A hydrological detection method, system, product and medium

Abstract

A hydrological detection method, system, product and medium. The method includes: controlling an unmanned ship to travel along a preset route and collecting real-time velocity profiles; splicing the real-time velocity profiles in the order of collection; in the case where a discontinuous part appears in the spliced ​​velocity profile, setting a corrected driving route according to the discontinuous position and the real-time position; controlling the unmanned ship to travel along the corrected driving route; when the unmanned ship reaches the discontinuous position, collecting a corrected velocity profile of the discontinuous part of the spliced ​​velocity profile; splicing the corrected velocity profile to the discontinuous part of the spliced ​​velocity profile; when the corrected driving route is completed, controlling the unmanned ship to travel along the preset route and continuing to collect real-time velocity profiles. The implementation of the technical solution provided by the present application improves the comprehensiveness of the collected hydrological detection data.

Description

Technical Field

[0001] The present application relates to the field of measurement that is not dedicated to a specific variable, and in particular to a hydrological detection method, system, product and medium. Background Art

[0002] Under the dual influence of global climate change and human activities, river flow has shown a complex development trend. In some areas, river flow has shown increased seasonal fluctuations, long-term flow reduction, or extreme flood and drought alternation due to changes in precipitation patterns and accelerated melting of glaciers. The purpose of detecting river flow is to lay a solid data foundation for water conservancy project construction and rationally plan water resource allocation. At the same time, it provides accurate early warning for flood control and drought relief and reduces disaster losses. It can also help ecological protection and maintain the stability of river ecosystems.

[0003] At present, ADCP (Acoustic Doppler Current Profiler) is mostly used in river flow detection. It emits sound waves to the water body, uses the Doppler effect to accurately measure the speed of water flow at different depths, and constructs a velocity profile. Combined with the synchronously acquired river section information, the river flow can be calculated quickly and efficiently. ADCP is divided into cruise ADCP and fixed ADCP. Among them, the cruise ADCP can dynamically detect the flow at different locations while driving; the fixed ADCP can record flow changes at a fixed point for a long time, providing key data support for water resources management, flood prevention and disaster reduction, ecological protection and other work.

[0004] However, whether it is a fixed ADCP or a cruise ADCP, it is difficult to fully and accurately obtain the distribution of water flow velocity, and it is easy to miss some places or blind spots. Summary of the invention

[0005] The present application provides a hydrological detection method, system, product and medium for improving the comprehensiveness of collecting hydrological detection data.

[0006] In a first aspect of the present application, a hydrological detection method is provided, the method comprising:

[0007] The unmanned boat is controlled to travel along a preset route, and real-time velocity profiles are collected and stored in a preset real-time velocity profile set; the velocity profiles in the real-time velocity profile set are spliced ​​in the order of collection to obtain a spliced ​​velocity profile; when a discontinuous part appears in the spliced ​​velocity profile, the discontinuous position of the discontinuous part and the real-time position of the unmanned boat are obtained; a corrected travel route is set according to the discontinuous position and the real-time position; the corrected travel route is a travel route from the real-time position to the discontinuous position and then back to the real-time position; the unmanned boat is controlled to travel along the corrected travel route; when the unmanned boat reaches the discontinuous position, a corrected velocity profile of the discontinuous part of the spliced ​​velocity profile is collected; the corrected velocity profile is spliced ​​to the discontinuous part of the spliced ​​velocity profile; when the corrected travel route is completed, the unmanned boat is controlled to travel along the preset route, and real-time velocity profiles are continuously collected and stored in a preset real-time velocity profile set.

[0008] In the above embodiment, the unmanned boat is controlled to travel along a preset route to collect real-time velocity profiles and splice them. When the spliced ​​image is discontinuous, the relevant position is obtained to set a round-trip corrected driving route, the unmanned boat is controlled to travel along this route and collect corrected profiles at discontinuous positions, the velocity profiles at discontinuous positions are spliced ​​into the spliced ​​image, and the preset route is returned to continue collection, thereby improving the comprehensiveness of the collected hydrological detection data.

[0009] In conjunction with some embodiments of the first aspect, in some embodiments, setting a corrected driving route according to the discontinuous position and the real-time position specifically includes:

[0010] Taking the real-time position as the center, a local environment information matrix is ​​constructed by combining the river terrain data in the current river picture and the water flow velocity and direction data in the flow profile. The force condition of the unmanned boat at each element position in the local environment information matrix is ​​calculated according to the preliminary path, and the direction of the external force corresponding to each element position is determined and stored in the local environment information matrix to obtain an updated local environment information matrix. According to the updated local environment information matrix, the direction of each element position in the preliminary path is changed in combination with the angle adjustment formula to obtain a corrected driving route.

[0011] In the above embodiment, by taking the real-time position as the center and integrating data such as river terrain, water flow speed and direction, a local environmental information matrix is ​​constructed, and then the force on the unmanned boat at each element position is calculated based on the preliminary path, the matrix is ​​updated, and finally the path direction is corrected in combination with the angle adjustment formula. This allows the unmanned boat's driving route to fully consider local environmental factors, improves the rationality and scientificity of path planning, and effectively avoids navigation problems caused by complex terrain and water flow.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, after the direction of each element position in the preliminary path is changed in combination with the angle adjustment formula according to the updated local environment information matrix to obtain the corrected driving route, the method further includes:

[0013] The corrected driving route is subdivided into multiple path points in combination with the position of each element in the local environment information matrix; the angle between every three adjacent path points is calculated to obtain multiple path point angle values; and the corrected driving route is angle-smoothed in combination with the multiple path point angle values.

[0014] In the above embodiment, the driving route is subdivided and corrected into multiple path points by combining the local environmental information matrix, the angles between adjacent path points are calculated, and then the route is angle-smoothed, so that the driving path of the unmanned ship is more in line with the actual environment and more stable, thereby ensuring equipment safety, reducing navigation risks, and improving the reliability and efficiency of hydrological monitoring tasks.

[0015] In combination with some embodiments of the first aspect, in some embodiments, before controlling the unmanned ship to travel along a preset route and collecting a real-time velocity profile and storing it in a preset real-time velocity profile atlas, the method further includes:

[0016] According to the current river channel image and the fixed detectable area of ​​the unmanned boat, the current river channel image is divided into multiple acquisition grids; the driving route with the shortest distance that can traverse all grids in the current river channel image is calculated and set as the preset driving route.

[0017] In the above embodiment, by dividing the collection grids according to the current river channel picture and the fixed detectable area of ​​the unmanned boat, and then calculating the shortest driving route traversing all grids and setting it as the preset driving route, the unmanned boat can complete the detection of the entire river channel area in the most efficient path, which not only reduces the driving distance of the unmanned boat, reduces energy consumption and time costs, but also ensures the comprehensiveness of data collection of various areas of the river, thereby improving data collection efficiency.

[0018] In combination with some embodiments of the first aspect, in some embodiments, when a discontinuous portion appears in the spliced ​​velocity profile, before obtaining the discontinuous position of the discontinuous portion and the real-time position of the unmanned vessel, the method further includes:

[0019] The spliced ​​velocity profile is segmented according to the size of the acquisition grid to obtain multiple real-time segmented grids; the multi-dimensional data of all real-time segmented grids are analyzed, and if there are areas where the multi-dimensional data of adjacent real-time segmented grids are discontinuous, they are judged as discontinuous parts; the multi-dimensional data includes geometric feature data and water flow velocity data of the real-time segmented grids.

[0020] In the above embodiment, the spliced ​​velocity profile is divided into real-time segmented grids according to the acquisition grid size, and the multi-dimensional data including geometric features and water flow velocity is analyzed to determine the discontinuous part of the data, so that the abnormal area in the velocity profile can be accurately located, which helps to timely discover data anomalies caused by measurement errors, sudden changes in water flow and other factors.

[0021] In conjunction with some embodiments of the first aspect, in some embodiments, splicing the modified velocity profile to the discontinuous portion of the spliced ​​velocity profile specifically includes:

[0022] All corrected velocity data in the corrected velocity profile are stored in the corrected velocity data group according to corresponding positions; all incomplete velocity data in the discontinuous part are stored in the discontinuous velocity data group according to corresponding positions; the corrected velocity profile is spliced ​​to the discontinuous part of the spliced ​​velocity profile at the same position.

[0023] In the above embodiment, the data in the corrected flow velocity profile are stored in the corrected flow velocity data group and the discontinuous flow velocity data group according to the position, and the corrected flow velocity profile is spliced ​​to the discontinuous part of the spliced ​​flow velocity profile at the same position, so that the data of the flow velocity profile can be systematically integrated and improved.

[0024] In combination with some embodiments of the first aspect, in some embodiments, when the unmanned ship reaches the discontinuous position, after collecting the corrected velocity profile of the discontinuous part of the spliced ​​velocity profile, the method further includes:

[0025] When an obstacle is detected in the preset driving route, the obstacle position information and obstacle size of the obstacle are determined; the preset driving route is modified based on the obstacle position information and obstacle size to obtain an obstacle-free driving route; and the unmanned boat is controlled to travel along the obstacle-free driving route.

[0026] In the above embodiment, when an obstacle is detected on the preset driving route, the location and size information of the obstacle is accurately determined, and then the preset route is modified in a targeted manner based on this information, an obstacle-free driving route that can avoid obstacles is generated, and the unmanned boat is controlled to travel along this route, thereby avoiding the risk of collision caused by obstacles.

[0027] In a second aspect, an embodiment of the present application provides a hydrological detection system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the hydrological detection system to perform the method described in the first aspect and any possible implementation method of the first aspect.

[0028] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when the computer program product is run on a hydrological detection system, enables the hydrological detection system to perform the method described in the first aspect and any possible implementation method of the first aspect.

[0029] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions. When the instructions are executed on a hydrological detection system, the hydrological detection system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0030] It is understandable that the hydrological detection system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the hydrological detection method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0031] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0032] 1. This application controls the unmanned boat to travel along a preset route to collect real-time velocity profiles and splice them. When the spliced ​​image is discontinuous, the relevant position is obtained to set a round-trip correction route, the unmanned boat is controlled to travel along this route and collect corrected profiles at discontinuous positions, the velocity profiles at discontinuous positions are spliced ​​into the spliced ​​image, and the preset route is returned to continue collection, thereby improving the comprehensiveness of the collected hydrological detection data.

[0033] 2. This application constructs a local environmental information matrix centered on the real-time position and integrates data such as river terrain, water flow speed and direction. It then calculates the forces acting on the unmanned boat at each element position based on the preliminary path, updates the matrix, and finally corrects the path direction using the angle adjustment formula. This allows the unmanned boat's driving route to fully consider local environmental factors, improves the rationality and scientific nature of path planning, and effectively avoids navigation problems caused by complex terrain and water flow.

[0034] 3. When an obstacle is detected on the preset driving route, the application accurately determines the location and size information of the obstacle, and then makes targeted modifications to the preset route based on this information, generates an obstacle-free driving route that can avoid obstacles, and controls the unmanned boat to travel along this route, thereby avoiding the risk of collision caused by obstacles. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a structural diagram of an applicable system architecture of the hydrological detection method in the embodiment of the present application;

[0036] Figure 2It is a flow chart of the hydrological detection method in the embodiment of the present application;

[0037] Figure 3 is another flow chart of the hydrological detection method in the embodiment of the present application;

[0038] Figure 4 It is a schematic diagram of an exemplary hardware structure of the hydrological detection system in the embodiment of the present application. DETAILED DESCRIPTION

[0039] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0040] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0041] To facilitate understanding, the relevant terms and concepts involved in the embodiments of the present application are first introduced below.

[0042] ADCP (Acoustic Doppler Current Profiler) is an advanced instrument used to measure parameters such as water velocity profile and flow rate. It uses the acoustic Doppler effect to measure water velocity. It emits sound waves into the water. When the sound waves encounter scatterers such as suspended particles in the water, they will be reflected. Since the scatterers move with the water flow, the reflected sound waves will produce a frequency shift, namely the Doppler frequency shift. ADCP calculates the velocity of water flow at different depths by measuring this frequency shift. It does not need to be in direct contact with the water body, avoiding interference with the water flow, and can more accurately measure water flow in a natural state. It can accurately measure the velocity of water flow at different depths, with a depth resolution of generally 0.1 meters or even higher, and the velocity measurement accuracy can usually reach 1% to 2%. It can quickly and real-time obtain water velocity profile data, and can complete a measurement in a short time, which is suitable for dynamically changing water flow environment detection.

[0043] Figure 1It is a structural diagram of a system architecture to which the hydrological detection method in the embodiment of the present application can be applied.

[0044] See also Figure 1 The system architecture includes a hull 101, a navigation device 102, an ADCP 103, a communication device 104, a storage device 105 and a controller 106.

[0045] The hull 101 provides physical support and operating platform for the entire unmanned ship system. It is required to have good stability, be able to maintain balance under different water flow speeds, wind and waves, ensure the safety of equipment and the normal progress of detection tasks, and have sufficient carrying capacity to carry various detection equipment and instruments such as navigation equipment 102, ADCP103, and communication equipment 104; it has good waterproof and corrosion resistance and is suitable for long-term work in water environments. It is usually composed of hull structure, deck, cabin and other parts. The hull structure is made of suitable materials such as high-strength plastic, carbon fiber, etc. to ensure strength and lightness; the deck is used to install various equipment; the cabin is used to place internal equipment such as batteries and controllers to protect and seal.

[0046] The navigation device 102 can determine the position, heading, attitude and other information of the unmanned ship, and provide navigation support for the unmanned ship to travel along a preset route or a corrected route. It has high-precision positioning capabilities, and can obtain the position coordinates of the unmanned ship in the river in real time and accurately; it has a fast response speed and good stability to ensure that reliable navigation data can be continuously provided even in complex environments. It is generally composed of a global positioning system (GPS) module, an inertial measurement unit (IMU), a compass, etc. The GPS module determines the position information by receiving satellite signals; the IMU uses an accelerometer and a gyroscope to measure the acceleration and angular velocity of the unmanned ship to obtain attitude information; the compass is used to determine the heading.

[0047] ADCP103 is used to collect real-time velocity profiles of water bodies and provide key water flow velocity and direction data for hydrological detection. It has high-resolution and high-precision velocity measurement capabilities, and can accurately measure water flow velocities at different depths and locations; it has good adaptability and can work normally under different water qualities and different water flow conditions; the data output must be stable and reliable to facilitate subsequent data processing and analysis. It mainly includes transducers, electronic control units, data storage units, etc. The transducer transmits and receives sound wave signals, and calculates the water flow velocity by measuring the Doppler frequency shift of the sound wave; the electronic control unit is responsible for controlling the measurement process and processing data; the data storage unit is used to temporarily store the measurement data for data transmission with the storage device of the unmanned ship.

[0048] The communication device 104 is used to enable data transmission and command interaction between the unmanned ship and the base station or control center on the shore. It usually includes a wireless communication module, such as a 4G / 5G module, a Wi-Fi module, a Bluetooth module, etc., and a corresponding antenna. The appropriate communication module is selected according to the actual application scenario and communication requirements, and the antenna is used to enhance the transmission and reception of signals.

[0049] The storage device 105 stores various data collected by the unmanned ship during the detection process, such as real-time velocity profiles, river channel images, navigation data, etc., for subsequent processing, analysis and query. It has a large-capacity storage capacity to meet the storage needs of long-term and large amounts of data; it has a fast data reading and writing speed to ensure that data can be stored and read in time without affecting the normal progress of the detection task; data storage must be safe and reliable to prevent data loss or damage. It is generally composed of storage media such as hard disks, solid-state drives (SSDs) or flash memory. Appropriate storage devices can be selected according to the data volume and reading and writing speed requirements. It may also include a data storage management module for classifying, organizing and storing data.

[0050] The controller 106 is the core control unit, responsible for coordinating and controlling the operation of each device to realize the autonomous driving and task execution of the unmanned ship. It has powerful computing and data processing capabilities, can process various information such as navigation data and sensor data in real time, and generate control signals according to preset algorithms and instructions; it has rich interfaces, can connect and communicate with the power system, navigation equipment, ADCP, communication equipment, storage equipment, etc. of the hull, and realize precise control of each device; it has high reliability and stability, and can operate stably for a long time under complex environmental conditions. It is usually based on a microcontroller (MCU) or an embedded computer, equipped with various interface circuits, communication buses, clock circuits, etc. The microcontroller or embedded computer is responsible for running the control software and algorithms, the interface circuit is used to connect external devices, the communication bus realizes data transmission between components, and the clock circuit provides a clock signal to ensure the synchronous operation of the system.

[0051] In the related art, fixed ADCP or cruise ADCP is generally used to detect river flow. Among them, the fixed ADCP cannot be moved. For large rivers or rivers with more complex flows, multiple fixed ADCPs need to be used, which is not flexible enough, and when using multiple fixed ADCPs, it is still easy to have blind spots; for cruise ADCP, although it can be moved, it is easy to miss some places during the detection process, making it difficult to fully detect river flow.

[0052] By adopting the hydrological detection method in the embodiment of the present application, the unmanned boat is controlled to travel along a preset route, and real-time velocity profiles are collected and spliced. When discontinuous parts appear in the spliced ​​velocity profiles, relevant incomplete position information is obtained, and a corrected driving route is set. The unmanned boat is allowed to travel along this route, and the corrected velocity profiles are collected at discontinuous positions, and finally spliced ​​to the discontinuous parts. This makes it possible to effectively deal with the problem that some places may be missed or blind spots may appear during the unmanned boat hydrological detection process, thereby improving the comprehensiveness of the collected hydrological detection data.

[0053] Figure 2 It is a flow chart of the hydrological detection method in the embodiment of the present application, comprising the following steps:

[0054] S201, controlling the unmanned boat to travel along a preset route, and collecting real-time velocity profiles, and storing them in a preset real-time velocity profile atlas;

[0055] The controller controls the power system and navigation equipment according to the planning instructions of the preset route, and guides the unmanned boat to travel along the preset route in the river. At the same time, the ADCP installed on the unmanned boat continuously emits sound waves to the water body, uses the Doppler effect to measure the water velocity at different depths, and generates real-time velocity profiles. After data processing, these profiles are transmitted to the storage device through the communication link and stored in the preset real-time velocity profile set.

[0056] S202, splicing the velocity profiles in the real-time velocity profile set according to the acquisition order to obtain a spliced ​​velocity profile;

[0057] From the preset real-time velocity profile set, the velocity profiles are called out in the order in which they were collected. Since each real-time velocity profile contains water flow information at a specific location and time, when splicing, the adjacent velocity profiles are reasonably connected in space with reference to the spatial location and time sequence to ensure the continuity of the water flow data in location and time.

[0058] Based on the spatial position, we must first ensure that all velocity profiles are based on the same coordinate system, such as establishing a plane rectangular coordinate system with a fixed point in the river as the origin. When collecting data, the unmanned ship will record the starting coordinates of each profile and other information to determine its position in the overall coordinate system. Analyze some characteristic points in the velocity profile, such as the center of the vortex in the water flow, the point of velocity mutation, etc., and determine their corresponding relationship in space by comparing these characteristic points in adjacent profiles, and then judge the relative position and angle between the profiles to achieve preliminary spatial docking.

[0059] Based on the time sequence, the unmanned ship will record the precise timestamp when collecting each velocity profile, and arrange the profiles in chronological order to ensure that they are in line with the actual collection order when splicing. The time interval between adjacent profiles is calculated. If the time interval is too large or too small, it may mean that there is a data anomaly or a collection frequency problem.

[0060] S203, determining whether there is a discontinuous part in the spliced ​​velocity profile;

[0061] If yes, execute the following step S204;

[0062] If not, execute the above step S201;

[0063] Perform a comprehensive analysis on the spliced ​​velocity profile, and determine whether there are discontinuous parts by comparing the consistency of water flow data in adjacent areas. For example, check whether the changes in key parameters such as velocity and flow direction on the profile are smooth and continuous. If a sudden change in velocity, an unreasonable turn in flow direction, or a shape of the spliced ​​part is found to be different from the shape in the known river channel image, it is determined that there is a discontinuous part, and step S204 is executed to supplement the collection of the velocity profile of the discontinuous part; if there is no discontinuous part in the spliced ​​velocity profile, return to step S201, continue to drive according to the preset route and collect real-time velocity profiles.

[0064] S204, obtaining the discontinuous position of the discontinuous part and the real-time position of the unmanned ship;

[0065] After determining that there is a discontinuous part in the spliced ​​velocity profile, first, according to the pre-set coordinate system, the acquisition coordinates of the two adjacent velocity profiles before and after the discontinuous part are determined as the discontinuous front coordinate and discontinuous rear coordinate; then, according to the acquisition rules of the velocity profile, such as the acquisition interval distance, direction and other information, the theoretical coordinate difference between the two adjacent profiles is calculated. Assume that the acquisition direction of each profile is fixed and the acquisition interval is a uniform unit length. Next, analyze the spatial relationship between the discontinuous front coordinate and the discontinuous rear coordinate, and find the position of the coordinate mutation by comparing the theoretical coordinate difference with the actual coordinate difference. Finally, combine the overall coordinate framework of the velocity profile and the analysis of the mutation position to obtain the specific coordinate position of the discontinuous part in the overall velocity profile, so as to obtain the discontinuous position. At the same time, with the help of the navigation equipment on the unmanned ship, such as the GPS module and the inertial measurement unit (IMU), the longitude and latitude, heading and other information of the unmanned ship in the river channel are obtained in real time, so as to accurately determine the real-time position of the unmanned ship.

[0066] S205, setting a corrected driving route according to the discontinuous position and the real-time position;

[0067] Specifically, the corrected driving route is a driving route from the real-time position to the discontinuous position and then back to the real-time position.

[0068] Based on the acquired real-time position of the unmanned ship and the position information of the discontinuous parts in the spliced ​​velocity profile, a specific path planning method is used to set the corrected driving route.

[0069] Taking the current location of the unmanned boat as the starting point and the location of the discontinuous part as the target point, a path is planned from the real-time location to the discontinuous location. Considering that the real-time location needs to be returned later, the path from the target point back to the starting point is reversely planned. The combination of the two forms a complete corrected driving route to ensure that the unmanned boat can smoothly travel back and forth between the two locations.

[0070] S206, controlling the unmanned boat to travel along the corrected travel route;

[0071] The control system of the unmanned boat receives the corrected route information, which includes a series of position coordinates and steering instructions. The navigation system locates the position of the unmanned boat in real time and compares it with the target position in the route. Based on the deviation between the two, the control system adjusts the power system, such as controlling the motor speed and the steering angle of the servo, to make the unmanned boat move toward the corrected route. During the driving process, the above positioning, comparison, and adjustment processes are repeated continuously to ensure that the unmanned boat accurately follows the corrected route.

[0072] S207, when the unmanned ship reaches the discontinuous position, collecting a corrected velocity profile of the discontinuous portion of the spliced ​​velocity profile;

[0073] When the unmanned boat relies on the navigation system and control system to accurately locate the position corresponding to the discontinuous part in the spliced ​​velocity profile, the ADCP equipment on the unmanned boat starts the measurement. ADCP emits sound waves to the surrounding water according to the established measurement mode. The sound waves propagate in the water and return after being scattered by suspended particles and other substances in the water. ADCP analyzes the Doppler frequency shift of the received reflected sound waves, accurately calculates the water velocity at different depths, generates a corrected velocity profile, and comprehensively records the water flow information in the area.

[0074] S208, splicing the corrected velocity profile to the discontinuous portion of the spliced ​​velocity profile;

[0075] When the modified driving route is completed, the process returns to the above step S201.

[0076] After obtaining the corrected velocity profile, the first step is to determine the splicing position. According to the boundary information of the discontinuous part recorded previously, find the starting and ending positions of the discontinuous part in the spliced ​​velocity profile, and embed the corrected velocity profile into the discontinuous part of the spliced ​​velocity profile.

[0077] When the unmanned ship completes the corrected route from the real-time position to the discontinuous position and then returns to the real-time position, the control system of the unmanned ship switches back to the control instructions of the preset route. At this time, the navigation system guides the unmanned ship to sail according to the coordinate information of the preset route, and the power system adjusts the navigation status according to the navigation feedback. At the same time, the ADCP on the ship remains in working condition and collects real-time velocity profiles at a set frequency. These newly collected data are stored in the preset real-time velocity profile atlas through the data transmission system.

[0078] In the above embodiment, the unmanned boat is controlled to travel along a preset route to collect real-time velocity profiles and splice them. When the spliced ​​image is discontinuous, the relevant positions are obtained to set a round-trip corrected driving route, the unmanned boat is controlled to travel along this route and collect corrected profiles at discontinuous positions, the velocity profiles at discontinuous positions are spliced ​​into the spliced ​​image, and the preset route is returned to continue collecting, thereby improving the comprehensiveness and completeness of the collected hydrological detection data.

[0079] In some other embodiments of the present application, when the unmanned ship is traveling along a preset route, an obstacle may be detected ahead of the route, in which case the route needs to be adjusted. In some embodiments of the present application, after the obstacle is detected, the route can be optimized by analyzing the location and size of the obstacle.

[0080] like Figure 3 FIG. 1 is another flow chart of the hydrological detection method provided in the embodiment of the present application, which can be used for Figure 1 The system architecture shown includes the following steps:

[0081] S301, dividing the current river channel image into a plurality of acquisition grids according to the current river channel image and the fixed detectable area of ​​the unmanned boat;

[0082] First, determine the fixed detectable area of ​​the unmanned boat. Then, analyze the current river channel image and determine the appropriate segmentation method based on the image size and the size and shape of the detectable area of ​​the unmanned boat. By calculating the number of detectable areas that the river channel image can accommodate in the length and width directions, the river channel image can be accurately segmented into multiple acquisition grids of relatively uniform size and shape.

[0083] For example, if the detectable area of ​​the unmanned ship is a square, the river channel image is divided into square grids.

[0084] S302, calculating a driving route that can traverse all grids in the current river channel image and has the shortest distance, and setting it as a preset driving route;

[0085] On the basis of dividing the river channel image into multiple collection grids, a path planning algorithm, such as the related algorithm of the traveling salesman problem or the path planning or optimization algorithm such as the genetic algorithm, is used to calculate the shortest driving route. After considering the position coordinates of each grid, different traversal orders are tried to calculate the length of various possible paths starting from one grid, passing through all grids and finally returning to the starting point. By comparing the lengths of these paths, the one with the shortest distance is selected and set as the preset driving route of the unmanned boat to ensure that the unmanned boat can complete the traversal of all grids in the optimal path.

[0086] S303, controlling the unmanned boat to travel along a preset route, and collecting real-time velocity profiles, and storing them in a preset real-time velocity profile atlas;

[0087] S304, when an obstacle is detected in the preset driving route, determining the obstacle position information and obstacle size of the obstacle;

[0088] The various sensors carried by the unmanned boat are in a continuous working state, continuously collecting data information about the surrounding environment. When the data collected by the sensor deviates from the data pattern in the normal state, the system determines that there may be an obstacle. At this time, the relevant information of the obstacle is determined based on the unique properties of different sensor data.

[0089] In some embodiments of the present application, radar is used to detect and determine obstacle information; in other embodiments of the present application, sonar, camera, etc. may also be used to detect and determine obstacle information, which is not limited here.

[0090] Take radar as an example. Radar works by emitting high-frequency electromagnetic waves and receiving reflected waves. When abnormal data is detected, that is, the reflected wave has signal characteristics different from the normal situation, the distance between the obstacle and the unmanned ship can be calculated based on the time it takes for the reflected wave to return. Because the propagation speed of electromagnetic waves is known, the distance is equal to the speed multiplied by half of the propagation time. At the same time, based on the intensity and Doppler shift of the reflected wave and other characteristics, the approximate size and shape of the obstacle and whether it is in motion can also be inferred. For example, the reflected wave intensity of a larger obstacle is relatively high, and a moving obstacle will cause the reflected wave to produce a Doppler shift. By analyzing the frequency shift, its movement speed and direction can be determined.

[0091] The working principle of sonar is to transmit sound waves and receive echoes. When the sonar data is abnormal, such as the echo time and intensity are inconsistent with the normal situation, it can be used to judge the obstacle. By measuring the time interval between the emission and reception of the sound wave, combined with the propagation speed of the sound wave in the water, the distance of the obstacle can be determined. Obstacles of different materials and shapes have different reflection and scattering characteristics for sound waves. By analyzing the spectral characteristics of the echo, the material, shape and other information of the obstacle can be identified. For example, the echo spectrum produced by hard and smooth objects and soft and porous objects is significantly different, based on which the types of obstacles can be distinguished.

[0092] The camera uses image recognition technology. When the pixel distribution, color pattern, etc. in the image are abnormal, it indicates that there may be obstacles. With the help of computer vision algorithms, the images taken by the camera are processed and analyzed to identify the shape, outline and other features of the objects in the image, thereby determining the appearance information of the obstacles. For example, by training a large number of images containing obstacles through deep learning algorithms, the system can accurately identify different types of obstacles and estimate the actual size and distance of the obstacles based on the image size of the obstacles in the image and known shooting parameters (such as focal length, shooting angle, etc.).

[0093] S305, adjusting the preset driving route based on the obstacle location information and obstacle size;

[0094] After the location information and size of the obstacle are determined, a safe avoidance range is defined with the obstacle as the center. The intersection of the preset driving route and the range is analyzed, and the part of the route that conflicts with the obstacle is extracted. According to the maneuverability of the unmanned boat and the surrounding water conditions, the path planning algorithm is used to replan the path in the surrounding area within the safe avoidance range. For example, if the obstacle is on the left side of the route, priority is given to planning an arc or broken line to bypass the obstacle in the open area on the right to ensure that the new path maintains a safe distance from the obstacle, and finally forms an adjusted preset driving route.

[0095] S306, splicing the velocity profiles in the real-time velocity profile set according to the acquisition order to obtain a spliced ​​velocity profile;

[0096] S307, segmenting the spliced ​​velocity profile according to the size of the acquisition grid to obtain multiple real-time segmented grids;

[0097] It is known that the acquisition grids were previously divided according to the fixed detectable area of ​​the unmanned boat and the river channel pictures. After the spliced ​​velocity profile is obtained, the size of these acquisition grids is used as the standard for segmentation. Starting from the starting position of the spliced ​​image, the spliced ​​velocity profile is divided in sequence according to the length and width of the acquisition grid. It is like using a fixed-size "grid template" to intercept one by one in sequence on the spliced ​​image, so as to obtain multiple real-time segmentation grids corresponding to the acquisition grids, and each real-time segmentation grid contains the velocity profile data of the corresponding area.

[0098] S308, analyzing the multi-dimensional data of all real-time segmented grids to determine whether there are discontinuous parts in the spliced ​​velocity profile;

[0099] If yes, execute the following step S309;

[0100] If not, execute the above step S301;

[0101] Specifically, the discontinuous part is an area where the multi-dimensional data of adjacent real-time segmented grids is discontinuous; the multi-dimensional data includes geometric feature data and water flow velocity data of the real-time segmented grid. For each real-time segmented grid, extract its geometric feature data (such as shape, area, etc.) and water flow velocity data. Compare the adjacent real-time segmented grids in turn to check whether these multi-dimensional data are continuous. For example, check whether the water flow velocity of the adjacent grids suddenly changes too much, or whether the geometric features have unreasonable jumps. Once it is found that the multi-dimensional data of the adjacent real-time segmented grids are discontinuous, it is determined that there are discontinuous parts in the spliced ​​velocity profile, and then step S309 is executed to supplement the collection of the velocity profile of the discontinuous part; if the data of all adjacent grids are continuous, it is determined that there is no discontinuous part, and return to step S301 to continue driving according to the preset driving route and collect the velocity profile in real time.

[0102] S309, obtaining the discontinuous position of the discontinuous part and the real-time position of the unmanned ship;

[0103] S310, taking the real-time position as the center, combining the river topography data in the current river picture and the water flow velocity and direction data in the flow profile, constructing a local environment information matrix;

[0104] Specifically, the local environment information matrix includes local river terrain data, local water flow velocity data and local water flow direction data between the real-time position and the discontinuous position. Each element in the local environment information matrix corresponds to a position area, and stores regional river terrain data, regional water flow velocity data and regional water flow direction data related to the corresponding position area.

[0105] First, determine the real-time position of the unmanned boat and use it as the center point. Extract the river terrain data within a certain range around the real-time position from the current river picture, and obtain the water flow speed and direction data within the range from the flow profile. Based on these data, divide the area between the real-time position and the discontinuous position into multiple position areas. For each position area, integrate the corresponding river terrain, water flow speed and direction data, and fill them into the corresponding elements of a matrix, thereby constructing a local environmental information matrix containing local river terrain, water flow speed and direction data.

[0106] The local environment information matrix is ​​a data structure used to store and organize environmental data of a specific area. The real-time position of the unmanned ship is taken as the center, which means that this position is used as the reference point to determine the scope covered by the matrix.

[0107] The matrix covers the area from the real-time position to the discontinuous position, and its purpose is to provide detailed environmental information for the unmanned ship in this path planning.

[0108] Each element in the matrix corresponds to a location region, which is a subdivision of the above-mentioned specific range space. Each element stores various data related to the corresponding location region.

[0109] Regional river terrain data, such as water depth, riverbed slope, whether there are reefs and other terrain features, these data come from the analysis of current river channel pictures. The terrain has a significant impact on water flow and unmanned ship navigation. For example, shallow water areas may limit ship speed, and reefs may cause collision risks; regional water flow velocity data is obtained from the velocity profile, which represents the speed of the water flow in the location area. The water flow speed affects the navigation speed and energy consumption of the unmanned ship. Fast water flow may cause the ship to deviate from the scheduled route; regional water flow direction data is derived from the velocity profile to clarify the direction of the water flow in the location area. Understanding the direction of the water flow helps the unmanned ship adjust its course and ensure smooth sailing to discontinuous locations.

[0110] Through this matrix form, complex local environmental information can be integrated in an orderly manner. Based on various data in the matrix elements, the terrain, water flow speed and direction and other factors can be comprehensively considered to plan the optimal or better driving path, helping the unmanned ship to efficiently navigate to the target location.

[0111] S311, calculating the force condition of the unmanned ship at each element position of the local environment information matrix according to the preliminary path, determining the direction of the external force corresponding to each element position, storing it in the local environment information matrix, and obtaining an updated local environment information matrix;

[0112] First, a preliminary path is set. The preliminary path is a straight line passing through the real-time position and the discontinuous position, from the real-time position to the discontinuous position and then back to the real-time position. The unmanned ship is assumed to travel along this preliminary path in the area covered by the local environmental information matrix. For each element position in the matrix, that is, each specific location area, the force exerted on the unmanned ship here is calculated based on the water flow speed, direction, river topography and other data in the area, combined with the principles of physical mechanics. For example, the thrust or resistance generated by the water flow, the additional force that may be caused by terrain changes, etc. Through these calculations, the direction of the external force exerted on the unmanned ship at this position is determined, and then the external force direction information is stored in the corresponding matrix element, thereby completing the update of the local environmental information matrix, and obtaining an updated local environmental information matrix that more comprehensively reflects the force conditions of the unmanned ship.

[0113] S312, according to the updated local environment information matrix, the direction of each element position in the preliminary path is changed in combination with the angle adjustment formula to obtain a corrected driving route;

[0114] After obtaining the updated local environment information matrix, each element position in the matrix contains the external force direction information of the unmanned ship at this location. According to these external force directions, combined with the angle adjustment formula, the direction of each element position corresponding to the preliminary path is corrected.

[0115] For example, if the direction of the external force at a certain position is biased to the left, the angle to turn right is calculated according to the angle adjustment formula, thereby changing the direction of the preliminary path at that position. This operation is performed on each element position in turn, gradually optimizing the entire preliminary path, and finally obtaining a corrected driving route that fully considers the influence of external forces at each position.

[0116] S313, combining the position of each element in the local environment information matrix, subdividing the corrected driving route into a plurality of path points;

[0117] Each element in the local environment information matrix corresponds to a specific location area. With these element positions as reference, the corrected driving route is divided according to certain rules. For example, according to the distance between adjacent element positions, terrain changes or differences in water flow conditions, appropriate points on the corrected driving route are selected as path points. These path points divide the corrected driving route into multiple small segments. Each path point is associated with the local environment information of the corresponding element position in the matrix, such as the water flow speed, direction and river terrain data at that location, thereby completing the subdivision of the corrected driving route.

[0118] S314, calculating the angle between every three adjacent path points to obtain multiple path point angle values;

[0119] On the basis of subdividing the corrected driving route into multiple path points, three adjacent path points are selected in sequence, and these path points are regarded as coordinate points on a plane, and the angles between them are calculated.

[0120] In some embodiments of the present application, the angle between path points may be calculated through vector operations; in other embodiments of the present application, the angle between path points may also be calculated with the aid of a graphics processing library based on a programming language, which is not limited here.

[0121] For example, through vector operations, first determine the vectors from the first path point to the second path point and from the second path point to the third path point, and then calculate the angle between the two vectors according to the vector angle formula. This angle is the angle between the three adjacent path points. This cycle is repeated to calculate all three adjacent path points, thereby obtaining multiple path point angle values.

[0122] S315, performing angle smoothing processing on the corrected driving route in combination with angle values ​​of multiple path points;

[0123] Get the angle values ​​of multiple path points calculated. These angle values ​​reflect the direction changes at different positions on the corrected driving route. Set a standard for angle smoothing, which is the maximum threshold for angle change. For adjacent path point areas where the angle value changes exceed the threshold, use curve fitting or interpolation algorithms to make adjustments. For example, by inserting new path points between these path points and recalculating the angle to make the angle change smoother, the angle of the corrected driving route can be smoothed, making the route more in line with the needs of the unmanned ship to travel smoothly.

[0124] S316, controlling the unmanned ship to travel along the corrected travel route;

[0125] S317, when the unmanned ship reaches the discontinuous position, collecting a corrected velocity profile of the discontinuous portion of the spliced ​​velocity profile;

[0126] S318, storing all the corrected flow velocity data in the corrected flow velocity profile in a corrected flow velocity data group according to corresponding positions;

[0127] After obtaining the corrected velocity profile, the corrected velocity data at each position in the profile is analyzed. For each velocity data, its corresponding precise position information in the profile is determined. Then, based on this position information, each corrected velocity data is stored in the corresponding position in the specially set corrected velocity data group.

[0128] For example, if the data corresponds to a specific coordinate position in the profile, the storage unit corresponding to the coordinate is found in the data group and the flow rate data is stored therein to ensure the accuracy and orderliness of data storage.

[0129] S319, storing all incomplete flow rate data of the discontinuous part in the discontinuous flow rate data group according to corresponding positions;

[0130] After identifying the discontinuous part in the spliced ​​velocity profile, the velocity data of the part is sorted out. For each incomplete velocity data, its specific position corresponding to the discontinuous part is clarified, which may involve information such as spatial coordinates or relative position. Then, based on this position information, each incomplete velocity data is sequentially stored in the corresponding position in the pre-set discontinuous velocity data group.

[0131] For example, if an incomplete flow velocity data corresponds to a specific coordinate region of a discontinuous part, a storage location corresponding to the coordinate region is found in the discontinuous flow velocity data group and the data is stored therein.

[0132] S320, splicing the corrected velocity profile to the discontinuous part of the spliced ​​velocity profile at the same position;

[0133] When the modified driving route is completed, the process returns to the above step S301.

[0134] First, the specific position range of the discontinuous part in the spliced ​​velocity profile is clarified, and the position information corresponding to each data in the modified velocity profile is determined. Then, according to the corresponding relationship of the spatial position, the data matching the position of the discontinuous part in the modified velocity profile is embedded into the discontinuous part of the spliced ​​velocity profile point by point or region by region. When the modified driving route is completed, the above step S301 is returned to be executed.

[0135] Steps S303, S306, S309, S316, S317, S320 and Figure 2 In the illustrated embodiment, steps S202, S204, S206-S208 are similar, and the descriptions of steps S202, S204, S206-S208 may be referred to, and will not be repeated here.

[0136] In the above embodiment, the unmanned boat is controlled to travel along the preset route to collect real-time velocity profiles, and after splicing the collected velocity profiles, a correction route is set for the discontinuous part and travels along it, and the corrected velocity profiles are collected for splicing, and the river channel image is segmented to determine the preset route before driving, and the environment, path smoothness, obstacles, etc. are also considered in the process. This enables hydrological detection to obtain velocity data more comprehensively and accurately, effectively respond to complex environments and emergencies, and improve data integrity and reliability.

[0137] The following introduces an exemplary hydrological detection system 400 provided in an embodiment of the present application. Figure 4 It is a schematic diagram of an exemplary hardware structure of the hydrological detection system 400 provided in an embodiment of the present application.

[0138] In some embodiments, the hydrological detection system 400 includes a computer device. The computer device includes a processor, a memory and a network interface connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with other external terminals or servers through a network connection. In some embodiments, the network interface can be a wired network interface, and in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, the method in the embodiment of the present application is implemented.

[0139] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0140] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0141] As used in the above embodiments, the term "when..." may be interpreted as "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted as "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0142] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0143] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A hydrological detection method, characterized in that: The following steps are involved: Control the unmanned boat to travel along a preset route, collect real-time velocity profiles, and store them in a preset real-time velocity profile atlas; splicing the velocity profiles in the real-time velocity profile set in the order of collection to obtain a spliced ​​velocity profile; When a discontinuous portion appears in the spliced ​​velocity profile, obtaining a discontinuous position of the discontinuous portion and a real-time position of the unmanned vessel; Setting a corrected driving route according to the discontinuous position and the real-time position; The modified driving route is a driving route from the real-time position to the discontinuous position and then back to the real-time position; Controlling the unmanned boat to travel along the corrected travel route; When the unmanned ship reaches the discontinuous position, collecting a corrected velocity profile of the discontinuous portion of the spliced ​​velocity profile; splicing the modified velocity profile to the discontinuous portion of the spliced ​​velocity profile; When the modified driving route is completed, the unmanned boat is controlled to travel according to the preset driving route, and the real-time flow velocity profile is continuously collected and stored in the real-time flow velocity profile atlas.

2. The method according to claim 1, characterized in that The step of setting a corrected driving route according to the discontinuous position and the real-time position specifically includes: Taking the real-time position as the center, combining the river channel topography data in the current river channel picture and the water flow velocity and direction data in the flow velocity profile, a local environment information matrix is ​​constructed; the local environment information matrix includes the local river channel topography data, local water flow velocity data and local water flow direction data between the real-time position and the discontinuous position, each element in the local environment information matrix corresponds to a position area, and stores the regional river channel topography data, regional water flow velocity data and regional water flow direction data related to the corresponding position area; Calculate the force of the unmanned ship at each element position of the local environment information matrix according to the preliminary path, determine the direction of the external force corresponding to each element position, store it in the local environment information matrix, and obtain an updated local environment information matrix; According to the updated local environment information matrix, the direction of each element position in the preliminary path is changed in combination with the angle adjustment formula to obtain a corrected driving route.

3. The method according to claim 2, characterized in that After the direction of each element position in the preliminary path is changed according to the updated local environment information matrix and the angle adjustment formula is combined to obtain a corrected driving route, the method further includes: In combination with the position of each element in the local environment information matrix, subdividing the corrected driving route into a plurality of path points; Calculate the angle between every three adjacent path points to obtain multiple path point angle values; Angle smoothing is performed on the corrected driving route in combination with the angle values ​​of the multiple path points.

4. The method according to claim 1, characterized in that: Before controlling the unmanned boat to travel along the preset route and collecting the real-time velocity profile and storing it in the preset real-time velocity profile atlas, the method further includes: According to the current river channel image and the fixed detectable area of ​​the unmanned boat, the current river channel image is divided into a plurality of acquisition grids; A driving route that can traverse all grids in the current river channel image and has the shortest distance is calculated and set as the preset driving route.

5. The method according to claim 4, characterized in that In the case where a discontinuous portion appears in the spliced ​​velocity profile, before obtaining the discontinuous position of the discontinuous portion and the real-time position of the unmanned ship, the method further includes: Segmenting the spliced ​​velocity profile according to the size of the acquisition grid to obtain a plurality of real-time segmented grids; The multi-dimensional data of all the real-time segmented grids are analyzed, and if there are areas where the multi-dimensional data of adjacent real-time segmented grids are discontinuous, they are determined to be discontinuous parts; the multi-dimensional data include geometric feature data and water flow velocity data of the real-time segmented grids.

6. The method according to claim 1, characterized in that The step of splicing the modified velocity profile to the discontinuous portion of the spliced ​​velocity profile specifically includes: storing all the corrected flow velocity data in the corrected flow velocity profile in a corrected flow velocity data group according to corresponding positions; storing all incomplete flow rate data of the discontinuous part in a discontinuous flow rate data group according to corresponding positions; The modified velocity profile is spliced ​​to the discontinuous portion of the spliced ​​velocity profile at the same position.

7. The method according to claim 4, characterized in that After collecting the corrected velocity profile of the discontinuous part of the spliced ​​velocity profile when the unmanned ship reaches the discontinuous position, the method further includes: When an obstacle is detected in the preset driving route, determining the obstacle position information and obstacle size of the obstacle; Based on the obstacle location information and the obstacle size, the preset driving route is modified to obtain an obstacle-free driving route; The unmanned boat is controlled to travel along the barrier-free travel route.

8. A hydrological detection system, characterized in that: The hydrological detection system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the hydrological detection system to execute the method described in any one of claims 1-7.

9. A computer program product comprising instructions, characterized in that When the computer program product runs on a hydrological detection system, the hydrological detection system is enabled to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a hydrological detection system, the hydrological detection system is caused to execute the method as described in any one of claims 1 to 7.

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