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

Abstract

The application relates to the technical field of data processing, and discloses a high-efficiency intelligent management method and system for pollution of a hydro-fluctuation belt. The method comprises the following steps: calculating water quality load of the hydro-fluctuation belt by using a SWAT model, obtaining a pollution 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 three-layer purification unit of biological filtration, adsorption and degradation; controlling a processing working condition through hydraulic regulation; calculating a processing efficiency based on long-term monitoring, and generating a target management strategy. Through the establishment of a water quality and water quantity coupling optimization regulation and control technology system, accurate identification of the pollution migration rule and intelligent regulation and control of a processing technology are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, and particularly relates to a high-efficiency intelligent management method and system for pollution of drawdown zone. BACKGROUND

[0002] As a water-land ecotone, the ecological environment quality of drawdown zone directly affects the water quality safety of reservoir. At present, the pollution control of drawdown zone mainly adopts the combination of ecological restoration and engineering management. In terms of ecological restoration, the commonly used technologies include vegetation restoration, ecological substrate improvement and biological reinforcement, etc.; in terms of engineering management, measures such as pollution interception, flow diversion and purification treatment are mainly adopted. These technologies have achieved certain effect in practice, especially in the aspects of water and soil conservation, pollution interception and ecological restoration, etc.

[0003] However, the existing pollution control technology of drawdown zone has some deficiencies. First of all, the traditional management method often separates ecological restoration and engineering measures, lacking systematic integration; secondly, the water quality purification effect is significantly affected by water level change, and it is difficult to maintain stable treatment effect when water level fluctuates sharply; thirdly, the migration and transformation law of pollutants in drawdown zone is complex, and the existing technology is difficult to realize precise control and dynamic optimization; finally, there is a lack of intelligent control means based on water quality and quantity coupling, resulting in low treatment efficiency and high operation cost. SUMMARY

[0004] The present application provides a high-efficiency intelligent management method and system for pollution of drawdown zone, which is used to realize precise identification of pollution migration law and intelligent control of treatment process by establishing a water quality and quantity coupling optimization control technology system, so as to solve the problem of pollution control under water level fluctuation in the prior art, and ensure the stability and reliability of treatment effect.

[0005] In a first aspect, the application provides a high-efficiency intelligent management method for pollution in a drawdown zone, which comprises: calculating water quality load of nitrogen and phosphorus concentrations, suspended matter content and organic matter in the drawdown zone by a SWAT model to obtain pollutant migration rule data; dividing the pollutant migration rule data into high, medium and low monitoring areas according to water level gradients, and determining dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll data of each area by using a multi-parameter water quality sensor; determining planting areas and density ratios of vetiver grass, mulberry and emergent plants according to water quality parameters of each monitoring area and combining soil physicochemical property analysis results; constructing a biological filtration layer, an adsorption layer and a degradation layer by using ecological substrates and modified materials to form a hierarchical purification unit according to the determined plant ratios; adjusting sewage retention time and treatment conditions by using a water quantity distribution device to perform water quantity distribution on the hierarchical purification unit in combination with online water quality monitoring data; calculating nitrogen and phosphorus removal rates, COD degradation rates and heavy metal adsorption rates of each purification unit based on long-term water quality monitoring data, screening target operation parameters, and generating a target management strategy according to the target operation parameters.

[0006] In a second aspect, the application provides a high-efficiency intelligent management system for pollution in a drawdown zone, which comprises:

[0007] A calculation module for calculating water quality load of nitrogen and phosphorus concentrations, suspended matter content and organic matter in the drawdown zone by a SWAT model to obtain pollutant migration rule data;

[0008] A division module for dividing the pollutant migration rule data into high, medium and low monitoring areas according to water level gradients, and determining dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll data of each area by using a multi-parameter water quality sensor;

[0009] An analysis module for determining planting areas and density ratios of vetiver grass, mulberry and emergent plants according to water quality parameters of each monitoring area and combining soil physicochemical property analysis results;

[0010] A generation module for constructing a biological filtration layer, an adsorption layer and a degradation layer by using ecological substrates and modified materials to form a hierarchical purification unit according to the determined plant ratios;

[0011] A detection module for adjusting sewage retention time and treatment conditions by using a water quantity distribution device to perform water quantity distribution on the hierarchical purification unit in combination with online water quality monitoring data;

[0012] A screening module for calculating nitrogen and phosphorus removal rates, COD degradation rates and heavy metal adsorption rates of each purification unit based on long-term water quality monitoring data, screening target operation parameters, and generating a target management strategy according to the target operation parameters.

[0013] In the technical scheme provided in the application, the water quality load of the nitrogen and phosphorus concentration, the suspended matter content and the organic matter in the drawdown zone is calculated by the SWAT model to accurately obtain the pollutant migration rule data, thereby providing a scientific basis for subsequent treatment; the pollutant migration rule data is divided into three monitoring areas of high, medium and low according to the water level gradient, and the dissolved oxygen, the pH value, the conductivity, the turbidity, the ammonia nitrogen content and the chlorophyll data of each area are measured by using a multi-parameter water quality sensor, thereby realizing dynamic monitoring of the water quality change under different water level conditions; according to the water quality parameters of each monitoring area and the analysis results of the soil physical and chemical properties, the planting area and the density ratio of the vetiver grass, the mulberry and the emergent plant are scientifically determined, and a strong adaptive ecological restoration system is constructed; the biological filtration layer, the adsorption layer and the degradation layer are constructed by using the ecological substrate and the modified material, thereby forming a hierarchical purification unit, and the pollutant interception and conversion efficiency is significantly improved; the water quantity distribution of the hierarchical purification unit is performed by using the hydraulic regulating device, the sewage retention time and the treatment working condition are adjusted in combination with the online monitoring data of the water quality, thereby realizing accurate regulation and control of the treatment process; the nitrogen and phosphorus removal rate, the COD degradation rate and the heavy metal adsorption rate of each purification unit are calculated, the target operation parameter is selected and the target treatment strategy is generated, thereby ensuring the continuous stability of the treatment effect. The overall scheme realizes the organic combination of the ecological restoration and the engineering measures, establishes an intelligent regulation and control system of water quality and water quantity coupling, and significantly improves the efficiency and reliability of the pollution treatment of the drawdown zone. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical scheme of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0015] Figure 1 An embodiment schematic diagram of the efficient intelligent treatment method for the pollution of the drawdown zone in the embodiments of the application;

[0016] Figure 2 An embodiment schematic diagram of the efficient intelligent treatment system for the pollution of the drawdown zone in the embodiments of the application. DETAILED DESCRIPTION

[0017] The embodiment of the present application provides a kind of for the efficient intelligent management method and system of pollution zone.The terms "first", "second", "third", "fourth" and the like (if exist) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" or "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0018] For ease of understanding, the specific process of the embodiment of the present application is described below, please refer to Figure 1 One embodiment of the efficient intelligent management method for pollution zone in the embodiment of the present application comprises:

[0019] Step S101, the water quality load of nitrogen and phosphorus concentration, suspended matter content and organic matter in the pollution zone is calculated by the SWAT model, and the pollutant migration rule data is obtained;

[0020] Step S102, the pollutant migration rule data is divided into high, medium and low monitoring areas according to water level gradient, and the dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll data of each area are measured by using multi-parameter water quality sensor;

[0021] Step S103, according to the water quality parameters of each monitoring area, combined with the analysis results of soil physical and chemical properties, the planting area and density ratio of vanilla, mulberry and emergent plants are determined;

[0022] Step S104, according to the determined plant ratio, the biological filtration layer, adsorption layer and degradation layer are constructed by using ecological substrate and modified material, and the hierarchical purification unit is formed;

[0023] Step S105, the water volume distribution of hierarchical purification unit is carried out by using hydraulic regulating device, and the sewage retention time and treatment condition are adjusted combined with water quality online monitoring data;

[0024] Step S106, based on long-term monitoring data of water quality, the nitrogen and phosphorus removal rate, COD degradation rate and heavy metal adsorption rate of each purification unit are calculated, target operation parameters are screened, and target treatment strategy is generated according to target operation parameters.

[0025] It can be understood that the execution subject of the present application can be an efficient intelligent management system for pollution of the drawdown zone, and can also be a terminal or a server, and the specific embodiments are not limited herein. The server is taken as an example for description of the embodiments of the present application.

[0026] Specifically, the SWAT model is a distributed watershed hydrological model used to simulate hydrological processes and pollutant migration and transformation processes in complex watershed systems. Through the model, the nitrogen and phosphorus concentrations, suspended solids content and organic matter in the drawdown zone are calculated to obtain pollutant migration rule 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 each sub-basin is calibrated by the measured data of the hydrological monitoring station. In the water quality load calculation process, different sections of water samples are collected for analysis of nitrogen and phosphorus concentrations, and total nitrogen and total phosphorus contents are measured; the suspended solids content is measured by gravity sedimentation method; and the organic matter content is measured by potassium dichromate oxidation-external heating method. After obtaining the pollutant migration rule data, it needs to be divided into high, medium and low monitoring areas according to the water level gradient. The high area is located above the normal water level and is mainly affected by rainfall; the medium area is located between the normal water level and the dead water level and is significantly affected by water level fluctuation; and the low area is located below the dead water level and is long-term submerged. 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 in each area. The multi-parameter water quality sensor contains multiple probes, and each probe corresponds to the measurement of one water quality index. The dissolved oxygen is measured by electrochemical method, the pH value is measured by glass electrode method, the conductivity is measured by conductivity electrode method, the turbidity is measured by scattering light method, and the ammonia nitrogen content is measured by ammonia gas sensitive electrode method.

[0027] Based on the obtained water quality parameter data and the analysis results of soil physicochemical properties, the planting area and density ratio of vetiver, mulberry and emergent plants are determined. The analysis of soil physicochemical 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, vetiver is mainly planted, which has developed root system to hold soil; in the middle-level drawdown zone, mulberry is planted, which has strong flooding resistance and developed root system; in the low-level drawdown zone, emergent plants are planted, and water-tolerant varieties are selected. The plant density ratio is determined according to the area, slope and pollution load of each region. After determining the plant ratio, a multi-level purification unit is constructed using ecological substrate and modified materials. The ecological substrate is mainly composed of porous materials such as sand, zeolite and volcanic rock, and the adsorption capacity of the substrate for pollutants is enhanced through modification treatment. The biological filtration layer is mainly composed of large-particle-size sand, which plays a role in intercepting suspended solids; the adsorption layer uses modified zeolite, which specifically adsorbs nitrogen and phosphorus pollutants; the degradation layer adds specific functional microbial flora to promote the degradation of organic matter.

[0028] When the hydraulic regulating device distributes water to the hierarchical purification unit, the hydraulic load of different levels needs to be adjusted according to the water quality and treatment target. By monitoring the water quality parameters online, including pH value, dissolved oxygen, conductivity and other indicators, the residence time of wastewater in each level unit is adjusted in real time. The adjustment of treatment conditions is based on factors such as water temperature, dissolved oxygen content, pollutant concentration, etc. Finally, based on the long-term accumulation of water quality monitoring data, the treatment effect of each purification unit is calculated. The nitrogen and phosphorus removal rates are calculated by the total nitrogen and total phosphorus concentrations in the influent and effluent; the COD degradation rate reflects the removal effect of organic matter; 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 treatment strategy.

[0029] For example, in a certain drawdown zone treatment project, the area of the region is about 50 hectares, and the water level fluctuates between 4-6 meters. First, the SWAT model is used to simulate the inflow water quantity, and combined with the measured water quality data, the pollutant migration law is calculated. The drawdown zone is divided into three monitoring areas according to 174-176 meters, 172-174 meters and 170-172 meters, and multi-parameter water quality sensors are installed for monitoring. The water quality monitoring results show that the ammonia nitrogen content is high in the high-level area, the dissolved oxygen is low in the middle-level area, and the suspended solids content is high in the low-level area. Based on the monitoring data, vetiver is planted in the high-level area with a density of 45 plants per square meter; mulberry is planted in the middle-level area with a plant 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 biological filtration layer is 40 cm thick, the adsorption layer is 30 cm thick, and the degradation layer is 30 cm thick. During operation, the residence time is controlled within 24-48 hours by the hydraulic regulating device. After half a year of operation, the nitrogen and phosphorus removal rates reach the expected target, the COD degradation rate is stable, and the heavy metals are effectively controlled.

[0030] In the embodiments of the present application, the water quality load of nitrogen and phosphorus concentration, suspended matter content and organic matter in the drawdown zone is calculated by the SWAT model to accurately obtain the pollutant migration rule data and provide a scientific basis for subsequent treatment; the pollutant migration rule data is divided into high, medium and low monitoring areas 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 using a multi-parameter water quality sensor to realize 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 planting area and density ratio of the roots of the plant, mulberry and emergent plants are scientifically determined to construct an adaptive ecological restoration system; the biological filtration layer, adsorption layer and degradation layer are constructed by using ecological substrate and modified materials to form a hierarchical purification unit, which significantly improves the interception and conversion efficiency of pollutants; the water quantity distribution of the hierarchical purification unit is adjusted by using the hydraulic regulating device, and the sewage retention time and treatment working condition are adjusted combined with the online monitoring data of water quality to realize accurate regulation and control of the treatment process; the target operation parameters are selected and the target treatment strategy is generated by calculating the nitrogen and phosphorus removal rate, COD degradation rate and heavy metal adsorption rate of each purification unit to ensure the continuous stability of the treatment effect. The overall scheme realizes the organic combination of ecological restoration and engineering measures, establishes an intelligent regulation and control system of water quality and quantity coupling, and significantly improves the efficiency and reliability of the pollution treatment in the drawdown zone.

[0031] In a specific embodiment, the process of performing step S101 can specifically include the following steps:

[0032] (1) Calculate the time period distribution of nitrogen and phosphorus concentration, suspended matter content and organic matter according to the water quality monitoring data to generate initial water quality load data;

[0033] (2) Perform partition calculation on the initial water quality load data according to 109 sub-basins to obtain partition water quality load data;

[0034] (3) Calculate the cumulative amount of nitrogen and phosphorus concentration, suspended matter content and organic matter in the partition water quality load data to form pollutant cumulative flux;

[0035] (4) Calculate the concentration distribution of each sub-basin in the drawdown zone by using the pollutant cumulative flux to obtain the pollutant migration trend;

[0036] (5) Correlate and analyze the pollutant migration trend and hydraulic retention time to output the pollutant migration rule data.

[0037] Specifically, the nitrogen and phosphorus concentrations are obtained by chemical analysis of water samples. Total nitrogen is determined by ultraviolet spectrophotometry with alkaline potassium persulfate digestion, and total phosphorus is determined by spectrophotometry with ammonium molybdate. The suspended solids content is calculated by filtering the water sample through a 0.45 micron filter, drying and weighing. The organic matter content is determined by potassium dichromate oxidation, and is converted by the amount of potassium dichromate consumed. These water quality indicators are correlated with the change in reservoir inflow data to generate initial water quality load data.

[0038] To improve the calculation accuracy, the initial water quality load data is calculated in 109 sub-basins. The division of sub-basins is based on topographic features, catchment area and hydrological connectivity, and each sub-basin has relatively independent hydrological characteristics. For each sub-basin, collect cross-section water quality monitoring data, and calculate pollutant flux combined with flow data to form sub-basin water quality load data. This sub-basin calculation method can reflect the pollution characteristics and water quality variation of different regions. When calculating the cumulative amount of each index in the sub-basin water quality load data, the hydraulic connectivity and the transformation law of pollutants in the transmission process need to be considered. Nitrogen and phosphorus will undergo adsorption, desorption, sedimentation and other effects during transmission, suspended solids will settle and resuspend with water flow, and organic matter will undergo degradation and transformation. By establishing a pollutant mass balance equation, the cumulative flux of each index is calculated. The formula for calculating the cumulative flux of pollutants is: In the formula: C represents the cumulative flux of pollutants (kg / d); Q represents the flow of the i-th sub-basin (m³ / d); C represents the pollutant concentration in the i-th sub-basin (mg / L); K represents the pollutant transmission attenuation coefficient (dimensionless); H represents the hydraulic connectivity coefficient (dimensionless); T represents the temperature correction coefficient (dimensionless); n represents the total number of sub-basins.

[0039] Based on the calculated cumulative flux of pollutants, the distribution of pollutant concentration in each sub-basin is further analyzed. The migration of pollutants in the water-storage area is affected by many factors such as terrain, water flow, vegetation, etc., and these factors need to be considered comprehensively. Combined with measured data and theoretical calculations, the spatial distribution characteristics and temporal variation trend 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 hydraulic retention time and pollutant degradation and transformation, the pollutant migration law under different hydraulic conditions is evaluated, and the complete pollutant migration law data is finally output.

[0040] For example, in water quality sampling analysis, water samples are collected for 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 inflow water quantity and water quality indicators, especially during rainfall, the suspended solids content increases with the increase of inflow water quantity. After the monitoring data is statistically divided into 109 sub-basins, the pollution characteristics of different regions are clearly reflected. For example, in sub-basins with better vegetation coverage, the suspended solids content is relatively low, while in sub-basins with larger slope, the pollutant input is larger during rainfall. 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 law of pollutants in the water fluctuation zone is determined.

[0041] In a specific embodiment, the process of performing step S102 can specifically include the following steps:

[0042] (1) Layered analysis is performed on the water level elevation data in the pollutant migration law data to generate the boundaries of the monitoring regions of high, medium and low water level gradients;

[0043] (2) Data collection is performed by the multi-parameter water quality sensor at the sampling points in the high, medium and low monitoring regions to obtain the initial monitoring values of dissolved oxygen;

[0044] (3) Temperature correction and air pressure compensation are performed on the initial monitoring values of dissolved oxygen to obtain the measured data of dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll;

[0045] (4) The measured data is classified and summarized according to the monitoring regions to form the time series table of water quality parameters of each monitoring region;

[0046] (5) The water quality change law under different water level conditions is calculated according to the time series table of water quality parameters to generate the water quality change curve of the monitoring region;

[0047] (6) The pollutant concentration distribution is analyzed using the water quality change curve of the monitoring region, and the monitoring results of dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll data are outputted.

[0048] Specifically, the key step is to analyze the water level data in the pollutant migration data. The water level data is obtained from the continuous record of water level monitoring station, by measuring the change of water level with time, combined with topographic survey data, to determine the vertical distribution range of the drawdown zone. The water level data is divided into three layers according to the normal water level, average water level and dead water level, forming three water level gradient monitoring areas of high, medium and low. The high area is above the normal water level, mainly affected by seasonal rainfall; the medium area is between the normal water level and the dead water level, with frequent water level fluctuations; the low area is below the dead water level, in a long-term submerged state. In the three monitoring areas, multi-parameter water quality sensors are laid out for data collection. Multi-parameter water quality sensor is an integrated monitoring device, which includes dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen and chlorophyll, etc. The sensor is laid out at the representative position of each monitoring area, and the selection of sampling points should consider the water flow characteristics, pollution source distribution and topographic features. 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 the dissolved oxygen concentration value is obtained after signal conversion.

[0049] The processing of the initial monitoring value of dissolved oxygen involves two important aspects of temperature correction and pressure compensation. Temperature correction is because the solubility of dissolved oxygen is significantly affected by temperature, and the increase of temperature will lead to the decrease of dissolved oxygen saturation value. The sensor has a built-in temperature compensation module, which automatically corrects according to the measured water temperature. The pressure compensation considers the influence of atmospheric pressure on the solubility of dissolved oxygen, and the real-time pressure value measured by the pressure sensor is used for correction. At the same time, other water quality parameters also need corresponding data processing: pH value is calibrated regularly with double buffer solution, conductivity needs temperature compensation, 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 need to establish a complete database structure. First, classify the data according to the monitoring area, and the data of each area includes sampling time, sampling point position and corresponding water quality parameter value. The construction of water quality parameter time series table needs to consider sampling frequency, data quality control and outlier processing. The sampling frequency is set according to the monitoring purpose, generally once an hour. Data quality control includes data validity test, continuity test and reasonableness test, and the obviously wrong data points are removed.

[0050] According to the formed water quality parameter time sequence table, the water quality change law under different water level conditions is analyzed. The time sequence data is processed by statistical method, including trend analysis, periodicity analysis and correlation analysis. In the data processing process, the influence factors such as hydrological and meteorological conditions and seasonal change are considered, and the corresponding relationship between water quality parameters and water level change is established. The water quality change curve of the monitoring area reflects the change characteristics of various indicators with time and water level, which helps to understand the migration and transformation law of pollutants and the growth dynamics of algae in the drawdown zone. Finally, the water quality change curve of the monitoring area is used for pollutant concentration and chlorophyll distribution analysis. Through spatial interpolation method, the discrete monitoring point data is extended to the whole monitoring area, forming the spatial distribution characteristics of pollutant concentration and algae biomass. The spatio-temporal variation law of various indicators is comprehensively analyzed, and the complete monitoring result report is output.

[0051] For example, according to the water level monitoring data, the monitoring area is divided into high zone (175-177 meters), middle zone (172-175 meters) and low zone (170-172 meters). Four monitoring points are arranged in each area, and multi-parameter water quality sensors are installed for continuous monitoring. Through temperature correction and pressure compensation, accurate water quality parameter values are obtained. The monitoring data shows that there are significant differences in water quality characteristics in different water level gradient areas: the dissolved oxygen content in the high zone is high but fluctuates greatly, the water quality parameters in the middle zone are relatively stable, and the low zone shows obvious seasonal changes. The chlorophyll content in different regions also shows unique distribution characteristics: the high zone is affected by sufficient light, and the chlorophyll concentration changes significantly with water level fluctuations; the chlorophyll content in the middle zone is relatively stable and mainly affected by water temperature; the chlorophyll concentration in the low zone is generally low, but local algae proliferation occurs in summer. Through analysis of one year's continuous monitoring data, the internal relationship between water quality parameters, chlorophyll content and water level change is revealed.

[0052] In a specific embodiment, the process of performing step S103 can specifically include the following steps:

[0053] (1) The water quality parameters are divided into dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll data for zonal statistics, and a water quality spatial distribution map is generated;

[0054] (2) The soil samples in each monitoring area are analyzed for organic matter content, pH value and nitrogen, phosphorus and potassium content to obtain the soil physical and chemical property analysis results;

[0055] (3) The water quality spatial distribution map and the soil physical and chemical property analysis results are superimposed and operated to form a plant planting suitability evaluation table;

[0056] (4) The high drawdown zone data in the plant planting suitability evaluation table are analyzed to determine the planting area of vetiver grass;

[0057] (5) Based on the median drawdown zone data in the plant planting suitability evaluation table, the optimal planting location of mulberry is screened;

[0058] (6) According to the low 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.

[0059] Specifically, the water quality parameters are statistically partitioned. The water quality parameters include six key indicators of dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll. Dissolved oxygen reflects the oxygen content of water body, which is monitored in real time by electrochemical probe; pH value represents the acidity and alkalinity of water body, which is determined by glass electrode method; conductivity reflects the total amount of ions in water, which is measured by conductivity electrode; turbidity represents the transparency of water body, which is determined by scattering light method; ammonia nitrogen content is determined by Nessler's reagent colorimetry; and chlorophyll content is determined by fluorescence method, which reflects the biomass of phytoplankton in water body. The measured values of these indicators are imported into geographic information processing software, and the spatial distribution map of water quality is generated by Kriging interpolation method. The spatial distribution map directly shows the spatial variation of each water quality indicator. The analysis of physical and chemical properties of soil samples is an important basis for determining the plant planting area. Surface soil samples are collected in each monitoring area, with a sampling depth of 0-20 cm, and the samples are ensured to be representative by quartering method. Soil organic matter content is determined by potassium dichromate oxidation-external heating method; soil pH value is determined by potential method; total nitrogen content is determined by Kjeldahl method; total phosphorus content is determined by molybdenum antimony colorimetric method; and total potassium content is determined by flame photometry. The determination results are arranged according to the sample position to form a complete analysis result of soil physical and chemical properties. The superposition operation of water quality spatial distribution map and soil physical and chemical property analysis result adopts multi-layer data superposition analysis method. First, an evaluation index system is established, including water quality index weight and soil index weight. Water quality indicators mainly consider the influence of dissolved oxygen, pH value and chlorophyll on plant growth, and soil indicators mainly focus on organic matter content and nutrient status. Chlorophyll content, as an important indicator of water eutrophication degree, has important guiding significance for plant configuration scheme. Through weighted superposition calculation, the plant planting suitability evaluation table reflecting the comprehensive quality of the environment is obtained. For the analysis of high drawdown zone data, the factors of soil moisture content change, nutrient status and slope are mainly investigated. Vetiver is a good water and soil conservation plant with developed root system and strong environmental adaptability. According to the scoring results in the plant planting suitability evaluation table, the areas with higher scores and suitable topographic conditions are preferentially selected as the planting areas of vetiver. The planting method of vetiver is strip planting, and the row spacing is generally 0.5 meters.

[0060] The analysis of the median drawdown zone data focuses on the matching degree of soil physical and chemical properties and water conditions. Mulberry, as an economic tree species that can tolerate waterlogging, needs to be planted in areas with good soil fertility and moderate drainage conditions. Through comprehensive analysis of each index in the plant planting suitability evaluation table, the most suitable location for mulberry growth is determined. The planting distance of mulberry needs to consider the canopy width of adult trees, which is generally controlled at 2-3 meters. The plant configuration in the low drawdown zone mainly considers the flooding tolerance and anchoring ability of emergent plants. According to the parameters of the plant planting suitability evaluation table, suitable emergent plant species such as reed and cattail are selected. The calculation of planting density needs to consider the growth characteristics of plants and the water purification demand, and the optimal planting density is determined through the relationship between unit area biomass and purification efficiency.

[0061] For example, through analysis of the monitoring area, it is found that the soil pH value in the high 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 grass; the soil water content in the median drawdown zone is high, and the nitrogen and phosphorus content is rich, so a water-tolerant mulberry variety is selected for planting; the low drawdown zone is mainly configured with reed. The chlorophyll monitoring results show that the chlorophyll content in the low drawdown zone is relatively high, with an average of 42 μg / L, indicating that there is a certain degree of eutrophication in this area, and the overgrowth of algae needs to be controlled through the planting of emergent plants. The specific planting scheme is: vetiver grass planting density 45 plants per square meter in the high drawdown zone, mulberry plant spacing 2.5 meters in the median drawdown zone, and reed planting density 8 plants per square meter in the low drawdown zone. Through field verification, the plants grow well and play a significant role in water quality purification and ecological restoration of the drawdown zone, and the chlorophyll content is significantly reduced, effectively controlling the degree of water eutrophication.

[0062] In a specific embodiment, the process of performing step S104 can specifically include the following steps:

[0063] (1) Import the vetiver grass root growth data in the plant ratio into the biological filtration layer design parameters to determine the void ratio and permeability coefficient of the biological filtration layer;

[0064] (2) Select the ecological substrate formula from the void ratio and permeability coefficient of the biological filtration layer, and prepare the porous ecological substrate through particle size distribution analysis;

[0065] (3) Perform surface modification treatment on the porous ecological substrate, add mulberry root exudates, and construct an adsorption layer with biological activity;

[0066] (4) Mix and match the adsorption layer with the emergent plant root system to form a degradation layer with microbial attachment function;

[0067] (5) Based on the biological filtration layer, superimpose the adsorption layer and the degradation layer to construct a three-dimensional ecological substrate body;

[0068] (6) The three-dimensional ecological matrix body is subjected to sewage interception performance verification to obtain a hierarchical purification unit.

[0069] Specifically, the acquisition of the root growth data of the vetiver is the basis for constructing the biological filtration layer. The vetiver roots grow vertically, and the root density decreases exponentially with depth. The root length density, root distribution depth, and root growth rate are obtained through the root growth amount determination. These growth data directly affect the design of the biological filtration layer. The spatial distribution characteristics of the root system determine the physical structure of the filtration layer. The void ratio of the biological filtration layer is calculated by the ratio of the root occupied space volume to the total volume. The permeability coefficient is determined by the variable head permeability test, which reflects the water transport characteristics in the filtration layer. Based on the determined void ratio and permeability coefficient, an appropriate ecological matrix formula is selected. The ecological matrix is mainly composed of sand, gravel, zeolite, volcanic rock and other materials. Different particle sizes of the matrix particles are obtained by sieving. The particle size distribution analysis adopts the standard sieving method. The matrix materials with particle sizes of 2-4 mm, 1-2 mm, and 0.5-1 mm are selected for proportioning. The preparation process of the porous ecological matrix includes material pretreatment, mixing and stirring, and curing. The pretreatment includes cleaning and drying. During mixing, it is necessary to ensure uniform distribution of materials of different particle sizes.

[0070] The surface modification treatment of the ecological matrix aims to improve its adsorption capacity for pollutants. First, the roots of mulberry trees are collected, and root exudates are extracted, mainly including organic acids, amino acids, and polysaccharide substances. The extracted root exudates are combined with the ecological matrix by immersion method, and the immersion time and temperature conditions are controlled. The modified matrix surface forms a biofilm with strong adsorption activity, which can effectively remove nitrogen and phosphorus nutrients in water. The construction of the degradation layer combines the adsorption layer with the emergent plant root tissue. The emergent plant root tissue has developed aeration tissue, providing a good attachment carrier for microorganisms. After crushing the root tissue, it is mixed with the modified matrix, and a composite microbial agent is added during the mixing process to cultivate a stable microbial community. This mixed structure not only maintains the adsorption function but also can degrade pollutants through the metabolic action of microorganisms.

[0071] The construction of the three-dimensional ecological matrix body 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 adsorbing dissolved pollutants; and the top is the degradation layer, which performs biological degradation of pollutants. The transition zone connects each layer to ensure hydraulic conductivity. The entire matrix body has a porous three-dimensional structure, which is beneficial to the uniform distribution of water flow and the full contact of pollutants. The performance verification of the hierarchical purification unit includes hydraulic property testing and pollutant removal effect evaluation. The hydraulic property testing mainly tests the permeability and hydraulic retention time, and the tracer method is used to determine the actual hydraulic retention time distribution. The pollutant removal effect evaluation is determined by measuring the water quality parameters of the inlet and outlet water, including COD, ammonia nitrogen, total nitrogen, total phosphorus, and other indicators.

[0072] For example, the particle size of the ecological substrate is 2-4 mm, the porosity is controlled at about 40%, and the permeability coefficient is about 10-3cm / s. The addition of mulberry root exudates significantly improves the ammonia nitrogen adsorption capacity of the substrate. The mixing ratio of emergent plant roots to modified substrate 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 substrate body is 80 cm, including a biological filtration layer of 30 cm, an adsorption layer of 25 cm, and a degradation layer of 25 cm. The operation results show that the multi-level purification unit has a significant removal effect on COD, ammonia nitrogen, and total nitrogen, and the treated effluent meets the standard stably. This multi-level ecological purification structure realizes the synergistic effect of physical interception, chemical adsorption, and biological degradation.

[0073] In a specific embodiment, the process of performing step S105 can specifically include the following steps:

[0074] (1) Collect the inlet water quantity 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;

[0075] (2) Perform dynamic calculation on the hydraulic load distribution to obtain the hydraulic retention time of each layer of the hierarchical purification unit;

[0076] (3) Correct the hydraulic retention time based on the online water quality monitoring data to generate sewage flow adjustment parameters;

[0077] (4) Based on the sewage flow adjustment parameters, adjust the opening of the hydraulic adjustment device to form a real-time water distribution scheme;

[0078] (5) Import the real-time water distribution scheme into the automatic control program to output the influent flow of each treatment unit;

[0079] (6) Online check the influent flow, adjust the sewage retention time and treatment conditions in combination with the online water quality monitoring data.

[0080] Specifically, the inlet water quantity 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 water inflow. 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, the adsorption layer and the degradation layer is obtained by monitoring the pressure sensor installed in each layer, which measures the water head loss and reflects the actual hydraulic load state of each layer. The hydraulic load distribution data is counted by hour to form 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, the void fraction and medium characteristics are considered; for the adsorption layer, the specific surface area and pore distribution of the adsorbent material need to be considered; and for the degradation layer, the spatial structure of the microbial carrier is mainly focused on. The calculation of the hydraulic retention time combines the characteristic parameters of each layer to ensure that the sewage is fully treated.

[0081] The water quality online monitoring data are used for dynamic correction of the hydraulic retention time. The online monitoring equipment includes a pH meter, a dissolved oxygen meter, a conductivity meter and the like, which can monitor the changes of various water quality indexes in real time. When the effluent water quality cannot meet the treatment requirements, the treatment effect needs to be improved by adjusting the hydraulic retention time. The correlation analysis of the water quality data and the hydraulic retention time generates specific flow adjustment parameters. The calculation formula of the adjustment parameter is: In the formula, is the flow adjustment parameter (dimensionless); is the hydraulic load weight of the i-th layer; is the water quality index deviation coefficient; is the temperature correction coefficient; is the biological activity coefficient; is the seasonal variation coefficient; i represents the layer (1-biological filtration layer, 2-adsorption layer, 3-degradation layer).

[0082] According to the flow adjustment parameter, the precise distribution of water quantity is realized through the hydraulic adjustment device. The hydraulic adjustment device mainly includes an electric valve and a flow controller, which can automatically adjust the opening degree according to the calculation results. The real-time water quantity distribution scheme considers multiple factors such as treatment load, effluent requirements and energy consumption optimization. The real-time water quantity distribution scheme realizes automatic operation through data acquisition and control program. The program receives real-time data of each measuring point, including flow, water level, water quality and other parameters, and outputs control signals according to the preset control logic. The water inflow of each treatment unit is realized through a shunt device to ensure the uniformity and stability of water quantity distribution.

[0083] The online verification of the influent flow is an important link to ensure the treatment effect. By comparing the measured flow with the set flow, the deviation can be found and corrected in time. Combined with online water quality monitoring data, the treatment conditions are dynamically adjusted. The condition adjustment includes the fine adjustment of the hydraulic retention time and the optimization of the flow distribution ratio. For example, the treatment scale of this project is 1000 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, that of the adsorption layer is 3.2 cubic meters per square meter per day, and that of 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 too high, the flow adjustment parameter is calculated to be 0.85, and accordingly the hydraulic retention time of the degradation layer is extended from 4 hours to 4.7 hours. By adjusting the opening degree of the electric valve, the accurate distribution of water quantity is realized. After adjustment, the treatment effluent indicators are stable and meet the standards, which confirms the effectiveness of the control method. The whole process realizes the automatic control of hydraulic regulation, and ensures the stable operation of the treatment process.

[0084] In a specific embodiment, the process of performing step S106 can specifically include the following steps:

[0085] (1) The long-term monitoring data of water quality is segmented and counted according to the biological filtration layer, the adsorption layer and the degradation layer, to obtain the water quality indicators of each layer;

[0086] (2) The water quality indicators of each layer are subjected to difference operation to calculate the nitrogen and phosphorus removal rates of each purification unit;

[0087] (3) The concentration attenuation analysis is performed on the water quality indicators of each layer to obtain the COD degradation rate;

[0088] (4) The ion concentration determination is performed on the water quality indicators of each layer to obtain the heavy metal adsorption rate;

[0089] (5) The nitrogen and phosphorus removal rates, the COD degradation rate and the heavy metal adsorption rate are comprehensively evaluated to screen the target operating parameters;

[0090] (6) The target treatment strategy is output by taking the target operating parameters as the benchmark for optimization analysis of the treatment unit performance.

[0091] Specifically, for the biological filtration layer, adsorption layer and degradation layer, water sampling points are set up respectively, and water samples are collected regularly by using standard monitoring methods. The water quality indicators of the influent include total nitrogen, total phosphorus, COD, heavy metals and other parameters, and the water quality indicators of the effluent 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 water quality indicators of each layer is the key step to calculate the purification effect. For the calculation of nitrogen and phosphorus removal rate, the total nitrogen and total phosphorus concentrations in the influent and effluent need to be determined. The total nitrogen is determined by the alkaline potassium persulfate digestion ultraviolet spectrophotometric method, and the total phosphorus is determined by the molybdenum antimony spectrophotometric method. The calculation of nitrogen and phosphorus removal rate takes into account the water balance and concentration change, reflecting the actual treatment effect.

[0092] The concentration decay analysis is mainly aimed at the removal effect of COD. As a comprehensive indicator representing the content of organic matter, the degradation process of COD is influenced by multiple factors. The COD concentration is determined by potassium dichromate method, and the decay law of COD is analyzed by continuous monitoring data. In the degradation process, the influence of environmental factors such as temperature and pH value on the degradation rate needs to be considered, and the change of microbial activity also needs to be paid attention to. The determination of heavy metal adsorption rate uses atomic absorption spectrophotometry. For different heavy metal ions, the corresponding determination method is selected: flame atomic absorption method for copper, zinc and lead, and graphite furnace atomic absorption method for cadmium and arsenic. By comparing the concentrations of heavy metal ions in the influent and effluent, combined with the water quantity data, the actual adsorption capacity of each layer for heavy metals is calculated.

[0093] When evaluating the removal rate data of each indicator, a scientific evaluation system needs to be established. The evaluation indicators include 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 target operating parameters, including hydraulic load, residence time, pH value control range, etc. Finally, based on the target operating parameters, the performance of the treatment unit is optimized. The optimization content includes process parameter adjustment, operation mode optimization and maintenance management measures, etc. Through multiple rounds of test verification, the best combination of operating conditions is determined, and a complete target treatment strategy is formed.

[0094] For example, through the analysis of three months of operation data, it is found that the biological filtration layer has a significant removal effect on suspended solids, and the effluent turbidity is stable; the adsorption layer performs well in nitrogen and phosphorus removal, but the adsorption effect decreases with the extension of operation time; the degradation layer has outstanding effect in organic matter degradation, and the COD removal rate is stable. After comprehensive evaluation, the optimal combination of operating parameters is determined: the hydraulic load of the biological filtration layer is 4.5 cubic meters per square meter per day, the pH value of the adsorption layer is controlled at 7.2-7.8, and the dissolved oxygen of the degradation layer is maintained above 3 mg / L. The determination of these parameters provides an important basis for subsequent operation optimization.

[0095] In a specific embodiment, the process of performing concentration decay analysis by the water quality indicators of each layer can specifically include the following steps:

[0096] (1) The COD concentration values in the water quality indicators of each layer are arranged in time sequence to form a COD concentration change curve;

[0097] (2) The COD concentration change curve is segmented and intercepted according to the biological filtration layer, the adsorption layer and the degradation layer to obtain layered COD decay data;

[0098] (3) The layered COD decay data is temperature corrected and hydraulic retention time corrected to generate standardized COD decay values;

[0099] (4) The COD reduction amount per unit time is calculated based on the standardized COD decay values, and the instantaneous COD degradation rate is output;

[0100] (5) The instantaneous COD degradation rate is weighted and averaged according to the hydraulic load to form a steady-state degradation coefficient;

[0101] (6) The steady-state degradation coefficient is cumulatively operated to obtain the COD degradation rate.

[0102] Specifically, the water quality long-term monitoring data is segmented and counted according to the biological filtration layer, the adsorption layer and the degradation layer. For each layer, the water quality data of the influent and effluent is collected by an online monitoring device, the sampling frequency is every 2 hours, and the continuous monitoring is not less than 90 days. The water quality indicators include total nitrogen, total phosphorus, COD, heavy metals and other parameters, an automatic sampler and an online analyzer are set at each sampling point to ensure the continuity and reliability of the data. For the difference operation of the water quality indicators of each layer, the following calculation formula is used: In the formula: 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 coefficient; is the hydraulic load correction coefficient; m is the number of pollutant types.

[0103] After formula calculation, the core indicators such as nitrogen and phosphorus removal rate, COD degradation rate and heavy metal adsorption rate are obtained. The total nitrogen is monitored by ultraviolet spectrophotometry, the total phosphorus is monitored by ammonium molybdate spectrophotometry, the COD is monitored by potassium dichromate method, and the heavy metal is monitored by atomic absorption spectrophotometry. The concentration decay analysis considers the influence of environmental factors such as water temperature, pH value and dissolved oxygen.

[0104] For example, through long-term monitoring, it is 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 outstanding performance in removing nitrogen and phosphorus, and needs to be regenerated as the running time increases; the degradation layer has good effect on organic matter degradation and shows stable treatment capacity. The optimal operating parameters determined through comprehensive evaluation include hydraulic retention time, pH range, dissolved oxygen concentration, and other key indicators.

[0105] In a specific embodiment, the process of performing the optimization analysis step of the performance of the treatment unit based on the target operating parameters can specifically include the following steps:

[0106] (1) Import the target operating parameters into the treatment unit performance evaluation matrix to generate a unit operation score table;

[0107] (2) Score the unit operation score table according to the nitrogen and phosphorus removal rate, COD degradation rate, and heavy metal adsorption rate to obtain performance evaluation indicators;

[0108] (3) Calculate the pollutant removal contribution rate of each treatment unit based on the performance evaluation indicators to form a unit treatment capacity table;

[0109] (4) Cross-verify the unit treatment capacity table with the pollutant migration rule data to output treatment efficiency optimization suggestions;

[0110] (5) Perform feasibility analysis and cost accounting on the treatment efficiency optimization suggestions to generate an improvement plan list;

[0111] (6) Determine the optimal operating condition based on the improvement plan list and output the target treatment strategy.

[0112] Specifically, a treatment unit performance evaluation matrix is established. The matrix includes multiple evaluation dimensions such as hydraulic load, retention time, inlet and outlet water quality, and operating cost. 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 table is generated. The itemized scoring of the unit operation score table involves quantitative evaluation of pollutant removal effect. For nitrogen and phosphorus removal rate, the removal effect of total nitrogen and total phosphorus is mainly investigated; COD degradation rate reflects the removal degree of organic matter; and heavy metal adsorption rate reflects the treatment capacity of heavy metal pollutants. Each indicator is scored according to the actual removal effect, and the scoring standard is based on the technical specification requirements of the relevant treatment process. The scoring results form a detailed performance evaluation indicator system.

[0113] Based on the performance evaluation index, 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 contributes significantly to the removal of nitrogen, phosphorus, and heavy metals; and the degradation layer is mainly responsible for the degradation and conversion of organic matter. By counting the removal amount of different pollutants by each unit, the contribution proportion of each unit to the overall treatment effect is calculated to form the unit treatment capacity table. Cross verification of the unit treatment capacity table and the pollutant migration rule data is the key step to optimize the treatment efficiency. By comparing the two sets of data, the improvement space and constraints of the treatment efficiency are found. Through in-depth analysis of the problems, combined with the process characteristics and operation experience, targeted optimization suggestions are put forward. The optimization suggestions cover process parameter adjustment, operation mode improvement, and equipment maintenance, etc.

[0114] The feasibility analysis is carried out from the technical, economic and management dimensions to evaluate the implementation difficulty and expected effect of the optimization measures. The cost accounting includes equipment modification cost, incremental operation cost and maintenance cost change, etc. Through comprehensive analysis, the improvement scheme list is formed by selecting the technically feasible and economically reasonable improvement schemes. Finally, the optimal operation condition is determined based on the improvement scheme list. The operation condition includes specific indicators such as hydraulic load, residence time, operation parameter, etc. By comparing and analyzing the comprehensive benefits of different schemes, the optimal operation condition combination is selected to form the target governance strategy.

[0115] For example, through the performance evaluation of the three treatment units, it is found that the biological filtration layer performs well in suspended solids removal, but the backwashing frequency needs to be optimized; the removal effect of nitrogen and phosphorus by the adsorption layer gradually decreases with the running time, and the regeneration period of the adsorption material needs to be considered; the degradation layer has stable effect in organic matter degradation, but its performance decreases in low temperature season. Based on these findings, improvement schemes including backwashing optimization, adsorption material regeneration and temperature regulation are developed. Economic analysis shows that the input-output ratio of these improvement measures is reasonable, and they have implementation value. The final determined operation condition includes: the backwashing period is adjusted to 48 hours once, the adsorption material regeneration period is 3 months, and the hydraulic residence time is adjusted to ensure the treatment effect in low temperature season. The implementation of this optimization scheme significantly improves the overall treatment effect.

[0116] The above describes the high-efficiency intelligent governance method for pollution in the drawdown zone in the embodiments of the present application, and the following describes the high-efficiency intelligent governance system for pollution in the drawdown zone in the embodiments of the present application. Please refer to Figure 2 An embodiment of the high-efficiency intelligent governance system for pollution in the drawdown zone in the embodiments of the present application includes:

[0117] The calculation module 201 is configured to calculate the water quality load of the nitrogen and phosphorus concentrations, the suspended solids content, and the organic matter in the drawdown zone by using the SWAT model, and obtain the pollutant migration rule data.

[0118] The division module 202 is configured to divide the pollutant migration law data into high, medium and low monitoring areas 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.

[0119] The analysis module 203 is configured to determine the planting area and density ratio of the vetiver grass, mulberry and emergent plants according to the water quality parameters of each monitoring area and the analysis results of the soil physicochemical properties.

[0120] The generation module 204 is configured to construct a biological filtration layer, an adsorption layer and a degradation layer using ecological substrates and modified materials according to the determined plant ratio, to form a hierarchical purification unit.

[0121] The detection module 205 is configured to use a hydraulic regulating device to distribute water to the hierarchical purification unit, and adjust the sewage retention time and treatment conditions in combination with the online water quality monitoring data.

[0122] The screening module 206 is configured 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 target operating parameters, and generate a target treatment strategy according to the target operating parameters.

[0123] Through the cooperation of the above components, the water quality load of nitrogen and phosphorus concentration, suspended solids content and organic matter in the drawdown zone is calculated by the SWAT model, the pollutant migration law data is accurately obtained, and a scientific basis is provided for subsequent treatment; the pollutant migration law data is divided into high, medium and low monitoring areas 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, realizing dynamic monitoring of water quality changes under different water level conditions; the planting area and density ratio of the vetiver grass, mulberry and emergent plants are scientifically determined according to the water quality parameters of each monitoring area and the analysis results of the soil physicochemical properties, and a strong adaptive ecological restoration system is constructed; a biological filtration layer, an adsorption layer and a degradation layer are constructed using ecological substrates and modified materials to form a hierarchical purification unit, significantly improving the interception and conversion efficiency of pollutants; the hydraulic regulating device is used to distribute water to the hierarchical purification unit, and the sewage retention time and treatment conditions are adjusted in combination with the online water quality monitoring data, realizing precise regulation and control of the treatment process; the target operating parameters are screened and the target treatment strategy is generated by calculating the nitrogen and phosphorus removal rate, COD degradation rate and heavy metal adsorption rate of each purification unit, ensuring the sustained and stable treatment effect. The overall scheme realizes the organic combination of ecological restoration and engineering measures, establishes an intelligent regulation and control system of water quality and quantity coupling, and significantly improves the efficiency and reliability of the pollution treatment in the drawdown zone.

[0124] The above-described embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A high-efficiency intelligent management method for pollution of a hydro-fluctuation belt, characterized in that, The efficient intelligent governance method for the pollution of the drawdown zone comprises: The water quality load calculation of nitrogen and phosphorus concentrations, suspended matter content and organic matter in the drawdown zone is performed by using the SWAT model to obtain pollutant migration rule data; The pollutant migration rule data is divided into high, medium and low monitoring areas 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; According to the water quality parameters of each monitoring area and the analysis results of the soil physical and chemical properties, the planting area and density ratio of the plantain grass, mulberry and emergent plants are determined; According to the determined plant ratio, an ecological substrate and a modified material are used to construct a biological filtration layer, an adsorption layer and a degradation layer to form a hierarchical purification unit; The water quantity distribution of the hierarchical purification unit is adjusted by using a hydraulic regulating device, and the sewage retention time and treatment working condition are adjusted in combination with the online monitoring data of water quality; Based on the long-term monitoring data of water quality, the nitrogen and phosphorus removal rates, COD degradation rate and heavy metal adsorption rate of each purification unit are calculated, the target operating parameters are screened, and the target governance strategy is generated according to the target operating parameters.

2. The efficient intelligent management method for pollution of the hydro-fluctuation belt according to claim 1, characterized in that, The water quality load calculation of nitrogen and phosphorus concentrations, suspended matter content and organic matter in the drawdown zone is performed by using the SWAT model to obtain pollutant migration rule data, comprising: The time period distribution of nitrogen and phosphorus concentrations, suspended matter content and organic matter is calculated according to the water quality monitoring data to generate initial water quality load data; The initial water quality load data is calculated by partitioning according to 109 sub-basins to obtain partitioned water quality load data; The cumulative amount of nitrogen and phosphorus concentrations, suspended matter content and organic matter in the partitioned water quality load data is calculated to form pollutant cumulative flux; The concentration distribution of each sub-basin in the drawdown zone is calculated by using the pollutant cumulative flux to obtain the pollutant migration trend; The pollutant migration trend is associated with the hydraulic retention time for analysis to output the pollutant migration rule data.

3. The efficient intelligent management method for pollution of the hydro-fluctuation belt according to claim 1, characterized in that, The pollutant migration rule data is divided into high, medium and low monitoring areas 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, comprising: The water level elevation data in the pollutant migration rule data is analyzed by layer to generate the monitoring area boundaries of the high, medium and low water level gradients; The dissolved oxygen initial monitoring value is obtained by data acquisition at the sampling points in the high, medium and low monitoring areas through the multi-parameter water quality sensor; The measured data of dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll are obtained by temperature correction and pressure compensation of the dissolved oxygen initial monitoring value; The measured data is classified and summarized according to the monitoring area to form the water quality parameter time sequence table of each monitoring area; The water quality and algal growth change rule under different water level conditions is calculated according to the water quality parameter time sequence table to generate the monitoring area water quality change curve; The dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll monitoring results are output by using the monitoring area water quality change curve to analyze the pollutant concentration and algal biomass distribution.

4. The efficient intelligent management method for pollution of the hydro-fluctuation belt according to claim 1, characterized in that, The water quality parameters are classified according to dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll, and a water quality spatial distribution map is generated. The soil samples in each monitoring area are analyzed for organic matter content, pH value and nitrogen, phosphorus and potassium content to obtain the soil physicochemical property analysis results. The water quality spatial distribution map and the soil physicochemical property analysis results are superimposed to form a plant planting suitability evaluation table. The high-elevation drawdown zone data in the plant planting suitability evaluation table are analyzed to determine the planting area of the vanilla plant. Based on the medium-elevation drawdown zone data in the plant planting suitability evaluation table, the optimal planting position of the mulberry tree is selected. According to the low-elevation drawdown zone parameters of the plant planting suitability evaluation table, the planting density of the emergent plant is calculated, and the planting area and density ratio of vanilla, mulberry and emergent plant are output. The plant ratio is determined, and an ecological substrate and modified material are used to construct a biological filtration layer, an adsorption layer and a degradation layer to form a hierarchical purification unit, including:

5. The efficient intelligent management method for pollution of the hydro-fluctuation belt according to claim 1, characterized in that, The vanilla root growth data in the plant ratio are imported into the biological filtration layer design parameters to determine the void ratio and permeability coefficient of the biological filtration layer. The void ratio and permeability coefficient of the biological filtration layer are selected to prepare a porous ecological substrate through particle size distribution analysis. The porous ecological substrate is subjected to surface modification treatment, and mulberry root exudates are added to construct a biologically active adsorption layer. The adsorption layer and emergent plant root tissue are mixed and matched 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 substrate body. The three-dimensional ecological substrate body is subjected to pollution interception performance verification to obtain a hierarchical purification unit. The water quantity distribution of the hierarchical purification unit is adjusted by using a hydraulic regulating device, and the sewage retention time and treatment condition are adjusted based on water quality online monitoring data, including:

6. The efficient intelligent management method for pollution of the hydro-fluctuation belt according to claim 1, characterized in that, The inlet water quantity data of the hierarchical purification unit is collected by time period, and the hydraulic load distribution of the biological filtration layer, adsorption layer and degradation layer is calculated. The hydraulic load distribution is dynamically calculated to obtain the hydraulic retention time of each layer of the hierarchical purification unit. The hydraulic retention time is corrected by water quality online monitoring data to generate sewage flow regulation parameters. Based on the sewage flow regulation parameters, the opening of the hydraulic regulating device is adjusted to form a real-time water quantity distribution scheme. The real-time water quantity distribution scheme is imported into an automatic control program to output the inflow of each treatment unit. The inflow is verified online, and the sewage retention time and treatment condition are adjusted based on water quality online monitoring data. Based on 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 selected, and the target treatment strategy is generated according to the target operating parameters, including:

7. The efficient intelligent management method for pollution of the hydro-fluctuation belt according to claim 1, characterized in that, ​ The water quality long-term monitoring data is segmented and counted according to the biological filtration layer, the adsorption layer and the degradation layer, so as to obtain water quality indexes of each layer; The water quality indexes of each layer are subjected to difference operation, so as to calculate nitrogen and phosphorus removal rates of each purification unit; Concentration attenuation analysis is performed on the water quality indexes of each layer, so as to obtain a COD degradation rate; Ion concentration determination is performed on the water quality indexes of each layer, so as to obtain a heavy metal adsorption rate; The nitrogen and phosphorus removal rates, the COD degradation rate and the heavy metal adsorption rate are comprehensively evaluated, so as to screen target operation parameters; The target operation parameters are taken as a benchmark to perform optimization analysis on the performance of the treatment unit, and a target treatment strategy is output.

8. The efficient intelligent management method for pollution of the hydro-fluctuation belt according to claim 7, characterized in that, The concentration attenuation analysis on the water quality indexes of each layer to obtain the COD degradation rate comprises: The COD concentration values in the water quality indexes of each layer are arranged in time sequence to form a COD concentration change curve; The COD concentration change curve is segmented and intercepted according to the biological filtration layer, the adsorption layer and the degradation layer, so as to obtain layered COD attenuation data; The layered COD attenuation data are subjected to temperature correction and hydraulic retention time correction to generate standardized COD attenuation values; The COD reduction amount per unit time is calculated based on the standardized COD attenuation values, and an instantaneous COD degradation rate is output; The instantaneous COD degradation rate is weighted and averaged according to the hydraulic load to form a steady-state degradation coefficient; The steady-state degradation coefficient is subjected to cumulative operation to obtain the COD degradation rate.

9. The efficient intelligent management method for pollution of the hydro-fluctuation belt according to claim 8, characterized in that, The optimization analysis on the performance of the treatment unit based on the target operation parameters to output the target treatment strategy comprises: The target operation parameters are imported into a treatment unit performance evaluation matrix to generate a unit operation score table; The unit operation score table is scored according to the nitrogen and phosphorus removal rates, the COD degradation rate and the heavy metal adsorption rate to obtain performance evaluation indexes; The performance evaluation indexes are used to calculate the pollutant removal contribution rate of each treatment unit to form a unit treatment capacity table; The unit treatment capacity table and pollutant migration rule data are cross-verified to output a treatment efficiency optimization suggestion; The treatment efficiency optimization suggestion is subjected to feasibility analysis and cost accounting to generate an improvement scheme list; The optimal operation condition is determined based on the improvement scheme list, and the target treatment strategy is output.

10. A high-efficiency intelligent management system for pollution of a hydro-fluctuation belt, for implementing the high-efficiency intelligent management method for pollution of a hydro-fluctuation belt according to any one of claims 1-9, characterized in that, The high-efficiency intelligent treatment system for pollution in the drawdown zone comprises: A calculation module configured to calculate nitrogen and phosphorus concentrations, suspended matter content and organic matter in the drawdown zone by a SWAT model to obtain pollutant migration rule data; A division module configured to divide the pollutant migration rule data into high, medium and low monitoring areas according to water level gradients, and measure dissolved oxygen, pH value, conductivity, turbidity, ammonia nitrogen content and chlorophyll data in each area by using a multi-parameter water quality sensor; An analysis module configured to determine planting areas and density ratios of vetiver grass, mulberry trees and emergent plants according to water quality parameters of each monitoring area and soil physicochemical property analysis results; A generation module configured to construct biological filtration layers, adsorption layers and degradation layers by using ecological substrates and modified materials according to the determined plant ratios to form hierarchical purification units. The detection module is configured to utilize the water regulating device to allocate water to the hierarchical purification units, and adjust sewage retention time and treatment conditions in combination with online monitoring data of water quality. The screening module is configured to calculate nitrogen and phosphorus removal rates, COD degradation rates, and heavy metal adsorption rates of the purification units based on long-term monitoring data of water quality, screen target operation parameters, and generate a target treatment strategy according to the target operation parameters.

Images