CN-121684222-B - Synergistic regulation and optimization method for water salt and pollutants

CN121684222BCN 121684222 BCN121684222 BCN 121684222BCN-121684222-B

Abstract

The invention relates to the technical field of collaborative regulation and optimization of water salt and pollutants, in particular to a collaborative regulation and optimization method of water salt and pollutants. The method comprises the steps of obtaining spatial position information and attribute information corresponding to a strip field, a drainage ditch, an ecological absorption tank, a strong drainage station and an external drainage river channel outlet in a target area, constructing a target directed graph network model according to the spatial position information and the attribute information corresponding to the strip field, the drainage ditch, the ecological absorption tank, the strong drainage station and the external drainage river channel outlet, and determining a water salt and pollutant collaborative regulation optimization scheme corresponding to the target area based on the target directed graph network model. The method has the advantages that the facility operation parameters (such as the split ratio) are optimized in a targeted manner, the conflict between salt control and pollution reduction is solved, the fine configuration is realized, the treatment efficiency is improved, and the comprehensive treatment requirement of the water environment in the irrigation area is met.

Inventors

LI YUANHANG
ZHANG ZIQI
ZHANG YUHANG
LIN HUIMEI
HUANG XUAN
XIA YONGQIU

Assignees

河海大学
中国科学院南京土壤研究所

Dates

Publication Date: 20260512
Application Date: 20260210

Claims (7)

1. The method for optimizing the cooperative regulation and control of the water salt and the pollutants is characterized by comprising the following steps: acquiring spatial position information and attribute information respectively corresponding to a strip field, a drainage ditch, an ecological absorption pool, a forced drainage station and an outlet of an external drainage river in a target area; constructing a target directed graph network model according to spatial position information and attribute information respectively corresponding to the strip field, the drainage ditch, the ecological absorption pool, the forced drainage station and the outlet of the external drainage river; Determining a water salt and pollutant cooperative regulation optimization scheme corresponding to the target area based on the target directed graph network model; based on the target directed graph network model, determining a water salt and pollutant cooperative regulation optimization scheme corresponding to the target area comprises the following steps: Calculating a first nitrogen load, a first phosphorus load and a first salt load corresponding to a source node according to attribute information and hydrological scene parameters corresponding to the source node in the target directed graph network model; According to the target directed graph network model, sequentially calculating nitrogen load, phosphorus load and salt load respectively corresponding to a transmission node, a processing node, a control node and a collection node in the target directed graph network model; Taking the target flow distribution ratio corresponding to each target directed edge in the target directed graph network model as a decision variable; Constructing an objective function, wherein the objective function comprises a first sub-function, a second sub-function and a third sub-function, the first sub-function is used for representing that the nitrogen load corresponding to the collection node is minimum, the second sub-function is used for representing that the phosphorus load corresponding to the collection node is minimum, and the third sub-function is used for representing that the salt load corresponding to the collection node is maximum; Solving the target flow distribution ratio corresponding to the target directed edge based on a preset multi-target evolution algorithm; According to the target flow distribution ratio, determining a water salt and pollutant cooperative regulation optimization scheme corresponding to the target area; the solving the target flow distribution ratio corresponding to the target directed edge based on a preset multi-target evolution algorithm comprises the following steps: constructing constraint conditions based on attribute information corresponding to each node in the target directed graph network model, wherein the constraint conditions comprise at least one of basic constraint, economic cost constraint and operation and maintenance constraint; solving the objective function based on the constraint condition by utilizing the preset multi-objective evolutionary algorithm to obtain the objective flow distribution ratio corresponding to the objective directed edge; the method for solving the objective function based on the constraint condition by using the preset multi-objective evolutionary algorithm to obtain the objective flow distribution ratio corresponding to the objective directed edge comprises the following steps: Randomly generating a preset number of initial decision variable vectors, wherein the initial decision variable vectors comprise random decision variable vectors, experience decision variable vectors and boundary decision variable vectors; Deleting the initial decision variable vector which does not meet the constraint condition from each initial decision variable vector based on the constraint condition to obtain a residual decision variable vector; Calculating a first sub-function value, a second sub-function value and a third sub-function value corresponding to each residual decision variable vector based on the objective function; determining a penalty coefficient corresponding to each residual decision variable vector according to the first sub-function value, the second sub-function value and the third sub-function value corresponding to each residual decision variable vector; marking the residual decision variable vector with the punishment coefficient larger than a preset threshold value as to-be-eliminated; calculating a dominant relationship between the residual effective decision variable vectors; Layering the residual effective decision variable vectors according to the dominance relation to obtain a plurality of levels, wherein the smaller the level corresponding to the residual effective decision variable vector is, the better the residual effective decision variable vector is represented; for each first decision variable vector in a first hierarchy, calculating the number of lower-level decision vectors governed by each first decision variable vector; Screening candidate decision variable vectors with the number of lower-level decision vectors larger than a preset number threshold value from the first decision variable vectors; determining a target decision variable vector based on each candidate decision variable vector; And obtaining the target flow distribution ratio corresponding to the target directed edge based on the target decision variable vector.
2. The method of claim 1, wherein constructing the target directed graph network model based on spatial location information and attribute information corresponding to the strip field, the drain trench, the ecological intake pool, the forced drainage station, and the outlet of the outward river respectively comprises: Determining each strip field in the target area as a source node; Determining each of the drain trenches in the target area as a transmission node; Determining each ecological digestion tank in the target area as a processing node; determining each strong-rank station in the target area as a control node; Determining each of the outlet river channels in the target area as a collection node; Determining node attributes respectively corresponding to the source node, the transmission node, the processing node, the control node and the collection node according to spatial position information and attribute information respectively corresponding to the strip field, the drainage ditch, the ecological absorption pool, the forced drainage station and the outlet of the outward-drainage river channel; And determining target directed edges respectively corresponding to the source node, the transmission node, the processing node, the control node and the collection node according to the space position information and the attribute information respectively corresponding to the strip field, the drainage ditch, the ecological absorption pool, the strong drainage station and the outlet of the external drainage river, so as to obtain the target directed graph network model.
3. The method of claim 2, wherein the transmission relationship of the target directed graph network model is that each of the source nodes transmits to each of the transmission nodes, each of the transmission nodes transmits to each of the processing nodes, each of the transmission nodes transmits to the control nodes, each of the processing nodes transmits to each of the control nodes, each of the control nodes transmits to each of the sink nodes; determining target directed edges corresponding to the source node, the transmission node, the processing node, the control node and the collection node according to spatial position information and attribute information corresponding to the strip field, the drain ditch, the ecological absorption pool, the forced drainage station and the outlet of the outward river respectively, wherein the target directed edges comprise; Aiming at two types of nodes with potential transmission relations, calculating the distance matching degree, the water flow matching degree and the service matching degree between the current node and the potential transmission nodes; calculating a space association degree according to the distance matching degree, the water flow adaptation degree and the service matching degree; determining the target directed edge between the current node and the potential transmission node according to the spatial association degree; And adding target side attribute information for each target directed side.
4. The method of claim 3, wherein the target-side attribute information includes at least one of base attribute information, dynamic attribute information, and optimization attribute information, wherein the dynamic attribute information includes a dynamic traffic threshold and an attenuation coefficient, and wherein adding target-side attribute information for each of the target-directed sides includes: determining basic attribute information corresponding to each target directed edge, wherein the basic attribute information comprises at least one of edge type, edge starting point, edge ending point, edge length and edge creation time corresponding to each target directed edge; calculating a dynamic flow threshold corresponding to each target directed edge according to the edge type corresponding to each target directed edge; According to the edge types corresponding to the target directed edges, calculating attenuation coefficients corresponding to the transmission type target directed edges, wherein the attenuation coefficients are used for representing the along-path attenuation parameters of water salt and pollutants; Calculating an initial flow distribution ratio corresponding to each target directed edge according to the edge type corresponding to each target directed edge; And determining the initial flow distribution ratio as the optimized attribute information corresponding to the target directed edge.
5. The method according to claim 1, wherein the calculating the first nitrogen load, the first phosphorus load, and the first salt load corresponding to the source node according to the attribute information corresponding to the source node in the target directed graph network model and the hydrological scene parameter includes: Obtaining hydrological scene parameters corresponding to each source node, wherein the hydrological scene parameters comprise rainfall, irrigation quantity, runoff coefficient, nitrogen leaching rate, phosphorus leaching rate and salinity concentration coefficient; For each source node, calculating the total drainage of the source node based on the area of the source node and the rainfall, irrigation and runoff coefficients in the hydrologic scene parameters; according to the nitrogen fertilizer application amount, the phosphate fertilizer application amount, the area, the nitrogen leaching rate and the phosphorus leaching rate of the source node, respectively calculating the first nitrogen load and the first phosphorus load of the source node; and calculating the first salt load of the source node based on the total drainage and the soil salinity attribute and the preset salt concentration coefficient of the source node.
6. The method according to claim 1, wherein the sequentially calculating the nitrogen load, the phosphorus load and the salt load corresponding to the transmission node, the processing node, the control node and the aggregation node in the target directed graph network model according to the target directed graph network model includes: determining a first nitrogen input amount, a first phosphorus input amount and a first salt input amount corresponding to each transmission node according to the target directed graph network model aiming at the transmission node; Multiplying the first nitrogen input amount, the first phosphorus input amount and the first salt input amount by attenuation coefficients corresponding to target directed edges between the source node and the transmission node to obtain a second nitrogen load, a second phosphorus load and a second salt load corresponding to the transmission node; determining a second nitrogen input amount, a second phosphorus input amount and a second salt input amount corresponding to each processing node according to the target directed graph network model aiming at the processing node; Calculating a third nitrogen load and a third phosphorus load corresponding to the processing node based on the second nitrogen input quantity, the second phosphorus input quantity, the nitrogen absorption rate and the phosphorus absorption rate corresponding to the processing node; calculating a third salt load corresponding to the processing node based on the second salt input quantity and the salt absorption rate corresponding to the processing node; Determining a fourth nitrogen load, a fourth phosphorus load and a fourth salt load corresponding to each control node according to the target directed graph network model aiming at the control node; and determining a fifth nitrogen load, a fifth phosphorus load and a fifth salt load corresponding to each collection node according to the target directed graph network model aiming at the collection node.
7. The method of claim 1, wherein said determining a target decision variable vector based on each of said candidate decision variable vectors comprises: sorting the candidate decision variable vectors according to the ascending order of the first sub-function values to obtain a first sequence; sorting the candidate decision variable vectors according to the ascending order of the second sub-function values to obtain a second sequence; sorting the candidate decision variable vectors according to a third sub-function value descending order to obtain a third sequence; Calculating a first range corresponding to the first sub-function value based on the first sequence; calculating a second range corresponding to the second sub-function value based on the second sequence; calculating a third range corresponding to the third sub-function value based on the third sequence; For each candidate decision variable vector, calculating the difference value between the candidate decision variable vector and the adjacent candidate decision variable vector in each sequence; dividing the difference value corresponding to each sequence by the global range of the sequence to obtain the distance component of the candidate decision variable vector in each sequence; Obtaining a crowding distance corresponding to each candidate decision variable vector according to the distance component of each candidate decision variable vector in each sequence; selecting a standby decision variable vector with the crowding distance larger than a preset distance threshold value according to the crowding distance corresponding to each candidate decision variable vector; selecting two standby decision variable vectors from the standby decision variable vectors as parent decision variable vectors to perform cross operation, and generating two child generation decision variable vectors; calculating mutation step length according to the current algebra corresponding to the offspring decision variable vector; Performing mutation operation on the child decision variable vector based on the mutation step length to obtain a mutation decision variable vector; Combining each father decision variable vector and each variation decision variable vector into a temporary population; And cycling the steps based on the temporary population until the preset iteration times, and obtaining the target decision variable vector.

Description

Synergistic regulation and optimization method for water salt and pollutants Technical Field The invention relates to the technical field of collaborative regulation and optimization of water salt and pollutants, in particular to a collaborative regulation and optimization method of water salt and pollutants. Background The traditional hydrologic model is difficult to adapt to actual characteristics of flat topography, ditch segmentation and scattered discharge of a coastal plain irrigation area, relies on a large number of parameters with high acquisition difficulty and high cost, has high calculation complexity, and cannot meet the real-time optimization management requirement of a large-scale irrigation area. Meanwhile, the existing treatment lacks a systematic optimization method, focuses on a single target, does not fully consider the water-salt-nutrient coupling effect, and the operating parameters of facilities such as an ecological digestion tank are set by experience, cannot be configured in a refined mode according to the pollution load of a strip field and the processing capacity of the facilities, and has low treatment efficiency. In the prior art, because the space and attribute characteristics of core elements such as strip fields, drainage ditches, ecological digestion tanks and the like in irrigation areas are not combined, an accurate quantization model cannot be constructed, collaborative simulation of water salt and pollutant migration and optimization of a regulation scheme cannot be realized, and the core problems of salt control and pollution reduction conflict and low treatment efficiency are difficult to solve. Therefore, a collaborative regulation optimization method for constructing a network model based on the key element information of the irrigation area is developed, and the method becomes an urgent need for comprehensive water environment treatment of the irrigation area of the coastal saline-alkali soil. Disclosure of Invention The invention provides a collaborative regulation and control optimization method for water salt and pollutants, which aims to solve the problems that collaborative simulation of migration of water salt and pollutants and optimization of regulation and control schemes cannot be realized, and the core problems of salt control pollution reduction conflict and low treatment efficiency are difficult to solve. The invention provides a water salt and pollutant collaborative regulation optimization method, which comprises the steps of obtaining spatial position information and attribute information corresponding to strip fields, drainage ditches, ecological absorption tanks, forced drainage stations and outlet of an outlet river channel in a target area, constructing a target directed graph network model according to the spatial position information and the attribute information corresponding to the strip fields, the drainage ditches, the ecological absorption tanks, the forced drainage stations and the outlet of the outlet river channel, and determining a water salt and pollutant collaborative regulation optimization scheme corresponding to the target area based on the target directed graph network model. In an alternative implementation mode, a target directed graph network model is built according to spatial position information and attribute information respectively corresponding to a strip field, a drainage ditch, an ecological absorption tank, a forced drainage station and an outward drainage river channel outlet, wherein the method comprises the steps of determining each strip field in a target area as a source node, determining each drainage ditch in the target area as a transmission node, determining each ecological absorption tank in the target area as a processing node, determining each forced drainage station in the target area as a control node, determining each outward drainage river channel outlet in the target area as a collection node, determining the spatial position information and attribute information respectively corresponding to the strip field, the drainage ditch, the ecological absorption tank, the forced drainage station and the outward drainage river channel outlet, determining the node attribute respectively corresponding to the source node, the transmission node, the processing node, the control node and the collection node, and determining the spatial position information and attribute information respectively corresponding to the strip field, the drainage ditch, the ecological absorption tank, the forced drainage station and the outward drainage river channel outlet, and the target directed graph network model is obtained. In an alternative implementation mode, the transmission relation of the target directed graph network model is that each source node transmits to each transmission node, each transmission node transmits to each processing node, each transmission node transmits to each control node, each processing node transmi