptance probability is used to determine whether to accept the new solution until the termination condition is reached and the global optimal solution is obtained. The optimal solution is further optimized by using a genetic algorithm. Through selection, crossover and mutation operations, the population is continuously evolved iteratively to optimize individual fitness and obtain an initial water resource allocation plan that meets multiple objectives.

6. The method according to claim 1, characterized in that After obtaining the initial water resource allocation plan, it also includes: According to the initial water resource allocation plan, combined with the water resource engineering system and scheduling rules, the water balance method is used to calculate the water revenue and expenditure balance of each region, and the linear programming optimization scheduling technology is used to optimize the allocation of water resources in time and space.

7. The method according to claim 1, characterized in that The process of optimizing the allocation of water resources in time and space includes: According to the water resource allocation plan and engineering system, the water supply capacity and water demand data of each region are obtained, and the water balance of each region is calculated by the water balance method to obtain the supply and demand difference data; Based on the supply-demand gap data, a linear programming optimization algorithm is used to establish an optimization model with the objective function of alleviating the contradiction between supply and demand to the greatest extent possible and the optimal allocation of water resources in time and space as the decision variable. According to the scheduling rules, the constraints of the optimization model are set, the optimization model is solved, the optimal configuration plan of water resources in different times and regions is obtained, and the optimized scheduling plan is obtained.

8. The method according to claim 7, characterized in that The linear programming optimization algorithm is used to alleviate the contradiction between supply and demand to the greatest extent possible as the objective function, and the optimal allocation of water resources in time and space is used as the decision variable. The process of establishing the optimization model includes: Based on water resources supply and demand data, a supply and demand contradiction assessment model is established to quantify the supply and demand gap; Use spatiotemporal analysis methods to obtain the distribution characteristics of water resources in time and space dimensions; Through the optimization algorithm, the optimal water resource allocation plan is determined to minimize the contradiction between supply and demand. If the gap between supply and demand exceeds the preset threshold, the early warning mechanism is triggered and emergency dispatch measures are taken; Based on the optimization allocation plan, a water resources scheduling plan is formulated to guide the optimal allocation of water resources, and a reinforcement learning algorithm is used to continuously optimize the water resources allocation strategy.

9. The method according to claim 1, characterized in that: Establish a water resource allocation coordination mechanism that balances the interests of multiple parties. According to the coordination results of stakeholders, adjust and optimize the initial water resource allocation plan. The process of obtaining the final water resource allocation plan includes: According to the coordination results of stakeholders, the optimization objectives and constraints of water resource allocation are obtained, and a water resource allocation optimization model is established; Use heuristic algorithms to solve the water resource allocation optimization model and obtain the optimized water resource allocation plan; and convert it into executable scheduling rules and management policies; Incorporate the optimized dispatching rules and management policies into the basin water resources management information system, and exchange data with the dispatching control system and monitoring system through the system interface; Obtain real-time monitoring data, dynamically evaluate the water resource allocation process, and determine whether the optimization objectives and constraints are met. If not, re-optimization of the water resource allocation plan is triggered; According to the optimized water resource allocation plan, the dispatching rules and management policies are dynamically adjusted and sent to the dispatching control system through the basin water resource management information system to guide water resource dispatching; Continuously obtain data on water resource utilization efficiency and benefits, use machine learning algorithms to establish efficiency and benefit evaluation models, and conduct quantitative evaluation of the comprehensive utilization efficiency and benefits of water resources; The results of comprehensive water resource utilization efficiency and benefit evaluation are fed back to the water resource allocation optimization model as new optimization objectives or constraints to continuously optimize water resource allocation.

10. A cold region water resources rational allocation system based on ecological protection, characterized in that: include: Ecological water demand assessment module, used to obtain the spatiotemporal distribution data of ecological water demand in different periods; A living and production water demand prediction module is used to establish a living and production water demand prediction model based on the spatiotemporal distribution data of ecological water demand through grey prediction and regression analysis methods, and determine the predicted values ​​of living and production water demand under high growth, medium growth and low growth development scenarios; A water resource allocation multi-objective optimization model building module is used to take ecological water demand as a constraint condition and the predicted value of living and production water demand as an optimization target to build a multi-objective optimization model for water resource allocation; A water resource allocation solution module is used to solve the multi-objective optimization model using a simulated annealing algorithm and a genetic algorithm to obtain an initial water resource allocation solution; A water resource optimization configuration module is used to calculate the water revenue and expenditure balance of each region according to the initial water resource allocation plan, in combination with the water resource engineering system and scheduling rules, and to optimize the configuration of water resources in time and space by using linear programming optimization scheduling technology; A water resource allocation coordination mechanism establishment module is used to define the weight coefficients of different water users according to the initial water resource allocation plan and establish a water resource allocation coordination mechanism with balanced interests among multiple parties; The water resource allocation plan adjustment and implementation module is used to adjust and optimize the initial water resource allocation plan according to the coordination results of stakeholders to obtain the final water resource allocation plan.

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