Water quality detection system and method based on unmanned aerial vehicle

Abstract

The invention is applicable to the technical field of water quality detection, and provides a water quality detection system and method based on an unmanned aerial vehicle, and the system comprises a water taking bottle, the unmanned aerial vehicle, a wireless communication module, a central control unit, a mobile terminal, a water quality detection sensor, a water pump, an interlayer and a battery. Under the condition that the unmanned aerial vehicle is carried, the limitation of environment and weather can be greatly reduced, the target water area can be rapidly positioned to be collected and detected, the labor cost is reduced, and meanwhile, the detection convenience and flexibility are improved, especially in areas where unmanned ships are difficult to reach or the environment is severe. High reliability of water sample collection and initial detection is ensured through equipment linkage and algorithm optimization, errors are reduced, and the function of a water quality detection station can be replaced to a certain extent. And meanwhile, remote automatic monitoring and real-time data analysis are supported, and an alarm is immediately given once water quality abnormity is found, so that the water quality supervision efficiency is improved, the labor cost is reduced, and powerful technical support is provided for environmental protection and scientific decision making.

Description

Technical Field

[0001] The present invention belongs to the technical field of water quality detection, and in particular relates to a water quality detection system and method based on an unmanned aerial vehicle. Background Art

[0002] Water quality testing currently faces many challenges, especially at widespread and dispersed testing points in the wild or polluted areas. Traditional manual testing methods are not only inefficient and time-consuming, but also difficult to respond quickly to sudden abnormalities in water quality, resulting in untimely and inaccurate data collection. Especially under severe weather conditions, the risks and costs of manual operations increase significantly. At the same time, although the construction of water quality stations can provide stable monitoring services, they are limited by high construction and maintenance costs, and their coverage is often limited to urban areas and some key waters, which cannot meet the monitoring needs of a wide range of wild water sources. The method of using unmanned boats to collect water samples can alleviate the burden of manpower to a certain extent, but it is limited by the ground environment and it is difficult to operate stably in complex waters such as shallows and rapids, and it is not universal.

[0003] When testing water quality in the target area, timely adjustment and feedback of abnormal situations in the testing process is the key to successful testing. The detection of abnormalities includes the detection of the deviation of the target area, whether the function of the deployment equipment is intact, whether the testing equipment is working, and whether the test data reaches abnormal values. The accuracy of abnormality detection is the key to ensuring the reasonable and effective testing process, that is, the test data, and is crucial to the design, implementation and credibility of the testing operation and testing plan.

[0004] Most of the current water quality detection schemes still use traditional detection methods. It is necessary to manually go to the target waters to collect samples and perform data detection and analysis on the samples in the rear laboratory to check whether there are any abnormal conditions. In the case of abnormal water quality, it is impossible to automatically alarm in time. Not only does it require a lot of manpower to detect the on-site scene, but it is also extremely inefficient. After an abnormal event occurs, it cannot respond quickly, and it is easy to miss the first time to solve the water quality problem. With the rapid development of drone technology and Internet of Things technology, new possibilities have been brought to water quality detection. By using drones equipped with advanced water quality intelligent detection systems and combining linkage detection algorithms, remote, fast and accurate water quality data collection and analysis can be achieved. For this purpose, a water quality detection system and method based on drones are proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a water quality detection system and method based on drone, aiming to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A water quality detection method based on drone, comprising the following steps:

[0008] Step 1: The central control unit starts and tries to establish a wireless connection with the mobile terminal; if there is an available network connection in the current environment, the connection is successfully established; if the network is not available, the central control unit enters the standby state and continues to wait until the wireless signal is restored;

[0009] Step 2: The drone sends the water bottle into the target water area. After receiving the command, the central control unit sends a sampling signal to the water pump, controls the water pump to start forward, extracts water samples from the target water area through the hose and pumps them into the lower part of the water bottle;

[0010] Step 3: After the water level reaches the initial inspection line, the central control unit issues a command to shut down the water pump, and at the same time, the water quality detection sensor in the water bottle starts working. The detection probe of the water quality detection sensor collects various water quality data in real time, including pH value, TDS value, turbidity and temperature. After the data changes stabilize, it is sent to the mobile terminal through the wireless module for processing and analysis;

[0011] Step 4: The mobile terminal receives the initial inspection information of the water bottle and makes a judgment;

[0012] If the water quality data is normal, the mobile terminal sends a command to the central control unit to control the water pump to start in the reverse direction, discharge the extracted water samples until the water level is lower than the initial inspection line, and then turn off the water pump to wait for the next sampling signal.

[0013] If the water quality data is abnormal, the mobile terminal sends a command to the central control unit to control the water pump to start in the forward direction and continue to extract water samples until the water level reaches the collection line. The water pump is then turned off and the collected water samples are saved and wait for the drone to send them back.

[0014] Step 5: The mobile terminal controls the central control unit to repeat steps 2 to 4 multiple times to obtain multiple water quality measurement values; then the mobile terminal performs arithmetic average calculation on the multiple water quality measurement values, and the obtained arithmetic average replaces the true value of the measurement value and compensates for the original error; finally, the processed result is saved.

[0015] Furthermore, the specific process of step 5 is as follows:

[0016] Step 51: After the water bottle starts pumping water, the detection probe delays for 20 seconds before detecting the water sample in contact;

[0017] Step 52: Collect data from the water sample in the water bottle once every 3 seconds, collect data 20 times in total, and obtain a set of original data X i (i=1,2…20);

[0018] Step 53: Detect abnormal data using the Chauville criterion: Calculate the arithmetic mean of the original data And the root mean square deviation σ, then by With σ and each individual X i Calculate the deviation K for each individual value i and the critical value K corresponding to n = 20 in the Chauveille criterion standard table max Compare and if the deviation of a single value is greater than the critical value, it will be marked as an abnormal value; if the number of detected abnormal values ​​N≥1, return to step 51 to re-collect data until no abnormal data is detected;

[0019] The formula of Chauville's criterion is:

[0020]

[0021] Step 54: Perform error analysis on the detection data;

[0022] Calculate the arithmetic mean error ΔX, The addition and subtraction with ΔX is taken as the true value T of the detection data and saved and recorded; the calculation formula is:

[0023]

[0024] Step 55: performing temperature compensation on a TDS sensor for detecting a TDS value and a pH sensor for detecting a pH value;

[0025] Step 56: The final data obtained in step 55 is saved as the result of a preliminary water quality inspection, and the above operation is repeated until the central control unit issues a drainage or pumping instruction.

[0026] Furthermore, the process of temperature compensation for the data of the TDS sensor is as follows:

[0027] Get the voltage value V without temperature compensation 测 And the corresponding temperature value T0, according to the temperature correction coefficient formula to calculate the temperature correction coefficient T 修 , take 25℃ as the standard temperature, and then calculate V by the temperature correction coefficient 修 , and finally bring it into the TDS standard curve formula to get the compensated TDS value; the calculation formula is:

[0028] T 修 =1+0.02*(T0-25);

[0029] V 修 =T 修 *V 测 ;

[0030] TDS=66.71V 修 3 -127.93V 修2 +428.7V 修 .

[0031] Furthermore, the process of temperature compensation of the pH sensor data is as follows:

[0032] By Nernst equation The relationship between potential E and pH value is:

[0033]

[0034] Where E0 is the equipotential point, R is the gas constant, n is the number of electrons gained or lost in the electrode reaction, F is the Faraday constant, and H + is the concentration of hydrogen ions in the solution;

[0035] When the temperature is constant, measure the PH1 of the standard buffer solution and record the voltage E1. Then measure the PH2 of another known buffer solution and record the voltage E2. Use the following formula to calculate the actual slope:

[0036]

[0037] The equipotential point is calculated using the following formula:

[0038]

[0039] The pH value after compensation is:

[0040]

[0041] A drone-based water quality detection system, used to implement the drone-based water quality detection method described above, comprising a water collection bottle, a drone, a wireless communication module, a central control unit, a mobile terminal, a water quality detection sensor, a water pump, an interlayer and a battery, wherein the wireless communication module, the central control unit and the water quality detection sensor are all integrated on the upper part of the water collection bottle;

[0042] The water bottle is used to store the extracted water sample;

[0043] The drone is equipped with a water bottle for transporting the water bottle;

[0044] The central control unit is used to control the opening and closing of the water pump by receiving instructions from the mobile terminal, and to receive data from the water quality detection sensor and send it to the mobile terminal through the wireless communication module;

[0045] The mobile terminal establishes a connection with the central control unit through a wireless communication module to receive and analyze water quality data and send control instructions to the central control unit;

[0046] The detection probe of the water quality detection sensor extends into the water collection bottle and is connected to the central control unit through a line to collect various water quality data in real time;

[0047] The water pump is connected to the central control unit through a line and is installed on the side wall of the water bottle for draining or pumping water;

[0048] The interlayer is located between the water bottles and is used to place the circuits;

[0049] The battery is used to provide power for the entire system.

[0050] Furthermore, the water quality detection sensor includes a pH sensor, a TDS sensor, a turbidity sensor and a temperature sensor.

[0051] Furthermore, the various water quality data include pH value, TDS value, turbidity and temperature of the water sample.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. The present invention can greatly reduce the restrictions of environment and weather when equipped with a drone, and can quickly locate the target waters for sampling and inspection, thereby reducing labor costs and improving the convenience and flexibility of inspection, especially in areas that are difficult for unmanned ships to reach or in harsh environments.

[0054] 2. The present invention aims at the design of equipment linkage and algorithm optimization, which ensures the accuracy of water sample collection and the reliability of initial inspection. Through the error detection and compensation mechanism, the error in the detection process is reduced, and the accuracy and reliability of the test results are improved. Under the condition that precise analysis is required, the present invention can quickly complete the initial inspection and collect water samples to bring back to the laboratory for in-depth analysis, which not only simplifies the detection process, but also ensures the accuracy of the data, and can replace the function of the water quality testing station to a certain extent.

[0055] 3. The present invention can collect water quality data in real time, store and analyze it through the cloud platform, and support historical data comparison and trend prediction. Once abnormal water quality is found, the system will immediately issue an alarm to remind relevant departments to take timely measures. This remote and automated monitoring method not only improves the timeliness of water quality supervision, but also reduces labor costs, providing strong technical support for scientific decision-making and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic diagram of the structure of the system in the present invention.

[0057] Figure 2 It is a schematic diagram of the cross-sectional structure of the water bottle in the present invention.

[0058] Figure 3The figure is a schematic diagram of the top view of the water bottle in the present invention.

[0059] Figure 4 Flow chart of the method of the present invention.

[0060] In the figure: drone 1, water bottle 2, wireless communication module 3, central control unit 4, water pump 5, interlayer 6, battery 7, pH sensor 8, TDS sensor 9, turbidity sensor 10, temperature sensor 11, detection probe 12. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0062] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0063] like Figure 1-3 As shown, a water quality detection system based on a drone is provided as an embodiment of the present invention, including a water bottle 2, a drone 1, a wireless communication module 3, a central control unit 4, a mobile terminal, a water quality detection sensor, a water pump 5, an interlayer 6 and a battery 7, wherein the wireless communication module 3, the central control unit 4 and the water quality detection sensor are all integrated in the upper part of the water bottle 2; the water quality detection sensor includes a pH sensor 8 (pH4502C), a TDS sensor 9, a turbidity sensor 10 and a temperature sensor 11 (DS18B20). Under the premise of being carried on a four-rotor drone 1, it is expected that the water bottle 2 can collect 1L of water sample, and the overall weight does not exceed 3kg. The initial inspection content of the water bottle 2 includes but is not limited to: temperature, turbidity, TDS value and pH value.

[0064] The water bottle 2 is used to store the extracted water sample;

[0065] The drone 1 is mounted with a water bottle 2 for transporting the water bottle 2;

[0066] The central control unit 4 is used to control the opening and closing of the water pump 5 by receiving instructions from the mobile terminal, and to receive data from the water quality detection sensor and send it to the mobile terminal through the wireless communication module 3;

[0067] The mobile terminal establishes a connection with the central control unit 4 through the wireless communication module 3 to receive and analyze water quality data and send control instructions to the central control unit 4;

[0068] The detection probe 12 of the water quality detection sensor extends into the water collection bottle 2 and is connected to the central control unit 4 through a line to collect various water quality data in real time;

[0069] The water pump 5 is connected to the central control unit 4 through a line and is installed on the side wall of the water bottle 2 for draining or pumping water;

[0070] The interlayer 6 is located between the water bottles 2 and is used to house circuits such as power supply and signal collection.

[0071] The battery 7 is connected to the central control unit 4, the water pump 5 and other components to provide power for the entire system.

[0072] In the embodiment of the present invention, the present invention integrates the central control unit 4 with water quality sensors such as pH sensor 8, TDS sensor 9, turbidity sensor 10, temperature sensor 11, and wireless WIFI module in the upper part of the bottle-like container and supplies power through an external battery 7, and the top is designed to facilitate the mounting of the drone 1. The lower part of the bottle-like container is waterproofly isolated from the upper part, and a water pump 5 is installed on the side wall to achieve the function of efficiently collecting water samples, and a detection probe 12 extending from the upper part is installed in the lower part.

[0073] like Figure 4 As shown, a water quality detection method based on a drone is provided in one embodiment of the present invention, which is applied to the above-mentioned water quality detection system based on a drone, and includes the following steps:

[0074] Step 1: The central control unit 4 starts and tries to establish a wireless connection with the mobile terminal; if there is an available network connection in the current environment, the connection is successfully established; if the network is not available, the central control unit 4 enters a standby state and continues to wait until the wireless signal is restored;

[0075] Step 2: The drone 1 sends the water bottle 2 into the target water area. After receiving the command, the central control unit 4 sends a sampling signal to the water pump 5, controls the water pump 5 to start forward, and extracts the water sample from the target water area through the hose and pumps it into the lower part of the water bottle 2;

[0076] Step 3: After the water level reaches the initial inspection line, the central control unit 4 issues a command to turn off the water pump 5, and at the same time, the water quality detection sensor in the water bottle 2 starts working. The detection probe 12 of the water quality detection sensor collects various water quality data in real time, including pH value, TDS value, turbidity and temperature. After the data changes stabilize, it is sent to the mobile terminal through the wireless module for processing and analysis;

[0077] Step 4: The mobile terminal receives the initial inspection information of the water bottle 2 and makes a judgment;

[0078] If the water quality data is normal, the mobile terminal sends a command to the central control unit 4 to control the water pump 5 to start in the reverse direction, discharge the extracted water sample until the water level is lower than the initial inspection line, and then turn off the water pump 5 to wait for the next sampling signal;

[0079] If the water quality data is abnormal, the mobile terminal sends a command to the central control unit 4 to control the water pump 5 to start in the forward direction, continue to extract water samples until the water level reaches the collection line, turn off the water pump 5, save the collected water samples, and wait for the drone 1 to send them back for in-depth laboratory analysis;

[0080] Step 5: The mobile terminal controls the central control unit 4 to repeat steps 2 to 4 multiple times to obtain multiple water quality measurement values; then the mobile terminal performs arithmetic average calculation on the multiple water quality measurement values, and the obtained arithmetic average approximately replaces the true value of the measurement value and compensates for the original error; finally, the processed result is saved.

[0081] As a preferred embodiment of the present invention, in the initial water quality inspection step, for field water quality inspections in the wild or urban areas, if the traditional direct measurement method is used to inspect the flowing water source, it will inevitably cause the detection data of the same water source to change at a high frequency in a short period of time, thereby causing frequent false alarms, which will not only increase the flow of storage and transmission data, but also increase the workload of the inspection personnel. Since the water quality inspection area is fixed, the position of the measured water sample at the same time is relatively unchanged. Therefore, the water sample can be taken out within a limited time, and the measurement can be repeated many times in the process. The arithmetic mean of these measured values ​​is used to approximately replace the true value of the measured value and compensate for the original error. Therefore, the specific process of step 5 is as follows:

[0082] Step 51: After the water bottle 2 starts pumping water, the detection probe 12 delays for 20 seconds before detecting the water sample in contact with it to prevent the initial error from being too large;

[0083] Step 52: Collect data from the water sample in the water bottle 2 once every 3 seconds, collect data 20 times in total, and obtain a set of original data X i (i=1,2…20);

[0084] Step 53: Detect abnormal data using the Chauvenet Criterion: Calculate the arithmetic mean of the original data And the root mean square deviation σ, then by With σ and each individual X i Calculate the deviation K for each individual value i and the critical value K corresponding to n = 20 in the Chauveille criterion standard table max Compare, if the deviation of the individual value is greater than the critical value, it will be marked as an outlier; since the sample capacity of each test is 20, no abnormal data is allowed when the capacity is small, so if the number of detected outliers N ≥ 1, return to step 51 to re-collect data until no abnormal data is detected;

[0085] The formula of Chauville's criterion is:

[0086]

[0087] Step 54: Perform error analysis on the detection data;

[0088] Calculate the arithmetic mean error ΔX, The addition and subtraction with ΔX is taken as the true value T of the detection data and saved and recorded; the calculation formula is:

[0089]

[0090]

[0091] Step 55: Perform temperature compensation on the TDS sensor 9 for detecting the TDS value and the pH sensor 8 for detecting the pH value;

[0092] The process of temperature compensation of the data of the TDS sensor 9 is as follows:

[0093] Get the voltage value V without temperature compensation 测 And the corresponding temperature value T0, according to the temperature correction coefficient formula to calculate the temperature correction coefficient T 修 (25℃ is used as the standard temperature), and then V is calculated by the temperature correction coefficient 修 , and finally bring it into the TDS standard curve formula to get the compensated TDS value; the calculation formula is:

[0094] T 修 =1+0.02*(T0-25);

[0095] V 修 =T 修 *V 测 ;

[0096] TDS=66.71V 修 3 -127.93V 修 2 +428.7V 修 .

[0097] The process of temperature compensation of the data of the pH sensor 8 is as follows:

[0098] By Nernst equation The relationship between potential E and pH value is:

[0099]

[0100] Where E0 is the equipotential point, R is the gas constant, n is the number of electrons gained or lost in the electrode reaction, F is the Faraday constant, and H + is the concentration of hydrogen ions in the solution;

[0101] When the temperature is constant, measure the PH1 of the standard buffer solution and record the voltage E1. Then measure the PH2 of another known buffer solution and record the voltage E2. Use the following formula to calculate the actual slope:

[0102]

[0103] The equipotential point is calculated using the following formula:

[0104]

[0105] The pH value after compensation is:

[0106]

[0107] Step 56: The final data obtained in step 55 is saved as the result of a preliminary water quality inspection, and the above operation is repeated until the central control unit 4 issues a drainage or pumping instruction.

[0108] In the embodiments of the present invention, the present invention can be applied to the following scenarios:

[0109] 1) Real-time detection of urban rivers: The present invention can conduct regular patrols in urban rivers to collect and analyze water quality data in real time. Through the high mobility and wide coverage of the drone 1 and the real-time detection and analysis, the system can quickly discover and locate areas with abnormal water quality, and issue an alarm in time to notify relevant departments for processing. After the implementation of this system, the efficiency of water quality detection in urban rivers has been significantly improved, and environmental management departments can grasp water quality conditions more timely and accurately, thereby improving the level of water environment protection.

[0110] 2) Field lake ecological detection: In mountain forests with complex terrain and environment, the present invention can quickly locate and detect water quality data of different lakes through autonomous cruising of the drone 1 and efficient water sampling of the detection module, and can track the ecological changes of lakes for a long time, providing a scientific basis for lake environmental protection and governance. Through real-time monitoring and data analysis, the system helps environmental protection departments to promptly discover and deal with lake pollution problems and maintain the ecological balance and health of lakes.

[0111] The above are only preferred embodiments of the present invention. It should be pointed out that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention. These should also be regarded as the protection scope of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent.

Claims

1. A water quality detection method based on drone, characterized in that: The following steps are involved: Step 1: The central control unit starts and tries to establish a wireless connection with the mobile terminal; if there is an available network connection in the current environment, the connection is successfully established; if the network is not available, the central control unit enters the standby state and continues to wait until the wireless signal is restored; Step 2: The drone sends the water bottle into the target water area. After receiving the command, the central control unit sends a sampling signal to the water pump, controls the water pump to start forward, extracts water samples from the target water area through the hose and pumps them into the lower part of the water bottle; Step 3: After the water level reaches the initial inspection line, the central control unit issues a command to shut down the water pump, and at the same time, the water quality detection sensor in the water bottle starts working. The detection probe of the water quality detection sensor collects various water quality data in real time, including pH value, TDS value, turbidity and temperature. After the data changes stabilize, it is sent to the mobile terminal through the wireless module for processing and analysis; Step 4: The mobile terminal receives the initial inspection information of the water bottle and makes a judgment; If the water quality data is normal, the mobile terminal sends a command to the central control unit to control the water pump to start in the reverse direction, discharge the extracted water samples until the water level is lower than the initial inspection line, and then turn off the water pump to wait for the next sampling signal. If the water quality data is abnormal, the mobile terminal sends a command to the central control unit to control the water pump to start in the forward direction and continue to extract water samples until the water level reaches the collection line. The water pump is then turned off and the collected water samples are saved and wait for the drone to send them back. Step 5: The mobile terminal controls the central control unit to repeat steps 2 to 4 multiple times to obtain multiple water quality measurement values; The mobile terminal then performs arithmetic average calculation on multiple water quality measurement values, and the obtained arithmetic average replaces the true value of the measurement value and compensates for the original error; finally, the processed result is saved.

2. The water quality detection method based on drone according to claim 1 is characterized in that: The specific process of step 5 is as follows: Step 51: After the water bottle starts pumping water, the detection probe delays for 20 seconds before detecting the water sample in contact; Step 52: Collect data from the water sample in the water bottle once every 3 seconds, collect data 20 times in total, and obtain a set of original data X i (i=1,2…20); Step 53: Detect abnormal data using the Chauville criterion: Calculate the arithmetic mean of the original data And the root mean square deviation σ, then by With σ and each individual X i Calculate the deviation K for each individual value i and the critical value K corresponding to n = 20 in the Chauveille criterion standard table max Compare and if the deviation of a single value is greater than the critical value, it will be marked as an abnormal value; if the number of detected abnormal values ​​N≥1, return to step 51 to re-collect data until no abnormal data is detected; The formula of Chauville's criterion is: Step 54: Perform error analysis on the detection data; Calculate the arithmetic mean error ΔX, The addition and subtraction with ΔX is taken as the true value T of the detection data and saved and recorded; the calculation formula is: Step 55: performing temperature compensation on a TDS sensor for detecting a TDS value and a pH sensor for detecting a pH value; Step 56: The final data obtained in step 55 is saved as the result of a preliminary water quality inspection, and the above operation is repeated until the central control unit issues a drainage or pumping instruction.

3. The water quality detection method based on drone according to claim 2 is characterized in that: The process of temperature compensation of the TDS sensor data is as follows: Get the voltage value V without temperature compensation 测 And the corresponding temperature value T0, according to the temperature correction coefficient formula to calculate the temperature correction coefficient T 修 , take 25℃ as the standard temperature, and then calculate V by the temperature correction coefficient 修 , and finally bring it into the TDS standard curve formula to get the compensated TDS value; The calculation formula is: T 修 =1+0.02*(T0-25); V 修 =T 修 *V 测 ; TDS=66.71V 修 3 -127.93V 修 2 +428.7V 修 。 4. The water quality detection method based on drone according to claim 2 is characterized in that: The process of temperature compensation of the pH sensor data is as follows: By Nernst equation The relationship between potential E and pH value is: Where E0 is the equipotential point, R is the gas constant, n is the number of electrons gained or lost in the electrode reaction, F is the Faraday constant, and H + is the concentration of hydrogen ions in the solution; When the temperature is constant, measure the PH1 of the standard buffer solution and record the voltage E1. Then measure the PH2 of another known buffer solution and record the voltage E2. Use the following formula to calculate the actual slope: The equipotential point is calculated using the following formula: The pH value after compensation is:

5. A water quality detection system based on drone, used to implement the water quality detection method based on drone according to any one of claims 1 to 4, characterized in that: It includes a water bottle, a drone, a wireless communication module, a central control unit, a mobile terminal, a water quality detection sensor, a water pump, an interlayer and a battery, wherein the wireless communication module, the central control unit and the water quality detection sensor are all integrated in the upper part of the water bottle; The water bottle is used to store the extracted water sample; The drone is mounted with a water bottle for transporting the water bottle; The central control unit is used to control the opening and closing of the water pump by receiving instructions from the mobile terminal, and to receive data from the water quality detection sensor and send it to the mobile terminal through the wireless communication module; The mobile terminal establishes a connection with the central control unit through a wireless communication module to receive and analyze water quality data and send control instructions to the central control unit; The detection probe of the water quality detection sensor extends into the water collection bottle and is connected to the central control unit through a line to collect various water quality data in real time; The water pump is connected to the central control unit through a line and is installed on the side wall of the water bottle for draining or pumping water; The interlayer is located between the water bottles and is used to place the circuits; The battery is used to provide power for the entire system.

6. The water quality detection system based on drone according to claim 5 is characterized in that: The water quality detection sensor includes a pH sensor, a TDS sensor, a turbidity sensor and a temperature sensor.

7. The water quality detection system based on drone according to claim 5 is characterized in that: The various water quality data include pH value, TDS value, turbidity and temperature of the water sample.

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