Efficient intelligent treatment method and system for pollution of hydro-fluctuation belt

Abstract

The invention relates to the technical field of data processing, and discloses an efficient intelligent treatment method and system for pollution of a hydro-fluctuation belt. The method comprises the following steps: calculating the water quality load of the hydro-fluctuation belt by adopting an SWAT model, obtaining a pollutant migration rule, dividing a monitoring area according to a water level gradient, and measuring water quality parameters; determining a plant configuration scheme according to water quality and soil analysis results; constructing a biological filtration, adsorption and degradation three-layer purification unit; the treatment working condition is controlled through hydraulic regulation; and generating a target treatment strategy based on the long-term monitoring calculation processing efficiency. By establishing a water quality and water quantity coupling optimization regulation and control technical system, accurate identification of a pollutant migration rule and intelligent regulation and control of a treatment process are realized.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to an efficient and intelligent method and system for managing drawdown zone pollution. Background Art

[0002] As a water-land transition zone, the ecological environment quality of the drawdown zone directly affects the water quality safety of the reservoir. At present, the pollution control of the drawdown zone mainly adopts a combination of ecological restoration and engineering management. In terms of ecological restoration, commonly used technologies include vegetation restoration, ecological matrix improvement and bio-enhancement; in terms of engineering management, measures such as sewage interception, diversion and purification are mainly adopted. These technologies have achieved certain results in practice, especially in soil and water conservation, pollutant interception and ecological restoration.

[0003] However, the existing technologies for pollution control in the drawdown zone have some shortcomings. First, traditional control methods often separate ecological restoration and engineering measures and lack systematic integration; second, the water purification effect is significantly affected by water level changes, and it is difficult to maintain a stable treatment effect when the water level fluctuates violently; third, the migration and transformation of pollutants in the drawdown zone are complex, and it is difficult for existing technologies to achieve precise regulation and dynamic optimization; finally, there is a lack of intelligent control methods based on the coupling of water quality and water quantity, resulting in low treatment efficiency and high operating costs. Summary of the invention

[0004] The present application provides an efficient and intelligent method and system for pollution control in the drawdown zone, which is used to achieve accurate identification of pollutant migration patterns and intelligent control of treatment processes by establishing a water quality and water quantity coupled optimization and control technology system, thereby solving the problem of pollution control under water level fluctuation conditions that is difficult to cope with in the prior art and ensuring the stability and reliability of the treatment effect.

[0005] In the first aspect, the present application provides an efficient and intelligent method for pollution control in the drawdown zone, which includes: calculating the water quality load of nitrogen and phosphorus concentrations, suspended solids content and organic matter in the drawdown zone through the SWAT model to obtain pollutant migration law data; dividing the pollutant migration law data into three monitoring areas of high, medium and low according to the water level gradient, and using a multi-parameter water quality sensor to measure the dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll data of each area; according to the water quality parameters of each monitoring area, combined with soil physical and chemical Based on the results of the property analysis, the planting areas and density ratios of vetiver, mulberry and emergent plants were determined; according to the determined plant ratios, ecological matrices and modified materials were used to construct biological filtration layers, adsorption layers and degradation layers to form hierarchical purification units; hydraulic regulating devices were used to distribute water to the hierarchical purification units, and the sewage residence time and treatment conditions were adjusted in combination with the online water quality monitoring data; based on the long-term water quality monitoring data, the nitrogen and phosphorus removal rates, COD degradation rates and heavy metal adsorption rates of each purification unit were calculated, the target operating parameters were screened, and the target governance strategy was generated based on the target operating parameters.

[0006] In a second aspect, the present application provides an efficient and intelligent management system for water-fluctuation zone pollution, the efficient and intelligent management system for water-fluctuation zone pollution comprising: The calculation module is used to calculate the water quality load of nitrogen and phosphorus concentrations, suspended solids content and organic matter in the drawdown zone through the SWAT model to obtain the data on the migration law of pollutants; A division module is used to divide the pollutant migration law data into three monitoring areas of high, medium and low according to the water level gradient, and use a multi-parameter water quality sensor to measure the dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll data of each area; The analysis module is used to determine the planting area and density ratio of vetiver, mulberry and emergent plants based on the water quality parameters of each monitoring area and the results of soil physical and chemical properties analysis; A generation module is used to construct a biological filtration layer, an adsorption layer and a degradation layer using ecological matrix and modified materials according to a determined plant ratio to form a hierarchical purification unit; The detection module is used to distribute water to the hierarchical purification units using the hydraulic regulating device and adjust the sewage retention time and treatment conditions in combination with the online water quality monitoring data; The screening module is used to calculate the nitrogen and phosphorus removal rate, COD degradation rate and heavy metal adsorption rate of each purification unit based on long-term water quality monitoring data, screen the target operating parameters, and generate a target treatment strategy based on the target operating parameters.

[0007] In the technical solution provided by this application, the water quality load of nitrogen and phosphorus concentrations, suspended solids content and organic matter in the drawdown zone is calculated through the SWAT model, and the data on the migration law of pollutants are accurately obtained to provide a scientific basis for subsequent treatment; the data on the migration law of pollutants are divided into three monitoring areas of high, medium and low according to the water level gradient, and multi-parameter water quality sensors are used to measure the dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll data of each area, thereby realizing dynamic monitoring of water quality changes under different water level conditions; according to the water quality parameters of each monitoring area, combined with the results of soil physical and chemical property analysis, the water quality of vetiver, mulberry and The planting area and density ratio of emergent plants have built an adaptable ecological restoration system; the ecological matrix and modified materials are used to construct the biological filtration layer, adsorption layer and degradation layer to form a hierarchical purification unit, which significantly improves the interception and conversion efficiency of pollutants; the water volume of the hierarchical purification unit is distributed by hydraulic regulating devices, and the sewage retention time and treatment conditions are adjusted in combination with the online water quality monitoring data to achieve precise control of the treatment process; by calculating the nitrogen and phosphorus removal rate, COD degradation rate and heavy metal adsorption rate of each purification unit, the target operating parameters are screened and the target governance strategy is generated to ensure the continuous stability of the treatment effect. The overall plan realizes the organic combination of ecological restoration and engineering measures, establishes an intelligent control system for water quality and water quantity coupling, and significantly improves the efficiency and reliability of pollution control in the drawdown zone. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0009] Figure 1 This is a schematic diagram of an embodiment of a highly efficient and intelligent method for managing pollution in the drawdown zone in an embodiment of the present application; Figure 2 This is a schematic diagram of an embodiment of a highly efficient and intelligent management system for drawdown zone pollution in an embodiment of the present application. DETAILED DESCRIPTION

[0010] The embodiments of the present application provide a highly efficient and intelligent method and system for the pollution control of the drawdown zone. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0011] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiments of the present application, an embodiment of an efficient and intelligent method for controlling pollution in the drawdown zone includes: Step S101, using the SWAT model to calculate the water quality load of nitrogen and phosphorus concentrations, suspended solids content and organic matter in the drawdown zone, and obtain pollutant migration law data; Step S102: Divide the pollutant migration law data into three monitoring areas: high, medium and low according to the water level gradient, and use a multi-parameter water quality sensor to measure the dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll data of each area; Step S103, determining the planting area and density ratio of vetiver, mulberry and emergent plants according to the water quality parameters of each monitoring area and the analysis results of soil physical and chemical properties; Step S104: constructing a biological filtration layer, an adsorption layer and a degradation layer using ecological matrix and modified materials according to the determined plant ratio to form a hierarchical purification unit; Step S105: using a hydraulic regulating device to distribute water to the hierarchical purification units, and adjusting the sewage residence time and treatment conditions in combination with the water quality online monitoring data; Step S106: Based on the long-term water quality monitoring data, the nitrogen and phosphorus removal rate, COD degradation rate and heavy metal adsorption rate of each purification unit are calculated, the target operating parameters are screened, and the target treatment strategy is generated according to the target operating parameters.

[0012] It is understandable that the execution subject of the present application can be an efficient intelligent management system for the pollution of the drawdown zone, or a terminal or a server, which is not limited here. The embodiment of the present application is described by taking the server as the execution subject as an example.

[0013] Specifically, the SWAT model, as a distributed watershed hydrological model, is used to simulate the hydrological process and pollutant migration and transformation process in a complex watershed system. The model is used to calculate the water quality load of nitrogen and phosphorus concentrations, suspended matter content and organic matter in the drawdown zone to obtain pollutant migration law data. Specifically, the SWAT model first needs to collect precipitation data and evapotranspiration data in the drawdown zone, including CMFD precipitation data set, CHMPRE precipitation data set, MSWEP precipitation data set, ERA5Land evapotranspiration data set, CR evapotranspiration data set and GLEAM evapotranspiration data set. After importing these data into the SWAT model, data processing is performed according to 109 sub-basins, and the inflow of water to each sub-basin is calibrated by the measured data of the hydrological monitoring station. In the process of water quality load calculation, water samples from different sections are collected for analysis of nitrogen and phosphorus concentrations, and the total nitrogen and total phosphorus contents are determined; the suspended matter content is determined by gravity sedimentation method; and the organic matter content is determined by potassium dichromate oxidation-external heating method. After obtaining the pollutant migration law data, it is necessary to divide it into three monitoring areas: high, medium and low according to the water level gradient. The high-level area is located above the normal water level and is mainly affected by rainfall; the middle-level area is located between the normal water level and the dead water level and is significantly affected by the fluctuation of the water level; the low-level area is located below the dead water level and is submerged for a long time. In the divided monitoring area, a multi-parameter water quality sensor is used to measure the dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll data of each area. The multi-parameter water quality sensor contains multiple probes, each of which corresponds to a water quality indicator. Dissolved oxygen is measured by electrochemical method, pH value is measured by glass electrode method, conductivity is measured by conductivity electrode method, turbidity is measured by scattered light method, and ammonia nitrogen content is measured by ammonia sensitive electrode method.

[0014] According to the water quality parameter data obtained, combined with the results of soil physical and chemical property analysis, the planting areas and density ratios of vetiver, mulberry and emergent plants are determined in a targeted manner. The analysis of soil physical and chemical properties includes the determination of soil organic matter content, pH value, total nitrogen, total phosphorus and total potassium content. In the high-level drawdown zone area, vetiver is the main plant, which has a developed root system that can hold the soil; mulberry trees are planted in the middle-level drawdown zone area, which have strong flood resistance and developed root systems; emergent plants are planted in the low-level drawdown zone area, and water-resistant varieties are selected. The plant density ratio is determined according to the area, slope and pollution load of each area. After determining the plant ratio, ecological matrix and modified materials are used to construct a multi-level purification unit. The ecological matrix is ​​mainly composed of porous materials such as sandstone, zeolite, and volcanic rock, and its adsorption capacity for pollutants is enhanced after modification. The biological filtration layer is mainly composed of sand and gravel with larger particle size, which plays the role of intercepting suspended matter; the adsorption layer uses modified zeolite to specifically adsorb pollutants such as nitrogen and phosphorus; the degradation layer adds microbial flora with specific functions to promote the degradation of organic matter.

[0015] When the hydraulic regulating device distributes water to the hierarchical purification units, it is necessary to adjust the hydraulic loads of different levels according to the influent water quality and treatment objectives. By online monitoring of influent and effluent water quality parameters, including pH value, dissolved oxygen, conductivity and other indicators, the residence time of sewage in each hierarchical unit is adjusted in real time. The basis for adjusting the treatment conditions includes factors such as water temperature, dissolved oxygen content, and pollutant concentration. Finally, based on the long-term accumulated water quality monitoring data, the treatment effect of each purification unit is calculated. The nitrogen and phosphorus removal rate is calculated by the total nitrogen and total phosphorus concentrations of the influent and effluent water; the COD degradation rate reflects the removal effect of organic matter; and the heavy metal adsorption rate reflects the adsorption performance of the modified material. By analyzing various indicators, the optimal combination of operating parameters is selected to form a targeted governance strategy.

[0016] For example, in a water level management project, the area is about 50 hectares, and the water level fluctuates by 4-6 meters. First, the SWAT model is used to simulate the amount of water entering the reservoir, and the pollutant migration law is calculated in combination with the measured water quality data. The water level management project divides the water level management project into three monitoring areas at 174-176 meters, 172-174 meters, and 170-172 meters, and multi-parameter water quality sensors are deployed for monitoring. The water quality monitoring results show that the ammonia nitrogen content in the high-level area is high, the dissolved oxygen in the middle-level area is low, and the suspended solids content in the low-level area is high. Based on the monitoring data, vetiver grass is planted in the high-level area with a density of 45 plants / square meter; mulberry trees are planted in the middle-level area with a spacing of 2 meters; and emergent plants are planted in the low-level area with a coverage of not less than 85%. In the constructed hierarchical purification unit, the thickness of the biological filtration layer is 40 cm, the thickness of the adsorption layer is 30 cm, and the thickness of the degradation layer is 30 cm. During operation, the retention time is controlled within the range of 24-48 hours by the hydraulic regulating device. After half a year of operation, the nitrogen and phosphorus removal rates reached the expected target, the COD degradation rate was stable, and heavy metals were effectively controlled.

[0017] In the embodiment of the present application, the water quality load of nitrogen and phosphorus concentrations, suspended solids content and organic matter in the drawdown zone is calculated through the SWAT model, and the data on the migration law of pollutants are accurately obtained to provide a scientific basis for subsequent treatment; the data on the migration law of pollutants are divided into three monitoring areas of high, medium and low according to the water level gradient, and the dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll data of each area are measured by a multi-parameter water quality sensor, so as to realize the dynamic monitoring of water quality changes under different water level conditions; according to the water quality parameters of each monitoring area, combined with the analysis results of soil physical and chemical properties, the water quality of vetiver, mulberry and water hyacinth is scientifically determined. The planting area and density ratio of plants have built an adaptable ecological restoration system; the ecological matrix and modified materials are used to construct the biological filtration layer, adsorption layer and degradation layer to form a hierarchical purification unit, which significantly improves the interception and conversion efficiency of pollutants; the water volume of the hierarchical purification unit is distributed by hydraulic regulating devices, and the sewage retention time and treatment conditions are adjusted in combination with the online water quality monitoring data to achieve precise control of the treatment process; by calculating the nitrogen and phosphorus removal rate, COD degradation rate and heavy metal adsorption rate of each purification unit, the target operating parameters are screened and the target governance strategy is generated to ensure the continuous stability of the treatment effect. The overall plan realizes the organic combination of ecological restoration and engineering measures, establishes an intelligent control system for water quality and water quantity coupling, and significantly improves the efficiency and reliability of pollution control in the drawdown zone.

[0018] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) Calculate the time distribution of nitrogen and phosphorus concentrations, suspended solids content and organic matter based on water quality monitoring data to generate initial water quality load data; (2) The initial water quality load data were divided into 109 sub-basins to obtain the sub-basin water quality load data; (3) Calculate the cumulative amount of nitrogen and phosphorus concentrations, suspended solids content and organic matter in the water quality load data of the sub-region to form the cumulative flux of pollutants; (4) Using the pollutant cumulative flux to calculate the concentration distribution of each sub-basin in the drawdown zone, obtain the pollutant migration trend; (5) Conduct correlation analysis between pollutant migration trends and hydraulic retention time, and output pollutant migration law data.

[0019] Specifically, the nitrogen and phosphorus concentrations are obtained by collecting water samples for chemical analysis, where total nitrogen is determined by alkaline potassium persulfate digestion ultraviolet spectrophotometry, and total phosphorus is determined by ammonium molybdate spectrophotometry. The suspended matter content is determined by gravity sedimentation, and the water sample is filtered through a 0.45-micron filter membrane, dried and weighed. The organic matter content is determined by potassium dichromate oxidation method and converted by the amount of potassium dichromate consumed. These water quality indicators are associated with the data on changes in the amount of water entering the reservoir to generate initial water quality load data.

[0020] In order to improve the calculation accuracy, the initial water quality load data is divided into 109 sub-basins for calculation. The division of sub-basins is based on terrain characteristics, catchment area and hydrological connectivity, and each sub-basin has relatively independent hydrological characteristics. For each sub-basin, cross-section water quality monitoring data is collected, and the pollutant flux is calculated in combination with the flow data to form the partitioned water quality load data. This partition calculation method can reflect the pollution characteristics and water quality change laws in different regions. When calculating the cumulative amount of each indicator in the partitioned water quality load data, it is necessary to consider the hydraulic connectivity and the transformation law of pollutants during the transmission process. Nitrogen and phosphorus will undergo adsorption, desorption, and sedimentation during the transmission process. Suspended matter will settle and resuspend with the water flow, and organic matter will undergo degradation and transformation. By establishing a pollutant mass balance equation, the cumulative flux of each indicator is calculated. The calculation formula for the cumulative flux of pollutants is: Where: It represents the cumulative flux of pollutants (kg / d); represents the flow rate of the ith sub-basin (m³ / d); represents the pollutant concentration of the ith sub-basin (mg / L); represents the pollutant transport attenuation coefficient (dimensionless); represents the hydraulic connectivity coefficient (dimensionless); represents the temperature correction factor (dimensionless); n represents the total number of sub-basins.

[0021] Based on the calculated pollutant cumulative flux, the pollutant concentration distribution of each sub-basin is further analyzed. The migration of pollutants in the drawdown zone is affected by multiple factors such as topography, water flow, and vegetation, and it is necessary to fully consider the effects of these factors on pollutant migration. Combining measured data and theoretical calculations, the spatial distribution characteristics and temporal change trends of pollutants are obtained. Finally, the pollutant migration trend is correlated with the hydraulic retention time, which is a key factor affecting the migration and transformation of pollutants. By establishing the relationship between the hydraulic retention time and the degradation and transformation of pollutants, the migration law of pollutants under different hydraulic conditions is evaluated, and finally the complete pollutant migration law data is output.

[0022] For example, in the water quality sampling and analysis, water samples are collected from different sections to determine the content of various indicators. Through the analysis of continuous monitoring data, it is found that there is a significant correlation between the amount of water entering the reservoir and the water quality indicators, especially during rainfall, the content of suspended matter increases with the increase of water entering the reservoir. After the monitoring data is divided into 109 sub-basins, the pollution characteristics of different regions are clearly reflected. For example, in sub-basins with good vegetation coverage, the content of suspended matter is relatively low, while in sub-basins with larger slopes, the input of pollutants during rainfall is large. Through cumulative flux calculation, the input intensity and spatial distribution characteristics of various pollutants are quantified. Combined with the analysis of hydraulic retention time, the migration and transformation laws of pollutants in the drawdown zone are determined.

[0023] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (1) Perform a stratified analysis of the water level elevation data in the pollutant migration law data to generate the monitoring area boundaries of three water level gradients: high, medium, and low; (2) Using multi-parameter water quality sensors to collect data at sampling points in the high, medium, and low monitoring areas, the initial monitoring value of dissolved oxygen is obtained; (3) Perform temperature correction and air pressure compensation on the initial monitoring value of dissolved oxygen to obtain the measured data of dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll; (4) Classify and summarize the measured data according to the monitoring area to form a time series table of water quality parameters in each monitoring area; (5) Calculate the water quality change pattern under different water level conditions based on the water quality parameter time series table and generate the water quality change curve of the monitoring area; (6) Use the water quality change curve of the monitoring area to analyze the distribution of pollutant concentrations and output the monitoring results of dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll data.

[0024] Specifically, the key step is to perform a stratified analysis of the water level elevation data in the pollutant migration law data. The water level elevation data comes from the continuous records of the water level monitoring station. By measuring the change of water level over time and combining it with topographic measurement data, the vertical distribution range of the drawdown zone is determined. The water level elevation data is stratified according to the normal water level, average water level and dead water level to form monitoring areas with high, medium and low water level gradients. The high-level area is located above the normal water level and is mainly affected by seasonal rainfall; the middle-level area is between the normal water level and the dead water level, and the water level fluctuates frequently; the low-level area is located below the dead water level and is submerged for a long time. In the three determined monitoring areas, multi-parameter water quality sensors are deployed for data collection. The multi-parameter water quality sensor is an integrated monitoring device that contains multiple measurement probes such as dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen and chlorophyll. The sensors are deployed at representative locations in each monitoring area, and the selection of sampling points needs to consider water flow characteristics, pollution source distribution and terrain characteristics. The initial monitoring value of dissolved oxygen is measured by an electrochemical sensor, which uses the reduction reaction of oxygen on the electrode surface to generate a current signal, and obtains the dissolved oxygen concentration value through signal conversion.

[0025] The processing of the initial monitoring value of dissolved oxygen involves two important links: temperature correction and air pressure compensation. Temperature correction is because the solubility of dissolved oxygen is significantly affected by temperature, and rising temperature will lead to a decrease in the dissolved oxygen saturation value. The sensor has a built-in temperature compensation module, which automatically performs corrections based on the measured water temperature. Air pressure compensation considers the influence of atmospheric pressure on the solubility of dissolved oxygen, and corrects the real-time air pressure value measured by the air pressure sensor. At the same time, other water quality parameters also require corresponding data processing: pH value is regularly calibrated with double buffer solution, conductivity needs to be temperature compensated, turbidity value needs to remove bubble interference, ammonia nitrogen content needs to consider the influence of pH value and temperature, and chlorophyll data needs to be corrected for light intensity and turbidity interference. The classification and summary of measured data requires the establishment of a complete database structure. First, the data is classified according to the monitoring area. The data of each area includes the sampling time, sampling point location and the corresponding water quality parameter value. The construction of the water quality parameter time series table needs to consider the sampling frequency, data quality control and abnormal value processing. The sampling frequency is set according to the monitoring purpose, generally once an hour. Data quality control includes data validity testing, continuity testing and rationality testing, and the elimination of obviously erroneous data points.

[0026] According to the formed time series table of water quality parameters, the water quality change law under different water level conditions is analyzed. Time series data are processed by statistical methods, including trend analysis, periodicity analysis and correlation analysis. In the process of data processing, the corresponding relationship between water quality parameters and water level changes is established by considering the influencing factors such as hydrological and meteorological conditions and seasonal changes. The water quality change curve of the monitoring area reflects the changing characteristics of various indicators over time and water level, which helps to understand the migration and transformation laws of pollutants in the drawdown zone and the growth dynamics of algae. Finally, the water quality change curve of the monitoring area is used to analyze the pollutant concentration and chlorophyll distribution. Through the spatial interpolation method, the discrete monitoring point data is expanded to the entire monitoring area to form the spatial distribution characteristics of pollutant concentration and algal biomass. Comprehensively analyze the temporal and spatial changes of various indicators and output a complete monitoring result report.

[0027] For example, the monitoring area division was determined based on the water level monitoring data: high-level area (175-177 meters), middle-level area (172-175 meters) and low-level area (170-172 meters). Four monitoring points were set up in each area, and multi-parameter water quality sensors were installed for continuous monitoring. Accurate water quality parameter values ​​were obtained through temperature correction and air pressure compensation. The monitoring data showed that there were significant differences in water quality characteristics in different water level gradient areas: the dissolved oxygen content in the high-level area was high but fluctuated greatly, the water quality parameters in the middle-level area were relatively stable, and the low-level area showed obvious seasonal changes. The chlorophyll content also showed unique distribution characteristics in different areas: the high-level area was affected by sufficient light, and the chlorophyll concentration changed significantly with the water level fluctuation; the chlorophyll content in the middle-level area was relatively stable, mainly affected by water temperature; the chlorophyll concentration in the low-level area was generally low, but local algae proliferation was prone to occur in summer. By analyzing the monitoring data for one consecutive year, the intrinsic connection between water quality parameters, chlorophyll content and water level changes was revealed.

[0028] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) The water quality parameters are divided into different regions according to dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll data to generate a water quality spatial distribution map; (2) Analyze the organic matter content, pH value, and nitrogen, phosphorus, and potassium content of soil samples in each monitoring area to obtain the results of soil physical and chemical properties analysis; (3) Superimpose the water quality spatial distribution map with the soil physical and chemical property analysis results to form a plant planting suitability evaluation table; (4) Analyze the high-level fluctuation zone data in the plant planting suitability evaluation table to determine the planting area of ​​vetiver grass; (5) Based on the median fluctuation zone data in the plant planting suitability evaluation table, select the optimal planting location for mulberry trees; (6) Calculate the planting density of emergent plants based on the low-level drawdown zone parameters in the plant planting suitability evaluation table, and output the planting area and density ratio of vetiver, mulberry and emergent plants.

[0029] Specifically, water quality parameters are divided into different regions for statistics. Water quality parameters include six key indicators: dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll. Dissolved oxygen reflects the oxygen content of the water body and is monitored in real time by an electrochemical probe; pH value represents the acidity and alkalinity of the water body and is measured by the glass electrode method; conductivity reflects the total amount of ions in the water and is measured by a conductivity electrode; turbidity indicates the transparency of the water body and is measured by the scattered light method; ammonia nitrogen content is measured by Nessler's reagent colorimetry; chlorophyll content is measured by fluorescence method, reflecting the phytoplankton biomass in the water body. The measured values ​​of these indicators are imported into geographic information processing software, and a water quality spatial distribution map is generated by the Kriging interpolation method. The distribution map intuitively shows the spatial variation patterns of various water quality indicators. The analysis of the physical and chemical properties of soil samples is an important basis for determining the plant planting area. Surface soil samples were collected in each monitoring area, with a sampling depth of 0-20 cm, and the quartering method was used to ensure that the samples were representative. The soil organic matter content was determined by potassium dichromate oxidation-external heating method; the soil pH value was determined by potentiometric method; the total nitrogen content was determined by Kjeldahl method; the total phosphorus content was determined by molybdenum antimony colorimetry; and the total potassium content was determined by flame photometry. The determination results were sorted according to the sample location to form a complete soil physical and chemical property analysis result. The superposition operation of the water quality spatial distribution map and the soil physical and chemical property analysis results adopted the multi-layer data superposition analysis method. First, an evaluation index system was established, including water quality index weights and soil index weights. The water quality index mainly considers the effects of dissolved oxygen, pH value and chlorophyll on plant growth, while the soil index focuses on organic matter content and nutrient status. As an important indicator of the degree of eutrophication of water bodies, the distribution characteristics of chlorophyll content have important guiding significance for plant configuration plans. Through weighted superposition calculation, a plant planting suitability evaluation table reflecting the comprehensive quality of the environment was obtained. For the analysis of the high-level drawdown zone data, the focus was on factors such as soil water content changes, nutrient status and slope. As an excellent soil and water conservation plant, Vetiver has a developed root system and strong environmental adaptability. According to the scoring results in the plant planting suitability evaluation table, areas with higher scores and suitable terrain conditions are preferred as vetiver planting areas. Vetiver is planted in strips, with a row spacing of 0.5 meters.

[0030] The analysis of median drawdown zone data focuses on the matching degree between soil physical and chemical properties and water conditions. As an economic tree species that is resistant to water and humidity, mulberry trees need to be selected in areas with good soil fertility and moderate drainage conditions. Through a comprehensive analysis of the various indicators in the plant planting suitability evaluation table, the most suitable location for mulberry tree growth is determined. The planting spacing of mulberry trees needs to take into account the crown width of adult trees, which is generally controlled at 2-3 meters. The plant configuration of the low-level drawdown zone mainly considers the flooding resistance and fixation ability of emergent plants. According to the parameters of the plant planting suitability evaluation table, select suitable emergent plant species, such as reeds and cattails. The calculation of planting density needs to take into account the growth characteristics of plants and water purification needs, and the optimal planting density is determined by the relationship between biomass per unit area and purification efficiency.

[0031] For example, through the analysis of the monitoring area, it was found that the soil pH value of the high-level drawdown zone is between 6.5-7.5, and the organic matter content is 1.5-2.5%, which is suitable for the growth of vetiver; the soil water content in the middle-level drawdown zone is high, and the nitrogen and phosphorus content is rich, so water-resistant mulberry varieties are selected for planting; the low-level drawdown zone is mainly equipped with reeds. The chlorophyll monitoring results show that the chlorophyll content in the low-level drawdown zone is high, with an average of 42μg / L, indicating that there is a certain degree of eutrophication in the area, and it is necessary to control the excessive growth of algae through the planting of emergent plants. The specific planting plan is: the planting density of vetiver in the high-level drawdown zone is 45 plants / square meter, the spacing of mulberry trees in the middle-level drawdown zone is 2.5 meters, and the planting density of reeds in the low-level drawdown zone is 8 plants / square meter. Through field verification, the plants are in good growth condition, which has played a significant role in water purification and ecological restoration in the drawdown zone. The chlorophyll content has been significantly reduced, and the eutrophication of the water body has been effectively controlled.

[0032] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Import the root growth data of vetiver in the plant mix into the design parameters of the biofilter layer to determine the porosity and permeability coefficient of the biofilter layer; (2) Selecting the ecological matrix formula based on the porosity and permeability coefficient of the biological filtration layer, and preparing the porous ecological matrix through particle size distribution analysis; (3) Surface modification of the porous ecological matrix, adding mulberry root secretions, and constructing a biologically active adsorption layer; (4) Mixing the adsorption layer with the root tissue of emergent plants to form a degradation layer with microbial attachment function; (5) Based on the biological filtration layer, the adsorption layer and the degradation layer are superimposed to construct a three-dimensional ecological matrix; (6) The pollution interception performance of the three-dimensional ecological matrix is ​​verified to obtain a hierarchical purification unit.

[0033] Specifically, the acquisition of vetiver root growth data is the basis for constructing the biological filter layer. Vetiver roots grow vertically, and the root density decays exponentially with depth. Parameters such as root length density, root distribution depth and root growth rate are obtained by measuring the root growth. These growth data directly affect the design of the biological filter layer, and the spatial distribution characteristics of the root system determine the physical structure of the filter layer. The porosity of the biological filter layer is calculated by the ratio of the volume of the space occupied by the root system to the total volume, and the permeability coefficient is determined by the variable head permeability test, which reflects the migration characteristics of water in the filter layer. Based on the determined porosity and permeability coefficient, a suitable ecological matrix formula is selected. The ecological matrix is ​​mainly composed of sandstone, zeolite, volcanic rock and other materials, and matrix particles of different particle sizes are obtained by screening. The particle size distribution analysis adopts the standard screening method, and the matrix materials with three particle size ranges of 2-4mm, 1-2mm and 0.5-1mm are selected for proportioning. The preparation process of the porous ecological matrix includes material pretreatment, mixing and curing. Pretreatment includes cleaning, impurity removal and drying, and mixing requires ensuring that materials of different particle sizes are evenly distributed.

[0034] The surface modification of the ecological matrix is ​​aimed at improving its adsorption capacity for pollutants. First, the mulberry roots are collected and the root secretions are extracted, mainly including organic acids, amino acids and polysaccharides. The extracted root secretions are combined with the ecological matrix by immersion method, and the immersion time and temperature conditions are controlled. A biofilm is formed on the surface of the modified matrix, which has strong adsorption activity and can effectively remove nutrients such as nitrogen and phosphorus in the water. The construction of the degradation layer combines the adsorption layer with the root tissue of emergent plants. The roots of emergent plants have developed aeration tissue, which provides a good attachment carrier for microorganisms. The root tissue is crushed and mixed with the modified matrix. During the mixing process, a composite bacterial agent is added to cultivate a stable microbial community. This mixed structure not only maintains the adsorption function, but also can degrade pollutants through the metabolism of microorganisms.

[0035] The construction of the three-dimensional ecological matrix adopts a layered stacking method. The bottom is the biological filtration layer, which mainly plays a mechanical interception role; the middle is the adsorption layer, which is responsible for the adsorption of soluble pollutants; the top is the degradation layer, which performs the biodegradation of pollutants. The layers are connected by transition zones to ensure hydraulic conductivity. The entire matrix has a porous three-dimensional structure, which is conducive to the uniform distribution of water flow and the full contact of pollutants. The performance verification of the hierarchical purification unit includes hydraulic characteristic testing and pollutant removal effect evaluation. The hydraulic characteristic test mainly examines the permeability and hydraulic retention time, and the tracer method is used to determine the actual hydraulic retention time distribution. The pollutant removal effect is evaluated by measuring the inlet and outlet water quality parameters, including COD, ammonia nitrogen, total nitrogen, total phosphorus and other indicators.

[0036] For example, an ecological matrix with a particle size of 2-4 mm is selected as the main filler, the porosity is controlled at about 40%, and the permeability coefficient is about 10^-3cm / s. The addition of mulberry root secretions significantly increases the ammonia nitrogen adsorption capacity of the matrix. The mixing ratio of the emergent plant roots and the modified matrix is ​​1:4 (volume ratio), and a stable microbial community is formed after 72 hours of cultivation. The total thickness of the three-dimensional ecological matrix body is 80 cm, including 30 cm of biological filtration layer, 25 cm of adsorption layer, and 25 cm of degradation layer. The operation results show that the hierarchical purification unit has a significant removal effect on COD, ammonia nitrogen, and total nitrogen, and the treated water is stable and up to standard. This multi-level ecological purification structure achieves the synergistic effect of physical interception, chemical adsorption and biodegradation.

[0037] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) Collect the inlet water volume data of the hierarchical purification unit by time period and calculate the hydraulic load distribution of the biological filtration layer, adsorption layer and degradation layer; (2) Dynamically calculate the hydraulic load distribution to obtain the hydraulic retention time of each layer of the hierarchical purification unit; (3) Correct the hydraulic retention time through online water quality monitoring data to generate sewage flow regulation parameters; (4) Based on the sewage flow regulation parameters, adjust the opening of the hydraulic regulating device to form a real-time water distribution plan; (5) Import the real-time water allocation plan into the automatic control program and output the water flow rate of each treatment unit; (6) Conduct online calibration of the inlet flow rate and adjust the sewage residence time and treatment conditions based on online water quality monitoring data.

[0038] Specifically, the inlet water volume data collection of the hierarchical purification unit is realized by an electromagnetic flowmeter, which has high precision and anti-interference characteristics and can accurately measure the inlet flow rate. The data collection frequency is set to once every 5 minutes, and the instantaneous flow value is recorded. The hydraulic load distribution of the biological filtration layer, adsorption layer and degradation layer is monitored by the pressure sensors installed on each layer. The pressure sensors measure the head loss and reflect the actual hydraulic load status of each layer. The hydraulic load distribution data is counted hourly to form a complete hydraulic load time series data. The dynamic calculation of the hydraulic load distribution mainly considers the permeability and hydraulic characteristics of each layer. The hydraulic retention time is an important parameter reflecting the treatment effect, which is calculated by the effective volume and actual flow of each layer. For the biological filtration layer, its porosity and medium characteristics are considered; the adsorption layer needs to consider the specific surface area and pore distribution of the adsorption material; the degradation layer focuses on the spatial structure of the microbial carrier. The calculation of the hydraulic retention time combines the characteristic parameters of each layer to ensure that the sewage is fully treated.

[0039] Online water quality monitoring data is used for dynamic correction of hydraulic retention time. Online monitoring equipment includes pH meter, dissolved oxygen meter, conductivity meter, etc., which monitor the changes of various water quality indicators in real time. When the effluent quality cannot meet the treatment requirements, it is necessary to improve the treatment effect by adjusting the hydraulic retention time. The correlation analysis between water quality data and hydraulic retention time generates specific flow adjustment parameters. The calculation formula of the adjustment parameter is: Where: is the flow regulation parameter (dimensionless); is the hydraulic load weight of the i-th layer; is the water quality index deviation coefficient; is the temperature correction factor; is the bioactivity coefficient; is the seasonal variation coefficient; i represents the level (1-biological filtration layer, 2-adsorption layer, 3-degradation layer).

[0040] According to the flow adjustment parameters, the hydraulic regulating device can achieve accurate water distribution. The hydraulic regulating device mainly includes electric valves and flow controllers, which can automatically adjust the opening according to the calculation results. The real-time water distribution plan takes into account multiple factors such as processing load, water output requirements and energy consumption optimization. The real-time water distribution plan realizes automatic operation through data acquisition and control programs. The program receives real-time data from each measuring point, including flow, water level, water quality and other parameters, and outputs control signals according to the preset control logic. The water inlet flow of each processing unit is realized through the diversion device to ensure the uniformity and stability of water distribution.

[0041] Online verification of the inlet flow rate is an important part of ensuring the treatment effect. By comparing the measured flow rate with the set flow rate, deviations can be discovered and corrected in a timely manner. Combined with online water quality monitoring data, the treatment conditions are dynamically adjusted. The working condition adjustment includes fine-tuning the hydraulic retention time and optimizing the flow distribution ratio. For example, the treatment scale of this project is 1,000 cubic meters per day. The hydraulic load distribution of the hierarchical purification unit shows that the hydraulic load of the biological filtration layer is 4.8 cubic meters per square meter per day, the adsorption layer is 3.2 cubic meters per square meter per day, and the degradation layer is 2.4 cubic meters per square meter per day. When it is found through online monitoring that the ammonia nitrogen in the effluent is high, the flow regulation parameter is calculated to be 0.85, and the hydraulic retention time of the degradation layer is extended from the original 4 hours to 4.7 hours. By adjusting the opening of the electric valve, the precise distribution of water is achieved. After adjustment, the various indicators of the treated effluent are stable and meet the standards, which confirms the effectiveness of the control method. The entire process realizes the automatic control of hydraulic regulation and ensures the stable operation of the treatment process.

[0042] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) The long-term water quality monitoring data is divided into sections according to the biological filtration layer, adsorption layer and degradation layer to obtain the water quality indicators of the inlet and outlet water of each layer; (2) Perform differential calculation on the water quality indicators of the inlet and outlet water of each layer and calculate the nitrogen and phosphorus removal rate of each purification unit; (3) The COD degradation rate is obtained by performing concentration decay analysis on the water quality indicators of the inlet and outlet water of each layer; (4) Measure the ion concentration of the inlet and outlet water quality indicators of each layer to obtain the heavy metal adsorption rate; (5) Comprehensively evaluate the nitrogen and phosphorus removal rate, COD degradation rate and heavy metal adsorption rate to screen the target operating parameters; (6) Optimize and analyze the performance of the processing unit based on the target operating parameters and output the target governance strategy.

[0043] Specifically, for the biological filtration layer, adsorption layer and degradation layer, inlet and outlet water sampling points are set respectively, and water samples are collected regularly using standard monitoring methods. The inlet water quality indicators include total nitrogen, total phosphorus, COD, heavy metals and other parameters, and the outlet water quality indicators reflect the treatment effect of each layer. The sampling frequency is once a day, and the continuous monitoring period is not less than 3 months to ensure the representativeness and reliability of the data. The difference calculation of the inlet and outlet water quality indicators of each layer is a key step in calculating the purification effect. For the calculation of nitrogen and phosphorus removal rates, it is necessary to determine the total nitrogen and total phosphorus concentrations in the inlet and outlet water. Total nitrogen is determined by alkaline potassium persulfate digestion ultraviolet spectrophotometry, and total phosphorus is determined by molybdenum antimony anti-spectrophotometry. The calculation of nitrogen and phosphorus removal rates takes into account water balance and concentration changes, reflecting the actual treatment effect.

[0044] Concentration decay analysis is mainly aimed at the removal effect of COD. As a comprehensive indicator to characterize the content of organic matter, the degradation process of COD is affected by multiple factors. The potassium dichromate method is used to determine the COD concentration, and the decay law of COD is analyzed through continuous monitoring data. During the degradation process, it is necessary to consider the effects of environmental factors such as temperature and pH value on the degradation rate, and also pay attention to changes in microbial activity. The heavy metal adsorption rate is determined by atomic absorption spectrophotometry. For different heavy metal ions, the corresponding determination method is selected: flame atomic absorption method is used for copper, zinc, and lead, and graphite furnace atomic absorption method is used for cadmium and arsenic. By comparing the heavy metal ion concentrations of the inlet and outlet water, combined with the water volume data, the actual adsorption capacity of each layer for heavy metals is calculated.

[0045] When comprehensively evaluating the removal rate data of various indicators, it is necessary to establish a scientific evaluation system. The evaluation indicators include three aspects: pollutant removal efficiency, operation stability and economy. The weight of each indicator is determined by principal component analysis, and a comprehensive scoring model is established. The scoring results are used as the basis for screening the target operating parameters, which include hydraulic load, residence time, pH control range, etc. Finally, the performance of the treatment unit is optimized and analyzed based on the target operating parameters. The optimization content includes adjustment of treatment process parameters, optimization of operation mode and maintenance management measures. Through multiple rounds of test verification, the best combination of operating conditions is determined to form a complete target governance strategy.

[0046] For example, analysis of three months of operation data revealed that: the biological filtration layer has a significant effect on the removal of suspended solids, and the effluent turbidity remains stable; the adsorption layer has a good performance in the removal of nitrogen and phosphorus, but as the operation time increases, the adsorption effect decreases; the degradation layer has an outstanding effect on the degradation of organic matter, and the COD removal rate is stable. After comprehensive evaluation, the optimal combination of operating parameters was determined: the hydraulic load of the biological filtration layer is 4.5 cubic meters / square meter·day, the pH value of the adsorption layer is controlled at 7.2-7.8, and the dissolved oxygen in the degradation layer is maintained above 3mg / L. The determination of these parameters provides an important basis for subsequent operation optimization.

[0047] In a specific embodiment, the process of performing concentration decay analysis based on the water quality indicators of the inlet and outlet water of each layer may specifically include the following steps: (1) Arrange the COD concentration values ​​in the water quality indicators of each layer inlet and outlet according to the time series to form a COD concentration change curve; (2) The COD concentration change curve is segmented according to the biological filtration layer, adsorption layer and degradation layer to obtain the layered COD attenuation data; (3) Perform temperature correction and hydraulic retention time correction on the stratified COD decay data to generate standardized COD decay values; (4) Calculate the COD reduction per unit time based on the standardized COD decay value and output the instantaneous COD degradation rate; (5) The instantaneous COD degradation rate is weighted averaged according to the hydraulic load to form a steady-state degradation coefficient; (6) Perform cumulative calculation on the steady-state degradation coefficient to obtain the COD degradation rate.

[0048] Specifically, the long-term water quality monitoring data is segmented and counted according to the biological filtration layer, adsorption layer and degradation layer. For each layer, the inlet and outlet water quality data are collected through online monitoring equipment, with a sampling frequency of once every 2 hours, and continuous monitoring for no less than 90 days. Water quality indicators include total nitrogen, total phosphorus, COD, heavy metals and other parameters. Each sampling point is equipped with an automatic sampler and an online analyzer to ensure the continuity and reliability of the data. For the difference calculation of the inlet and outlet water quality indicators of each layer, the following calculation formula is used: Where: is the comprehensive removal efficiency (%); is the influent concentration of the jth pollutant (mg / L); is the effluent concentration of the jth pollutant (mg / L); is the pollutant weight coefficient; is the temperature correction factor; is the hydraulic load correction coefficient; m is the number of pollutant types.

[0049] Through formula calculation, core indicators such as nitrogen and phosphorus removal rate, COD degradation rate and heavy metal adsorption rate are obtained. Total nitrogen monitoring adopts ultraviolet spectrophotometry, total phosphorus adopts ammonium molybdate spectrophotometry, COD adopts potassium dichromate method, and heavy metals adopt atomic absorption spectrophotometry. Concentration decay analysis takes into account the influence of environmental factors such as water temperature, pH value, and dissolved oxygen.

[0050] For example, through long-term monitoring, it was found that the biological filtration layer has a significant effect on the removal of suspended solids, and the effluent turbidity remains stable; the adsorption layer has an outstanding performance in the removal of nitrogen and phosphorus, and needs to be regenerated as the operation time goes by; the degradation layer has a good effect on the degradation of organic matter and shows a stable treatment capacity. The optimal operating parameters determined through comprehensive evaluation include key indicators such as hydraulic retention time, pH range, and dissolved oxygen concentration.

[0051] In a specific embodiment, the process of performing the step of optimizing and analyzing the performance of the processing unit based on the target operating parameters may specifically include the following steps: (1) Import the target operating parameters into the processing unit performance evaluation matrix to generate a unit operation score sheet; (2) The unit operation score sheet is scored according to the nitrogen and phosphorus removal rate, COD degradation rate and heavy metal adsorption rate to obtain performance evaluation indicators; (3) Calculate the pollutant removal contribution rate of each treatment unit based on the performance evaluation index and form a unit treatment capacity table; (4) Cross-validate the unit treatment capacity table with the pollutant migration law data and output treatment efficiency optimization suggestions; (5) Conduct feasibility analysis and cost accounting for processing efficiency optimization suggestions and generate a list of improvement plans; (6) Determine the optimal operating conditions based on the list of improvement plans and output the target governance strategy.

[0052] Specifically, a treatment unit performance evaluation matrix is ​​established. The matrix includes multiple evaluation dimensions such as hydraulic load, residence time, inlet and outlet water quality, and operating costs. The target operating parameters include key operating indicators of each treatment unit, such as the filtration rate of the biological filtration layer, the adsorption capacity of the adsorption layer, and the microbial activity of the degradation layer. By importing these parameters into the evaluation matrix and combining the weight coefficients of each indicator, a unit operation score sheet is generated. The sub-item scoring of the unit operation score sheet involves the quantitative evaluation of the pollutant removal effect. For nitrogen and phosphorus removal rates, the removal effects of total nitrogen and total phosphorus are mainly examined; the COD degradation rate reflects the degree of removal of organic matter; and the heavy metal adsorption rate reflects the treatment capacity for heavy metal pollutants. Each indicator is scored according to the actual removal effect, and the scoring criteria refer to the technical specifications of the relevant treatment process. The scoring results form a detailed performance evaluation indicator system.

[0053] Based on the performance evaluation indicators, the contribution of each treatment unit to pollutant removal is quantitatively analyzed. The biological filtration layer mainly contributes to the removal of suspended solids; the adsorption layer makes significant contributions to the removal of nitrogen, phosphorus and heavy metals; the degradation layer is mainly responsible for the degradation and transformation of organic matter. By counting the removal amount of different pollutants by each unit, the proportion of its contribution to the overall treatment effect is calculated to form a unit treatment capacity table. Cross-validating the unit treatment capacity table with the pollutant migration law data is a key step in optimizing treatment efficiency. By comparing the two sets of data, the room for improvement and constraints in treatment efficiency are found. An in-depth analysis of the problems found is conducted, and targeted optimization suggestions are put forward based on the process characteristics and operating experience. The optimization suggestions cover aspects such as process parameter adjustment, operation mode improvement and equipment maintenance.

[0054] The feasibility analysis is carried out from three dimensions: technology, economy and management, to evaluate the implementation difficulty and expected effect of the optimization measures. Cost accounting includes equipment transformation costs, operating cost increments and maintenance cost changes. Through comprehensive analysis, technically feasible and economically reasonable improvement plans are screened out to form a list of improvement plans. Finally, the optimal operating conditions are determined based on the list of improvement plans. The operating conditions include specific indicators such as hydraulic load, residence time, and operating parameters. By comparing and analyzing the comprehensive benefits of different plans, the optimal combination of operating conditions is selected to form a target governance strategy.

[0055] For example, through the performance evaluation of the three treatment units, it was found that: the biological filtration layer performed well in the removal of suspended solids, but the backwashing frequency needed to be optimized; the removal effect of nitrogen and phosphorus in the adsorption layer gradually decreased with the operating time, and the regeneration cycle of the adsorption material needed to be considered; the degradation layer had a stable effect on the degradation of organic matter, but its performance decreased in the low temperature season. Based on these findings, an improvement plan including backwashing optimization, adsorption material regeneration and temperature control was formulated. Economic analysis shows that the input-output ratio of these improvement measures is reasonable and has implementation value. The final operating conditions include: the backwashing cycle is adjusted to once every 48 hours, the adsorption material regeneration cycle is 3 months, and the treatment effect is guaranteed by adjusting the hydraulic retention time in the low temperature season. The implementation of this set of optimization plans has significantly improved the overall treatment effect.

[0056] The above describes the efficient and intelligent treatment method for the pollution of the drawdown zone in the embodiment of the present application. The following describes the efficient and intelligent treatment system for the pollution of the drawdown zone in the embodiment of the present application. Figure 2 In the embodiment of the present application, an efficient and intelligent system for managing pollution in the drawdown zone includes: The calculation module 201 is used to calculate the water quality load of nitrogen and phosphorus concentrations, suspended solids content and organic matter in the drawdown zone through the SWAT model to obtain pollutant migration law data; A division module 202 is used to divide the pollutant migration law data into three monitoring areas of high, medium and low according to the water level gradient, and use a multi-parameter water quality sensor to measure the dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll data of each area; Analysis module 203, for determining the planting area and density ratio of vetiver, mulberry and emergent plants according to the water quality parameters of each monitoring area and the analysis results of soil physical and chemical properties; The generation module 204 is used to construct a biological filtration layer, an adsorption layer and a degradation layer using ecological matrix and modified materials according to the determined plant ratio to form a hierarchical purification unit; The detection module 205 is used to distribute water to the hierarchical purification units using a hydraulic regulating device, and to adjust the sewage retention time and treatment conditions in combination with the online water quality monitoring data; The screening module 206 is used to calculate the nitrogen and phosphorus removal rate, COD degradation rate and heavy metal adsorption rate of each purification unit based on the long-term water quality monitoring data, screen the target operating parameters, and generate a target treatment strategy according to the target operating parameters.

[0057] Through the coordinated cooperation of the above components, the SWAT model is used to calculate the water quality load of nitrogen and phosphorus concentrations, suspended solids content and organic matter in the drawdown zone, accurately obtain the data on pollutant migration patterns, and provide a scientific basis for subsequent governance; the pollutant migration pattern data is divided into three monitoring areas: high, medium and low according to the water level gradient, and multi-parameter water quality sensors are used to measure the dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll data of each area, realizing dynamic monitoring of water quality changes under different water level conditions; according to the water quality parameters of each monitoring area, combined with the results of soil physical and chemical properties analysis, scientific determination of vetiver and mulberry The planting area and density ratio of trees and emergent plants have built an adaptable ecological restoration system; the biological filtration layer, adsorption layer and degradation layer are constructed by using ecological matrix and modified materials to form a hierarchical purification unit, which significantly improves the interception and conversion efficiency of pollutants; the water volume of the hierarchical purification unit is distributed by hydraulic regulating devices, and the sewage retention time and treatment conditions are adjusted in combination with the online water quality monitoring data to achieve precise control of the treatment process; by calculating the nitrogen and phosphorus removal rate, COD degradation rate and heavy metal adsorption rate of each purification unit, the target operating parameters are screened and the target governance strategy is generated to ensure the continuous stability of the treatment effect. The overall plan realizes the organic combination of ecological restoration and engineering measures, establishes an intelligent control system for water quality and water quantity coupling, and significantly improves the efficiency and reliability of pollution control in the drawdown zone.

[0058] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An efficient and intelligent method for pollution control in the water-fluctuation zone, characterized in that: The efficient and intelligent treatment method for the pollution of the drawdown zone includes: The SWAT model is used to calculate the water quality load of nitrogen and phosphorus concentrations, suspended solids content and organic matter in the drawdown zone to obtain data on the migration patterns of pollutants. The pollutant migration law data is divided into three monitoring areas: high, medium and low according to the water level gradient, and the dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll data of each area are measured using a multi-parameter water quality sensor; Based on the water quality parameters of each monitoring area and the results of soil physical and chemical property analysis, determine the planting area and density ratio of vetiver, mulberry and emergent plants; According to the determined plant ratio, ecological matrix and modified materials are used to construct biological filtration layer, adsorption layer and degradation layer to form a hierarchical purification unit; Use hydraulic regulators to distribute water to the hierarchical purification units, and adjust sewage retention time and treatment conditions based on online water quality monitoring data; Based on the long-term water quality monitoring data, the nitrogen and phosphorus removal rates, COD degradation rates and heavy metal adsorption rates of each purification unit are calculated, the target operating parameters are screened, and the target treatment strategy is generated according to the target operating parameters.

2. The efficient and intelligent treatment method for the pollution in the drawdown zone according to claim 1 is characterized in that: The SWAT model is used to calculate the water quality load of nitrogen and phosphorus concentrations, suspended solids content and organic matter in the drawdown zone to obtain pollutant migration law data, including: Calculate the time distribution of nitrogen and phosphorus concentrations, suspended solids content and organic matter based on water quality monitoring data to generate initial water quality load data; The initial water quality load data is divided into 109 sub-basins for calculation to obtain sub-basin water quality load data; Calculating the cumulative amount of nitrogen and phosphorus concentrations, suspended solids content and organic matter in the water quality load data of the sub-area to form a pollutant cumulative flux; The pollutant cumulative flux is used to calculate the concentration distribution of each sub-basin in the drawdown zone to obtain the pollutant migration trend; The pollutant migration trend is correlated with the hydraulic retention time and the pollutant migration law data is output.

3. The efficient and intelligent treatment method for water-fluctuation zone pollution according to claim 1 is characterized in that: The pollutant migration law data is divided into three monitoring areas: high, medium and low according to the water level gradient, and a multi-parameter water quality sensor is used to measure the dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll data of each area, including: Performing a hierarchical analysis on the water level elevation data in the pollutant migration law data to generate monitoring area boundaries of three water level gradients: high, medium and low; The multi-parameter water quality sensor is used to collect data at sampling points in the three monitoring areas of high, medium and low to obtain the initial monitoring value of dissolved oxygen; The initial monitoring value of dissolved oxygen is subjected to temperature correction and air pressure compensation to obtain measured data of dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll; Classify and summarize the measured data according to the monitoring area to form a time series table of water quality parameters in each monitoring area; Calculate the changing rules of water quality and algae growth under different water level conditions according to the water quality parameter time series table, and generate a water quality change curve for the monitoring area; The water quality change curve of the monitoring area is used to analyze the pollutant concentration and algae biomass distribution, and the monitoring results of dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll are output.

4. The efficient and intelligent treatment method for water-falling zone pollution according to claim 1 is characterized in that: The above-mentioned method determines the planting area and density ratio of vetiver, mulberry and emergent plants according to the water quality parameters of each monitoring area and the analysis results of soil physical and chemical properties, including: The water quality parameters are divided into different zones according to dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll to generate a water quality spatial distribution map; The soil samples in each monitoring area were analyzed for organic matter content, pH value, and nitrogen, phosphorus, and potassium content to obtain the results of soil physical and chemical properties analysis; The water quality spatial distribution map is superimposed on the soil physical and chemical property analysis results to form a plant planting suitability evaluation table; Analyze the high-level fluctuation zone data in the plant planting suitability evaluation table to determine the planting area of ​​vetiver grass; Based on the median fluctuation zone data in the plant planting suitability evaluation table, select the optimal planting location for mulberry trees; According to the low-level drawdown zone parameters of the plant planting suitability evaluation table, the planting density of emergent plants is calculated, and the planting area and density ratio of vetiver, mulberry and emergent plants are output.

5. The efficient and intelligent treatment method for water-falling zone pollution according to claim 1 is characterized in that: According to the determined plant ratio, the ecological matrix and modified materials are used to construct the biological filtration layer, adsorption layer and degradation layer to form a hierarchical purification unit, including: Importing the root growth data of vetiver in the plant mix into the design parameters of the biological filter layer to determine the porosity and permeability coefficient of the biological filter layer; The ecological matrix formula is selected according to the porosity and permeability coefficient of the biological filtration layer, and the porous ecological matrix is ​​prepared by particle size distribution analysis; The porous ecological matrix is ​​subjected to surface modification treatment, and mulberry root secretions are added to construct an adsorption layer with biological activity; The adsorption layer is mixed with the root tissue of emergent plants to form a degradation layer with microbial attachment function; Based on the biological filtration layer, the adsorption layer and the degradation layer are superimposed to construct a three-dimensional ecological matrix; The pollution interception performance of the three-dimensional ecological matrix is ​​verified to obtain a hierarchical purification unit.

6. The efficient and intelligent treatment method for water-fluctuation zone pollution according to claim 1 is characterized in that: The method of using a hydraulic regulating device to distribute water to the hierarchical purification units and adjusting the sewage retention time and treatment conditions in combination with online water quality monitoring data includes: The inlet water volume data of the hierarchical purification unit is collected by time period, and the hydraulic load distribution of the biological filtration layer, the adsorption layer and the degradation layer is statistically analyzed; Dynamically calculate the hydraulic load distribution to obtain the hydraulic retention time of each layer of the hierarchical purification unit; The hydraulic retention time is corrected by online water quality monitoring data to generate a sewage flow adjustment parameter; Based on the sewage flow regulation parameters, the opening of the hydraulic regulating device is adjusted to form a real-time water distribution plan; Importing the real-time water distribution plan into the automatic control program and outputting the water inlet flow of each processing unit; The water inlet flow is verified online, and the sewage residence time and treatment conditions are adjusted in combination with the water quality online monitoring data.

7. The efficient and intelligent treatment method for water-fluctuation zone pollution according to claim 1 is characterized in that: Based on the long-term water quality monitoring data, the nitrogen and phosphorus removal rate, COD degradation rate and heavy metal adsorption rate of each purification unit are calculated, the target operating parameters are screened, and the target treatment strategy is generated according to the target operating parameters, including: The long-term water quality monitoring data is segmented and counted according to the biological filtration layer, adsorption layer and degradation layer to obtain the water quality indicators of the inlet and outlet water of each layer; Performing difference calculation on the water quality indicators of the inlet and outlet water of each layer, and calculating the nitrogen and phosphorus removal rate of each purification unit; The COD degradation rate is obtained by performing concentration decay analysis on the water quality indicators of the inlet and outlet water of each layer; Determine the ion concentration of the water quality indicators of the inlet and outlet water of each layer to obtain the heavy metal adsorption rate; Comprehensively evaluate the nitrogen and phosphorus removal rate, COD degradation rate and heavy metal adsorption rate to screen target operating parameters; The processing unit performance is optimized and analyzed based on the target operating parameters, and the target governance strategy is output.

8. The efficient and intelligent treatment method for the pollution in the drawdown zone according to claim 7 is characterized in that: The concentration decay analysis is performed on the water quality indicators of the inlet and outlet water of each layer to obtain the COD degradation rate, including: Arranging the COD concentration values ​​in the water quality indicators of the inlet and outlet water of each layer in a time series to form a COD concentration change curve; The COD concentration variation curve is segmented according to the biological filtration layer, the adsorption layer and the degradation layer to obtain layered COD attenuation data; Performing temperature correction and hydraulic retention time correction on the stratified COD decay data to generate a standardized COD decay value; The COD reduction per unit time is calculated based on the standardized COD decay value, and the instantaneous COD degradation rate is output; The instantaneous COD degradation rate is weighted averaged according to the hydraulic load to form a steady-state degradation coefficient; The steady-state degradation coefficient is cumulatively calculated to obtain the COD degradation rate.

9. The efficient and intelligent treatment method for the pollution of the drawdown zone according to claim 8 is characterized in that: The optimizing and analyzing the processing unit performance based on the target operating parameters and outputting the target governance strategy includes: Importing the target operating parameters into a processing unit performance evaluation matrix to generate a unit operation score sheet; The unit operation score sheet is scored according to nitrogen and phosphorus removal rate, COD degradation rate and heavy metal adsorption rate to obtain performance evaluation indicators; Calculating the pollutant removal contribution rate of each treatment unit based on the performance evaluation index to form a unit treatment capacity table; Cross-validate the unit processing capacity table with the pollutant migration law data and output processing efficiency optimization suggestions; Conduct feasibility analysis and cost accounting for the processing efficiency optimization suggestions and generate a list of improvement plans; Based on the list of improvement plans, the optimal operating conditions are determined and the target governance strategy is output.

10. An efficient and intelligent management system for water-fluctuation zone pollution, used to implement the efficient and intelligent management method for water-fluctuation zone pollution as described in any one of claims 1 to 9, characterized in that: The efficient and intelligent pollution control system for the drawdown zone includes: The calculation module is used to calculate the water quality load of nitrogen and phosphorus concentrations, suspended solids content and organic matter in the drawdown zone through the SWAT model to obtain the data on the migration law of pollutants; A division module is used to divide the pollutant migration law data into three monitoring areas of high, medium and low according to the water level gradient, and use a multi-parameter water quality sensor to measure the dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll data of each area; The analysis module is used to determine the planting area and density ratio of vetiver, mulberry and emergent plants based on the water quality parameters of each monitoring area and the results of soil physical and chemical properties analysis; A generation module is used to construct a biological filtration layer, an adsorption layer and a degradation layer using ecological matrix and modified materials according to a determined plant ratio to form a hierarchical purification unit; The detection module is used to distribute water to the hierarchical purification units using the hydraulic regulating device and adjust the sewage retention time and treatment conditions in combination with the online water quality monitoring data; The screening module is used to calculate the nitrogen and phosphorus removal rate, COD degradation rate and heavy metal adsorption rate of each purification unit based on long-term water quality monitoring data, screen the target operating parameters, and generate a target treatment strategy based on the target operating parameters.

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