An intelligent interactive river management consulting management system and method

Abstract

The present invention relates to the field of river management technology, and in particular to an intelligent interactive river management consulting and management system and method. The method comprises: deploying a multi-level water quality monitoring probe array at preset monitoring points in the river, and dynamically monitoring the river water quality based on an adaptive sampling frequency method through the multi-level water quality monitoring probe array to obtain multi-level water quality original data, wherein the multi-level water quality original data includes surface probe data, middle probe data, and bottom probe data; performing gas-liquid interface disturbance analysis on the surface probe data to obtain gas-liquid impact data; performing water body mixing analysis on the middle probe data to obtain vertical mixing data; and performing sediment resuspension analysis on the bottom probe data to obtain sediment release intensity data. The present invention, by deploying a multi-level water quality monitoring probe array and combining it with adaptive sampling frequency technology, can accurately capture the vertical distribution and dynamic changes of river water quality, thereby improving the timeliness and accuracy of monitoring.

Description

Technical Field

[0001] The present invention relates to the field of river management technology, and in particular to an intelligent interactive river management consulting management system and method. Background Art

[0002] River management is a complex, integrated project aimed at improving the river environment, enhancing flood control capabilities, and promoting ecological restoration, while also meeting the needs of socioeconomic development. River management consulting and management is a multifaceted process, primarily encompassing the following aspects: River basin management is a holistic approach to river management aimed at reducing pollution and promoting ecological restoration and protection. It encompasses land use management, sewage treatment, and water resources. River management engineering technologies and management practices include comprehensive surveys of the river's environmental status, such as water quality monitoring, ecological and environmental assessments, and pollution source analysis. Based on these survey results, a systematic management plan is developed, clarifying the specific content and implementation steps of each management measure. Scientific planning should also emphasize the integrated use of engineering and ecological restoration methods to ensure the sustainability of management results. Furthermore, a robust management system and mechanism should be established, clarifying the responsibilities and tasks of management departments at all levels, to ensure the smooth progress of river management and the effective implementation of management measures. Effective river basin management can reduce pollution emissions and protect the river's ecological environment. Intelligent, interactive river management consulting and management is a comprehensive approach to river management that incorporates modern information technology, particularly artificial intelligence, the Internet of Things, and big data.

[0003] However, traditional intelligent interactive river management consulting and management methods often suffer from the following problems: They typically rely on manual fixed-point monitoring and periodic sampling, with long sampling intervals and limited spatial coverage. These methods fail to reflect the dynamic changes in river water quality in real time, particularly its vertical distribution and spatial transmission characteristics. Water quality assessments are often based on single parameters or simple statistical analysis, making it difficult to reveal complex temporal and spatial correlations in water quality and pollution diffusion mechanisms. Summary of the Invention

[0004] Based on this, it is necessary for the present invention to provide an intelligent interactive river management consulting management system and method to solve at least one of the above technical problems.

[0005] To achieve the above objectives, an intelligent interactive river management consulting and management method is provided, comprising the following steps:

[0006] A multi-layer water quality monitoring probe array is deployed at preset monitoring points in the river. The river water quality is dynamically monitored by the multi-layer water quality monitoring probe array based on an adaptive sampling frequency method to obtain multi-layer water quality raw data, where the multi-layer water quality raw data includes surface probe data, middle probe data, and bottom probe data;

[0007] The surface probe data is analyzed for gas-liquid interface disturbance to obtain gas-liquid impact data; the middle layer probe data is analyzed for water mixing to obtain vertical mixing data; the bottom layer probe data is analyzed for sediment resuspension to obtain sediment release intensity data;

[0008] A water quality vertical distribution model is constructed based on gas-liquid impact data, vertical mixing data, and sediment release intensity data. Based on the water quality vertical distribution model, spatiotemporal correlation analysis of water quality parameters at adjacent monitoring points is performed to draw a water quality transmission network topology map.

[0009] Based on the water quality vertical distribution model and the water quality transmission network topology, the degree centrality and betweenness centrality of each monitoring point are calculated, and the key propagation nodes of water quality fluctuations are identified to obtain water quality tomography data. Based on the water quality tomography data, a river management plan is generated and the management plan is simulated and verified to form a river management plan for river management consultation.

[0010] The multi-layered monitoring probe array of this invention enables the acquisition of three-dimensional spatial distribution data on river water quality, covering the surface, middle, and bottom layers, thereby obtaining more comprehensive water quality information. Dynamically adjusting the monitoring frequency based on an adaptive sampling frequency method improves data acquisition accuracy under drastic water quality fluctuations while reducing data redundancy during periods of stable water quality, optimizing the efficient use of monitoring resources. Data analysis from surface probes reveals the dynamic impact of air-liquid interface disturbances on dissolved oxygen and volatile pollutants in water, providing a scientific basis for assessing the interaction between the atmosphere and water. Data analysis from middle-layer probes quantifies the intensity of material and energy exchange between different water layers using water mixing, facilitating the study of vertical pollutant migration. Data analysis from bottom-layer probes assesses the resuspension release of nutrients or pollutants from sediments, helping to predict the long-term impact of sediment on overall water quality. By integrating data from air-liquid interface disturbances, vertical mixing, and sediment release intensity into a vertical distribution model, the vertical distribution characteristics of water quality parameters can be systematically quantified. This model not only provides a more accurate description of the spatial structure of water quality but also provides a basis for analyzing parameter correlations between monitoring points. Spatiotemporal correlation analysis of water quality data from adjacent monitoring points can reveal the dynamic patterns of pollutant diffusion and migration. Topological maps visualize the complex transmission relationships of river water quality, helping management personnel intuitively identify pollution sources, diffusion pathways, and key treatment areas. Calculating the degree centrality and betweenness centrality of monitoring points quantifies the importance of each monitoring point in the water quality transmission network. Identifying key transmission nodes allows for more precise localization of sources of water quality fluctuations or high-risk transmission pathways, thereby improving the relevance and effectiveness of treatment plans. Generating treatment plans based on water quality tomography data ensures that the plans are based on comprehensive scientific data, increasing their feasibility. The simulation verification process assesses the actual effectiveness and potential risks of the plans through virtual simulations, avoiding unnecessary issues that may arise from direct implementation and improving treatment efficiency and success rates. Combining all key data and simulation verification results, the resulting treatment recommendations are highly scientific and targeted, effectively addressing water quality issues in complex river environments and achieving precise treatment. This process also significantly shortens decision-making time and improves treatment effectiveness. This method establishes a spatiotemporal dynamic model of river water quality through multi-level and multi-dimensional data collection and analysis. It systematically identifies key factors and transmission nodes influencing water quality and generates optimal treatment plans through an intelligent, data-driven approach. Combined with simulation verification, the reliability of the treatment plans is further ensured, significantly improving river management efficiency and reducing treatment costs, while also providing strong support for scientific decision-making.

[0011] Preferably, the water mixing degree analysis is specifically as follows:

[0012] The temperature, conductivity and dissolved oxygen parameters of water bodies at different depths are extracted based on the data from the mid-layer probe to obtain vertical profile data of the water body;

[0013] The water stratification intensity index is calculated based on the vertical profile data of the water body to obtain the water stratification intensity data. The water stratification intensity index calculation specifically includes calculating the thermal stratification coefficient based on the vertical temperature gradient, calculating the salt stratification coefficient based on the vertical conductivity gradient, and calculating the oxygen stratification coefficient based on the vertical dissolved oxygen gradient. The thermal stratification coefficient, the salt stratification coefficient, and the oxygen stratification coefficient are weighted and integrated to obtain the water stratification intensity index.

[0014] Based on the mixed water stratification intensity data, the water body is vertically divided into n calculation layers, and the mass and energy transfer equations between adjacent calculation layers are established. The vertical turbulent diffusion coefficient describing the vertical mixing intensity is dynamically adjusted to construct a vertical mixing model and obtain vertical mixing data.

[0015] The present invention uses a method to measure the variations in water parameters such as temperature, conductivity, and dissolved oxygen at different depths, reflecting the vertical structural characteristics of the water body. By extracting these parameters from the mid-layer probe data, vertical profile data of the water body can be obtained, providing a comprehensive understanding of the vertical distribution of the water body. Water quality data at different depths can reveal the hierarchical structure of the water body, help identify water quality differences between different depth layers, and provide basic data support for subsequent analysis of water stratification, mixing, and pollutant diffusion. The stratification of a water body affects its physicochemical properties such as temperature, salinity, and dissolved oxygen. By calculating the water stratification intensity index, the vertical hierarchical structure and stability of the water body can be quantified. Specifically, the vertical gradients of temperature, salinity, and dissolved oxygen are used to calculate the thermal, salinity, and oxygen stratification coefficients, respectively. These are then decentralized and integrated to obtain comprehensive water stratification intensity data. This data can help identify whether the water body is in a stable stratification state and the stratification intensity between different layers, providing a basis for further water management and pollutant control. The stratification state of a water body directly affects the mixing process, which in turn affects the efficiency of material and energy exchange. By analyzing water stratification intensity data, it is possible to assess the mixing efficiency of water bodies and, in turn, derive the mixing efficiency coefficient. This analysis helps understand the material exchange capacity of various layers within a water body, as well as the transport and diffusion rates between different layers. It is of great significance for water management, pollutant control, and water quality early warning. In particular, when water bodies exhibit varying degrees of stratification, the mixing efficiency coefficient helps determine water quality trends and the stability of the ecological environment. Vertical mixing of water bodies involves the exchange of matter and energy within the water body, and this process is significantly affected by stratification. By dividing the water body into multiple computational layers based on the mixing efficiency coefficient data and establishing mass and energy transfer equations between adjacent computational layers, the vertical mixing process can be accurately simulated. By dynamically adjusting the vertical turbulent diffusion coefficient, which describes the vertical mixing intensity, the model accurately reflects material transport and energy exchange within the water body. This model can help identify the mixing efficiency between different layers within a water body and predict water quality trends. In particular, in stratified water bodies where the exchange rates of substances such as temperature and dissolved oxygen are low, the vertical mixing model can effectively guide pollutant control and water quality restoration measures.

[0016] The present invention also provides an intelligent interactive river management consultation and management system for executing the above-mentioned intelligent interactive river management consultation and management method, wherein the intelligent interactive river management consultation and management system comprises:

[0017] A water quality monitoring unit is used to deploy a multi-layer water quality monitoring probe array at preset monitoring points in the river. The multi-layer water quality monitoring probe array dynamically monitors the river water quality based on an adaptive sampling frequency method to obtain multi-layer water quality raw data, wherein the multi-layer water quality raw data includes surface probe data, middle probe data, and bottom probe data;

[0018] The water quality characteristic analysis unit is used to perform gas-liquid interface disturbance analysis on surface probe data to obtain gas-liquid impact data; perform water mixing analysis on mid-layer probe data to obtain vertical mixing data; and perform sediment resuspension analysis on bottom layer probe data to obtain sediment release intensity data.

[0019] The water quality distribution modeling unit is used to construct a water quality vertical distribution model based on gas-liquid impact data, vertical mixing data, and sediment release intensity data; based on the water quality vertical distribution model, the spatiotemporal correlation analysis of water quality parameters at adjacent monitoring points is performed to draw a water quality transmission network topology map;

[0020] The treatment plan generation unit is used to calculate the degree centrality and betweenness centrality of each monitoring point based on the water quality vertical distribution model and the water quality transmission network topology diagram, and identify the key propagation nodes of water quality fluctuations to obtain water quality tomography data; generate river treatment plans based on water quality tomography data, and simulate and verify the treatment plans to form a river treatment plan for river treatment consultation.

[0021] By deploying a multi-layered array of monitoring probes, the present invention can comprehensively cover all layers of a water body, thereby obtaining water quality data at different levels. This multi-layered data provides detailed information for subsequent analysis, ensuring the comprehensiveness and accuracy of water quality monitoring. Adaptive sampling frequency dynamically adjusts the sampling frequency based on water quality changes, avoiding resource waste due to oversampling while ensuring improved accuracy and real-time data acquisition even when water quality fluctuates dramatically. This strategy enhances the responsiveness of the monitoring system, facilitating timely identification and response to water quality fluctuations. By separately analyzing the characteristics of surface, middle, and bottom layer data, the dynamic changes in each layer of the water body can be more accurately understood and assessed. For example, gas-liquid interface disturbance analysis helps identify the impact of airflow in the surface layer, while water mixing analysis reveals the fluidity and exchange characteristics of the middle layer. Sediment resuspension analysis helps assess bottom-level pollution sources and the intensity of sediment release. Different water quality characteristics provide multi-dimensional information, which helps to fully understand the factors affecting water quality and provides accurate input data for subsequent water quality modeling and treatment plan development. By establishing a vertical water quality distribution model, we can comprehensively understand water quality variations at different levels within a water body, thereby gaining a deeper understanding of its distribution characteristics. This modeling can reveal the spatial and temporal evolution of water quality, providing a theoretical basis for water quality management and remediation. Spatiotemporal correlation analysis can reveal the relationships between water quality parameters at different monitoring points, forming a topological map of the water quality transmission network. This map helps identify water quality transmission channels and key nodes, clarifying the path and impact range of water quality changes, and provides a valuable tool for tracing pollution sources and analyzing the dynamic evolution of water quality. By calculating degree centrality and betweenness centrality, we can identify key transmission nodes of water quality fluctuations. Nodes with high centrality are often the main transmission points or bottlenecks of water quality changes. Addressing these nodes can effectively control the spread of water quality changes in a short period of time and optimize remediation effectiveness. Water quality tomography data can more clearly identify the distribution and variation characteristics of water quality problems, helping decision-makers identify the root causes of water quality issues. Water quality tomography data provides a precise map of water quality problems, making remediation plans more targeted and scientific. Through simulation and verification of governance solutions, we can predict the effects of different governance measures, assess their feasibility in practice, and avoid uncertainty and risk in implementation. Simulation and verification can help select the most effective and economical governance measures, ensuring the efficiency and feasibility of the final governance recommendations. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0023] Figure 1 A schematic flow chart of the steps of the intelligent interactive river management consultation and management method of the present invention;

[0024] Figure 2 Detailed process flow diagram of the gas-liquid interface disturbance analysis;

[0025] Figure 3 Schematic diagram of the detailed steps of the water mixing analysis. DETAILED DESCRIPTION

[0026] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0027] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0028] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0029] To achieve this, please refer to Figures 1 to 3 The present invention provides an intelligent interactive river management consultation and management method, which includes the following steps:

[0030] A multi-layer water quality monitoring probe array is deployed at preset monitoring points in the river. The river water quality is dynamically monitored by the multi-layer water quality monitoring probe array based on an adaptive sampling frequency method to obtain multi-layer water quality raw data, where the multi-layer water quality raw data includes surface probe data, middle probe data, and bottom probe data;

[0031] The surface probe data is analyzed for gas-liquid interface disturbance to obtain gas-liquid impact data; the middle layer probe data is analyzed for water mixing to obtain vertical mixing data; the bottom layer probe data is analyzed for sediment resuspension to obtain sediment release intensity data;

[0032] A water quality vertical distribution model is constructed based on gas-liquid impact data, vertical mixing data, and sediment release intensity data. Based on the water quality vertical distribution model, spatiotemporal correlation analysis of water quality parameters at adjacent monitoring points is performed to draw a water quality transmission network topology map.

[0033] Based on the water quality vertical distribution model and the water quality transmission network topology, the degree centrality and betweenness centrality of each monitoring point are calculated, and the key propagation nodes of water quality fluctuations are identified to obtain water quality tomography data. Based on the water quality tomography data, a river management plan is generated and the management plan is simulated and verified to form a river management plan for river management consultation.

[0034] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart showing the steps of an intelligent interactive river management consultation and management method according to the present invention. In this example, the intelligent interactive river management consultation and management method includes the following steps:

[0035] S1: A multi-layer water quality monitoring probe array is deployed at preset monitoring points in the river. The river water quality is dynamically monitored by the multi-layer water quality monitoring probe array based on an adaptive sampling frequency method to obtain multi-layer water quality raw data, where the multi-layer water quality raw data includes surface probe data, mid-layer probe data, and bottom-layer probe data;

[0036] In an embodiment of the present invention, a multi-layer array of water quality monitoring probes is deployed at preset monitoring points in the river. By selecting probes of different water depths, multi-layer monitoring of surface, middle, and bottom water quality is achieved to improve the spatial resolution of water quality parameters. In a specific implementation, the surface probe is deployed at a water depth of 0.5 meters, the middle probe is deployed at a water depth of 5 meters, and the bottom probe is deployed at a water depth of 0.5 meters from the riverbed. The sampling frequency is dynamically adjusted using an adaptive sampling frequency method. For example, when the rate of change of water quality parameters is greater than 0.8 units / minute, the sampling interval is adjusted to 10 seconds; when the rate of change is between 0.3-0.8 units / minute, the sampling interval is 2 minutes; when the rate of change is less than 0.3 units / minute, the sampling interval is 15 minutes, thereby balancing detailed monitoring and resource conservation.

[0037] S2: Perform gas-liquid interface disturbance analysis on surface probe data to obtain gas-liquid impact data; perform water mixing analysis on mid-layer probe data to obtain vertical mixing data; perform sediment resuspension analysis on bottom layer probe data to obtain sediment release intensity data;

[0038] The embodiment of the present invention analyzes the gas-liquid interface disturbance based on the surface probe data. By combining the surface water quality parameters collected by the probe with the wind speed and air pressure change data monitored by the meteorological sensor, the impact of the water surface disturbance on the water quality is evaluated. In the specific implementation, the wind-induced turbulence intensity is calculated at a wind speed of 6 m / s, and the intensity coefficient obtained is 0.72; the air pressure data is used to predict the air pressure from 0.1% to 0.2% in the next 24 hours. Down to The disturbance degree is predicted to be significant, and the surface probe data is combined to analyze the impact of fluctuations on water quality parameters (such as dissolved oxygen), and finally generate gas-liquid impact data. The water mixing degree analysis is performed on the middle-layer probe data. The vertical profile of the water body is constructed by extracting temperature, conductivity and dissolved oxygen data at different depths, and the vertical mixing characteristics are analyzed using the stratification intensity index. In the specific implementation, the vertical temperature gradient is m, the calculated thermal stratification coefficient is 0.6; the vertical gradient of conductivity is m, the calculated salt stratification coefficient is 0.5; the vertical gradient of dissolved oxygen is The oxygen stratification coefficient is calculated to be 0.7, and the stratification intensity index after weighting the stratification coefficient is 0.62. Based on this intensity index, the vertical turbulent diffusion coefficient is dynamically adjusted to Seconds, the vertical mixing model is constructed to generate vertical mixing data. Sediment resuspension analysis is performed on the bottom probe data to identify turbidity mutation events by monitoring the instantaneous change of turbidity. In the specific implementation, when the turbidity rise rate reaches When the turbidity changes, the concentration of total phosphorus in the water increases, and the turbidity increases. , and generate sediment release intensity data.

[0039] S3: Construct a water quality vertical distribution model based on gas-liquid impact data, vertical mixing data, and sediment release intensity data; perform spatiotemporal correlation analysis of water quality parameters at adjacent monitoring points based on the water quality vertical distribution model to draw a water quality transmission network topology map;

[0040] The embodiment of the present invention establishes a water quality vertical distribution model based on the coupling of vertical turbulent diffusion and stratification characteristics based on gas-liquid impact data, vertical mixing data, and sediment release intensity data. In the specific implementation, the surface dissolved oxygen is corrected for gas-liquid interface disturbance, and the correction value is Analysis of the transmission efficiency of the middle layer water quality showed that the transmission efficiency of the vertical gradient was reduced to 0.8; combined with the bottom layer release intensity, the total phosphorus concentration increase was corrected to , ultimately dynamically calculating and forming a water quality model describing the vertical distribution. Based on the vertical distribution model, time lag analysis and spatial correlation analysis are performed on water quality parameters at adjacent monitoring points, and a spatiotemporal correlation coefficient matrix is calculated between each monitoring point. In practice, when two points have a time lag correlation coefficient of 0.85, a spatial distance of 2 kilometers, and similar hydrological and geographical characteristics, they are determined to have a water quality transmission relationship and are marked as connected nodes in the topological map.

[0041] S4: Based on the water quality vertical distribution model and the water quality transmission network topology, the degree centrality and betweenness centrality of each monitoring point are calculated, and the key propagation nodes of water quality fluctuations are identified to obtain water quality tomography data; based on the water quality tomography data, a river management plan is generated and the management plan is simulated and verified to form a river management plan for river management consultation.

[0042] The present invention uses degree centrality and betweenness centrality analysis based on a topological graph to identify propagation nodes and generate a river management plan based on tomographic data. In a specific implementation, degree centrality was used to screen out key nodes (nodes with a centrality value greater than 0.75). Analysis revealed that the primary pollution problem was excessive nitrogen. Based on the empirical database, the management measures matched were planting aquatic plants and using aeration devices. Simulation verification showed that nitrogen concentration decreased after the management. , and finally output the recommended governance solution.

[0043] The multi-layered monitoring probe array of this invention enables the acquisition of three-dimensional spatial distribution data on river water quality, covering the surface, middle, and bottom layers, thereby obtaining more comprehensive water quality information. Dynamically adjusting the monitoring frequency based on an adaptive sampling frequency method improves data acquisition accuracy under drastic water quality fluctuations while reducing data redundancy during periods of stable water quality, optimizing the efficient use of monitoring resources. Data analysis from surface probes reveals the dynamic impact of air-liquid interface disturbances on dissolved oxygen and volatile pollutants in water, providing a scientific basis for assessing the interaction between the atmosphere and water. Data analysis from middle-layer probes quantifies the intensity of material and energy exchange between different water layers using water mixing, facilitating the study of vertical pollutant migration. Data analysis from bottom-layer probes assesses the resuspension release of nutrients or pollutants from sediments, helping to predict the long-term impact of sediment on overall water quality. By integrating data from air-liquid interface disturbances, vertical mixing, and sediment release intensity into a vertical distribution model, the vertical distribution characteristics of water quality parameters can be systematically quantified. This model not only provides a more accurate description of the spatial structure of water quality but also provides a basis for analyzing parameter correlations between monitoring points. Spatiotemporal correlation analysis of water quality data from adjacent monitoring points can reveal the dynamic patterns of pollutant diffusion and migration. Topological maps visualize the complex transmission relationships of river water quality, helping management personnel intuitively identify pollution sources, diffusion pathways, and key treatment areas. Calculating the degree centrality and betweenness centrality of monitoring points quantifies the importance of each monitoring point in the water quality transmission network. Identifying key transmission nodes allows for more precise localization of sources of water quality fluctuations or high-risk transmission pathways, thereby improving the relevance and effectiveness of treatment plans. Generating treatment plans based on water quality tomography data ensures that the plans are based on comprehensive scientific data, increasing their feasibility. The simulation verification process assesses the actual effectiveness and potential risks of the plans through virtual simulations, avoiding unnecessary issues that may arise from direct implementation and improving treatment efficiency and success rates. Combining all key data and simulation verification results, the resulting treatment recommendations are highly scientific and targeted, effectively addressing water quality issues in complex river environments and achieving precise treatment. This process also significantly shortens decision-making time and improves treatment effectiveness. This method establishes a spatiotemporal dynamic model of river water quality through multi-level and multi-dimensional data collection and analysis. It systematically identifies key factors and transmission nodes influencing water quality and generates optimal treatment plans through an intelligent, data-driven approach. Combined with simulation verification, the reliability of the treatment plans is further ensured, significantly improving river management efficiency and reducing treatment costs, while also providing strong support for scientific decision-making.

[0044] Preferably, the adaptive sampling frequency method is specifically:

[0045] Obtain the water quality parameter change rate at the current moment. When the water quality parameter change rate is greater than the preset first threshold, adjust the sampling time interval to 10 seconds. When the water quality parameter change rate is between the preset first threshold and the preset second threshold, adjust the sampling time interval to 2 minutes. When the water quality parameter change rate is less than the preset second threshold, adjust the sampling time interval to 15 minutes to achieve fine sampling of areas with drastic changes and resource conservation in stable areas.

[0046] The embodiment of the present invention uses a real-time monitoring system to obtain the change rate of water quality parameters at the current moment. Water quality parameters include dissolved oxygen (DO), turbidity (TURB), conductivity (EC), etc. In a specific implementation, a multi-level water quality monitoring probe array is used to collect water quality data every 5 minutes, and the change rate is obtained by performing a differential calculation on the water quality parameters of two consecutive samples. For example, in a certain river section, when the DO concentration of two consecutive samples is respectively and , when the time interval is 5 minutes, the change rate calculation formula is The change rate data will be used as the basis for subsequent sampling interval adjustment. When the water quality parameter change rate is greater than the preset first threshold, the sampling interval is adjusted to 10 seconds. For example, if the first threshold is set to 0.8 units / minute, when the calculated DO change rate reaches When the water quality parameter change rate is between the preset first threshold and the preset second threshold, the sampling time interval is adjusted to 2 minutes. For example, the first threshold is set to 0.8 units / minute and the second threshold is set to 0.3 units / minute. When the DO change rate calculated at a certain monitoring point is When the water quality parameter change rate is less than the preset second threshold, the sampling time interval is adjusted to 15 minutes to save resources in the stable area. For example, when the DO change rate calculated at a certain monitoring point is less than 0. If the water quality is below the second threshold of 0.3 units / minute, the system triggers the low-frequency sampling mode and adjusts the sampling interval to 15 minutes to reduce data redundancy and equipment power consumption. At the same time, the long-term trend of low water quality change rate is recorded through the background database for subsequent ecological environment assessment and optimization of treatment plans.

[0047] The present invention's real-time monitoring of the rate of change of water quality parameters accurately reflects dynamic changes in the aquatic environment and captures dramatic fluctuations in water quality caused by emergencies (such as pollutant discharges or natural disturbances). This real-time response capability provides data support for timely response measures, significantly improving the sensitivity and accuracy of the water quality monitoring system. When the water quality change rate exceeds a preset first threshold, the sampling interval is quickly shortened to 10 seconds, enabling high-frequency monitoring. This precise sampling captures details of rapidly changing processes, revealing the precise trajectory of pollutant concentration changes and providing high-resolution data for subsequent analysis. When the water quality change rate is between the first and second thresholds, the sampling interval is adjusted to 2 minutes, balancing monitoring accuracy and resource efficiency. This setting ensures that even when changes are relatively gradual, sufficient data is captured to reflect changing trends, avoiding the increased equipment load and data redundancy caused by overly frequent sampling. This allows for faster and more accurate localization of pollution sources and assessment of the impact of pollution incidents. When the water quality change rate is below the second threshold, extending the sampling interval to 15 minutes significantly reduces system resource consumption. Reducing the sampling frequency in areas with stable water quality not only saves energy and data storage space for monitoring equipment, but also alleviates the burden of data storage and processing, while still maintaining effective monitoring of environmental conditions. By dynamically adjusting the sampling interval, critical data can be captured in areas of rapid fluctuations. This refined monitoring strategy facilitates in-depth research into the driving mechanisms and diffusion pathways of pollution incidents, providing high-quality data to support the development of targeted remediation and early warning measures. In areas with stable water quality parameters, reducing the sampling frequency avoids unnecessary resource waste and enhances the sustainability of the monitoring system. This resource-saving strategy also frees up more equipment and human resources for centralized monitoring of key areas. Dynamically adjusting the sampling interval combines flexibility and efficiency, adapting to the varying characteristics of water quality in different regions to achieve refined and cost-effective monitoring. This strategy not only enhances the intelligence of the water quality monitoring system but also optimizes the monitoring process, significantly improving its operational efficiency and effectiveness. By combining real-time monitoring of the rate of change of water quality parameters with an adaptive sampling interval adjustment strategy, this approach achieves refined monitoring in areas of rapid fluctuation and resource conservation in stable areas. It not only ensures the timely discovery and in-depth analysis of water quality emergencies, but also significantly reduces resource waste in long-term monitoring, providing a scientific and efficient solution for water quality monitoring in large-scale, dynamic environments.

[0048] Preferably, the gas-liquid interface disturbance analysis is specifically as follows:

[0049] Obtain wind speed and air pressure change data based on surface probe data;

[0050] The coupling relationship between wind speed and water quality parameters is calculated based on surface probe data and wind speed and air pressure change data, thereby obtaining wind-induced turbulence intensity data;

[0051] The interface disturbance degree within the next 24 hours is predicted based on the pressure change trend of the wind speed and pressure change data, thereby obtaining the disturbance degree prediction data;

[0052] Based on the disturbance degree prediction data and wind-induced turbulence intensity data, the water quality parameters of the surface probe data are analyzed to analyze the impact of water surface fluctuations on water quality, thereby obtaining gas-liquid impact data.

[0053] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 The detailed step flow chart of the gas-liquid interface disturbance analysis in the embodiment of the present invention is as follows:

[0054] S211: Obtain wind speed and air pressure change data based on surface probe data;

[0055] The embodiment of the present invention uses a surface probe to collect water quality data such as water temperature, dissolved oxygen (DO), pH value, etc. in real time, and combines it with the wind speed and air pressure data provided by the environmental monitoring station to extract wind speed and air pressure change data using a data integration algorithm. For example, a river monitoring point is selected, and the surface probe collects water quality data every 10 minutes. The weather station also provides the wind speed at the corresponding time point. , the air pressure is 1015 By analyzing the changing trends of wind speed and air pressure, the dynamic change sequence of wind speed and air pressure during the monitoring period was obtained, providing basic data for subsequent analysis.

[0056] S212: Calculating the coupling relationship between wind speed and water quality parameters based on surface probe data and wind speed and air pressure change data, thereby obtaining wind-induced turbulence intensity data;

[0057] The embodiment of the present invention uses the water quality data and wind speed and air pressure change data collected by the surface probe, and adopts the regression analysis method to calculate the coupling relationship between wind speed and water quality parameters. In the specific implementation, the wind speed is used as the independent variable and the DO concentration as the dependent variable, and the least squares method is used for regression fitting to obtain the coupling model between wind speed and DO. For example, the relationship between the DO concentration change rate and wind speed is DO_rate=0.05windspeed^2-0.2windspeed+0.5. Then, the wind-induced turbulence intensity at a specific wind speed is calculated based on the coupling model. For example, the wind speed is 8 When the wind-induced turbulence intensity is calculated to be 1.2 units, it is one of the main driving forces for the changes in water quality parameters.

[0058] S213: Predicting the interface disturbance degree within the next 24 hours based on the pressure change trend based on the wind speed and pressure change data, thereby obtaining disturbance degree prediction data;

[0059] The embodiment of the present invention performs pressure change trend analysis on wind speed and pressure change data, and uses a time series prediction model to predict the degree of interface disturbance within the next 24 hours. For example, based on the time series of pressure change ( ), the ARIMA model is used to fit and predict the future pressure change trend. Combined with the coupling influence relationship between wind speed and air pressure, the degree of interface disturbance in the next 24 hours is calculated by the disturbance index formula, for example, disturbance index = wind speed The rate of change of air pressure is used to obtain the predicted data of disturbance degree.

[0060] S214: Analyze the impact of water surface fluctuations on water quality based on the disturbance degree prediction data and the wind-induced turbulence intensity data on the water quality parameters of the surface probe data, thereby obtaining gas-liquid impact data.

[0061] In this embodiment of the present invention, a CFD (computational fluid dynamics) model is used to analyze the impact of surface fluctuations on surface water quality parameters based on predicted disturbance level data and wind-induced turbulence intensity data. For example, the predicted wind speed, air pressure changes, and wind-induced turbulence intensity are input into the CFD model as boundary conditions to simulate the distribution changes of DO concentration under fluctuation conditions. The results show that under the predicted high wind speed and low air pressure conditions, the DO concentration fluctuation amplitude is , indicating that fluctuations in the gas-liquid interface have a significant impact on dissolved oxygen. The analysis results were ultimately compiled into gas-liquid impact data for subsequent optimization of river management strategies.

[0062] Wind speed and air pressure are important external factors affecting surface water quality, particularly in terms of water surface fluctuations, gas-liquid interface disturbances, and water mixing. By acquiring wind speed and air pressure change data correlated with surface probe data, environmental conditions can be systematically correlated and analyzed with water quality monitoring data, providing a more comprehensive water quality status monitoring framework. This enables the system to predict water quality fluctuation trends based on climate change, improving the accuracy and timeliness of water quality monitoring. The impact of wind speed on surface water quality is reflected in the intensity of wind-induced turbulence (water disturbance caused by wind). By coupling wind speed and air pressure data with surface probe data, the intensity of wind-induced turbulence can be quantified, revealing the potential impact of wind on water quality. Wind-induced turbulence can affect important water quality parameters such as oxygen exchange, dissolved oxygen concentration, and the distribution of suspended particulate matter. Therefore, this analysis can provide a deeper understanding of the complex relationship between wind and water quality, particularly its impact on dynamic processes such as water surface fluctuations, gas-liquid exchange, and water mixing. Changes in wind speed and air pressure directly affect the degree of gas-liquid interface disturbance, which in turn affects water quality. By analyzing air pressure trends and combining them with wind speed data for forecasts, it is possible to predict surface disturbances within the next 24 hours. This predictive capability helps identify potential water quality fluctuations in advance, especially when air pressure fluctuations trigger abnormal atmospheric pressure, which can lead to significant water surface fluctuations. The predicted disturbance level data can provide data support for early warning of water quality trends and the preemptive deployment of control measures. Water surface fluctuations are a significant phenomenon caused by wind-induced turbulence, directly affecting air-liquid interface exchange and water quality changes. By combining predicted disturbance level data with wind-induced turbulence intensity data for water quality analysis, we can gain a deeper understanding of how water surface fluctuations affect water quality parameters through air-liquid exchange (such as dissolved oxygen concentration and gas exchange). This analysis helps identify the key drivers of water quality fluctuations, especially during periods of high wind speed or drastic pressure fluctuations. Quantifying the impact of water surface fluctuations provides strong data support for dynamic predictions of water quality changes, pollutant dispersion models, and water management. This analysis fully considers the potential impacts of wind speed and air pressure on water quality, particularly the effects of wind-induced turbulence on water surface fluctuations and air-liquid interface disturbances. This comprehensive analytical approach provides more accurate forecasts of water quality trends, helping monitoring systems better respond to dynamic disturbances in the natural environment. Furthermore, by precisely identifying the key drivers of water quality changes, it enhances the early warning capabilities and accuracy of water quality monitoring, enabling timely adjustments to remediation measures and water quality management strategies to ensure the stability and protection of the aquatic ecosystem.

[0063] Preferably, the water mixing degree analysis is specifically as follows:

[0064] The temperature, conductivity and dissolved oxygen parameters of water bodies at different depths are extracted based on the data from the mid-layer probe to obtain vertical profile data of the water body;

[0065] The water stratification intensity index is calculated based on the vertical profile data of the water body to obtain the water stratification intensity data. The water stratification intensity index calculation specifically includes calculating the thermal stratification coefficient based on the vertical temperature gradient, calculating the salt stratification coefficient based on the vertical conductivity gradient, and calculating the oxygen stratification coefficient based on the vertical dissolved oxygen gradient. The thermal stratification coefficient, the salt stratification coefficient, and the oxygen stratification coefficient are weighted and integrated to obtain the water stratification intensity index.

[0066] Based on the water stratification intensity data, the water body is vertically divided into n calculation layers, and the mass and energy transfer equations between adjacent calculation layers are established. The vertical turbulent diffusion coefficient describing the vertical mixing intensity is dynamically adjusted to construct a vertical mixing model and obtain vertical mixing data.

[0067] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 The detailed step flow diagram of the water mixing degree analysis in the embodiment of the present invention is as follows:

[0068] S221: Extract the temperature, conductivity and dissolved oxygen parameters of water bodies at different depths based on the data from the mid-layer probe, thereby obtaining vertical profile data of the water body;

[0069] S222: Calculating a water stratification intensity index based on the water body vertical profile data to obtain water stratification intensity data, wherein the water stratification intensity index calculation specifically includes calculating a thermal stratification coefficient based on the vertical temperature gradient, calculating a salt stratification coefficient based on the vertical conductivity gradient, and calculating an oxygen stratification coefficient based on the vertical dissolved oxygen gradient. The thermal stratification coefficient, the salt stratification coefficient, and the oxygen stratification coefficient are weighted and integrated to obtain the water stratification intensity index.

[0070] S223: Based on the water stratification intensity data, the water body is vertically divided into n calculation layers, and the mass and energy transfer equations between adjacent calculation layers are established. The vertical turbulent diffusion coefficient describing the vertical mixing intensity is dynamically adjusted to construct a vertical mixing model and obtain vertical mixing data.

[0071] The embodiment of the present invention uses mid-level probes to collect water temperature, conductivity, and dissolved oxygen parameters at different depths in the river in a vertically distributed manner. The specific operation is to deploy probes at three depths of 1 meter, 3 meters, and 5 meters, and collect data every 5 minutes. For example, at a certain monitoring point, the temperatures collected at different depths are 22.5°C, 21.3°C, and 20.1°C, and the conductivity is 450 , 470 and 500 , dissolved oxygen is 8.2 , 7.5 and 6.8 . These data are organized into vertical profile data of water bodies to provide data support for subsequent stratification calculations. Based on the extracted vertical profile data of water bodies, the water stratification intensity index is calculated. The specific operations include using the vertical temperature gradient to calculate the thermal stratification coefficient. For example, the temperature gradient between 1 meter and 3 meters is -0.6°C / m, and the corresponding thermal stratification coefficient is 2.5; using the vertical conductivity gradient to calculate the salt stratification coefficient. For example, the conductivity gradient between 3 meters and 5 meters is 15 , the corresponding salt stratification coefficient is 1.8; the oxygen stratification coefficient is calculated using the vertical gradient of dissolved oxygen. For example, the dissolved oxygen gradient between 1 meter and 3 meters is , the corresponding oxygen stratification coefficient is 2.0. Then the water stratification intensity index is obtained as the water stratification intensity data through the weighted formula. Based on the water stratification intensity index, the water body is vertically divided into three calculation layers (1 meter, 3 meters and 5 meters), and the mass and energy transfer equations between adjacent calculation layers are established. For example, the inter-layer mass transfer rate is set to , the energy transfer rate is The vertical turbulent diffusion coefficient is dynamically adjusted according to the stratification intensity data. For example, when the stratification intensity index increases from 2.2 to 2.8, the vertical turbulent diffusion coefficient increases from Reduce to These parameters are used to construct a vertical mixing model. By solving the continuity equation and turbulent momentum equation, vertical mixing data are finally obtained, which provides support for the quantitative analysis of the vertical mixing characteristics of water bodies.

[0072] The present invention uses a method to measure the variations in water parameters such as temperature, conductivity, and dissolved oxygen at different depths, reflecting the vertical structural characteristics of the water body. By extracting these parameters from the mid-layer probe data, vertical profile data of the water body can be obtained, providing a comprehensive understanding of the vertical distribution of the water body. Water quality data at different depths can reveal the hierarchical structure of the water body, help identify water quality differences between different depth layers, and provide basic data support for subsequent analysis of water stratification, mixing, and pollutant diffusion. The stratification of a water body affects its physicochemical properties such as temperature, salinity, and dissolved oxygen. By calculating the water stratification intensity index, the vertical hierarchical structure and stability of the water body can be quantified. Specifically, the vertical gradients of temperature, salinity, and dissolved oxygen are used to calculate the thermal, salinity, and oxygen stratification coefficients, respectively. These are then decentralized and integrated to obtain comprehensive water stratification intensity data. This data can help identify whether the water body is in a stable stratification state and the stratification intensity between different layers, providing a basis for further water management and pollutant control. The stratification state of a water body directly affects the mixing process, which in turn affects the efficiency of material and energy exchange. By analyzing water stratification intensity data, it is possible to assess the mixing efficiency of water bodies and, in turn, derive the mixing efficiency coefficient. This analysis helps understand the material exchange capacity of various layers within a water body, as well as the transport and diffusion rates between different layers. It is of great significance for water management, pollutant control, and water quality early warning. In particular, when water bodies exhibit varying degrees of stratification, the mixing efficiency coefficient helps determine water quality trends and the stability of the ecological environment. Vertical mixing of water bodies involves the exchange of matter and energy within the water body, and this process is significantly affected by stratification. By dividing the water body into multiple computational layers based on the mixing efficiency coefficient data and establishing mass and energy transfer equations between adjacent computational layers, the vertical mixing process can be accurately simulated. By dynamically adjusting the vertical turbulent diffusion coefficient, which describes the vertical mixing intensity, the model accurately reflects material transport and energy exchange within the water body. This model can help identify the mixing efficiency between different layers within a water body and predict water quality trends. In particular, in stratified water bodies where the exchange rates of substances such as temperature and dissolved oxygen are low, the vertical mixing model can effectively guide pollutant control and water quality restoration measures.

[0073] Preferably, the sediment resuspension analysis is specifically:

[0074] The instantaneous change of turbidity of the bottom water is calculated based on the bottom probe data, thereby obtaining the instantaneous change data of turbidity;

[0075] Turbidity mutation detection is performed based on the preset turbidity instantaneous change threshold and turbidity instantaneous change data. When a turbidity mutation is detected, the high-frequency sampling mode of the bottom probe is triggered, and the turbidity rising rate and diffusion range are recorded to obtain turbidity mutation data;

[0076] Resuspension type identification is performed on the turbidity mutation data based on typical patterns in the historical database, thereby obtaining resuspension type data;

[0077] Based on the resuspension type data and bottom probe data, the impact of resuspension on water quality and the duration are evaluated to obtain sediment release intensity data.

[0078] The embodiment of the present invention uses the bottom probe to record the turbidity data of the bottom water body every second. For example, the turbidity data collected by the probe set in a certain river section within 10 minutes are 20, 21, 23, 25, 50, and 55 (unit: NTU). According to the turbidity change rate formula Calculate the instantaneous rate of change of turbidity, where For 1 second, we can calculate The values are 1, 2, 2, 25, and 5 respectively, and the turbidity instantaneous change data is finally obtained, which shows that the turbidity changes dramatically in the 5th second, providing a basis for subsequent analysis. The calculated turbidity instantaneous change data is compared with the preset turbidity instantaneous change threshold (such as 10NTU / s). It is found that the change rate in the 5th and 6th seconds is greater than the threshold, so the high-frequency sampling mode is triggered, and the sampling frequency is increased from 1 time per second to 5 times per second, and the rising rate and diffusion range of the turbidity are recorded. For example, the high-frequency sampling results show that the turbidity rise rate in 10 seconds is , the diffusion range is , generate turbidity mutation data to reflect the diffusion characteristics of the mutation area. Match and identify the turbidity mutation data with typical resuspension patterns in the historical database (such as ship stirring, benthic biological activities, etc.), and use pattern recognition algorithms such as support vector machines (SVM) to classify and determine the mutation type. For example, by matching the turbidity rise rate, diffusion range and historical data, the cause of the mutation is determined to be the resuspension of bottom sediment caused by ship activities, and the resuspension type data is obtained to provide a basis for subsequent evaluation. Based on the resuspension type data and the water quality data collected by the bottom probe (such as nitrogen and phosphorus concentrations, dissolved oxygen changes), the degree and duration of the impact of resuspension on water quality are evaluated. For example, through numerical simulation, it was found that the resuspension caused by ship activities increased the nitrogen concentration in the bottom water to , dissolved oxygen decreased to The radius of the affected area is , lasting for 4 hours, to obtain sediment release intensity data, providing a scientific basis for water quality management and improvement.

[0079] The instantaneous change in turbidity used in this invention is a key indicator for assessing sediment disturbance in water bodies. By calculating the instantaneous change in bottom water turbidity in real time, it can promptly reflect the disturbance and suspension of sediment in the bottom water. This process is crucial for monitoring dynamic changes in water quality, especially when bottom water is disturbed, allowing for the timely detection of turbidity, providing early warning for subsequent water quality management and pollution source control. Sudden turbidity changes are often a sign of water disturbance (such as wind, tidal changes, or human activity) or sediment resuspension. By setting a preset threshold for instantaneous turbidity changes and combining it with real-time data for sudden change detection, it is possible to accurately capture sudden changes in the water body. This not only provides a dynamic response mechanism for water quality monitoring but also offers an opportunity for timely intervention in water management. Especially in bottom waters, sudden turbidity changes often indicate sediment disturbance or pollutant release. Prompt detection of sudden changes can effectively prevent the spread of pollution and reduce environmental risks. When a sudden turbidity change is detected, the bottom probe automatically triggers its high-frequency sampling mode and records the rate of turbidity rise and the extent of its spread, enabling real-time monitoring and recording of detailed turbidity changes in the water. This process helps more accurately track the origin, propagation path, and spread of turbidity, providing scientific data for subsequent water quality warnings, pollution source tracing, and environmental restoration plans. The use of a high-frequency sampling pattern ensures more detailed analysis of water quality changes in the event of sudden changes, helping to understand the real-time dynamics of water quality fluctuations and improve the timeliness and effectiveness of decision-making. Resuspension is a form of water turbidity caused by sediment disturbance, and different types of disturbance can have different water quality impacts. Resuspension type identification based on typical patterns in a historical database can help distinguish different types of resuspension and determine their specific impacts on water quality. This step helps accurately identify the root cause of turbidity changes and, by comparing historical patterns, can predict and understand the long-term impacts of similar disturbances, providing a scientific basis for pollution prevention, environmental restoration, and water quality protection strategies. The severity of a resuspension event is closely related not only to its intensity but also to its duration and spread. By combining resuspension type data with bottom-level probe data, it is possible to quantitatively assess the specific impact of resuspension on water quality, such as the amount of sediment released, the extent of the affected area, and changes in water quality parameters. This assessment can help water resource management departments accurately formulate treatment measures, predict the long-term and short-term impacts of resuspension on water quality, and provide decision-making support for water restoration and pollution control. Sediment release intensity data is an important parameter for water pollution and water quality changes. Through a comprehensive evaluation of resuspension type data, the intensity of sediment release can be accurately quantified, revealing the amount of pollutant release and its negative impact on water quality. This data helps to further judge the difficulty of water quality restoration and provides a basis for water quality restoration measures after sediment disturbance. Quantifying sediment release intensity not only helps to understand changes in water pollution sources, but also provides a detailed monitoring basis for future environmental management, which can effectively reduce the long-term impact of sediment pollution on water quality.

[0080] Preferably, the construction of the water quality vertical distribution model is specifically as follows:

[0081] According to the gas-liquid impact data, the surface water quality parameters are corrected for gas-liquid interface disturbance, thereby obtaining the surface water quality correction data;

[0082] The vertical layered structure of the water body is characterized by vertical mixing data, and the transmission of water quality parameters between adjacent calculation layers is quantified based on the vertical turbulent diffusion coefficient to obtain inter-layer transmission data.

[0083] The sediment resuspension impact assessment was conducted on bottom water quality parameters based on the sediment release intensity data, thus obtaining bottom water quality impact data;

[0084] A mathematical model describing the vertical distribution of water quality parameters is established based on surface water quality correction data, interlayer transmission data, and bottom water quality impact data. The dynamic evolution of water quality parameters in each layer is calculated through layered coupling to construct a water quality vertical distribution model.

[0085] The embodiment of the present invention uses gas-liquid impact data to perform gas-liquid interface disturbance correction on surface water quality parameters (such as dissolved oxygen, pH value and temperature). By coupling analysis of disturbance degree prediction data and wind-induced turbulence intensity data, the influence of gas-liquid interface disturbance on dissolved oxygen transmission rate is calculated. For example, the dissolved oxygen transmission rate is calculated by Upgrade to ; Substitute the corrected transmission rate into the dissolved oxygen concentration evolution formula to dynamically correct the surface dissolved oxygen concentration. At the same time, correct the pH value and temperature change trend of the surface water quality. Finally, the surface water quality correction data is obtained to optimize the evaluation and prediction of surface water quality parameters. The layered structure characteristics of the water body are extracted using vertical mixing data. Based on the vertical gradients of temperature, conductivity and dissolved oxygen, the density difference and turbulent diffusion coefficient of each layer of the water body are calculated. The formula = ( ) quantifies the transmission of water quality parameters between adjacent computational layers, where is the turbulent diffusion coefficient, The concentration gradient is used to generate interlayer transport data to describe the vertical migration of water quality parameters. The sediment release intensity data is used to evaluate the changes in bottom water quality parameters (such as ammonia nitrogen, total phosphorus and COD). Based on the release rate and diffusion range of sediment resuspension on bottom water quality parameters, the formula Evaluate concentration changes in sediment release, where The release strength of the sediment, is the duration, The bottom water volume is used to generate bottom water quality impact data to quantify the degree of water quality impact of sediment resuspension. The surface water quality correction data, interlayer transmission data and bottom water quality impact data are input into the water quality vertical distribution mathematical model. The dynamic evolution of water quality parameters of each layer is calculated by layered coupling method. The finite difference method with a time step of 1 hour is used to simulate the vertical distribution change of water quality parameters over time. For example, the simulation results show that the surface dissolved oxygen concentration changes from 0.01% to 0.01% after 6 hours. Reduce to , while the bottom ammonia nitrogen concentration is Increase to , and finally constructed a water quality vertical distribution model to provide support for the prediction of dynamic changes in water quality and decision-making on water quality improvement.

[0086] The surface water quality parameters of the present invention are affected by disturbances at the gas-liquid interface. In particular, factors such as wind speed and air pressure changes can cause water surface fluctuations, which can affect the accuracy of water quality monitoring data. By correcting surface water quality using gas-liquid impact data, errors caused by surface disturbances can be removed or mitigated, thereby improving the accuracy of water quality data. This step ensures that the true water quality of the water body can still be accurately obtained in a fluctuating or disturbed environment, providing more reliable basic data for subsequent water quality management and decision-making. The vertical stratification structure of a water body reflects the differences in water quality between different depth layers. Understanding the vertical mixing of water bodies is crucial for predicting water quality changes. By extracting the vertical stratification characteristics of a water body and quantifying the transmission of water quality parameters between layers, the diffusion and transmission of water quality between different layers can be more accurately assessed. This process can help identify the material exchange mechanisms (such as dissolved oxygen, nutrients, etc.) between different depths of a water body and contribute to the establishment of more refined water quality models to improve the accuracy of water quality predictions and optimize water resource management. The intensity of sediment release directly affects the water quality of the bottom water. Sediment may contain a large number of pollutants (such as heavy metals, nutrients, and organic pollutants). These pollutants are released into the water body when the sediment is disturbed, thus affecting water quality. By assessing the impact of sediment release intensity on water quality, we can better understand changes in bottom water quality and their contribution to overall water quality. This step can help predict and identify potential threats to water quality from sediment disturbance, providing a scientific basis for water pollution control, sediment management, and ecological restoration. Mathematical models describing the vertical distribution of water quality parameters can accurately reflect the water quality status at different layers within a water body, helping to study the vertical variation of water quality. Combining surface correction data, interlayer transport data, and bottom-level impact data can more comprehensively characterize the water quality distribution characteristics of a water body and reveal the drivers of water quality changes. This provides a more multidimensional analytical perspective for water quality management, enabling optimized management at different levels and ensuring refined water quality protection. By establishing a vertical water quality distribution model, we can predict water quality trends between different water layers and provide a theoretical basis for future water quality assessment and management. The vertical distribution of water quality in a water body is dynamic and influenced by multiple factors, such as temperature, dissolved oxygen, and sediment resuspension. A layered coupling approach allows for simultaneous consideration of water quality changes in each water layer, and dynamic evolution calculations simulate the combined impact of these factors on water quality. This dynamic modeling not only reflects short-term water quality changes but also enables long-term predictions to assess water quality evolution trends under different scenarios. This model helps improve the accuracy of water quality monitoring, early warning, and assessment, and can provide decision support for the protection, management, and restoration of water resources.

[0087] Preferably, the spatiotemporal correlation analysis is specifically:

[0088] Based on the water quality vertical distribution model, the time series data of water quality parameters at each monitoring point are extracted, and the time lag correlation analysis of the time series of water quality parameters at adjacent monitoring points is performed to obtain time correlation characteristic data;

[0089] The spatial distance and hydrological and geographical characteristics between adjacent monitoring points are analyzed according to the water quality vertical distribution model, and spatial correlation characteristic data are obtained according to the spatial distribution characteristics of water quality parameters;

[0090] The spatiotemporal correlation coefficient of water quality parameters between any two adjacent monitoring points is calculated based on spatiotemporal weighting according to the temporal correlation characteristic data and the spatial correlation characteristic data, thereby obtaining a spatiotemporal correlation coefficient matrix;

[0091] The spatiotemporal correlation coefficient matrix is threshold judged based on the preset spatiotemporal correlation threshold. When the spatiotemporal correlation coefficient is greater than the preset threshold, it is determined that there is a water quality transmission channel between the two monitoring points. Otherwise, there is no water quality transmission channel between the two monitoring points, thereby drawing a water quality transmission network topology map reflecting the water quality transmission relationship between the monitoring points.

[0092] The embodiment of the present invention extracts the time series data of water quality parameters of each monitoring point based on the vertical distribution model of water quality, and processes the hourly sampling records of dissolved oxygen, ammonia nitrogen, total phosphorus and other parameters of the monitoring point within 24 consecutive hours to obtain the time series data of each parameter, and uses the cross-correlation function to perform time lag correlation analysis on the time series data of adjacent monitoring points. For example, for two monitoring points A and B with a distance of 1 km, the analysis found that there was a time lag of 3 hours in the dissolved oxygen parameter, and the lag correlation coefficient was 0.85. Time-related characteristic data was generated, and the time dependence of the change of water quality parameters was quantified. According to the vertical distribution model of water quality, the spatial distance between adjacent monitoring points is measured, and the spatial distribution characteristics of water quality parameters are analyzed in combination with the hydrological and geographical characteristics of the river (such as river flow velocity, curvature coefficient, and terrain slope). For example, the average flow velocity of a certain section of river is , the curvature coefficient is 1.2, and the spatial distribution law of dissolved oxygen is calculated by combining the distance and terrain data of the monitoring points. The spatial correlation characteristic data between adjacent monitoring points are obtained to describe the characteristics of spatial water quality transmission. Using the time correlation characteristic data and the spatial correlation characteristic data, the spatiotemporal correlation coefficient of the water quality parameters between any two adjacent monitoring points is calculated based on spatiotemporal weighting. The calculation formula is ,in is the time correlation coefficient, is the spatial correlation coefficient, and are the spatiotemporal weighting factors, set to 0.6 and 0.4, respectively. For example, if the time correlation coefficient of dissolved oxygen between two monitoring points is 0.85 and the spatial correlation coefficient is 0.75, the spatiotemporal correlation coefficients are calculated to generate a spatiotemporal correlation coefficient matrix, providing data support for subsequent water quality transmission relationship analysis. The spatiotemporal correlation coefficient matrix is compared with the preset spatiotemporal correlation threshold. When the correlation coefficient is greater than the preset threshold, it is determined that a water quality transmission channel exists between the two monitoring points. For example, in a certain river section, the spatiotemporal correlation coefficient between monitoring points 1 and 2 exceeds the threshold, indicating the presence of a transmission channel; while the spatiotemporal correlation coefficient between monitoring points 2 and 3 is 0.75, which is below the threshold, indicating the absence of a transmission channel. Ultimately, the transmission relationships between all monitoring points are mapped in the form of a network topology, generating a water quality transmission network topology map that reflects the dynamic transmission paths of river water quality, providing an important reference for water quality monitoring and transmission law analysis in river management.

[0093] By extracting time series data of water quality parameters at each monitoring point from a vertical water quality distribution model, the present invention can comprehensively capture the dynamic changes in water quality. Time-lagged correlation analysis of water quality time series at adjacent monitoring points can identify the temporal relationships between water quality parameters, revealing the comparison of water quality changes between different monitoring points and their temporal sequence. This temporal correlation characteristic data helps identify the lag effects of water quality changes, such as the time delay in the diffusion and reaction of certain pollutants, providing strong support for subsequent water quality change predictions and real-time monitoring. The spatial distribution characteristics of water quality parameters can reflect the heterogeneity of water quality between different monitoring points in a water body. By analyzing the spatial distances and hydrological and geographical characteristics between adjacent monitoring points, the spatial propagation patterns and regional differences in water quality can be revealed. This provides important information for understanding the spatial sources of pollution, flow trends, and spatial heterogeneity of water quality changes in water bodies. Spatially correlated characteristic data helps further optimize the layout of water quality monitoring networks and improve the accuracy of water quality early warning systems. The calculation of the spatiotemporal weighted correlation coefficient comprehensively considers the water quality correlations in both time and space. Such analysis can reveal the spatiotemporal dynamic relationships of water quality changes between monitoring points. By calculating the spatiotemporal correlation coefficient matrix, we can effectively quantify the intensity and relationships of water quality transmission between different monitoring points, thereby identifying the spatial propagation patterns of water quality changes. This spatiotemporal correlation coefficient matrix lays the foundation for constructing a water quality transmission network, making subsequent analysis more accurate and detailed. Applying thresholds to the spatiotemporal correlation coefficient matrix can help identify pairs of monitoring points with significant water quality transmission relationships. By determining whether water quality transmission channels exist between these monitoring points, a clearer and more accurate water quality transmission network topology can be constructed. This topology not only reveals the transmission paths of water quality but also helps predict the transmission trends and diffusion ranges of water quality pollutants. This has important practical applications for water quality management, pollution source tracing, and the development of water quality optimization plans.

[0094] Preferably, the river regulation plan is generated as follows:

[0095] Classify and identify river water quality problems based on water quality chromatography data, and match problem characteristics based on a preset historical governance experience database to obtain data on the types of problems to be addressed;

[0096] Analyze the spatial distribution and propagation trends of various water quality problems based on the type of problem to be treated, and determine the priority treatment areas based on the distribution of key nodes in the water quality transmission network topology map, thereby obtaining treatment area division data;

[0097] Based on the data of the governance area division, the causes of water quality problems in different regions are analyzed, and the causal relationship network of water quality problems in each region is constructed according to the water quality vertical distribution model to obtain the cause data of the problem;

[0098] Based on the cause data of the problem, matching treatment technology combinations are screened from the preset treatment measures library. The applicability of the treatment technologies is evaluated based on the hydrological characteristics and environmental constraints of each region to obtain a preliminary treatment plan.

[0099] The implementation sequence and timing of each control measure in the preliminary control plan are optimized, and the optimal combination of control technologies is determined based on cost-benefit analysis to form a river control plan.

[0100] The embodiment of the present invention uses a support vector machine classification model to classify and identify river water quality problems based on water quality chromatography data, by analyzing the spatiotemporal variation characteristics of water quality parameters such as dissolved oxygen, ammonia nitrogen and total phosphorus, and combining it with the typical pollution problem feature templates in the historical governance experience database. For example, in a certain section of the river, the dissolved oxygen concentration is detected to be lower than , ammonia nitrogen concentration is higher than The matching result identifies eutrophication as a problem, generating data on the type of problem to be addressed. Based on this data, combined with the spatial distribution of water quality issues and the topology of the water quality transmission network, a simulation analysis of the propagation trend of river water quality issues was conducted. For example, analysis revealed a clear trend of eutrophication spreading from the upper reaches to the middle reaches of a river, with key propagation nodes concentrated at three monitoring points in the middle reaches. The middle reaches were prioritized for remediation, broken down into 500-meter zones, and remediation zone demarcation data was generated to provide a reference for key areas for subsequent remediation efforts. Based on this remediation zone demarcation data and combined with a water quality vertical distribution model, the causes of water quality issues within the priority remediation areas were analyzed. For example, analysis of the vertical distribution data revealed that phosphorus loading in sediments was the primary source of pollution. Further analysis, combined with surrounding land use data, confirmed that the primary source of phosphorus was agricultural non-point source pollution. A Bayesian network was used to construct a causal network for the problem, generating data on the cause of the problem and providing a scientific basis for selecting remediation strategies. Based on this data on the cause of the problem, matching remediation technology combinations were selected from a library of remediation measures. For example, for the eutrophication problem of agricultural non-point source pollution, three technologies were selected: "constructed wetland construction", "agricultural non-point source runoff control" and "sediment dredging". , the flow rate is ) and environmental constraints (such as limiting the construction period to June-September) to conduct a suitability assessment and generate a preliminary treatment plan. The implementation sequence and time schedule of each treatment measure in the preliminary treatment plan are optimized, and a linear programming model is used to analyze resource allocation and construction period. The economic efficiency and treatment effect of the technical combination are evaluated in combination with cost-benefit analysis. For example, through optimization, it is determined that "agricultural non-point source runoff control" will be implemented first, followed by "constructed wetland construction" and finally "sediment desilting", which will reduce the total construction cost. , governance effect improved Ultimately, an optimal management plan is formed that includes time schedule, technology selection, and resource allocation, providing a scientific and efficient reference for actual river management.

[0101] The water quality chromatography data presented in this paper provides information on the spatial distribution of different water quality parameters within water bodies, effectively identifying water quality issues within rivers. Classification and identification accurately distinguish the types of water quality issues (such as pollution sources, eutrophication, and hypoxia). Combined with a database of historical governance experience, these issues can be matched to their characteristics, further confirming the type of water quality issue. Obtaining data on the types of issues to be addressed provides clear targets for subsequent water quality governance, making governance plans more precise and targeted, and reducing unnecessary resource waste. Analysis of the spatial distribution and propagation trends of water quality issues can reveal the distribution of pollution sources and their diffusion paths. Combined with a topological map of the water quality transmission network, key nodes of water quality issues can be located, identifying the areas most severely affected by water quality issues and prioritizing remediation efforts. This analysis can effectively optimize the division of remediation areas, focusing limited remediation resources on areas most in need of intervention, improving remediation efficiency, and maximizing water quality improvement outcomes. Analyzing the causes of water quality issues in each region can provide a deeper understanding of the root causes of water quality issues, such as pollution sources and inadequate watershed management. The vertical distribution model of water quality allows for vertical analysis of the impact of water stratification on water quality issues, thereby constructing a causal network. This analysis reveals interactions between different layers and factors, helping to identify the primary and secondary causes of water quality issues. This provides a basis for developing more precise remediation measures, avoiding superficial treatments. By matching problem causal data with a pre-defined remediation measure library, the most appropriate remediation technology combination can be selected for each water quality issue. This approach ensures the scientific and targeted nature of remediation measures. An assessment of the hydrological characteristics and environmental constraints of each region determines the applicability of remediation measures in the actual environment, ensuring the feasibility and effectiveness of the selected technical solutions. This comprehensive analysis results in preliminary remediation plans that better meet actual needs and provide strong support for subsequent implementation. Optimizing the sequence and timing of remediation measures can improve the overall efficiency of the remediation process, avoiding ineffective results due to inappropriate timing or sequencing. For example, some remediation measures may need to be implemented in advance, while others require specific conditions to be effective. Cost-benefit analysis ensures that the selected remediation technology combination maximizes remediation effectiveness within limited resources. This optimization can not only improve the management effect, but also ensure the maximization of economic and social benefits, thus forming an optimal river management plan.

[0102] The present invention also provides an intelligent interactive river management consultation and management system for executing the above-mentioned intelligent interactive river management consultation and management method, wherein the intelligent interactive river management consultation and management system comprises:

[0103] A water quality monitoring unit is used to deploy a multi-layer water quality monitoring probe array at preset monitoring points in the river. The multi-layer water quality monitoring probe array dynamically monitors the river water quality based on an adaptive sampling frequency method to obtain multi-layer water quality raw data, wherein the multi-layer water quality raw data includes surface probe data, middle probe data, and bottom probe data;

[0104] The water quality characteristic analysis unit is used to perform gas-liquid interface disturbance analysis on surface probe data to obtain gas-liquid impact data; perform water mixing analysis on mid-layer probe data to obtain vertical mixing data; and perform sediment resuspension analysis on bottom layer probe data to obtain sediment release intensity data.

[0105] The water quality distribution modeling unit is used to construct a water quality vertical distribution model based on gas-liquid impact data, vertical mixing data, and sediment release intensity data; based on the water quality vertical distribution model, the spatiotemporal correlation analysis of water quality parameters at adjacent monitoring points is performed to draw a water quality transmission network topology map;

[0106] The treatment plan generation unit is used to calculate the degree centrality and betweenness centrality of each monitoring point based on the water quality vertical distribution model and the water quality transmission network topology diagram, and identify the key propagation nodes of water quality fluctuations to obtain water quality tomography data; generate river treatment plans based on water quality tomography data, and simulate and verify the treatment plans to form a river treatment plan for river treatment consultation.

[0107] By deploying a multi-layered array of monitoring probes, the present invention can comprehensively cover all layers of a water body, thereby obtaining water quality data at different levels. This multi-layered data provides detailed information for subsequent analysis, ensuring the comprehensiveness and accuracy of water quality monitoring. Adaptive sampling frequency dynamically adjusts the sampling frequency based on water quality changes, avoiding resource waste due to oversampling while ensuring improved accuracy and real-time data acquisition even when water quality fluctuates dramatically. This strategy enhances the responsiveness of the monitoring system, facilitating timely identification and response to water quality fluctuations. By separately analyzing the characteristics of surface, middle, and bottom layer data, the dynamic changes in each layer of the water body can be more accurately understood and assessed. For example, gas-liquid interface disturbance analysis helps identify the impact of airflow in the surface layer, while water mixing analysis reveals the fluidity and exchange characteristics of the middle layer. Sediment resuspension analysis helps assess bottom-level pollution sources and the intensity of sediment release. Different water quality characteristics provide multi-dimensional information, which helps to fully understand the factors affecting water quality and provides accurate input data for subsequent water quality modeling and treatment plan development. By establishing a vertical water quality distribution model, we can comprehensively understand water quality variations at different levels within a water body, thereby gaining a deeper understanding of its distribution characteristics. This modeling can reveal the spatial and temporal evolution of water quality, providing a theoretical basis for water quality management and remediation. Spatiotemporal correlation analysis can reveal the relationships between water quality parameters at different monitoring points, forming a topological map of the water quality transmission network. This map helps identify water quality transmission channels and key nodes, clarifying the path and impact range of water quality changes, and provides a valuable tool for tracing pollution sources and analyzing the dynamic evolution of water quality. By calculating degree centrality and betweenness centrality, we can identify key transmission nodes of water quality fluctuations. Nodes with high centrality are often the main transmission points or bottlenecks of water quality changes. Addressing these nodes can effectively control the spread of water quality changes in a short period of time and optimize remediation effectiveness. Water quality tomography data can more clearly identify the distribution and variation characteristics of water quality problems, helping decision-makers identify the root causes of water quality issues. Water quality tomography data provides a precise map of water quality problems, making remediation plans more targeted and scientific. Through simulation and verification of governance solutions, we can predict the effects of different governance measures, assess their feasibility in practice, and avoid uncertainty and risk in implementation. Simulation and verification can help select the most effective and economical governance measures, ensuring the efficiency and feasibility of the final governance recommendations.

[0108] For example, the intelligent interactive river management consultation and management system may be an intelligent interactive river management management system, and the intelligent interactive river management consultation and management method may be an intelligent interactive river management management method.

[0109] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced within the present invention.

[0110] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent interactive river management consulting and management method, characterized by: The following steps are involved: A multi-layer water quality monitoring probe array is deployed at preset monitoring points in the river. The river water quality is dynamically monitored by the multi-layer water quality monitoring probe array based on an adaptive sampling frequency method to obtain multi-layer water quality raw data, where the multi-layer water quality raw data includes surface probe data, middle probe data, and bottom probe data; The surface probe data is analyzed for gas-liquid interface disturbance to obtain gas-liquid impact data; the middle layer probe data is analyzed for water mixing to obtain vertical mixing data; the bottom layer probe data is analyzed for sediment resuspension to obtain sediment release intensity data; A water quality vertical distribution model is constructed based on gas-liquid impact data, vertical mixing data, and sediment release intensity data. Based on the water quality vertical distribution model, spatiotemporal correlation analysis of water quality parameters at adjacent monitoring points is performed to draw a water quality transmission network topology map. The construction of the water quality vertical distribution model is specifically as follows: According to the gas-liquid impact data, the surface water quality parameters are corrected for gas-liquid interface disturbance, thereby obtaining the surface water quality correction data; The vertical layered structure of the water body is characterized by vertical mixing data, and the transmission of water quality parameters between adjacent calculation layers is quantified based on the vertical turbulent diffusion coefficient to obtain inter-layer transmission data. The sediment resuspension impact assessment was conducted on bottom water quality parameters based on the sediment release intensity data, thus obtaining bottom water quality impact data; Based on surface water quality correction data, interlayer transmission data, and bottom water quality impact data, a mathematical model describing the vertical distribution of water quality parameters is established. The dynamic evolution of water quality parameters in each layer is calculated through layered coupling to construct a water quality vertical distribution model. The spatiotemporal correlation analysis is specifically as follows: Based on the water quality vertical distribution model, the time series data of water quality parameters at each monitoring point are extracted, and the time lag correlation analysis of the time series of water quality parameters at adjacent monitoring points is performed to obtain time correlation characteristic data; The spatial distance and hydrological and geographical characteristics between adjacent monitoring points are analyzed according to the water quality vertical distribution model, and spatial correlation characteristic data are obtained according to the spatial distribution characteristics of water quality parameters; The spatiotemporal correlation coefficient of water quality parameters between any two adjacent monitoring points is calculated based on spatiotemporal weighting according to the temporal correlation characteristic data and the spatial correlation characteristic data, thereby obtaining a spatiotemporal correlation coefficient matrix; Based on the preset spatiotemporal correlation threshold, the spatiotemporal correlation coefficient matrix is threshold judged. When the spatiotemporal correlation coefficient is greater than the preset threshold, it is determined that there is a water quality transmission channel between the two monitoring points. Otherwise, there is no water quality transmission channel between the two monitoring points, thereby drawing a water quality transmission network topology map reflecting the water quality transmission relationship between the monitoring points; Based on the water quality vertical distribution model and the water quality transmission network topology, the degree centrality and betweenness centrality of each monitoring point are calculated, and the key propagation nodes of water quality fluctuations are identified to obtain water quality tomography data. Based on the water quality tomography data, a river management plan is generated and the management plan is simulated and verified to form a river management plan for river management consultation.

2. The intelligent interactive river management consulting and management method according to claim 1 is characterized in that: The adaptive sampling frequency method is specifically as follows: Obtain the water quality parameter change rate at the current moment. When the water quality parameter change rate is greater than the preset first threshold, adjust the sampling time interval to 10 seconds. When the water quality parameter change rate is between the preset first threshold and the preset second threshold, adjust the sampling time interval to 2 minutes. When the water quality parameter change rate is less than the preset second threshold, adjust the sampling time interval to 15 minutes to achieve fine sampling of areas with drastic changes and resource conservation in stable areas.

3. The intelligent interactive river management consulting and management method according to claim 2 is characterized in that: The gas-liquid interface disturbance analysis is specifically as follows: Obtain wind speed and air pressure change data based on surface probe data; The coupling relationship between wind speed and water quality parameters is calculated based on surface probe data and wind speed and air pressure change data, thereby obtaining wind-induced turbulence intensity data; The interface disturbance degree within the next 24 hours is predicted based on the pressure change trend of the wind speed and pressure change data, thereby obtaining the disturbance degree prediction data; Based on the disturbance degree prediction data and wind-induced turbulence intensity data, the water quality parameters of the surface probe data are analyzed to analyze the impact of water surface fluctuations on water quality, thereby obtaining gas-liquid impact data.

4. The intelligent interactive river management consulting and management method according to claim 3 is characterized in that: The water mixing degree analysis is specifically as follows: The temperature, conductivity and dissolved oxygen parameters of water bodies at different depths are extracted based on the data from the mid-layer probe to obtain vertical profile data of the water body; The water stratification intensity index is calculated based on the vertical profile data of the water body to obtain the water stratification intensity data. The water stratification intensity index calculation specifically includes calculating the thermal stratification coefficient based on the vertical temperature gradient, calculating the salt stratification coefficient based on the vertical conductivity gradient, and calculating the oxygen stratification coefficient based on the vertical dissolved oxygen gradient. The thermal stratification coefficient, the salt stratification coefficient, and the oxygen stratification coefficient are weighted and integrated to obtain the water stratification intensity index. Based on the water stratification intensity data, the water body is vertically divided into n calculation layers, and the mass and energy transfer equations between adjacent calculation layers are established. The vertical turbulent diffusion coefficient describing the vertical mixing intensity is dynamically adjusted to construct a vertical mixing model and obtain vertical mixing data.

5. The intelligent interactive river management consulting and management method according to claim 4 is characterized in that: The sediment resuspension analysis is specifically as follows: The instantaneous change of turbidity of the bottom water is calculated based on the bottom probe data, thereby obtaining the instantaneous change data of turbidity; Turbidity mutation detection is performed based on the preset turbidity instantaneous change threshold and turbidity instantaneous change data. When a turbidity mutation is detected, the high-frequency sampling mode of the bottom probe is triggered, and the turbidity rising rate and diffusion range are recorded to obtain turbidity mutation data; Resuspension type identification is performed on the turbidity mutation data based on typical patterns in the historical database, thereby obtaining resuspension type data; Based on the resuspension type data and bottom probe data, the impact of resuspension on water quality and the duration are evaluated to obtain sediment release intensity data.

6. The intelligent interactive river management consulting and management method according to claim 5 is characterized in that: The specific steps for generating river regulation scheme are as follows: Classify and identify river water quality problems based on water quality chromatography data, and match problem characteristics based on a preset historical governance experience database to obtain data on the types of problems to be addressed; Analyze the spatial distribution and propagation trends of various water quality problems based on the type of problem to be treated, and determine the priority treatment areas based on the distribution of key nodes in the water quality transmission network topology map, thereby obtaining treatment area division data; Based on the data of the governance area division, the causes of water quality problems in different regions are analyzed, and the causal relationship network of water quality problems in each region is constructed according to the water quality vertical distribution model to obtain the cause data of the problem; Based on the cause data of the problem, matching treatment technology combinations are screened from the preset treatment measures library. The applicability of the treatment technologies is evaluated based on the hydrological characteristics and environmental constraints of each region to obtain a preliminary treatment plan. The implementation sequence and timing of each control measure in the preliminary control plan are optimized, and the optimal combination of control technologies is determined based on cost-benefit analysis to form a river control plan.

7. An intelligent interactive river management consultation and management system, characterized by: For executing the intelligent interactive river management consultation and management method according to claim 1, the intelligent interactive river management consultation and management system comprises: A water quality monitoring unit is used to deploy a multi-layer water quality monitoring probe array at preset monitoring points in the river. The multi-layer water quality monitoring probe array dynamically monitors the river water quality based on an adaptive sampling frequency method to obtain multi-layer water quality raw data, wherein the multi-layer water quality raw data includes surface probe data, middle probe data, and bottom probe data; The water quality characteristic analysis unit is used to perform gas-liquid interface disturbance analysis on surface probe data to obtain gas-liquid impact data; perform water mixing analysis on mid-layer probe data to obtain vertical mixing data; and perform sediment resuspension analysis on bottom layer probe data to obtain sediment release intensity data. The water quality distribution modeling unit is used to construct a water quality vertical distribution model based on gas-liquid impact data, vertical mixing data, and sediment release intensity data; based on the water quality vertical distribution model, the spatiotemporal correlation analysis of water quality parameters at adjacent monitoring points is performed to draw a water quality transmission network topology map; The treatment plan generation unit is used to calculate the degree centrality and betweenness centrality of each monitoring point based on the water quality vertical distribution model and the water quality transmission network topology diagram, and identify the key propagation nodes of water quality fluctuations to obtain water quality tomography data; generate river treatment plans based on water quality tomography data, and simulate and verify the treatment plans to form a river treatment plan for river treatment consultation.

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