A method and system for measuring the flow velocity of an under-ice river by bottom attachment and upward measurement

Abstract

This invention relates to a method and system for measuring the flow velocity of subglacial rivers using a bottom-feeding, upward-facing approach. The system includes an underwater robot body. The underwater robot, equipped with an ADCP (Advanced Divergence Probe) and a DVL (Diverterless Velocity Profiler), is lowered into the water through an opening. The transducer probe of the ADCP is vertically positioned upwards at the top of the underwater robot body to acquire measured flow velocity profile data of the water above. The transducer probe of the DVL is vertically positioned downwards at the bottom of the underwater robot body to measure the robot's actual distance from the riverbed in real time. The system controls the underwater robot body to navigate at a preset distance from the riverbed. Based on the flow velocity measured at a reference point by the ADCP, the flow velocity at various target heights within the measurement blind zone is calculated, and data is completed and stitched together to generate a complete vertical flow velocity profile. This invention achieves blind-zone-free, high-efficiency, and high-precision continuous acquisition of flow velocity across the entire cross-section of a frozen river. The measurement process is highly automated, significantly improving the efficiency and accuracy of flow measurement operations.

Description

Technical Field

[0001] This invention relates to the field of hydrological monitoring technology, specifically to a method and system for measuring the flow velocity of subglacial rivers by bottom-feeding and upward-facing measurements. Background Technology

[0002] In winter, rivers in northern my country and high-latitude regions often form thick ice sheets. Accurately understanding the distribution of sub-ice flow velocity is of paramount importance for ice jam prevention and disaster reduction, water resource allocation, and hydrological research in cold regions. Traditional methods for obtaining sub-ice flow velocity mainly rely on manually drilling holes in the ice surface and lowering current meters. However, this single-point, discrete approach is not only inefficient but also fails to fully reflect the continuous flow field characteristics of the river.

[0003] In recent years, with the development of underwater robot technology, using autonomous underwater vehicles (AUVs) or remotely operated vehicles (ROVs) equipped with acoustic Doppler current profilers (ADCPs) to conduct sub-ice current measurement has become a new trend. However, existing underwater robot current measurement solutions still have significant limitations in the unique enclosed environment of sub-ice: First, conventional equipment typically mounts the Acoustic Doppler Current Profiler (ADCP) at the bottom of the robot for downward measurements. In this mode, if the robot chooses to navigate close to the ice surface to obtain a complete vertical velocity profile, it is highly susceptible to collisions with ice ridges beneath the ice, posing a significant safety risk. Conversely, if it chooses to navigate close to the riverbed for safety, the downward-measuring ADCP can only detect the riverbed and cannot obtain crucial velocity data for the main body of water above. Furthermore, if the ADCP is mounted upwards for a downward-looking measurement, although velocity data from above the robot to the lower surface of the ice sheet can be obtained, the bottom water between the robot's depth and the riverbed (i.e., the area below the robot) becomes a "measurement blind spot." Existing technologies often cannot effectively handle this blind spot, and directly ignoring the data in this area will lead to significant deviations in the final cross-sectional flow calculation.

[0004] Therefore, it is necessary to study a method and system for measuring the flow velocity of subglacial rivers by bottom-feeding and upward-looking measurement. Summary of the Invention

[0005] Therefore, the purpose of this invention is to provide a method and system for measuring the flow velocity of rivers under ice by bottom-view and upward-viewing, which effectively solves the problems of existing methods for measuring the flow velocity of rivers under ice, such as the danger of underwater robots colliding with ice sheets during underwater flow measurement, and the inaccurate flow calculation caused by discontinuous profile data and bottom data blind spots due to simple downward or upward-viewing measurements.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a method for measuring the flow velocity of an ice-covered river by bottom-feeding and upward-looking measurement, comprising the following steps: Step 1: Drill holes in the ice cover at the predetermined test section of the frozen river surface, and lower the underwater robot equipped with ADCP and DVL into the water through the holes. The ADCP and DVL are respectively set on the upper and lower sides of the underwater robot. Then control the underwater robot to dive to the preset height from the riverbed. Step 2: Use DVL to measure the actual height of the underwater robot from the riverbed in real time, and control the underwater robot to lock onto the trajectory at the preset height based on the actual height, and sail from one side of the riverbank to the other at a fixed altitude; Step 3: During navigation, control the ADCP to emit a sonic beam vertically upward to acquire measured velocity profile data of the water above the top of the underwater robot and the lower surface of the ice sheet. Step 4: Based on the measured velocity profile data of the water body above, select the measurement layer that is closest to the underwater robot and whose signal quality meets the preset threshold as the reference point. According to the measured velocity, actual height from the bottom, first-layer blind zone distance of ADCP, and preset vertical velocity distribution index of the reference point, calculate the velocity at each target height in the measurement blind zone below the underwater robot to complete the data of the measurement blind zone. Step 5: The measured velocity profile data of the upper water body and the calculated velocity data of the measurement blind zone are spliced ​​together according to the depth sequence to generate a complete vertical velocity profile from the riverbed to the lower surface of the ice sheet.

[0007] Further, in step 4, the process of calculating the flow velocity at each target height within the measurement blind zone below the underwater robot is as follows: the actual height above the bottom is added to the distance of the first layer of the ADCP blind zone to obtain the absolute height of the reference point from the riverbed; the ratio of the target height to the absolute height of the reference point is calculated, and a proportionality coefficient is constructed by combining it with the preset vertical flow velocity distribution index; the measured flow velocity at the reference point is multiplied by the proportionality coefficient to calculate the flow velocity at the target height.

[0008] Furthermore, in step 4, the vertical velocity distribution index is set according to the riverbed geological conditions: 1 / 7 when the riverbed is silt or smooth bedrock, 1 / 5 when the riverbed is gravel or rough, and 1 / 6 by default.

[0009] Furthermore, in step 3, the step of obtaining the measured flow velocity profile data of the upper water body is as follows: based on the peak characteristics of the echo intensity of different depth units received by ADCP appearing at the bottom of the ice layer, the specific location of the lower surface of the ice sheet is identified, thereby extracting the effective measured data from the top of the underwater robot to the lower surface of the ice sheet as the measured flow velocity profile data of the upper water body.

[0010] Furthermore, in step 2, during the underwater robot's altitude-fixed navigation, the underwater robot continuously scans the terrain information of the riverbed ahead. When it detects a protruding obstacle ahead, it automatically controls the underwater robot to temporarily increase its navigation altitude to avoid the obstacle, and after passing the obstacle, it automatically returns to the trajectory at the preset altitude above the bottom to continue navigation.

[0011] Furthermore, in step 4, the signal quality satisfies the preset threshold that the echo correlation coefficient of the measurement layer is higher than 0.7, and can be dynamically adjusted within the data range of 0.5 to 0.8 echo correlation coefficient and 3dB to 5dB signal-to-noise ratio according to the signal-to-noise ratio when ADCP is working.

[0012] This invention also provides a bottom-feeding, upward-looking system for measuring the flow velocity of an underwater river, comprising an underwater robot body, a measurement and sensing unit, an onboard control unit, and a communication unit for data transmission mounted on the underwater robot body. The measurement and sensing unit includes an ADCP and a DVL arranged vertically. The transducer probe of the ADCP is vertically upward-mounted at the top of the underwater robot body to acquire measured flow velocity profile data of the water above. The transducer probe of the DVL is vertically downward-mounted at the bottom of the underwater robot body to measure the actual height of the underwater robot from the riverbed in real time. The onboard control unit is connected to the measurement and sensing unit via the communication unit to control the underwater robot body to stay at a preset height above the bottom, and to calculate the flow velocity at each target height within the measurement blind zone based on the reference point flow velocity measured by the ADCP, and to perform data completion and stitching to generate a complete vertical flow velocity profile.

[0013] Furthermore, it also includes an image recognition unit, which includes a forward-viewing sonar, a camera, and an obstacle avoidance sonar. The forward-viewing sonar and camera are located at the front end of the underwater robot body and are used to detect the riverbed topography and obstacles in front. The obstacle avoidance sonar is located on the top of the underwater robot body and is used to detect ice ridge obstacles at the bottom of the ice layer above.

[0014] Furthermore, an inertial navigation unit is also sealed and integrated inside the underwater robot body. The inertial navigation unit is communicatively connected to the airborne control unit and is used to calculate the attitude data of the underwater robot body in real time during the underwater robot body's constant altitude navigation and feed it back to the airborne control unit to ensure the stability of the underwater robot body's navigation trajectory.

[0015] Furthermore, it also includes shore-based monitoring equipment and a zero-buoyancy cable connected to the tail of the underwater robot body. The communication unit includes a communication sonar and an ultra-short baseline beacon mounted on the underwater robot body. The airborne control unit uses the communication sonar in conjunction with the zero-buoyancy cable to perform real-time data interaction and underwater positioning and tracking with the shore-based monitoring equipment.

[0016] The present invention provides a method and system for measuring the flow velocity of an ice-covered river by bottom-feeding and upward-looking measurement. It utilizes a downward-firing DVL (Direct Vulnerability) sensor to measure the height above the ice surface in real time, controlling the robot to maintain stable navigation on a plane at a preset height above the riverbed. By using the riverbed as a solid navigation reference, the system ensures navigation safety and trajectory stability in extremely cold and enclosed environments. Simultaneously, with the dual detection of front-view imaging sonar and top obstacle avoidance sonar, the system can actively identify and avoid potential collision risks from both the riverbed and the ice sheet, significantly improving the safety and reliability of autonomous navigation under ice.

[0017] This invention uses measured flow velocity data above as a reference point and employs a flow velocity extrapolation algorithm based on the power law to accurately fill in the blank data in the measurement blind zone at the bottom of the underwater robot, thereby eliminating the measurement blind zone and successfully reconstructing a complete flow velocity profile across the entire water depth from the riverbed surface to the ice sheet.

[0018] To address the problems of time-consuming and labor-intensive traditional single-point, discrete measurements, and their inability to effectively reflect continuous flow field changes, this invention utilizes an underwater robot that only needs to perform a single traverse from one side of the riverbank to the other to efficiently and accurately acquire continuous flow velocity across the entire cross-section under the ice, without human intervention. The underwater robot navigates along the relatively flat riverbed at a constant altitude, using an upward-facing acoustic Doppler current profiler to obtain the flow velocity of the main water body. Simultaneously, for blind spots that cannot be directly measured on the hull and below, the physical assumption that the flow velocity distribution of the river boundary layer follows a power function is introduced, using near-hull measurement points as a benchmark for downward data extrapolation. Finally, the measured data above and the extrapolated data below are stitched together to reconstruct the complete flow field under the ice, greatly improving the quality and efficiency of the measurement operation. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the measurement method of the present invention; Figure 2 This is a schematic diagram of the implementation structure of the underwater robot of the present invention; Figure 3 This is a schematic diagram of the underwater robot measurement process of the present invention; Figure 4 This is a schematic diagram illustrating the derivation of the flow velocity in the measurement blind zone according to the present invention; Figure 5 This is a connection block diagram of the measurement system of the present invention.

[0020] Reference numerals: 101-Underwater robot body, 102-Main thruster, 103-Zero buoyancy cable, 104-Inertial navigation unit, 105-Onboard computer, 106-DVL, 107-Forward-viewing sonar, 108-Camera, 109-Obstacle avoidance sonar, 110-ADCP, 111-Depth gauge, 112-Ultra-short baseline beacon, 113-Communication sonar, 121-Ice cap, 122-Ice hole, 123-Wind. Detailed Implementation

[0021] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: Example 1: This example aims to provide a bottom-mounted, upward-looking system for measuring the flow velocity of subglacial rivers. It is mainly used in subglacial rivers in cold regions. This invention uses the riverbed as a stable reference to ensure the navigation safety of underwater robots. By combining "upward-looking measurement + algorithm deduction", it eliminates measurement blind spots and achieves high-efficiency and high-precision continuous acquisition of the flow velocity across the entire subglacial cross section.

[0022] In the specific implementation structure, such as Figures 1-4 As shown, the underwater river current velocity measurement system provided in this embodiment includes an underwater robot body 101 with a streamlined shell, and a measurement and sensing unit, an onboard control unit, an image recognition unit, an inertial navigation unit 104, a main thruster 102, and a communication unit for data transmission mounted on the underwater robot body 101. The main thruster 102 is located at the tail of the underwater robot body 101 and is used to provide propulsion for the underwater robot. In combination with the shape of the underwater robot body 101, it minimizes water flow resistance and ensures the stability of its underwater navigation. In practical applications, vertical thrusters and lateral thrusters (not shown in the figure) can also be installed on the underwater robot body to achieve multi-degree-of-freedom navigation control.

[0023] like Figure 2 As shown, in this embodiment, the measurement and sensing unit includes an ADCP (Acoustic Doppler Current Profiler) 110 and a DVL (Doppler Velocimeter) 106, which are arranged vertically. Both are installed in the middle of the underwater robot body. The ADCP is fixed to the upper part of the underwater robot body 101, and its transducer probe is set vertically upward. It is used to emit a fan-shaped acoustic beam into the ice bottom without obstruction during navigation to obtain the measured flow velocity profile data of the water body above the top of the robot and the lower surface of the ice sheet 121. The DVL is fixed to the lower part of the underwater robot body 101. The two are located on the same vertical axis, and the transducer probe of the DVL is set vertically downward. It is used to measure the actual height of the underwater robot from the riverbed and the absolute speed of the underwater robot relative to the bottom in real time.

[0024] In this embodiment, the image recognition unit includes a forward-viewing sonar 107, a camera 108, and an obstacle avoidance sonar 109. The forward-viewing sonar 107 and the camera 108 are both installed at the front end of the underwater robot body 101 along the direction of movement of the underwater robot. The forward-viewing sonar 107 is located below the camera 108 and is mainly used to continuously scan the three-dimensional terrain of the riverbed in front to detect whether there are obstacles such as protruding large rocks or steep slopes. Then, in conjunction with the airborne control unit, it drives the underwater robot to temporarily increase its height to avoid obstacles. The obstacle avoidance sonar 109 is set at the top front end of the underwater robot body 101 and is used to detect sudden ice ridge obstacles at the bottom of the ice layer above when the underwater robot is in shallow water or during the ascent process to prevent the underwater robot body 101 from colliding.

[0025] The inertial navigation unit 104 is integrated into the internal sealed compartment of the underwater robot body 101. The inertial navigation unit 104 is connected to the airborne control unit and can calculate key attitude data such as roll angle, pitch angle and yaw angle of the underwater robot body 101 in real time in a completely enclosed environment under ice where satellite signals are completely lost, and transmit the data to the airborne control unit.

[0026] The communication unit mounted on the underwater robot body 101 includes a communication sonar 113 and an ultra-short baseline beacon 112 located on the top. In this invention, a zero-buoyancy cable 103 is also fixedly connected to the tail end of the underwater robot body 101. This zero-buoyancy cable 103 contains optical fiber or multi-core shielded twisted-pair cable, exhibiting neutral buoyancy in water and thus not exerting vertical drag on the robot's constant-altitude navigation trajectory. Therefore, after the underwater robot is lowered into the water, the onboard control unit uses the zero-buoyancy cable 103 as the primary high-bandwidth communication channel to exchange data in real time with shore-based monitoring equipment. When in complex navigation sections or when the cable is temporarily jammed, the system can automatically switch to wireless underwater acoustic communication via the communication sonar. Simultaneously, the ultra-short baseline beacon 112 cooperates with the underwater acoustic positioning array of the shore-based monitoring equipment deployed on the shore to achieve real-time dynamic tracking of the underwater robot's three-dimensional coordinates under the ice.

[0027] Furthermore, the airborne control unit in this invention is an airborne computer 105 integrated in the pressure-resistant cabin inside the underwater robot body 101. The airborne computer 105 is connected to each unit via signal and is used to read the actual sea clearance height fed back by DVL in real time during the underwater robot's navigation, perform a high-speed closed-loop comparison with the preset sea clearance height, and dynamically adjust the rotation speed of the main thruster 102 to ensure that the underwater robot stays close to the bottom and navigates on the preset height plane. At the same time, when it receives a terrain obstacle alarm from the forward-view imaging sonar 107, it automatically interrupts the current altitude-keeping trajectory, manipulates the propulsion system to execute the command to increase the altitude, and after crossing the riverbed obstacle, it reguides the underwater robot to descend and lock back to the original altitude-keeping trajectory based on the DVL feedback data.

[0028] The underwater river flow velocity measurement system provided in this embodiment utilizes a stable riverbed as a navigation reference for underwater robots in a frozen river environment, ensuring safe navigation and high stability. It aligns ADCP with DVL (Depth-to-Volume Flow) and uses DVL to lock the height above the bottom for bottom-hugging, constant-altitude navigation. ADCP is used to measure upwards, and the ice sheet boundary is accurately identified based on echo intensity peaks. A flow velocity extrapolation algorithm based on height ratios is then used to supplement data for the physical laws supporting the measurement blind spots below. This achieves blind-spot-free, continuous, and high-resolution acquisition of flow velocity across the entire cross-section of frozen rivers. The measurement process is highly automated and has strong data integrity, significantly improving the efficiency and accuracy of underwater river flow measurement.

[0029] Example 2: This example provides a method for measuring subglacial river flow velocity by bottom-feeding and upward measurement. It utilizes the subglacial river flow velocity measurement system described in Example 1. The measurement method provided in this example specifically includes the following steps: Step 1: Deployment and Water Deployment like Figure 3 As shown, an ice hole 122 is drilled in the ice cap 121 at a predetermined test section on the frozen river surface to create an ice hole 122 for lowering the underwater robot. A winch 123 is then deployed on the ice surface near the bank of the river near the ice hole 122. Using a zero-buoyancy cable 103 installed at the tail of the underwater robot, the robot is slowly lowered. A depth gauge 111 mounted on the underwater robot body 101 identifies its diving depth, and an onboard computer controls the underwater robot until it reaches a preset height above the riverbed. This preset height can be flexibly set according to the actual riverbed topography and water depth to ensure that the underwater robot can stay close to the riverbed to reduce the blind spot below while maintaining sufficient navigational safety redundancy. To ensure that the blind zone below is within the dominant range of the riverbed boundary layer and thus control the power-law calculation error, preferably, the preset height h from the bottom is controlled within 5% to 15% of the local total water depth. At the same time, considering the robot's own size limitations, it is set by the on-site operator to ensure that the underwater robot can both get close to the riverbed to reduce the range of the blind zone below and maintain sufficient navigation safety redundancy.

[0030] Step 2: Bottom-hugging, altitude-maintaining navigation Once the underwater robot reaches the preset height above the bottom and stabilizes, the onboard computer 105 activates the DVL and enters the bottom-hugging, altitude-holding navigation control mode. At this time, the DVL emits sound waves downwards in real time to measure the actual height above the bottom of the underwater robot. The onboard computer compares this actual measurement (actual height above the bottom) with the target preset value (preset height above the bottom) and controls the thrusters in real time to adjust the underwater robot's ascent or descent, keeping the actual measurement value consistent with the preset height as much as possible, so that the underwater robot is always locked on the trajectory at the preset height above the bottom and sails stably from one side of the riverbank to the other.

[0031] Furthermore, during navigation, to ensure safety while skimming the bottom, the inertial navigation unit 104 integrated inside the underwater robot calculates and outputs the robot's attitude and heading data in real time, assisting the onboard control unit in maintaining the stability of the navigation trajectory. At the same time, the forward-view imaging sonar 107 and camera 108 at the front of the underwater robot body 101 continuously scan the riverbed terrain information ahead. When a protruding obstacle such as a large rock is detected ahead, the onboard control unit automatically controls the vertical thruster to temporarily increase the robot's navigation altitude to complete obstacle avoidance, and automatically returns to the trajectory at the preset height above the bottom after passing the obstacle. Simultaneously, the obstacle avoidance sonar on the underwater robot body is controlled to detect sudden ice ridge obstacles at the bottom of the ice layer above in advance when the underwater robot is in shallow water or during the ascent, preventing the underwater robot body from colliding with the lower surface of the ice sheet 121.

[0032] Step 3: Actual measurement of the flow field of the upper water body like Figure 3 and 4 As shown, during the stable navigation of the underwater robot, the ADCP on its top is activated synchronously. The ADCP emits a fan-shaped acoustic beam vertically upward. Utilizing the time propagation characteristics of sound waves in water, the entire water column from the top of the underwater robot to the water surface is divided into multiple continuous virtual depth units at equal intervals according to the set layer thickness. Then, the ADCP receives the acoustic echoes reflected back from water layers of different depths and calculates the flow velocity vector of each depth unit based on the Doppler frequency shift of the echoes, including the three-dimensional velocity magnitude and direction of the water flow, thereby obtaining a detailed internal profile structure of the water flow above.

[0033] Furthermore, during the upward propagation of sound waves, due to the significant difference in acoustic impedance between the flowing liquid river water and the solid ice cap above, a strong reflection occurs when the sound waves reach the bottom of the ice layer. This causes a significant peak in the echo intensity received by the ADCP at the bottom of the ice layer. In other words, the echo intensity data of each depth unit received by the ADCP will exhibit an unusually obvious peak characteristic at this time. The onboard computer uses this characteristic to accurately determine the specific depth of the lower surface of the ice cap by identifying the peak position of the echo intensity. Using this boundary as the upper limit threshold, it automatically removes invalid noise data that penetrates into the ice layer or air, accurately extracts the effective acoustic velocity vector data from the top of the underwater robot to the lower surface of the ice cap, and uses it as the measured velocity profile of the main water body above at that moment.

[0034] Therefore, during a single voyage, this step is used to perform refined stratified flow measurement on the entire water body above the underwater robot to obtain a continuous, high-resolution velocity vector distribution from the lower surface of the ice sheet to the top of the robot. This provides complete and reliable measured flow field benchmark data for the upper half of the flow field for subsequent cross-sectional flow calculations and the deduction of the blind zone below.

[0035] Step 4: Calculation of flow velocity in the bottom blind zone To address the measurement blind zone at the depth of the underwater robot body 101 and below it, which cannot be directly measured, this embodiment uses a physical model based on the power function law of the river boundary layer velocity distribution to complete the data, and then reverse-engineers the velocity of the water in the blind zone below the underwater robot. Specifically, due to the near-field blind zone of the ADCP transducer and the robot body occupying a certain height, there is a region that cannot be directly measured from the riverbed to the top of the robot, or more precisely, to the first effective measurement layer of the ADCP. This region is the measurement blind zone. Therefore, this invention uses a velocity deduction algorithm based on fluid dynamics to complete the velocity distribution within this blind zone, thereby achieving full-depth velocity profile coverage from the riverbed to the ice sheet. That is, in the closed river flow under the ice sheet, due to the influence of the frictional force of the riverbed boundary layer, the water velocity decreases closer to the bottom of the riverbed. In the double boundary layer flow in the closed river channel under the ice cover, the upper water flow is affected by the roughness of the ice cover bottom, while the near-bed area close to the river bed bottom is mainly dominated by the river bed boundary layer friction. In the present invention, the measurement blind area to be calculated is only in the near-bed area, and the vertical distribution of the near-bed flow velocity in this area still conforms to the following basic power law formula: ​ ​ ​

[0036] In the above formula, V For flow rate, z The height from the riverbed. C For environmental constants, α This is the velocity distribution index.

[0037] The actual calculation includes the following steps: First, the onboard computer scans the effective flow velocity data measured upwards from the ADCP, from near to far, and selects the first measurement layer that is closest to the underwater robot and whose echo correlation coefficient (signal-to-noise ratio) is greater than a preset safety threshold. This layer is used as a reference point to read its measured flow velocity value. In this embodiment, the preset safety threshold specifically refers to a normalized echo correlation coefficient higher than 0.7 and a signal-to-noise ratio greater than 3dB. In practical applications, the onboard computer can also dynamically and adaptively adjust this safety threshold within a data range of 0.5 to 0.8 echo correlation coefficient and 3dB to 5dB signal-to-noise ratio, based on the current water turbidity and ADCP operating frequency band, to ensure that the selected reference point flow velocity is not disturbed by near-field sidelobes and underwater suspended particle noise. The onboard computer characterizes the water turbidity based on the real-time echo signal intensity (RSSI) received by the ADCP and executes dynamic threshold adjustment logic: In clear water (RSSI below the preset lower limit), the background noise is minimal, so the system raises the signal-to-noise ratio threshold to 5dB and the correlation coefficient to 0.8 to pursue ultimate accuracy; in turbid or sediment-laden water (RSSI above the preset upper limit), sound attenuation is severe, so the system lowers the signal-to-noise ratio threshold to 3dB and the correlation coefficient to 0.5 to ensure that effective data can still be obtained under the cover of natural low noise, thereby dynamically locking the undisturbed natural flow field layer.

[0038] Then, the current physical height of the underwater robot from the riverbed, as fed back by the bottom DVL, is read in real time. h ; Retrieve the factory-set or calibrated first-layer blind zone distance of the current ADCP device d This refers to the distance between the probe surface and the first depth unit from which valid data can be measured. Based on the above data, since the physical assumption model takes the riverbed bottom (flow velocity of 0) as the starting point, the data of all points must be uniformly converted into the absolute height from the riverbed. Therefore, the actual physical height of the selected reference point from the riverbed can be calculated, that is, the absolute distance of the reference point from the bottom can be calculated. The formula is:

[0039] Next, since the river boundary layer velocity distribution follows the above power law formula, and the environmental constant... C Since these environmental constants are difficult to obtain directly in actual measurements, in this embodiment, the airborne control unit uses a ratio relationship to eliminate the environmental constant. Based on the assumption, the reference point satisfies the following:

[0040] For any target height to be calculated within the measurement blind zone , (i.e., within the blind zone below the underwater robot), the flow velocity also satisfies:

[0041] Dividing the two equations above will eliminate the unknown environmental constant. C The ratio derivation formula is obtained as follows:

[0042] Therefore, the flow velocity at any height within the blind zone is equal to the measured flow velocity at the reference point multiplied by the ratio of that height to the height of the reference point. α The power, where the velocity distribution index is... α This reflects the degree of influence of riverbed roughness on the velocity gradient. Its value is preset according to the geological conditions of the riverbed: 1 / 7 (about 0.143) when the riverbed is silt or smooth bedrock, 1 / 5 (0.200) when the riverbed is gravel or rough, and 1 / 6 (about 0.167) by default. This is to ensure that the velocity at the selected reference point is not affected by turbulent noise from the near-field side lobes and the wake of the robot's propulsion, and to ensure that the reference point truly represents the flow field state of the natural boundary layer.

[0043] As a preferred method to further improve the accuracy of the calculation, the aforementioned vertical velocity distribution index α can also be obtained through dynamic fitting: the airborne computer extracts data from multiple consecutive effective water layers with the highest signal-to-noise ratio in the ADCP measured profile as sample points, substitutes them into the log-linearized power-law equation, and performs linear regression using the least squares method. The resulting real-time slope is the dynamic velocity distribution index α of the current measured vertical line. This method can effectively eliminate errors caused by changes in Reynolds number and relative roughness.

[0044] It should be noted that the "target height" mentioned above refers to any location within the blind zone at different heights from the riverbed (and this height must be less than the robot's travel altitude). h For example, if a robot is navigating 1 meter away from the riverbed, the algorithm will treat 0.1 meters, 0.2 meters, 0.3 meters... all the way up to 0.9 meters as "target heights," substitute them into the formula to calculate the flow velocity, and thus fit a smooth projection curve.

[0045] Finally, based on the above deduction formula, the airborne control unit, within the blind zone, from the riverbed surface ( z= 0) Begin by cutting layers according to the set vertical resolution, for example, every 0.1m as a slice, in 0- h Within the blind zone height range, a series of discrete values ​​are generated. Then, input these target heights one by one into the above deduction formula to calculate the calculated flow velocity at the corresponding height in batches. Thus, the bottom water flow velocity, which was originally impossible to measure due to the instrument's installation location and physical limitations, was completely deduced.

[0046] Step 5: Reconstructing the full-section velocity profile Based on the measured water velocity profile data obtained in step 3 above, and the bottom measurement blind zone velocity data calculated by the algorithm in step 4 above, the data is seamlessly stitched together according to the depth sequence to synthesize a complete vertical velocity profile curve extending from the riverbed (the boundary point where the velocity is 0) all the way to the lower surface of the ice sheet. Finally, after the robot completes its lateral navigation, a complete sub-ice velocity distribution map of this section is generated. The specific process is as follows: First, for the spatial position of the underwater robot at any moment during its navigation, the onboard control unit sorts the above measured velocity profile data and the below blind zone projection data according to the absolute height of each data point from the riverbed, achieving seamless splicing of the depth sequence. During splicing, the height from the riverbed is used as the vertical axis coordinate and the velocity magnitude as the horizontal axis coordinate. The blind zone projection data covers the range from the riverbed surface (height 0) to the bottom of the first effective measurement layer of ADCP. The above measured data starts from the first effective measurement layer of ADCP and extends upward to the lower surface of the ice sheet. The two sets of data are naturally connected at the starting depth of the effective measurement layer of ADCP, with continuous height coordinates and smooth transition of velocity values ​​at the junction without overlap or gaps. Furthermore, at the riverbed boundary, according to the fluid dynamics no-slip boundary condition, the velocity at the riverbed surface is zero. The system of this invention uses the boundary point with a height of 0 and a velocity of 0 as the lowest endpoint of the blind zone projection data, which together with the projected near-bottom velocity values ​​of each layer constitutes a complete near-bottom velocity distribution curve. In this embodiment, the riverbed surface (the boundary point where the flow velocity is 0) is geometrically referenced by the composite acoustic reflection surface detected by the DVL and ADCP bottom tracking functions; in the hydrological engineering calculation approximation, the relative water flow velocity at this acoustic reference surface is treated as zero.

[0047] Then, at the upper part of the profile, step 3 has accurately identified the specific location of the lower surface of the ice sheet through the peak characteristics of the echo intensity. Therefore, the depth cell corresponding to this location can be used as the upper boundary of the measured data above. The flow velocity value at the lower surface of the ice sheet is obtained by ADCP in this depth cell, and invalid data above the ice sheet is discarded.

[0048] Thus, through the above splicing and boundary processing, the airborne control unit can synthesize a continuous vertical velocity profile curve extending from the riverbed (flow velocity is 0) to the lower surface of the ice sheet. This profile curve contains full-depth velocity information from the near-bottom region to the near-ice region, eliminating the near-field blind zone inherent in the downward or upward measurement of ADCP in traditional measurement methods, and realizing a complete characterization of the velocity distribution of the entire vertical water column.

[0049] Simultaneously, the aforementioned single-point vertical profile synthesis process continues throughout the underwater robot's journey along the predetermined route. That is, while the robot navigates along the riverbed at a constant altitude, steps 3-5 are repeatedly performed at preset time intervals or spatial intervals, thereby generating a complete vertical velocity profile at each sampling point on the track. After the underwater robot completes its traversal of the river from one side to the other, the onboard computer, combining the relative horizontal displacement data calculated by DVL and the inertial navigation unit, arranges and combines the complete vertical velocity profiles at all sampling points on the track according to their spatial position in the cross-sectional direction, ultimately generating a full-profile velocity distribution map of the sub-ice profile covering the entire test section.

[0050] This velocity distribution map fully presents the velocity information of the water body at every point within the cross-section of the frozen river, from the bottom of the riverbed to the bottom of the ice sheet, extending from the left bank to the right bank, covering the entire measurement blind area. During this process, the shore-based monitoring equipment can receive the velocity distribution map in real time through the communication unit, allowing hydrological survey personnel to intuitively grasp the velocity distribution pattern of the cross-section and accurately calculate key hydrological parameters such as cross-sectional flow, thus providing a complete and reliable data foundation for accurate calculation of subglacial river flow and hydrological analysis.

[0051] Once the underwater robot has completed its traversal of the river from one side to the other, it obtains the full cross-sectional velocity distribution of the test section. The robot then returns along the original route or is retrieved to the ice surface by a winch via a zero-buoyancy cable, thus concluding the measurement operation.

[0052] The present invention provides a bottom-mounted, upward-looking method for measuring the flow velocity of rivers under ice, which solves the problems of traditional underwater robots easily colliding with ice sheets and inevitably generating bottom data blind spots when measuring flow under ice. The present invention uses the riverbed as a stable benchmark, and combines upward-looking measurement to obtain the water body above and algorithm inversion to repair blind spots to reconstruct the full profile data under ice, thereby restoring the complete flow field under ice. It achieves blind-spot-free, continuous, and high-resolution acquisition of the flow velocity of the entire cross section of the frozen river. The measurement process has a high degree of automation and strong data integrity, which significantly improves the efficiency and accuracy of flow measurement operations.

[0053] The embodiments of the present invention described above do not constitute a limitation on the scope of protection of the present invention. The basic concept of the present invention lies in using an underwater robot equipped with an upward-pointing ADCP and a downward-pointing DVL for operation, and using the riverbed as a stable reference to ensure navigation safety. The combination of "overhead measurement + algorithm deduction" eliminates measurement blind spots, achieving high-efficiency and high-precision continuous acquisition of sub-ice cross-sectional flow velocity. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for measuring the flow velocity of an ice-covered river under ice by bottom-feeding and upward-looking measurement, characterized in that: Includes the following steps: Step 1: Drill holes in the ice cover at the predetermined test section of the frozen river surface, and lower the underwater robot equipped with ADCP and DVL into the water through the holes. The ADCP and DVL are respectively set on the upper and lower sides of the underwater robot. Then control the underwater robot to dive to the preset height from the riverbed. Step 2: Use DVL to measure the actual height of the underwater robot from the riverbed in real time, and control the underwater robot to lock onto the trajectory at the preset height based on the actual height, and sail from one side of the riverbank to the other at a fixed altitude; Step 3: During navigation, control the ADCP to emit a sonic beam vertically upward to acquire measured velocity profile data of the water above the top of the underwater robot and the lower surface of the ice sheet. Step 4: Based on the measured velocity profile data of the water body above, select the measurement layer that is closest to the underwater robot and whose signal quality meets the preset threshold as the reference point. According to the measured velocity, actual height from the bottom, first-layer blind zone distance of ADCP, and preset vertical velocity distribution index of the reference point, calculate the velocity at each target height in the measurement blind zone below the underwater robot to complete the data of the measurement blind zone. Step 5: The measured velocity profile data of the upper water body and the calculated velocity data of the measurement blind zone are spliced ​​together according to the depth sequence to generate a complete vertical velocity profile from the riverbed to the lower surface of the ice sheet.

2. The method for measuring subglacial river flow velocity by bottom-feeding and upward-looking measurement according to claim 1, characterized in that: In step 4, the process of calculating the flow velocity at each target height within the measurement blind zone below the underwater robot is as follows: add the actual height above the bottom to the first layer blind zone distance of the ADCP to obtain the absolute height of the reference point from the riverbed; calculate the ratio of the target height to the absolute height of the reference point, and construct a proportionality coefficient by combining it with the preset vertical flow velocity distribution index; multiply the measured flow velocity at the reference point by the proportionality coefficient to calculate the flow velocity at the target height.

3. The method for measuring subglacial river flow velocity by bottom-feeding and upward-looking measurement according to claim 2, characterized in that: In step 4, the vertical velocity distribution index is set according to the riverbed geological conditions: 1 / 7 when the riverbed is silt or smooth bedrock, 1 / 5 when the riverbed is gravel or rough, and 1 / 6 by default.

4. The method for measuring subglacial river flow velocity by bottom-feeding and upward-looking measurement according to claim 1, characterized in that: In step 3, the step of obtaining the measured flow velocity profile data of the upper water body is as follows: based on the peak characteristics of the echo intensity of different depth units received by ADCP at the bottom of the ice layer, the specific location of the lower surface of the ice sheet is identified, and the effective measured data from the top of the underwater robot to the lower surface of the ice sheet is extracted as the measured flow velocity profile data of the upper water body.

5. The method for measuring subglacial river flow velocity by bottom-feeding and upward-looking measurement according to claim 1, characterized in that: In step 2, during the underwater robot's altitude-fixed navigation, the underwater robot continuously scans the terrain information of the riverbed ahead. When a protruding obstacle is detected ahead, the underwater robot is automatically controlled to temporarily increase its navigation altitude to avoid the obstacle. After passing the obstacle, it automatically returns to the trajectory at the preset altitude above the bottom and continues to navigate.

6. The method for measuring subglacial river flow velocity by bottom-feeding and upward-looking measurement according to claim 1, characterized in that: In step 4, the signal quality meets the preset threshold when the echo correlation coefficient of the measurement layer is higher than 0.7, and can be dynamically adjusted within the data range of 0.5 to 0.8 echo correlation coefficient and 3dB to 5dB signal-to-noise ratio according to the signal-to-noise ratio when ADCP is working.

7. A system for measuring subglacial river flow velocity by bottom-to-top measurement, employing the bottom-to-top measurement method for subglacial river flow velocity according to any one of claims 1-6, characterized in that: The system includes an underwater robot body, and a measurement and sensing unit, an onboard control unit, and a communication unit for data transmission mounted on the underwater robot body. The measurement and sensing unit includes an ADCP and a DVL arranged vertically. The transducer probe of the ADCP is vertically upward at the top of the underwater robot body to acquire measured flow velocity profile data of the water above. The transducer probe of the DVL is vertically downward at the bottom of the underwater robot body to measure the actual height of the underwater robot from the riverbed in real time. The onboard control unit is connected to the measurement and sensing unit via the communication unit to control the underwater robot body to stay at a preset height above the bottom and to calculate the flow velocity at each target height within the measurement blind zone based on the reference point flow velocity measured by the ADCP, and to perform data completion and stitching to generate a complete vertical flow velocity profile.

8. The ice-covered river flow velocity measurement system according to claim 7, characterized in that: It also includes an image recognition unit, which includes a forward-viewing sonar, a camera, and an obstacle avoidance sonar. The forward-viewing sonar and camera are located at the front end of the underwater robot body and are used to detect the riverbed topography and obstacles in front. The obstacle avoidance sonar is located on the top of the underwater robot body and is used to detect ice ridge obstacles at the bottom of the ice layer above.

9. The ice-covered river flow velocity measurement system based on bottom-feeding and upward measurement according to claim 7, characterized in that: The underwater robot body also has an inertial navigation unit sealed and integrated inside. The inertial navigation unit is communicatively connected to the airborne control unit and is used to calculate the attitude data of the underwater robot body in real time during the underwater robot body's constant altitude navigation and feed it back to the airborne control unit to ensure the stability of the underwater robot body's navigation trajectory.

10. The ice-covered river flow velocity measurement system based on bottom-feeding and upward measurement according to claim 7, characterized in that: It also includes shore-based monitoring equipment and a zero-buoyancy cable connected to the tail of the underwater robot body. The communication unit includes a communication sonar and an ultra-short baseline beacon mounted on the underwater robot body. The airborne control unit uses the communication sonar in conjunction with the zero-buoyancy cable to perform real-time data interaction and underwater positioning and tracking with the shore-based monitoring equipment.

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