Regional water resource management optimization method

Abstract

The invention discloses a regional water resource management optimization method, which is applied to the technical field of water resource management. Comprising the following steps: collecting water resource related data of a target area, and identifying key variables; predicting water demand data of the target area in a future time period based on the key variables; constructing a multi-task objective function for water resource management optimization, wherein the multi-task objective function comprises an economic objective, a production objective, an ecological objective and an environment objective; setting weights of different targets, and solving the water resource scheduling scheme by adopting a multi-target optimization algorithm to obtain an optimal solution set; and converting the optimal solution set into a specific water resource scheduling scheme, performing quantitative index evaluation, recommending an optimal scheduling scheme, converting the optimal scheduling scheme into a specific operation instruction, issuing the specific operation instruction to an execution unit, and continuously monitoring an actual effect after instruction execution. According to the method, water resource data sensing, prediction, optimization, execution and evaluation are performed on the target area, so that refined, adaptive and sustainable management of regional water resources is realized.

Description

Technical Field

[0001] This invention relates to the field of water resources management technology, and more specifically to a method for optimizing regional water resources management. Background Technology

[0002] With the intensification of global climate change and human activities, regional water scarcity and management issues are becoming increasingly prominent. Therefore, optimizing water resource allocation schemes and rationally distributing water resources is of increasing importance for water resource management. However, traditional water resource management schemes mainly rely on manual experience and static numerical calculations for allocation, which is insufficient to meet the complex needs of regional water resource management. Furthermore, traditional water demand forecasting models have limited accuracy and struggle to capture the complex nonlinear spatiotemporal characteristics of water demand, especially for agricultural water use, as they cannot respond to meteorological influences in real time. Therefore, providing a regional water resource management optimization method is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0003] In view of this, the present invention provides a method for optimizing regional water resource management, which solves the problems in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A method for optimizing regional water resource management includes the following steps: S1. Collect water resource-related data for the target area within a set historical time period and preprocess it; S2. Identify key variables affecting regional water demand changes from preprocessed historical water resources data; S3. A deep learning model for water demand prediction is trained based on key variables and historical water demand to predict water demand data for the target area in future time periods. S4. Construct a multi-task objective function for water resource management optimization by combining water demand data, including economic objectives, production objectives, ecological objectives, and environmental objectives; S5. Set the weights for different objectives, and use a multi-objective optimization algorithm to solve the water resource scheduling scheme under constraints to obtain the optimal solution set; S6. Transform the optimized solution set into specific water resource scheduling schemes, evaluate each water resource scheduling scheme with quantitative indicators, and recommend the optimal scheduling scheme. S7. Transform the optimal scheduling scheme into specific operation instructions and issue them to the execution unit, and continuously monitor the actual effect after the instructions are executed.

[0005] Optionally, S2 specifically involves: performing preliminary filtering of historical water resources-related data through missing value removal, variance filtering, and correlation coefficient calculation to obtain a set of candidate key variables; calculating the mutual information between each candidate key variable in the set and the regional water demand; sorting the candidate key variables from high to low according to the mutual information value; and selecting candidate key variables with mutual information values ​​greater than a preset threshold as key variables affecting changes in regional water demand.

[0006] Optionally, S3 specifically involves: constructing a CNN-LSTM-based water demand prediction model, including a CNN module, an LSTM module, and a fully connected layer. Key variables and historical water demand data for the region across multiple historical time steps are used to construct the input vector. The CNN module extracts features for each time step, the LSTM module extracts the hidden state for each time step, and the fully connected layer converts the output of the LSTM module into predicted water demand values ​​for future time steps. A mean squared error loss function is used to measure the difference between the predicted water demand and the actual water demand, and the Adam algorithm is used to minimize the loss function for model training.

[0007] Optionally, the multi-task objective function in S4 is as follows: ; ; ; ; ; In the formula, , , and These are the economic objective function, the production objective function, the ecological objective function, and the environmental objective function, respectively. , , and They are respectively , , and The weight, X As decision variables, For water source s Water supply costs, for t Water source during the period s Water supply For water source s Energy consumption cost for t Water source during the period s Energy consumption, To cover the costs of water shortage, fort Water shortage during the period for t Agricultural areas during the period n The actual evaporation rate for t Agricultural areas during the period n Potential evaporation, for t Agricultural areas during the period n The amount of water shortage, for t Industrial Zone during the period j The output value, for t River section within the time period r Missing values ​​from standard flow for t Lakes during the period m The missing value for the distance from the standard water level, for t Pollutant concentration values ​​in river sections and lakes during the specified time period These are the water quality standard values ​​for river sections and lakes.

[0008] Optionally, the constraints in S5 include supply and demand balance constraints, water quantity balance constraints, water supply capacity constraints, ecological standard constraints, water quality constraints, and compliance constraints.

[0009] Optionally, in S5, setting the weights of different objectives specifically involves: setting the initial weights and weight adjustment rules for different objectives, obtaining the weight driving factor data and matching it with the weight adjustment rules, and adjusting the weights of different objectives if a corresponding adjustment rule exists.

[0010] Optionally, the multi-objective optimization algorithm used in S5 to solve the water resource scheduling scheme under constraints is as follows: the water resource scheduling scheme is encoded as individual variables, the population size and maximum number of iterations are determined, an initial population is initially generated based on historical scheduling data and water resource allocation rules, the objective function value and constraint penalty value of all individuals in the initial population are calculated, all individuals are sorted in a non-dominated manner, individuals are randomly selected from the population for crossover and mutation operations to generate offspring populations, the parent population and offspring populations are merged, the merged population is sorted in a non-dominated manner, the individuals are divided into different non-dominated layers, and individuals from each non-dominated layer are added to the new population in turn until the size of the new population reaches the initial population size. The non-dominated sorting, crossover and mutation operations, and new population generation are repeated until the maximum number of iterations is reached, and the first layer in the non-dominated layer is taken as the optimal solution set.

[0011] Optionally, S6 performs quantitative evaluation of each water resource scheduling scheme and recommends the optimal scheduling scheme as follows: evaluate the scores of each water resource scheduling scheme from four dimensions: optimization efficiency, allocation fairness, stability and risk. Determine the weight of each dimension according to management priorities, use the TOPSIS method to calculate the comprehensive evaluation score, select the scheme with the highest comprehensive evaluation score as the optimal scheduling scheme, and at the same time, use the scheme with the highest score in each evaluation dimension as the scenario alternative scheme.

[0012] As can be seen from the above technical solution, compared with the prior art, the present invention provides a regional water resource management optimization method with the following beneficial effects: by sensing, predicting, optimizing, executing, and evaluating water resource data in the target area, it achieves refined, adaptive, and sustainable management of regional water resources; it establishes a water demand prediction model to capture the spatiotemporal dynamics of water resource demand, providing accurate predictions and laying the foundation for refined management; it introduces a dynamic weight mechanism, enabling the optimization target to respond to real-time changes, solving the problem of rigid schemes caused by fixed weights, and improving the flexibility of management; through multi-dimensional comprehensive quantitative evaluation, it overcomes the one-sidedness of traditional evaluation, ensuring the comprehensiveness and effectiveness of recommended schemes; and through a closed loop of monitoring, feedback, and scheduling, it enables the system to have self-optimization capabilities. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0014] Figure 1 This is a flowchart of the regional water resources management optimization method of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] This invention discloses an optimization method for regional water resource management, such as... Figure 1 As shown, it includes the following steps: S1. Collect water resource-related data for the target area within a set historical time period and perform preprocessing; S2. Identify key variables affecting regional water demand changes from preprocessed historical water resources data; S3. A deep learning model for water demand prediction is trained based on key variables and historical water demand to predict water demand data for the target area in future time periods. S4. Construct a multi-task objective function for water resource management optimization by combining water demand data, including economic objectives, production objectives, ecological objectives, and environmental objectives; S5. Set the weights for different objectives, and use a multi-objective optimization algorithm to solve the water resource scheduling scheme under constraints to obtain the optimal solution set; S6. Transform the optimized solution set into specific water resource scheduling schemes, evaluate each water resource scheduling scheme with quantitative indicators, and recommend the optimal scheduling scheme. S7. Transform the optimal scheduling scheme into specific operation instructions and issue them to the execution unit, and continuously monitor the actual effect after the instructions are executed.

[0017] Furthermore, S2 specifically involves: performing preliminary filtering of historical water resources-related data through missing value removal, variance filtering, and correlation coefficient calculation to obtain a set of candidate key variables; calculating the mutual information between each candidate key variable in the set and the regional water demand; sorting the candidate key variables from high to low according to their mutual information values; and selecting candidate key variables with mutual information values ​​greater than a preset threshold as key variables affecting changes in regional water demand.

[0018] In this embodiment of the invention, historical water resources related data include topography, soil type, crop distribution, water conservancy project network topology, precipitation, evaporation, temperature, humidity, wind speed, river flow, reservoir water level, groundwater level, industrial water use, agricultural water use, domestic water use, ecological zoning, population, industrial structure, soil moisture, water quality parameters, and project operation status data. If the missing value of a certain indicator exceeds 40%, it will be removed. For the remaining indicators, missing values ​​can be filled by interpolation. Variance filtering is used to remove variables with variance close to zero. The correlation coefficient is calculated using the Pearson correlation coefficient. In the initial filtering, only water resource-related data with a correlation coefficient greater than 0.3 are retained to achieve rapid screening. Mutual information calculation specifically involves: ; In the formula, Representing variables A and variables B mutual information value, yes A and B The joint probability mass function, and They are Aand B The marginal probability mass function.

[0019] Furthermore, S3 specifically involves: constructing a CNN-LSTM-based water demand prediction model, including a CNN module, an LSTM module, and a fully connected layer. Key variables and historical water demand data for the region across multiple historical time steps are used to construct input vectors. The CNN module extracts features for each time step, the LSTM module extracts the hidden states for each time step, and the fully connected layer converts the output of the LSTM module into predicted water demand values ​​for future time steps. A mean squared error loss function is used to measure the difference between the predicted and actual water demand values, and the Adam algorithm is used to minimize the loss function for model training.

[0020] In this embodiment of the invention, the LSTM module includes two LSTM layers. LSTM is a specially designed recurrent neural network (RNN) architecture that can capture the long-term temporal dependence of water demand and its driving factors. Through gating mechanisms (input gate, forget gate, output gate), it can selectively memorize and forget information, effectively overcoming the gradient vanishing problem of traditional RNNs. Its calculation process is as follows: ; ; ; ; ; ; In the formula, , , and They represent t Outputs of the time forget gate, input gate, unit state, and output gate; Indicates that LSTM in time t The output; and tan h Let these represent the sigmoid activation function and the hyperbolic tangent function, respectively. , , and These are the weights for the forget gate, input gate, unit state, and output gate, respectively. , , and These are the biases for the forget gate, input gate, unit state, and output gate, respectively.

[0021] LSTM outputs a hidden state at each time step. This state encodes all historical information up to the current moment.

[0022] Furthermore, the multi-task objective function in S4 is as follows: ; ; ; ; ; In the formula, , , and These are the economic objective function, the production objective function, the ecological objective function, and the environmental objective function, respectively. , , and They are respectively , , and The weight, X As decision variables, For water source s Water supply costs, for t Water source during the period s Water supply For water source s Energy consumption cost for t Water source during the period s Energy consumption, To cover the costs of water shortage, for t Water shortage during the period for t Agricultural areas during the period n The actual evaporation rate for t Agricultural areas during the period n Potential evaporation, for t Agricultural areas during the period n The amount of water shortage, for t Industrial Zone during the period j The output value, for t River section within the time period r Missing values ​​from standard flow for t Lakes during the period m The missing value for the distance from the standard water level, for t Pollutant concentration values ​​in river sections and lakes during the specified time period These are the water quality standard values ​​for river sections and lakes. In this embodiment, the output value... It can be determined by fitting a functional relationship between water consumption and output value.

[0023] Furthermore, the constraints in S5 include supply and demand balance constraints, water quantity balance constraints, water supply capacity constraints, ecological standard constraints, water quality constraints, and compliance constraints.

[0024] In this embodiment of the invention, the constraint conditions are specifically expressed as follows: Supply and demand balance constraints: ; The total water supply from all water sources must meet a certain proportion, where, This is the proportionality coefficient. To predict total water demand; Water balance constraints: ; In the formula, for t Reservoir capacity during +1 time period for t Reservoir capacity during a given period for t Inbound volume during a given period for t Water supply during different time periods for t Evaporation over a period of time for t The amount of loss during the period; Water supply capacity constraints: ; The water supply from each water source must be less than its maximum water supply capacity at any given time. ; Ecological standards constraints: ; ; The flow rate of each river segment must be greater than its minimum standard flow rate at each time period. The water level of each lake must be higher than its minimum standard water level at every time period. ; Water quality constraints: ; Compliance constraints: ; ; In the formula, The maximum allowed total water consumption. This refers to the amount of groundwater extracted. This is the maximum permitted amount of groundwater extraction. In this embodiment, the constraints may also include constraints on crop yield, water consumption in various industries, and the proportion of reclaimed water.

[0025] Furthermore, in S5, setting the weights of different objectives specifically involves: setting the initial weights and weight adjustment rules for different objectives; obtaining weight driving factor data and matching it with the weight adjustment rules; and adjusting the weights of different objectives if a corresponding adjustment rule exists.

[0026] In this embodiment of the invention, the initial weights of the different objectives are: economic objective 0.3, production objective 0.4, ecological objective 0.15, and environmental objective 0.15. The weight adjustment rules may include: When the drought index is greater than the preset threshold and the remaining water volume of the reservoir is less than a certain proportion, the weight of the production target is increased. At this time, the weight is adjusted as follows: economic target 0.2, production target 0.5, ecological target 0.2, and environmental target 0.1. If the current time is the fish migration season and the river's ecological flow is less than 80%, the weights are adjusted as follows: economic target 0.25, production target 0.25, ecological target 0.4, and environmental target 0.1. If pollutant concentrations are detected to exceed standards for a period of time, the weighting is adjusted as follows: economic target 0.2, production target 0.2, ecological target 0.2, and environmental target 0.4. In addition, other rules can be preset. If multiple rules are met at the same time, the weight adjustment can be based on the preset priority to determine which rule is the priority rule.

[0027] Furthermore, the multi-objective optimization algorithm used in S5 to solve the water resource scheduling scheme under constraints is as follows: the water resource scheduling scheme is encoded as individual variables, the population size and maximum number of iterations are determined, an initial population is initially generated based on historical scheduling data and water resource allocation rules, the objective function value and constraint penalty value of all individuals in the initial population are calculated, all individuals are sorted in a non-dominated manner, individuals are randomly selected from the population for crossover and mutation operations to generate offspring populations, the parent and offspring populations are merged, the merged population is sorted in a non-dominated manner, individuals are divided into different non-dominated layers, and individuals from each non-dominated layer are added to the new population in turn until the size of the new population reaches the initial population size. The non-dominated sorting, crossover and mutation operations, and new population generation are repeated until the maximum number of iterations is reached, and the first layer in the non-dominated layer is taken as the optimal solution set.

[0028] In this embodiment of the invention, the constraint penalty value is calculated as follows: ; In the formula, Individual variables X The constraint penalty value, For inequality constraints, For equality constraints, , The corresponding maximum value; The non-dominated sort is specifically as follows: First, identify all individuals that are not dominated by any other individuals and designate them as the first non-dominated level. Remove the individuals from the first non-dominated level and then identify the non-dominated individuals among the remaining individuals and designate them as the second non-dominated level. Continue this process of establishing non-dominated levels to complete the non-dominated sorting.

[0029] Furthermore, S6 conducts quantitative evaluation of each water resource scheduling scheme and recommends the optimal scheduling scheme as follows: evaluate the scores of each water resource scheduling scheme from four dimensions: optimization efficiency, allocation fairness, stability and risk. Determine the weight of each dimension according to management priorities, use the TOPSIS method to calculate the comprehensive evaluation score, select the scheme with the highest comprehensive evaluation score as the optimal scheduling scheme, and at the same time, select the scheme with the highest score in each evaluation dimension as the scenario alternative scheme.

[0030] In this embodiment of the invention, the evaluation index for the efficiency dimension is the direct achievement of the scheduling scheme of the four objectives; the evaluation index for the allocation fairness dimension is the Gini coefficient of the regional water security rate; the evaluation index for the stability dimension is the target completion rate of the scheduling scheme under different scenarios; and the evaluation index for the risk dimension is the risk of water quality deterioration and excessive water use. A severity level scoring system is used to determine the index values. The weight of each dimension is determined based on the current management scheme's priorities combined with human experience. Preset weight allocations are used for each of the four dimensions when they are prioritized, and when priorities shift, the system directly switches to the corresponding preset weight scheme.

[0031] In this embodiment of the invention, the actual effect after the command is executed is continuously monitored, including water consumption, water level, flow rate, water quality, etc. The actual effect is compared with the predicted result and the optimization result. If the deviation exceeds the set threshold, the model update mechanism or the optimization rescheduling mechanism is automatically triggered to re-execute the process from S3 to S7, so as to realize the system's self-adaptation and continuous optimization.

[0032] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0033] Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing regional water resource management, characterized in that, Includes the following steps: S1. Collect water resource-related data for the target area within a set historical time period and preprocess it; S2. Identify key variables affecting regional water demand changes from preprocessed historical water resources data; S3. A deep learning model for water demand prediction is trained based on key variables and historical water demand to predict water demand data for the target area in future time periods. S4. Construct a multi-task objective function for water resource management optimization by combining water demand data, including economic objectives, production objectives, ecological objectives, and environmental objectives; S5. Set the weights for different objectives, and use a multi-objective optimization algorithm to solve the water resource scheduling scheme under constraints to obtain the optimal solution set; S6. Transform the optimized solution set into specific water resource scheduling schemes, evaluate each water resource scheduling scheme with quantitative indicators, and recommend the optimal scheduling scheme. S7. Transform the optimal scheduling scheme into specific operation instructions and issue them to the execution unit, and continuously monitor the actual effect after the instructions are executed.

2. The regional water resources management optimization method according to claim 1, characterized in that, S2 specifically involves: performing preliminary filtering of historical water resources data through missing value removal, variance filtering, and correlation coefficient calculation to obtain a set of candidate key variables; calculating the mutual information between each candidate key variable in the set and the regional water demand; sorting the candidate key variables from high to low according to the mutual information value; and selecting candidate key variables with mutual information values ​​greater than a preset threshold as key variables affecting changes in regional water demand.

3. The regional water resources management optimization method according to claim 1, characterized in that, S3 specifically involves constructing a CNN-LSTM-based water demand prediction model, including a CNN module, an LSTM module, and a fully connected layer. Key variables and historical water demand data for the region across multiple historical time steps are used to construct the input vector. The CNN module extracts features for each time step, the LSTM module extracts the hidden states for each time step, and the fully connected layer converts the output of the LSTM module into predicted water demand values ​​for future time steps. A mean squared error loss function is used to measure the difference between the predicted and actual water demand, and the Adam algorithm is used to minimize the loss function for model training.

4. The regional water resources management optimization method according to claim 1, characterized in that, The multi-task objective function in S4 is as follows: ； ； ； ； ； In the formula, , , and These are the economic objective function, the production objective function, the ecological objective function, and the environmental objective function, respectively. , , and They are respectively , , and The weight, X As decision variables, For water source s Water supply costs for t Water source during the period s Water supply For water source s Energy consumption cost for t Water source during the period s Energy consumption, To cover the costs of water shortage, for t Water shortage during the period for t Agricultural areas during the period n The actual evaporation rate for t Agricultural areas during the period n Potential evaporation, for t Agricultural areas during the period n The amount of water shortage, for t Industrial Zone during the period j The output value, for t River section within the time period r Missing values ​​from standard flow for t Lakes during the period m The missing value for the distance from the standard water level, for t Pollutant concentration values ​​in river sections and lakes during the specified time period These are the water quality standard values ​​for river sections and lakes.

5. The regional water resources management optimization method according to claim 1, characterized in that, The constraints in S5 include supply and demand balance constraints, water quantity balance constraints, water supply capacity constraints, ecological standard constraints, water quality constraints, and compliance constraints.

6. The regional water resources management optimization method according to claim 1, characterized in that, In S5, setting the weights of different objectives involves: setting the initial weights and weight adjustment rules for different objectives; obtaining the weight driving factor data and matching it with the weight adjustment rules; and adjusting the weights of different objectives if a corresponding adjustment rule exists.

7. The regional water resources management optimization method according to claim 1, characterized in that, The S5 algorithm employs a multi-objective optimization approach to solve water resource scheduling schemes under constraints. Specifically, it encodes the water resource scheduling scheme as individual variables, determines the population size and maximum number of iterations, initially generates an initial population based on historical scheduling data and water resource allocation rules, calculates the objective function value and constraint penalty value for all individuals in the initial population, performs non-dominated sorting on all individuals, randomly selects individuals from the population for crossover and mutation operations to generate offspring populations, merges the parent and offspring populations, performs non-dominated sorting on the merged population, divides individuals into different non-dominated layers, and sequentially adds individuals from each non-dominated layer to the new population until the new population size reaches the initial population size. This process of non-dominated sorting, crossover and mutation operations, and new population generation is repeated until the maximum number of iterations is reached. The first layer in the non-dominated layer is taken as the optimal solution set.

8. The regional water resources management optimization method according to claim 1, characterized in that, In S6, each water resource scheduling scheme is evaluated using quantitative indicators. The recommended optimal scheduling scheme is as follows: each water resource scheduling scheme is evaluated from four dimensions: optimization efficiency, allocation fairness, stability, and risk. The weight of each dimension is determined according to the management priorities. The TOPSIS method is used to calculate the comprehensive evaluation score. The scheme with the highest comprehensive evaluation score is selected as the optimal scheduling scheme. At the same time, the scheme with the highest score in each evaluation dimension is selected as the scenario alternative scheme.

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