Water quality and water quantity accurate simulation method and system based on SWAT model and storage medium

Abstract

The invention relates to the technical field of data processing, and discloses a water quality and water quantity accurate simulation method and system based on an SWAT model and a storage medium. The method comprises the following steps: analyzing rainfall and evapotranspiration data through a two-parameter trend test, and generating standard hydrological data by adopting a triple configuration algorithm; dividing sub-basins by combining topographic information, and calculating a hydrological response unit; performing water balance operation and water conservation evaluation; pollutant diffusion is analyzed, and a diffusion equation is solved; running state monitoring is implemented, and an early warning system is designed; and comparing and correcting measured data, and constructing a hierarchical monitoring mechanism. According to the invention, the whole-process precise management from hydrological data acquisition, water quality evaluation to early warning monitoring is realized, and the accuracy and reliability of water quality and water quantity monitoring are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method, system and storage medium for accurately simulating water quality and quantity based on a SWAT model. Background Art

[0002] The existing water quality and quantity monitoring methods mainly use traditional methods such as fixed monitoring point sampling and analysis, manual inspections, etc., and combine remote sensing monitoring, online monitoring and other technical means to monitor the water quality and quantity of the water source area. In terms of water quality assessment, it mainly relies on physical and chemical index analysis and biological monitoring to evaluate the concentration of water pollutants and the degree of eutrophication of water bodies. In terms of water quantity regulation, it mainly collects hydrological data through hydrological station networks, precipitation monitoring networks, etc., and analyzes and predicts them in combination with hydrological models. Currently, the more commonly used hydrological models include the SWAT model and the MIKE model, which play an important role in simulating and predicting hydrological processes.

[0003] However, the existing technologies have the following shortcomings: traditional monitoring methods often have problems such as insufficient spatiotemporal resolution of monitoring data and incomplete coverage of monitoring points; water quality assessment methods find it difficult to accurately describe the spatial diffusion patterns and temporal evolution characteristics of water pollutants; water quantity regulation lacks accurate assessment methods for water conservation capacity, making it difficult to achieve coordinated optimization of water quality and quantity; although existing hydrological models can simulate hydrological processes, when faced with complex water quality and quantity coupling problems, the simulation accuracy and prediction accuracy still need to be improved; at the same time, the existing monitoring and early warning systems lack dynamic correction and accuracy assessment mechanisms for monitoring data, making it difficult to ensure the reliability of monitoring results. Summary of the invention

[0004] This application provides a method, system and storage medium for simulating water quality and quantity based on the SWAT model, which is used to solve the data accuracy problem in the process of coupling optimization of water quality and quantity in water source areas. By establishing a complete data processing system, the whole process from hydrological data collection, water quality assessment to early warning monitoring is managed accurately, which significantly improves the accuracy and reliability of water quality and quantity monitoring.

[0005] In a first aspect, the present application provides a method for accurately simulating water quality and quantity based on a SWAT model, and the method for accurately simulating water quality and quantity based on a SWAT model includes: The temporal and spatial characteristics of multi-source precipitation and evapotranspiration data are analyzed by dual-parameter trend test and slope evaluation to obtain initial hydrological data, which are processed by triple configuration algorithm to generate a standard hydrological data set; Based on the standard hydrological data set, regional hydrological division is performed in combination with terrain elevation information to generate sub-basin division data, and the hydrological response unit of the sub-basin is calculated according to the sub-basin division data to construct a basic hydrological calculation data set; Using the basic hydrological calculation data set to perform water balance calculations, extract water conservation characteristic data, conduct data evaluation on the water conservation characteristic data and land use changes, and generate water conservation evaluation data; Based on the water conservation assessment data, pollutant diffusion analysis of the incoming water is performed, water quality change prediction data is output, the water quality change prediction data is integrated and the pollutant diffusion equation is solved to establish water quality distribution data; Implement reservoir operation status monitoring based on the water quality distribution data to obtain pollution diffusion situation data; design an early warning indicator system based on the pollution diffusion situation data to determine early warning threshold data; The warning threshold data is compared and corrected with the measured data to establish accuracy assessment data, a hierarchical monitoring mechanism is constructed based on the accuracy assessment data, and water quality and quantity monitoring results are output.

[0006] In a second aspect, the present application provides a water quality and water quantity accurate simulation system based on a SWAT model, and the water quality and water quantity accurate simulation system based on a SWAT model comprises: An acquisition module is used to analyze the spatiotemporal characteristics of multi-source precipitation and evapotranspiration data through dual-parameter trend test and slope evaluation, obtain initial hydrological data, process the initial hydrological data using a triple configuration algorithm, and generate a standard hydrological data set; A division module, used to perform regional hydrological division based on the standard hydrological data set and terrain elevation information, generate sub-basin division data, calculate the hydrological response unit of the sub-basin according to the sub-basin division data, and construct a basic hydrological calculation data set; A calculation module, used to perform water balance calculation using the basic hydrological calculation data set, extract water conservation characteristic data, perform data evaluation on the water conservation characteristic data and land use change, and generate water conservation evaluation data; An analysis module, for performing pollutant diffusion analysis on the incoming water body based on the water conservation assessment data, outputting water quality change prediction data, integrating the water quality change prediction data and solving the pollutant diffusion equation to establish water quality distribution data; A monitoring module is used to implement reservoir operation status monitoring based on the water quality distribution data and obtain pollution diffusion situation data; design an early warning indicator system based on the pollution diffusion situation data and determine early warning threshold data; The correction module is used to compare and correct the warning threshold data with the measured data, establish accuracy evaluation data, build a hierarchical monitoring mechanism for the accuracy evaluation data, and output water quality and quantity monitoring results.

[0007] A third aspect of the present application provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is run on a computer, the computer executes the above-mentioned water quality and quantity precise simulation method based on the SWAT model.

[0008] In the technical solution provided by the present application, the spatiotemporal characteristics of multi-source precipitation and evapotranspiration data are analyzed through dual-parameter trend test and slope evaluation, and the initial hydrological data are processed by a triple configuration algorithm to ensure the accuracy and reliability of the hydrological data; regional hydrological division is performed based on the standard hydrological data set combined with terrain elevation information, sub-basin division data is generated and hydrological response units are calculated, thereby improving the spatial accuracy of hydrological analysis; water balance calculations are performed using basic hydrological calculation data sets, water conservation characteristic data are extracted and data evaluation is performed with land use changes, thereby achieving an accurate evaluation of water conservation capacity; pollutant diffusion analysis of water bodies entering the reservoir is performed based on water conservation evaluation data, and water quality distribution data is established by solving the pollutant diffusion equation, thereby improving the accuracy of water quality evaluation; reservoir operation status monitoring is implemented based on water quality distribution data, pollution diffusion trend data is obtained and an early warning indicator system is designed, thereby enhancing the real-time nature of water quality monitoring; and the reliability of the monitoring results is ensured by comparing and correcting the early warning threshold data with the measured data, establishing accuracy evaluation data and building a hierarchical monitoring mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] 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.

[0010] Figure 1 This is a schematic diagram of an embodiment of a method for accurately simulating water quality and quantity based on a SWAT model in an embodiment of the present application; Figure 2 This is a schematic diagram of an embodiment of a water quality and quantity accurate simulation system based on the SWAT model in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The embodiments of the present application provide a method, system and storage medium for accurately simulating water quality and quantity based on the SWAT model. 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 interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. 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 comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0012] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of the water quality and quantity accurate simulation method based on the SWAT model includes: Step S101, analyzing the spatiotemporal characteristics of multi-source precipitation and evapotranspiration data through dual-parameter trend test and slope evaluation, obtaining initial hydrological data, processing the initial hydrological data using a triple configuration algorithm, and generating a standard hydrological data set; Step S102: Perform regional hydrological division based on the standard hydrological data set and terrain elevation information, generate sub-basin division data, calculate the hydrological response unit of the sub-basin based on the sub-basin division data, and construct a basic hydrological calculation data set; Step S103, using the basic hydrological calculation data set to perform water balance calculation, extract water conservation characteristic data, conduct data evaluation on the water conservation characteristic data and land use change, and generate water conservation evaluation data; Step S104: Based on the water conservation assessment data, pollutant diffusion analysis of the incoming water is performed, water quality change prediction data is output, the water quality change prediction data is integrated and the pollutant diffusion equation is solved to establish water quality distribution data; Step S105: monitor the reservoir operation status based on the water quality distribution data to obtain pollution diffusion situation data; design an early warning indicator system based on the pollution diffusion situation data to determine the early warning threshold data; Step S106: Compare and calibrate the warning threshold data with the measured data, establish accuracy assessment data, build a hierarchical monitoring mechanism based on the accuracy assessment data, and output water quality and quantity monitoring results.

[0013] It is understandable that the execution subject of the present application can be a water quality and quantity precision simulation system based on the SWAT model, or a terminal or a server, which is not limited here. The present application embodiment is described by taking the server as the execution subject as an example.

[0014] Specifically, the spatiotemporal characteristics of multi-source precipitation and evapotranspiration data were analyzed by dual-parameter trend test and slope assessment. The dual-parameter trend test uses the Mann-Kendall method, which calculates the statistic S value for time series data, and then obtains the Z statistic through standardization to determine the change trend of the data series. The slope assessment uses the Sen slope method, which estimates the overall rate of change by calculating the median slope between any two points in the time series. The three precipitation data sets of CMFD, CHMPRE and MSWEP are time-aligned, and data fusion is achieved through the extended triple configuration method. The extended triple configuration method establishes data quality assessment indicators, assigns corresponding weights to different data sources, and performs weighted average calculations. The same method is used to process the three evapotranspiration data sets of ERA5Land, CR and GLEAM. In the data fusion process, each data set is first tested for spatiotemporal consistency, outliers are removed, and then weight configuration is used to achieve effective integration of multi-source data.

[0015] Based on the terrain elevation information, the flow direction is extracted, the cumulative flow is calculated, and the river network structure is determined. The D8 algorithm is used to extract the flow direction, that is, the study area is divided into regular grids, and the flow direction of each grid unit points to the direction with the lowest elevation among the eight adjacent units around it. The cumulative flow calculation is based on a recursive algorithm, and the catchment area is accumulated step by step from upstream to downstream. The sub-basin division adopts the critical catchment area threshold method, and the appropriate threshold is set according to the terrain characteristics of different regions to complete the division of 109 sub-basins. The calculation of the hydrological response unit is based on factors such as land use type, soil characteristics and slope. The regional units with similar hydrological characteristics are determined by overlay analysis. The land use type data comes from remote sensing image interpretation, and the soil characteristics include physical and chemical properties such as texture, organic matter content, and permeability. The slope is calculated by the digital elevation model. These data are spatially overlaid and analyzed through the geographic information system to generate a hydrological response unit distribution map.

[0016] The water balance calculation is based on the principle of water conservation, taking into account hydrological elements such as precipitation, runoff, evaporation and infiltration, and establishing a water balance equation. In the process of extracting water conservation characteristics, combined with land use change data, the impact of different land use types on water conservation is analyzed, and the conservation capacity index is calculated. The water balance equation considers three components: surface runoff, subsoil flow and groundwater recharge. The spatiotemporal distribution of each hydrological element is obtained by solving the continuity equation. A pollutant diffusion equation is established, considering convection diffusion and degradation processes, and the pollutant concentration distribution is obtained by numerical solution methods. The pollutant diffusion equation is solved by the finite difference method, discretizing the spatial and temporal dimensions, and obtaining a steady-state solution through iterative calculation. The parameters in the diffusion equation are calibrated by measured data, including key parameters such as the diffusion coefficient and the degradation coefficient.

[0017] A monitoring network is established based on water quality distribution data, and the pollutant diffusion situation is analyzed through real-time data collection. The layout of the monitoring network takes into account the characteristics of water flow and the law of pollutant diffusion, and monitoring points are set up at key sections. The design of the early warning indicator system takes into account multiple dimensions such as water quality parameters, diffusion rate and impact range, and adopts a multi-level classification standard to determine the early warning threshold. The early warning indicators include conventional water quality indicators and characteristic pollutant indicators. The early warning threshold of each indicator is determined through statistical analysis. By comparing the predicted value with the measured data, the error statistics are calculated, and the accuracy evaluation system is established. Error statistics use multiple indicators such as root mean square error and relative error to comprehensively evaluate the prediction accuracy. The hierarchical monitoring mechanism sets the monitoring frequency and response measures according to different accuracy levels to ensure the reliability of the monitoring results. The monitoring mechanism includes two levels: conventional monitoring and emergency monitoring, and dynamically adjusts the monitoring frequency according to changes in water quality.

[0018] Take the Guanshan River Basin, a typical tributary entering the reservoir, as an example. The precipitation data was processed, and the significant change trend was identified through the dual-parameter trend test, and then the change rate was determined in combination with the slope evaluation. In the process of sub-basin division, the study area was divided into multiple sub-basins according to the terrain characteristics, and each sub-basin was further subdivided into several hydrological response units. The water balance calculation results show the quantitative relationship between various hydrological elements, and the water conservation capacity is inferred through these relationships. The pollutant diffusion analysis is based on the measured data, and a diffusion model is established to predict the migration and transformation process of pollutants in the water body. The accuracy evaluation of the monitoring data shows that this method can accurately reflect the dynamic changes of water quality and quantity, and provide a scientific basis for water environment management.

[0019] For example, in the division of hydrological response units, the critical catchment area threshold is determined by measured hydrological data; in the pollutant diffusion analysis, the diffusion coefficient and degradation coefficient are calibrated by experimental data. This parameter calibration method based on measured data ensures the reliability of the simulation results. At the same time, the simulation accuracy is improved through multi-source data fusion and multi-scale analysis. In the data fusion stage, outliers are eliminated through consistency testing; in the parameter calibration process, a multi-objective optimization method is used to determine the optimal parameter combination; in the model verification stage, independent data sets are used for cross-validation. These quality control measures ensure the standardization of the implementation of the method and the reliability of the results.

[0020] In the embodiment of the present application, the spatiotemporal characteristics of multi-source precipitation and evapotranspiration data are analyzed through dual-parameter trend test and slope evaluation, and the initial hydrological data are processed by a triple configuration algorithm to ensure the accuracy and reliability of the hydrological data; regional hydrological division is performed based on the standard hydrological data set combined with terrain elevation information, sub-basin division data is generated and hydrological response units are calculated, thereby improving the spatial accuracy of hydrological analysis; water balance calculations are performed using basic hydrological calculation data sets, water conservation characteristic data are extracted and data evaluation is performed with land use changes, thereby achieving an accurate evaluation of water conservation capacity; pollutant diffusion analysis of incoming water bodies is performed based on water conservation evaluation data, and water quality distribution data is established by solving the pollutant diffusion equation, thereby improving the accuracy of water quality evaluation; reservoir operation status monitoring is implemented based on water quality distribution data, pollution diffusion trend data is obtained and an early warning indicator system is designed, thereby enhancing the real-time nature of water quality monitoring; by comparing and correcting the early warning threshold data with the measured data, accuracy evaluation data is established and a hierarchical monitoring mechanism is constructed, thereby ensuring the reliability of the monitoring results.

[0021] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) Extract the time weight coefficients of multi-source precipitation and evapotranspiration data, use weighted filtering to eliminate random disturbance components, and generate a time series matrix; (2) Perform the least squares operation on the time series matrix, calculate the mutation detection threshold, construct the trend test sequence based on the MK statistic, and output the significance level; (3) Based on the significance level as the benchmark parameter, perform segmented calculation of the time series change rate, use Sen slope evaluation to determine the trend change, and generate spatiotemporal characteristic parameters through quantile correction; (4) Based on the spatiotemporal characteristic parameters as constraints, perform Kriging spatial interpolation operations, calculate the spatial autocorrelation coefficient, and output the initial hydrological data through co-evolution analysis; (5) Perform heteroscedasticity test matrix operations on the initial hydrological data, remove outliers by combining the Mahalanobis distance discriminant method, and use Bayesian estimation to supplement the data; (6) Perform triple configuration weight calculation on the processed hydrological data, conduct adaptive fusion iterative operation, and generate a standard hydrological data set through normalization processing.

[0022] Specifically, the time weight coefficients are extracted from multi-source precipitation and evapotranspiration data. The multi-source data include three precipitation data sets, CMFD, CHMPRE and MSWEP, and three evapotranspiration data sets, ERA5Land, CR and GLEAM. The time weight coefficient extraction process performs a time consistency test on each data set. By calculating the data change rate of adjacent time points, a lower weight is given to abnormal fluctuation points, and a higher weight is given to stable change areas. The weighted filtering process uses the sliding window method. The window size is set according to the data sampling frequency. The data in each time window is weighted averaged to eliminate the influence of random disturbances. After these two steps, the original data is converted into a matrix form with time attributes.

[0023] The least squares operation is performed on the time series matrix. This process is to identify the mutation points in the data sequence. The least squares operation fits the trend line of the time series and calculates the residual sum of squares between the actual value and the fitted value. When the residual exceeds the preset threshold, it is determined to be a mutation point. The determination of the mutation detection threshold is based on the statistical characteristics of historical data, and the trend test sequence is constructed through the MK statistic. During the calculation of the MK statistic, the time series data are compared pairwise, the number of rises or falls is counted, and then the statistic Z value is obtained through standardization, and the significance level is output. With the significance level as the benchmark parameter, the segmented calculation of the time series change rate begins. The time series is divided into different change stages according to the significance level, and the change rate is calculated for each stage. The Sen slope evaluation method determines the overall trend change by calculating the median of the slopes between all possible two points in the time series. Quantile correction is to eliminate the influence of extreme values. The spatiotemporal characteristic parameters are obtained by calculating the change rate under different quantiles. Based on the obtained spatiotemporal characteristic parameters, the spatial interpolation stage is entered. Kriging spatial interpolation is an optimal linear unbiased estimation method based on regionalized variable theory. It calculates the spatial correlation between the interpolation points and the known points by constructing a variogram model. The spatial autocorrelation coefficient is calculated using the Moran index method to quantify the relationship between spatial units. Coevolution analysis constructs a spatial correlation matrix to analyze the variation patterns of hydrological elements between different regions and outputs initial hydrological data.

[0024] Further processing of the initial hydrological data includes heteroscedasticity test and outlier processing. Heteroscedasticity test matrix operation tests the spatial heterogeneity of the data by calculating the variance of different spatial locations. The Mahalanobis distance discriminant method is a multidimensional distance calculation method that considers the correlation between variables and identifies and removes outliers by setting a threshold. For the data gaps after removing outliers, the Bayesian estimation method is used to supplement them, and the optimal solution for the missing values ​​is estimated based on the distribution characteristics of the known data.

[0025] The triple configuration algorithm is a multi-source data fusion method based on data quality, spatiotemporal distribution characteristics and physical consistency. The weight calculation takes into account the accuracy, spatiotemporal coverage and physical rationality of the data, and continuously optimizes the weight coefficient through adaptive iteration. Through normalization processing, data from different sources are converted to a unified scale to generate a standard hydrological data set.

[0026] For example, the precipitation data processing process for a certain study area. Precipitation data are obtained from three data sources: CMFD, CHMPRE and MSWEP. Through the calculation of the time weight coefficient, it is found that the data show different change characteristics in the flood season and the non-flood season, and different weight coefficients are assigned accordingly. After weighted filtering, the obvious abnormal fluctuations are smoothed. The least squares fitting and MK test show that the data has a significant upward trend on the interannual scale, and the specific rate of change is determined by the Sen slope evaluation. In the spatial interpolation process, considering the influence of terrain factors, the Kriging method is used to interpolate the area between the stations. The heteroscedasticity test found that there are differences in precipitation variability between mountainous and plain areas, and the Mahalanobis distance discriminant method is used to identify outliers that do not conform to the regional precipitation law. The triple configuration algorithm fuses the processed multi-source data to obtain a standard precipitation data set that is continuous in time and space and physically reasonable.

[0027] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (1) Multiply the precipitation in the standard hydrological data set by the terrain elevation information to calculate the water yield per unit area, and divide it by the confluence time to obtain the initial flow data; spatially superimpose the initial flow data to generate a water flow confluence map; (2) Extract the confluence path from the water flow convergence map, calculate the cumulative flow according to the flow direction relationship matrix, multiply it by the slope coefficient, divide it by the critical threshold, and output the coordinates of the sub-basin boundary points; connect the coordinates of the sub-basin boundary points into lines to generate sub-basin division data; (3) Using the sub-basin division data as the boundary, extract the land use type area ratio of each sub-basin, multiply it by the permeability coefficient, and calculate the underlying surface characteristic value; combine the underlying surface characteristic value with the soil parameters to obtain the hydrological response parameters; (4) Classify and count the hydrological response parameters, calculate the area weights of different response types, multiply them by the hydrological characteristic coefficient, and output the hydrological response unit distribution; superimpose the hydrological response unit distribution with the sub-basin boundary to generate the response unit spatial data; (5) Calculate the runoff coefficient of each unit using the spatial data of the response unit. Multiply the runoff coefficient by the precipitation and divide it by the area to obtain the unit runoff. Perform spatial integration on the unit runoff and output the watershed water volume data. (6) Combine the basin water volume data with the hydrological parameter relationship, calculate the hydrological response coefficient matrix, establish the water volume balance equation, and generate the basic hydrological calculation data set.

[0028] Specifically, the precipitation data in the standard hydrological data set is processed and combined with the terrain elevation information to calculate the water yield per unit area. The initial flow data is obtained by multiplying the precipitation value of each grid cell by the corresponding elevation correction coefficient and then dividing it by the confluence time of the grid. The confluence time is the time required for water to flow from a certain point to the outlet section, which is calculated by the slope and flow path length. By spatially superimposing these initial flow data, a water flow convergence map reflecting the convergence of water flows in the entire region can be generated.

[0029] The confluence path is extracted based on the water flow convergence map, and the D8 algorithm is used to determine the flow direction of each grid unit. The flow direction relationship matrix records the water flow transmission relationship between each grid and its eight adjacent grids, and the cumulative flow is obtained by cumulative calculation. The cumulative flow is multiplied by the slope coefficient reflecting the degree of terrain undulation, and then divided by the critical threshold determined according to the characteristics of the study area to obtain the coordinates of the boundary points of the sub-basin. These boundary points are connected into closed boundary lines through vector processing to form sub-basin division data. After the sub-basin division is completed, it is necessary to extract the land use information in each sub-basin. Land use types include different categories such as forest land, cultivated land, and grassland, and each type has its own specific permeability coefficient. By calculating the area proportion of each type of land and multiplying it by the corresponding permeability coefficient, a comprehensive parameter reflecting the characteristics of the underlying surface is obtained. This underlying surface characteristic value is then combined with the physical parameters of the soil (such as porosity, bulk density, permeability, etc.) to generate hydrological response parameters.

[0030] The processing of hydrological response parameters is carried out through classification and statistical methods. The levels are divided according to the distribution characteristics of the parameter values, and the area weights of each level are calculated. Then, the weights are multiplied by the coefficients reflecting the characteristics of the hydrological process to obtain the spatial distribution of the hydrological response units. This distribution information is superimposed with the sub-basin boundary data obtained previously to form a spatial data set of the response unit.

[0031] Based on the spatial data of the response unit, the runoff coefficient of each unit is calculated. The runoff coefficient is the ratio of rainfall to surface runoff, which is affected by factors such as land use type, soil characteristics, and terrain slope. The runoff coefficient is multiplied by the precipitation and divided by the unit area to obtain the runoff per unit area. The unit runoff of all units is spatially integrated to obtain the water volume data of the entire basin.

[0032] The relationship between the watershed water volume data and various hydrological parameters is integrated to calculate the hydrological response coefficient matrix. The water volume balance equation takes into account the relationship between various hydrological elements such as precipitation, evaporation, infiltration, and surface runoff, which can be expressed as:

[0033] in: is the water balance value; is the precipitation correction coefficient of the ith unit; is the precipitation of the ith unit; is the evaporation coefficient of the i-th unit; is the evaporation amount of the i-th unit; is the infiltration coefficient of the ith unit; is the infiltration amount of the ith unit; is the runoff coefficient of the ith unit; is the runoff of the ith unit; is the area of ​​the i-th unit; is the coefficient of the jth groundwater exchange term; is the jth groundwater exchange amount.

[0034] For example, the hydrological process is analyzed. The initial flow is calculated based on precipitation data and DEM data, and the main confluence path is determined by cumulative flow analysis. In the process of sub-basin division, the study area is divided into multiple sub-basins taking into account the terrain characteristics. In the land use analysis, the distribution of various types of land is extracted, and the hydrological response parameters are determined in combination with soil survey data. By solving the water balance equation, the hydrological characteristic data of the study area are obtained, which provides a basis for subsequent water quality simulation. In the entire process of hydrological unit division and water balance calculation, the accuracy and continuity of data processing are crucial. The standard hydrological dataset is the basic data source, which contains time series precipitation data, which has been pre-processed and standardized. The terrain elevation information comes from the digital elevation model (DEM), which reflects the three-dimensional terrain characteristics of the study area. When multiplying these two types of data, the spatial overlay analysis method is used to ensure the spatial registration and unit unification of the data.

[0035] The analysis of the water flow convergence process adopts the cumulative flow calculation method. The flow direction matrix is ​​constructed, and the flow direction of each grid unit is determined by the direction of the maximum slope. The cumulative flow is calculated using a recursive algorithm, and the contribution area is accumulated step by step from upstream to downstream. The determination of the critical threshold takes into account the size of the study area and the characteristics of the water system, and the most appropriate threshold value is optimized by the trial calculation method. The sub-basin boundary is determined by a tracking algorithm, starting from the watershed and tracing along the contour until it meets other tributaries. The extraction of land use types uses the results of remote sensing image interpretation, including different types such as forests, farmlands, grasslands, and waters. The permeability coefficient is an important parameter reflecting the water infiltration capacity of the underlying surface, which is determined by field tests or reference standard values. The calculation of the underlying surface characteristic value comprehensively considers factors such as surface coverage, soil structure, and terrain slope to form a comprehensive indicator.

[0036] The division of hydrological response units is a process of comprehensive analysis of multiple factors. Cluster analysis is performed on hydrological response parameters to determine the main response types. The calculation of area weights is based on the spatial distribution characteristics of each type, reflecting the importance of different response types in the study area. The hydrological characteristic coefficient is obtained based on the analysis of historical hydrological data, reflecting the runoff characteristics of different regions. Calculation of runoff is the core link of hydrological simulation. The determination of runoff coefficients requires consideration of multiple influencing factors, including rainfall intensity, previous soil moisture content, vegetation coverage, etc. The calculation of unit runoff adopts a distributed method to fully consider the impact of spatial heterogeneity. The grid overlay method is used in the spatial integration process to ensure the spatial continuity of the calculation results.

[0037] The construction of the hydrological response coefficient matrix reflects the relationship between various hydrological elements. Each element in the matrix represents the conversion relationship between different hydrological processes, and the specific value is determined by parameter calibration. The establishment of the water balance equation follows the principle of water conservation and considers all water input and output items in the system.

[0038] Taking a large reservoir basin as an example, ten years of precipitation data and 90-meter resolution DEM data of the basin were collected, and the initial flow data was obtained through spatial analysis and calculation. According to the cumulative flow analysis, the confluence paths of the main rivers were determined, and the entire basin was divided into multiple sub-basins based on the critical catchment area threshold of 500 square kilometers. Land use analysis shows that the study area mainly includes three types: forest land, farmland and grassland. The permeability coefficient of each type was determined through field sampling. The division of hydrological response units considers the three main factors of slope, soil and vegetation to form spatial distribution data. The calculation of the runoff coefficient is based on rainfall intensity and underlying surface characteristics, and the reliability of the calculation results is verified by measured data. By solving the water balance equation, the hydrological cycle process data is obtained. In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) First, multiply the daily precipitation data in the basic hydrological calculation data set by the basin area, subtract the product of daily evaporation and daily runoff, and divide it by the total water volume to calculate the daily scale water balance value; then accumulate the daily scale water balance value on a monthly basis to obtain the monthly scale water volume control data; (2) Calculate the monthly water conservation coefficient by multiplying the monthly water control data by the monthly average soil infiltration rate, subtracting the monthly groundwater recharge, and dividing by the monthly precipitation. Multiply the monthly water conservation coefficient by the underlying surface characteristic value to obtain the initial value of water conservation. (3) Calculate the daily average of the initial value of water conservation, compare the calculated result with the upper limit of soil water holding capacity, and determine the effective conservation depth; calculate the water conservation flux based on the effective conservation depth, and extract water conservation characteristic data; (4) Classify the water conservation characteristic data according to cultivated land, forest land and grassland, and calculate the annual average conservation capacity of different land use types; standardize the annual average conservation capacity to obtain the land use conversion rate; (5) Based on the land use conversion rate, multiply the change in each type of land area by its corresponding conservation contribution to obtain the total annual conservation change; perform standard deviation analysis on the total annual conservation change to output the water conservation stability index; (6) Substitute the water conservation stability index into the water conservation capacity assessment function, calculate the regional water conservation capacity value, and generate water conservation assessment data.

[0039] Specifically, the basic hydrological calculation data set includes daily precipitation data, watershed area data, daily evaporation data, and daily runoff data. By processing and calculating these basic data, water conservation assessment data is obtained, thereby achieving accurate simulation of water quality and quantity.

[0040] When calculating the daily water balance value, it is necessary to multiply the daily precipitation data by the basin area to obtain the total regional precipitation. Then subtract the product of daily evaporation and daily runoff from it. This step reflects the regional water balance. Divide the result by the total water volume to obtain the daily water balance value. The daily water balance value reflects the balance of water input and output in a single day. These daily data are accumulated by month to form monthly water control data, which provides a basis for subsequent analysis.

[0041] After obtaining the monthly water regulation data, the impact of soil infiltration and groundwater recharge needs to be considered. The monthly average soil infiltration rate is an important indicator to measure the soil's ability to absorb water. The monthly water conservation coefficient can be calculated by multiplying the monthly water regulation data by the monthly average soil infiltration rate, subtracting the monthly groundwater recharge, and then dividing it by the total monthly precipitation. This coefficient reflects the region's water regulation capacity on a monthly scale. Multiply the monthly water conservation coefficient by the underlying surface characteristic value to obtain the initial value of water conservation. The underlying surface characteristic value includes the impact of factors such as surface cover type and vegetation conditions on water conservation capacity.

[0042] The purpose of calculating the daily average of the initial value of water conservation is to obtain more detailed time scale characteristics. The calculated daily average is compared with the upper limit of soil water holding capacity to determine the effective conservation depth. The effective conservation depth refers to the depth range in the soil layer that can effectively store and regulate water. Based on this depth range, the water conservation flux is calculated, that is, the amount of water passing through a unit area per unit time, thereby extracting water conservation characteristic data.

[0043] Water conservation characteristic data need to be classified and counted according to different land use types. Mainly including cultivated land, forest land and grassland, the annual average conservation capacity of different land use types is calculated. These annual average conservation data are standardized to obtain the land use conversion rate. Standardization eliminates the impact of different units and magnitudes, making the data comparable.

[0044] Based on the land use conversion rate, the annual conservation change is calculated by combining the change in the area of ​​various types of land use with the corresponding conservation contribution. This step considers the impact of land use change on water conservation capacity. The standard deviation analysis of the annual conservation change is performed to output the water conservation stability index, which reflects the stability of the regional water conservation capacity.

[0045] Finally, the water conservation stability index is substituted into the water conservation capacity evaluation function to calculate the regional water conservation capacity. The expression of the water conservation capacity evaluation function is: in: Indicates the regional water conservation capacity value; , , represent the weight coefficients of vegetation cover, soil properties and terrain conditions respectively; Indicates the degree of root development of the i-th vegetation type; represents the coverage of the i-th vegetation; represents the growth condition index of the i-th vegetation; represents the water holding capacity of the jth type of soil; represents the permeability of the jth type of soil; represents the thickness coefficient of the jth type of soil; represents the slope factor of the kth terrain unit; represents the aspect factor of the kth terrain unit; represents the elevation factor of the kth terrain unit; n, m, and l represent the vegetation type, soil type, and the number of terrain units, respectively.

[0046] This function comprehensively considers the impact of vegetation cover, soil characteristics and terrain conditions on water conservation capacity. By calculating the regional water conservation capacity value, water conservation assessment data is generated to provide a scientific basis for water quality and quantity regulation.

[0047] For example: collect daily precipitation, evaporation and runoff data for the basin for one year. Through daily water balance calculation, basic data reflecting the water balance status are obtained. After monthly summary, the water conservation coefficient is calculated in combination with the soil infiltration characteristics and groundwater recharge of the basin. Combined with the dynamic change data of different types of land use areas in the basin, its impact on water conservation capacity is analyzed. Finally, the water conservation capacity of the basin is comprehensively evaluated through the water conservation capacity evaluation function. For example, for an area with high vegetation coverage, good soil structure and moderate terrain undulation, its water conservation capacity value will be relatively high, which reflects that the area has a strong water conservation function. The entire evaluation process forms a data processing chain, and the logical connection between each link is close, ensuring the scientificity and accuracy of the evaluation results.

[0048] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Multiply the water conservation assessment data by the cross-sectional flow rate of the water body entering the reservoir, subtract the average daily evaporation loss of the reservoir, and divide by the cross-sectional area to calculate the flow rate of the water body entering the reservoir; combine the flow rate of the water body entering the reservoir with the monitored pollutant concentration value to obtain the initial pollution load; (2) Calculate the pollutant diffusion per unit time by multiplying the initial pollutant load by the water diffusion coefficient, adding the pollutant degradation coefficient, and dividing by the cross-sectional area of ​​the water flow. Accumulate the pollutant diffusion per unit time by hour to obtain the daily diffusion flux. (3) Perform spatial stratification calculations on the daily diffusion flux, multiply the surface, middle, and bottom concentration values ​​by the corresponding weight coefficients, and calculate the vertical mixing coefficient; deduce the horizontal diffusion intensity based on the vertical mixing coefficient, and output the water quality change prediction data; (4) Based on the water quality change prediction data, extract the pollutant concentration gradient, multiply the concentration gradient value by the diffusion coefficient, divide it by the characteristic length, calculate the convection diffusion term; substitute the pollutant degradation reaction term, and construct the diffusion equation group; (5) Configure boundary conditions for the diffusion equations, substitute the inlet boundary concentration values, outlet boundary concentration values, and shore concentration values ​​into the solution to obtain a steady-state concentration field; superimpose the steady-state concentration field with the hydrodynamic field to output a dynamic concentration distribution; (6) Based on the dynamic concentration distribution, calculate the spatial diffusion range of pollutants, determine the area ratio of areas exceeding the standard, and establish water quality distribution data.

[0049] Specifically, by analyzing the water conservation assessment data and water flow characteristics, the pollutant diffusion process can be accurately simulated. The water conservation assessment data contains key information such as water conservation capacity and water balance status. By combining this data with the cross-sectional flow of water entering the reservoir, pollutant diffusion analysis can be carried out.

[0050] The calculation of the flow rate of water entering the reservoir involves multiple hydrological parameters. Multiplying the water conservation assessment data with the cross-sectional flow of the water entering the reservoir reflects the total amount of water entering the reservoir. Subtract the average daily evaporation loss of the reservoir from it to consider the impact of water loss. Dividing the result by the cross-sectional area, the flow rate of the water entering the reservoir is obtained, which is an important driving force for the migration and diffusion of pollutants. The flow rate of the water entering the reservoir is combined with the pollutant concentration monitoring value to calculate the initial pollution load, which reflects the total amount characteristics of pollutants.

[0051] The initial pollution load will gradually distribute in space after water diffusion. The water diffusion coefficient is an important parameter that describes the diffusion capacity of pollutants in water bodies and is related to factors such as water temperature and turbulence intensity. The initial pollution load is multiplied by the water diffusion coefficient and the pollutant degradation coefficient is added to consider the natural degradation process of pollutants. The result is divided by the cross-sectional area of ​​the water flow to obtain the pollutant diffusion per unit time. By accumulating the pollutant diffusion per unit time on an hourly scale, the daily diffusion flux is obtained, which reflects the diffusion law of pollutants within a day. Water pollutants show stratified characteristics in the vertical direction. The daily diffusion flux needs to consider the concentration distribution of the three water layers of the surface, middle and bottom layers. Different water layers have different weight coefficients, which reflects the importance of each layer in the process of pollutant diffusion. By multiplying the concentration value of each layer with the corresponding weight coefficient, the vertical mixing coefficient is calculated to describe the degree of mixing of pollutants in the vertical direction. Based on the vertical mixing coefficient, the horizontal diffusion intensity is deduced and the water quality change prediction data is output.

[0052] The water quality change prediction data contains the spatial distribution information of pollutant concentration. By extracting the pollutant concentration change gradient, the concentration change trend of pollutants in space is analyzed. The concentration gradient value is multiplied by the diffusion coefficient and divided by the characteristic length to calculate the convection diffusion term. At the same time, the degradation reaction of pollutants is considered to construct the diffusion equation group. The expression of the diffusion equation group is: in: represents the pollutant concentration field; represents the time variable; represents the diffusion coefficient tensor; represents the flow velocity vector; represents the source-sink function; represents the second-order diffusion coefficient of the i-th spatial dimension; represents the i-th spatial coordinate; represents the kinetic coefficient of the jth reaction; represents the concentration of the jth reaction component; n represents the number of spatial dimensions; m represents the number of reaction components.

[0053] The solution of the diffusion equations requires appropriate boundary conditions. By configuring the concentration values ​​of the inlet boundary, outlet boundary and shore, a boundary condition system is established. Solving the equations obtains a steady-state concentration field, which reflects the spatial distribution of pollutants when they reach equilibrium. The steady-state concentration field is superimposed on the hydrodynamic field to obtain a dynamic concentration distribution that takes into account the effect of water flow. The dynamic concentration distribution is the basis for assessing the impact range of pollutants. By analyzing the area where the pollutant concentration exceeds the standard limit, the area proportion of the exceeded area is determined, thereby establishing water quality distribution data. These data provide an important basis for water quality management and pollution control.

[0054] For example: collect hydrological data and pollutant monitoring data of the inlet section, including section flow, water level, pollutant concentration, etc. Determine the initial conditions through flow velocity calculation and pollution load analysis. Analyze the migration and transformation laws of pollutants by combining the diffusion effect and degradation process of water bodies. Consider the stratification characteristics of water bodies and establish a vertical mixing model. Obtain the spatial distribution characteristics of pollutants by solving the diffusion equation group. The entire analysis process forms a data processing chain to ensure the accuracy and reliability of the simulation results.

[0055] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) Divide the water quality distribution data into monitoring grids according to the cross-section location, multiply it by the corresponding water quality parameter weight, and divide it by the total number of grids to calculate the water quality status index; perform time series sampling on the water quality status index to obtain the water quality change series; (2) For the water quality change sequence, calculate the concentration difference between adjacent moments, divide the difference by the time interval, and multiply by the diffusion coefficient to obtain the pollution diffusion rate; superimpose the pollution diffusion rate with the water flow direction data to output the diffusion direction vector; (3) Based on the diffusion direction vector, the migration trajectory of the pollution group is extracted, the coordinates of the trajectory points are associated with the time series, and the diffusion range change rate is calculated; based on the diffusion range change rate, the pollution load flux is analyzed to generate pollution diffusion situation data; (4) Classify the pollution diffusion trend data, calculate the mean and standard deviation of pollutant concentrations at each level, multiply the mean concentration by the weight coefficient, and establish a classification evaluation index; compare the classification evaluation index with the water quality standard, and output the risk coefficient of exceeding the standard; (5) Use the risk coefficient of exceeding the standard to construct the warning level classification standard, correspond the risk coefficient range with the warning level, calculate the trigger probability of each level, and form a warning indicator system; set the graded response conditions based on the warning indicator system and generate warning threshold data.

[0056] Specifically, water quality distribution data contains information such as pollutant concentration and spatial distribution. By gridding and time series analysis of these data, a water quality monitoring and early warning system is established. Gridding of water quality distribution data is the basis for building a monitoring system. The monitoring section area is divided into several grid units, each of which represents a specific monitoring point. Each grid unit is assigned a corresponding water quality parameter weight, and the weight value reflects the importance of the monitoring point. The water quality status index is calculated by multiplying the water quality data of the grid unit by the corresponding weight and dividing it by the total number of grids. This index is a comprehensive indicator to measure the overall water quality status. The water quality status index is sampled at fixed time intervals to form time series data, which reflects the dynamic changes in the water quality status.

[0057] The concentration change is obtained by calculating the concentration difference between adjacent sampling moments. The concentration change rate is obtained by dividing the difference by the sampling time interval. The concentration change rate is multiplied by the diffusion coefficient to calculate the pollution diffusion rate, which reflects how fast the pollutants diffuse in space. The pollution diffusion rate is superimposed and analyzed with the water flow direction data to output the diffusion direction vector, which describes the directional characteristics of pollutant migration. The extraction of the migration trajectory of the pollution group is based on the diffusion direction vector. The movement path of the pollution group is determined by tracking the diffusion direction vector in a continuous time period. The spatial coordinates of the trajectory points are associated with the corresponding time series to calculate the rate of change of the diffusion range over time. The rate of change of the diffusion range reflects the dynamic characteristics of the diffusion process of the pollution group. Based on this rate of change, the transport flux of pollutants is analyzed to generate data reflecting the overall situation of pollution diffusion.

[0058] The situation data is graded to determine the levels of different pollution degrees. The mean and standard deviation of the pollutant concentration data at each level are calculated to evaluate the pollution characteristics of that level. The mean concentration is multiplied by the corresponding weight coefficient to establish a graded evaluation index system. By comparing these evaluation indicators with the water quality standards, the risk coefficient of exceeding the standard is calculated to quantify the risk degree of pollution at different levels. The division of warning levels is based on the risk coefficient of exceeding the standard. By dividing the risk coefficient into intervals, a corresponding relationship with the warning level is established. The trigger probability of each warning level is calculated to construct a warning index system. Based on this system, the response conditions of different levels are set, the trigger thresholds of each level of warning are clarified, and the warning threshold data is formed.

[0059] For example: by laying out monitoring grids at key sections, water quality data can be collected in real time. The collected data is processed in a grid format to calculate the water quality status index. Through continuous monitoring, a water quality change sequence is obtained. Based on the water quality change data, the diffusion characteristics of pollutants are analyzed and the migration trajectory of the pollution group is tracked. The pollution degree is graded and evaluated, and an early warning trigger mechanism is established. The entire monitoring and early warning process forms a data analysis chain, from data collection to early warning triggering, and each link is closely connected to ensure the timeliness and accuracy of monitoring and early warning.

[0060] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) Align the warning threshold data with the measured data according to the time node, calculate the predicted deviation value at the corresponding moment, multiply it by the weight coefficient, and obtain the weighted error sequence; perform statistical analysis on the weighted error sequence and output the correction coefficient; (2) Use the correction coefficient to correct the warning threshold, calculate the correlation coefficient between the corrected value and the measured value, and divide it by the number of samples to obtain the fitting accuracy value; classify the fitting accuracy value according to the monitoring section to generate the spatial accuracy distribution; (3) Based on the spatial accuracy distribution, calculate the prediction accuracy of each monitoring section, multiply the accuracy value by the section importance coefficient, divide it by the total number of sections, and output the section weight coefficient; compare the section weight coefficient with the accuracy threshold to establish accuracy evaluation data; (4) Classify the accuracy assessment data, count the data proportions in different accuracy intervals, calculate the credibility coefficients of each level, and generate a graded evaluation matrix; associate the graded evaluation matrix with the monitoring threshold and output the graded monitoring standard; (5) Based on the hierarchical monitoring standards, calculate the early warning trigger conditions for each monitoring section, subtract the monitoring value from the standard value, and divide it by the allowable deviation to obtain the early warning discrimination coefficient; classify the early warning discrimination coefficient and determine the monitoring response level; (6) Match the monitoring response level with the water quality category, calculate the monitoring frequency requirements for each level, and generate water quality and quantity monitoring results.

[0061] Specifically, the comparative analysis of warning threshold data and measured data is the basis for evaluating monitoring accuracy. By aligning the two types of data according to time nodes, the data is ensured to have time consistency, providing an accurate data basis for subsequent analysis. After alignment, the predicted deviation value at the corresponding moment is calculated, which reflects the degree of difference between the predicted result and the actual situation. Considering the differences in the importance of data in different time periods, it is necessary to introduce a weight coefficient, and obtain a weighted error sequence by multiplying it with the deviation value. The sequence is statistically analyzed, including calculating statistics such as the mean and standard deviation, and outputting the correction coefficient for data correction. The application of the correction coefficient is intended to improve the accuracy of the warning threshold. The correction of the data is achieved by multiplying the original warning threshold with the correction coefficient. To verify the correction effect, the corrected value needs to be compared with the measured value and the correlation coefficient is calculated. The correlation coefficient is divided by the number of samples to obtain the fitting accuracy value, which reflects the degree of fit between the corrected data and the measured data. The fitting accuracy values ​​are classified and sorted according to different monitoring sections to generate accuracy distribution data reflecting the spatial distribution characteristics.

[0062] The analysis of spatial accuracy distribution data is the key to evaluating the monitoring effect. The prediction accuracy of each monitoring section is calculated, which reflects the monitoring reliability of a specific section. Considering the different status of different sections in the overall monitoring network, it is necessary to introduce the section importance coefficient, multiply the accuracy value with it, and divide it by the total number of sections to obtain the section weight coefficient that reflects the relative importance of each section. By comparing the section weight coefficient with the pre-set accuracy threshold, an accuracy evaluation data system is established. The hierarchical processing of accuracy evaluation data is the basis for establishing a monitoring mechanism. The evaluation data is graded, the quantity distribution of data in each accuracy interval is statistically analyzed, and the proportion of data in different intervals to the total is calculated. Based on these statistical results, the credibility coefficients of each level are calculated to reflect the reliability of data at different levels. This information is integrated to form a hierarchical evaluation matrix, which contains the accuracy characteristics of each level. By associating the hierarchical evaluation matrix with the monitoring threshold, a hierarchical monitoring standard is generated.

[0063] The application of graded monitoring standards is reflected in specific monitoring practices. By calculating the early warning trigger conditions of each monitoring section, the actual monitoring value is compared with the standard value, and the difference is calculated. The difference is divided by the allowable deviation range to obtain the early warning discrimination coefficient, which is an important basis for judging whether it is necessary to trigger an early warning. The early warning discrimination coefficient is graded to determine the monitoring response level that needs to be taken in different situations. Finally, a corresponding relationship is established between the monitoring response level and the water quality category, and the monitoring frequency required for different water quality categories is clarified. According to the characteristics of the water quality category and regulatory requirements, the corresponding monitoring frequency requirements are formulated to form water quality and quantity monitoring results. These results not only include monitoring data, but also management information such as monitoring frequency and early warning level.

[0064] For example, after obtaining the measured data and forecast data of the early warning system for a certain monitoring section for 30 consecutive days, the data time is aligned. For example, the predicted value at 8:00 on January 1 is 0.85 mg / L, and the measured value is 0.82 mg / L. The predicted deviation value is calculated to be 0.03 mg / L. Considering that this time point is the morning peak water consumption period, the weight coefficient is set to 1.5, and the weighted error is 0.045 mg / L. The data of 30 days are processed similarly to obtain the weighted error sequence, and the correction coefficient 1.08 is output through statistical analysis. The correction coefficient is applied to the correction of the original warning threshold. For example, the original warning threshold is 1.0 mg / L, which is corrected to 1.08 mg / L after correction. The correlation coefficient between the corrected value and the measured value is calculated to be 0.92. The number of samples is 30, and the fitting accuracy value is calculated to be 0.0307. The same analysis was performed on sections at different locations 500 m, 1000 m, and 1500 m upstream of the monitoring section, and the fitting accuracy values ​​were 0.0312, 0.0298, 0.0320, etc., respectively, forming a spatial accuracy distribution.

[0065] Based on the accuracy distribution of these sections, the prediction accuracy is calculated. For example, if the deviation between the predicted value and the measured value of the section is within the allowable range for 27 days out of 30 days, the accuracy is 90%. Since the section is located upstream of the water intake, the section importance coefficient is set to 1.2, the total number of sections is 10, and the section weight coefficient is calculated to be 0.108. This weight coefficient is compared with the set accuracy threshold of 0.1 to establish the accuracy evaluation data. The accuracy evaluation data is graded and counted according to intervals such as above 90%, 80-90%, and 70-80%, and the credibility coefficients of different levels are calculated. For example, the credibility coefficient of the level above 90% is 0.95, and that of the level 80-90% is 0.85, etc. These data are organized into a graded evaluation matrix, associated with the monitoring threshold, and a graded monitoring standard is formed.

[0066] According to the graded monitoring standards, calculate the early warning trigger conditions of the monitoring section. For example, if the monitoring value is 0.95mg / L, the standard value is 1.0mg / L, and the allowable deviation is 0.1mg / L, the early warning discrimination coefficient is calculated to be -0.5. The coefficient is graded and determined as the secondary response level. Finally, the monitoring frequency requirements are formulated according to the response level. For example, the first-level response requires monitoring once every 2 hours, the second-level response once every 4 hours, and the third-level response once every 6 hours, etc., to form a monitoring specification. In this way, a dynamic monitoring system that is continuously optimized based on measured data is established.

[0067] The above describes the water quality and water quantity accurate simulation method based on the SWAT model in the embodiment of the present application. The following describes the water quality and water quantity accurate simulation system based on the SWAT model in the embodiment of the present application. Figure 2In the embodiment of the present application, an embodiment of the water quality and quantity accurate simulation system based on the SWAT model includes: An acquisition module is used to analyze the spatiotemporal characteristics of multi-source precipitation and evapotranspiration data through dual-parameter trend test and slope evaluation, obtain initial hydrological data, process the initial hydrological data using a triple configuration algorithm, and generate a standard hydrological data set; A division module, used to perform regional hydrological division based on the standard hydrological data set and terrain elevation information, generate sub-basin division data, calculate the hydrological response unit of the sub-basin according to the sub-basin division data, and construct a basic hydrological calculation data set; A calculation module, used to perform water balance calculation using the basic hydrological calculation data set, extract water conservation characteristic data, perform data evaluation on the water conservation characteristic data and land use change, and generate water conservation evaluation data; An analysis module, for performing pollutant diffusion analysis on the incoming water body based on the water conservation assessment data, outputting water quality change prediction data, integrating the water quality change prediction data and solving the pollutant diffusion equation to establish water quality distribution data; A monitoring module is used to implement reservoir operation status monitoring based on the water quality distribution data and obtain pollution diffusion situation data; design an early warning indicator system based on the pollution diffusion situation data and determine early warning threshold data; The correction module is used to compare and correct the warning threshold data with the measured data, establish accuracy evaluation data, build a hierarchical monitoring mechanism for the accuracy evaluation data, and output water quality and quantity monitoring results.

[0068] Through the coordinated cooperation of the above components, the spatiotemporal characteristics of multi-source precipitation and evapotranspiration data are analyzed through dual-parameter trend test and slope evaluation, and the triple configuration algorithm is used to process the initial hydrological data to ensure the accuracy and reliability of the hydrological data; regional hydrological division is carried out according to the standard hydrological data set combined with terrain elevation information, sub-basin division data is generated and hydrological response units are calculated, thereby improving the spatial accuracy of hydrological analysis; water balance calculations are carried out using basic hydrological calculation data sets, water conservation characteristic data are extracted and data evaluation is carried out with land use changes, thereby achieving an accurate evaluation of water conservation capacity; pollutant diffusion analysis of water bodies entering the reservoir is carried out based on water conservation evaluation data, and water quality distribution data is established by solving the pollutant diffusion equation, thereby improving the accuracy of water quality evaluation; reservoir operation status monitoring is implemented based on water quality distribution data, pollution diffusion trend data is obtained and an early warning indicator system is designed, thereby enhancing the real-time nature of water quality monitoring; the reliability of monitoring results is ensured by comparing and correcting the early warning threshold data with the measured data, establishing accuracy evaluation data and building a hierarchical monitoring mechanism.

[0069] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the method for accurately simulating water quality and quantity based on the SWAT model.

[0070] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0071] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0072] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described 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. A method for accurately simulating water quality and quantity based on the SWAT model, characterized in that: The water quality and quantity accurate simulation method based on the SWAT model includes: The temporal and spatial characteristics of multi-source precipitation and evapotranspiration data are analyzed by dual-parameter trend test and slope evaluation to obtain initial hydrological data, which are processed by triple configuration algorithm to generate a standard hydrological data set; Based on the standard hydrological data set, regional hydrological division is performed in combination with terrain elevation information to generate sub-basin division data, and the hydrological response unit of the sub-basin is calculated according to the sub-basin division data to construct a basic hydrological calculation data set; Using the basic hydrological calculation data set to perform water balance calculations, extract water conservation characteristic data, conduct data evaluation on the water conservation characteristic data and land use changes, and generate water conservation evaluation data; Based on the water conservation assessment data, pollutant diffusion analysis of the incoming water is performed, water quality change prediction data is output, the water quality change prediction data is integrated and the pollutant diffusion equation is solved to establish water quality distribution data; Implement reservoir operation status monitoring based on the water quality distribution data to obtain pollution diffusion situation data; design an early warning indicator system based on the pollution diffusion situation data to determine early warning threshold data; The warning threshold data is compared and corrected with the measured data to establish accuracy assessment data, a hierarchical monitoring mechanism is constructed based on the accuracy assessment data, and water quality and quantity monitoring results are output.

2. The water quality and quantity accurate simulation method based on the SWAT model according to claim 1 is characterized in that: The method analyzes the spatiotemporal characteristics of multi-source precipitation and evapotranspiration data through dual-parameter trend test and slope evaluation, obtains initial hydrological data, processes the initial hydrological data using a triple configuration algorithm, and generates a standard hydrological data set, including: Extracting time weight coefficients from the multi-source precipitation and evapotranspiration data, eliminating random disturbance components using weighted filtering, and generating a time series matrix; Performing least squares operation on the time series matrix, calculating the mutation detection threshold, constructing a trend test sequence based on the MK statistic, and outputting the significance level; Based on the significance level as the benchmark parameter, the time series change rate is calculated in segments, the trend change is determined by using the Sen slope evaluation, and the spatiotemporal characteristic parameters are generated by quantile correction; Based on the spatiotemporal characteristic parameters as constraint conditions, a Kriging spatial interpolation operation is performed to calculate the spatial autocorrelation coefficient, and the initial hydrological data is output through co-evolution analysis; Perform heteroscedasticity test matrix operations on the initial hydrological data, remove outliers by combining Mahalanobis distance discrimination method, and use Bayesian estimation to supplement data; The processed hydrological data are subjected to triple configuration weight calculation, adaptive fusion iterative operation is performed, and the standard hydrological data set is generated through normalization processing.

3. The water quality and quantity accurate simulation method based on the SWAT model according to claim 1 is characterized in that: The method of performing regional hydrological division based on the standard hydrological data set and combining terrain elevation information to generate sub-basin division data, calculating the hydrological response unit of the sub-basin according to the sub-basin division data, and constructing a basic hydrological calculation data set includes: The precipitation in the standard hydrological data set is multiplied by the terrain elevation information to calculate the water yield per unit area, and the result is divided by the confluence time to obtain the initial flow data; the initial flow data is spatially superimposed to generate a water flow confluence map; Extract the confluence path from the water flow convergence map, calculate the cumulative flow according to the flow direction relationship matrix, multiply it by the slope coefficient, divide it by the critical threshold, and output the coordinates of the sub-basin boundary points; connect the coordinates of the sub-basin boundary points into lines to generate the sub-basin division data; Taking the sub-basin division data as the boundary, extracting the land use type area ratio of each sub-basin, multiplying it by the permeability coefficient, and calculating the underlying surface characteristic value; combining the underlying surface characteristic value with the soil parameter to obtain the hydrological response parameter; Classify and count the hydrological response parameters, calculate the area weights of different response types, multiply them by the hydrological characteristic coefficients, and output the hydrological response unit distribution; superimpose the hydrological response unit distribution with the sub-basin boundary to generate response unit spatial data; Calculate the runoff coefficient of each unit using the spatial data of the response unit, multiply the runoff coefficient by the precipitation, and divide it by the area to obtain the unit runoff; perform spatial integration on the unit runoff and output the watershed water volume data; The water volume data of the watershed is combined with the hydrological parameter relationship, the hydrological response coefficient matrix is ​​calculated, the water volume balance equation is established, and the basic hydrological calculation data set is generated.

4. The water quality and quantity accurate simulation method based on the SWAT model according to claim 1 is characterized in that: The method of using the basic hydrological calculation data set to perform water balance calculation, extracting water conservation characteristic data, and performing data evaluation on the water conservation characteristic data and land use changes to generate water conservation evaluation data includes: First, the daily precipitation data in the basic hydrological calculation data set is multiplied by the basin area, the product of daily evaporation and daily runoff is subtracted, and the result is divided by the total water volume to calculate the daily scale water balance value; then the daily scale water balance value is accumulated monthly to obtain the monthly scale water volume control data; The monthly water conservation coefficient is calculated by multiplying the monthly water control data by the monthly average soil infiltration rate, subtracting the monthly groundwater recharge, and then dividing by the monthly precipitation. The monthly water conservation coefficient is multiplied by the underlying surface characteristic value to obtain the initial value of water conservation capacity. Calculate the daily average of the initial value of the water conservation capacity, compare the calculated result with the upper limit of the soil water holding capacity, and determine the effective conservation depth; calculate the water conservation flux based on the effective conservation depth, and extract the water conservation characteristic data; The water conservation characteristic data are classified and counted according to cultivated land, forest land and grassland, and the annual average conservation amount of different land use types is calculated; the annual average conservation amount is standardized to obtain the land use conversion rate; Based on the land use conversion rate, the changes in the area of ​​each type of land are multiplied by their corresponding conservation contributions to obtain the total annual conservation change; the standard deviation analysis of the total annual conservation change is performed to output the water conservation stability index; Substitute the water conservation stability index into the water conservation capacity evaluation function, calculate the regional water conservation capacity value, and generate the water conservation evaluation data.

5. The water quality and quantity accurate simulation method based on the SWAT model according to claim 1 is characterized in that: Based on the water conservation assessment data, pollutant diffusion analysis of the incoming water is performed, water quality change prediction data is output, the water quality change prediction data is integrated and the pollutant diffusion equation is solved to establish water quality distribution data, including: The water conservation assessment data is multiplied by the cross-sectional flow rate of the water body entering the reservoir, the average daily evaporation loss of the reservoir is subtracted, and the result is divided by the cross-sectional area to calculate the flow rate of the water body entering the reservoir; the flow rate of the water body entering the reservoir is combined with the pollutant concentration monitoring value to obtain the initial pollution load; The pollutant diffusion amount per unit time is calculated by multiplying the initial pollution load by the water body diffusion coefficient, adding the pollutant degradation coefficient, and dividing by the cross-sectional area of ​​the water flow; the pollutant diffusion amount per unit time is accumulated hourly to obtain the daily diffusion flux; The daily diffusion flux is spatially layered and the surface, middle and bottom concentration values ​​are multiplied by the corresponding weight coefficients to calculate the vertical mixing coefficient; the horizontal diffusion intensity is deduced based on the vertical mixing coefficient, and the water quality change prediction data is output; Based on the water quality change prediction data, extract the pollutant concentration change gradient, multiply the concentration gradient value by the diffusion coefficient, divide it by the characteristic length, calculate the convection diffusion term; substitute the pollutant degradation reaction term, and construct a diffusion equation group; The boundary conditions of the diffusion equations are configured, and the concentration values ​​of the inlet boundary, the outlet boundary and the shore are substituted into the solution to obtain a steady-state concentration field; the steady-state concentration field is superimposed with the hydrodynamic field to output a dynamic concentration distribution; Based on the dynamic concentration distribution, the spatial diffusion range of pollutants is calculated, the area ratio of areas exceeding the standard is determined, and the water quality distribution data is established.

6. The water quality and quantity accurate simulation method based on the SWAT model according to claim 1 is characterized in that: The monitoring of reservoir operation status is carried out based on the water quality distribution data to obtain pollution diffusion situation data; Design an early warning indicator system based on the pollution diffusion situation data and determine the early warning threshold data, including: The water quality distribution data is divided into monitoring grids according to the cross-sectional location, multiplied by the corresponding water quality parameter weight, and divided by the total number of grids to calculate the water quality status index; the water quality status index is sampled in time series to obtain a water quality change sequence; For the water quality change sequence, the concentration difference between adjacent moments is calculated, the difference is divided by the time interval, and multiplied by the diffusion coefficient to obtain the pollution diffusion rate; the pollution diffusion rate is superimposed with the water flow direction data to output the diffusion direction vector; Based on the diffusion direction vector, the migration trajectory of the pollution group is extracted, the coordinates of the trajectory points are associated with the time series, and the diffusion range change rate is calculated; based on the diffusion range change rate, the pollution load flux is analyzed to generate the pollution diffusion situation data; The pollution diffusion situation data is classified, the mean value and standard deviation of pollutant concentration at each level are calculated, the mean concentration is multiplied by a weight coefficient, and a classification evaluation index is established; the classification evaluation index is compared with the water quality standard, and an exceeding risk coefficient is output; The excessive risk coefficient is used to construct a warning level classification standard, the risk coefficient interval is matched with the warning level, the trigger probability of each level is calculated, and an early warning indicator system is formed; based on the early warning indicator system, the graded response conditions are set to generate the early warning threshold data.

7. The water quality and quantity accurate simulation method based on the SWAT model according to claim 1 is characterized in that: The method of comparing and correcting the warning threshold data with the measured data, establishing accuracy evaluation data, constructing a hierarchical monitoring mechanism for the accuracy evaluation data, and outputting water quality and quantity monitoring results includes: Aligning the warning threshold data with the measured data according to the time nodes, calculating the predicted deviation value at the corresponding moment, multiplying it by the weight coefficient, and obtaining a weighted error sequence; performing statistical analysis on the weighted error sequence and outputting a correction coefficient; The correction coefficient is used to correct the warning threshold, and the correlation coefficient between the corrected value and the measured value is calculated, and the result is divided by the number of samples to obtain the fitting accuracy value; the fitting accuracy value is classified according to the monitoring section to generate the spatial accuracy distribution; Based on the spatial accuracy distribution, the prediction accuracy of each monitoring section is calculated, the accuracy value is multiplied by the section importance coefficient, divided by the total number of sections, and the section weight coefficient is output; the section weight coefficient is compared with the accuracy threshold to establish the accuracy evaluation data; The accuracy assessment data is graded, the data proportions of different accuracy intervals are counted, the credibility coefficients of each level are calculated, and a graded evaluation matrix is ​​generated; the graded evaluation matrix is ​​associated with the monitoring threshold, and a graded monitoring standard is output; Based on the hierarchical monitoring standard, the early warning triggering condition of each monitoring section is calculated, the monitoring value is subtracted from the standard value, and the difference is divided by the allowable deviation to obtain the early warning discrimination coefficient; the early warning discrimination coefficient is graded to determine the monitoring response level; The monitoring response level is matched with the water quality category, the monitoring frequency requirements of each level are calculated, and the water quality and quantity monitoring results are generated.

8. A water quality and water quantity accurate simulation system based on the SWAT model, used to implement the water quality and water quantity accurate simulation method based on the SWAT model as described in any one of claims 1 to 7, characterized in that: The water quality and quantity accurate simulation system based on the SWAT model includes: An acquisition module is used to analyze the spatiotemporal characteristics of multi-source precipitation and evapotranspiration data through dual-parameter trend test and slope evaluation, obtain initial hydrological data, process the initial hydrological data using a triple configuration algorithm, and generate a standard hydrological data set; A division module, used to perform regional hydrological division based on the standard hydrological data set and terrain elevation information, generate sub-basin division data, calculate the hydrological response unit of the sub-basin according to the sub-basin division data, and construct a basic hydrological calculation data set; A calculation module, used to perform water balance calculation using the basic hydrological calculation data set, extract water conservation characteristic data, perform data evaluation on the water conservation characteristic data and land use change, and generate water conservation evaluation data; An analysis module, for performing pollutant diffusion analysis on the incoming water body based on the water conservation assessment data, outputting water quality change prediction data, integrating the water quality change prediction data and solving the pollutant diffusion equation to establish water quality distribution data; A monitoring module is used to implement reservoir operation status monitoring based on the water quality distribution data and obtain pollution diffusion situation data; design an early warning indicator system based on the pollution diffusion situation data and determine early warning threshold data; The correction module is used to compare and correct the warning threshold data with the measured data, establish accuracy evaluation data, build a hierarchical monitoring mechanism for the accuracy evaluation data, and output water quality and quantity monitoring results.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the water quality and quantity accurate simulation method based on the SWAT model as described in any one of claims 1 to 7 is implemented.

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