A control system and method for an aerial robot used for water quality monitoring

Abstract

This application provides a control system and method for an aerial robot used for water quality monitoring. The control system includes: a collaborative and autonomous navigation module that accurately plans the flight path for water quality monitoring based on the monitored water area and location information; and intelligently and on demand, based on the regional water quality analysis results from the remote sensing monitoring module, that ensures comprehensive coverage of the monitoring area while promptly focusing on abnormal areas, significantly improving monitoring efficiency; a multi-rotor UAV control module to ensure stable flight; and a robotic arm control module to assist in precise measurement. The remote sensing and precise monitoring modules respectively utilize different sensors to acquire regional and point-based water quality data. A data fusion and analysis module integrates the two types of data, determines and corrects the pollution range and source, and locates the pollution source based on the pollution gradient, improving the efficiency and accuracy of water quality monitoring.

Description

Technical Field

[0001] This application relates to the field of water quality monitoring technology, and in particular to a control system and method for an aerial robot used for water quality monitoring. Background Technology

[0002] Currently, commonly used water quality monitoring technologies mainly include fixed-point sensor monitoring, mobile manual monitoring, and remote sensing. Fixed-point sensor monitoring, by deploying sensors at fixed locations, can acquire water quality data from monitoring points in real time, offering a high level of automation and rapid response to water quality changes. Mobile manual monitoring relies on personnel carrying monitoring equipment to collect and analyze water samples at different locations. This method is highly flexible, allowing for the selection of monitoring points based on actual needs, but it requires a significant investment of manpower, making it difficult to guarantee monitoring frequency, and the results are easily affected by human error. Remote sensing, utilizing sensors mounted on satellites or drones, can quickly cover large areas of water, acquiring macroscopic water quality information, making it suitable for large-scale water quality monitoring tasks. However, its accuracy is relatively low, and it cannot accurately obtain detailed water quality parameters for specific areas.

[0003] However, fixed-point sensor monitoring suffers from drawbacks such as high equipment costs, difficulty in large-scale deployment, limited monitoring range, inability to comprehensively reflect the overall water quality status, and susceptibility to environmental interference. Manual mobile monitoring is inefficient, costly, and highly susceptible to human factors, making it difficult to guarantee data reliability and consistency. While remote sensing technology offers wide coverage, its accuracy is insufficient, failing to accurately capture some localized and subtle water quality changes, thus failing to meet the demands for refined water quality monitoring. In summary, existing water quality monitoring technologies are insufficient to meet the current requirements for comprehensive, accurate, and efficient water quality monitoring. Summary of the Invention

[0004] In view of this, this application provides a control system and method for an aerial robot used for water quality monitoring, so as to achieve large-scale and high-precision water quality monitoring.

[0005] Specifically, this application is implemented through the following technical solution:

[0006] The first aspect of this application provides a control system for an aerial robot used for water quality monitoring, the control system comprising:

[0007] The collaborative and autonomous navigation module is used to generate a water quality monitoring flight path based on the monitored water area information and monitoring location information, and to trigger the start of the fine monitoring module based on the regional water quality analysis results of the remote sensing monitoring module.

[0008] A multi-rotor drone control module is used to control the aerial work robot to fly along the water quality monitoring flight path;

[0009] The robotic arm control module is used to respond to the start signal of the fine monitoring module and control the robotic arm to move the water quality sensor to the water surface to perform multi-point water quality measurement;

[0010] The remote sensing monitoring module is used to scan and detect the reflection information of the water body under different wavebands using various types of primary sensors to obtain regional water quality analysis results;

[0011] The fine monitoring module is used to detect water quality information at multiple points using various types of second sensors to obtain point water quality analysis results. The first sensor and the second sensor are of different types.

[0012] The data fusion and analysis module is used to fuse the regional water quality analysis results and the point water quality analysis results to obtain the location of the pollution source and the pollution area of ​​the monitored water area.

[0013] A second aspect of this application provides a control method for an aerial robot used for water quality monitoring, the method comprising:

[0014] The flight path for water quality monitoring is determined based on information about the monitored water area and monitoring location points.

[0015] The aerial operation robot is controlled to fly along the water quality monitoring flight path to perform remote sensing monitoring of the monitored water area and obtain remote sensing monitoring results.

[0016] The extent of pollution is determined based on the water quality information obtained from the remote sensing monitoring results.

[0017] New monitoring points are generated based on the pollution range, and the water quality monitoring flight path is updated based on the new monitoring points;

[0018] The aerial robot is controlled to fly according to the updated water quality monitoring flight path. When it reaches the monitoring point in the updated water quality monitoring flight path, the second sensor is used to detect the water quality of the pollution area and obtain the water quality detection result.

[0019] The remote sensing monitoring results are corrected based on the water quality test results to obtain the water quality identification results.

[0020] The aerial robot control system and method for water quality monitoring provided in this application include a collaborative and autonomous navigation module, a multi-rotor UAV control module, a robotic arm control module, a remote sensing monitoring module, a fine monitoring module, and a data fusion and analysis module. Through the close collaboration of these modules, an efficient, multi-layered monitoring network is formed. The remote sensing monitoring module can quickly identify problems in a wide range of water bodies and provide accurate water quality data through fine measurements. Combined with the data fusion and analysis module, it helps to detect water quality changes in a timely manner, thus protecting the ecological environment and public safety. First, the collaborative and autonomous navigation module generates a flight path based on the monitored water area and location information. It intelligently triggers the fine monitoring module based on remote sensing monitoring results, enabling flexible and precise monitoring, greatly improving efficiency and targeting. The multi-rotor UAV control module controls the aerial robot to fly along the planned path. After the fine monitoring module's activation signal, the robotic arm control module precisely controls the robotic arm to drive the water quality sensor deep into the water surface, conducting multi-point water quality measurements and collecting detailed and accurate water quality data. The remote sensing monitoring module uses multiple primary sensors to scan and quickly obtain regional water quality analysis results, enabling preliminary identification of the pollution range in a wide area. The fine monitoring module uses different types of secondary sensors to further obtain high-precision point water quality analysis results. Finally, the data fusion and analysis module combines the remote sensing and fine monitoring results to ultimately determine the location of the pollution source. By providing accurate data on water quality through fine measurement and combining data fusion and analysis functions, it promptly captures water quality changes, achieving large-scale and high-precision water quality monitoring. Attached Figure Description

[0021] Figure 1 A schematic diagram of an embodiment of the aerial robot control system for water quality monitoring provided in this application;

[0022] Figure 2 This is a flowchart of an embodiment of the aerial robot control method for water quality monitoring provided in this application. Detailed Implementation

[0023] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0024] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used herein are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0025] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0026] The following specific embodiments are given to illustrate the technical solution of this application in detail.

[0027] Figure 1 This is a schematic diagram of an embodiment of the aerial robot control system for water quality monitoring provided in this application. Please refer to... Figure 1 The control system provided in this embodiment includes:

[0028] The collaborative and autonomous navigation module is used to generate a water quality monitoring flight path based on the monitored water area information and monitoring location information, and to trigger the start of the fine monitoring module based on the regional water quality analysis results of the remote sensing monitoring module.

[0029] A multi-rotor drone control module is used to control the aerial work robot to fly along the water quality monitoring flight path;

[0030] The robotic arm control module is used to respond to the start signal of the fine monitoring module and control the robotic arm to move the water quality sensor to the water surface to perform multi-point water quality measurement;

[0031] The remote sensing monitoring module is used to scan and detect the reflection information of the water body under different wavebands using various types of primary sensors to obtain regional water quality analysis results;

[0032] The fine monitoring module is used to detect water quality information at multiple points using various types of second sensors to obtain point water quality analysis results. The first sensor and the second sensor are of different types.

[0033] The data fusion and analysis module is used to fuse the regional water quality analysis results and the point water quality analysis results to obtain the location of the pollution source and the pollution area of ​​the monitored water area.

[0034] Specifically, the collaborative and autonomous navigation module is used to receive monitoring water area information and monitoring location information. The monitoring water area information usually includes the scope of the monitoring water area, key monitoring locations, preset flight paths, and environmental characteristics of the target area. The monitoring location information includes the coordinates, altitude, and other information of the pre-set operation points that need to be monitored for water quality.

[0035] Furthermore, the steps for generating a water quality monitoring flight path based on the monitored water area information and monitoring location information include:

[0036] (1) Generate coverage constraints based on the monitored water area information and the monitored location point information;

[0037] Specifically, the purpose of coverage constraints is to ensure that the flight path can cover all the locations that need to be monitored. Assume the set of monitored locations is P = {P1, P2, ..., P...} n The flight path consists of a series of path points Q = {Q1, Q2, ..., Q}. m The coverage constraint requires that for any monitoring location point P... i There exists a corresponding path point Q for each. i Coverage constraints can be determined using the following formula:

[0038] ;

[0039] in, Let i be the i-th monitoring location point;

[0040] Let i be the i-th path point;

[0041] This represents the distance between the i-th monitoring location and the i-th path point.

[0042] This is the maximum error distance, which is set according to actual needs.

[0043] (2) Generate flight state constraints based on the safe flight restrictions of the aerial operation robot, wherein the flight state constraints include altitude constraints, speed constraints and acceleration constraints;

[0044] Specifically, considering the flight safety of aerial work robots and the effectiveness of monitoring data, it is necessary to restrict the flight status of aerial work robots. Flight status constraints include flight altitude, speed, and acceleration constraints. First, minimum and maximum flight altitudes are set in combination with surface airflow, signal transmission, and energy consumption to keep the flight altitude of aerial work robots between the minimum and maximum flight altitudes. Similarly, minimum and maximum speeds, as well as minimum and maximum accelerations, are set to keep the flight speed of aerial work robots between the minimum and maximum speeds and accelerations between the minimum and maximum accelerations.

[0045] (3) Summarize the coverage constraints and the flight state constraints to generate constraint conditions;

[0046] Specifically, when generating the water quality monitoring flight path, coverage constraints and flight state constraints are set. Coverage constraints ensure that the flight path fully covers all monitoring locations, making the acquired water quality data complete and representative, laying the foundation for accurate assessment of water quality conditions. Flight altitude constraints limit the flight altitude range of the aerial robot, effectively preventing it from being interfered with by the water surface environment due to being too close to the water surface, or from having a decrease in monitoring accuracy due to being too high, ensuring flight safety while ensuring the normal operation of the monitoring equipment. Speed ​​constraints keep the flight speed of the aerial robot within a reasonable range, preventing flight instability and malfunction of the monitoring equipment due to excessive speed, and preventing monitoring costs and time from being too slow, achieving a balance between monitoring efficiency and data quality. Acceleration constraints prevent the aerial robot from moving too violently during takeoff, landing, and turning, protecting the onboard water quality monitoring equipment from damage and ensuring that it acquires reliable data in a stable state. These four constraints work together to ensure that the aerial robot can efficiently and safely complete the water quality monitoring task during its flight path from different perspectives.

[0047] (4) Determine the area range based on the monitored water area information, and use the minimum sum of the Euclidean distances between two adjacent locations on the water quality detection flight path as the objective function, and generate the water quality monitoring flight path in combination with the constraints.

[0048] Specifically, by monitoring water area information, the area range including all monitoring locations can be determined. This area range is the flight area range of the aerial work robot. The flight path of the aerial work robot cannot exceed this range. At the same time, when planning the path, it is also necessary to consider the obstacles that may exist in the water area, such as islands and buildings, to avoid collisions between the drone and them.

[0049] Assuming the detection area is a two-dimensional planar region Its boundary is Furthermore, the detection area contains several obstacles (such as nearby islands, floating objects, other drones, etc.), which can be represented by polygonal or circular regions. It can be represented as:

[0050] ;

[0051] in, These are the center coordinates of the obstacle.

[0052] It is the radius of the obstacle.

[0053] It is a point Distance to the center of the obstacle.

[0054] The core of obstacle avoidance is to avoid collisions by calculating the relative position of the obstacle to the flight path. For waypoints... It can calculate it to each obstacle. minimum distance To avoid collisions, it is necessary to ensure that the distance between each waypoint and the obstacle is greater than a safe distance. (Minimum distance is greater than safe distance)

[0055] ;

[0056] If it exists If this condition is met, it indicates that the path point is valid. Avoid collisions with obstacles.

[0057] Furthermore, the Euclidean distance between two adjacent points refers to the straight-line distance between two adjacent points on the flight path. The objective is to minimize the sum of the Euclidean distances between all adjacent points on the flight path, using optimization algorithms (such as A* algorithm, Dijkstra's algorithm, etc.) to solve this objective function.

[0058] Assuming the path is Then minimize the total path length ,in It is the Euclidean distance between two adjacent waypoints. In addition, constraints need to be added to ensure that the path covers the detection area, avoids obstacles, and meets flight conditions.

[0059] By simultaneously satisfying the previously generated coverage constraints and flight state constraints, an optimal or near-optimal flight path can be obtained, enabling the UAV to complete the water quality monitoring task efficiently and safely.

[0060] Furthermore, the multi-rotor drone control module includes:

[0061] The positioning module is used to provide feedback on the position information of the aerial robot based on real-time dynamic positioning and visual methods.

[0062] Multiple status sensors are used to detect different flight attitudes of the aerial robot in real time;

[0063] An ultrasonic sensor is used to monitor the distance between the aerial work robot and the water surface in real time when the distance between the aerial work robot and the water surface of the monitored water area is less than a preset distance.

[0064] Specifically, the positioning module utilizes Real-Time Kinematic (RTK) technology to achieve high-precision positioning by receiving satellite signals. Simultaneously, it combines visual methods (such as visual SLAM technology, simultaneous localization and mapping) with camera-captured images of the surrounding environment to assist positioning. These two methods complement each other, providing real-time and accurate feedback on the drone's position in the air. Multiple status sensors (such as accelerometers, gyroscopes, and barometers) work collaboratively to monitor the drone's flight attitude in real time, including pitch, roll, and yaw angles, allowing the system to constantly monitor the drone's status. Furthermore, when the drone approaches the surface of the monitored water area, if the positioning module detects a distance less than a preset distance, it triggers the ultrasonic sensors to monitor the distance between the drone and the water surface in real time. This ensures that the drone maintains a safe and appropriate altitude during low-altitude flight or contact water quality monitoring, avoiding collisions with the water surface or excessively close proximity affecting flight stability. It should be noted that the preset distance is set according to actual needs and is not limited in this embodiment.

[0065] Furthermore, localized airflow or wind speed changes often occur over water, especially during low-altitude flight. These wind speed variations can cause instability in the flight trajectory of aerial robots. The multi-rotor UAV control module also includes devices such as wind speed sensors and barometers, which can monitor wind speed and airflow direction in real time and adjust flight strategies according to environmental conditions to ensure flight stability.

[0066] Furthermore, the robotic arm control module is used to precisely place the water quality sensor below the water surface during refined water quality monitoring, enabling contact measurements. The control module dynamically controls the extension and angle of the robotic arm based on the flight mission and water quality measurement requirements, ensuring the sensor accurately reaches the desired water depth. Sensor depth control is achieved through motors and encoders, ensuring measurements at each depth are performed within a preset range. Moreover, it's worth noting that the robotic arm control module can not only perform measurements at a single depth but also at different water levels, obtaining water quality data at multiple depths. This is particularly important for assessing the vertical distribution of water, especially when conducting in-depth analysis of data such as dissolved oxygen, temperature, and pH.

[0067] Furthermore, as the aerial robot flies along its flight path, it conducts a comprehensive scan of the monitored water area using various types of primary sensors during the flight.

[0068] Optionally, the first sensor of various types includes: multiple optical sensors for scanning reflectance information under different spectra of the monitored water area, and analyzing water quality parameters of the monitored water area based on the reflectance information;

[0069] A temperature sensor is used to monitor the temperature of the monitored water area;

[0070] A lidar sensor is used to monitor the water structure of the monitored water area.

[0071] Specifically, multiple optical sensors (such as multispectral sensors, which can capture the reflectance information of water in different spectral bands such as visible light, near-infrared, and infrared) capture the reflectance of water in multiple spectral bands, such as visible light, near-infrared, and infrared, based on the reflectance characteristics of different bands, which are related to various physical and chemical properties of water. These reflectance characteristics can reflect water quality parameters such as color changes, suspended solids concentration, and chlorophyll content. By analyzing the spectral information of water, water quality can be effectively assessed, especially for monitoring algal blooms and pollutant accumulation. Temperature sensors (such as thermal infrared sensors) can identify areas of abnormal temperature by detecting changes in water surface temperature. For example, when there are areas of thermal pollution or large temperature fluctuations in a water body, thermal infrared sensors can quickly detect and mark these areas, aiding in further water quality analysis. LiDAR sensors, on the other hand, emit laser pulses and receive reflected signals to detect the water surface and internal structure, obtaining information on turbidity and depth, and are particularly suitable for monitoring turbid water or water bodies with complex underwater structures. The high resolution of lidar helps identify sediments, plankton, and pollutants in water bodies, especially in deeper waters or areas with poor visibility. Through the collaborative work of multiple types of primary sensors, the remote sensing monitoring module can quickly acquire water quality information for large areas of water, obtaining regional water quality analysis results that include the water quality characteristics of the monitored water area.

[0072] Optionally, temperature field information, water structure characteristics, and spectral data of the monitored water area are determined based on multi-source detection from various types of first sensors; a quantitative model of temperature-dissolved oxygen-microbial activity is established based on the temperature field information, wherein the quantitative model is used to quantify the dynamic influence coefficient of temperature on the dissolved oxygen content and microbial metabolic rate of the water body, and the influence coefficient is used to correct the predicted dissolved oxygen value and pollution diffusion rate in the regional water quality analysis results; a pollutant diffusion model is established based on the water body structure characteristics combined with the water flow velocity field simulated by computational fluid dynamics (CFD), wherein the pollutant diffusion model is used to predict the horizontal migration path and concentration decay curve of pollutants; the temperature field information, water body structure characteristics, and spectral data are input into a pre-trained machine learning algorithm, and the machine learning algorithm outputs the regional water quality analysis results.

[0073] Specifically, machine learning algorithms (such as random forests and neural networks) can learn the influence coefficients of water temperature and dissolved oxygen concentration, microbial metabolic rate, and, based on the water structure, use computational fluid dynamics (CFD) simulation results to analyze parameters such as water flow velocity field, eddy distribution, and the location of water stratification interfaces, as well as the relationship between spectral data and the concentration distribution of indicators such as chlorophyll and chemical oxygen demand (COD). For example, in one possible implementation, given the input spectral reflectance data of a certain water body (680nm reflectance 0.15, corresponding to an initial chlorophyll a concentration of 20 μg / L), temperature 28℃, and water depth 5 meters, the neural network model outputs a corrected chlorophyll a concentration of 25 μg / L. Combined with temperature field analysis (high temperature promotes algal reproduction), the area is determined to be a moderate eutrophication risk zone, triggering a fine monitoring module to perform targeted detection of dissolved oxygen and nitrogen and phosphorus content in that area.

[0074] Furthermore, the remote sensing monitoring module sends the regional water quality analysis results to the collaborative and autonomous navigation module. The collaborative and autonomous navigation module analyzes the regional water quality analysis results, and the steps to trigger the activation of the fine monitoring module based on the regional water quality analysis results from the remote sensing monitoring module include:

[0075] By comparing the detection value of each first sensor with the corresponding threshold value of each first sensor, if the detection value of any first sensor is greater than the threshold value of the corresponding first sensor, the corresponding area is determined to be an abnormal area.

[0076] The regional water quality analysis results in the abnormal area are sorted, and a predetermined number of locations with the highest ranking in the sorting results are selected as new monitoring points for the abnormal area.

[0077] Specifically, in water quality monitoring, the remote sensing monitoring module utilizes various types of primary sensors (such as multiple optical sensors, temperature sensors, and lidar sensors) to scan and detect reflection information of the monitored water area at different wavelengths, thereby obtaining regional water quality analysis results. The remote sensing monitoring module can collect data in real time and transmit the data to the ground station for analysis via the UAV's communication system. This process can provide real-time water quality change trends, offering timely support for decision-making.

[0078] Furthermore, another significant advantage of the remote sensing monitoring module is its non-contact measurement capability, enabling water quality assessment without direct contact with the water body. This technology can identify a variety of water quality anomalies, primarily including:

[0079] 1. Algal Overgrowth: By analyzing specific bands of water reflectance, the presence of algal blooms can be identified. Algal blooms typically lead to an increase in chlorophyll concentration in the water. Remote sensing technology can detect this change on the water surface, providing early warning for controlling and preventing algal bloom outbreaks.

[0080] 2. Pollutant Accumulation: Remote sensing sensors can detect the concentration of suspended solids in water bodies and determine whether pollutants have accumulated in specific areas by analyzing the reflectivity of the water surface. For example, in areas with industrial pollution or sewage outfalls, remote sensing technology can quickly detect pollutant deposits, facilitating subsequent detailed investigations.

[0081] 3. Temperature Anomalies: By detecting changes in water temperature using thermal infrared sensors, areas of temperature anomalies caused by industrial emissions or environmental changes can be identified. Changes in water temperature can impact aquatic ecosystems, and temperature monitoring provides additional clues for assessing water quality.

[0082] 4. Water Turbidity and Transparency: LiDAR and multispectral sensors can effectively assess water turbidity and identify the presence of high concentrations of sediment or suspended matter in the water. For waters with low transparency, remote sensing technology can quickly acquire information to ensure timely intervention.

[0083] Once the remote sensing monitoring module detects an abnormal area in the water body or reaches the key monitoring area set in the task, the refined monitoring module will be automatically activated. Specifically, the remote sensing module identifies water quality anomalies (such as algal blooms, pollutant accumulation, temperature anomalies, etc.) by scanning data over a wide area. Once an abnormal area is identified, the system will guide the drone to that area through the drone control module, and then trigger the refined monitoring module to perform more precise contact water quality measurements.

[0084] Furthermore, each first sensor has a corresponding first sensor threshold for different water quality indicators. The first sensor threshold represents the reasonable range of the water quality indicator under normal conditions. The detection value of each first sensor in the regional water quality analysis results is compared with each first sensor threshold. If any detection value is greater than the corresponding first sensor threshold, it means that the water quality indicator in that area has exceeded the normal range, and this area will be identified as an abnormal area. For example, when an optical sensor monitors the chlorophyll content in water, if the detection value corresponding to the chlorophyll content in a certain area is greater than the sensor's preset threshold, it indicates that there may be abnormal conditions such as algal blooms in that area, and this area will be marked as an abnormal area.

[0085] Furthermore, after identifying the abnormal area, to conduct more accurate water quality monitoring, it is necessary to select a number of locations within the abnormal area as new monitoring points. The regional water quality analysis results for the abnormal area are then ranked. The ranking is based on the degree of correlation with the anomaly; the greater the deviation of the water quality indicators from the first sensor threshold, the more severe the impact of pollution or anomalies on the water quality analysis results, and the higher the ranking. A predetermined number of locations with the highest rankings are selected as new monitoring points. It should be noted that this predetermined number is set according to actual needs and is not limited in this embodiment. These locations are then used as new monitoring points in the abnormal area, and a fine monitoring module is used to perform fine monitoring on these new monitoring points, providing more reliable data support for subsequently determining the location of pollution sources and the polluted area.

[0086] In addition to being triggered by the remote sensing monitoring module, the fine monitoring module can also be triggered based on the monitoring task. When the monitoring task includes precise dissolved oxygen or pH data, the system will directly select to trigger the fine monitoring module, even if the area does not trigger the judgment conditions for abnormal monitoring.

[0087] Furthermore, after identifying new monitoring points in the abnormal area, the flight path also needs to be updated. The specific steps include:

[0088] (1) Determine whether the newly added monitoring point is in the water quality monitoring flight path. If so, keep the water quality monitoring flight path unchanged.

[0089] Specifically, determining whether a new monitoring point is already included in the existing water quality monitoring flight path is a fundamental check step to avoid unnecessary path adjustments. If the new monitoring point is already in the original flight path, it means that monitoring these new monitoring points can be carried out by flying according to the original plan. Therefore, keeping the water quality monitoring flight path unchanged and continuing to fly according to the established flight path can ensure the continuity and efficiency of the monitoring mission and reduce the additional energy consumption and time costs caused by frequent path adjustments.

[0090] (2) If not, calculate the first distance between the next flight point and the current flight point in the water quality monitoring flight path, and the second distance between the newly added monitoring point closest to the current flight point and the current flight point;

[0091] (3) If the first distance is less than or equal to the second distance, control the aerial operation robot to fly to the next flight point, set the next flight point as the latest current flight point, return to the step of calculating the first distance, until all flight points in the water quality monitoring flight path have been traversed;

[0092] (4) If the first distance is greater than the second distance, control the aerial operation robot to fly to the newly added monitoring point, and use the newly added monitoring point as the latest current flight point. The next flight point remains unchanged. Return to the step of calculating the first distance until all flight points in the water quality monitoring flight path have been traversed.

[0093] Specifically, if the new monitoring point is not on the original water quality monitoring flight path, a first distance and a second distance need to be calculated. The first distance represents the distance from the current position to the next target point according to the original plan. The second distance represents the shortest additional distance the drone needs to fly if the flight path is changed to monitor the new monitoring point. Comparing the first and second distances, if the first distance is less than or equal to the second distance, it means that the distance required to fly to the next flight point according to the original plan is not longer than the distance to the nearest new monitoring point, and may even be shorter. In this case, considering flight efficiency and the overall monitoring task, the aerial robot is controlled to continue flying to the next flight point on the original flight path, maintaining the original monitoring path. After the aerial robot reaches the next flight point, the flight path is updated again. If the first distance is greater than the second distance, it means that the distance to the nearest new monitoring point is shorter than the distance to the next flight point on the original path. At this time, in order to conduct more timely and efficient fine monitoring of the newly added abnormal area, the aerial robot is controlled to change its flight direction and fly to the new monitoring point closest to the current position. After the aerial robot reaches the new monitoring point, the next flight point is determined again, using that monitoring point as the current flight point. This allows for the rapid acquisition of water quality information from these critical locations, avoiding redundant flights and improving efficiency. It enables the system to flexibly respond to newly discovered anomalies while ensuring the smooth operation of the overall monitoring mission, thereby improving the efficiency and accuracy of water quality monitoring and ensuring timely and effective monitoring of potentially polluted areas.

[0094] Furthermore, when conducting detailed monitoring of newly added monitoring points, various types of second sensors are used, including:

[0095] pH meter, used to monitor the pH value of the monitored water area;

[0096] A dissolved oxygen meter is used to monitor the dissolved oxygen content of the monitored water area;

[0097] A turbidity meter is used to monitor the concentration of suspended particulate matter in the monitored water area;

[0098] A heavy metal sensor is used to monitor the concentration of heavy metal ions in the monitored water area.

[0099] Specifically, pH meters are used to measure the acidity or alkalinity of water bodies. pH value is an important parameter for assessing water quality and directly reflects the health status of water bodies. For example, abnormal pH values ​​may indicate the presence of organic matter, pollutants, or acidic precipitation. Dissolved oxygen meters are used to measure the dissolved oxygen content in water bodies. Dissolved oxygen is one of the key indicators for measuring the health of aquatic ecosystems; low dissolved oxygen may indicate eutrophication or the influence of potential pollution sources. Turbidity meters assess the turbidity of water by measuring the concentration of suspended particulate matter. High turbidity is usually associated with water pollution or algal blooms and can help identify potential pollution areas or water quality problems. Heavy metal sensors are used to detect the concentration of heavy metal ions in water bodies, such as lead, mercury, and arsenic. Heavy metal pollution usually originates from industrial wastewater or agricultural activities, and its long-term presence can threaten aquatic ecosystems and human health.

[0100] pH reflects the concentration of hydrogen ions in water. Under natural conditions, different bodies of water have their specific pH ranges. Accurately measuring and monitoring the pH of a body of water with a pH meter can promptly detect abnormal changes in acidity or alkalinity, providing important information for determining whether the water body is polluted and the type of pollution. Dissolved oxygen content in water directly affects the survival of aquatic organisms and the self-purification capacity of the water body. Under normal circumstances, the dissolved oxygen content in water is affected by various factors such as water temperature, water flow velocity, and photosynthesis of aquatic plants. Dissolved oxygen meters can monitor the dissolved oxygen content in water in real time, promptly detect changes in dissolved oxygen, assess the ecological health of the water body, and evaluate the impact of pollution on dissolved oxygen. Turbidity meters are mainly used to monitor the concentration of suspended particulate matter in water. Suspended particulate matter includes silt, algae, microorganisms, and organic matter; changes in turbidity reflect the quantity and properties of suspended particulate matter in the water body. Accurate measurement of water turbidity using a turbidity meter allows us to understand the degree of turbidity and determine whether and to what extent the water is polluted. Heavy metal sensors precisely monitor the concentration of heavy metal ions in the water, enabling timely detection of heavy metal pollution. With the assistance of a robotic arm, measurements are taken at different locations and depths of water. Based on parameters obtained from these various types of secondary sensors, multi-point water quality information is determined, yielding point-based water quality analysis results.

[0101] Furthermore, after obtaining multiple measurement results from various types of second sensors, the multiple measurement results are subjected to range standardization to eliminate dimensional differences. The processed multiple measurement results are mapped to the same spatiotemporal grid. The multiple measurement results are weighted and summed using a pre-constructed judgment matrix and weights to obtain the comprehensive score corresponding to the point water quality analysis result. The pollution range in the regional water quality analysis result is corrected based on the comprehensive score.

[0102] Specifically, range standardization is used to map sensor data of different dimensions (such as pH, dissolved oxygen concentration, and heavy metal concentration) to the [0,1] interval through linear transformation. This eliminates dimensional differences in pH (dimensionless), dissolved oxygen (mg / L), and heavy metals (μg / L), making different parameters comparable. Further, the processed sensor data is allocated to a three-dimensional spatiotemporal grid according to the geographical coordinates (longitude, latitude, and depth) of the monitoring points and the sampling time, forming a gridded dataset. This facilitates spatial interpolation and time series analysis during subsequent data fusion. A judgment matrix is ​​constructed based on the analytic hierarchy process (AHP) to determine the relative importance of each water quality parameter through expert experience or historical data. For example, the weight for heavy metal toxicity is set to 0.4, the weight for dissolved oxygen ecological impact is set to 0.3, the weight for pH chemical stability is set to 0.2, and the weight for suspended solids concentration pollution indication is set to 0.1. This quantifies the comprehensive impact of different parameters on water quality, avoids misjudgment based on a single parameter, and determines the pollution range of the monitored water area based on the pre-set relationship between the comprehensive score and the pollution range. For example, a monitoring water body with a comprehensive score of less than or equal to 0.5 is defined as slightly polluted, a monitoring water body with a comprehensive score of greater than 0.5 but less than 0.7 is defined as moderately polluted, and a monitoring water body with a comprehensive score of greater than or equal to 0.7 is defined as heavily polluted.

[0103] The refined monitoring module is an important component of the water quality monitoring system, possessing several significant characteristics that enable it to perform water quality measurement tasks efficiently and accurately, ensuring the acquisition of more comprehensive and accurate water quality data:

[0104] 1. High-precision measurement: One of the key features of the refined monitoring module is its ability to achieve high-precision water quality measurement. This module employs multiple high-sensitivity sensors to ensure extremely high accuracy in the data collected during water quality monitoring. These sensors not only acquire multiple key water quality parameters in real time but also provide reliable readings under various water body conditions.

[0105] 2. Multi-sensor combined application: This module integrates multiple types of water quality sensors, enabling simultaneous measurement of multiple water quality parameters, such as pH value, dissolved oxygen, suspended solids concentration, and heavy metal content. This multi-sensor combined application allows the refined monitoring module to comprehensively assess water quality, identify potential water quality problems or pollution sources, and provide more in-depth water quality analysis results.

[0106] 3. Precise Depth and Position Control: The sensors in the refined monitoring module can precisely control their depth and position within the water body. Through collaboration with the control modules of the drone and robotic arm, the sensors can be adjusted to specific water depths (such as the surface, bottom, or middle layer) as needed, thereby acquiring water quality data at different levels. This function is particularly suitable for analyzing the vertical distribution characteristics of water bodies, further improving monitoring accuracy.

[0107] 4. Efficient Real-Time Data Transmission and Analysis: The refined monitoring module, in conjunction with the UAV's communication system, can instantly upload real-time collected data to the onboard computer and control center. This allows operators to monitor measurement data in real-time during flight and make rapid decisions based on data feedback. Furthermore, real-time data transmission also supports the UAV in autonomously adjusting its flight path and optimizing the measurement process.

[0108] Furthermore, after obtaining the regional and point water quality analysis results, the data fusion and analysis module integrates remote sensing data with contact water quality data to provide comprehensive water quality analysis results. The large-scale spatial data provided by the remote sensing monitoring module and the precise water quality parameters provided by the contact water quality measurement module will be integrated into a unified data platform for analysis. Remote sensing data mainly obtains macroscopic information about water bodies (such as water quality hotspots, pollutant distribution, algae growth, etc.) through technologies such as multispectral, thermal infrared, or lidar, while contact water quality measurement data provides high-precision specific water quality parameters (such as pH value, dissolved oxygen, heavy metal content, etc.).

[0109] Furthermore, all remote sensing and contact measurement data are uploaded in real time to a unified data platform for management and analysis. The platform efficiently processes data from different sensors, performing format conversion and standardization to ensure data consistency and accuracy during analysis. In addition, the platform supports real-time data updates and visualization, allowing users to easily view and analyze the current water quality status at any time.

[0110] Furthermore, the data fusion analysis module analyzes the monitored water area based on the regional water quality analysis results and the point water quality analysis results, including the following steps:

[0111] (1) Calculate the water quality analysis results of the area to determine the pollution range and pollution source type;

[0112] Specifically, the remote sensing monitoring module utilizes various types of primary sensors (such as optical sensors, temperature sensors, and lidar sensors) to scan and detect reflectance information in different wavelengths of the monitored water area, thereby obtaining regional water quality analysis results. Analysis of these results allows for the preliminary delineation of the pollution range. Furthermore, based on the characteristic behavior of different pollutants in the regional water quality analysis results, the type of pollution source can be preliminarily determined. For example, in one possible implementation, an abnormal reflectance in the spectral reflectance of the regional water quality analysis results indicates pollution in the water area, thus defining its approximate location as a polluted region. Similarly, an abnormally high temperature in the regional water quality analysis results may indicate pollution caused by industrial thermal wastewater discharge, thereby determining the pollution range and the pollution source type as industrial thermal wastewater discharge.

[0113] (2) Correct the pollution source type based on the water quality analysis results of the point;

[0114] Specifically, the fine monitoring module utilizes various types of secondary sensors (such as pH meters, dissolved oxygen meters, turbidity meters, and heavy metal sensors) to conduct detailed tests on the water quality in abnormal areas, obtaining point water quality analysis results. Point water quality analysis provides more precise and specific data, while regional water quality analysis can only offer a preliminary assessment of the pollution source type and may contain some errors. Point water quality analysis results can correct for these errors. For example, if the initial assessment identifies agricultural non-point source pollution, but point water quality analysis shows abnormally high concentrations of heavy metal ions in the water, this indicates that the pollution source may also include industrial pollution, requiring a revision of the previous assessment. In this way, the type of pollution source can be determined more accurately.

[0115] (3) Correct the pollution range based on the point water quality analysis results within the pollution range to obtain the corrected pollution range and pollution source type.

[0116] Specifically, based on the point water quality analysis results, it can be found that the pollution range previously determined based on the regional water quality analysis results may have been biased. The areas within the pollution range where the point water quality analysis results are normal are removed from the pollution range, and the areas not included in the pollution range but where the point water quality analysis results show pollution are updated into the pollution range, thereby obtaining the corrected pollution range and pollution source type.

[0117] Furthermore, after obtaining the corrected extent and type of pollution sources, it also includes:

[0118] (1) Determine the pollution gradient within the polluted area based on the water quality analysis results at the point;

[0119] Specifically, within the polluted area, the spatial distribution patterns of pollutant concentrations in the water quality monitoring data are analyzed. The differences in pollutant concentrations between monitoring points are calculated to determine the direction of the pollution gradient, i.e., the direction of the greatest change in pollutant concentration.

[0120] (2) Determine the location of the pollution source based on the pollution gradient.

[0121] Specifically, pollution sources are usually located at the beginning of the pollution gradient or at the highest concentration. The pollution source is located by combining the changing trends of multiple water quality parameters, and the area with the highest degree of pollution in the pollution gradient is taken as the location of the pollution source.

[0122] Furthermore, through the data fusion analysis module, the system can intelligently allocate patrol and measurement resources, optimize the task scheduling and flight path of aerial operation robots, and also use data fusion technology to identify the location and scope of pollution sources, providing a scientific basis for subsequent water quality remediation work. In addition, the trend prediction and anomaly detection functions provided by the system can assess the effectiveness of current water quality management measures and propose improvement measures based on the prediction results to optimize water quality protection strategies.

[0123] The data fusion and analysis module effectively integrates remote sensing data and contact-based water quality data, providing powerful analytical capabilities for the water quality monitoring system. Through big data analytics and machine learning technologies, the system can perform multiple functions such as trend prediction, anomaly detection, and early warning, supporting scientific water quality management and decision-making. This module not only improves the accuracy and comprehensiveness of water quality data but also provides water quality managers with more intelligent and efficient management tools.

[0124] The aerial robot control system for water quality monitoring provided in this embodiment achieves high efficiency, accuracy, and intelligence in water quality monitoring through the collaborative work of its various modules. First, the collaborative and autonomous navigation module generates a flight path based on the monitored water area and location information, flexibly adjusts and initiates fine-grained monitoring in conjunction with remote sensing monitoring results, ensuring comprehensive coverage of monitoring points while promptly focusing on abnormal areas, greatly improving monitoring efficiency. The multi-rotor drone control module, utilizing positioning, status, and ultrasonic sensors, ensures stable and safe flight of the aerial robot, laying the foundation for accurate monitoring. The robotic arm control module precisely drives the water quality sensor to perform multi-point measurements, acquiring detailed water quality data. At the data acquisition level, the robotic arm control module drives the water quality sensor to perform multi-point measurements, obtaining rich and accurate point water quality data. The remote sensing monitoring module utilizes multiple primary sensors to rapidly scan water bodies, obtaining regional water quality analysis results and initially delineating the pollution area. The fine-grained monitoring module supplements this with high-precision point water quality information using different types of secondary sensors, providing multi-dimensional data support for subsequent analysis. The data fusion analysis module integrates regional and point water quality analysis results, gradually determining and correcting the pollution range and source type. Finally, based on the pollution gradient, it identifies the pollution source location, improving the accuracy of pollution assessment and providing a scientific basis for targeted remediation. This system combines the efficiency of remote monitoring with the precision of contact monitoring, enabling rapid identification of problems in a wide range of water bodies and providing accurate water quality data through refined measurements. This helps in the timely detection of water quality changes, safeguarding the ecological environment and public safety.

[0125] Corresponding to the aforementioned embodiment of an aerial robot control system for water quality monitoring, this application also provides an embodiment of an aerial robot control method for water quality monitoring.

[0126] Figure 2This is a flowchart of an embodiment of the aerial robot control method for water quality monitoring provided in this application. Please refer to... Figure 2 The method provided in this embodiment includes:

[0127] S201. Determine the flight path for water quality monitoring based on the information of the monitored water area and the monitoring location.

[0128] Specifically, the system collects information such as the boundaries, terrain, and obstacle distribution of the monitored water area, as well as the coordinates of preset monitoring locations. Based on this information, a path planning algorithm is used, taking into account factors such as flight safety and coverage of all monitoring locations, to generate a flight path for the aerial robot to monitor water quality, ensuring comprehensive and efficient patrol of the monitored water area.

[0129] S202. Control the aerial operation robot to fly along the water quality monitoring flight path, perform remote sensing monitoring of the monitored water area, and obtain remote sensing monitoring results.

[0130] Specifically, the aerial robot flies along a generated flight path, during which its onboard remote sensing monitoring equipment is activated. This equipment includes multiple sensors that scan and detect reflection information in different wavelengths of the monitored water area. By analyzing this information, remote sensing monitoring results about the water quality of the monitored water area are obtained, providing a preliminary overview of the water quality of a large area.

[0131] S203. Determine the pollution range based on the water quality information from the remote sensing monitoring results.

[0132] Specifically, the water quality information from remote sensing monitoring is compared with normal standard values. When the water quality parameters of certain areas exceed the normal range, these areas are identified as polluted areas, thereby determining the scope of pollution and delineating the water areas that require special attention.

[0133] S204. Generate new monitoring points based on the pollution range, and update the water quality monitoring flight path based on the new monitoring points.

[0134] For a detailed explanation of step S204, please refer to the description above; further details will not be repeated here.

[0135] S205. Control the aerial operation robot to fly according to the updated water quality monitoring flight path. When the robot reaches the monitoring point in the updated water quality monitoring flight path, use the second sensor to detect the water quality of the pollution area and obtain the water quality detection result.

[0136] Specifically, the aerial robot flies along the updated flight path. Upon reaching the newly added monitoring point, its various types of secondary sensors begin to operate. The pH meter, through contact with the water, accurately measures the water's acidity or alkalinity, reflecting whether the water's chemical properties are normal. The dissolved oxygen meter measures the dissolved oxygen content in the water, a key indicator of the health of the aquatic ecosystem; changes in its content directly reflect the water's pollution and self-purification capacity. The turbidity meter uses optical principles to detect the concentration of suspended particulate matter in the water, determining the degree of turbidity and assessing the presence of pollution or algal blooms. The heavy metal sensor employs advanced analytical techniques to specifically detect the concentration of heavy metal ions in the water, such as lead, mercury, cadmium, and chromium. Even at low concentrations, these heavy metals can cause serious harm to the ecological environment and human health.

[0137] Under the precise control of the robotic arm's control module, these second sensors penetrate to different depths below the water surface to conduct multi-point water quality detection. The collected real-time water quality data is quickly transmitted back to the ground station or onboard data processing unit to obtain the water quality detection results, providing high-precision data support for subsequent water quality analysis and pollution range correction.

[0138] S206. Correct the remote sensing monitoring results based on the water quality detection results to obtain water quality identification results.

[0139] Specifically, the water quality detection results obtained from the second sensor are compared and analyzed in depth with the previous remote sensing monitoring results. Because remote sensing monitoring results have certain limitations in accuracy, while water quality detection results are more targeted and accurate, the differences in water quality parameters between the two methods in the same area are compared to correct information in the remote sensing monitoring results regarding the scope and degree of pollution. For example, if the water quality detection results indicate that the concentration of heavy metal ions in a certain area is far above normal levels, while the remote sensing monitoring results assess the pollution level in that area relatively low, then it is necessary to appropriately expand the pollution range in that area and raise the assessment level of pollution based on the actual detection data.

[0140] The method in this embodiment can be used to execute Figure 1 The steps of the system embodiment shown are similar in principle and process, and will not be repeated here.

[0141] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A control system for an aerial robot used for water quality monitoring, characterized in that, The control system includes: The collaborative and autonomous navigation module is used to generate a water quality monitoring flight path based on the monitored water area information and monitoring location information, and to trigger the start of the fine monitoring module based on the regional water quality analysis results of the remote sensing monitoring module. A multi-rotor drone control module is used to control the aerial work robot to fly along the water quality monitoring flight path; The robotic arm control module is used to respond to the start signal of the fine monitoring module and control the robotic arm to move the water quality sensor to the water surface to perform multi-point water quality measurement; The remote sensing monitoring module utilizes multiple types of primary sensors to scan and detect reflectance information in different wavelength bands of the monitored water area, obtaining regional water quality analysis results. Specifically, it determines the temperature field information, water structure characteristics, and spectral data of the monitored water area based on multi-source detection from various types of primary sensors. A quantitative model of temperature-dissolved oxygen-microbial activity is established based on the temperature field information. This model quantifies the dynamic influence coefficient of temperature on dissolved oxygen content and microbial metabolic rate, and the dynamic influence coefficient is used to correct the predicted dissolved oxygen value and pollution diffusion rate in the regional water quality analysis results. A pollutant diffusion model is established based on the water structure characteristics and the water flow velocity field simulated by computational fluid dynamics. This model predicts the horizontal migration path and concentration decay curve of pollutants. The temperature field information, water structure characteristics, and spectral data are input into a pre-trained machine learning algorithm, which outputs the regional water quality analysis results. The fine monitoring module is used to detect water quality information at multiple points using various types of second sensors to obtain point water quality analysis results. The first sensor and the second sensor are of different types. The data fusion and analysis module is used to fuse the regional water quality analysis results and the point water quality analysis results to obtain the location of the pollution source and the pollution area of ​​the monitored water area.

2. The control system according to claim 1, characterized in that, The step of generating a water quality monitoring flight path based on the monitored water area information and monitoring location point information includes: A coverage constraint is generated based on the monitored water area information and the monitored location point information; Flight state constraints are generated based on the safe flight restrictions of the aerial operation robot. The flight state constraints include altitude constraints, speed constraints, and acceleration constraints. By summing the coverage constraints and the flight state constraints, constraint conditions are generated; The area is determined based on the monitored water area information. The objective function is to minimize the sum of the Euclidean distances between two adjacent locations on the water quality monitoring flight path. The water quality monitoring flight path is then generated in combination with the constraints.

3. The control system according to claim 1, characterized in that, The step of triggering the fine monitoring module based on the regional water quality analysis results from the remote sensing monitoring module includes: By comparing the detection value of each first sensor with the corresponding threshold value of each first sensor, if the detection value of any first sensor is greater than the threshold value of the corresponding first sensor, the corresponding area is determined to be an abnormal area. The regional water quality analysis results in the abnormal area are sorted, and a predetermined number of locations with the highest ranking in the sorting results are selected as new monitoring points for the abnormal area.

4. The control system according to claim 3, characterized in that, The cooperative and autonomous navigation module also includes: Determine whether the newly added monitoring point is in the water quality monitoring flight path; if so, keep the water quality monitoring flight path unchanged. If not, calculate the first distance between the next flight point and the current flight point in the water quality monitoring flight path, and the second distance between the newly added monitoring point closest to the current flight point and the current flight point; If the first distance is less than or equal to the second distance, then control the aerial operation robot to fly to the next flight point, set the next flight point as the latest current flight point, return to the step of calculating the first distance, until all flight points in the water quality monitoring flight path have been traversed; If the first distance is greater than the second distance, the aerial robot is controlled to fly to the newly added monitoring point, and the newly added monitoring point is used as the latest current flight point. The next flight point remains unchanged. The process returns to the step of calculating the first distance until all flight points in the water quality monitoring flight path have been traversed.

5. The control system according to claim 1, characterized in that, The multi-rotor UAV control module includes: The positioning module is used to provide feedback on the position information of the aerial robot based on real-time dynamic positioning and visual methods. Multiple status sensors are used to detect different flight attitudes of the aerial robot in real time; An ultrasonic sensor is used to monitor the distance between the aerial work robot and the water surface in real time when the distance between the aerial work robot and the water surface of the monitored water area is less than a preset distance.

6. The control system according to claim 1, characterized in that, The various types of first sensors include: Multiple optical sensors are used to scan the reflectance information of the monitored water area under different spectra, and the water quality parameters of the monitored water area are analyzed based on the reflectance information; A temperature sensor is used to monitor the temperature of the monitored water area; A lidar sensor is used to monitor the water structure of the monitored water area.

7. The control system according to claim 1, characterized in that, The various types of second sensors include: pH meter, used to monitor the pH value of the monitored water area; A dissolved oxygen meter is used to monitor the dissolved oxygen content of the monitored water area; A turbidity meter is used to monitor the concentration of suspended particulate matter in the monitored water area; A heavy metal sensor is used to monitor the concentration of heavy metal ions in the monitored water area.

8. The control system according to claim 1, characterized in that, The data fusion and analysis module includes: The water quality analysis results of the area are calculated to determine the extent and type of pollution sources; The pollution source type is corrected based on the point water quality analysis results; The pollution range is corrected based on the point water quality analysis results within the pollution range to obtain the corrected pollution range and pollution source type.

9. The control system according to claim 8, characterized in that, After obtaining the corrected pollution range and pollution source type, the process also includes: The pollution gradient within the polluted area is determined based on the water quality analysis results. The location of the pollution source is determined based on the pollution gradient.

10. A control method for an aerial robot used for water quality monitoring, characterized in that, The aerial robot control method for water quality monitoring is implemented based on the control system described in any one of claims 1-9, and the aerial robot control method for water quality monitoring includes: The flight path for water quality monitoring is determined based on information about the monitored water area and monitoring location points. The aerial operation robot is controlled to fly along the water quality monitoring flight path to perform remote sensing monitoring of the monitored water area and obtain remote sensing monitoring results. The extent of pollution is determined based on the water quality information obtained from the remote sensing monitoring results. New monitoring points are generated based on the pollution range, and the water quality monitoring flight path is updated based on the new monitoring points; The aerial robot is controlled to fly according to the updated water quality monitoring flight path. When it reaches the monitoring point in the updated water quality monitoring flight path, the second sensor is used to detect the water quality of the pollution area and obtain the water quality detection result. The remote sensing monitoring results are corrected based on the water quality test results to obtain the water quality identification results.

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