Intelligent interactive river regulation consultation management system and method

Abstract

The invention relates to the technical field of river regulation, in particular to an intelligent interactive river regulation consultation management system and method. The method comprises the following steps: arranging a multi-layer water quality monitoring probe array at preset monitoring points of a river channel, and dynamically monitoring the water quality of the river channel based on a self-adaptive sampling frequency method through the multi-layer water quality monitoring probe array to obtain multi-layer water quality original data, wherein the multi-level water quality original data comprises surface-layer probe data, middle-layer probe data and bottom-layer probe data; performing gas-liquid interface disturbance analysis on the surface probe data to obtain gas-liquid influence data; performing water mixing degree analysis on the middle-layer probe data to obtain vertical mixing data; and carrying out sediment resuspension analysis on the bottom layer probe data to obtain sediment release strength data. By arranging the multi-layer water quality monitoring probe array and combining the self-adaptive sampling frequency technology, the vertical distribution and dynamic change of the water quality of the river channel can be accurately captured, and the monitoring timeliness and accuracy are improved.

Description

Technical Field

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

[0002] River management is a complex and comprehensive project aimed at improving the river environment, enhancing flood control capabilities, promoting ecological restoration, and meeting the needs of social and economic development. River management consulting and management is a complex process involving many aspects, mainly including the following aspects: River basin management is a method of overall river management aimed at reducing river pollution and promoting ecological restoration and protection. It includes land use management, sewage treatment, water resources, river management engineering technology and management practices, including a comprehensive survey of the current status of the river environment, such as water quality monitoring, ecological environment assessment, and pollution source analysis. Based on the survey results, a systematic management plan is formulated to clarify the specific content and implementation steps of various management measures. Scientific planning should also focus on the comprehensive use of engineering means and ecological restoration means to ensure the sustainability of the management effect. In addition, a sound management system and mechanism should be established to clarify 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. Through effective river basin management, the discharge of pollution sources can be reduced and the ecological environment of the river can be protected. Intelligent interactive river management consulting and management is a method of comprehensive management of rivers that combines modern information technology, especially artificial intelligence, the Internet of Things, big data and other technologies.

[0003] However, traditional intelligent interactive river management consulting and management methods often have the following problems: the methods usually rely on manual fixed-point monitoring and regular sampling, with long sampling intervals and limited spatial coverage, and cannot reflect the dynamic changes of river water quality in real time, especially the vertical distribution and spatial transmission characteristics. Water quality assessment is mostly based on a single parameter or simple statistical analysis, which makes it difficult to reveal the complex spatiotemporal correlation of water quality and the pollution diffusion mechanism. Summary of the invention

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

[0005] To achieve the above purpose, an intelligent interactive river management consulting management method includes the following steps:

[0006] A multi-level water quality monitoring probe array is arranged at preset monitoring points in the river, and the river water quality is dynamically monitored based on the adaptive sampling frequency method through the multi-level water quality monitoring probe array to obtain multi-level water quality raw data, wherein the multi-level water quality raw data includes surface probe data, middle probe data and bottom probe data;

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

[0008] A water quality vertical distribution model is constructed based on the gas-liquid impact data, vertical mixing data, and sediment release intensity data; a spatiotemporal correlation analysis of water quality parameters at adjacent monitoring points is performed based on the water quality vertical distribution model 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. According to the water quality tomography data, the 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-level monitoring probe array in the present invention can realize the three-dimensional spatial distribution data collection of river water quality, covering the surface, middle and bottom layers, so as to obtain more comprehensive water quality information. The method based on adaptive sampling frequency dynamically adjusts the monitoring frequency, which can improve the data collection accuracy when the water quality changes drastically, and reduce data redundancy when the water quality is stable, thereby optimizing the utilization efficiency of monitoring resources. The data analysis of the surface probe reveals the dynamic influence of gas-liquid interface disturbance on dissolved oxygen and volatile pollutants in water bodies, and provides a scientific basis for judging the interaction process between the atmosphere and water bodies. The data analysis of the middle-level probe quantifies the material and energy exchange intensity between water bodies at different levels through water mixing, which is helpful for studying the vertical migration law of pollutants. The data analysis of the bottom probe evaluates the resuspension release process of nutrients or pollutants in the sediment, thereby helping to predict the long-term impact of the sediment on the overall water quality. The data of gas-liquid interface disturbance, vertical mixing degree and sediment release intensity are integrated into a vertical distribution model, which can systematically quantify the distribution characteristics of water quality parameters in the vertical direction. This model not only provides a more accurate description of the spatial structure of water quality, but also provides a basis for parameter correlation analysis between monitoring points. The spatiotemporal correlation analysis of water quality data from adjacent monitoring points can reveal the dynamic laws of pollutant diffusion and migration. The complex transmission relationship of river water quality can be visualized through topological maps, helping governance personnel to intuitively identify pollution sources, diffusion paths and key governance areas. Calculating the degree centrality and betweenness centrality of monitoring points can quantify the importance of each monitoring point in the water quality transmission network. After identifying key transmission nodes, the source of water quality fluctuations or high-risk transmission paths can be more accurately located, thereby improving the pertinence and effectiveness of governance plans. Generating governance plans based on water quality chromatography data ensures that the plans are based on comprehensive scientific data, increasing the feasibility of implementation. The simulation verification process evaluates the actual effects and potential risks of the plans through virtual simulation, avoiding unnecessary problems that may be caused by direct implementation, and improving governance efficiency and success rate. The governance recommendations output by combining all key data and simulation verification results are highly scientific and targeted, and can effectively deal with water quality problems in complex river environments and achieve precise governance. At the same time, this process greatly shortens decision-making time and improves governance benefits. This method establishes a spatiotemporal dynamic model of river water quality through multi-level and multi-dimensional data collection and analysis, which can systematically identify key factors and propagation nodes that affect water quality, and generate the optimal governance plan in an intelligent and data-driven way. Combined with simulation verification, the reliability of the governance plan is further guaranteed, thereby significantly improving the efficiency of river governance and reducing governance costs, while 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 middle layer probe, thus obtaining the vertical profile data of the water body;

[0013] The water stratification intensity index is calculated according to the vertical profile data of the water body, so as to obtain the water stratification intensity data, wherein the water stratification intensity index calculation specifically includes calculating the thermal stratification coefficient based on the vertical gradient of temperature, calculating the salt stratification coefficient based on the vertical gradient of conductivity, and calculating the oxygen stratification coefficient based on the vertical gradient of dissolved oxygen, and 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] Conduct water body hybrid power analysis based on water body stratification intensity data to obtain mixing efficiency coefficient data;

[0015] Based on the mixing efficiency coefficient 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 according to the water body stratification intensity data, thereby constructing a vertical mixing model and obtaining vertical mixing data.

[0016] The changes in parameters such as temperature, conductivity and dissolved oxygen of the water body at different depths of the present invention reflect the vertical structural characteristics of the water body. By extracting these parameters from the middle-layer probe data, the vertical profile data of the water body can be obtained, so as to fully understand the vertical distribution of the water body. Water quality data at different depths can reveal the hierarchical structure of the water body, help identify the 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 the water body affects the physical and chemical properties of the water body such as temperature, salinity, and dissolved oxygen. By calculating the water stratification intensity index, the hierarchical structure and stability of the water body in the vertical direction can be quantified. Specifically, the thermal, salt, and oxygen stratification coefficients are calculated respectively through the vertical gradients of temperature, salinity, and dissolved oxygen, and then decentralized fusion is performed to obtain comprehensive water stratification intensity data. This data can help identify whether the water body is in a stable stratified state, as well as the stratification intensity between different layers, and provide a basis for further water management and pollutant control. The stratified state of the water body directly affects the mixing process of the water body, and then affects the exchange efficiency of matter and energy. By analyzing the stratification intensity data of water bodies, the mixing efficiency of water bodies can be evaluated, and then the mixing efficiency coefficient can be obtained. This analysis helps to understand the material exchange capacity of each layer in the water body, as well as the material transport and diffusion rate between different layers. It is of great significance for water body management, pollutant control and water quality early warning. In particular, when the water body is stratified with different intensities, the mixing efficiency coefficient helps to judge the change trend of water quality and the stability of the ecological environment. The vertical mixing of water bodies is the exchange process of matter and energy in the water body. Especially when there is stratification of water bodies, the mixing process is greatly affected. By dividing the water body into multiple calculation layers based on the data of the mixing efficiency coefficient and establishing the mass and energy transfer equations between adjacent calculation layers, the vertical mixing process of the water body can be accurately simulated. By dynamically adjusting the vertical turbulent diffusion coefficient that describes the vertical mixing intensity, the material transport and energy exchange in the water body can be accurately reflected in the model. The model can help identify the mixing efficiency between different layers in the water body and predict the trend of water quality changes in the water body. In particular, in stratified water bodies with low material exchange rates such as temperature and dissolved oxygen, the vertical mixing model can effectively guide pollutant control and water quality restoration measures.

[0017] Preferably, the water body hybrid power analysis is specifically as follows:

[0018] Obtain mid-water environmental data;

[0019] The water body kinetic energy input flux is calculated based on the mid-layer water body environmental data to obtain the total input flux data. The formula used to calculate the water body kinetic energy input flux is as follows:

[0020] Pe=Pe 1 +Pe 2 ;

[0021]

[0022] Where Pe is the total input flux, Pe 1 is the wind kinetic energy input flux, Pe 2 is the velocity kinetic energy input flux, ρ 1 is the air density, Cd is the drag coefficient, U is the wind speed, ρ 2 is water density, v is water velocity, A is water surface area, and h is water depth;

[0023] The energy required for complete mixing of water from a stratified state is calculated based on the vertical profile data of the water body, thereby obtaining the data on the change in water body potential energy. The calculation formula for the change in water body potential energy is as follows:

[0024]

[0025] Among them, Ep is the change of water body potential energy, Ep 2 is the potential energy after complete mixing, Ep 1 is the potential energy of the initial stratified state, is the average density of all layers, g is the acceleration of gravity, H is the total water depth, ρ i is the density of the water body in the i-th layer, h i is the water depth of the i-th layer;

[0026] Based on the total input flux data and the water body potential energy change data, the mixing effect of the water body under the action of external driving force is quantified according to the water body stratification intensity data, so as to obtain the mixing efficiency coefficient data.

[0027] The middle-layer water body environmental data of the present invention includes important water quality parameters, such as temperature, dissolved oxygen, salinity, etc., and their changes in the middle layer of the water body. These data provide a basis for subsequent calculations and analysis, and help to gain a deeper understanding of the vertical distribution structure of the water body. By obtaining these data, the state of different depths in the water body can be identified, which is helpful to evaluate the mixing and vertical structure of the water body, thereby providing an important basis for model construction and water quality monitoring. Calculating the kinetic energy input flux (Pe) of the water body is a key step in evaluating the dynamic process of the water body. By calculating the wind kinetic energy input flux and the flow velocity kinetic energy input flux, we can understand the impact of the external environment (such as wind speed and flow velocity) on the kinetic energy input of the water body, especially when there are changes in wind speed and flow velocity. The impact of kinetic energy input on water mixing and vertical stratification is crucial. Use the given formula to calculate the wind kinetic energy (Pe 1 ) and flow velocity kinetic energy (Pe 1), the impact of different driving forces on water bodies can be further quantified. This helps to accurately evaluate the dynamic changes of water bodies and provide numerical support for water mixing intensity analysis, pollutant diffusion prediction, etc. The stability of water stratification is closely related to its energy. By calculating the energy (potential energy change) required for water bodies to go from stratified state to complete mixing, the potential energy difference of water stratification and the difficulty of the mixing process can be quantified. The calculation formula of potential energy change (Ep) can help evaluate the energy required for the process from stratification to complete mixing of water bodies under the action of external driving forces. The change in potential energy reflects the stratification intensity and stability of water bodies, and provides key data on whether water bodies are easy to mix or have mixing obstacles. This helps to understand and optimize water governance and management strategies, especially in terms of pollutant distribution and water body self-purification capacity, so that more effective intervention measures can be formulated. By combining total input flux data, potential energy change data and water stratification intensity data, the mixing effect of water bodies under the action of external driving forces can be quantified. The core purpose of this step is to calculate the mixing efficiency coefficient of water bodies, which can reflect the efficiency of material exchange between different layers in water bodies. The mixing efficiency coefficient helps to determine whether the water body can effectively exchange substances and energy between the upper and lower layers, thereby affecting the speed and breadth of water quality changes. This indicator is crucial in water body management, pollutant control and ecological restoration. By quantifying the mixing effect, more refined decision support can be provided for ecological and environmental management. These steps combine the kinetic energy input, potential energy change and mixing effect quantification of the water body, and can comprehensively evaluate the mixing dynamics of the water body under the action of external driving forces. By quantifying the energy changes and driving factors in the stratification and mixing process of water bodies, the transmission efficiency of substances and energy in the water body, the diffusion range of pollutants and the trend of water quality changes can be accurately predicted. This provides a scientific basis for water body management, pollution prevention and control, ecological restoration and environmental monitoring, so that water body management can be implemented more accurately and efficiently. At the same time, the quantification of the mixing effect of water bodies provides an important reference for the prediction of water quality changes, which can play an important role in water resource utilization, ecological protection and environmental impact assessment.

[0028] 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:

[0029] A water quality monitoring unit is used to deploy a multi-level water quality monitoring probe array at a preset monitoring point in a river, and dynamically monitor the water quality of the river based on an adaptive sampling frequency method through the multi-level water quality monitoring probe array to obtain multi-level water quality raw data, wherein the multi-level water quality raw data includes surface probe data, middle probe data, and bottom probe data;

[0030] The water quality characteristic analysis unit is used to perform gas-liquid interface disturbance analysis on the surface probe data to obtain gas-liquid impact data; perform water mixing degree analysis on the middle layer probe data to obtain vertical mixing data; perform sediment resuspension analysis on the bottom layer probe data to obtain sediment release intensity data;

[0031] 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; conduct spatiotemporal correlation analysis on water quality parameters at adjacent monitoring points based on the water quality vertical distribution model, thereby drawing a water quality transmission network topology map;

[0032] The management 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 management plans based on water quality tomography data, and simulate and verify the management plans, so as to form a river management plan for river management consultation.

[0033] The present invention can fully cover all levels of the water body by deploying a multi-level monitoring probe array, thereby obtaining water quality data at different levels. This multi-level data provides detailed information for subsequent analysis, ensuring the comprehensiveness and accuracy of water quality monitoring. The adaptive sampling frequency can dynamically adjust the sampling frequency according to the changes in water quality, avoiding the waste of resources due to oversampling, while ensuring that the accuracy and real-time performance of data acquisition can be improved when the water quality changes drastically. Such a strategy improves the responsiveness of the monitoring system and helps to identify and respond to water quality fluctuations in a timely manner. By performing characteristic analysis on the surface, middle and bottom layer data respectively, the dynamic changes of each layer in the water body can be more accurately understood and evaluated. For example, gas-liquid interface disturbance analysis helps identify the impact of airflow on the surface, while water mixing analysis can reveal the fluidity and exchange characteristics of the middle layer water body, and sediment resuspension analysis helps to evaluate the intensity of bottom pollution sources and sediment release. Different water quality characteristics provide multi-dimensional information, which helps to fully understand the changing factors of the water body and provide accurate input data for subsequent water quality modeling and governance plan formulation. By establishing a vertical distribution model of water quality, we can fully understand the changes in water quality at different levels in the water body, so as to deeply understand the distribution characteristics of water quality. This modeling can reveal the evolution of water quality in space and time, and provide a theoretical basis for water quality management and governance. Spatiotemporal correlation analysis can reveal the relationship between water quality parameters between different monitoring points and form a topological map of the water quality transmission network. This map helps to identify the propagation channels and key nodes of water quality, clarify the path and impact range of water quality changes, and provide an important tool for tracing pollution sources and analyzing the dynamic evolution of water quality. By calculating degree centrality and betweenness centrality, the key propagation nodes of water quality fluctuations can be identified. Nodes with higher centrality are usually the main propagation points or bottlenecks of water quality changes. Governing these nodes can effectively control the propagation of water quality in a short time and optimize the governance effect. Through water quality chromatography data, the distribution and change characteristics of water quality problems can be more clearly identified, helping decision makers to clarify the root causes of water quality problems. Water quality chromatography data provides an accurate map of water quality problems, making the governance plan more targeted and scientific. Through simulation verification of governance solutions, we can predict the effects of different governance measures, evaluate their feasibility in practical applications in advance, and avoid uncertainty and risks in the implementation of the solutions. Simulation verification can help select the most effective and economical governance measures, ensuring the efficiency and operability of the final governance recommendations. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0035] Figure 1 A schematic diagram of the steps of the intelligent interactive river management consultation management method of the present invention;

[0036] Figure 2 A schematic diagram of the detailed steps of the gas-liquid interface disturbance analysis;

[0037] Figure 3 It is a schematic diagram of the detailed steps of the water mixing degree analysis. DETAILED DESCRIPTION

[0038] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0039] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0040] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0041] To achieve this, please refer to Figures 1 to 3 The present invention provides an intelligent interactive river management consultation method, which comprises the following steps:

[0042] A multi-level water quality monitoring probe array is arranged at preset monitoring points in the river, and the river water quality is dynamically monitored based on the adaptive sampling frequency method through the multi-level water quality monitoring probe array to obtain multi-level water quality raw data, wherein the multi-level water quality raw data includes surface probe data, middle probe data and bottom probe data;

[0043] The surface probe data are analyzed for gas-liquid interface disturbance to obtain gas-liquid impact data; the middle layer probe data are analyzed for water mixing to obtain vertical mixing data; the bottom layer probe data are analyzed for sediment resuspension to obtain sediment release intensity data;

[0044] A water quality vertical distribution model is constructed based on the gas-liquid impact data, vertical mixing data, and sediment release intensity data; a spatiotemporal correlation analysis of water quality parameters at adjacent monitoring points is performed based on the water quality vertical distribution model to draw a water quality transmission network topology map;

[0045] 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. According to the water quality tomography data, the river management plan is generated and the management plan is simulated and verified to form a river management plan for river management consultation.

[0046] In the embodiment of the present invention, reference Figure 1 As shown, it is a schematic diagram of the steps of an intelligent interactive river management consultation management method of the present invention. In this example, the intelligent interactive river management consultation management method includes the following steps:

[0047] S1: A multi-level water quality monitoring probe array is deployed at preset monitoring points in the river, and the river water quality is dynamically monitored based on the adaptive sampling frequency method through the multi-level water quality monitoring probe array to obtain multi-level water quality raw data, wherein the multi-level water quality raw data includes surface probe data, middle probe data and bottom probe data;

[0048] The embodiment of the present invention deploys a multi-level array of water quality monitoring probes at preset monitoring points in the river, and realizes multi-level monitoring of surface, middle and bottom water quality by selecting probes of different water depths, so as to improve the spatial resolution of water quality parameters. In the 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 river bottom. The sampling frequency is dynamically adjusted using an adaptive sampling frequency method. For example, when the water quality parameter change rate is greater than 0.8 units / minute, the sampling interval is adjusted to 10 seconds; when the change rate is between 0.3-0.8 units / minute, the sampling interval is 2 minutes; when the change rate is less than 0.3 units / minute, the sampling interval is 15 minutes, thereby balancing fine monitoring and resource conservation.

[0049] S2: Perform gas-liquid interface disturbance analysis on the surface probe data to obtain gas-liquid impact data; perform water mixing degree analysis on the middle layer probe data to obtain vertical mixing data; perform sediment resuspension analysis on the bottom layer probe data to obtain sediment release intensity data;

[0050] The embodiment of the present invention performs gas-liquid interface disturbance analysis on the surface probe data, and evaluates the degree of influence of water surface disturbance on water quality by combining the surface water quality parameters collected by the probe and the wind speed and air pressure change data monitored by the meteorological sensor. In the specific implementation, the wind-induced turbulence intensity is calculated when the wind speed is 6 meters per second, and the intensity coefficient obtained is 0.72; the air pressure data is used to predict that the air pressure will drop from 1015hPa to 1010hPa in the next 24 hours, and the degree of disturbance is predicted to be a significant disturbance, and the water quality parameters (such as dissolved oxygen) affected by the fluctuation are analyzed in combination with the surface probe data, and finally the gas-liquid impact data is generated. The water mixing degree is analyzed on the middle-layer probe data, and the vertical profile of the water body is constructed by extracting the temperature, conductivity and dissolved oxygen data at different depths, and the vertical mixing characteristics are analyzed using the stratified intensity index. In the specific implementation, the vertical temperature gradient is 0.3℃ / meter, and the calculated thermal stratification coefficient is 0.6; the vertical conductivity gradient is 0.02mS / cm / meter, and the calculated salt stratification coefficient is 0.5; the vertical dissolved oxygen gradient is 0.4mg / L / meter, and the calculated oxygen stratification coefficient is 0.7. After weighting the stratification coefficient, the stratification intensity index is 0.62. Based on this intensity index, the vertical turbulent diffusion coefficient is dynamically adjusted to 0.03m 2 / second, the constructed vertical mixing model generates vertical mixing data. Sediment resuspension analysis is performed on the bottom probe data, and turbidity mutation events are identified by monitoring the instantaneous changes in turbidity. In specific implementation, when the turbidity rise rate reaches 10NTU / minute and spreads to a radius of 3 meters, high-frequency sampling is triggered to record the trend of turbidity changes. Combined with the typical pattern recognition of the historical database, it is identified as "resuspension caused by bottom flow", and the impact on the total phosphorus concentration in the water quality is evaluated as an increase of 0.02mg / L, and sediment release intensity data is generated.

[0051] 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 on water quality parameters at adjacent monitoring points based on the water quality vertical distribution model, and draw a water quality transmission network topology map;

[0052] According to the gas-liquid impact data, vertical mixing data and sediment release intensity data, the embodiment of the present invention establishes a water quality vertical distribution model based on the coupling of vertical turbulent diffusion and stratification characteristics. In the specific implementation, the surface dissolved oxygen is corrected for gas-liquid interface disturbance, and the correction value is 0.15 mg / L; the transmission efficiency analysis of the middle layer water quality shows that the transmission efficiency of the vertical gradient is reduced to 0.8; combined with the bottom layer release intensity, the concentration increase of total phosphorus is corrected to 0.03 mg / L, and finally a water quality model describing the vertical distribution is formed by dynamic calculation. According to the water quality vertical distribution model, the water quality parameters of adjacent monitoring points are subjected to time lag analysis and spatial correlation analysis, and the spatiotemporal correlation coefficient matrix between each monitoring point is calculated. In the specific implementation, when the time lag correlation coefficient of two points is 0.85, the spatial distance is 2 kilometers and they have similar hydrological and geographical characteristics, it is determined that there is a water quality transmission relationship between them, and they are marked as connected nodes in the topological map.

[0053] 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.

[0054] The embodiment of the present invention performs degree centrality and betweenness centrality analysis based on the topological graph, identifies propagation nodes, and generates a river management plan in combination with tomographic data. In the specific implementation, key nodes (nodes with a centrality value greater than 0.75) are screened out through degree centrality, and the pollution problem is mainly excessive nitrogen. The management measures matched according to the empirical database are planting aquatic plants and using aeration devices. After simulation verification, it is shown that the nitrogen concentration decreases by 15% after management, and finally the recommended management plan is output.

[0055] The multi-level monitoring probe array in the present invention can realize the three-dimensional spatial distribution data collection of river water quality, covering the surface, middle and bottom layers, so as to obtain more comprehensive water quality information. The method based on adaptive sampling frequency dynamically adjusts the monitoring frequency, which can improve the data collection accuracy when the water quality changes drastically, and reduce data redundancy when the water quality is stable, thereby optimizing the utilization efficiency of monitoring resources. The data analysis of the surface probe reveals the dynamic influence of gas-liquid interface disturbance on dissolved oxygen and volatile pollutants in water bodies, and provides a scientific basis for judging the interaction process between the atmosphere and water bodies. The data analysis of the middle-level probe quantifies the material and energy exchange intensity between water bodies at different levels through water mixing, which is helpful for studying the vertical migration law of pollutants. The data analysis of the bottom probe evaluates the resuspension release process of nutrients or pollutants in the sediment, thereby helping to predict the long-term impact of the sediment on the overall water quality. The data of gas-liquid interface disturbance, vertical mixing degree and sediment release intensity are integrated into a vertical distribution model, which can systematically quantify the distribution characteristics of water quality parameters in the vertical direction. This model not only provides a more accurate description of the spatial structure of water quality, but also provides a basis for parameter correlation analysis between monitoring points. The spatiotemporal correlation analysis of water quality data from adjacent monitoring points can reveal the dynamic laws of pollutant diffusion and migration. The complex transmission relationship of river water quality can be visualized through topological maps, helping governance personnel to intuitively identify pollution sources, diffusion paths and key governance areas. Calculating the degree centrality and betweenness centrality of monitoring points can quantify the importance of each monitoring point in the water quality transmission network. After identifying key transmission nodes, the source of water quality fluctuations or high-risk transmission paths can be more accurately located, thereby improving the pertinence and effectiveness of governance plans. Generating governance plans based on water quality chromatography data ensures that the plans are based on comprehensive scientific data, increasing the feasibility of implementation. The simulation verification process evaluates the actual effects and potential risks of the plans through virtual simulation, avoiding unnecessary problems that may be caused by direct implementation, and improving governance efficiency and success rate. The governance recommendations output by combining all key data and simulation verification results are highly scientific and targeted, and can effectively deal with water quality problems in complex river environments and achieve precise governance. At the same time, this process greatly shortens decision-making time and improves governance benefits. This method establishes a spatiotemporal dynamic model of river water quality through multi-level and multi-dimensional data collection and analysis, which can systematically identify key factors and propagation nodes that affect water quality, and generate the optimal governance plan in an intelligent and data-driven way. Combined with simulation verification, the reliability of the governance plan is further guaranteed, thereby significantly improving the efficiency of river governance and reducing governance costs, while providing strong support for scientific decision-making.

[0056] Preferably, the adaptive sampling frequency method is specifically:

[0057] Get 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, so as to achieve fine sampling of the drastically changing areas and save resources in the stable areas.

[0058] The embodiment of the present invention obtains the change rate of water quality parameters at the current moment through a real-time monitoring system. The water quality parameters include dissolved oxygen (DO), turbidity (TURB), electrical conductivity (EC), etc. In the specific implementation, a multi-level water quality monitoring probe array is used to collect water quality data every 5 minutes initially set, and the change rate is obtained by differential calculation of the water quality parameters of two consecutive samples. For example, in a certain river section, when the DO concentrations of two consecutive samples are 7.8 mg / L and 8.3 mg / L respectively, and the time interval is 5 minutes, the change rate calculation formula is (8.3-7.8) / 5=0.1 mg / L / minute. The change rate data will be used as the basis for subsequent sampling interval adjustments. When the water quality parameter change rate is greater than the preset first threshold, the sampling time interval is adjusted to 10 seconds. For example, the first threshold is set to 0.8 units / minute. When the calculated DO change rate reaches 1.2 mg / L / minute, the system automatically issues a sampling interval adjustment instruction to shorten the sampling interval of the current monitoring point from 5 minutes to 10 seconds, and starts the high-frequency sampling mode to obtain the rapid change data of DO, and transmits it to the background in real time for dynamic monitoring, so as to capture the key details of sudden water quality events, such as the characteristics of short-term high-concentration inflow of pollutants. 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 calculated DO change rate of a certain monitoring point is 0.5 mg / L / minute, the system triggers the sampling interval adjustment algorithm, shortens the original 5-minute interval to 2 minutes, and enters the medium-frequency sampling mode to dynamically track smaller water quality fluctuations. At the same time, the sampling data is curve-fitted by the smoothing algorithm in the background to generate a trend analysis report to determine whether the sampling strategy needs to be further adjusted. 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 stable areas. For example, when the DO change rate calculated at a monitoring point is 0.1 mg / L / minute, which is lower than 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 governance plans.

[0059] The present invention monitors the rate of change of water quality parameters in real time, which can accurately reflect the dynamic changes of the water environment and capture the drastic fluctuations in water quality caused by emergencies (such as pollutant discharge 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 is greater than the preset first threshold, high-frequency monitoring can be achieved by quickly shortening the sampling time interval to 10 seconds. This fine sampling can capture the details of the rapid change process, reveal the precise trajectory of the change in pollutant concentration, and provide high-resolution data support for subsequent analysis. When the water quality change rate is between the first and second thresholds, the sampling interval is adjusted to 2 minutes, which can take into account monitoring accuracy and resource utilization efficiency. This setting ensures that in the case of relatively gentle changes, enough data can still be captured to reflect trend changes, avoiding equipment load increases and data redundancy caused by too frequent sampling. Thereby locating the pollution source and assessing the impact range of the pollution event faster and more accurately. When the water quality change rate is lower than the second threshold, extending the sampling time interval to 15 minutes can significantly reduce system resource consumption. Reducing the sampling frequency in areas with stable water quality not only saves the operating energy consumption and data storage space of the monitoring equipment, but also reduces the pressure of data transmission and processing, while maintaining effective monitoring of the environmental status. By dynamically adjusting the sampling interval, key data can be captured in areas with drastic changes. This refined monitoring strategy helps to deeply study the driving mechanism and diffusion path of pollution events, and provide high-quality data support for the formulation of precise governance and early warning measures. In areas with stable water quality parameters, unnecessary resource waste is avoided by reducing the sampling frequency, and the sustainability of the monitoring system is enhanced. This resource-saving strategy also reserves more equipment and human resources for centralized monitoring of key areas. The method of dynamically adjusting the sampling interval is both flexible and efficient, and can adapt to the characteristics of water quality changes in different regions to achieve refined and economical monitoring goals. This strategy not only improves the intelligence level of the water quality monitoring system, but also optimizes the monitoring process, greatly improving the operating efficiency and effectiveness of the system. This method achieves fine monitoring of drastic change areas and resource conservation in stable areas by real-time monitoring of the change rate of water quality parameters and combining an adaptive sampling time interval adjustment strategy. 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.

[0060] Preferably, the gas-liquid interface disturbance analysis is specifically:

[0061] Obtain wind speed and air pressure change data based on surface probe data;

[0062] The coupling relationship between wind speed and water quality parameters is calculated based on the surface probe data and the wind speed and air pressure change data, thereby obtaining the wind-induced turbulence intensity data;

[0063] The interface disturbance degree in the next 24 hours is predicted based on the pressure change trend of the wind speed and air pressure change data, so as to obtain the disturbance degree prediction data;

[0064] 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 determine the impact of water surface fluctuations on water quality, thereby obtaining gas-liquid impact data.

[0065] 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 described in the embodiment of the present invention is as follows:

[0066] S211: Obtain wind speed and air pressure change data according to surface probe data;

[0067] The embodiment of the present invention collects water quality data such as water temperature, dissolved oxygen (DO), pH value, etc. in real time through the surface probe, and extracts wind speed and air pressure change data by using the data integration algorithm in combination with the wind speed and air pressure data provided by the environmental monitoring station. For example, a river monitoring point is selected, and the surface probe collects water quality data every 10 minutes. The meteorological station also provides the wind speed of 6m / s and the air pressure of 1015hPa at the corresponding time point. 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 is obtained, providing basic data for subsequent analysis.

[0068] S212: Calculate the coupling relationship between wind speed and water quality parameters based on the surface probe data and the wind speed and air pressure change data, so as to obtain wind-induced turbulence intensity data;

[0069] 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 is used as the dependent variable. 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=0.05 wind speed^2-0.2 wind speed+0.5. Then, the wind-induced turbulence intensity at a specific wind speed is calculated according to the coupling model. For example, when the wind speed is 8m / s, the calculated wind-induced turbulence intensity is 1.2 units, which is one of the main driving forces for the change of water quality parameters.

[0070] S213: predicting the interface disturbance degree within the next 24 hours based on the pressure change trend for the wind speed and pressure change data, thereby obtaining disturbance degree prediction data;

[0071] The embodiment of the present invention performs pressure change trend analysis on the 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 changes (1015hPa, 1014hPa, 1012hPa...), the ARIMA model is used for fitting and prediction to obtain the future pressure change trend. Combined with the coupling influence relationship between wind speed and pressure, the degree of interface disturbance in the next 24 hours is calculated by the disturbance index formula, for example, disturbance index = wind speed * pressure change rate, to obtain disturbance degree prediction data.

[0072] 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.

[0073] The embodiment of the present invention uses a CFD (computational fluid dynamics) model to analyze the impact of water surface fluctuations on surface water quality parameters based on disturbance degree prediction 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 range is ±1.5 mg / L, indicating that the fluctuation of the gas-liquid interface has a significant impact on dissolved oxygen. Finally, the analysis results are organized into gas-liquid impact data for subsequent optimization of river management strategies.

[0074] The wind speed and air pressure of the present invention are important external factors that affect the surface water quality of water bodies, especially in terms of water surface fluctuations, gas-liquid interface disturbances, and water mixing. By obtaining wind speed and air pressure change data related to surface probe data, environmental conditions can be systematically correlated and analyzed with water quality monitoring data, thereby providing a more comprehensive water quality status monitoring framework. This enables the system to predict water quality fluctuation trends based on climate change and improve the accuracy and timeliness of water quality monitoring. The impact of wind speed on the surface water quality of water bodies is reflected in the intensity of wind-induced turbulence (water body disturbance caused by wind). By coupling and calculating wind speed and air pressure data with surface probe data, the intensity of wind-induced turbulence can be quantified, thereby 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 distribution of suspended particles in water bodies, so this analysis can help to gain a deeper understanding of the complex relationship between wind and water quality, especially the 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 disturbance at the gas-liquid interface, thereby affecting water quality. By analyzing the trend of air pressure changes and combining it with wind speed data for prediction, the interface disturbance within the next 24 hours can be predicted. This prediction capability helps to identify possible water quality fluctuations in advance, especially when the air pressure changes cause abnormal atmospheric pressure, the water surface may fluctuate greatly. The predicted disturbance degree data can provide data support for early warning of water quality change trends and early deployment of control measures. Water surface fluctuation is an important phenomenon caused by wind-induced turbulence, which directly affects the gas-liquid interface exchange and water quality changes in the water body. By combining the disturbance degree prediction data and wind-induced turbulence intensity data for water quality analysis, we can deeply understand how water surface fluctuations affect water quality parameters through gas-liquid exchange (such as dissolved oxygen concentration, gas exchange, etc.). This analysis helps to identify the key driving factors of water quality fluctuations, especially when the wind speed is high or the air pressure changes drastically. By quantitatively analyzing the impact of water surface fluctuations, it can provide strong data support for the dynamic prediction of water quality changes, pollutant diffusion models and water body management. Through this step of analysis, the potential impact of wind speed and air pressure on water quality can be fully considered, especially the impact of wind-induced turbulence on water surface fluctuations and gas-liquid interface disturbances. This comprehensive analysis method can provide more accurate predictions of water quality trends and help the monitoring system better cope with the dynamic disturbances caused by the natural environment to water quality. At the same time, accurate identification of the key driving factors of water quality changes improves the early warning capability and accuracy of water quality monitoring, helps to adjust treatment measures and water quality management strategies in a timely manner, and ensures the stability and protection of the water ecological environment.

[0075] Preferably, the water mixing degree analysis is specifically as follows:

[0076] The temperature, conductivity and dissolved oxygen parameters of water bodies at different depths are extracted based on the data from the middle layer probe, thus obtaining the vertical profile data of the water body;

[0077] The water stratification intensity index is calculated according to the vertical profile data of the water body, so as to obtain the water stratification intensity data, wherein the water stratification intensity index calculation specifically includes calculating the thermal stratification coefficient based on the vertical gradient of temperature, calculating the salt stratification coefficient based on the vertical gradient of conductivity, and calculating the oxygen stratification coefficient based on the vertical gradient of dissolved oxygen, and the thermal stratification coefficient, the salt stratification coefficient and the oxygen stratification coefficient are weighted and integrated to obtain the water stratification intensity index;

[0078] Conduct water body hybrid power analysis based on water body stratification intensity data to obtain mixing efficiency coefficient data;

[0079] Based on the mixing efficiency coefficient 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 according to the water body stratification intensity data, thereby constructing a vertical mixing model and obtaining vertical mixing data.

[0080] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 1 The detailed step flow chart of the water mixing degree analysis described in the embodiment of the present invention is as follows:

[0081] S221: Extract the temperature, conductivity and dissolved oxygen parameters of water bodies at different depths based on the data from the middle layer probe, thereby obtaining the vertical profile data of the water body;

[0082] S222: Calculating the water body stratification intensity index according to the water body vertical profile data, thereby obtaining the water body stratification intensity data, wherein the water body stratification intensity index calculation specifically includes calculating the thermal stratification coefficient based on the vertical gradient of temperature, calculating the salt stratification coefficient based on the vertical gradient of conductivity, and calculating the oxygen stratification coefficient based on the vertical gradient of dissolved oxygen, and weightedly integrating the thermal stratification coefficient, the salt stratification coefficient, and the oxygen stratification coefficient, thereby obtaining the water body stratification intensity index;

[0083] S223: Performing water body hybrid power analysis according to the water body stratification intensity data, thereby obtaining mixing efficiency coefficient data;

[0084] S224: Based on the mixing efficiency coefficient 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 according to the water body stratification intensity data, thereby constructing a vertical mixing model and obtaining vertical mixing data.

[0085] The embodiment of the present invention collects water temperature, conductivity and dissolved oxygen parameters at different depths in the river channel in the form of vertical distribution through the middle layer probe. The specific operation is to arrange the probe 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, the conductivity is 450μS / cm, 470μS / cm and 500μS / cm, and the dissolved oxygen is 8.2mg / L, 7.5mg / L and 6.8mg / L. These data are organized into vertical profile data of the water body to provide data support for subsequent stratification calculations. According to the extracted vertical profile data of the water body, the water body stratification intensity index is calculated. The specific operation includes calculating the thermal stratification coefficient using the vertical temperature gradient. For example, the temperature gradient between 1 meter and 3 meters is -0.6℃ / m, and the corresponding thermal stratification coefficient is 2.5; calculating the salt stratification coefficient using the vertical conductivity gradient. For example, the conductivity gradient between 3 meters and 5 meters is 15μS / cm / m, and the corresponding salt stratification coefficient is 1.8; calculating the oxygen stratification coefficient using the vertical dissolved oxygen gradient. For example, the dissolved oxygen gradient between 1 meter and 3 meters is -0.35mg / L / m, and the corresponding oxygen stratification coefficient is 2.0. Then, through the weighted formula, such as stratification intensity index = 0.4 thermal stratification coefficient + 0.3 salt stratification coefficient + 0.3*oxygen stratification coefficient, the water stratification intensity index is finally obtained as 2.23 as the water stratification intensity data. According to the water stratification intensity data, the mixed dynamic behavior of the water body is analyzed. In the specific implementation, the mixing efficiency coefficient is calculated based on the numerical model (such as the k-ε model). For example, by simulating the surface wind speed of 3m / s and the bottom flow velocity of 0.1m / s, the mixing efficiency coefficient is 0.7, indicating that the water mixing efficiency is relatively high. In addition, by observing the correlation between the trend of stratification intensity change and the mixing efficiency, the important influence of stratification intensity on hybrid power is verified. Based on the mixing efficiency coefficient data, 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 interlayer mass transfer rate is set to 0.02kg / s and the energy transfer rate is set to 1.5W / m^2. 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 decreases from 0.5×10^-4m^2 / s to 0.3×10^-4m^2 / s. These parameters are used to construct a vertical mixing model. By solving the continuity equation and turbulent momentum equation, the vertical mixing data are finally obtained, providing support for the quantitative analysis of the vertical mixing characteristics of water bodies.

[0086] The changes in parameters such as temperature, conductivity and dissolved oxygen of the water body at different depths of the present invention reflect the vertical structural characteristics of the water body. By extracting these parameters from the middle-layer probe data, the vertical profile data of the water body can be obtained, so as to fully understand the vertical distribution of the water body. Water quality data at different depths can reveal the hierarchical structure of the water body, help identify the 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 the water body affects the physical and chemical properties of the water body such as temperature, salinity, and dissolved oxygen. By calculating the water stratification intensity index, the hierarchical structure and stability of the water body in the vertical direction can be quantified. Specifically, the thermal, salt, and oxygen stratification coefficients are calculated respectively through the vertical gradients of temperature, salinity, and dissolved oxygen, and then decentralized fusion is performed to obtain comprehensive water stratification intensity data. This data can help identify whether the water body is in a stable stratified state, as well as the stratification intensity between different layers, and provide a basis for further water management and pollutant control. The stratified state of the water body directly affects the mixing process of the water body, and then affects the exchange efficiency of matter and energy. By analyzing the stratification intensity data of water bodies, the mixing efficiency of water bodies can be evaluated, and then the mixing efficiency coefficient can be obtained. This analysis helps to understand the material exchange capacity of each layer in the water body, as well as the material transport and diffusion rate between different layers. It is of great significance for water body management, pollutant control and water quality early warning. In particular, when the water body is stratified with different intensities, the mixing efficiency coefficient helps to judge the change trend of water quality and the stability of the ecological environment. The vertical mixing of water bodies is the exchange process of matter and energy in the water body. Especially when there is stratification of water bodies, the mixing process is greatly affected. By dividing the water body into multiple calculation layers based on the data of the mixing efficiency coefficient and establishing the mass and energy transfer equations between adjacent calculation layers, the vertical mixing process of the water body can be accurately simulated. By dynamically adjusting the vertical turbulent diffusion coefficient that describes the vertical mixing intensity, the material transport and energy exchange in the water body can be accurately reflected in the model. The model can help identify the mixing efficiency between different layers in the water body and predict the trend of water quality changes in the water body. In particular, in stratified water bodies with low material exchange rates such as temperature and dissolved oxygen, the vertical mixing model can effectively guide pollutant control and water quality restoration measures.

[0087] Preferably, the water body hybrid power analysis is specifically as follows:

[0088] Obtain mid-water environmental data;

[0089] The water body kinetic energy input flux is calculated based on the mid-layer water body environmental data to obtain the total input flux data. The formula used to calculate the water body kinetic energy input flux is as follows:

[0090] Pe=Pe 1 +Pe 2 ;

[0091]

[0092] Where Pe is the total input flux, Pe 1 is the wind kinetic energy input flux, Pe 2 is the velocity kinetic energy input flux, ρ 1 is the air density, Cd is the drag coefficient, U is the wind speed, ρ 2 is water density, v is water velocity, A is water surface area, and h is water depth;

[0093] The energy required for complete mixing of water from a stratified state is calculated based on the vertical profile data of the water body, thereby obtaining the data on the change in water body potential energy. The calculation formula for the change in water body potential energy is as follows:

[0094]

[0095] Among them, Ep is the change of water body potential energy, Ep 2 is the potential energy after complete mixing, Ep 1 is the potential energy of the initial stratified state, is the average density of all layers, g is the acceleration of gravity, H is the total water depth, ρ i is the density of the water body in the i-th layer, h i is the water depth of the i-th layer;

[0096] Based on the total input flux data and the water body potential energy change data, the mixing effect of the water body under the action of external driving force is quantified according to the water body stratification intensity data, so as to obtain the mixing efficiency coefficient data.

[0097] The embodiment of the present invention obtains water environment data through the middle layer probe, including wind speed, water flow speed, water depth and water surface area. For example, in a certain river section, the wind speed is 5m / s, the water flow speed is 0.3m / s, the water depth is 3m, and the water surface area is 200m 2 , and assume that the air density ρ 1 1.2kg / m 3 , water density ρ 2 1000kg / m 3 , the drag coefficient Cd is 0.0015. According to the formula Pe 1 =((ρ 1 QUR 3 )) / ρ 2 , calculate the wind kinetic energy input flux Pe 1 =0.0015; According to the formula Pe 2 =0.5ρ 2 v 3 *A / h, calculate the flow rate kinetic energy input flux Pe 2=15. Adding the two together, we get the total input flux Pe = 15.0015 W / m 2 , as the total input flux data, provides a quantitative indicator of kinetic energy input for subsequent analysis. Based on the vertical profile data of the water body and the stratification intensity index obtained in the previous step, calculate the potential energy change required for the water body to be completely mixed from the stratified state. For example, the water depth of 3m is divided into three layers, namely 1m, 1m, and 1m, and the water density of each layer is 998kg / m 3 , 1000kg / m 3 and 1002kg / m 3 First, calculate the average density after complete mixing: ρ = (998 + 1000 + 1002) / 3 = 1000 kg / m 3 ; Then calculate the total potential energy Ep in the initial stratified state 1 =ρ 1 gh 1 +ρ 2 gh 2 +ρ 3 gh 3 =9989.81+10009.81+10029.81=29402.8J / m 2 ; Calculate the potential energy after complete mixing Finally, the change in water potential energy Ep=|Ep 2 ―Ep 1 |=|29400-29402.8|=2.8J / m 2 . This data reflects the energy required for water mixing and provides a basis for the evaluation of mixing efficiency. Combined with the total input flux data and the potential energy change data, the mixing efficiency coefficient of the water body under the action of external driving force is quantified. For example, using the mixing efficiency formula η=Ep / Pe, in the above case η=2.8 / 15.0015≈0.1867. Further considering the water body stratification intensity data, for example, the stratification intensity index is 2.2, by comparing the changing trends of stratification intensity and mixing efficiency, it is concluded that under the action of external driving force, the mixing effect of the middle layer water body is better, and the efficiency coefficient is 0.19, which provides a reference for formulating water body management measures.

[0098] The middle-layer water body environmental data of the present invention includes important water quality parameters, such as temperature, dissolved oxygen, salinity, etc., and their changes in the middle layer of the water body. These data provide a basis for subsequent calculations and analysis, and help to gain a deeper understanding of the vertical distribution structure of the water body. By obtaining these data, the state of different depths in the water body can be identified, which is helpful to evaluate the mixing and vertical structure of the water body, thereby providing an important basis for model construction and water quality monitoring. Calculating the kinetic energy input flux (Pe) of the water body is a key step in evaluating the dynamic process of the water body. By calculating the wind kinetic energy input flux and the flow velocity kinetic energy input flux, we can understand the impact of the external environment (such as wind speed and flow velocity) on the kinetic energy input of the water body, especially when there are changes in wind speed and flow velocity. The impact of kinetic energy input on water mixing and vertical stratification is crucial. Use the given formula to calculate the wind kinetic energy (Pe 1 ) and flow velocity kinetic energy (Pe 1), the impact of different driving forces on water bodies can be further quantified. This helps to accurately evaluate the dynamic changes of water bodies and provide numerical support for water mixing intensity analysis, pollutant diffusion prediction, etc. The stability of water stratification is closely related to its energy. By calculating the energy (potential energy change) required for water bodies to go from stratified state to complete mixing, the potential energy difference of water stratification and the difficulty of the mixing process can be quantified. The calculation formula of potential energy change (Ep) can help evaluate the energy required for the process from stratification to complete mixing of water bodies under the action of external driving forces. The change in potential energy reflects the stratification intensity and stability of water bodies, and provides key data on whether water bodies are easy to mix or have mixing obstacles. This helps to understand and optimize water governance and management strategies, especially in terms of pollutant distribution and water body self-purification capacity, so that more effective intervention measures can be formulated. By combining total input flux data, potential energy change data and water stratification intensity data, the mixing effect of water bodies under the action of external driving forces can be quantified. The core purpose of this step is to calculate the mixing efficiency coefficient of water bodies, which can reflect the efficiency of material exchange between different layers in water bodies. The mixing efficiency coefficient helps to determine whether the water body can effectively exchange substances and energy between the upper and lower layers, thereby affecting the speed and breadth of water quality changes. This indicator is crucial in water body management, pollutant control and ecological restoration. By quantifying the mixing effect, more refined decision support can be provided for ecological and environmental management. These steps combine the kinetic energy input, potential energy change and mixing effect quantification of the water body, and can comprehensively evaluate the mixing dynamics of the water body under the action of external driving forces. By quantifying the energy changes and driving factors in the stratification and mixing process of water bodies, the transmission efficiency of substances and energy in the water body, the diffusion range of pollutants and the trend of water quality changes can be accurately predicted. This provides a scientific basis for water body management, pollution prevention and control, ecological restoration and environmental monitoring, so that water body management can be implemented more accurately and efficiently. At the same time, the quantification of the mixing effect of water bodies provides an important reference for the prediction of water quality changes, which can play an important role in water resource utilization, ecological protection and environmental impact assessment.

[0099] Preferably, the sediment resuspension analysis is specifically:

[0100] 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;

[0101] Turbidity mutation detection is performed according to 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;

[0102] Resuspension type identification is performed on the turbidity mutation data based on typical patterns in the historical database, thereby obtaining resuspension type data;

[0103] Based on the resuspension type data and bottom probe data, the impact degree and duration of resuspension on water quality are evaluated to obtain sediment release intensity data.

[0104] 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, 55 (unit: NTU). According to the turbidity change rate formula Calculate the instantaneous rate of change of turbidity, where Δt is 1 second, and the calculated ΔT is 1, 2, 2, 25, and 5 respectively. Finally, the instantaneous change data of turbidity is obtained, which shows that the turbidity changes dramatically in the 5th second, providing a basis for subsequent analysis. Compare the calculated instantaneous change data of turbidity with the preset instantaneous change threshold of turbidity (such as 10NTU / s), and find that the change rate in the 5th and 6th seconds is greater than the threshold. Therefore, 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 turbidity are recorded. For example, the high-frequency sampling results show that the rising rate of turbidity in 10 seconds is 20NTU / s, and the diffusion range is 20m 2 , 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 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 of the bottom water to 0.5 mg / L and reduced the dissolved oxygen to 5 mg / L. The radius of the affected area was 30m and the duration was 4 hours. The sediment release intensity data was obtained to provide a scientific basis for water quality management and improvement.

[0105] The instantaneous change of turbidity of the present invention is an important indicator for evaluating the disturbance of sediment in water bodies. By calculating the instantaneous change of turbidity of the bottom water body in real time, the disturbance and suspension of sediment in the bottom water body can be reflected in time. This process is crucial for monitoring the dynamic changes of water quality, especially when the bottom water body is disturbed, turbidity can be found in time, providing early warning basis for subsequent water quality management and pollution source control. Turbidity mutation is usually a manifestation of disturbance of the water body (such as wind, tidal changes, human activities, etc.) or sediment resuspension. By setting a preset turbidity instantaneous change threshold and combining real-time data for mutation detection, sudden changes in the water body can be accurately captured. This not only provides a dynamic response mechanism for water quality monitoring, but also provides an opportunity for timely intervention for water management. Especially in the bottom water body, turbidity mutation often means disturbance of sediment or release of pollutants. Timely detection of mutation can effectively avoid pollution diffusion and reduce environmental risks. When a turbidity mutation is detected, the high-frequency sampling mode of the bottom probe is automatically triggered and the turbidity rise rate and diffusion range are recorded, which can monitor and record the details of turbidity changes in the water body in real time. This process can help more accurately track the source, propagation path and diffusion range of turbidity, and provide scientific data for subsequent water quality warning, pollution source tracking and environmental restoration plans. The use of high-frequency sampling mode ensures a more detailed analysis of water quality changes in the case of mutations, helps to grasp the real-time dynamics of water quality fluctuations, and improves the timeliness and effectiveness of decision-making. Resuspension is one of the turbidity phenomena caused by sediment disturbance, and different types of disturbances may have different water quality impacts. By identifying the resuspension type based on typical patterns in the historical database, it can help distinguish different types of resuspension phenomena and judge their specific impact on water quality. This step helps to accurately identify the root cause of turbidity changes, and by comparing historical patterns, it can predict and understand the long-term impact of similar disturbances, providing a scientific basis for pollution prevention and control, environmental restoration and water quality protection strategies. The severity of the resuspension phenomenon is not only related to its intensity, but also closely related to its duration and diffusion range. By combining the resuspension type data and the bottom probe data, it is possible to quantitatively evaluate the specific impact of resuspension on water quality, such as the amount of sediment released, the scope of the affected water area and the 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 provide a basis for water quality restoration measures after sediment disturbance. Quantification of sediment release intensity not only helps to understand the 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.

[0106] Preferably, the construction of the water quality vertical distribution model is specifically as follows:

[0107] According to the gas-liquid impact data, the surface water quality parameters are corrected for gas-liquid interface disturbance, thereby obtaining surface water quality correction data;

[0108] 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, thereby obtaining inter-layer transmission data.

[0109] The sediment resuspension impact assessment was performed on bottom water quality parameters based on the sediment release intensity data, thus obtaining bottom water quality impact data;

[0110] 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.

[0111] The embodiment of the present invention uses gas-liquid impact data to correct the gas-liquid interface disturbance of surface water quality parameters (such as dissolved oxygen, pH value and temperature). By coupling analysis of the disturbance degree prediction data and wind-induced turbulence intensity data, the effect of the gas-liquid interface disturbance on the dissolved oxygen transmission rate is calculated. For example, the dissolved oxygen transmission rate is increased from 0.01 mg / L / s to 0.03 mg / L / s. The corrected transmission rate is substituted into the dissolved oxygen concentration evolution formula to dynamically correct the surface dissolved oxygen concentration. At the same time, the pH value and temperature change trends of the surface water quality are corrected to finally obtain the surface water quality correction data for optimizing 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 Quantify the transmission of water quality parameters between adjacent computational layers, where vt is the turbulent diffusion coefficient, is the concentration gradient; for example, the vertical diffusion coefficient is calculated to be 5×10 ―5 m 2 / s, the concentration gradient is 0.1 mg / L / m, and the dissolved oxygen transmission flux between adjacent layers is 5×10 ―6mg / (L·s), generating 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 to bottom water quality parameters, the concentration change of sediment release is evaluated by the formula ΔC=F·t / V, where F is the sediment release intensity, t is the duration, and V is the volume of the bottom water body; for example, the sediment release intensity is 0.02mg / (L·h) and the diffusion range is 50m 2 , lasting for 6 hours, the calculated increase in ammonia nitrogen concentration in the bottom water body was 0.12 mg / L, and the bottom water quality impact data was generated 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 were input into the water quality vertical distribution mathematical model, and the dynamic evolution of water quality parameters in each layer was calculated by the layered coupling method. The finite difference method with a time step of 1 hour was used to simulate the vertical distribution changes of water quality parameters over time. For example, the simulation results showed that the surface dissolved oxygen concentration decreased from 9 mg / L to 8 mg / L after 6 hours, while the bottom ammonia nitrogen concentration increased from 0.3 mg / L to 0.42 mg / L. Finally, a water quality vertical distribution model was constructed to provide support for the prediction of dynamic changes in water quality and decision-making on water quality improvement.

[0112] The surface water quality parameters of the present invention are affected by the disturbance of the gas-liquid interface, especially factors such as wind speed and air pressure changes will cause water surface fluctuations, and these disturbances may affect the accuracy of water quality monitoring data. By correcting the surface water quality through gas-liquid impact data, the error caused by surface disturbance can be removed or reduced, thereby improving the accuracy of water quality data. This step can ensure 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 the water body reflects the water quality differences between different depth layers. Understanding the vertical mixing status of the water body is crucial for predicting water quality changes. By extracting the vertical stratification characteristics of the 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 evaluated. This process can help identify the material exchange mechanism between different depths of the water body (such as dissolved oxygen, nutrients, etc.), and help to establish a more sophisticated water quality model to improve the accuracy of water quality prediction and optimize water resource management. The intensity of sediment release directly affects the water quality of the bottom water body. There may be a large number of pollutants (such as heavy metals, nutrients, organic pollutants, etc.) in the sediment. These pollutants are released into the water body when the sediment is disturbed, thus affecting the water quality. By evaluating the impact of sediment release intensity on water quality, we can better understand the changes in bottom water quality and its contribution to the overall water quality. This step can help predict and identify the potential threats to water quality caused by sediment disturbance, and provide a scientific basis for water pollution control, sediment management and ecological restoration. The mathematical model describing the vertical distribution of water quality parameters can accurately reflect the water quality status of different layers in the water body, and help study the law of water quality changes in the vertical direction of the water body. Combining surface correction data, interlayer transmission data and bottom impact data, the water quality distribution characteristics of the water body can be more comprehensively characterized, and the driving factors of water quality changes can be revealed. This provides a more multi-dimensional analysis perspective for water quality management, and can optimize management at different levels to ensure the refinement of water quality protection. By establishing a vertical water quality distribution model, the trend of water quality changes between different water layers can be predicted, and a theoretical basis can be provided for future water quality assessment and governance. The vertical water quality distribution of water bodies is dynamically changing and is affected by many factors (such as temperature, dissolved oxygen, sediment resuspension, etc.). Through layered coupling, the water quality changes of each water layer can be considered at the same time, and the comprehensive impact of different factors on water quality can be simulated through dynamic evolution calculation. This dynamic modeling can not only reflect short-term water quality changes, but also evaluate the evolution trend of water quality under different scenarios through long-term prediction. The construction of this model helps to improve the accuracy of water quality monitoring, early warning, and evaluation, and can provide decision support for the protection, management, and restoration of water resources.

[0113] Preferably, the spatiotemporal correlation analysis is specifically:

[0114] Based on the water quality vertical distribution model, the time series data of water quality parameters of each monitoring point are extracted, and the time lag correlation analysis of the time series of water quality parameters of adjacent monitoring points is performed to obtain time-related characteristic data;

[0115] According to the water quality vertical distribution model, the spatial distance and hydrological and geographical characteristics between adjacent monitoring points are analyzed, and according to the spatial distribution characteristics of water quality parameters, spatial correlation characteristic data are obtained;

[0116] 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;

[0117] Based on the preset space-time correlation threshold, the space-time correlation coefficient matrix is ​​judged by threshold. When the space-time 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.

[0118] 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, processes the hourly sampling records of parameters such as dissolved oxygen, ammonia nitrogen, and total phosphorus at the monitoring point within 24 consecutive hours, obtains 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 separated by 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, generating time-related characteristic data, and quantifying the time dependence of the change of water quality parameters. According to the vertical distribution model of water quality, the spatial distance of 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 section of the river is 0.5m / s, and the curvature coefficient is 1.2. Combined with the distance and terrain data of the monitoring points, the spatial distribution law of dissolved oxygen is calculated, and the spatial correlation characteristic data between adjacent monitoring points is obtained, which is used to describe the characteristics of spatial water quality transmission. Using the time-related characteristic data and space-related characteristic data, the time-space correlation coefficient of water quality parameters between any two adjacent monitoring points is calculated based on time-space weighting. The calculation formula is R st =ω t ·R t +ω s R s , where R t is the time correlation coefficient, R s is the spatial correlation coefficient, ω s and ω tare the spatiotemporal weighting factors, which are set to 0.6 and 0.4 respectively. For example, the time correlation coefficient of dissolved oxygen between two monitoring points is 0.85, the spatial correlation coefficient is 0.75, the calculated spatiotemporal correlation coefficient is 0.81, and the spatiotemporal correlation coefficient matrix is ​​generated to provide data support for the 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 (such as 0.8), it is determined that there is a water quality transmission channel between the two monitoring points. For example, in a certain river section, the spatiotemporal correlation coefficient between monitoring points 1 and 2 is 0.81, which exceeds the threshold and determines that there is a transmission channel; while the spatiotemporal correlation coefficient between monitoring points 2 and 3 is 0.75, which is lower than the threshold and determines that there is no transmission channel. Finally, the transmission relationship between all monitoring points is drawn in the form of network topology, and a water quality transmission network topology map reflecting the dynamic transmission path of river water quality is generated, which provides an important reference for water quality monitoring and transmission law analysis in river management.

[0119] The present invention can capture the dynamic change process of water quality comprehensively by extracting the time series data of water quality parameters of each monitoring point from the vertical distribution model of water quality. By performing time lag correlation analysis on the water quality time series of adjacent monitoring points, the temporal relationship between water quality parameters can be identified, and the comparison of water quality changes between different monitoring points and their temporal precedence can be revealed. This time-related characteristic data helps to identify the hysteresis effect of water quality changes, such as the time delay of the diffusion and reaction of certain pollutants, and provides strong support for subsequent water quality change prediction and real-time monitoring. The spatial distribution characteristics of water quality parameters can reflect the water quality heterogeneity between different monitoring points in the water body. By analyzing the spatial distance and hydrological and geographical characteristics between adjacent monitoring points, the spatial propagation pattern and regional differences of water quality can be revealed. This provides important information for understanding the spatial pollution sources, flow trends and spatial heterogeneity of water quality changes in water bodies. Spatial correlation characteristic data helps to further optimize the layout of water quality monitoring networks and improve the accuracy of water quality early warning systems. The time-space weighted correlation coefficient calculation comprehensively considers the water quality correlation in the two dimensions of time and space, and such analysis can reveal the temporal and spatial dynamic relationship of water quality changes between monitoring points. By calculating the spatiotemporal correlation coefficient matrix, the water quality transmission intensity and relationship between different monitoring points can be effectively quantified, thereby identifying the spatial propagation pattern of water quality changes. The spatiotemporal correlation coefficient matrix lays the foundation for building a water quality transmission network, making subsequent analysis more accurate and detailed. Threshold judgment of the spatiotemporal correlation coefficient matrix can help screen out pairs of monitoring points with significant water quality transmission relationships. By judging whether there are water quality transmission channels between these monitoring points, a clearer and more accurate water quality transmission network topology map can be constructed. This topology map not only reveals the water quality transmission path, but also helps predict the transmission trend and diffusion range of water quality pollutants. This has important practical application value for water quality management, pollution source tracking, and the formulation of water quality optimization plans.

[0120] Preferably, the river management plan is generated as follows:

[0121] 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 governed;

[0122] Analyze the spatial distribution and propagation trends of various water quality problems based on the type of problem data 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 the treatment area division data;

[0123] Based on the data of governance area division, the causes of water quality problems in different regions are analyzed, and the cause-effect 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;

[0124] According to the cause data of the problem, a matching combination of governance technologies is selected from the preset governance measures library, and the applicability of the governance technologies is evaluated based on the hydrological characteristics and environmental constraints of each region, so as to obtain a preliminary governance plan;

[0125] The implementation sequence and timing of each treatment measure in the preliminary treatment plan are optimized, and the optimal combination of treatment technologies is determined based on cost-benefit analysis to form a river treatment plan.

[0126] The embodiment of the present invention is based on water quality chromatography data, analyzes the spatiotemporal variation characteristics of water quality parameters such as dissolved oxygen, ammonia nitrogen and total phosphorus, and combines the characteristic templates of typical pollution problems in the historical governance experience database, and uses a support vector machine classification model to classify and identify river water quality problems. For example, in a certain section of the river, it is detected that the dissolved oxygen concentration is lower than 2 mg / L for a long time, and the ammonia nitrogen concentration is higher than 1.5 mg / L. The matching result is identified as a eutrophication problem, and the type data of the problem to be governed is generated. According to the type data of the problem to be governed, combined with the spatial distribution information of the water quality problem and the topological map of the water quality transmission network, the propagation trend of the river water quality problem is simulated and analyzed. For example, through analysis, it is found that a certain eutrophication problem has an obvious propagation trend from the upper reaches to the middle reaches of the river, and the key propagation nodes are concentrated at three monitoring points in the middle reaches. The priority governance area is set as the middle reaches and refined into a range of 500 meters, and the governance area division data is generated to provide a reference for key areas for subsequent governance. Based on the governance area division data, combined with the water quality vertical distribution model, the causes of water quality problems in the priority governance area are traced and analyzed. For example, by analyzing the vertical distribution data, it is found that the phosphorus load in the sediment is the main source of pollution. Further combined with the surrounding land use data, it is confirmed that the main source of phosphorus is agricultural non-point source pollution. The Bayesian network is used to construct the causal relationship network of the problem, generate the problem cause data, and provide a scientific basis for the selection of governance strategies. According to the problem cause data, the matching governance technology combination is selected from the governance measure library. For example, for the eutrophication problem of agricultural non-point source pollution, three technologies, "artificial wetland construction", "agricultural non-point source runoff control" and "sediment dredging" are selected, and the applicability is evaluated in combination with the hydrological characteristics of the priority governance area (such as a river slope of 0.5% and a flow rate of 0.3m / s) and environmental constraints (such as limiting the construction period to June-September), and a preliminary governance plan is generated. The implementation order and time arrangement of each governance measure in the preliminary governance plan are optimized, and the linear programming model is used to analyze the resource allocation and construction period, and the cost-benefit analysis is combined to evaluate the economy and governance effect of the technology combination. For example, through optimization, it was determined that "agricultural non-point source runoff control" should be implemented first, followed by "construction of artificial wetlands" and finally "desilting of sediments", which reduced the total construction cost by 15% and improved the treatment effect by 20%. Finally, the optimal treatment plan including time schedule, technology selection and resource allocation was formed, providing a scientific and efficient reference for actual river management.

[0127] The water quality chromatography data of the present invention provides spatial distribution information of different water quality parameters in water bodies, which can effectively identify water quality problems in rivers. Through classification and identification, the types of water quality problems (such as pollution sources, eutrophication, hypoxia, etc.) can be accurately distinguished. Combined with the historical governance experience database, the characteristics of these problems can be matched to further confirm the types of water quality problems. Obtaining the type data of problems to be governed provides a clear goal for subsequent water quality governance, making the governance plan more accurate and targeted, and reducing unnecessary waste of resources. The spatial distribution and propagation trend analysis of water quality problems can reveal the distribution of pollution sources and their diffusion paths. Combined with the water quality transmission network topology map, the key nodes of water quality problems can be located, the areas with the most serious impact of water quality problems can be identified, and the priority governance areas can be determined. Through this analysis, the division of governance areas can be effectively optimized, limited governance resources can be concentrated in the areas that need intervention most, governance efficiency can be improved, and the water quality improvement effect can be maximized. Cause analysis of water quality problems in various regions can help to more deeply understand the root causes of water quality problems, such as pollution sources, insufficient watershed management and other factors. Through the vertical distribution model of water quality, the impact of water body stratification on water quality problems can be analyzed in the vertical direction, and then a causal network can be constructed. This analysis can reveal the interactions between different levels and different factors, help identify the main and secondary causes of water quality problems, and provide a basis for formulating more accurate governance measures to avoid simple surface governance. By matching the cause data of the problem with the preset governance measure library, the most suitable combination of governance technologies can be screened for each water quality problem. This approach can ensure the scientificity and pertinence of the governance measures. By evaluating the hydrological characteristics and environmental constraints of each region, the applicability of the governance measures in the actual environment can be judged, thereby ensuring that the selected technical solutions are operational and efficient. Through this comprehensive analysis, the preliminary governance plan obtained is more in line with actual needs and provides strong support for subsequent specific implementation. The optimization of the implementation sequence and timing of governance measures can improve the overall efficiency of the governance process and avoid poor results caused by inappropriate timing or inappropriate sequence of measures. For example, some governance measures may need to be implemented in advance, while other measures need to take effect under certain conditions. Through cost-benefit analysis, it can be ensured that the selected governance technology combination maximizes the governance effect under 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.

[0128] 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:

[0129] A water quality monitoring unit is used to deploy a multi-level water quality monitoring probe array at a preset monitoring point in a river, and dynamically monitor the water quality of the river based on an adaptive sampling frequency method through the multi-level water quality monitoring probe array to obtain multi-level water quality raw data, wherein the multi-level water quality raw data includes surface probe data, middle probe data, and bottom probe data;

[0130] The water quality characteristic analysis unit is used to perform gas-liquid interface disturbance analysis on the surface probe data to obtain gas-liquid impact data; perform water mixing degree analysis on the middle layer probe data to obtain vertical mixing data; perform sediment resuspension analysis on the bottom layer probe data to obtain sediment release intensity data;

[0131] 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; conduct spatiotemporal correlation analysis on water quality parameters at adjacent monitoring points based on the water quality vertical distribution model, thereby drawing a water quality transmission network topology map;

[0132] The management 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 management plans based on water quality tomography data, and simulate and verify the management plans, so as to form a river management plan for river management consultation.

[0133] The present invention can fully cover all levels of the water body by deploying a multi-level monitoring probe array, thereby obtaining water quality data at different levels. This multi-level data provides detailed information for subsequent analysis, ensuring the comprehensiveness and accuracy of water quality monitoring. The adaptive sampling frequency can dynamically adjust the sampling frequency according to the changes in water quality, avoiding the waste of resources due to oversampling, while ensuring that the accuracy and real-time performance of data acquisition can be improved when the water quality changes drastically. Such a strategy improves the responsiveness of the monitoring system and helps to identify and respond to water quality fluctuations in a timely manner. By performing characteristic analysis on the surface, middle and bottom layer data respectively, the dynamic changes of each layer in the water body can be more accurately understood and evaluated. For example, gas-liquid interface disturbance analysis helps identify the impact of airflow on the surface, while water mixing analysis can reveal the fluidity and exchange characteristics of the middle layer water body, and sediment resuspension analysis helps to evaluate the intensity of bottom pollution sources and sediment release. Different water quality characteristics provide multi-dimensional information, which helps to fully understand the changing factors of the water body and provide accurate input data for subsequent water quality modeling and governance plan formulation. By establishing a vertical distribution model of water quality, we can fully understand the changes in water quality at different levels in the water body, so as to deeply understand the distribution characteristics of water quality. This modeling can reveal the evolution of water quality in space and time, and provide a theoretical basis for water quality management and governance. Spatiotemporal correlation analysis can reveal the relationship between water quality parameters between different monitoring points and form a topological map of the water quality transmission network. This map helps to identify the propagation channels and key nodes of water quality, clarify the path and impact range of water quality changes, and provide an important tool for tracing pollution sources and analyzing the dynamic evolution of water quality. By calculating degree centrality and betweenness centrality, the key propagation nodes of water quality fluctuations can be identified. Nodes with higher centrality are usually the main propagation points or bottlenecks of water quality changes. Governing these nodes can effectively control the propagation of water quality in a short time and optimize the governance effect. Through water quality chromatography data, the distribution and change characteristics of water quality problems can be more clearly identified, helping decision makers to clarify the root causes of water quality problems. Water quality chromatography data provides an accurate map of water quality problems, making the governance plan more targeted and scientific. Through simulation verification of governance solutions, we can predict the effects of different governance measures, evaluate their feasibility in practical applications in advance, and avoid uncertainty and risks in the implementation of the solutions. Simulation verification can help select the most effective and economical governance measures, ensuring the efficiency and operability of the final governance recommendations.

[0134] For example, the intelligent interactive river management consulting and management system may be an intelligent interactive river management management system, and the intelligent interactive river management consulting and management method may be an intelligent interactive river management management method.

[0135] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0136] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be 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 will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An intelligent interactive river management consulting method, characterized in that: The following steps are involved: A multi-level water quality monitoring probe array is arranged at preset monitoring points in the river, and the river water quality is dynamically monitored based on the adaptive sampling frequency method through the multi-level water quality monitoring probe array to obtain multi-level water quality raw data, wherein the multi-level water quality raw data includes surface probe data, middle probe data and bottom probe data; The surface probe data are analyzed for gas-liquid interface disturbance to obtain gas-liquid impact data; the middle layer probe data are analyzed for water mixing to obtain vertical mixing data; the bottom layer probe data are analyzed for sediment resuspension to obtain sediment release intensity data; A water quality vertical distribution model is constructed based on the gas-liquid impact data, vertical mixing data, and sediment release intensity data; a spatiotemporal correlation analysis of water quality parameters at adjacent monitoring points is performed based on the water quality vertical distribution model to draw a water quality transmission network topology map; 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. According to the water quality tomography data, the 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: Get 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, so as to achieve fine sampling of the drastically changing areas and save resources in the 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 the surface probe data and the wind speed and air pressure change data, thereby obtaining the wind-induced turbulence intensity data; The interface disturbance degree in the next 24 hours is predicted based on the pressure change trend of the wind speed and air pressure change data, so as to obtain 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 determine 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 middle layer probe, thus obtaining the vertical profile data of the water body; The water stratification intensity index is calculated according to the vertical profile data of the water body, so as to obtain the water stratification intensity data, wherein the water stratification intensity index calculation specifically includes calculating the thermal stratification coefficient based on the vertical gradient of temperature, calculating the salt stratification coefficient based on the vertical gradient of conductivity, and calculating the oxygen stratification coefficient based on the vertical gradient of dissolved oxygen, and the thermal stratification coefficient, the salt stratification coefficient and the oxygen stratification coefficient are weighted and integrated to obtain the water stratification intensity index; Conduct water body hybrid power analysis based on water body stratification intensity data to obtain mixing efficiency coefficient data; Based on the mixing efficiency coefficient 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 according to the water body stratification intensity data, thereby constructing a vertical mixing model and obtaining vertical mixing data.

5. The intelligent interactive river management consulting and management method according to claim 4 is characterized in that: The water body hybrid power analysis is specifically as follows: Obtain mid-water environmental data; The water body kinetic energy input flux is calculated based on the mid-layer water body environmental data to obtain the total input flux data. The formula used to calculate the water body kinetic energy input flux is as follows: Pe=Pe1+Pe2; Among them, Pe is the total input flux, Pe1 is the wind kinetic energy input flux, Pe2 is the flow velocity kinetic energy input flux, ρ1 is the air density, Cd is the drag coefficient, U is the wind speed, ρ2 is the water density, v is the water flow velocity, A is the water surface area, and h is the water depth; The energy required for complete mixing of water from a stratified state is calculated based on the vertical profile data of the water body, thereby obtaining the data on the change in water body potential energy. The calculation formula for the change in water body potential energy is as follows: Among them, Ep is the change in water potential energy, Ep2 is the potential energy after complete mixing, and Ep 1 is the potential energy of the initial stratified state, ρ is the average density of all layers, g is the gravitational acceleration, H is the total water depth, and ρ i is the density of the water body in the i-th layer, h i is the water depth of the i-th layer; Based on the total input flux data and the water body potential energy change data, the mixing effect of the water body under the action of external driving force is quantified according to the water body stratification intensity data, so as to obtain the mixing efficiency coefficient data.

6. The intelligent interactive river management consultation and management method according to claim 5 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 according to 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 degree and duration of resuspension on water quality are evaluated to obtain sediment release intensity data.

7. The intelligent interactive river management consultation and management method according to claim 6 is characterized in that: 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 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, thereby obtaining inter-layer transmission data. The sediment resuspension impact assessment was performed on bottom water quality parameters based on the sediment release intensity data, thus obtaining bottom water quality impact data; 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.

8. The intelligent interactive river management consultation and management method according to claim 7 is characterized in that: The spatiotemporal correlation analysis is specifically as follows: Based on the water quality vertical distribution model, the time series data of water quality parameters of each monitoring point are extracted, and the time lag correlation analysis of the time series of water quality parameters of adjacent monitoring points is performed to obtain time-related characteristic data; According to the water quality vertical distribution model, the spatial distance and hydrological and geographical characteristics between adjacent monitoring points are analyzed, and according to the spatial distribution characteristics of water quality parameters, spatial correlation characteristic data are obtained; 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 space-time correlation threshold, the space-time correlation coefficient matrix is ​​judged by threshold. When the space-time 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.

9. The intelligent interactive river management consultation and management method according to claim 8 is characterized in that: The specific generation of river management plan is 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 governed; Analyze the spatial distribution and propagation trend of various water quality problems based on the type of problem data to be treated, and determine the priority treatment areas based on the distribution of key nodes in the water quality transmission network topology map, so as to obtain the treatment area division data; Based on the data of governance area division, the causes of water quality problems in different regions are analyzed, and the cause-effect 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; According to the cause data of the problem, a matching combination of governance technologies is selected from the preset governance measures library, and the applicability of the governance technologies is evaluated based on the hydrological characteristics and environmental constraints of each region, so as to obtain a preliminary governance plan; The implementation sequence and timing of each treatment measure in the preliminary treatment plan are optimized, and the optimal combination of treatment technologies is determined based on cost-benefit analysis to form a river treatment plan.

10. An intelligent interactive river management consultation and management system, characterized in that: Used to execute the intelligent interactive river management consulting management method according to claim 1, the intelligent interactive river management consulting management system comprises: A water quality monitoring unit is used to deploy a multi-level water quality monitoring probe array at a preset monitoring point in a river, and dynamically monitor the water quality of the river based on an adaptive sampling frequency method through the multi-level water quality monitoring probe array to obtain multi-level water quality raw data, wherein the multi-level 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 the surface probe data to obtain gas-liquid impact data; perform water mixing degree analysis on the middle layer probe data to obtain vertical mixing data; perform sediment resuspension analysis on the 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; conduct spatiotemporal correlation analysis on water quality parameters at adjacent monitoring points based on the water quality vertical distribution model, thereby drawing a water quality transmission network topology map; The management 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 management plans based on water quality tomography data, and simulate and verify the management plans, so as to form a river management plan for river management consultation.

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