Water quality comprehensive regulation and control intelligent model based on water quality hydrodynamic force

Abstract

The invention discloses a water quality comprehensive regulation and control intelligent model based on water quality hydrodynamic force, and relates to the technical field of water quality comprehensive regulation and control, which comprises the following steps: constructing a comprehensive meteorological monitoring network, capturing key meteorological parameters of air temperature, rainfall, humidity and wind speed in real time, and formulating water area monitoring paths of an unmanned aerial vehicle and an unmanned ship according to detected environment data; the method comprises the following steps: setting an unmanned aerial vehicle monitoring team to monitor the water surface condition in real time, setting an unmanned ship formation to run according to a formulated route, carrying out key monitoring and sampling on a specific polluted area, constructing a water quality standard-reaching early warning system, and carrying out prediction and early warning simulation calculation and correction, and analyzing and evaluating the water quality condition. According to the water quality comprehensive regulation and control intelligent model based on the water quality hydrodynamic force, water area pollution can be quickly found, pollutants can be traced and researched in time, pollution expansion is prevented, meanwhile, pollution early warning can be conducted according to data, potential environmental risks can be found, and powerful technical support is provided for environmental protection work.

Description

Technical Field

[0001] The present invention relates to the technical field of comprehensive water quality regulation, and in particular to an intelligent model for comprehensive water quality regulation based on water quality and hydrodynamics. Background Art

[0002] The coupled water quantity and quality model of a water body is an advanced simulation tool that cleverly integrates the complex principles of hydrology, hydraulics and water quality, aiming to deeply explore the interaction mechanism between water quantity and water quality in a water body. The model can not only accurately capture the subtle effects of changes in water quantity on water quality in a water body, but also reveal how changes in water quality react to the dynamic balance of water quantity. By using precise mathematical equations and calculation methods, the coupled water quantity and quality model of a water body can simulate the behavior of water bodies under different environmental conditions, including rainfall, evaporation, runoff, and the diffusion and transformation of pollutants.

[0003] In practical applications, the detection of water quality data, hydraulic data and hydrological data parameters can be achieved through the high-precision monitoring sensors installed on the hull to accurately collect information on a variety of parameters, so that ship-borne water quality monitoring can continuously monitor and sample the same point and monitor the pollution range centered on the pollution source. However, hull monitoring cannot detect information in a large area of ​​water, quickly and efficiently find the source of water pollution and issue emergency warnings to staff, especially in severe weather conditions. Heavy rainfall and drought will cause great interference to the water area, making emergency pollution accidents prone to occur, such as industrial water flooding or the rotting of drowned animal bodies that produce pathogens. The pollution source needs to be dealt with quickly and safely before the pollution expands. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent model for comprehensive water quality control based on water quality and hydrodynamics to solve the problem raised in the prior art that in severe and changeable weather, heavy rainfall and drought weather will cause great interference to the water area, and emergency pollution accidents are prone to occur, such as industrial water flooding or the rotting of drowned animal bodies that produce pathogens, and the pollution source needs to be dealt with quickly and safely before the pollution expands.

[0005] To achieve the above object, the present invention provides the following technical solution: an intelligent model for comprehensive water quality control based on water quality and hydrodynamics, specifically comprising the following steps: Step 1: Build a comprehensive meteorological monitoring network to capture key meteorological parameters such as temperature, rainfall, humidity and wind speed in real time. By analyzing meteorological information, more accurate and effective management strategies can be developed. Response measures can be adjusted in real time according to changes in temperature, rainfall, humidity and wind speed, and water quality management strategies can be changed. Step 2: Develop water monitoring routes for drones and unmanned boats based on the detection environment data; Step 3: Set up a drone monitoring fleet to perform water patrol missions according to the pre-set flight path. The high-resolution camera equipment on board can monitor the water surface conditions in real time. Through these high-quality image data, monitoring personnel can accurately identify colored pollutants on the water surface, including oil pollution, chemical leakage, plastic and industrial waste. These pollutants form obvious color spots on the water surface and cause damage to the natural environment. The camera equipment captures animal carcasses on the water surface and transmits real-time images to the monitoring center; Step 4: The drone is simultaneously equipped with temperature sensors, pH sensors, dissolved oxygen sensors and turbidity meters. The temperature sensors, pH sensors, dissolved oxygen sensors and turbidity meters work synchronously with the camera equipment, and the monitoring center records the coordinate position of each landing measurement water body, and synchronizes the water body image information and the detected water quality information according to the coordinate position, providing more comprehensive data support for environmental monitoring; Step 5: Set up an unmanned boat formation to travel according to the established route. The unmanned boats are simultaneously equipped with temperature sensors, pH sensors, dissolved oxygen sensors and turbidity meters. When performing sampling tasks, the unmanned boats can perform grid or fixed-point sampling of specific areas according to the preset routes or mission requirements, ensuring the comprehensiveness and accuracy of the data. The unmanned boats receive the location information of colored polluted water bodies, animal carcasses and solid waste monitored by drones. In an emergency, the unmanned boats can quickly adjust their driving plans and focus on monitoring specific polluted areas to ensure that potential environmental risks are discovered and reported in a timely manner, providing strong technical support for environmental protection work. The unmanned boat takes samples of the water body at a fixed point. The unmanned boat can automatically adjust the sampling depth and position to adapt to the changes in water quality in different water layers. After completing the sampling, the unmanned boat sends the water samples back to the laboratory for further analysis. Using efficient analytical instruments and advanced detection technology, the unmanned boat conducts a detailed analysis of the chemicals, microorganisms, heavy metals and other pollutants in the water samples. The unmanned boat may be equipped with machine learning models that can learn from a large amount of water quality data and identify the patterns and laws of water quality changes. When the water quality fluctuates abnormally, the unmanned boat will be able to automatically trigger an alarm, which will enable environmental protection departments to respond to water quality problems more quickly and take more effective measures to protect water resources; Step 6: The information collected by the drone and the unmanned boat are synchronously transmitted to the control center, and the detection results of the drone and the unmanned boat are compared and analyzed. The moving trajectory and moving end point of the polluted water body are calculated according to the detection coordinates. The types of water pollution sources and emission characteristics are analyzed according to the detection data of the drone and the unmanned boat. The water quality model is used to simulate the transport and diffusion process of pollutants in the water area, and the impact of different pollution sources on water quality is evaluated. The source analysis technology of chemical mass balance, isotope tracing, and biomarkers is used to determine the source of pollutants. Combined with the above analysis results, a comprehensive evaluation of the types of water pollution sources and emission characteristics is conducted, and the priority and strategy sources and contribution ratios of pollution control and governance are proposed; Step 7: Build a water quality compliance early warning system. The real-time monitoring data of these parameters are transmitted to the data processing center and analyzed by algorithms and models to ensure accurate assessment of water quality conditions. During the monitoring process, if the detected indicators exceed the established safety threshold, the early warning system will trigger the early warning mechanism, and the early warning information release platform will be activated immediately to issue timely alarms to managers, relevant institutions and the public. The early warning system can reveal the potential trends and patterns of water quality changes by using advanced data mining technology and machine learning algorithms. The early warning system can also combine the spatial analysis tools of the Geographic Information System (GIS) to spatially locate and simulate the diffusion of pollution sources, providing decision support for the formulation of scientific and reasonable water resources management strategies; Step 8: Analyze and evaluate the water quality through prediction and early warning simulation calculation and correction, carry out simulation and prediction of water environment early warning process, form visual simulation results through computer, simulate and predict the dynamic changes of pollutants in water bodies and the flow and storage of water according to the detection data, establish a water quality model according to the detection data of unmanned boats, and establish a hydrodynamic and hydrological model according to the receiving images of drones and the flow sensors and flow sensors installed on the river bank. The changes in pollutant concentration calculated in the water quality model affect the water quality parameters in the water quantity model, thereby adjusting the movement state of the water flow. The regional water quality and water quantity mutual feedback coupling control model provides scientific management measures for water resource managers based on the simulation results of the model, including optimal scheduling of reservoirs, strict control of pollution sources, and reasonable protection of ecological flow.

[0006] Preferably, under high temperature weather conditions, water bodies are prone to eutrophication, which leads to excessive reproduction of algae and deterioration of water quality. It is necessary to adjust the circulation and purification measures of the water bodies, increase the amount of aeration, promote the exchange of oxygen in the water bodies, and release appropriate amounts of biological agents to control the growth of algae. Through scientific analysis of meteorological information, more accurate and effective management strategies can be formulated to ensure the health and stability of aquatic ecosystems.

[0007] Preferably, in case of increased rainfall, response measures should be taken in advance to prevent floods from damaging the surrounding environment of the water area, check and clean the drainage system, ensure that the drainage channels are unobstructed, and adjust the water purification and disinfection procedures in time according to the changes in water quality after rainfall, increase the amount of disinfectant added to ensure water quality safety, and protect the lives and property of residents. The application of meteorological information in water area management has improved the scientific nature of management and enhanced the foresight and response capabilities of water area management.

[0008] Preferably, the unmanned boat is also combined with satellite remote sensing technology to achieve real-time monitoring and data analysis of a large area of ​​water, and the unmanned boat is combined with Internet of Things technology to achieve real-time transmission and sharing of water quality data, thereby improving the effectiveness and utilization of the data.

[0009] Preferably, the A-star algorithm is used to find the shortest path, the Dijkstra algorithm is used for complex networks that need to consider multiple constraints, and the genetic algorithm and particle swarm optimization heuristic algorithm are used to solve more complex optimization problems. By optimizing the path, unnecessary flight or navigation distance can be reduced, thereby reducing fuel consumption or electricity use. This not only helps to extend the duration of the mission, but also reduces the impact on the environment.

[0010] Preferably, through simulation experiments and field tests, the effectiveness of the path planning of drones and unmanned ships can be verified, and corresponding adjustments can be made according to the needs of actual application scenarios. The simulation experiment provides a virtual environment that allows planners to test different path plans without actual risks. The field test verifies the path under real operating conditions to ensure that drones and unmanned ships can perform tasks safely and efficiently according to the predetermined path. Through continuous iteration and optimization processes, a path planning solution that meets safety standards and has high efficiency can be obtained. Through these simulations, potential problems in path planning can be discovered and corrected to improve the reliability of the system. Field testing provides an opportunity to verify the results of simulation experiments to ensure that in the real world, drones and unmanned ships can adapt to changing environments, such as natural factors such as wind speed changes and ocean current fluctuations. By collecting test data, the path planning algorithm can be further refined and adjusted to meet the special requirements of specific tasks and environments. Finally, combined with the feedback from simulation experiments and field tests, the path planning solution can be continuously optimized to achieve efficient and safe operation of drones and unmanned ships in various complex environments.

[0011] Preferably, by being equipped with high-precision sensors and advanced image recognition technology, drones can carefully analyze the various components in sewage and detect potential harmful chemicals and heavy metals in sewage, such as mercury, lead, cadmium, etc. The image recognition system of the drone can accurately identify and classify garbage in sewage and count the number of various types of garbage. The drone detects floating carcasses of animals and uses image recognition technology to detect the type and degree of decay of the animal, identify whether the animal has diseased tissue, and provide detailed data support for subsequent cleaning work. The monitoring systems of drones and unmanned boats are combined with the existing water quality monitoring network to build a comprehensive, efficient and coordinated water environment monitoring network.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. The drone of this application transmits the collected images and data to the ground control center in real time for in-depth analysis by environmental monitoring personnel. This information is crucial for quickly discovering and responding to water pollution problems, and helps maintain the health and biodiversity of aquatic ecosystems. Through the precise monitoring and efficient data transmission of drones, relevant departments can take timely measures to perform necessary cleanup and protection actions to reduce the long-term impact of pollution on the environment and organisms. The drone transmits the collected images and data to the ground control center in real time. During this process, the accuracy and integrity of the data are fully guaranteed. Monitoring personnel can view and analyze this data in real time and respond quickly. Through the precise observation and efficient data transmission of drones, relevant departments can promptly discover and respond to water pollution problems, facilitate environmental protection departments to take corresponding measures in a timely manner, effectively prevent the further spread of pollution, and maintain the health and biodiversity of aquatic ecosystems.

[0013] 2. When the unmanned boat of this application performs sampling tasks, it can perform grid or fixed-point sampling of specific areas according to the preset route or task requirements, ensuring the comprehensiveness and accuracy of the data. The unmanned boat receives the location information of colored polluted water bodies, animal carcasses and solid garbage monitored by the drone. In an emergency, the unmanned boat can quickly adjust the driving plan and focus on monitoring specific polluted areas to ensure timely discovery and reporting of potential environmental risks, providing strong technical support for environmental protection work. Through the unmanned boat, the unmanned boat can automatically adjust the sampling depth and position to adapt to the changes in water quality in different water layers. After completing the sampling, the unmanned boat will send the water sample back to the laboratory for further analysis. Using efficient analytical instruments and advanced detection technology, the chemical substances, microorganisms, heavy metals and other pollutants in the water samples are analyzed in detail. The water quality data collected by the unmanned boat is cross-compared with the images of polluted water bodies taken by the drone, which can further verify and supplement each other's information. For example, by comparing the polluted area shown in the drone image with the water quality data collected by the unmanned boat, we can more accurately locate the source of pollution and its scope of influence. In addition, by combining the two data types, the dynamic changing trends of pollution can also be analyzed, providing a scientific basis for pollution control and environmental protection.

[0014] 3. The comparative analysis of water quality data sampled by unmanned boats and polluted water data taken by drones in this application provides us with a comprehensive, accurate and efficient means of water environment monitoring and pollution control. By deeply mining and intelligently analyzing these data, we can better understand and respond to water pollution problems and provide strong support for protecting and improving water environment quality.

[0015] 4. Construct a water quality compliance early warning system. The real-time monitoring data of these parameters are transmitted to the data processing center and analyzed by algorithms and models to ensure accurate assessment of water quality conditions. During the monitoring process, if the detected indicators exceed the established safety threshold, the early warning system will trigger the early warning mechanism, and the early warning information release platform will be activated immediately to issue timely alarms to managers, relevant agencies and the public. The early warning system can reveal the potential trends and patterns of water quality changes by using advanced data mining technology and machine learning algorithms. The early warning system can also combine the spatial analysis tools of the geographic information system (GIS) to spatially locate and simulate the diffusion of pollution sources, providing decision support for the formulation of scientific and reasonable water resources management strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the overall steps of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example: Figure 1 As shown, the present invention provides a technical solution of an intelligent model for comprehensive water quality control based on water quality and hydrodynamics, which specifically includes the following steps: Step 1: Build a comprehensive meteorological monitoring network to capture key meteorological parameters such as temperature, rainfall, humidity and wind speed in real time. By analyzing meteorological information, more accurate and effective management strategies can be developed. Response measures can be adjusted in real time according to changes in temperature, rainfall, humidity and wind speed, and water quality management strategies can be changed. Step 2: Develop water monitoring routes for drones and unmanned boats based on the detection environment data; Step 3: Set up a drone monitoring fleet to perform water patrol missions according to the pre-set flight path. The high-resolution camera equipment on board can monitor the water surface conditions in real time. Through these high-quality image data, monitoring personnel can accurately identify colored pollutants on the water surface, including oil pollution, chemical leakage, plastic and industrial waste. These pollutants form obvious color spots on the water surface and cause damage to the natural environment. The camera equipment captures animal carcasses on the water surface and transmits real-time images to the monitoring center; Step 4: The drone is simultaneously equipped with temperature sensors, pH sensors, dissolved oxygen sensors and turbidity meters. The temperature sensors, pH sensors, dissolved oxygen sensors and turbidity meters work synchronously with the camera equipment, and the monitoring center records the coordinate position of each landing measurement water body, and synchronizes the water body image information and the detected water quality information according to the coordinate position, providing more comprehensive data support for environmental monitoring; Step 5: Set up an unmanned boat formation to travel according to the established route. The unmanned boats are simultaneously equipped with temperature sensors, pH sensors, dissolved oxygen sensors and turbidity meters. When performing sampling tasks, the unmanned boats can perform grid or fixed-point sampling of specific areas according to the preset routes or mission requirements, ensuring the comprehensiveness and accuracy of the data. The unmanned boats receive the location information of colored polluted water bodies, animal carcasses and solid waste monitored by drones. In an emergency, the unmanned boats can quickly adjust their driving plans and focus on monitoring specific polluted areas to ensure that potential environmental risks are discovered and reported in a timely manner, providing strong technical support for environmental protection work. The unmanned boat takes samples of the water body at a fixed point. The unmanned boat can automatically adjust the sampling depth and position to adapt to the changes in water quality in different water layers. After completing the sampling, the unmanned boat sends the water samples back to the laboratory for further analysis. Using efficient analytical instruments and advanced detection technology, the unmanned boat conducts a detailed analysis of the chemicals, microorganisms, heavy metals and other pollutants in the water samples. The unmanned boat may be equipped with machine learning models that can learn from a large amount of water quality data and identify the patterns and laws of water quality changes. When the water quality fluctuates abnormally, the unmanned boat will be able to automatically trigger an alarm, which will enable environmental protection departments to respond to water quality problems more quickly and take more effective measures to protect water resources; Step 6: The information collected by the drone and the unmanned boat are synchronously transmitted to the control center, and the detection results of the drone and the unmanned boat are compared and analyzed. The moving trajectory and moving end point of the polluted water body are calculated according to the detection coordinates. The types of water pollution sources and emission characteristics are analyzed according to the detection data of the drone and the unmanned boat. The water quality model is used to simulate the transport and diffusion process of pollutants in the water area, and the impact of different pollution sources on water quality is evaluated. The source analysis technology of chemical mass balance, isotope tracing, and biomarkers is used to determine the source of pollutants. Combined with the above analysis results, a comprehensive evaluation of the types of water pollution sources and emission characteristics is conducted, and the priority and strategy sources and contribution ratios of pollution control and governance are proposed; Step 7: Build a water quality compliance early warning system. The real-time monitoring data of these parameters are transmitted to the data processing center and analyzed by algorithms and models to ensure accurate assessment of water quality conditions. During the monitoring process, if the detected indicators exceed the established safety threshold, the early warning system will trigger the early warning mechanism, and the early warning information release platform will be activated immediately to issue timely alarms to managers, relevant institutions and the public. The early warning system can reveal the potential trends and patterns of water quality changes by using advanced data mining technology and machine learning algorithms. The early warning system can also combine the spatial analysis tools of the Geographic Information System (GIS) to spatially locate and simulate the diffusion of pollution sources, providing decision support for the formulation of scientific and reasonable water resources management strategies; Step 8: Analyze and evaluate the water quality through prediction and early warning simulation calculation and correction, carry out simulation and prediction of water environment early warning process, form visual simulation results through computer, simulate and predict the dynamic changes of pollutants in water bodies and the flow and storage of water according to the detection data, establish a water quality model according to the detection data of unmanned boats, and establish a hydrodynamic and hydrological model according to the receiving images of drones and the flow sensors and flow sensors installed on the river bank. The changes in pollutant concentration calculated in the water quality model affect the water quality parameters in the water quantity model, thereby adjusting the movement state of the water flow. The regional water quality and water quantity mutual feedback coupling control model provides scientific management measures for water resource managers based on the simulation results of the model, including optimal scheduling of reservoirs, strict control of pollution sources, and reasonable protection of ecological flow.

[0019] Under high temperature weather conditions, water bodies are prone to eutrophication, which leads to excessive reproduction of algae and deterioration of water quality. It is necessary to adjust the circulation and purification measures of water bodies, increase aeration volume, promote oxygen exchange in water bodies, release appropriate amounts of biological agents, and control the growth of algae. Through scientific analysis of meteorological information, more accurate and effective management strategies can be formulated to ensure the health and stability of aquatic ecosystems.

[0020] In the case of increased rainfall, take countermeasures in advance to prevent floods from damaging the surrounding environment of the water area, check and clean the drainage system, ensure that the drainage channels are unobstructed, and adjust the water purification and disinfection procedures in time according to the changes in water quality after rainfall, increase the amount of disinfectants added to ensure water quality safety, and protect the lives and property of residents. The application of meteorological information in water area management has improved the scientific nature of management and enhanced the foresight and response capabilities of water area management.

[0021] Unmanned boats are also combined with satellite remote sensing technology to achieve real-time monitoring and data analysis of large areas of water. Unmanned boats are combined with Internet of Things technology to achieve real-time transmission and sharing of water quality data, thereby improving the effectiveness and utilization of data.

[0022] The A-star algorithm is used to find the shortest path, the Dijkstra algorithm is used for complex networks that need to consider multiple constraints, and the genetic algorithm and particle swarm optimization heuristic algorithm are used to solve more complex optimization problems. By optimizing the path, unnecessary flight or navigation distance can be reduced, thereby reducing fuel consumption or electricity use. This not only helps to extend the duration of the mission, but also reduces the impact on the environment.

[0023] Through simulation experiments and field tests, the effectiveness of the path planning of drones and unmanned ships can be verified, and corresponding adjustments can be made according to the needs of actual application scenarios. The simulation experiment provides a virtual environment that allows planners to test different path plans without actual risks. The field test verifies the path under real operating conditions to ensure that drones and unmanned ships can perform tasks safely and efficiently according to the predetermined path. Through continuous iteration and optimization, a path planning solution that meets safety standards and has high efficiency can be obtained. Through these simulations, potential problems in path planning can be discovered and corrected to improve the reliability of the system. Field tests provide an opportunity to verify the results of simulation experiments to ensure that in the real world, drones and unmanned ships can adapt to changing environments, such as natural factors such as wind speed changes and ocean current fluctuations. By collecting test data, the path planning algorithm can be further refined and adjusted to meet the special requirements of specific tasks and environments. Finally, combined with the feedback from simulation experiments and field tests, the path planning solution can be continuously optimized to achieve efficient and safe operation of drones and unmanned ships in various complex environments.

[0024] By being equipped with high-precision sensors and advanced image recognition technology, drones can carefully analyze the various components in sewage and detect potential harmful chemicals and heavy metals in sewage, such as mercury, lead, cadmium, etc. The drone's image recognition system can accurately identify and classify garbage in sewage and count the number of each type of garbage. Drones can detect floating animal carcasses and use image recognition technology to detect the animal species and degree of decay, identify whether the animal has diseased tissue, and provide detailed data support for subsequent cleanup work. The monitoring systems of drones and unmanned boats combined with the existing water quality monitoring network can build a comprehensive, efficient and coordinated water environment monitoring network.

[0025] Specifically, through simulation experiments and field tests, the effectiveness of path planning can be verified, and corresponding adjustments can be made according to the needs of actual application scenarios. Simulation experiments provide a virtual environment that allows planners to test different path plans without actual risks. Field tests verify the path under real operating conditions to ensure that drones and unmanned ships can perform tasks safely and efficiently according to the predetermined path. Through continuous iteration and optimization processes, a path planning solution that meets safety standards and is efficient can be obtained. By collecting test data, the path planning algorithm can be further refined and adjusted to meet the special requirements of specific tasks and environments. Finally, combined with the feedback from simulation experiments and field tests, the path planning solution can be continuously optimized to achieve efficient and safe operation of drones and unmanned ships in various complex environments.

[0026] Environmental conditions are factors that cannot be ignored in path planning. For drones, terrain undulations, wind speed and direction, temperature and humidity will affect the flight path and stability. Unmanned ships need to consider factors such as ocean currents, tides, wave heights and wind force, which will affect navigation speed and safety. Therefore, these environmental data must be taken into account when planning the path to ensure the smooth progress of the mission. By optimizing the path, unnecessary flight or navigation distances can be reduced, thereby reducing fuel consumption or electricity use. This not only helps to extend the duration of the mission, but also reduces the impact on the environment. For example, drones can save energy by choosing to fly with the wind, while unmanned ships can use the help of ocean currents and tides to reduce power consumption.

[0027] The drone performs water patrol missions according to the pre-set flight path, and the high-resolution camera equipment on board can monitor the water surface conditions in real time. Through these high-quality image data, monitoring personnel can accurately identify colored pollutants on the water surface, including oil pollution, chemical leaks, and various types of plastics and industrial waste. These pollutants form obvious color spots on the water surface, causing damage to the natural environment. At the same time, the camera equipment captures the carcasses of animals on the water surface. Whether they die due to disease, pollution or other factors, these images have important environmental monitoring value. During the observation process, the drone's high-definition camera equipment can capture tiny details on the water surface with its high resolution and wide-angle field of view. The drone transmits the collected images and data to the ground control center in real time for in-depth analysis by environmental monitoring personnel. This information is crucial for quickly discovering and responding to water pollution problems, and helps maintain the health and biodiversity of aquatic ecosystems. Through the drone's precise monitoring and efficient data transmission, relevant departments can take timely measures to carry out necessary cleanup and protection actions to reduce the long-term impact of pollution on the environment and organisms. The drone transmits the collected images and data to the ground control center in real time. In this process, the accuracy and integrity of the data are fully guaranteed. Monitoring personnel can view and analyze this data in real time and respond quickly. Through the precise observation and efficient data transmission of drones, relevant departments can promptly detect and respond to water pollution problems, making it easier for environmental protection departments to take appropriate measures in a timely manner, effectively preventing the further spread of pollution and maintaining the health and biodiversity of water ecosystems.

[0028] Water quality sampling using unmanned vessels can provide us with detailed data sets on water conditions. These data sets cover water temperature, pH value, dissolved oxygen content, turbidity and other key water quality parameters. Unmanned vessels have the ability to penetrate into areas that are difficult for humans to reach, thereby providing more comprehensive and accurate water quality information.

[0029] It helps to intuitively reveal the scope, extent and distribution of pollution. When performing sampling tasks, unmanned ships can conduct grid or fixed-point sampling of specific areas according to preset routes or task requirements, ensuring the comprehensiveness and accuracy of the data. The unmanned ships receive the location information of colored polluted water bodies, animal carcasses and solid waste monitored by drones. In emergency situations, the unmanned ships can quickly adjust their driving plans and focus on monitoring specific polluted areas to ensure that potential environmental risks are discovered and reported in a timely manner, providing strong technical support for environmental protection work. Through the unmanned ship's fixed-point sampling of the water body, the unmanned ship can automatically adjust the sampling depth and position to adapt to changes in water quality in different water layers. After completing the sampling, the unmanned ship will send the water samples back to the laboratory for further analysis, and use efficient analytical instruments and advanced detection technologies to conduct detailed analysis of chemicals, microorganisms, heavy metals and other pollutants in the water samples.

[0030] Cross-comparison analysis of water quality data collected by unmanned boats and images of polluted water bodies taken by drones can further verify and supplement each other's information. For example, by comparing the polluted areas shown in drone images with the water quality data collected by unmanned boats, we can more accurately locate the source of pollution and its scope of influence. In addition, by combining the two types of data, the dynamic trend of pollution can also be analyzed, providing a scientific basis for pollution control and environmental protection.

[0031] This type of comprehensive analysis method not only improves the reliability and accuracy of the data, but also provides decision makers with comprehensive reference information, so as to formulate more effective environmental protection measures. Through this multi-dimensional and multi-level data comparative analysis, we can more deeply understand and respond to water pollution problems, protect and improve the quality of the water environment. In the further data comparative analysis process, we can use advanced algorithms and models to conduct in-depth mining and intelligent analysis of the data collected by unmanned ships and drones. These algorithms and models can identify the potential laws and trends in the data, helping us to more deeply understand the mechanism of water quality changes and pollution diffusion. The data collected by unmanned ships and drones are compared with historical data to analyze the changing trends of water quality and pollution conditions. This helps us discover potential pollution risks and early warning signals, and take timely measures to intervene and control. The comparative analysis of water quality data sampled by unmanned ships and polluted water data taken by drones provides us with a comprehensive, accurate and efficient means of water environment monitoring and pollution control. Through in-depth mining and intelligent analysis of these data, we can better understand and respond to water pollution problems, and provide strong support for protecting and improving water environment quality.

[0032] Build a water quality compliance early warning system. The real-time monitoring data of these parameters are transmitted to the data processing center and analyzed through algorithms and models to ensure accurate assessment of water quality conditions. During the monitoring process, if the detected indicators exceed the established safety threshold, the early warning system will trigger the early warning mechanism, and the early warning information release platform will be activated immediately to issue timely alarms to managers, relevant agencies and the public. The early warning system can reveal the potential trends and patterns of water quality changes by using advanced data mining technology and machine learning algorithms. The early warning system can also combine geographic information system (GIS) spatial analysis tools to spatially locate and simulate the diffusion of pollution sources, providing decision support for the formulation of scientific and reasonable water resources management strategies.

[0033] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. The intelligent model for comprehensive water quality control based on water quality and hydrodynamics specifically includes the following steps, which are characterized by: Step 1: Build a comprehensive meteorological monitoring network to capture key meteorological parameters such as temperature, rainfall, humidity and wind speed in real time. By analyzing meteorological information, more accurate and effective management strategies can be developed. Response measures can be adjusted in real time according to changes in temperature, rainfall, humidity and wind speed, and water quality management strategies can be changed. Step 2: Develop water monitoring routes for drones and unmanned boats based on the detection environment data; Step 3: Set up a drone monitoring fleet to perform water patrol missions according to the pre-set flight path. The high-resolution camera equipment on board can monitor the water surface conditions in real time. Through these high-quality image data, monitoring personnel can accurately identify colored pollutants on the water surface, including oil pollution, chemical leakage, plastic and industrial waste. These pollutants form obvious color spots on the water surface and cause damage to the natural environment. The camera equipment captures animal carcasses on the water surface and transmits real-time images to the monitoring center; Step 4: The drone is simultaneously equipped with temperature sensors, pH sensors, dissolved oxygen sensors and turbidity meters. The temperature sensors, pH sensors, dissolved oxygen sensors and turbidity meters work synchronously with the camera equipment, and the monitoring center records the coordinate position of each landing measurement water body, and synchronizes the water body image information and the detected water quality information according to the coordinate position, providing more comprehensive data support for environmental monitoring; Step 5: Set up an unmanned boat formation to travel according to the established route. The unmanned boats are simultaneously equipped with temperature sensors, pH sensors, dissolved oxygen sensors and turbidity meters. When performing sampling tasks, the unmanned boats can perform grid or fixed-point sampling of specific areas according to the preset routes or mission requirements, ensuring the comprehensiveness and accuracy of the data. The unmanned boats receive the location information of colored polluted water bodies, animal carcasses and solid waste monitored by drones. In an emergency, the unmanned boats can quickly adjust their driving plans and focus on monitoring specific polluted areas to ensure that potential environmental risks are discovered and reported in a timely manner, providing strong technical support for environmental protection work. The unmanned boat takes samples of the water body at a fixed point. The unmanned boat can automatically adjust the sampling depth and position to adapt to the changes in water quality in different water layers. After completing the sampling, the unmanned boat sends the water samples back to the laboratory for further analysis. Using efficient analytical instruments and advanced detection technology, the unmanned boat conducts a detailed analysis of the chemicals, microorganisms, heavy metals and other pollutants in the water samples. The unmanned boat may be equipped with machine learning models that can learn from a large amount of water quality data and identify the patterns and laws of water quality changes. When the water quality fluctuates abnormally, the unmanned boat will be able to automatically trigger an alarm, which will enable environmental protection departments to respond to water quality problems more quickly and take more effective measures to protect water resources; Step 6: The information collected by the drone and the unmanned boat are synchronously transmitted to the control center, and the detection results of the drone and the unmanned boat are compared and analyzed. The moving trajectory and moving end point of the polluted water body are calculated according to the detection coordinates. The types of water pollution sources and emission characteristics are analyzed according to the detection data of the drone and the unmanned boat. The water quality model is used to simulate the transport and diffusion process of pollutants in the water area, and the impact of different pollution sources on water quality is evaluated. The source analysis technology of chemical mass balance, isotope tracing, and biomarkers is used to determine the source of pollutants. Combined with the above analysis results, a comprehensive evaluation of the types of water pollution sources and emission characteristics is conducted, and the priority and strategy sources and contribution ratios of pollution control and governance are proposed; Step 7: Build a water quality compliance early warning system. The real-time monitoring data of these parameters are transmitted to the data processing center and analyzed by algorithms and models to ensure accurate assessment of water quality conditions. During the monitoring process, if the detected indicators exceed the established safety threshold, the early warning system will trigger the early warning mechanism, and the early warning information release platform will be activated immediately to issue timely alarms to managers, relevant institutions and the public. The early warning system can reveal the potential trends and patterns of water quality changes by using advanced data mining technology and machine learning algorithms. The early warning system can also combine the spatial analysis tools of the Geographic Information System (GIS) to spatially locate and simulate the diffusion of pollution sources, providing decision support for the formulation of scientific and reasonable water resources management strategies; Step 8: Analyze and evaluate the water quality through prediction and early warning simulation calculation and correction, carry out simulation and prediction of water environment early warning process, form visual simulation results through computer, simulate and predict the dynamic changes of pollutants in water bodies and the flow and storage of water according to the detection data, establish a water quality model according to the detection data of unmanned boats, and establish a hydrodynamic and hydrological model according to the receiving images of drones and the flow sensors and flow sensors installed on the river bank. The changes in pollutant concentration calculated in the water quality model affect the water quality parameters in the water quantity model, thereby adjusting the movement state of the water flow. The regional water quality and water quantity mutual feedback coupling control model provides scientific management measures for water resource managers based on the simulation results of the model, including optimal scheduling of reservoirs, strict control of pollution sources, and reasonable protection of ecological flow.

2. The water quality comprehensive control intelligent model based on water quality and hydrodynamics according to claim 1 is characterized by: In step 1, under high temperature weather conditions, eutrophication of water bodies is prone to occur, leading to excessive reproduction of algae and deterioration of water quality. The circulation and purification measures of the water bodies are adjusted, the aeration volume is increased, the exchange of oxygen in the water bodies is promoted, and an appropriate amount of biological agents are added to control the growth of algae.

3. The water quality comprehensive control intelligent model based on water quality and hydrodynamics according to claim 1 is characterized by: In the step 1, when the rainfall increases, countermeasures are taken in advance to prevent floods from causing damage to the surrounding environment of the water area, the drainage system is checked and cleaned, and the drainage channels are ensured to be unobstructed. According to the changes in water quality after rainfall, the water purification and disinfection procedures are adjusted in time, and the amount of disinfectant added is increased to ensure water quality safety.

4. The water quality comprehensive control intelligent model based on water quality and hydrodynamics according to claim 1 is characterized by: Unmanned boats are also combined with satellite remote sensing technology to achieve real-time monitoring and data analysis of large areas of water. Unmanned boats are combined with Internet of Things technology to achieve real-time transmission and sharing of water quality data, thereby improving the effectiveness and utilization of data.

5. The water quality comprehensive control intelligent model based on water quality and hydrodynamics according to claim 1 is characterized by: The A-star algorithm is used to find the shortest path, the Dijkstra algorithm is used for complex networks that need to consider multiple constraints, and the genetic algorithm and particle swarm optimization heuristic algorithm are used to solve more complex optimization problems.

6. The water quality comprehensive control intelligent model based on water quality and hydrodynamics according to claim 1 is characterized by: Through simulation experiments and field tests, the effectiveness of drone and unmanned ship path planning can be verified, and corresponding adjustments can be made according to the needs of actual application scenarios. Simulation experiments provide a virtual environment that allows planners to test different path plans without actual risks. Field tests verify the path under real operating conditions to ensure that drones and unmanned ships can perform tasks safely and efficiently according to the predetermined path. Through continuous iteration and optimization, we can eventually obtain a path planning solution that meets safety standards and is efficient.

7. The water quality comprehensive control intelligent model based on water quality and hydrodynamics according to claim 1 is characterized by: By being equipped with high-precision sensors and advanced image recognition technology, drones can carefully analyze the various components in sewage and detect potential harmful chemicals and heavy metals in sewage, such as mercury, lead, cadmium, etc. The drone's image recognition system can accurately identify and classify garbage in sewage and count the amount of each type of garbage. Drones can detect floating animal carcasses and use image recognition technology to detect the animal species and degree of decay, identify whether the animal has diseased tissue, and provide detailed data support for subsequent cleaning work.

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