Unmanned ship monitoring system and method suitable for river and lake water body multi-field scanning perception

Abstract

The invention discloses an unmanned ship monitoring system and method suitable for river and lake water body multi-field scanning sensing. The system comprises an upper computer, an unmanned ship, a lower computer carried by the unmanned ship and functional modules. The upper computer controls the unmanned ship to cruise through the lower computer and collects data and water samples; the lower computer is used for performing data interaction with the upper computer, receiving an instruction issued by the upper computer to control the unmanned ship and each functional module, and feeding various data sensed by each functional module back to the upper computer; each functional module comprises a positioning module, a wireless communication module, a data storage module, a water sample collection module, an environment monitoring module, a power driving module, an obstacle avoidance module, an early warning indication module and a power supply module. According to the system and the method, multi-factor automatic sensing of the river and lake water body can be carried out, full-field traversal efficient and accurate monitoring of the unmanned ship on the river and lake water body is completed, and scientific interpolation from point data to plane data of the river and lake water body under multi-factor disturbance is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of river and lake ecological environment monitoring and protection, and in particular relates to an unmanned boat monitoring system and method suitable for multi-field scanning perception of river and lake water bodies. Background Art

[0002] The use of unmanned boats to perceive the hydrological, water quality, and water ecological elements of rivers and lakes is currently a common means of monitoring and protecting the ecological environment of rivers and lakes. The realization of multiple benign interactions among the hydrodynamic field, pollution field, and ecological field of rivers and lakes is the basis for maintaining the recovery of the ecological environment of rivers and lakes. Therefore, how to use unmanned boats to achieve accurate and efficient perception of the hydrodynamic field, pollution field, and ecological field of rivers and lakes is a key issue that needs to be urgently solved in the field of river and lake ecological environment monitoring and protection technology.

[0003] At present, there are the following difficulties in using unmanned boats to perceive the hydrology, water quality, and water ecological elements of rivers and lakes in real time, as well as to conduct multi-field scanning of rivers and lakes:

[0004] 1. The size of various monitoring sensors on the market does not match the volume of the unmanned ship cabin:

[0005] At present, most of the water quality monitoring equipment on the market are cylindrical detection equipment. When they are carried on unmanned boats, they are often fixed to the hull through fixed equipment or connected to the unmanned boat through fixed-length ropes. They can only complete the water quality parameter monitoring of a single fixed surface water depth, and cannot perform three-dimensional monitoring of stratified water bodies. In addition, when unmanned boats are released in lakes, rivers, and reservoir shores, the shallow water depth on the shore can easily cause damage to the water quality monitoring equipment by dragging, friction, collision, etc. At the same time, when the multi-parameter water quality monitoring equipment carried by the unmanned boat monitors the water quality status, conventional mobile detection equipment does not have the ability to conduct in-depth analysis and comprehensive measurement of water quality parameters. Depending on the water quality risk status, water quality sampling and retention are sometimes required.

[0006] 2. Unmanned boat path planning problem in the dynamic environment of rivers and lakes:

[0007] Traditional methods usually divide river and lake water bodies into multiple uniform grids according to the range of river and lake water bodies, set the unmanned boat's patrol track points in each grid unit according to a certain rule, let the unmanned boat patrol one by one, and then perceive the relevant elements. This method solves the problem of slow efficiency and insufficient coverage of manual sampling monitoring to a certain extent, but it also exposes certain shortcomings. First, the dynamic environmental factors of river and lake water bodies, such as flow rate, obstacles, etc., are not considered. The planned path is difficult to fully realize in a dynamic environment. The local dynamic environment easily causes the unmanned boat's cruise track to fall into an endless loop; second, the artificially delineated trajectory route does not fully utilize the boost of the river and lake water flow field, but simply covers the water body range. However, due to the limited cabin range and limited power carried by the unmanned boat, it fully covers a large range of water bodies at once, which causes a time difference in the perception data. Therefore, how to efficiently and accurately determine the navigation path of the unmanned boat in the dynamic river and lake water environment is a difficulty faced.

[0008] 3. Irregular multi-point monitoring data interpolation problem in the dynamic environment of river and lake water bodies:

[0009] Affected by uncontrollable environmental factors such as the flow velocity and wind speed of rivers and lakes, as well as the uneven speed of unmanned boats, the points of water ecological environment conditions in rivers and lakes sensed by various sensors are uneven. So for this uneven, multi-point distributed monitoring data affected by multiple environmental factors, how to form surface data from point data, and further form a multi-field scan of the hydrodynamic field, pollution field, and biological field of rivers and lakes, is the bottleneck problem currently faced by unmanned boats in monitoring the water ecological environment of rivers and lakes. Summary of the invention

[0010] The purpose of the present invention is to provide an unmanned boat monitoring system and method suitable for multi-field scanning perception of river and lake water bodies, so as to solve the above-mentioned technical problems.

[0011] To achieve the above object, the present invention provides the following technical solutions:

[0012] The present invention discloses an unmanned boat monitoring system suitable for multi-field scanning perception of river and lake water bodies, the system comprises a host computer, an unmanned boat, and a lower computer and various functional modules carried by the unmanned boat; the host computer controls the unmanned boat to cruise and collect data and water samples through the lower computer; the lower computer is used to exchange data with the host computer, accept instructions issued by the host computer to control the unmanned boat and various functional modules, and feed back various types of data perceived by various functional modules to the host computer; the various functional modules comprise a positioning module, a wireless communication module, a data storage module, a water sample collection module, an environmental monitoring module, a power drive module, an obstacle avoidance module, an early warning indication module and an electric The source module is used for providing wireless communication data transmission function; the positioning module is used for real-time positioning of the unmanned boat; the data storage module is used for real-time receiving and storing data; the water sample collection module is used for collecting water samples at different monitoring points; the environmental monitoring module is used for monitoring environmental factor data at different water depths; the power drive module is used for providing power for the operation of the unmanned boat; the obstacle avoidance module is used for monitoring whether there are obstacles in front of the unmanned boat; the early warning indication module is used for indicating the location of the unmanned boat in unfavorable light or actual needs; the power supply module is used for supplying power to the unmanned boat and other functional modules.

[0013] Furthermore, the water sample collection module includes a collection device and a base device connected to each other; the collection device includes a water pump, a water pipe and multiple sampling bottles, the water pump is connected to the water pipe, and the water pump has a built-in water stop valve; the base device includes a turntable and a rotating motor, the rotating motor is arranged in the center of the turntable for driving the turntable to rotate, and a plurality of grooves are evenly arranged on the turntable for placing and fixing each sampling bottle; the collection device extracts water samples through the water pump, and guides the water to a water outlet at a fixed position through the water pipe, and the water outlet is aligned with the bottle mouth of the sampling bottle. After each sampling by the collection device is completed, the rotating motor controls the turntable of the base device to rotate a fixed angle, and the rotation angle is set according to the number of grooves, so that after each sampling is completed, the turntable automatically rotates to the next sampling bottle to align with the water outlet.

[0014] Furthermore, the environmental monitoring module includes a hydrological and water quality monitoring device and an underwater sonar monitoring device. The hydrological and water quality monitoring device includes a connected winch lifting device and a monitor; the winch lifting device includes a motor, a winch, a pulley, a cylinder and a rope, the motor is used to control the rotation of the winch, the pulley and the cylinder are fixed to the rear of the sealed cabin, the pulley is arranged above the cylinder, one end of the rope is fixedly wound around the winch, and the other end is connected to the monitor located inside the cylinder through the pulley, the cylinder is used to ensure the vertical lifting of the monitor, the upper part of the monitor is connected to the rope, and the outer wall of the monitor is stably operated against the cylinder during lifting, the motor controls the rotation of the winch to control the retraction length of the rope and thus controls the water measuring depth, and the probe of the monitor is provided with a multi-parameter environmental factor sensing sensor for monitoring environmental factor data at different water depths; the underwater sonar monitoring device is used for water depth monitoring, underwater terrain scanning and underwater biological image shooting.

[0015] Furthermore, the environmental factor data include flow rate, water temperature, dissolved oxygen, total suspended solids, pH, turbidity, chemical oxygen demand, ammonia nitrogen, total phosphorus, algae biomass, and ionic state.

[0016] Furthermore, the host computer includes a data processing module and a terminal display module, the data processing module is used for sending, receiving and processing data, and the terminal display module is used for displaying data results.

[0017] Furthermore, the unmanned boat comprises a sealed cabin and a floating airbag wrapped around the sealed cabin, and an openable and waterproof protective cover is provided on the top of the cabin.

[0018] Furthermore, the system also includes a manual remote controller, which is used to manually control the operation path of the unmanned boat.

[0019] The present invention also discloses an unmanned boat monitoring method based on the system and applicable to multi-field scanning perception of river and lake water bodies, the method comprising the following steps:

[0020] Step 1: Place the unmanned boat on the shore of the water body to be monitored, debug the unmanned boat, the lower computer and various functional modules to ensure that the unmanned boat can operate normally;

[0021] Step 2: Turn on the host computer and lower the unmanned boat into the water body to be monitored. The host computer divides the water body grid according to the range of the water body to be monitored on the electronic map. According to the built-in unmanned boat path planning algorithm, the unmanned boat operation path is set to traverse the entire water body to be monitored. The unmanned boat cruises along the set operation path.

[0022] Step 3: Every time the unmanned boat arrives at a monitoring point, it records the monitoring point number, collects water samples at different monitoring points using the water sample collection module, monitors environmental element data at different water depths using the environmental monitoring module, and simultaneously performs underwater terrain scanning and underwater biological image shooting; the monitoring data of the monitoring point is transmitted to the upper computer in real time through the lower computer, and is also transmitted to the data storage module for storage until the unmanned boat completes the monitoring of all monitoring points of the entire water body to be monitored;

[0023] Step 4: The host computer processes the received monitoring data, realizes scanning and rendering of the hydrodynamic field, pollution field and biological field of the water body to be monitored through the built-in interpolation algorithm, and displays the rendering of the hydrodynamic field, pollution field and biological field of the water body to be monitored;

[0024] Step 5: After the monitoring work is completed, the host computer controls the unmanned boat to return to the shore, recovers the unmanned boat, and shuts down the host computer.

[0025] Furthermore, the unmanned ship path planning algorithm in step 2 is a flow adaptive dynamic window planning algorithm, namely, a FADWP algorithm, which is specifically:

[0026] Assume that the unmanned boat traverses the river and lake area grid R, R contains a series of grid cells, each cell has a velocity vector v(i, j) and an obstacle mark o(i, j), the FADWP algorithm is a nonlinear function relationship expression with the river and lake water body grid, starting point, and target point as independent variables, and the path planning result and path cost function as dependent variables:

[0027] FADWP(R,S,G)={P,C} (1)

[0028] Where: R is the river and lake area grid; S is the starting point; G is the target point; P is the planning path result; C is the path cost function;

[0029] The steps to implement the algorithm are:

[0030] ① Algorithm initialization, the unmanned ship starts from the starting point S, initializes the path P = {S} and the cost C = 0;

[0031] ② Define the comprehensive cost function C, which comprehensively considers the path length of the unmanned ship, the water body grid traversal time, obstacle avoidance and flow velocity, and its expression is:

[0032] C(P) = w1 * L(P) + w2 * T(P) + w3 * O(P) + w4 * V(P) (2)

[0033] Where: C(P) is the representative function value of a certain path planning result; L(P) is the path length; T(P) is the estimated traversal time; O(P) is the obstacle avoidance cost; V(P) is the flow velocity impact cost, and its calculation formula is shown in formula (3); w1, w2, w3, w4 are weight coefficients;

[0034] V(P) takes into account the impact of flow velocity in each grid cell of river and lake water bodies on path planning, and considers the angle between the perceived flow velocity direction and the ship's travel direction. Its expression is:

[0035] V(p) = Σ [1 - cos(θ(v(i, j), d(i, j)))] * Δt (3)

[0036] Where: θ is the angle between the velocity vector v(i, j) and the ship's travel direction d(i, j); Δt is the time in unit (i, j); i, j represent the grid numbers of rivers and lakes;

[0037] ③ In each step of the unmanned ship's forward movement, the unmanned ship will evaluate the path cost function values ​​of all neighboring grid cells, and select the grid cell with the smallest cost as the next direction of travel, and continue to iterate until the unmanned ship moves to the target point G.

[0038] Furthermore, the interpolation algorithm in step 4 is the environmentally adaptive dynamic kriging interpolation algorithm, namely the EADK algorithm, which is specifically:

[0039] Assume that the set of monitoring points is P = {P1, P2, ..., P n}, monitoring point P i The attribute value is Z i , the target interpolation point is Q, the interpolation result of point Q is Z^Q, and the environmental factors include flow velocity U, wind direction W, and water temperature T;

[0040] The formula of the EADK algorithm is as follows:

[0041] Z^Q= Σ(λ i * Z i ) (4)

[0042] Where: i is the weight of the i-th monitoring point to the interpolation point Q, and the calculation formula is:

[0043] λ i = (μ * γ(P i , P j ) + δ * Ψ(P i , P j )) / (Σ(μ * γ(P i , P j) + δ * Ψ(P i , P j ))) (5)

[0044] Where: γ(P i ,P j ) is the semivariogram function, indicating the monitoring point P i and P j The spatial correlation between them is calculated as follows:

[0045] γ(P i , P j )= (Z i -Z j )^2 / 2 + φ(U i , U j , T i , T j ) (6)

[0046] Where: Z i and Z j Indicates monitoring point P i and P j The attribute value of φ(U i ,U j ,T i ,T j ) is the environmental factor influence function, and the calculation formula is:

[0047] φ(U i , U j , T i , T j ) = αU * | U i -U j | + αT * | T i -T j | + αW * (1 -cos(θ i -θ j )) (7)

[0048] Where: αU, αT and αW are the influence coefficients of flow velocity U, wind direction W and water temperature T respectively, θ i and θ j They are monitoring points P i and P j The wind direction angle;

[0049] In formula (5), Ψ(P i ,P j ) is the dynamic weight adjustment function, and the calculation formula is:

[0050] Ψ(P i , Pj ) = exp(-β * D i,j / L) (8)

[0051] Where: D i,j is the monitoring point P i and P j The Euclidean distance between them, L is the range, indicating the maximum distance of spatial correlation, and β is the adjustment parameter;

[0052] In formula (5), μ is the Lagrange multiplier, which is used to ensure the unbiasedness of interpolation; δ is the dynamic adjustment coefficient, which is used to balance the influence of spatial correlation and environmental factors.

[0053] The beneficial effects of the present invention are as follows:

[0054] 1. Ability to realize automatic perception of multiple elements of river and lake water bodies: The present invention gives full play to the advantages of maneuverability and automation of unmanned boats. By carrying various functional modules, optimizing the layout structure, and using mechanical transmission devices such as winches and turntables, it can realize the three-dimensional real-time perception function of hydrological elements, water quality elements and water ecological elements of river and lake water bodies. The present invention can complete the environmental element data monitoring at different water depths, and prevent the hydrological and water quality monitoring device from being damaged by dragging, friction, collision, etc. caused by the shallow water depth when the unmanned boat is released from the shore; at the same time, it can complete the multi-point sampling of water quality and water sample storage during the unmanned boat water body monitoring.

[0055] 2. Ability to realize full-field traversal of river and lake water bodies: The present invention can realize full-field traversal perception of river and lake water bodies by unmanned boats under the interference of dynamic power fields and static obstacles, and complete efficient and accurate monitoring of water bodies.

[0056] 3. Able to realize multi-field scanning of river and lake water bodies: The present invention can realize the scientific interpolation of point data of river and lake water bodies into surface data under the disturbance of multiple factors, forming multi-field scanning of the hydrodynamic field, pollution field, and biological field of river and lake water bodies, so that users can fully understand the spatial distribution characteristics of water environment elements.

[0057] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is the principle block diagram of the unmanned ship monitoring system;

[0059] Figure 2 This is a top view of the overall structure of the unmanned ship monitoring system;

[0060] Figure 3 This is a side view of the overall structure of the unmanned ship monitoring system;

[0061] Figure 4This is a schematic diagram of the water sample collection module structure decomposition;

[0062] Figure 5 Schematic diagram of the hydrological and water quality monitoring device structure Figure 1 ;

[0063] Figure 6 Schematic diagram of the hydrological and water quality monitoring device structure Figure 2 .

[0064] In the figure: 1. Lower computer; 2. Positioning module; 3. Wireless communication module; 4. Data storage module; 5. Power module; 6. Collection device; 7. Base device; 8. Power drive module; 9. Winch lifting device; 10. Monitor; 11. Obstacle avoidance module; 12. Transmission antenna; 13. Early warning indication module; 14. Underwater sonar monitoring device; 15. Cabin; 16. Floating airbag; 20. Motor; 21. Winch; 22. Pulley; 23. Cylinder; 24. Rope; 25. Water pump; 26. Rotating motor; 27. Water stop valve; 28. Groove; 29. ​​Water pipe; 30. Sampling bottle. DETAILED DESCRIPTION

[0065] The present invention discloses an unmanned boat monitoring system suitable for multi-field scanning perception of river and lake water bodies, such as Figures 1 to 6 As shown, the system includes a host computer, an unmanned boat, and a lower computer 1 and various functional modules carried by the unmanned boat. The host computer controls the unmanned boat to cruise and collect data and water samples through the lower computer; the lower computer is used to exchange data with the host computer, accept instructions issued by the host computer to control the unmanned boat and various functional modules, and feed back various types of data perceived by each functional module to the host computer; each functional module includes a positioning module 2, a wireless communication module 3, a data storage module 4, a water sample collection module, an environmental monitoring module, a power drive module 8, an obstacle avoidance module 11, an early warning indication module 13 and a power supply module 5. The system also includes a manual remote control for manually controlling the running path of the unmanned boat; generally, in emergency scenarios, humans intervene in the running path of the unmanned boat to prevent accidents such as collisions, capsizing and other accidents.

[0066] The host computer includes a data processing module and a terminal display module. The data processing module is used to send, receive and process data, and the terminal display module is used to display data results. The host computer is generally composed of a computer or a mobile phone and a dedicated app software installed. The app software contains electronic maps and various control instructions. When in use, the host computer controls the instructions through the app software and sends the instructions to the lower computer. The lower computer sets the cruise route, cruise mode, monitoring points, water sample collection, and environmental element data monitoring of the unmanned boat; when the unmanned boat runs to the monitoring point for monitoring, the lower computer feeds the monitoring data back to the host computer, and the host computer processes the received monitoring data and displays the results on the screen. The computer data processing module uses a Core I7 or above processor or a processor with equivalent performance, and the mobile phone data processing module uses a third-generation Snapdragon 7 or above processor or a processor with equivalent performance, with a memory of 4GB or more and a hard disk of 100GB or more; the terminal display module uses an integrated or independent graphics card with a video memory of 512M or more.

[0067] The unmanned boat includes a sealed cabin 15 and a floating airbag 16 wrapped around the sealed cabin. The top of the cabin is provided with an openable and waterproof protective cover. The cabin is made of high-strength fiberglass composite material or high-strength and high-performance alloy material. The floating airbag 16 is made of reinforced polyethylene material that is resistant to corrosion, frost, oxidation, and purple. The hull airbag is divided into left and right parts and is inflated when used. The floating airbag wraps the sealed cabin to provide buoyancy and reduce damage to the hull caused by collisions, and protect the unmanned boat. It usually uses bright colors such as orange and red to facilitate rapid identification on large areas of water.

[0068] The lower computer 1 is set inside the cabin of the unmanned ship. It is responsible for data transfer and command distribution. It is used to interact with the upper computer. It is based on the ARM-Cortex-M3 core and integrates the electronic speed regulator and MPU6000 module. After receiving the command from the upper computer or the manual remote control, the lower computer decompresses and dispatches it to control the operation of the hull and monitor the water body. At the same time, the monitoring data of each module is compressed and encrypted and transmitted to the upper computer. The frequency of data transmission can be set to 10s / 20s / 30s according to the monitoring requirements.

[0069] The positioning module 2 is used to locate the monitoring position of the unmanned ship in real time and transmit the positioning data to the lower computer. The present invention adopts a GPS / BDS (Beidou) positioning module, which is integrated with a GPS positioning module and a BDS positioning module. The positioning modes of GPS and BDS can be switched, and it has the advantages of high positioning accuracy, high speed, and low energy consumption.

[0070] The wireless communication module 3 is used to provide a wireless communication data transmission function, that is, the lower computer 1 transmits data with the upper computer through the wireless communication module. The present invention adopts a GSM / GPRS wireless communication module, which can provide a stable, high-speed, and long-distance data transmission service function. The transmission antenna 12 of the wireless communication module 3 is an antenna group, which is arranged at the front middle side of the cabin top, and can ensure the effectiveness and timeliness of signal transmission and reception.

[0071] The data storage module 4 is used to receive and store various monitoring data, positioning data, etc. transmitted by the lower computer in real time. The present invention adopts a large-capacity portable data storage device and uses SD card storage technology to realize data collection and storage. It has the characteristics of simple structure, low power consumption (module power consumption ≤ 0.3W), stable performance, small size and low cost. It adopts a 32-bit / 64-bit high-performance ARM processor with fast processing speed and stable performance; the data storage type is EXCEL / TXT file, which is convenient for software batch processing; it has a data storage interface and a system control interface; it has a built-in power supply, which can work continuously for more than 24 hours under full power; it has strong anti-interference ability and is suitable for complex meteorological and hydrological conditions. The power supply of the data storage device is provided by the built-in power supply and the power module. The power module is powered first. When the power module is insufficient, the built-in power supply will power the data storage device.

[0072] The water sample collection module is used for water sample collection at different monitoring points. The water sample collection module includes a collection device 6 and a base device 7 connected to each other. The collection device 6 includes a water pump 25, a water pipe 29 and a plurality of sampling bottles 30. The water pump is connected to the water pipe, and the water pump has a water stop valve 27 to prevent backflow. The base device 7 includes a turntable and a rotating motor 26, which generally adopts a 5V DC motor. The rotating motor is arranged in the center of the turntable to drive the turntable to rotate. A plurality of grooves 28 are evenly arranged on the turntable for placing and fixing each sampling bottle 30. The collection device extracts water samples through the water pump 25, and guides water to the water outlet at a fixed position through the water pipe 29. The water outlet is aligned with the position of the sampling bottle mouth. After the collection device completes sampling each time, the rotating motor controls the turntable of the base device to rotate a fixed angle. The rotation angle is set according to the number of grooves (for example, 6 grooves rotate 60 degrees, and 8 grooves rotate 45 degrees), so that after each sampling is completed, the turntable automatically rotates to the next sampling bottle to align with the water outlet. The base device has perfect braking and precise adjustment of the rotation angle function.

[0073] The turntable is provided with X (X is an even number, preferably 6 or 8) grooves, which are filled with blank sampling bottles. By default, it is aimed at a certain sampling bottle. The default water sampling volume of the sampling bottle is V, and the water pumping flow rate is Q. When in use, the upper computer sets the sampling time for each time to V / Q. When the sampling is completed, the upper computer records the sampled amount as Y, and the motor automatically controls the rotation angle of the turntable to 360 / X degrees. Repeat the above operation at the next sampling point.

[0074] The environmental monitoring module includes a hydrological and water quality monitoring device and an underwater sonar monitoring device 14. The hydrological and water quality monitoring device is used to monitor environmental factor data at different water depths; the hydrological and water quality monitoring device includes a connected winch hoisting device 9 and a monitoring instrument 10; the winch hoisting device includes a motor 20, a winch 21, a pulley 22, a cylinder 23 and a rope 24, the motor is used to control the rotation of the winch, the pulley and the cylinder are fixed to the rear of the sealed cabin, the pulley is arranged above the cylinder, one end of the rope is fixedly wound around the winch, and the other end is pulled through the pulley The wheel is connected to the monitor inside the cylinder. The cylinder is used to ensure the vertical lifting of the monitor. The upper part of the monitor is connected to the winch. When lifting, the outer wall of the monitor is stable against the cylinder. The motor controls the rotation of the winch to control the length of the winch and then the water depth. The probe of the monitor is equipped with a multi-parameter environmental factor perception sensor to monitor environmental factor data at different water depths, including flow rate, water temperature, dissolved oxygen, total suspended solids, pH, turbidity, chemical oxygen demand, ammonia nitrogen, total phosphorus, algae biomass, ion state and other data. The monitor has a built-in power supply and can be used continuously for a long time.

[0075] The rope is usually made of nylon rope, steel cable or hemp rope. The rope is strong and thin, and the length of each circle wound on the winch will not change significantly due to multiple windings. The radius of the winch is R, and the circumference of a circle of rope is πR 2 In the initial position, the monitor probe is located at the interface between the water surface and the air; when it is necessary to measure the water quality and hydrology of the water body at an underwater depth H, the host computer inputs the measurement depth H, and the host computer automatically calculates and controls the motor shaft to rotate 180H / πR degrees. When it is stable, the data is read to complete the hydrology and water quality measurement of the water body at a depth of H.

[0076] The underwater sonar monitoring device 14 is used for water depth monitoring, underwater terrain scanning, and underwater biological image shooting, and transmits the data to the lower computer, which is fed back to the upper computer after being processed by the lower computer. The present invention adopts Lawrence 3D stereo sonar monitoring equipment, which consists of a 3D wide-angle high-definition probe and a recording and display device, and can support a maximum scanning of 100 meters, and a scanning range of 200 meters on both sides. Through sonar feedback signal processing, it has the functions of rapid target recognition and high-definition wide-angle 3D scanning, can rotate and replay the recorded stereo image, view and record the underwater landform structure from any angle, and scan and image medium and large aquatic plants and various animals at the same time, and can complete the mapping of topographic maps and the monitoring and recording of medium and large aquatic plants and animal biomass.

[0077] The power drive module 8 is used to provide power for the hull operation. The power drive module consists of two AC (DC) motors and two jet thrusters. The AC (DC) motors are connected to the lower computer, and the lower computer controls the speed of the left and right motors to control the hull obstacle avoidance and operation.

[0078] The obstacle avoidance module 11 is used to monitor whether there are obstacles in front of the unmanned boat and transmit the data to the lower computer. The obstacle avoidance module is implemented by the HC series ultrasonic ranging module, which is set at the bow of the unmanned boat to monitor in real time whether there are obstacles in front of the unmanned boat. When the distance of the obstacle is less than 3m, the unmanned boat automatically slows down and turns to avoid the obstacle, and prompts an alarm message on the upper computer.

[0079] The function of the early warning indicator module 13 is to facilitate the monitoring personnel to quickly find the location of the unmanned boat under unfavorable light conditions or actual needs. The early warning indicator module 13 is provided with a switch controller and an indicator light. In actual monitoring, the indicator light can be remotely controlled to be turned on and off according to light conditions and actual needs.

[0080] The above-mentioned unmanned boat monitoring system fully solves the problems of the incompatibility between the commonly used environmental monitoring modules and the volume of the unmanned boat cabin and the inability to automatically switch the water sample collection module. It increases the diversity of monitoring positions and helps to complete the three-dimensional monitoring of water bodies; it can realize the automatic collection of water samples at different monitoring points.

[0081] The present invention also discloses an unmanned boat monitoring method suitable for multi-field scanning perception of river and lake water bodies, the method comprising the following steps:

[0082] Step 1: Place the unmanned boat on the shore of the water body to be monitored (including rivers, lakes, reservoirs, etc.), debug the unmanned boat, lower computer and various functional modules to ensure that the unmanned boat can operate normally.

[0083] Step 2: Turn on the host computer and lower the unmanned boat into the water body to be monitored. The host computer divides the water body grid according to the range of the water body to be monitored on the electronic map. According to the built-in unmanned boat path planning algorithm, the unmanned boat operation path is set to traverse the entire water body to be monitored. The unmanned boat cruises along the set operation path.

[0084] The main factors affecting the path planning of unmanned boats in rivers and lakes are dynamic flow velocity and static obstacles. In particular, the change of dynamic flow velocity has a greater impact on the path planning of unmanned boats. The present invention fully considers the impact of the dynamic power field of rivers and lakes on the path planning of unmanned boats, and proposes an unmanned boat path planning algorithm that considers the dynamic flow velocity and static obstacles of the water body, namely, the flow-adaptive dynamic window planning (FADWP) algorithm, which is specifically:

[0085] Assume that the unmanned boat traverses the river and lake area grid R, R contains a series of grid cells, each cell has a velocity vector v(i, j) and an obstacle mark o(i, j), the FADWP algorithm is a nonlinear function relationship expression with the river and lake water body grid, starting point, and target point as independent variables, and the path planning result and path cost function as dependent variables:

[0086] FADWP(R,S,G)={P,C} (1)

[0087] Where: R is the river and lake area grid; S is the starting point; G is the target point; P is the planning path result; C is the path cost function;

[0088] The steps to implement the algorithm are:

[0089] ① Algorithm initialization, the unmanned ship starts from the starting point S, initializes the path P = {S} and the cost C = 0;

[0090] ② Define the comprehensive cost function C, which comprehensively considers the path length of the unmanned ship, the water body grid traversal time, obstacle avoidance and flow velocity, and its expression is:

[0091] C(P) = w1 * L(P) + w2 * T(P) + w3 * O(P) + w4 * V(P) (2)

[0092] Where: C(P) is the representative function value of a certain path planning result; L(P) is the path length; T(P) is the estimated traversal time; O(P) is the obstacle avoidance cost; V(P) is the flow velocity impact cost, and its calculation formula is shown in formula (3); w1, w2, w3, w4 are weight coefficients;

[0093] V(P) takes into account the impact of flow velocity in each grid cell of river and lake water bodies on path planning, and considers the angle between the perceived flow velocity direction and the ship's travel direction. Its expression is:

[0094] V(p) = Σ [1 - cos(θ(v(i, j), d(i, j)))] * Δt (3)

[0095] Where: θ is the angle between the velocity vector v(i, j) and the ship's travel direction d(i, j); Δt is the time in unit (i, j); i, j represent the grid numbers of rivers and lakes;

[0096] ③ In each step of the unmanned ship's forward movement, the unmanned ship will evaluate the path cost function values ​​of all neighboring grid cells, and select the grid cell with the smallest cost as the next direction of travel, and continue to iterate until the unmanned ship moves to the target point G.

[0097] The pseudo code to implement this algorithm is as follows:

[0098]

[0099] Compared with the conventional unmanned boat static path planning algorithm, the above algorithm takes into account the response of factors such as path length, water body grid traversal time, obstacle avoidance and flow rate in the path cost function, which is suitable for unmanned boats to plan paths in the dynamic environment of rivers and lakes. In practical applications, the weight coefficient and cost function can be adjusted according to the actual situation to adapt to different river and lake environments and unmanned boat characteristics.

[0100] Step 3: Every time the unmanned boat arrives at a monitoring point, it records the monitoring point number, collects water samples at different monitoring points using the water sample collection module, monitors environmental element data at different water depths using the environmental monitoring module, and simultaneously performs underwater terrain scanning and underwater biological image shooting; the monitoring data of the monitoring point is transmitted to the upper computer in real time through the lower computer, and is also transmitted to the data storage module for storage until the unmanned boat completes the monitoring of all monitoring points of the entire water body to be monitored;

[0101] Step 4: The host computer processes the received monitoring data, and scans and renders the hydrodynamic field, pollution field and biological field of the water body to be monitored through the built-in interpolation algorithm, obtains and displays the renderings of the hydrodynamic field, pollution field and biological field of the water body to be monitored.

[0102] The built-in interpolation algorithm is the Environmental Adaptability Dynamic Kriging interpolation (EADK) algorithm, which fully considers environmental factors such as flow velocity U, wind direction W, and water temperature T. The specific algorithm is:

[0103] Assume that the set of monitoring points is P = {P1, P2, ..., P n}, monitoring point P i The attribute value is Z i , the target interpolation point is Q, the interpolation result of point Q is Z^Q, and the environmental factors include flow velocity U, wind direction W, and water temperature T;

[0104] The formula of the EADK algorithm is as follows:

[0105] Z^Q= Σ(λ i * Z i ) (4)

[0106] Where: i is the weight of the i-th monitoring point to the interpolation point Q, and the calculation formula is:

[0107] λ i = (μ * γ(P i , P j ) + δ * Ψ(P i , P j)) / (Σ(μ * γ(P i , P j ) + δ * Ψ(P i , P j ))) (5)

[0108] Where: γ(P i ,P j ) is the semivariogram function, indicating the monitoring point P i and P j The spatial correlation between them is calculated as follows:

[0109] γ(P i , P j )= (Z i -Z j )^2 / 2 + φ(U i , U j , T i , T j ) (6)

[0110] Where: Z i and Z j Indicates monitoring point P i and P j The attribute value of φ(U i ,U j ,T i ,T j ) is the environmental factor influence function, and the calculation formula is:

[0111] φ(U i , U j , T i , T j ) = αU * | U i -U j | + αT * | T i -T j | + αW * (1 -cos(θ i -θ j )) (7)

[0112] Where: αU, αT and αW are the influence coefficients of flow velocity U, wind direction W and water temperature T respectively, θ i and θ j They are monitoring points P i and P j The wind direction angle;

[0113] In formula (5), Ψ(P i ,P j ) is the dynamic weight adjustment function, and the calculation formula is:

[0114] Ψ(P i , P j ) = exp(-β * D i,j / L) (8)

[0115] Where: D i,j is the monitoring point P i and P j The Euclidean distance between them, L is the range, indicating the maximum distance of spatial correlation, and β is the adjustment parameter;

[0116] In formula (5), μ is the Lagrange multiplier, which is used to ensure the unbiasedness of interpolation; δ is the dynamic adjustment coefficient, which is used to balance the influence of spatial correlation and environmental factors.

[0117] The EADK algorithm takes into account the influence of the dynamic flow velocity of the water body and the distance between points. Compared with traditional inverse distance interpolation, Kriging interpolation and other algorithms, this algorithm fully considers the influence of environmental factors such as distance between points, flow velocity, wind direction, water temperature, etc. on different monitoring points when assigning weights to the monitoring values ​​of different monitoring points. It is suitable for unmanned boats to scan hydrodynamic fields, pollution fields, and biological fields in the dynamic environment of river and lake water bodies, and can more accurately estimate the spatial distribution characteristics of irregular point monitoring data of river and lake water bodies.

[0118] Step 6: After the monitoring work is completed, the host computer controls the unmanned boat to return to the shore, recover the unmanned boat, and shut down the host computer. By using the unmanned boat monitoring system and following the above steps, users can master the cruising path of the unmanned boat and fully understand the spatial distribution characteristics of the water environment elements to be monitored.

[0119] Finally, it should be noted that the above is only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred arrangement scheme, a person skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. An unmanned boat monitoring system suitable for multi-field scanning and sensing of river and lake water bodies, characterized in that: The system includes a host computer, an unmanned boat, and a lower computer and various functional modules carried by the unmanned boat; the host computer controls the unmanned boat to cruise and collect data and water samples through the lower computer; the lower computer is used to exchange data with the host computer, accept instructions issued by the host computer to control the unmanned boat and various functional modules, and feed back various types of data perceived by various functional modules to the host computer; the various functional modules include a positioning module, a wireless communication module, a data storage module, a water sample collection module, an environmental monitoring module, a power drive module, an obstacle avoidance module, an early warning indication module and a power module; the wireless communication module is used to provide wireless communication data transmission function; the positioning module is used to locate the position of the unmanned boat in real time; the data storage module is used to receive and store data in real time; the water sample collection module is used to collect water samples at different monitoring points; the environmental monitoring module is used to monitor environmental element data at different water depths; the power drive module is used to provide power for the operation of the unmanned boat; the obstacle avoidance module is used to monitor whether there are obstacles in front of the unmanned boat; the early warning indication module is used to indicate the location of the unmanned boat under unfavorable light or actual needs; the power module is used to supply power to the unmanned boat and other functional modules.

2. The unmanned boat monitoring system suitable for multi-field scanning and sensing of river and lake water bodies according to claim 1 is characterized in that: The water sample collection module includes a collection device and a base device connected to each other; the collection device includes a water pump, a water pipe and multiple sampling bottles, the water pump is connected to the water pipe, and the water pump has a water stop valve; the base device includes a turntable and a rotating motor, the rotating motor is arranged in the center of the turntable to drive the turntable to rotate, and a plurality of grooves are evenly arranged on the turntable for placing and fixing each sampling bottle; the collection device extracts water samples through the water pump, and guides the water to a water outlet at a fixed position through the water pipe, and the water outlet is aligned with the bottle mouth of the sampling bottle. After the collection device completes sampling each time, the rotating motor controls the turntable of the base device to rotate a fixed angle, and the rotation angle is set according to the number of grooves, so that after each sampling is completed, the turntable automatically rotates to the next sampling bottle to align with the water outlet.

3. The unmanned boat monitoring system suitable for multi-field scanning and sensing of river and lake water bodies according to claim 1 is characterized in that: The environmental monitoring module includes a hydrological and water quality monitoring device and an underwater sonar monitoring device. The hydrological and water quality monitoring device includes a connected winch lifting device and a monitor; the winch lifting device includes a motor, a winch, a pulley, a cylinder and a rope. The motor is used to control the rotation of the winch. The pulley and the cylinder are fixed to the rear of the sealed cabin. The pulley is arranged above the cylinder. One end of the rope is fixedly wound around the winch, and the other end is connected to the monitor located inside the cylinder through the pulley. The cylinder is used to ensure the vertical lifting of the monitor. The upper part of the monitor is connected to the rope. When lifting, the outer wall of the monitor runs stably against the cylinder. The motor controls the rotation of the winch to control the retraction length of the rope and thus controls the water measurement depth. The probe of the monitor is provided with a multi-parameter environmental factor perception sensor for monitoring environmental factor data at different water depths; the underwater sonar monitoring device is used for water depth monitoring, underwater terrain scanning and underwater biological image shooting.

4. The unmanned boat monitoring system suitable for multi-field scanning and sensing of river and lake water bodies according to claim 3 is characterized in that: The environmental factor data include flow rate, water temperature, dissolved oxygen, total suspended solids, pH, turbidity, chemical oxygen demand, ammonia nitrogen, total phosphorus, algae biomass, and ionic state.

5. The unmanned boat monitoring system suitable for multi-field scanning and sensing of river and lake water bodies according to claim 1 is characterized in that: The host computer includes a data processing module and a terminal display module. The data processing module is used for sending, receiving and processing data, and the terminal display module is used for displaying data results.

6. The unmanned boat monitoring system suitable for multi-field scanning and sensing of river and lake water bodies according to claim 1 is characterized in that: The unmanned boat comprises a sealed cabin and a floating airbag wrapped around the sealed cabin, and an openable and waterproof protective cover is provided on the top of the cabin.

7. The unmanned boat monitoring system suitable for multi-field scanning and sensing of river and lake water bodies according to claim 1 is characterized in that: The system also includes a manual remote controller, which is used to manually control the running path of the unmanned boat.

8. An unmanned boat monitoring method for multi-field scanning perception of river and lake water bodies based on the system of claim 1, characterized in that: The method comprises the following steps: Step 1: Place the unmanned boat on the shore of the water body to be monitored, debug the unmanned boat, the lower computer and various functional modules to ensure that the unmanned boat can operate normally; Step 2: Turn on the host computer and lower the unmanned boat into the water body to be monitored. The host computer divides the water body grid according to the range of the water body to be monitored on the electronic map. According to the built-in unmanned boat path planning algorithm, the unmanned boat operation path is set to traverse the entire water body to be monitored. The unmanned boat cruises along the set operation path. Step 3: Every time the unmanned boat arrives at a monitoring point, it records the monitoring point number, collects water samples at different monitoring points using the water sample collection module, monitors environmental element data at different water depths using the environmental monitoring module, and simultaneously performs underwater terrain scanning and underwater biological image shooting; the monitoring data of the monitoring point is transmitted to the upper computer in real time through the lower computer, and is also transmitted to the data storage module for storage until the unmanned boat completes the monitoring of all monitoring points of the entire water body to be monitored; Step 4: The host computer processes the received monitoring data, realizes scanning and rendering of the hydrodynamic field, pollution field and biological field of the water body to be monitored through the built-in interpolation algorithm, and displays the rendering of the hydrodynamic field, pollution field and biological field of the water body to be monitored; Step 5: After the monitoring work is completed, the host computer controls the unmanned boat to return to the shore, recovers the unmanned boat, and shuts down the host computer.

9. The unmanned boat monitoring method for multi-field scanning perception of river and lake water bodies according to claim 8 is characterized in that: The unmanned ship path planning algorithm described in step 2 is the flow adaptive dynamic window planning algorithm, namely the FADWP algorithm, which is specifically: Assume that the unmanned boat traverses the river and lake area grid R, R contains a series of grid cells, each cell has a velocity vector v(i, j) and an obstacle mark o(i, j), the FADWP algorithm is a nonlinear function relationship expression with the river and lake water body grid, starting point, and target point as independent variables, and the path planning result and path cost function as dependent variables: FADWP(R,S,G)={P,C} (1) Where: R is the river and lake area grid; S is the starting point; G is the target point; P is the planning path result; C is the path cost function; The steps to implement the algorithm are: ① Algorithm initialization, the unmanned ship starts from the starting point S, initializes the path P = {S} and the cost C = 0; ② Define the comprehensive cost function C, which comprehensively considers the path length of the unmanned ship, the water body grid traversal time, obstacle avoidance and flow velocity, and its expression is: C(P)=w1*L(P)+w2*T(P)+w3*O(P)+w4*V(P) (2) Where: C(P) is the representative function value of a certain path planning result; L(P) is the path length; T(P) is the estimated traversal time; O(P) is the obstacle avoidance cost; V(P) is the flow velocity impact cost, and its calculation formula is shown in formula (3); w1, w2, w3, w4 are weight coefficients; V(P) takes into account the impact of flow velocity in each grid cell of river and lake water bodies on path planning, and considers the angle between the perceived flow velocity direction and the ship's travel direction. Its expression is: V(p)=Σ[1-cos(θ(v(i,j),d(i,j)))]*Δt (3) Where: θ is the angle between the velocity vector v(i, j) and the ship's travel direction d(i, j); Δt is the time in unit (i, j); i, j represent the grid numbers of rivers and lakes; ③ In each step of the unmanned ship's forward movement, the unmanned ship will evaluate the path cost function values ​​of all neighboring grid cells, and select the grid cell with the smallest cost as the next direction of travel, and continue to iterate until the unmanned ship moves to the target point G.

10. The unmanned boat monitoring method for multi-field scanning perception of river and lake water bodies according to claim 8 is characterized in that: The interpolation algorithm in step 4 is the environmentally adaptive dynamic kriging interpolation algorithm, namely the EADK algorithm, which is specifically: Assume that the set of monitoring points is P = {P1, P2, ..., P n }, monitoring point P i The attribute value is Z i , the target interpolation point is Q, the interpolation result of point Q is Z^Q, and the environmental factors include flow velocity U, wind direction W, and water temperature T; The formula of the EADK algorithm is as follows: Z^Q=Σ(λ i *WITH i ) (4) Where: i is the weight of the i-th monitoring point to the interpolation point Q, and the calculation formula is: l i =(μ*γ(P i ,P j )+δ*Ψ(P i ,P j )) / (Σ(μ*γ(P i ,P j )+δ*Ψ(P i ,P j ))) (5) Where: γ(P i ,P j ) is the semivariogram function, indicating the monitoring point P i and P j The spatial correlation between them is calculated as: γ(P i ,P j )=(Z i -Z j )^2 / 2+φ(U i ,U j ,T i ,T j ) (6) Where: Z i and Z j Indicates monitoring point P i and P j The attribute value of φ(U i ,U j ,T i ,T j ) is the environmental factor influence function, and the calculation formula is: φ(U i ,U j ,T i ,T j )=αU*|U i -U j |+αT*|T i -T j |+αW*(1-cos(θ i -θ j )) (7) Where: αU, αT and αW are the influence coefficients of flow velocity U, wind direction W and water temperature T respectively, θ i and θ j They are monitoring points P i and P j The wind direction angle; In formula (5), Ψ(P i ,P j ) is the dynamic weight adjustment function, and the calculation formula is: Ψ(P i ,P j )=exp(-β*D i,j / L) (8) Where: D i,j is the monitoring point P i and P j The Euclidean distance between them, L is the range, indicating the maximum distance of spatial correlation, and β is the adjustment parameter; In formula (5), μ is the Lagrange multiplier, which is used to ensure the unbiasedness of interpolation; δ is the dynamic adjustment coefficient, which is used to balance the influence of spatial correlation and environmental factors.

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