Water network system optimization layout method and system

Abstract

The present invention discloses a method and system for optimizing the layout of a water network system, collects data of a study area, calculates the accuracy of a DEM and the threshold value of an optimal catchment area, identifies watersheds in the study area, and divides the study area into several primary and secondary basins; calculates the water demand for life, industry, agriculture, and ecology in each basin; constructs a system dynamics model of the water network system, calculates the mutation point of the capacity building efficiency index, and obtains the optimal layout plan of the nodes and the corresponding water supply; constructs a water resources optimization configuration model, optimizes the system dynamics model parameters, and obtains the optimal layout plan of the primary basin; calculates the difference between the water supply and water demand of each primary basin and inputs it into a pre-constructed water resources optimization scheduling model to obtain the water resources optimization scheduling plan of the study area. The present invention can support the formation of a national basin regional water network optimization layout plan for medium- and long-term water resources security, proposes major engineering layouts and related measures, and reduces engineering construction costs.

Description

Technical Field

[0001] The invention relates to a method for optimizing the layout of a water network system. Background Art

[0002] The water network system is a comprehensive system based on natural rivers and lakes, with diversion and drainage projects as channels, storage projects as nodes, and intelligent regulation as a means. It integrates the functions of optimizing water resources allocation, flood control and disaster reduction in river basins, and protection of water ecosystems. It is an effective measure to solve the uneven spatial distribution of water resources, improve the water resources guarantee rate in the receiving areas, alleviate the contradiction between water supply and demand in water-scarce areas, and achieve rational allocation of water resources. It is an important way to promote economic development and comprehensive development and utilization of water resources in water-scarce areas.

[0003] With the influence of factors such as population growth, accelerated urbanization and climate change, the contradiction between water supply and demand has become increasingly prominent. The optimized layout of the water network system can effectively regulate the supply and demand of water resources, meet different water needs, reduce the risk of water disasters such as floods and droughts, maintain the ecological balance of water bodies, maintain the health of the ecosystem, maximize the economic benefits of water resources, reduce operating costs, improve resource utilization efficiency, and promote the sustainable use of water resources.

[0004] The present invention proposes a method and system for optimizing the layout of a water network system, forms a national river basin regional water network optimization layout plan for mid- and long-term water resources security, proposes major project layouts and related measures, and reduces project construction costs. Summary of the invention

[0005] Purpose of the invention: to provide a method for optimizing the layout of a water network system to solve the above problems existing in the prior art. On the other hand, to provide a system for optimizing the layout of a water network system.

[0006] Technical solution: A method for optimizing the layout of a water network system, comprising the following steps:

[0007] Step S1, collect data of the study area, calculate the DEM accuracy and the optimal catchment area threshold based on the terrain data of the study area, grid the study area and automatically identify the watershed in the study area, divide the study area into m first-level basins based on the watershed, each basin contains several second-level basins, and m is a positive integer greater than 2;

[0008] Step S2, extracting the domestic, industrial, agricultural and ecological water demands of each administrative division in the study area, calculating the ratio of the number of grids in each secondary basin in each primary basin to the total number of grids in the administrative division to which the secondary basin belongs, obtaining the domestic, industrial, agricultural and ecological water demands of each secondary basin in each primary basin, and summing the water demands of all secondary basins in each primary basin to obtain the domestic, industrial, agricultural and ecological water demands of each primary basin;

[0009] Step S3, constructing a system dynamics model of the water network system based on the historical construction input of each node in the study area and the historical GDP total data corresponding to the node, the nodes in the study area include: reservoirs and lakes, solving the system dynamics model to obtain the change curve of GDP with the increase of node construction input and sequentially calculating the capacity building efficiency index mutation points corresponding to all nodes, and obtaining the optimal layout plan of the nodes in the water network system and the corresponding water supply based on the capacity building efficiency index mutation points; Step S4, extracting the historical water supply, historical rainfall and historical runoff data of all secondary basins in each primary basin and inputting them into the pre-constructed water resources optimization allocation model respectively, obtaining the long-term optimal allocation plan of all secondary basins in the primary basin, calibrating the system dynamics model parameters based on the long-term optimal allocation plan, obtaining the water supply of the secondary basin based on the sum of the water supply of all nodes in the secondary basin, inputting the water supply, rainfall and runoff data of all secondary basins in the primary basin into the optimized system dynamics model, and obtaining the water resources optimization allocation plan of the primary basin;

[0010] Step S5: Based on the water supply and water demand of m primary river basins, the difference between the water supply and water demand of each river basin is calculated in turn and input into the pre-built water resources optimization scheduling model to obtain the water resources optimization scheduling plan for the study area. The water network system optimization layout plan includes: the optimization layout plan of the nodes in the water network system, the water resources optimization configuration plan of m primary river basins and the water resources optimization scheduling plan for the study area.

[0011] According to one aspect of the present application, step S1 further comprises:

[0012] Step S11, collect data of the study area, including topographic data, forecasts of domestic, industrial, agricultural and ecological water use in each administrative division, historical construction investment of nodes, historical GDP data, historical water supply forecasts, historical rainfall forecasts and historical runoff forecasts;

[0013] Step S12: setting the DEM accuracy based on the terrain data of the study area and dividing the study area into a number of grids according to the DEM accuracy, and using the river network density method to calculate the optimal catchment area threshold;

[0014] Step S13, based on the optimal catchment area threshold, automatically identify all watersheds in the study area and divide the study area into several basins, and divide the basins into m primary basins and n secondary basins based on the fact that the main river channel of each basin belongs to a category or an order in the water network system. The primary basin is the category in the water network system, and the secondary basin is the order in the water network system. m and n are positive integers greater than 0.

[0015] According to one aspect of the present application, step S12 further comprises:

[0016] Step S12a, setting a plurality of different DEM resolutions and calculating the river length, drainage area and river network density extracted under different thresholds;

[0017] Step S12b, fitting the second-order derivative of the river network density curve and finding the point where the second-order derivative is 0 to obtain the range of the optimal threshold;

[0018] Step S12c, superimposing and comparing the river network extracted within the determined optimal threshold range with the actual river network to determine the optimal thresholds under four resolutions.

[0019] According to one aspect of the present application, step S2 further comprises:

[0020] Step S21, extract each watershed in turn, and classify the grids in the watershed according to the administrative divisions to which they belong;

[0021] Step S22, calculate the proportion of all grids belonging to a certain administrative division in each river basin to the total number of grids in the administrative division, and calculate the domestic, industrial, agricultural and ecological water demand of the part of the river basin belonging to the administrative division, that is, the product of the proportion and the total domestic, industrial, agricultural and ecological water demand of the administrative division;

[0022] Step S23, calculate the domestic, industrial, agricultural and ecological water demands of all categories in each river basin in turn, and sum them up to obtain the domestic, industrial, agricultural and ecological water demands of the river basin;

[0023] Step S24: Calculate S21-S23 for all primary and secondary river basins to obtain the domestic, industrial, agricultural and ecological water demands of all primary and secondary river basins.

[0024] According to one aspect of the present application, step S3 further comprises:

[0025] Step S31, extract the historical construction investment of each node in the study area and its corresponding GDP total data, build a system dynamics model of the water network system, and solve the system dynamics model to obtain a change curve of GDP with the change of historical construction investment of the node;

[0026] Step S32: Based on the system dynamics model, extend the curve of GDP changing with the historical construction investment of the node until the GDP growth is lower than the node construction investment, set the GDP increment brought by each 100 million yuan of construction investment as the capacity building efficiency index, and obtain several index mutation points on the change curve based on the index;

[0027] Step S33: adopt the mutation point monitoring method to select the best index mutation point from several index mutation points as the optimization point of the node, that is, the optimal layout plan of the node in the water network system, and obtain the water supply forecast of the node based on the optimal layout plan of the node in the water network system.

[0028] According to one aspect of the present application, step S4 is further:

[0029] Step S41, constructing a water resource optimization allocation model, the objective function is: the total water shortage of the system is minimized, the water shortage rate is uniform during each period, and the cost is minimized, and the constraints include: water balance constraint, water storage constraint, water supply capacity constraint, water demand constraint and variable non-negative constraint;

[0030] Step S42, extracting the historical water supply volume, historical rainfall and historical runoff data of all secondary basins in each primary basin and inputting them into the water resources optimization allocation model to obtain the historical optimization allocation plan of the primary basin;

[0031] Step S43: construct an optimization model based on the historical optimization configuration scheme and the corresponding real historical optimization configuration scheme, and solve the optimization model using the REVA algorithm based on the improved particle swarm algorithm and chaos mapping optimization to obtain the optimal parameter scheme of the system dynamics model;

[0032] Step S44: obtain the water supply forecast of all secondary basins in the primary basin based on the sum of the water supply volumes of all nodes in all secondary basins in the primary basin, input the water supply volume, rainfall and runoff data of all secondary basins in the primary basin into the optimized system dynamics model, and obtain the water resources optimization allocation plan for the primary basin.

[0033] According to one aspect of the present application, step S43 is further:

[0034] Step S43a, based on the historical optimization configuration scheme and the corresponding real historical optimization configuration scheme, the objective function is set to minimize the cumulative deviation between the optimal solution to be solved and the actual optimal solution, and the optimization model is constructed;

[0035] Step S43b: Use the optimized REVA algorithm to solve the optimization model and obtain the optimal parameter solution of the system dynamics model.

[0036] According to one aspect of the present application, step S43b is further:

[0037] Step S43b1, using multi-chaotic mapping to generate an initial population to improve the particle swarm algorithm, and applying the improved particle swarm algorithm to calculate the optimal basic parameters of the REVA algorithm;

[0038] Step S43b2, using multiple chaotic maps to generate an initial population to generate an initial population for the REVA algorithm;

[0039] Step S43b3, set the center vector, the preference radius, and generate the preference vector;

[0040] Step S43b4, using traditional genetic operations such as crossover and mutation to generate offspring populations, and then using elite strategy to merge the offspring population with the parent population;

[0041] Step S43b5, generating N sub-populations by associating each population member with one of the N reference vectors;

[0042] Step S43b6, calculate the angle penalty distance, take the individual with the smallest APD value in the sub-population as the elite retainer, pass it to the next generation, and calculate the i-th adaptive reference vector of the next generation based on the vector adaptation strategy;

[0043] Step S43b7, repeat steps S43b4 to S43b6 until the stopping criterion is reached, and then output the current population as the final result.

[0044] According to one aspect of the present application, step S5 is further:

[0045] Step S51, constructing a water resources optimization scheduling model, the objective function includes: the lowest overall water shortage and the lowest water transfer cost, and the constraints include: water balance constraint, water storage constraint, water supply capacity constraint, water demand constraint and variable non-negative constraint;

[0046] Step S52: based on the water demand and water supply of the primary river basins, calculate the supply and demand relationship of water resources of the m primary river basins in turn, and divide the m primary river basins into three categories according to the supply and demand relationship, namely: supply exceeds demand, supply is less than demand, and supply and demand are balanced;

[0047] Step S53, respectively calculating the excess water volume of the watershed with surplus water and the shortage water volume of the watershed with water shortage and inputting them into the water resources optimization scheduling model, and using a multi-objective optimization algorithm to obtain a non-inferior solution set of the water resources optimization scheduling scheme for the study area;

[0048] Step S54, use the grey correlation analysis method to optimize the non-inferior solution set, and obtain the best optimization scheduling plan as the water resources optimization scheduling plan for the study area. The water network system optimization layout plan includes: the optimization layout plan of the nodes in the water network system, the water resources optimization allocation plan of m first-level river basins and the water resources optimization scheduling plan for the study area.

[0049] According to another aspect of the present application, a water network system optimization layout system is provided, comprising:

[0050] at least one processor; and

[0051] a memory communicatively connected to at least one of the processors; wherein,

[0052] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the water network system optimization layout method described in any of the above technical solutions.

[0053] Beneficial effects: The water network system optimization layout method can support the formation of a national basin regional water network optimization layout plan for medium- and long-term water resources security, propose major project layouts and related measures, and reduce project construction costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flow chart of the present invention.

[0055] Figure 2 It is a flow chart of step S1 of the present invention.

[0056] Figure 3 It is a flow chart of step S2 of the present invention.

[0057] Figure 4 It is a flow chart of step S3 of the present invention.

[0058] Figure 5 It is a flow chart of step S4 of the present invention. DETAILED DESCRIPTION

[0059] like Figure 1 According to one aspect of the present application, a method for optimizing the layout of a water network system is provided, which is characterized by comprising the following steps:

[0060] Step S1, collect data of the study area, calculate the DEM accuracy and the optimal catchment area threshold based on the terrain data of the study area, grid the study area and automatically identify the watershed in the study area, divide the study area into m first-level basins based on the watershed, each basin contains several second-level basins, and m is a positive integer greater than 2;

[0061] Step S2, extracting the domestic, industrial, agricultural and ecological water demands of each administrative division in the study area, calculating the ratio of the number of grids in each secondary basin in each primary basin to the total number of grids in the administrative division to which the secondary basin belongs, obtaining the domestic, industrial, agricultural and ecological water demands of each secondary basin in each primary basin, and summing the water demands of all secondary basins in each primary basin to obtain the domestic, industrial, agricultural and ecological water demands of each primary basin;

[0062] Step S3, constructing a system dynamics model of the water network system based on the historical construction investment of each node in the study area and the historical GDP total data corresponding to the node, wherein the nodes in the study area include reservoirs and lakes, solving the system dynamics model to obtain a curve of GDP changing with the increase of node construction investment, and calculating the capacity building efficiency index mutation points corresponding to all nodes in turn, and obtaining the optimal layout plan of the nodes in the water network system and the corresponding water supply based on the capacity building efficiency index mutation points;

[0063] Step S4, extracting the historical water supply, historical rainfall and historical runoff data of all sub-basins in each basin and inputting them into the pre-built water resources optimization allocation model respectively, obtaining the long-term optimization allocation plan of all sub-basins in the basin, calibrating the system dynamics model parameters based on the long-term optimization allocation plan, obtaining the water supply of the secondary basin based on the sum of the water supply of all nodes in the secondary basin, inputting the water supply, rainfall and runoff data of all secondary basins in the primary basin into the optimized system dynamics model, and obtaining the water resources optimization allocation plan of the primary basin;

[0064] Step S5: Based on the water supply and water demand of m primary river basins, the difference between the water supply and water demand of each river basin is calculated in turn and input into the pre-built water resources optimization scheduling model to obtain the water resources optimization scheduling plan for the study area. The water network system optimization layout plan includes: the optimization layout plan of the nodes in the water network system, the water resources optimization configuration plan of m primary river basins and the water resources optimization scheduling plan for the study area.

[0065] According to one aspect of the present application, step S1 further comprises:

[0066] Step S11, collect data of the study area, including topographic data, forecasts of domestic, industrial, agricultural and ecological water use in each administrative division, historical construction investment of nodes, historical GDP data, historical water supply forecasts, historical rainfall forecasts and historical runoff forecasts;

[0067] Step S12: setting the DEM accuracy based on the terrain data of the study area and dividing the study area into a number of grids according to the DEM accuracy, and using the river network density method to calculate the optimal catchment area threshold;

[0068] Step S13, based on the optimal catchment area threshold, automatically identify all watersheds in the study area and divide the study area into several basins, and divide the basins into m primary basins and n secondary basins based on the fact that the main river channel of each basin belongs to a category or an order in the water network system. The primary basin is the category in the water network system, and the secondary basin is the order in the water network system. m and n are positive integers greater than 0.

[0069] In the extraction of digital river network based on DEM, the determination of catchment area threshold is crucial for the accurate extraction and analysis of watershed and river network structure. Different DEM resolutions will lead to different extraction results of digital river network, which in turn affects the determination of catchment area threshold. At the same time, the change of threshold has an uncertain impact on river network and watershed parameters. Selecting a suitable threshold is crucial for the accuracy of digital river network and watershed extraction. A smaller threshold can extract more small rivers, but it will increase the amount of calculation and data storage requirements; a larger threshold is more suitable for extracting large rivers and can improve calculation efficiency. At the same resolution, as the threshold increases, the river network becomes sparse. Topographic factors will have a greater impact on the selection of thresholds, and DEM resolution directly affects the extraction and analysis of terrain factors. High-resolution DEM can provide more reliable terrain information, thereby improving the precision and accuracy of the results. Therefore, different DEM resolutions will affect the selection of the optimal threshold. Low-resolution DEM may cause the digital river network extraction results to be inaccurate, but too high a resolution is also more susceptible to noise interference.

[0070] According to one aspect of the present application, step S12 further comprises:

[0071] Step S12a, setting four different DEM resolutions and calculating the river length, drainage area and river network density extracted under different thresholds;

[0072] Step S12b, fitting the second-order derivative of the river network density curve and finding the point where the second-order derivative is 0 to obtain the range of the optimal threshold;

[0073] Step S12c, superimposing and comparing the river network extracted within the determined optimal threshold range with the actual river network to determine the optimal thresholds under four resolutions.

[0074] The optimal catchment area threshold was first determined by trial and error, in which the appropriate threshold was determined by repeated experiments and comparison of the river network extracted under different thresholds with the actual river network. However, this method is subjective and has low calculation efficiency and accuracy. Subsequently, some researchers proposed more new threshold determination methods based on the study of hydrological information, including the frequency distribution method of slope water flow paths, the moderation index method, the multifractal analysis method, the river network density method, and the mean change point analysis method. Among them, the river network density method is simple, has the best simulation effect, and is widely applicable. Therefore, the river network density method is used in this embodiment to determine the optimal catchment area threshold.

[0075] According to one aspect of the present application, step S2 further comprises:

[0076] Step S21, extract each watershed and classify the grids in the watershed according to the administrative divisions to which they belong;

[0077] Step S22, calculating the proportion of all grids belonging to a certain administrative division in the basin to the total number of grids in the administrative division, and calculating the domestic, industrial, agricultural and ecological water demand of the part of the basin belonging to the administrative division, that is, the product of the proportion and the total domestic, industrial, agricultural and ecological water demand of the administrative division;

[0078] Step S23, calculate the domestic, industrial, agricultural and ecological water demands of all categories in the basin in turn, and sum them up to obtain the domestic, industrial, agricultural and ecological water demands of the basin;

[0079] Step S24: Calculate S21-S23 for all primary and secondary river basins to obtain the domestic, industrial, agricultural and ecological water demands of all primary and secondary river basins.

[0080] According to one aspect of the present application, step S3 further comprises:

[0081] Step S31, extract the historical construction investment of each node in the study area and its corresponding GDP total data, build a system dynamics model of the water network system, and solve the system dynamics model to obtain a change curve of GDP with the change of historical construction investment of the node;

[0082] System dynamics is a comprehensive discipline that studies the intersection of system information feedback and system problems. It emphasizes the viewpoints of system, wholeness, connection, development, and movement, and can solve multidisciplinary problems such as quantification of water resources carrying capacity, which involves society, economy, ecology, water resources, etc.

[0083] System dynamics believes that a system is composed of units and the movement and information transmitted through the units. System dynamics describes the complex feedback relationship between the subsystem substructures based on the integrity and nonlinearity of the system. According to the inherent characteristics of the system, the system is divided into several subsystems on the basis of a complete description of the feedback relationship between the subsystem substructures.

[0084] The structure of each subsystem in system dynamics is in the form of a feedback loop, and is composed of a first-order feedback loop. The first-order feedback loop is composed of state variables, rate variables, and auxiliary variables. Corresponding to the above three variables, three types of equations are set in system dynamics to describe them, namely, state equations, rate equations, and auxiliary equations. The main modeling steps for solving problems in system dynamics are:

[0085] Identify the various contradictions in the system, clarify the problems to be solved, and determine the system boundaries and system variables;

[0086] Identify the substructures and feedback loops of the system and clarify the connections between the substructures;

[0087] Determine the flow diagrams and system equations in each feedback link to describe the qualitative and semi-qualitative variable relationships;

[0088] Input the original data into the model for calculation to obtain the values ​​of each relevant variable and its change chart;

[0089] Evaluate and analyze the model operation results to determine whether the simulation results are consistent with objective reality. If there are deviations, find out the reasons and correct them;

[0090] Conduct historical tests and sensitivity analysis on the model, and make final adjustments to the variable parameters to provide a scientific basis for future decision-making and regulation.

[0091] Step S32: Based on the system dynamics model, extend the curve of GDP changing with the historical construction investment of the node until the GDP growth is lower than the node construction investment, set the GDP increment brought by each 100 million yuan of construction investment as the capacity building efficiency index, and obtain several index mutation points on the change curve based on the index;

[0092] In this embodiment, by constructing a system dynamics model of a water network system, improving the construction of water conservancy project capacity can increase the available amount of water resources, which will bring about social and economic growth. In this embodiment, the growth of GDP is directly used to represent the growth of social economy. At the same time, with the increase in capacity building, the increasing trend of GDP will slow down due to marginal benefits. The GDP increment brought by each 100 million yuan of capacity building is set as the indicator of capacity building efficiency, and the mutation point of this indicator is found as the optimization point of the knot. When the knot is optimized, the water resource supply forecast of the secondary basin is obtained.

[0093] Step S33: adopt the mutation point monitoring method to select the optimal index mutation point from a number of index mutation points as the optimization point of the node, that is, the optimal layout plan of the node in the water network system, and obtain the water supply forecast of the node based on the optimal layout plan of the node in the water network system.

[0094] According to one aspect of the present application, step S4 is further:

[0095] Step S41, constructing a water resource optimization allocation model, the objective function is: the total water shortage of the system is minimized, the water shortage rate is uniform during each period, and the cost is minimized, and the constraints include: water balance constraint, water storage constraint, water supply capacity constraint, water demand constraint and variable non-negative constraint;

[0096] Step S42, extracting the historical water supply volume, historical rainfall and historical runoff data of all secondary basins in each primary basin and inputting them into the water resources optimization allocation model to obtain the historical optimization allocation plan of the primary basin;

[0097] Step S43: construct an optimization model based on the historical optimization configuration scheme and the corresponding real historical optimization configuration scheme, and solve the optimization model using the REVA algorithm based on the improved particle swarm algorithm and chaos mapping optimization to obtain the optimal parameter scheme of the system dynamics model;

[0098] Step S44: obtain the water supply forecast of all secondary basins in the primary basin based on the sum of the water supply volumes of all nodes in all secondary basins in the primary basin, input the water supply volume, rainfall and runoff data of all secondary basins in the primary basin into the optimized system dynamics model, and obtain the water resources optimization allocation plan for the primary basin.

[0099] After obtaining the water supply forecasts for all secondary basins in the previous step, since the water resource allocation plan obtained by directly using the system dynamics model is more of a generalization and description of the historical situation, it is not as good as the plan obtained by directly using the water resource allocation model. Therefore, in this embodiment, historical data is first extracted and input into the water resource allocation model to obtain a historical allocation plan, and then the predicted historical allocation plan is compared with the actual historical allocation plan to optimize the system dynamics model and improve the accuracy of the model. The optimized system dynamics model is then used to simulate the water supply plan. The obtained water supply plan is more scientific and accurate than the water supply plan obtained by using the two models alone.

[0100] According to one aspect of the present application, step S43 is further:

[0101] Step S43a, based on the historical optimization configuration scheme and the corresponding real historical optimization configuration scheme, the objective function is set to minimize the cumulative deviation between the optimal solution to be solved and the actual optimal solution, and the optimization model is constructed;

[0102] Step S43b: Use the optimized REVA algorithm to solve the optimization model and obtain the optimal parameter solution of the system dynamics model.

[0103] According to one aspect of the present application, step S43b is further:

[0104] Step S43b1, using Henon mapping, Lorenz mapping, Tent mapping, Arnold mapping, Rossler mapping, and Koopman mapping with good ergodicity to construct a mapping set, when the particle swarm algorithm generates an initial population, a random algorithm is used to randomly extract mappings from the mapping set to generate the initial population, while maintaining the initial population with good ergodicity, further improving the randomness of the initial population, improving the efficiency and robustness of the algorithm, and applying the improved particle swarm algorithm to calculate the optimal basic parameters of the REVA algorithm;

[0105] Step S43b2, using Henon mapping, Lorenz mapping, Tent mapping, Arnold mapping, Rossler mapping, and Koopman mapping with good ergodicity to construct a mapping set. When the REVA algorithm generates an initial population, a random algorithm is used to randomly extract mappings from the mapping set to generate the initial population. While maintaining the initial population with good ergodicity, the randomness of the initial population is further improved, thereby improving the efficiency and robustness of the algorithm.

[0106] Step S43b3, set the center vector, the preference radius, and generate the preference vector;

[0107] Step S43b4, using traditional genetic operations such as crossover and mutation to generate offspring populations, and then using elite strategy to merge the offspring population with the parent population;

[0108] Step S43b5, generating N sub-populations by associating each population member with one of the N reference vectors;

[0109] Step S43b6, calculate the angle penalty distance, take the individual with the smallest APD value in the sub-population as the elite retainer, pass it to the next generation, and calculate the i-th adaptive reference vector of the next generation based on the vector adaptation strategy;

[0110] Step S43b7, repeat steps S43b4 to S43b6 until the stopping criterion is reached, and then output the current population as the final result.

[0111] According to one aspect of the present application, step S5 is further:

[0112] Step S51, constructing a water resources optimization scheduling model, the objective function includes: the lowest overall water shortage and the lowest water transfer cost, and the constraints include: water balance constraint, water storage constraint, water supply capacity constraint, water demand constraint and variable non-negative constraint;

[0113] Step S52: based on the water demand and water supply of the primary river basins, calculate the supply and demand relationship of water resources of the m primary river basins in turn, and divide the m primary river basins into three categories according to the supply and demand relationship, namely: supply exceeds demand, supply is less than demand, and supply and demand are balanced;

[0114] Step S53, respectively calculating the excess water volume of the watershed with surplus water and the shortage water volume of the watershed with water shortage and inputting them into the water resources optimization scheduling model, and using a multi-objective optimization algorithm to obtain a non-inferior solution set of the water resources optimization scheduling scheme for the study area;

[0115] Step S54, use the grey correlation analysis method to optimize the non-inferior solution set, and obtain the best optimization scheduling plan as the water resources optimization scheduling plan for the study area. The water network system optimization layout plan includes: the optimization layout plan of the nodes in the water network system, the water resources optimization allocation plan of m first-level river basins and the water resources optimization scheduling plan for the study area.

[0116] Grey correlation analysis is an analytical method used to study the degree of correlation of uncertain systems. It is often used in multi-factor decision-making problems, evaluation index selection and quality analysis. This method is based on grey system theory and reveals its internal connections and laws by analyzing the degree of correlation between data.

[0117] First, identify multiple factors or indicators that need to be evaluated and represent them as time series or data matrices;

[0118] The original data of each evaluation factor is standardized to make the processed data comparable;

[0119] Calculate the correlation coefficient between each factor;

[0120] The grey correlation factor method is used to calculate the correlation between various factors. The larger the correlation value, the higher the correlation between the factors.

[0121] Based on the calculated correlation values, the optimal factor or factor combination is determined.

[0122] Grey correlation analysis is based on grey system theory and can handle situations where information is incomplete or uncertain, making the analysis closer to the actual situation. Grey correlation analysis has low requirements for data sample size and can perform effective analysis even if the data volume is small. This method is suitable for correlation analysis between multiple factors and can comprehensively consider the degree of influence between multiple factors, reveal the internal connection between factors as a whole, and provide more comprehensive information. Grey correlation analysis has better flexibility in data processing than other methods, and helps to better specify decision-making plans. Therefore, in this embodiment, grey correlation analysis is used to make decisions on the non-inferior solution set obtained in the previous step to select the optimal solution.

[0123] Through steps S3-S4, the water resources in each river basin are optimized. At this time, the water use gap between river basins can only be achieved through spatial balance by transferring water between river basins. Therefore, in this embodiment, water is transferred by calculating whether the supply and demand of each river basin is balanced, so as to achieve an optimized layout of the entire water network system.

[0124] In the present invention, the optimal point is first found through the relationship between the construction investment of the nodes of the water network system and the regional GDP, the nodes are optimized and the water supply of each node is obtained at the same time, the water supply of the sub-basin is obtained by summing the water supply of all nodes in the sub-basin, and the water resources of each basin are configured based on the supply and demand relationship of the sub-basin in each basin to obtain the optimal configuration scheme between the sub-basins in each basin, and then the water is transferred between the basins in the study area according to whether each basin itself is short of water or has surplus water to achieve the optimal layout of the water network system. The obtained optimal layout scheme includes: the optimal layout scheme of the nodes in the water network system, the water resources optimal configuration scheme of m basins and the water resources optimal scheduling scheme of the study area.

[0125] According to another aspect of the present application, a water network system optimization layout system is provided, characterized in that it includes:

[0126] at least one processor; and

[0127] a memory communicatively connected to at least one of the processors; wherein,

[0128] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement any of the above-mentioned methods for optimizing the layout of the water network system.

[0129] The preferred embodiments of the present invention are described in detail above; however, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all belong to the protection scope of the present invention.

Claims

1. A method for optimizing the layout of a water network system, characterized in that: The steps include: Step S1, collect data of the study area, calculate the DEM accuracy and the optimal catchment area threshold based on the terrain data of the study area, grid the study area and automatically identify the watershed in the study area, divide the study area into m first-level basins based on the watershed, each basin contains several second-level basins, and m is a positive integer greater than 2; Step S2, extracting the domestic, industrial, agricultural and ecological water demands of each administrative division in the study area, calculating the ratio of the number of grids in each secondary basin in each primary basin to the total number of grids in the administrative division to which the secondary basin belongs, obtaining the domestic, industrial, agricultural and ecological water demands of each secondary basin in each primary basin, and summing the water demands of all secondary basins in each primary basin to obtain the domestic, industrial, agricultural and ecological water demands of each primary basin; Step S3, constructing a system dynamics model of the water network system based on the historical construction investment of each node in the study area and the historical GDP total data corresponding to the node, wherein each node in the study area includes: reservoirs and lakes, solving the system dynamics model to obtain a curve of GDP changing with the increase of node construction investment, and sequentially calculating the capacity building efficiency index mutation points corresponding to all nodes, and obtaining the optimal layout plan of the nodes in the water network system and the corresponding water supply based on the capacity building efficiency index mutation points; Step S4, extracting the historical water supply, historical rainfall and historical runoff data of all secondary basins in each primary basin and inputting them into the pre-built water resources optimization allocation model respectively, obtaining the long-term optimization allocation plan of all secondary basins in the primary basin, calibrating the system dynamics model parameters based on the long-term optimization allocation plan, obtaining the water supply of the secondary basin based on the sum of the water supply of all nodes in the secondary basin, inputting the water supply, rainfall and runoff data of all secondary basins in the primary basin into the optimized system dynamics model, and obtaining the water resources optimization allocation plan of the primary basin; Step S5: based on the water supply and water demand of the m first-level river basins, the difference between the water supply and water demand of each river basin is calculated in turn and input into the pre-built water resources optimization scheduling model to obtain the water resources optimization scheduling plan for the study area. The water network system optimization layout plan includes: the optimization layout plan of the nodes in the water network system, the water resources optimization configuration plan of the m first-level river basins and the water resources optimization scheduling plan for the study area; The step S1 is further as follows: Step S11, collect data of the study area, including topographic data, forecasts of domestic, industrial, agricultural and ecological water use in each administrative division, historical construction investment of nodes, historical GDP data, historical water supply forecasts, historical rainfall forecasts and historical runoff forecasts; Step S12: setting the DEM accuracy based on the terrain data of the study area and dividing the study area into a number of grids according to the DEM accuracy, and using the river network density method to calculate the optimal catchment area threshold; Step S13, based on the optimal catchment area threshold, automatically identify all watersheds in the study area and divide the study area into several basins, and divide the several basins into m primary basins and n secondary basins based on the fact that the main river channel of each basin belongs to a category or an order in the water network system, wherein the primary basin is a category in the water network system, and the secondary basin is an order in the water network system, and m and n are positive integers greater than 0; The step S12 is further as follows: Step S12a, setting a plurality of different DEM resolutions and calculating the river length, drainage area and river network density extracted under different thresholds; Step S12b, fitting the second-order derivative of the river network density curve and finding the point where the second-order derivative is 0 to obtain the range of the optimal threshold; Step S12c, superimposing and comparing the river network extracted under the determined optimal threshold range with the actual river network to determine the optimal thresholds under four resolutions; The step S3 is further as follows: Step S31, extract the historical construction investment of each node in the study area and its corresponding GDP total data, build a system dynamics model of the water network system, and solve the system dynamics model to obtain a change curve of GDP with the change of historical construction investment of the node; Step S32: Based on the system dynamics model, extend the curve of GDP changing with the historical construction investment of the node until the GDP growth is lower than the node construction investment, set the GDP increment brought by each 100 million yuan of construction investment as the capacity building efficiency index, and obtain several index mutation points on the change curve based on the index; Step S33: using a mutation point monitoring method to select the best index mutation point from a number of index mutation points as the optimization point of the node, that is, the optimal layout plan of the node in the water network system, and obtaining the water supply forecast of the node based on the optimal layout plan of the node in the water network system; The step S4 is further as follows: Step S41, constructing a water resource optimization allocation model, the objective function is: the total water shortage of the system is minimized, the water shortage rate is uniform during each period, and the cost is minimized, and the constraints include: water balance constraint, water storage constraint, water supply capacity constraint, water demand constraint and variable non-negative constraint; Step S42, extracting the historical water supply volume, historical rainfall and historical runoff data of all secondary basins in each primary basin and inputting them into the water resources optimization allocation model to obtain the historical optimization allocation plan of the primary basin; Step S43: construct an optimization model based on the historical optimization configuration scheme and the corresponding real historical optimization configuration scheme, and solve the optimization model using the REVA algorithm based on the improved particle swarm algorithm and chaos mapping optimization to obtain the optimal parameter scheme of the system dynamics model; Step S44: obtaining a water supply forecast for all secondary basins in the primary basin based on the sum of the water supply volumes of all nodes in all secondary basins in the primary basin, inputting the water supply volumes, rainfall and runoff data of all secondary basins in the primary basin into the optimized system dynamics model, and obtaining a water resources optimization allocation plan for the primary basin; The step S43 is further as follows: Step S43a, based on the historical optimization configuration scheme and the corresponding real historical optimization configuration scheme, the objective function is set to minimize the cumulative deviation between the optimal solution to be solved and the actual optimal solution, and the optimization model is constructed; Step S43b, using the optimized REVA algorithm to solve the optimization model and obtain the optimal parameter solution of the system dynamics model; The step S43b is further as follows: Step S43b1, using multi-chaotic mapping to generate an initial population to improve the particle swarm algorithm, and applying the improved particle swarm algorithm to calculate the optimal basic parameters of the REVA algorithm; Step S43b2, using multi-chaotic mapping to generate the initial population of the REVA algorithm; Step S43b3, set the center vector, the preference radius, and generate the preference vector; Step S43b4, using traditional crossover and mutation genetic operations to generate offspring populations, and then using elite strategy to merge the offspring populations with the parent populations; Step S43b5, generating N sub-populations by associating each population member with one of the N reference vectors; Step S43b6, calculate the angle penalty distance, take the individual with the smallest APD value in the sub-population as the elite retainer, pass it to the next generation, and calculate the i-th adaptive reference vector of the next generation based on the vector adaptation strategy; Step S43b7, repeating steps S43b4 to S43b6 until the stopping criterion is reached, and then outputting the current population as the final result; The step S5 is further as follows: Step S51, constructing a water resources optimization scheduling model, the objective function includes: the lowest overall water shortage and the lowest water transfer cost, and the constraints include: water balance constraint, water storage constraint, water supply capacity constraint, water demand constraint and variable non-negative constraint; Step S52: based on the water demand and water supply of the primary river basins, calculate the supply and demand relationship of water resources of the m primary river basins in turn, and divide the m primary river basins into three categories according to the supply and demand relationship, namely: supply exceeds demand, supply is less than demand, and supply and demand are balanced; Step S53, respectively calculating the excess water volume of the watershed with surplus water and the shortage water volume of the watershed with water shortage and inputting them into the water resources optimization scheduling model, and using a multi-objective optimization algorithm to obtain a non-inferior solution set of the water resources optimization scheduling scheme for the study area; Step S54, use the grey correlation analysis method to optimize the non-inferior solution set, and obtain the best optimization scheduling plan as the water resources optimization scheduling plan for the study area. The water network system optimization layout plan includes: the optimization layout plan of the nodes in the water network system, the water resources optimization allocation plan of m first-level river basins and the water resources optimization scheduling plan for the study area.

2. The water network system optimization layout method according to claim 1, characterized in that: The step S2 is further as follows: Step S21, extract each watershed in turn, and classify the grids in the watershed according to the administrative divisions to which they belong; Step S22, calculate the proportion of all grids belonging to a certain administrative division in each river basin to the total number of grids in the administrative division, and calculate the domestic, industrial, agricultural and ecological water demand of the part of the river basin belonging to the administrative division, that is, the product of the proportion and the total domestic, industrial, agricultural and ecological water demand of the administrative division; Step S23, calculate the domestic, industrial, agricultural and ecological water demands of all categories in each river basin in turn, and sum them up to obtain the domestic, industrial, agricultural and ecological water demands of the river basin; Step S24: Calculate S21-S23 for all primary and secondary river basins to obtain the domestic, industrial, agricultural and ecological water demands of all primary and secondary river basins.

3. A water network system optimization layout system, characterized in that: include: at least one processor; as well as a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the water network system optimization layout method described in claim 1 or 2.

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