Method and device for determining the flow of a tidal river

Abstract

The present application relates to the technical field of tidal river flow determination, and particularly relates to a tidal river flow determination method and device. Initial flow corresponding to each to-be-measured point in a target section of a tidal river is obtained. Water temperature data, salinity data and depth data corresponding to each to-be-measured point are obtained. The initial flow corresponding to each to-be-measured point is corrected based on the water temperature data, the salinity data and the depth data, so as to obtain target flow corresponding to each to-be-measured point in the target section. Through point-by-point correction, it is ensured that the flow data of each to-be-measured point in the target section is adapted to the local environment where the to-be-measured point is located, and the finally summarized total flow of the section can more truly reflect the hydrodynamic characteristics of the tidal river, thereby providing high-precision data support for flood control scheduling, water resource management and the like.

Description

Technical Field

[0001] This invention relates to the field of tidal channel flow determination technology, specifically to methods and equipment for determining tidal channel flow. Background Technology

[0002] The flow of tidal channels is "bidirectional" (the tidal current propels upstream during high tide and the runoff discharges downstream during low tide) and "periodic" (it exhibits a regular variation of 12-24 hours with the tidal cycle), and the density stratification formed by the mixing of fresh and salt water (such as saltwater wedges) further alters the vertical distribution of flow velocity.

[0003] Currently, mainstream flow monitoring methods can be divided into two categories: "direct measurement" and "indirect estimation." Both attempt to obtain the velocity-area relationship (flow rate = velocity × water area) at the measurement point through technical means. For example, the Doppler frequency shift of sound waves emitted by an acoustic Doppler current meter is used to calculate the water velocity. Sensors are deployed at the measurement point to directly output the three-dimensional velocity, and the flow rate is calculated by combining the area of ​​the measurement point (such as the vertical stratification area). However, the density stratification (halocele) of tidal channels leads to "non-uniformity" of the vertical velocity distribution: the velocity of the upper freshwater layer and the velocity of the lower saline water layer may be opposite, but existing methods mostly assume "vertical uniform flow" and ignore the difference in the contribution of stratification to the flow rate, resulting in deviations in the flow rate calculation at the measurement point (errors can reach 15%-30%).

[0004] Therefore, accurately calculating the flow rate at each measurement point in a tidal channel has become an urgent problem to be solved. Summary of the Invention

[0005] In view of this, the present invention provides a method and apparatus for determining the flow rate of a tidal channel, in order to solve the problem of how to accurately calculate the flow rate corresponding to each measurement point in a tidal channel.

[0006] In a first aspect, the present invention provides a method for determining the flow rate of a tidal river channel, applied to a control device in an underwater robot, the method comprising:

[0007] Obtain the initial flow rate at each measurement point in the target cross section of the tidal channel;

[0008] Acquire water temperature, salinity, and depth data for each measurement point;

[0009] The initial flow rate at each test point is corrected based on water temperature, salinity, and depth data to obtain the target flow rate at each test point in the target cross section.

[0010] The tidal channel flow determination method provided in this application obtains the initial flow rate corresponding to each measurement point in the target cross-section of the tidal channel. By initially collecting the flow rate at each measurement point within the cross-section, full coverage of the flow distribution of the entire target cross-section is achieved, avoiding overall flow estimation deviations due to single-point omissions and providing a complete initial reference for subsequent correction. Water temperature, salinity, and depth data corresponding to each measurement point are acquired to ensure their accuracy, avoiding correction deviations caused by distortion of basic parameters and guaranteeing the effectiveness of subsequent flow correction from the source. Based on the water temperature, salinity, and depth data, the initial flow rate corresponding to each measurement point is corrected to obtain the target flow rate corresponding to each measurement point in the target cross-section. The cross-sectional area calculation is optimized using water temperature and salinity data, combined with depth data, to accurately correct flow deviations in complex areas such as the mixing layer and halocline, making the results closer to the actual water flow state. In addition, by performing point-by-point calibration, it is ensured that the flow data of each measurement point within the target section is adapted to its local environment. The final total flow of the section can more accurately reflect the hydrodynamic characteristics of the tidal channel, providing high-precision data support for flood control scheduling and water resource management.

[0011] In one optional implementation, the initial flow rate corresponding to each test point is corrected based on water temperature data, salinity data, and depth data to obtain the target flow rate corresponding to each test point in the target cross section. This includes: acquiring the original sound velocity corresponding to each test point; calculating the true sound velocity corresponding to each test point based on the water temperature data, salinity data, and depth data corresponding to each test point; correcting the initial flow rate corresponding to each test point based on the relationship between the original sound velocity and the true sound velocity to obtain the candidate flow rate corresponding to each test point; identifying the target test point located in the mixing layer from among the test points; the mixing layer characterizes the cross-sectional layer where river water and seawater mix in the target cross section of the tidal channel; correcting the candidate flow rate corresponding to each target test point to obtain the target flow rate corresponding to each target test point; and determining the candidate flow rates corresponding to other test points besides the target test points as the target flow rates corresponding to other test points.

[0012] The method for determining tidal river flow provided in this application obtains the original sound velocity corresponding to each measurement point, providing a benchmark for flow correction. By comparing it with the subsequently calculated true sound velocity, the impact of sound velocity error on the initial flow can be quantified, laying the foundation for accurate correction. Based on the water temperature, salinity, and depth data corresponding to each measurement point, the true sound velocity corresponding to each measurement point is calculated. In tidal rivers, water temperature and salinity data vary significantly with depth data, directly affecting the sound wave propagation speed. Calculating the true sound velocity using real-time parameters accurately reflects the actual sound velocity environment at each measurement point, avoiding flow velocity measurement errors caused by discrepancies between preset and actual sound velocities, thereby ensuring the reliability of subsequent flow correction. Based on the relationship between the original and true sound velocities, the initial flow corresponding to each measurement point is corrected to obtain the candidate flow for each measurement point. By comparing the difference between the original sound velocity and the true sound velocity, the error caused by inaccurate sound velocity in the initial flow rate can be deduced in reverse. The same logic (sound velocity deviation correlation) is applied to all measurement points for correction, ensuring that the flow rate data at each point are comparable under the same benchmark. This makes the candidate flow rates closer to the true values, providing consistent basic data for subsequent specific corrections of the mixing layer. Target measurement points located in the mixing layer are identified from among the measurement points. The mixing layer is a turbulent region in tidal channels where fresh and saline water meet, with chaotic velocity distribution and susceptibility to disturbance. Identifying target measurement points individually allows for "precise policy implementation," avoiding the confusion of flow rate data between the mixing layer and non-mixing layers. After clarifying the range of the mixing layer, a more stringent correction strategy can be applied to the flow rate data in this region, while maintaining the stability of candidate flow rates in non-mixing layer regions, balancing efficiency and accuracy. The candidate flow rates corresponding to each target measurement point are corrected to obtain the target flow rates for each target measurement point. Flow velocity within the mixed layer is significantly affected by shear force and density difference. Specialized calibration can correct velocity measurement distortions caused by turbulence, making the flow rate at the target measurement point more consistent with actual hydrodynamic characteristics. Candidate flow rates corresponding to other measurement points besides the target point are designated as the target flow rates for those other measurement points. In non-mixed layer regions (such as pure river water layers or pure seawater layers), the flow is stable, and the candidate flow rates after sound velocity correction are sufficiently accurate, requiring no additional processing, reducing computational costs, and avoiding over-calibration that introduces new errors.

[0013] In one optional implementation, determining the target test point in the mixed layer from each test point includes: calculating the first vertical salinity gradient data corresponding to each test point based on the salinity data corresponding to each test point; determining the mixed layer in the target section based on the first vertical salinity gradient data corresponding to each test point; and determining the target test point in the mixed layer from each test point.

[0014] The method for determining tidal channel flow provided in this application calculates the first vertical salinity gradient data corresponding to each measurement point based on the salinity data corresponding to each measurement point. The first vertical salinity gradient can intuitively reflect the intensity of water stratification. In tidal channels, the gradient value in the salinity-freshwater confluence area is significantly higher than that in pure river water or pure seawater areas. By calculating the first vertical salinity gradient data, the "qualitative difference" of salinity can be transformed into a "quantitative indicator," providing a quantifiable basis for identifying the mixed layer, ensuring that the edge areas of the mixed layer are not missed, and improving the sensitivity of identification. Based on the first vertical salinity gradient data corresponding to each measurement point, the mixed layer in the target section is determined. The salinity gradient in pure river water or pure seawater areas is gentle and stable. The first vertical salinity gradient data can clearly distinguish these areas from the mixed layer, ensuring that the boundary delineation of the mixed layer is not affected by the overall salinity of the water body, thus guaranteeing the accuracy of the determined mixed layer in the target section. Target measurement points located in the mixed layer are determined from each measurement point. By associating the range of the hybrid layer with specific test points, the target test points located within the hybrid layer can be directly screened out, providing a clear target for subsequent special correction of the traffic data in this area and avoiding invalid processing of non-hybrid layer points.

[0015] In one optional implementation, determining the mixing layer in the target section based on the first vertical salinity gradient data corresponding to each test point includes: comparing the first vertical salinity gradient data corresponding to each test point with a preset dynamic gradient threshold; the preset dynamic gradient threshold is correlated with the mean and standard deviation of the first salinity gradient corresponding to each test point; if there is a test point with first vertical salinity gradient data greater than the preset dynamic gradient threshold, then the test point is determined as a candidate halocline point, and the preset area where the candidate halocline point is located is determined as an initial salinity anomaly area; if there is a test point with first vertical salinity gradient data less than or equal to the preset dynamic gradient threshold, and the salinity data abruptly changes beyond the preset abrupt change threshold, then the test point is determined as a potential disturbance point, and the preset area where the potential disturbance point is located is determined as a potential disturbance area. In the disturbed region; extract the first vertical salinity gradient data of multiple consecutive test points in the initial salinity anomaly region and the potential disturbance region respectively; if the first vertical salinity gradient data of multiple consecutive test points in the initial salinity anomaly region and / or the potential disturbance region have the same direction and all exceed the preset dynamic gradient threshold, then the initial salinity anomaly region and / or the potential disturbance region are determined to be time-stable regions; if the first vertical salinity gradient data of multiple consecutive test points in the initial salinity anomaly region and / or the potential disturbance region change direction abruptly, or if there is a test point whose corresponding first vertical salinity gradient data is less than or equal to the preset dynamic gradient threshold, then the initial salinity anomaly region and / or the potential disturbance region are determined to be time-unstable regions; perform spatial verification on the time-stable regions and / or time-unstable regions to determine the mixing layer in the target section.

[0016] The tidal channel flow determination method provided in this application compares the first vertical salinity gradient data corresponding to each test point with a preset dynamic gradient threshold. The preset dynamic gradient threshold is based on automatically adapting to the stratification intensity of different tidal river sections, avoiding missed or false judgments that may occur in complex environments with a fixed threshold. By comparing the first vertical salinity gradient data corresponding to each test point with the preset dynamic gradient threshold, the "anomaly" of the salinity gradient is transformed into an executable quantification condition, providing a unified benchmark for subsequent area labeling and reducing human judgment errors. If the first vertical salinity gradient data corresponding to a test point is greater than the preset dynamic gradient threshold, the test point is identified as a candidate halocline point, and the preset area where the candidate halocline point is located is identified as the initial salinity anomaly area. The candidate halocline point is an area where the salinity gradient significantly exceeds the threshold, directly corresponding to the area of ​​most intense saline-freshwater mixing. Marking its preset area as the "initial salinity anomaly area" can quickly locate the core range of the mixing layer, providing a key object for subsequent verification. If the first vertical salinity gradient data corresponding to a test point is less than or equal to a preset dynamic gradient threshold, and the salinity data mutation corresponding to the test point is greater than a preset mutation threshold, then the test point is identified as a potential perturbation point, and the preset region where the potential perturbation point is located is identified as a potential perturbation region. Points where "the first vertical salinity gradient data is less than or equal to the preset dynamic gradient threshold, and the salinity data mutation is greater than the preset mutation threshold" are marked as "potential perturbation regions" to avoid missing the edges of the mixing layer or instantaneous perturbation regions, thus improving the comprehensiveness of the mixing layer identification. The first vertical salinity gradient data of multiple consecutive test points are extracted from the initial salinity anomaly region and the potential perturbation region, respectively. This reflects the temporal stability of the salinity gradient, avoids misjudgment of regions due to single-point anomalies, and provides data support for temporal stability verification. If the first vertical salinity gradient data directions of multiple consecutive test points in the initial salinity anomaly region and / or potential disturbance region are consistent and all exceed the preset dynamic gradient threshold, then the initial salinity anomaly region and / or potential disturbance region are determined to be time-stable regions. This can preliminarily confirm the possibility that they are true mixing layers and reduce short-term turbulence interference. If the first vertical salinity gradient data directions of multiple consecutive test points in the initial salinity anomaly region and / or potential disturbance region change abruptly, or if the first vertical salinity gradient data corresponding to a test point is less than or equal to the preset dynamic gradient threshold, then the initial salinity anomaly region and / or potential disturbance region are determined to be time-unstable regions. Further screening through subsequent spatial verification is required to avoid including non-mixing layer regions in the correction range. Spatial verification is performed on time-stable regions and / or time-unstable regions to determine the mixing layer in the target section. This can confirm whether the time-stable region has lateral continuity or exclude isolated disturbance points in the time-unstable region, ultimately accurately delineating the range of the mixing layer.Furthermore, by combining the dual verification of temporal stability and spatial continuity, it is possible to effectively distinguish between "real mixing layer" and "instantaneous disturbance" and "instrument noise," ensuring that the determined mixing layer has both temporal continuity and spatial distribution rationality, thus providing a reliable regional boundary basis for subsequent flow correction.

[0017] In one optional implementation, spatial verification is performed on time-stable regions and / or time-unstable regions to determine the mixed layer in the target section, including: for time-stable regions and / or time-unstable regions, calculating the absolute value of the salinity difference between the current survey line and adjacent survey lines at the target depth; if the absolute value of the salinity difference is greater than a preset salinity difference threshold, then the time-stable regions and / or time-unstable regions are determined as candidate regions for the mixed layer; if the absolute value of the salinity difference is less than or equal to the preset salinity difference threshold, then the time-stable regions and / or time-unstable regions are removed from the candidate regions for the mixed layer; the candidate regions for the mixed layer are identified to determine the mixed layer in the target section.

[0018] The tidal channel flow determination method provided in this application calculates the absolute value of the salinity difference between the current survey line and adjacent survey lines at the target depth for time-stable and / or time-unstable regions. By comparing the absolute value of the salinity difference between the current survey line and adjacent survey lines at the same depth, the identification of the mixed layer is extended from the time stability analysis of a single survey line to the spatial continuity analysis of multiple survey lines. This avoids misjudgments caused by abnormal data from a single survey line, improves the spatial reliability of the identification, reduces interference from non-critical depth data, and improves the efficiency and accuracy of spatial verification. If the absolute value of the salinity difference is greater than a preset salinity difference threshold, the time-stable and / or time-unstable regions are identified as candidate regions for the mixed layer. An absolute value of the salinity difference greater than the preset salinity difference threshold indicates that there is a significant lateral difference in the salinity distribution between the current survey line and adjacent survey lines at the target depth, which is consistent with the spatial gradient distribution characteristics of the mixed layer (the confluence of fresh and saline water bodies) (such as salinity increasing from river to sea). Including such regions in the candidates can effectively capture the true range of the mixed layer with lateral continuity. A preset salinity difference threshold provides a clear standard for spatial verification, avoiding subjective judgment and ensuring consistency in comparisons between different survey lines. If the absolute value of the salinity difference is less than or equal to the preset salinity difference threshold, time-stable and / or time-instantaneous regions are removed from the candidate regions for the mixing layer, reducing "false samples" in the candidate regions and improving the purity of the mixing layer identification. The candidate regions for the mixing layer are identified to determine the mixing layer in the target section. Candidate regions that have passed time stability verification and spatial difference verification can be confirmed as real mixing layers, and their boundaries more closely match the actual physical process of saline-freshwater mixing.

[0019] In one optional implementation, identifying a candidate region for a mixed layer and determining the mixed layer in the target section includes: detecting whether the second vertical salinity gradient data corresponding to the current survey line at the target depth in the candidate region for the mixed layer is greater than a preset dynamic gradient threshold; if the second vertical salinity gradient data is greater than the preset dynamic gradient threshold, then detecting whether the third vertical salinity gradient data of the adjacent survey line corresponding to the current survey line at the target depth in the candidate region for the mixed layer is greater than the preset dynamic gradient threshold; if the third vertical salinity gradient data is greater than the preset dynamic gradient threshold, and the target depth is from the water surface in the candidate region for the mixed layer... If the first depth detected downwards satisfies the following conditions: the second vertical salinity gradient data is greater than the preset dynamic gradient threshold, the third vertical salinity gradient data is greater than the preset dynamic gradient threshold, and the absolute value of the salinity difference is greater than the preset salinity difference threshold, then the target depth is determined as the upper layer of the mixing layer. If the target depth is the first depth detected upwards from the bottom of the water body in the candidate region of the mixing layer that satisfies the following conditions: the second vertical salinity gradient data is greater than the preset dynamic gradient threshold, the third vertical salinity gradient data is greater than the preset dynamic gradient threshold, and the absolute value of the salinity difference is greater than the preset salinity difference threshold, then the target depth is determined as the lower layer of the mixing layer.

[0020] The tidal channel flow determination method provided in this application detects whether the second vertical salinity gradient data corresponding to the current survey line at the target depth in the candidate area of ​​the mixed layer is greater than a preset dynamic gradient threshold. By verifying whether the second vertical salinity gradient of the current survey line exceeds the dynamic threshold, areas within the mixed layer that may have significant salinity changes can be initially identified, providing a basis for subsequent boundary identification. If the second vertical salinity gradient data is greater than the preset dynamic gradient threshold, the method then detects whether the third vertical salinity gradient data of the adjacent survey line corresponding to the target depth in the candidate area of ​​the mixed layer is greater than the preset dynamic gradient threshold. By comparing the second vertical salinity gradient data corresponding to the current survey line with the third vertical salinity gradient data corresponding to the adjacent survey line, the method verifies whether the salinity gradient anomaly is spatially continuous (not an isolated phenomenon of a single survey line), eliminating vertical gradient anomalies caused by local disturbances (such as water flow vortices), and ensuring that the identified mixed layer boundary has spatial representativeness. If the third vertical salinity gradient data is greater than the preset dynamic gradient threshold, and the target depth is the first depth detected downwards from the surface of the water body in the candidate region of the mixing layer that satisfies the following conditions: the second vertical salinity gradient data is greater than the preset dynamic gradient threshold, the third vertical salinity gradient data is greater than the preset dynamic gradient threshold, and the absolute value of the salinity difference is greater than the preset salinity difference threshold, then the target depth is determined as the upper layer of the mixing layer. The setting of "the first qualified depth downwards from the surface" directly locks in the vertical starting point where saline-freshwater mixing begins significantly (such as the boundary between the freshwater layer and the mixing layer), solving the problem of ambiguity in the upper boundary of the mixing layer (because the surface water is easily disturbed by wind and waves). In addition, it is necessary to simultaneously satisfy "the current measuring line's vertical gradient meets the standard, the adjacent measuring line's vertical gradient meets the standard, and the absolute value of the salinity difference meets the standard", combining the significance of the vertical gradient with the difference in lateral distribution, avoiding misjudging occasional surface salinity fluctuations as the boundary of the mixing layer. If the target depth is the first depth detected upwards from the bottom of the water body in the candidate region of the mixing layer that satisfies the following conditions: the second vertical salinity gradient data is greater than the preset dynamic gradient threshold, the third vertical salinity gradient data is greater than the preset dynamic gradient threshold, and the absolute value of the salinity difference is greater than the preset salinity difference threshold, then the target depth is determined as the lower layer of the mixing layer. The "first qualified depth upwards from the bottom" corresponds to the boundary between the mixing layer and the lower seawater (or high-salinity water body). By tracing back from the bottom, the interference of factors such as bottom sediment disturbance on the salinity gradient can be effectively eliminated, and the vertical endpoint of the mixing layer can be accurately located. Furthermore, symmetrical with the upper layer boundary identification logic (both using "first qualified depth" + "multiple condition verification"), the upper and lower boundaries of the mixing layer form a closed vertical range, providing a clear spatial boundary reference for subsequent flow correction (especially flow within the mixing layer), avoiding correction deviations caused by boundary ambiguity.

[0021] In one optional implementation, the candidate flow rate corresponding to each target test point is corrected to obtain the target flow rate corresponding to each target test point, including: determining the thickness of the mixing layer and the average second salinity gradient of the mixing layer; determining the thickness threshold of the mixing layer based on the average second salinity gradient; comparing the thickness of the mixing layer with the thickness threshold; and correcting the candidate flow rate corresponding to each target test point based on the comparison result to obtain the target flow rate corresponding to each target test point.

[0022] The tidal channel flow determination method provided in this application determines the thickness of the mixing layer and the average second salinity gradient of the mixing layer. The thickness of the mixing layer directly reflects the vertical range of salinity mixing, while the average second salinity gradient reflects the overall intensity of salinity changes within the mixing layer. Combining these two factors comprehensively characterizes the physical features of the mixing layer, providing a quantitative basis for subsequent correction and avoiding the bias caused by relying on a single indicator. By calculating the average second salinity gradient of the mixing layer, the thickness threshold can be matched with the actual salinity gradient level of the mixing layer, ensuring the rationality and relevance of the thickness threshold. The thickness threshold is determined based on the average second salinity gradient of the mixing layer itself, rather than a preset fixed value. This allows for adaptation to changes in the characteristics of the mixing layer under different hydrological scenarios (such as high / low tide, wet / dry seasons) (for example, a mixing layer with a large salinity gradient may correspond to a smaller reasonable thickness threshold), improving the flexibility and applicability of the threshold. The thickness of the mixing layer is compared with a thickness threshold. This comparison allows for a quick determination of whether the current mixing layer thickness is within a reasonable range (e.g., a thickness exceeding the threshold may indicate the presence of atypical areas or data deviation), providing a clear basis for flow rate correction (intensified correction is needed for abnormal conditions). Based on the comparison results, candidate flow rates corresponding to each target measurement point are corrected to obtain the target flow rate for each target measurement point. By combining the comparison results of thickness and gradient for targeted correction, the sound velocity measurement deviation caused by drastic salinity changes (which in turn affects flow rate calculation) can be eliminated, making the target flow rate closer to the actual water flow state. A more refined correction method is used for mixing layers with abnormal thickness (e.g., too thick or too thin), while maintaining a reasonable correction intensity for mixing layers of normal thickness. This ensures accuracy while avoiding over-correction, balancing computational efficiency and data reliability.

[0023] In one optional implementation, based on the comparison results, the candidate flow rates corresponding to each target test point are corrected to obtain the target flow rates corresponding to each target test point. This includes: if the thickness of the mixing layer is less than a thickness threshold, obtaining the upper boundary flow rate and the lower boundary flow rate corresponding to the mixing layer; obtaining the first pose of the underwater robot device when collecting the upper boundary flow rate and the second pose when collecting the lower boundary flow rate; determining the first weight corresponding to the upper boundary flow rate based on the first pose; determining the second weight corresponding to the lower boundary flow rate based on the second pose; and correcting the candidate flow rates corresponding to each target test point based on the upper boundary flow rate, the lower boundary flow rate, the first weight, and the second weight to obtain the target flow rates corresponding to each target test point.

[0024] The tidal channel flow determination method provided in this application obtains the upper and lower boundary flows of the mixing layer if the thickness of the mixing layer is less than a thickness threshold. When the thickness of the mixing layer is small (less than the thickness threshold), its internal salinity and flow state are more easily affected by the upper and lower boundaries. Prioritizing the extraction of the upper and lower boundary flows can capture the core elements affecting the mixing layer flow, avoid correction deviations caused by internal data redundancy, and improve the targeting of correction. The method also obtains the first pose of the underwater robot when collecting the upper boundary flow and the second pose when collecting the lower boundary flow. The pose of the underwater robot (such as depth, horizontal position, and attitude angle) directly affects the accuracy of flow measurement (for example, the flow velocity and direction may differ at different depths). By recording the pose, flow data can be bound to specific spatial locations, providing a physical basis for subsequent weight allocation. The first weight corresponding to the upper boundary flow is determined based on the first pose; the second weight corresponding to the lower boundary flow is determined based on the second pose. The first weight is determined based on the first pose, and the second weight is determined based on the second pose (rather than a fixed value). This reflects the measurement reliability of different boundaries (e.g., measurement points with more stable poses correspond to higher weights), ensuring that the contribution of the upper and lower boundary flows in the calibration matches the actual measurement quality and avoiding calibration deviations caused by a "one-size-fits-all" approach. Based on the upper boundary flow, lower boundary flow, first weight, and second weight, the candidate flows corresponding to each target measurement point are calibrated to obtain the target flow for each target measurement point. By weighted fusion of key flow information from the upper and lower boundaries, the influence of the mixing layer on the interaction between the upper and lower water bodies can be comprehensively reflected, especially suitable for thin mixing layers (where internal data is not representative enough), making the calibrated target flow closer to the actual flow state. Furthermore, the dynamic adjustment of weights can adapt to different measurement scenarios (e.g., assigning a higher weight to a measurement with a better pose at the upper boundary). Even if there is a slight error in the flow of a single boundary, its impact on the final result can be reduced through weight allocation, enhancing the anti-interference capability of the calibration process.

[0025] In one optional implementation, the candidate flow rates corresponding to each target test point are corrected based on the comparison results to obtain the target flow rates corresponding to each target test point. This includes: if the thickness of the mixing layer is greater than or equal to a thickness threshold, obtaining the upper boundary momentum flux and lower boundary momentum flux of the mixing layer; correcting the initial momentum flux corresponding to each target test point based on the upper boundary momentum flux and lower boundary momentum flux to obtain the target momentum flux; and calculating the target flow rate corresponding to each target test point based on the target momentum flux.

[0026] The tidal channel flow determination method provided in this application obtains the upper and lower boundary momentum fluxes of the mixing layer if the thickness of the mixing layer is greater than or equal to a thickness threshold. When the thickness of the mixing layer is greater than or equal to the thickness threshold, its internal flow state is mainly dominated by momentum transfer between the upper and lower boundaries (such as momentum flux caused by wind, bottom friction, etc.). Extracting the upper and lower boundary momentum fluxes can directly pinpoint the core power source affecting the overall movement of the mixing layer, avoiding the neglect of macroscopic driving mechanisms due to focusing on local details, and improving the physical correlation of flow calculation. Based on the upper and lower boundary momentum fluxes, the initial momentum fluxes corresponding to each target measurement point are corrected to obtain the target momentum flux. The initial momentum flux may be affected by measurement errors (such as instrument accuracy, environmental interference), while the boundary momentum flux reflects the energy exchange between the mixing layer and the external water body, and has stronger stability and representativeness. Correction based on the upper and lower boundary momentum fluxes can effectively offset local measurement errors, making the target momentum flux closer to the true dynamic state. For each target measurement point, the target flow rate corresponding to that point is calculated based on the target momentum flux. Momentum flux and flow rate (velocity × area) have a direct dynamic relationship (e.g., conservation of momentum, Newton's second law). Calculating the flow rate based on the corrected target momentum flux ensures that the flow rate data matches the actual dynamic process of the mixing layer, rather than relying solely on statistical regularities, thus enhancing the scientific rigor of the results. Furthermore, for thick mixing layers, the target momentum flux incorporates the overall influence of the boundary. The flow rate at each measurement point calculated based on this flux better reflects the spatial distribution trend of the flow rate within the layer (e.g., whether there are velocity differences due to momentum gradients), avoiding misjudgments of overall flow characteristics due to local data fluctuations and improving the reliability of the results.

[0027] Secondly, the present invention provides an underwater robot device, comprising: an underwater robot body, an acoustic Doppler flow profiler, an integrated conductivity-temperature-depth meter, and a control device; wherein the acoustic Doppler flow profiler, the integrated conductivity-temperature-depth meter, and the control device are all mounted on the underwater robot body, and the acoustic Doppler flow profiler and the integrated conductivity-temperature-depth meter are communicatively connected to the control device; wherein:

[0028] Acoustic Doppler flow profiler is used to detect the initial flow rate at each test point in a target section of a tidal channel.

[0029] It integrates a conductivity-temperature-depth meter to monitor water temperature, salinity, and depth data at each measurement point;

[0030] A control device for performing the tidal channel flow determination method of the first aspect or any corresponding embodiment described above.

[0031] The underwater robot device provided in this application uses an acoustic Doppler flow profiler to acquire the initial flow rate at each test point in the target cross-section of a tidal channel. By initially collecting the flow rate at each test point within the cross-section, full coverage of the flow distribution across the entire target cross-section is achieved, avoiding overall flow estimation deviations due to single-point omissions and providing a complete initial reference for subsequent correction. Based on an integrated conductivity-temperature-depth meter, water temperature, salinity, and depth data are acquired for each test point, ensuring the accuracy of these data and avoiding correction deviations caused by distortion of basic parameters, thus guaranteeing the effectiveness of subsequent flow correction from the source. The control device corrects the initial flow rate at each test point based on the water temperature, salinity, and depth data to obtain the target flow rate at each test point in the target cross-section. By utilizing water temperature and salinity data, combined with depth data, the cross-sectional area calculation is optimized, accurately correcting flow deviations in complex areas such as the mixing layer and halocline, making the results closer to the actual water flow state. In addition, by performing point-by-point calibration, it is ensured that the flow data of each measurement point within the target section is adapted to its local environment. The final total flow of the section can more accurately reflect the hydrodynamic characteristics of the tidal channel, providing high-precision data support for flood control scheduling and water resource management. Attached Figure Description

[0032] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0033] Figure 1 This is a flowchart illustrating a method for determining tidal river flow according to an embodiment of the present invention.

[0034] Figure 2 This is a flowchart illustrating another method for determining the flow rate of a tidal channel according to an embodiment of the present invention;

[0035] Figure 3This is a structural schematic diagram of an underwater robot device according to an embodiment of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] It should be noted that the method for determining tidal river flow provided in this application can be executed by a device for determining tidal river flow. This device can be implemented as part or all of the control equipment in an underwater robot through software, hardware, or a combination of both. In the following method embodiments, the execution subject is described using a control device as an example.

[0038] According to an embodiment of the present invention, a method for determining the flow rate of a tidal channel is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0039] This embodiment provides a method for determining the flow rate of a tidal river channel, which can be used in the control equipment of the aforementioned underwater robot device. Figure 1 This is a flowchart of a method for determining tidal channel flow according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0040] Step S101: Obtain the initial flow rate corresponding to each measurement point in the target cross-section of the tidal channel.

[0041] Specifically, the underwater robot is equipped with an Acoustic Doppler Current Profiler (ADCP). The ADCP can perform full-coverage detection of the "target section" of a tidal channel.

[0042] The control equipment can divide specific measurement points according to the spatial distribution of the target cross section (such as dividing it into several columns along the width direction or several layers along the depth direction) to ensure coverage of different locations of the cross section (surface, middle, bottom, or main stream area, shore area, etc.).

[0043] Then, the control equipment controls the ADCP to emit sound waves and receive the reflected signals of suspended particles in the water flow. It uses the Doppler effect to calculate the flow velocity at each test point, and then combines the corresponding water flow area (determined by the cross-sectional size and the location of the test point) to obtain the initial flow velocity at each test point.

[0044] Then, by multiplying the initial flow velocity by the area of ​​the target cross section, the initial flow rate corresponding to each measurement point in the target cross section of the tidal channel is obtained.

[0045] Step S102: Obtain water temperature data, salinity data, and depth data corresponding to each test point.

[0046] Specifically, the underwater robot is equipped with an integrated conductivity-temperature-depth (CTD) meter. The control equipment can control the integrated CTD meter to synchronously acquire key hydrological parameters corresponding to the initial flow rate, achieving spatiotemporal matching of "flow rate-environmental parameters".

[0047] Specifically, the control equipment can control the CTD and ADCP to work synchronously. While measuring the initial flow rate at a certain point, it records the water temperature, salinity, and depth data at that point, ensuring that the flow rate data of each point corresponds one-to-one with the environmental parameters and avoiding errors caused by time or space misalignment.

[0048] Among them, water temperature data reflects the temperature characteristics of the water body, and temperature affects water density (density decreases as temperature increases) and sound wave propagation speed (affecting ADCP velocity measurement accuracy); salinity data is particularly important for tidal channels (affected by tides, freshwater and seawater mix, leading to salinity stratification), and salinity directly affects water density (higher salinity means greater density), which in turn affects the dynamic characteristics of water flow (such as the formation of stratified flow); depth data clarifies the vertical position of each measurement point in the water body (depth from the water surface or riverbed), providing a spatial coordinate reference for identifying mixed layers and stratified structures.

[0049] In one optional embodiment of this application, the underwater robot is equipped with a positioning device and an attitude monitoring module. The positioning device can determine the position information of the underwater robot, and the attitude monitoring module can detect the navigation attitude of the underwater robot, including pitch and roll angles. The control device can determine the stability of the underwater robot's navigation attitude based on the pitch and roll angles monitored by the attitude monitoring module. When an attitude deviation exceeding a preset threshold is detected (e.g., pitch and / or roll angles exceeding the corresponding first preset angle threshold), it is considered an abnormal measurement state, and the ADCP flow data at that moment is immediately marked as abnormal data. If the deviation is too large (e.g., reaching the first preset angle threshold, e.g., ±15° or more) and the duration exceeds a preset value, the initial flow, water temperature, salinity, and depth data corresponding to each measurement point in that segment are determined to be invalid and discarded. Through this mechanism, data with large measurement errors due to violent swaying and tilting are filtered out, ensuring that only the flow velocity profile data obtained during periods of stable attitude are used in subsequent flow calculations.

[0050] Step S103: Based on water temperature data, salinity data, and depth data, the initial flow rate corresponding to each test point is corrected to obtain the target flow rate corresponding to each test point in the target cross section.

[0051] Specifically, water temperature and salinity data together determine water density, and density differences lead to changes in hydrodynamic forces (such as pressure gradient and buoyancy), which in turn affect the actual flow velocity. By calculating water density using water temperature and salinity data, the sound velocity error caused by density changes in ADCP (sound velocity changes with density, affecting Doppler frequency shift calculation) is corrected, indirectly correcting the flow velocity and flow rate.

[0052] This step will be explained in detail below.

[0053] The tidal channel flow determination method provided in this application obtains the initial flow rate corresponding to each measurement point in the target cross-section of the tidal channel. By initially collecting the flow rate at each measurement point within the cross-section, full coverage of the flow distribution of the entire target cross-section is achieved, avoiding overall flow estimation deviations due to single-point omissions and providing a complete initial reference for subsequent correction. Water temperature, salinity, and depth data corresponding to each measurement point are acquired to ensure their accuracy, avoiding correction deviations caused by distortion of basic parameters and guaranteeing the effectiveness of subsequent flow correction from the source. Based on the water temperature, salinity, and depth data, the initial flow rate corresponding to each measurement point is corrected to obtain the target flow rate corresponding to each measurement point in the target cross-section. The cross-sectional area calculation is optimized using water temperature and salinity data, combined with depth data, to accurately correct flow deviations in complex areas such as the mixing layer and halocline, making the results closer to the actual water flow state. In addition, by performing point-by-point calibration, it is ensured that the flow data of each measurement point within the target section is adapted to its local environment. The final total flow of the section can more accurately reflect the hydrodynamic characteristics of the tidal channel, providing high-precision data support for flood control scheduling and water resource management.

[0054] This embodiment provides a method for determining the flow rate of a tidal river channel, which can be used in the control equipment of the aforementioned underwater robot device. Figure 2 This is a flowchart of a method for determining tidal channel flow according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0055] Step S201: Obtain the initial flow rate corresponding to each measurement point in the target cross-section of the tidal channel.

[0056] Please refer to the above description of step S101 for details on this step, which will not be repeated here.

[0057] Step S202: Obtain water temperature data, salinity data, and depth data corresponding to each test point.

[0058] Please refer to the above description of step S102 for details on this step, which will not be repeated here.

[0059] Step S203: Based on water temperature data, salinity data, and depth data, the initial flow rate corresponding to each test point is corrected to obtain the target flow rate corresponding to each test point in the target cross section.

[0060] Specifically, step S203 above may include the following steps:

[0061] Step S2031: Obtain the original sound velocity corresponding to each test point.

[0062] Specifically, the control device can receive the raw sound velocity corresponding to each test point acquired by the ADCP.

[0063] Step S2032: Calculate the true sound velocity corresponding to each test point based on the water temperature data, salinity data, and depth data corresponding to each test point.

[0064] Specifically, the control device can calculate the true sound velocity corresponding to each measurement point using the following formula, based on the water temperature data, salinity data, and depth data corresponding to each measurement point.

[0065]

[0066] Where v(S,T,p) is the specific volume of seawater, usually expressed in cubic meters (m³). 3 / kg represents the volume occupied by 1 kg of seawater under the conditions of salinity S, temperature T, and pressure p, where S is the salinity data, T is the temperature data, p is the pressure data, and a is the pressure data. i x i y i z i These are all fitting parameters, with 75 representing the number of terms.

[0067] Density is derived from specific volume:

[0068]

[0069] Where ρ is the density of seawater, and v = v(S,T,p) is the specific volume of seawater.

[0070] Then, based on the state equations in TEOS-10, the isentropic compression ratio is calculated:

[0071]

[0072] Among them, K T Isoentropy compression ratio, in dbar -1 (Since pressure p is commonly referred to as dbar, it can also be converted to Pa⁻¹ in the International System of Units (SI). Note the unit conversion.)

[0073]

[0074] Among them, c true This represents the actual speed of sound in seawater, measured in m / s.

[0075] Step S2033: Based on the relationship between the original sound velocity and the true sound velocity, the initial flow rate corresponding to each test point is corrected to obtain the candidate flow rate corresponding to each test point.

[0076] Specifically, the control equipment can calculate the sound velocity deviation rate k = (c 真实 -c原始 ) / c 原始 And establish a mapping relationship between deviation rate and flow rate.

[0077] When |k| ≤ the first preset sound velocity deviation threshold (e.g., 1%) (weak deviation), linear correction is used: Q 候选 =Q 初始 ×(1+k);

[0078] When |k| > the first preset sound velocity deviation threshold (e.g., 1%) (strong deviation), a depth compensation factor α = D / D is introduced. 平均 (D 平均 (where Q is the average depth of the cross section), the correction formula is: Q 候选 =W 初始 ×(1+k×α) is used to correct the difference in the impact of sound velocity error on flow rate at different depths (e.g., deep water flow is more affected by sound velocity error).

[0079] Outlier protection mechanism: If |k| of a certain test point is greater than the second preset sound velocity deviation threshold (e.g., 5%) (wherein the second preset sound velocity deviation threshold is greater than the first preset sound velocity deviation threshold), it is determined that the CTD or ADCP data is abnormal. At this time, the candidate flow rate is not corrected by sound velocity, but the average flow rate of the adjacent preset number of valid points is taken instead to avoid abnormal data pollution.

[0080] Step S2034: Determine the target test point in the mixed layer from each test point.

[0081] Among them, the mixed layer represents the cross-sectional layer in the target section of the tidal channel where river water and seawater mix.

[0082] Specifically, step S2034 above may include the following steps:

[0083] Step a1: Calculate the first vertical salinity gradient data corresponding to each test point based on the salinity data corresponding to each test point.

[0084] Optionally, it should be noted that the CTD salinity measurement resolution is ≤0.001 PSU, accuracy is ≤±0.01 PSU, and the spatial sampling interval is controlled within 0.05–0.10 m. Significance: The 0.001 PSU resolution can capture minute salinity differences, and the 0.05–0.10 m vertical sampling interval can adapt to fine-scale changes in salinity gradients in tidal channels (e.g., the halocline thickness may be only 0.3–0.5 m), avoiding the omission of gradient features due to insufficient sampling. If the salinity within the halocline gradually changes from 30 PSU to 32 PSU, with a thickness of 0.4 m, four points can be collected at 0.10 m intervals, clearly depicting the continuous change in salinity from 30→30.5→31→32; if the interval is 0.2 m, only two points (30→32) are collected, and the gradual change process will be lost.

[0085] The control device can calculate the median S from salinity data (S1, S2, S3) from three consecutive vertical sampling points. med =median(S1,S2,S3) replaces the median point S2 with the median (or performs point-by-point sliding processing). This suppresses random noise (such as salinity jumps caused by instantaneous CTD jitter) and preserves the continuous trend of change.

[0086] The control device can calculate the median absolute deviation (MAD) of the vertical salinity sequence. For a single point Si, if |S i -S med If the value is greater than 3×MAD, it is considered an outlier and replaced with an interpolated value from an adjacent valid point. Compared to fixed threshold rejection, this method is more adaptable to the dynamic distribution of salinity (different background salinity values ​​during high and low tides) and accurately identifies "isolated spikes" (such as salinity abrupt changes caused by sensor bubble interference).

[0087] Next, the control device uses a third-order Savitzky-Golay filter (window m=15) to fit a third-order polynomial and reconstruct the data for the vertical sequences of temperature data T and salinity data S using a sliding window with a length of 15 sampling points (corresponding to vertical distances of 0.75–1.5m, with intervals of 0.05–0.10m).

[0088] This filters out high-frequency noise (such as small fluctuations in salinity caused by sensor electronic noise), making the salinity sequence smoother. The third-order polynomial can adapt to nonlinear changes in salinity. When there is a salinity abrupt change at the scale of 0.2m (such as the halocline interface), the abrupt change characteristics can still be retained after filtering (because when the window length of 15 corresponds to a vertical scale greater than 0.2m, the abrupt change will be captured by polynomial fitting).

[0089] Then, the preprocessed salinity sequence S(z) (where z is the vertical depth and the sampling point depth z) is processed. j =z0 + j × Δz, Δz = 0.05 - 0.10m, j = 1, 2, ..., n), the first vertical salinity gradient data GS(j) is defined as: It means the change in salinity per unit vertical distance (m) (PSU / m), reflecting the rate of change of salinity with depth.

[0090] Since the sampling interval Δz is basically uniform (0.05–0.10m), it can be simplified to Reduce computational load; for surface points (j=1), only the downward gradient can be calculated. For the bottom point (j=n), only the upward gradient can be calculated (or discarded, depending on the requirements).

[0091] Step a2: Determine the mixed layer in the target section based on the first vertical salinity gradient data corresponding to each test point.

[0092] Specifically, step a2 above may include the following steps:

[0093] Step a21: Compare the first vertical salinity gradient data corresponding to each test point with the preset dynamic gradient threshold.

[0094] Among them, the preset dynamic gradient threshold is related to the mean value of the first salinity gradient and the standard deviation of the salinity gradient corresponding to each test point.

[0095] Specifically, the control device can traverse the first vertical salinity gradient sequence G S (j), calculate the global mean and standard deviation

[0096] The control device is set to a preset dynamic gradient threshold. (Adjustable multiplier, such as 2–3 times the standard deviation). Compared to a fixed threshold (such as 0.2 PSU / m), the preset dynamic gradient threshold adapts to the differences in salinity gradients at different tidal stages and cross sections (the salinity gradient is generally larger during high tide and smaller during low tide), thus more accurately identifying gradient anomalies.

[0097] Then, the control device compares the first vertical salinity gradient data corresponding to each test point with the preset dynamic gradient threshold.

[0098] Step a22: If the first vertical salinity gradient data corresponding to the test point is greater than the preset dynamic gradient threshold, then the test point is determined as a candidate halocline point, and the preset area where the candidate halocline point is located is determined as the initial salinity anomaly area.

[0099] Specifically, if the first vertical salinity gradient data corresponding to the point to be measured is greater than the preset dynamic gradient threshold, the control device will determine the point to be measured as a candidate halocline point and the preset area where the candidate halocline point is located will be determined as the initial salinity anomaly area.

[0100] Optionally, if a preset number of candidate halocline points exist within a preset region, then the preset region is determined as the initial salinity anomaly region.

[0101] The preset quantity can be 3, 4, or other values.

[0102] Step a23: If there exists a first vertical salinity gradient data corresponding to the test point that is less than or equal to a preset dynamic gradient threshold, and the salinity data corresponding to the test point changes abruptly by more than a preset change threshold, then the test point is determined as a potential disturbance point, and the preset region where the potential disturbance point is located is determined as a potential disturbance region.

[0103] Specifically, if the first vertical salinity gradient data corresponding to the point to be measured is less than or equal to the preset dynamic gradient threshold, and the salinity data corresponding to the point to be measured changes abruptly, which is greater than the preset change threshold, then the control device will determine the point to be measured as a potential disturbance point and the preset area where the potential disturbance point is located will be determined as a potential disturbance area.

[0104] Optionally, if a preset number of potential disturbance points exist within a preset area, then the preset area is defined as a potential disturbance area.

[0105] The preset quantity can be 3, 4, or other values.

[0106] Step a24: Extract the first vertical salinity gradient data of multiple consecutive test points in the initial salinity anomaly region and the potential disturbance region, respectively.

[0107] Specifically, the control equipment can construct a three-dimensional spatiotemporal cube of "vertical depth-lateral position-time" based on the initial salinity anomaly area and potential disturbance area: vertical: extracted at intervals of 0.05–0.10m; lateral: extracted by extending the measurement line interval of the target section (e.g., 2m); time: backtracking / pre-fetching data of adjacent time moments according to the tidal cycle (e.g., 15-minute interval).

[0108] Then, the historical task library of the underwater robot equipment is called up to simultaneously extract the first vertical salinity gradient data of the lateral adjacent points and multiple test points one hour before and after the initial salinity anomaly area and potential disturbance area, forming a three-dimensional data block.

[0109] Step a25: If the first vertical salinity gradient data of multiple consecutive test points in the initial salinity anomaly region and / or potential disturbance region are consistent in direction and all exceed the preset dynamic gradient threshold, then the initial salinity anomaly region and / or potential disturbance region are determined to be time-stable regions.

[0110] Specifically, if the first vertical salinity gradient data of multiple consecutive test points in the initial salinity anomaly region and / or potential disturbance region are in the same direction and all exceed the preset dynamic gradient threshold, then the initial salinity anomaly region and / or potential disturbance region are determined to be time-stable regions.

[0111] Step a26: If the direction of the first vertical salinity gradient data of multiple consecutive test points in the initial salinity anomaly region and / or potential disturbance region changes abruptly, or if the first vertical salinity gradient data corresponding to a test point is less than or equal to a preset dynamic gradient threshold, then the initial salinity anomaly region and / or potential disturbance region is determined as a time-unstable region.

[0112] Specifically, if the direction of the first vertical salinity gradient data of multiple consecutive test points in the initial salinity anomaly region and / or potential disturbance region changes abruptly, or if the first vertical salinity gradient data corresponding to a test point is less than or equal to a preset dynamic gradient threshold, then the initial salinity anomaly region and / or potential disturbance region is determined to be a time-unstable region.

[0113] Step a27: Spatial verification is performed on the time-stable and / or time-unstable regions to determine the mixed layer in the target section.

[0114] Specifically, step a27 above may include the following steps:

[0115] Step a271: For time-stable regions and / or time-unstable regions, calculate the absolute value of the salinity difference between the current survey line and the adjacent survey line at the target depth.

[0116] Specifically, for time-stable regions and / or time-unstable regions, the control equipment can calculate the current survey line L at the target depth z based on the following formula. i Its adjacent survey line L i-1 The absolute value of the salinity difference.

[0117]

[0118] Among them, S i (z) represents the current survey line L at the target depth. i The corresponding salinity data, S represents the average salinity data corresponding to the current measurement line Li. i-1 (z) represents the adjacent survey line L at the target depth. i-1 The corresponding salinity data, For adjacent survey line L i-1 The corresponding average salinity data, For the current survey line L i The corresponding standard deviation of salinity data, For adjacent survey line L i-1 The corresponding standard deviation of salinity data, C T λ is the riverbed topographic curvature (obtained through multibeam bathymetry), representing the steepness of the terrain below the survey line; λ is the topographic influence coefficient (a learnable parameter, default 0.3), which amplifies the salinity difference weight in areas of abrupt topographic changes.

[0119] Step a272: If the absolute value of the salinity difference is greater than the preset salinity difference threshold, then the time-stable region and / or the time-unstable region are determined as candidate regions for the mixing layer.

[0120] Optionally, the control device can divide the target cross-section into a main channel area (preset salinity difference threshold F1 = μ1 + 1.5σ1), a side beach area (preset salinity difference threshold F2 = μ2 + 2σ2), and an estuary area (preset salinity difference threshold F3 = μ3 + 2.5σ3), where μ and σ are historical lateral salinity difference statistics. Then, based on the division of the target cross-section, the corresponding preset salinity difference thresholds are determined.

[0121] Optionally, the control device can also input the current water depth, current flow velocity, and current temperature parameters corresponding to the target cross-section into the preset salinity difference threshold determination model, and output a threshold correction factor α. Then, based on the correction factor α, the preset salinity difference threshold is calculated: Tfinal = T(t)·α. The training data for determining the preset salinity difference threshold comes from the correlation between historical mixing layer identification results and environmental parameters.

[0122] If the absolute value of the salinity difference is greater than the preset salinity difference threshold, the control device will identify the time-stable region and / or the time-unstable region as candidate regions for the mixing layer.

[0123] Step a273: If the absolute value of the salinity difference is less than or equal to the preset salinity difference threshold, then the time-stable region and / or time-unstable region are removed from the candidate regions of the mixing layer.

[0124] Optionally, if the absolute value of the salinity difference is less than or equal to a preset salinity difference threshold, then the time-stable region and / or time-unstable region are removed from the candidate regions of the mixing layer.

[0125] Optionally, if the absolute value of the salinity difference is less than or equal to a preset salinity difference threshold, but the ADCP displays a vertical velocity gradient G... U >0.5s -1 (Strong shear flow), and temperature gradient G T If the temperature is >0.2℃ / m, it is retained as a candidate region (possibly a region of strong mixing turbulence).

[0126] Step a274: Identify candidate regions of the mixed layer and determine the mixed layer in the target section.

[0127] Specifically, step a274 above may include the following steps:

[0128] Step a2741: Detect whether the second vertical salinity gradient data corresponding to the current survey line at the target depth in the candidate region of the mixed layer is greater than the preset dynamic gradient threshold.

[0129] Specifically, the control device acquires the second vertical salinity gradient data corresponding to the current survey line at the target depth (i.e., the vertical salinity gradient data corresponding to the intersection point of the target depth and the current survey line), and compares the second vertical salinity gradient data with a preset dynamic gradient threshold to detect whether the second vertical salinity gradient data is greater than the preset dynamic gradient threshold.

[0130] Step a2742: If the second vertical salinity gradient data is greater than the preset dynamic gradient threshold, then detect whether the third vertical salinity gradient data of the adjacent survey line corresponding to the current survey line corresponding to the target depth in the mixed layer candidate area is greater than the preset dynamic gradient threshold.

[0131] Specifically, if the second vertical salinity gradient data is greater than the preset dynamic gradient threshold, the control device detects whether the third vertical salinity gradient data (i.e., the vertical salinity gradient data corresponding to the intersection point of the target depth and the adjacent measurement line corresponding to the current measurement line) in the candidate area of ​​the mixed layer is greater than the preset dynamic gradient threshold.

[0132] Step a2743: If the third vertical salinity gradient data is greater than the preset dynamic gradient threshold, and the target depth is the first depth detected from the water surface downward in the candidate region of the mixed layer that satisfies the condition that the second vertical salinity gradient data is greater than the preset dynamic gradient threshold, the third vertical salinity gradient data is greater than the preset dynamic gradient threshold, and the absolute value of the salinity difference is greater than the preset salinity difference threshold, then the target depth is determined as the upper layer of the mixed layer.

[0133] Specifically, if the third vertical salinity gradient data is greater than the preset dynamic gradient threshold, and the target depth is the first depth detected from the water surface downwards in the candidate region of the mixing layer that satisfies the condition that the second vertical salinity gradient data is greater than the preset dynamic gradient threshold, the third vertical salinity gradient data is greater than the preset dynamic gradient threshold, and the absolute value of the salinity difference is greater than the preset salinity difference threshold, then the target depth is determined as the upper layer of the mixing layer.

[0134] Step a2744: If the target depth is the first depth detected from the bottom of the water body in the candidate region of the mixed layer that satisfies the condition that the second vertical salinity gradient data is greater than the preset dynamic gradient threshold, the third vertical salinity gradient data is greater than the preset dynamic gradient threshold, and the absolute value of the salinity difference is greater than the preset salinity difference threshold, then the target depth is determined as the lower layer of the mixed layer.

[0135] Specifically, if the third vertical salinity gradient data is greater than the preset dynamic gradient threshold, and the target depth is the first depth detected from the bottom of the water body in the candidate region of the mixing layer that satisfies the condition that the second vertical salinity gradient data is greater than the preset dynamic gradient threshold, the third vertical salinity gradient data is greater than the preset dynamic gradient threshold, and the absolute value of the salinity difference is greater than the preset salinity difference threshold, then the target depth is determined as the lower layer of the mixing layer.

[0136] Step a3: Determine the target test point in the mixed layer from all test points.

[0137] Specifically, after determining the lower and upper layers of the hybrid layer, the control device identifies the target test point located between the upper and lower layers of the hybrid layer from among the test points.

[0138] Step S2035: Correct the candidate flow rate corresponding to each target test point to obtain the target flow rate corresponding to each target test point.

[0139] Specifically, step S2035 above may include the following steps:

[0140] Step b1: Determine the thickness of the mixing layer and the mean value of the second salinity gradient corresponding to the mixing layer.

[0141] Specifically, the control device can calculate the thickness h = z of the hybrid layer based on the upper and lower boundaries of the hybrid layer. 下 -z 上 .

[0142] The control device can also calculate the mean value of the second salinity gradient of the mixed layer based on the first vertical salinity gradient data corresponding to each target test point in the mixed layer.

[0143] Optionally, the control device can calculate the weight information corresponding to each target test point based on the depth information corresponding to each target test point in the hybrid layer.

[0144] For example, w i =exp(-|z i -z mid | / h), where w i z represents the weight information corresponding to each target test point in the hybrid layer. i z represents the depth information corresponding to each target point in the hybrid layer. mid denoted as the center depth of the hybrid layer, and h as the thickness of the hybrid layer.

[0145] Then, the control device uses a weighted average method based on the weight information corresponding to each target test point to calculate the mean value of the second salinity gradient corresponding to the mixed layer.

[0146] For example, in, G represents the mean of the second salinity gradient corresponding to the mixed layer. S，i This represents the first vertical salinity gradient data corresponding to each target measurement point in the mixed layer.

[0147] Step b2: Determine the thickness threshold corresponding to the mixing layer based on the average value of the second salinity gradient.

[0148] Optionally, the control device can determine the thickness threshold of the mixing layer based on the correspondence between the average value of the second salinity gradient and the thickness threshold corresponding to the mixing layer.

[0149] Optionally, the control device can calculate the ratio of the mean of the second salinity gradient to the critical gradient: Among them G crit =0.1PSU / m (empirical critical value, corresponding to weak stratification).

[0150] When R G When the value is greater than 3, it is determined to be a "strongly stratified mixed layer" (dominated by salinity gradient, with suppressed turbulence), and the scale factor is determined to be c = 0.4; when 1 ≤ R G When R ≤ 3, it is determined to be a "transitional stratified mixed layer" (co-effect of gradient and turbulence), and the scale factor is determined to be c = 0.5; when R G When the value is less than 1, it is determined to be a "weakly stratified mixed layer" (turbulence-dominated, gradient influence is weak), and the scale factor is determined to be c = 0.6.

[0151] Then, the turbulent kinetic energy dissipation rate ε is calculated using ADCP velocity fluctuations, for example:

[0152]

[0153] Where ν is the fluid kinematic viscosity coefficient (approximately 1.002×10⁻⁶ m² / s for water at 20℃); u′, v′, and w′ are the velocity fluctuation components in the x, y, and z directions, respectively (the difference between instantaneous velocity and time-averaged velocity); the horizontal line “” indicates time averaging; each term is the squared average of the spatial partial derivatives of the fluctuating velocity, reflecting the turbulent contribution of the velocity gradient.

[0154] The mean value of the second salinity gradient is corrected based on the turbulent kinetic energy dissipation rate ε. For example: in, The corrected mean of the second salinity gradient, k = 0.05m 2 / J, the greater the turbulent kinetic energy, the weaker the actual impact of the gradient on stratification, and the corrected value is closer to the true stratification intensity.

[0155] Then, based on the corrected mean of the second salinity gradient and the scale factor, the thickness threshold corresponding to the mixing layer is calculated. For example:

[0156]

[0157] Wherein, δ = 0.05 PSU / m (a minimum value to avoid an infinite threshold when the gradient is zero). The larger the salinity gradient (the stronger the stratification), the smaller the threshold for the mixing layer thickness (which conforms to the rule of "strong stratification inhibits mixing, and the thickness is limited").

[0158] Step b3: Compare the thickness of the hybrid layer with the thickness threshold.

[0159] Specifically, the control device can compare the thickness of the hybrid layer with a thickness threshold.

[0160] Step b4: Based on the comparison results, correct the candidate flow rates corresponding to each target test point to obtain the target flow rates corresponding to each target test point.

[0161] Specifically, step b4 above may include the following steps:

[0162] Step b41: If the thickness of the hybrid layer is less than the thickness threshold, then obtain the upper boundary flow and lower boundary flow of the hybrid layer.

[0163] Specifically, if the thickness of the hybrid layer is less than the thickness threshold, the control device acquires the upper boundary flow and lower boundary flow corresponding to the hybrid layer.

[0164] Step b42: Obtain the first pose of the underwater robot when collecting the upper boundary flow rate and the second pose when collecting the lower boundary flow rate.

[0165] Specifically, the control device can obtain the first pose of the underwater robot when collecting the upper boundary flow and the second pose when collecting the lower boundary flow based on the positioning system mounted on the target robot.

[0166] Step b43: Determine the first weight corresponding to the upper boundary flow based on the first pose.

[0167] Step b44: Determine the second weight corresponding to the lower boundary flow based on the second pose.

[0168] Specifically, the attitude angle influence function:

[0169]

[0170] Where θ0 = 0 (ideal heading), φ0 = 0 (no roll), ψ0 = 0 (no pitch), σ θ =15°, σ φ =5°, σ ψ = 5° (angle error tolerance).

[0171] Depth stability weights: ; where σ z The standard deviation of depth during the measurement period is σ0 = 0.1m (allowable depth fluctuation).

[0172] The first weight is

[0173] The second weight is

[0174] Step b45: Based on the upper boundary flow, lower boundary flow, first weight, and second weight, the candidate flow corresponding to each target test point is corrected to obtain the target flow corresponding to each target test point.

[0175] Specifically, the control equipment can calculate the target flow rate corresponding to the target measurement point based on the following formula:

[0176]

[0177] in, (Thickness deviation weight, σ) h =T h / 3), when h < <T h When α→0, the correction mainly depends on the measured boundary flow.

[0178] Step b46: If the thickness of the hybrid layer is greater than or equal to the thickness threshold, then obtain the upper boundary momentum flux and lower boundary momentum flux of the hybrid layer.

[0179] Specifically, if the thickness of the hybrid layer is greater than or equal to the thickness threshold, the control device calculates the upper boundary momentum flux and the lower boundary momentum flux of the hybrid layer.

[0180] Upper boundary momentum flux M 上 M 上 =ρ·Q 上 ·U 上 (ρ is the density of water, U) 上 (The average velocity at the upper boundary);

[0181] Lower boundary momentum flux M 下 Similarly, M 下 =ρ·Q 下 ·U 下 .

[0182] Step b47: Based on the upper boundary momentum flux and the lower boundary momentum flux, correct the initial momentum flux corresponding to each target measurement point to obtain the target momentum flux.

[0183] Step b48: For each target measurement point, calculate the target flow rate corresponding to the target measurement point based on the target momentum flux.

[0184] Specifically, the initial momentum flux M 初始 : by candidate flow Q 候选 and cross-sectional average flow velocity U 平均 Calculate: M 初始 =ρ·Q 候选 ·U 平均 .

[0185] Momentum flux correction:

[0186] in (Thickness ratio function), when h >> Th, β → 1, and the correction strength is the maximum.

[0187] Flow rate is inferred from the corrected momentum flux:

[0188] in (Weighted average effective flow rate) is incorporated into pose weights to improve accuracy.

[0189] Step S2036: The candidate traffic corresponding to other test points besides the target test point is determined as the target traffic corresponding to the other test points.

[0190] Specifically, the control device determines the candidate flow rates corresponding to other test points besides the target test point as the target flow rates corresponding to those other test points.

[0191] The tidal channel flow determination method provided in this application obtains the original sound velocity corresponding to each measurement point, providing a benchmark for flow correction. By comparing it with the subsequently calculated true sound velocity, the impact of sound velocity error on the initial flow can be quantified, laying the foundation for accurate correction. Based on the water temperature, salinity, and depth data corresponding to each measurement point, the true sound velocity corresponding to each measurement point is calculated. Calculating the true sound velocity using real-time parameters accurately reflects the actual sound velocity environment at each measurement point, avoiding flow velocity measurement errors caused by discrepancies between preset and actual sound velocities, thereby ensuring the reliability of subsequent flow correction. Based on the relationship between the original and true sound velocities, the initial flow corresponding to each measurement point is corrected to obtain the candidate flow for each measurement point. By comparing the difference between the original and true sound velocities, the error caused by inaccurate sound velocity in the initial flow can be deduced in reverse. The same logic (sound velocity deviation correlation) is used to correct all measurement points, ensuring that the flow data at each point are comparable under the same benchmark, making the candidate flow closer to the true value, and providing consistent basic data for subsequent specific correction of the mixing layer. Based on the salinity data corresponding to each measurement point, the first vertical salinity gradient data for each measurement point is calculated. The first vertical salinity gradient directly reflects the intensity of water stratification. In tidal channels, the gradient value in the saline-freshwater confluence area is significantly higher than in areas with pure river water or pure seawater. By calculating the first vertical salinity gradient data, the "qualitative difference" in salinity can be transformed into a "quantitative indicator," providing a quantifiable basis for identifying the mixed layer, ensuring that no edge areas of the mixed layer are missed, and improving the sensitivity of identification. The first vertical salinity gradient data corresponding to each measurement point is compared with a preset dynamic gradient threshold. The preset dynamic gradient threshold is based on automatically adapting to the stratification intensity of different tidal river sections, avoiding missed or false judgments that may occur in complex environments with a fixed threshold. By comparing the first vertical salinity gradient data corresponding to each measurement point with the preset dynamic gradient threshold, the "anomaly" in the salinity gradient is transformed into an executable quantitative condition, providing a unified benchmark for subsequent area marking and reducing human judgment errors. If the first vertical salinity gradient data corresponding to a test point is greater than a preset dynamic gradient threshold, the test point is identified as a candidate halocline point, and the preset region where the candidate halocline point is located is identified as an initial salinity anomaly region. Candidate halocline points are regions where the salinity gradient significantly exceeds the threshold, directly corresponding to the region of most intense salt-freshwater mixing. Marking their preset region as an "initial salinity anomaly region" can quickly locate the core range of the mixing layer, providing a key object for subsequent verification. If the first vertical salinity gradient data corresponding to a test point is less than or equal to a preset dynamic gradient threshold, and the salinity data abrupt change corresponding to the test point is greater than a preset abrupt change threshold, the test point is identified as a potential perturbation point, and the preset region where the potential perturbation point is located is identified as a potential perturbation region.Points where the first vertical salinity gradient data is less than or equal to a preset dynamic gradient threshold, and where the salinity data abruptly changes beyond a preset abrupt change threshold, are marked as "potential disturbance regions." This avoids overlooking the edges of the mixing layer or transient disturbance regions, improving the comprehensiveness of mixing layer identification. The first vertical salinity gradient data of multiple consecutive test points are extracted from both the initial salinity anomaly region and the potential disturbance region. This reflects the temporal stability of the salinity gradient, avoiding misjudgments of regions due to single-point anomalies and providing data support for temporal stability verification. If the first vertical salinity gradient data of multiple consecutive test points in the initial salinity anomaly region and / or the potential disturbance region are aligned and all exceed the preset dynamic gradient threshold, then the initial salinity anomaly region and / or the potential disturbance region are determined to be time-stable regions. This preliminarily confirms the possibility that they are true mixing layers, reducing short-term turbulence interference. If the first vertical salinity gradient data direction of multiple consecutive test points in the initial salinity anomaly region and / or potential disturbance region changes abruptly, or if the first vertical salinity gradient data corresponding to a test point is less than or equal to a preset dynamic gradient threshold, then the initial salinity anomaly region and / or potential disturbance region are identified as temporally unstable regions. Further screening through subsequent spatial verification is required to avoid including non-mixed layer regions in the correction range. For temporally stable and / or temporally unstable regions, the absolute value of the salinity difference between the current survey line and adjacent survey lines at the target depth is calculated. By comparing the absolute value of the salinity difference between the current survey line and adjacent survey lines at the same depth, the mixed layer identification is extended from the temporal stability analysis of a single survey line to the spatial continuity analysis of multiple survey lines. This avoids misjudgments caused by anomalies in single survey line data, improves the spatial reliability of identification, reduces interference from non-critical depth data, and improves the efficiency and accuracy of spatial verification. If the absolute value of the salinity difference is greater than a preset salinity difference threshold, then the temporally stable and / or temporally unstable regions are identified as candidate mixed layer regions. If the absolute value of the salinity difference is greater than the preset salinity difference threshold, it indicates a significant lateral difference in salinity distribution between the current survey line and adjacent survey lines at the target depth, consistent with the spatial gradient distribution characteristics of the mixed layer (the confluence of fresh and saline water bodies) (e.g., salinity increasing from river to sea). Including such areas in the candidate pool can effectively capture the true range of the mixed layer with lateral continuity. The preset salinity difference threshold provides a clear standard for spatial verification, avoiding subjective judgment and ensuring consistency in comparisons between different survey lines. If the absolute value of the salinity difference is less than or equal to the preset salinity difference threshold, time-stable and / or time-unstable areas are removed from the mixed layer candidate areas, reducing "false samples" in the candidate areas and improving the purity of mixed layer identification. The second vertical salinity gradient data corresponding to the current survey line at the target depth in the mixed layer candidate area is checked to see if it is greater than the preset dynamic gradient threshold. By verifying whether the second vertical salinity gradient of the current survey line exceeds the dynamic threshold, areas within the mixed layer that may have significant salinity changes can be preliminarily identified, providing a basis for subsequent boundary identification.If the second vertical salinity gradient data is greater than the preset dynamic gradient threshold, then the third vertical salinity gradient data of the adjacent survey line corresponding to the target depth in the mixing layer candidate area is checked to see if it is greater than the preset dynamic gradient threshold. By comparing the second vertical salinity gradient data corresponding to the current survey line with the third vertical salinity gradient data corresponding to the adjacent survey line, it is verified whether the salinity gradient anomaly is spatially continuous (not an isolated phenomenon of a single survey line), eliminating vertical gradient anomalies caused by local disturbances (such as water flow eddies), and ensuring that the identified mixing layer boundary has spatial representativeness. If the third vertical salinity gradient data is greater than the preset dynamic gradient threshold, and the target depth is the first depth detected from the water surface downwards in the mixing layer candidate area that satisfies the conditions that the second vertical salinity gradient data is greater than the preset dynamic gradient threshold, the third vertical salinity gradient data is greater than the preset dynamic gradient threshold, and the absolute value of the salinity difference is greater than the preset salinity difference threshold, then the target depth is determined as the upper layer of the mixing layer. The setting of "the first depth to reach the target from the surface" directly identifies the significant vertical starting point of salinity mixing (such as the boundary between the freshwater layer and the mixing layer), resolving the issue of ambiguous upper boundary of the mixing layer (due to the susceptibility of surface water to wind and wave disturbances). Furthermore, it must simultaneously satisfy "the current survey line's vertical gradient meets the target, the adjacent survey line's vertical gradient meets the target, and the absolute value of the salinity difference meets the target," combining the significance of the vertical gradient with the differences in lateral distribution to avoid misjudging occasional surface salinity fluctuations as the boundary of the mixing layer. If the target depth is the first depth detected upwards from the bottom of the water body in the candidate region of the mixing layer that satisfies the following conditions: the second vertical salinity gradient data is greater than the preset dynamic gradient threshold, the third vertical salinity gradient data is greater than the preset dynamic gradient threshold, and the absolute value of the salinity difference is greater than the preset salinity difference threshold, then the target depth is determined as the lower layer of the mixing layer. The "first compliant depth upwards" corresponds to the boundary between the mixed layer and the lower seawater (or high-salinity water). By tracing back from the bottom, interference from factors such as bottom sediment disturbance on the salinity gradient can be effectively eliminated, accurately locating the vertical endpoint of the mixed layer. Furthermore, symmetrical with the upper boundary identification logic (both using "first compliant depth" + "multiple condition verification"), the upper and lower boundaries of the mixed layer form a closed vertical range, providing a clear spatial boundary reference for subsequent flow correction (especially flow within the mixed layer), avoiding correction deviations caused by boundary ambiguity.

[0192] Identify the target test points located in the hybrid layer from among the test points. By associating the range of the hybrid layer with specific test points, the target test points located within the hybrid layer can be directly screened out, providing a clear target for subsequent special correction of the traffic data in this area and avoiding invalid processing of non-hybrid layer points.

[0193] The thickness of the mixing layer and the mean of its second salinity gradient are determined. The thickness of the mixing layer directly reflects the vertical range of salinity mixing, while the mean of the second salinity gradient reflects the overall intensity of salinity changes within the mixing layer. Combining these two factors comprehensively characterizes the physical features of the mixing layer, providing a quantitative basis for subsequent calibration and avoiding the bias caused by relying on a single indicator. By calculating the mean of the second salinity gradient corresponding to the mixing layer, the thickness threshold can be matched with the actual salinity gradient level of the mixing layer, ensuring the rationality and relevance of the thickness threshold. The thickness threshold is determined based on the mean of the second salinity gradient of the mixing layer itself, rather than a preset fixed value. This allows for adaptation to changes in the characteristics of the mixing layer under different hydrological scenarios (such as high / low tide, wet / dry seasons) (for example, a mixing layer with a large salinity gradient may correspond to a smaller reasonable thickness threshold), improving the flexibility and applicability of the threshold. The thickness of the mixing layer is compared with a thickness threshold. This comparison allows for a quick determination of whether the current mixing layer thickness is within a reasonable range (e.g., a thickness exceeding the threshold may indicate the presence of atypical areas or data deviation), providing a clear basis for flow correction (correction needs to be strengthened for abnormal states). If the thickness of the mixing layer is less than the thickness threshold, the upper and lower boundary flows of the mixing layer are obtained. When the mixing layer thickness is small (less than the thickness threshold), its internal salinity and flow state are more easily affected by the upper and lower boundaries. Prioritizing the extraction of upper and lower boundary flows can capture the core elements affecting the mixing layer flow, avoiding correction deviations caused by internal data redundancy and improving the specificity of correction. The first pose of the underwater robot when collecting the upper boundary flow and the second pose when collecting the lower boundary flow are obtained. The pose of the underwater robot (e.g., depth, horizontal position, attitude angle) directly affects the accuracy of flow measurement (e.g., the flow velocity and direction may differ at different depths). Recording the pose allows the flow data to be bound to a specific spatial location, providing a physical basis for subsequent weight allocation. The first weight corresponding to the upper boundary flow rate is determined based on the first pose; the second weight corresponding to the lower boundary flow rate is determined based on the second pose. The first weight is determined based on the first pose, and the second weight is determined based on the second pose (rather than a fixed value). This reflects the measurement reliability of different boundaries (e.g., measurement points with more stable poses correspond to higher weights), ensuring that the contribution of the upper and lower boundary flow rates in the calibration matches the actual measurement quality, avoiding calibration bias caused by a "one-size-fits-all" approach. Based on the upper boundary flow rate, lower boundary flow rate, first weight, and second weight, the candidate flow rates corresponding to each target measurement point are calibrated to obtain the target flow rate for each target measurement point. By weighted fusion of key flow information from the upper and lower boundaries, the influence of the interaction between the upper and lower water bodies on the mixed layer can be comprehensively reflected, especially suitable for thin mixed layers (where internal data is not representative enough), making the calibrated target flow rate closer to the actual water flow state.Furthermore, the dynamic adjustment of weights can adapt to different measurement scenarios (e.g., if the pose of a certain upper boundary measurement is better, it will be given higher weight). Even if there is a slight error in the flow of a single boundary, its impact on the final result can be reduced through weight allocation, thereby enhancing the anti-interference ability of the correction process.

[0194] If the thickness of the mixing layer is greater than or equal to a thickness threshold, the upper and lower boundary momentum fluxes of the mixing layer are obtained. When the thickness of the mixing layer is greater than or equal to the thickness threshold, its internal flow state is mainly dominated by momentum transfer between the upper and lower boundaries (such as momentum flux caused by wind, bottom friction, etc.). Extracting the upper and lower boundary momentum fluxes can directly pinpoint the core power source affecting the overall movement of the mixing layer, avoiding the neglect of macroscopic driving mechanisms due to focusing on local details, and improving the physical correlation of flow calculation. Based on the upper and lower boundary momentum fluxes, the initial momentum fluxes corresponding to each target measurement point are corrected to obtain the target momentum flux. The initial momentum flux may be affected by measurement errors (such as instrument accuracy, environmental interference), while the boundary momentum flux reflects the energy exchange between the mixing layer and the external water body, and has stronger stability and representativeness. Correction based on the upper and lower boundary momentum fluxes can effectively offset local measurement errors, making the target momentum flux closer to the true dynamic state. For each target measurement point, the target flow rate corresponding to that point is calculated based on the target momentum flux. Momentum flux and flow rate (velocity × area) have a direct dynamic relationship (e.g., conservation of momentum, Newton's second law). Calculating the flow rate based on the corrected target momentum flux ensures that the flow rate data matches the actual dynamic process of the mixing layer, rather than relying solely on statistical regularities, thus enhancing the scientific rigor of the results. Furthermore, for thick mixing layers, the target momentum flux incorporates the overall influence of the boundary. The flow rate at each measurement point calculated based on this flux better reflects the spatial distribution trend of the flow rate within the layer (e.g., whether there are velocity differences due to momentum gradients), avoiding misjudgments of overall flow characteristics due to local data fluctuations and improving the reliability of the results.

[0195] This application provides an underwater robot device, such as... Figure 3 As shown, the underwater robot equipment includes: an underwater robot body, an acoustic Doppler flow profiler, an integrated conductivity-temperature-depth meter, and control equipment; wherein, the acoustic Doppler flow profiler, the integrated conductivity-temperature-depth meter, and the control equipment are all mounted on the underwater robot body, and the acoustic Doppler flow profiler and the integrated conductivity-temperature-depth meter are both communicatively connected to the control equipment; wherein:

[0196] Acoustic Doppler flow profiler is used to detect the initial flow rate at each test point in a target section of a tidal channel.

[0197] It integrates a conductivity-temperature-depth meter to monitor water temperature, salinity, and depth data at each measurement point;

[0198] A control device for performing the tidal channel flow determination method according to any of the above embodiments.

[0199] For a detailed introduction to underwater robotic equipment, please refer to the above introduction on the method for determining the flow rate of tidal channels, which will not be repeated here.

[0200] The underwater robot device provided in this application uses an acoustic Doppler flow profiler to acquire the initial flow rate at each test point in the target cross-section of a tidal channel. By initially collecting the flow rate at each test point within the cross-section, full coverage of the flow distribution across the entire target cross-section is achieved, avoiding overall flow estimation deviations due to single-point omissions and providing a complete initial reference for subsequent correction. Based on an integrated conductivity-temperature-depth meter, water temperature, salinity, and depth data are acquired for each test point, ensuring the accuracy of these data and avoiding correction deviations caused by distortion of basic parameters, thus guaranteeing the effectiveness of subsequent flow correction from the source. The control device corrects the initial flow rate at each test point based on the water temperature, salinity, and depth data to obtain the target flow rate at each test point in the target cross-section. By utilizing water temperature and salinity data, combined with depth data, the cross-sectional area calculation is optimized, accurately correcting flow deviations in complex areas such as the mixing layer and halocline, making the results closer to the actual water flow state. In addition, by performing point-by-point calibration, it is ensured that the flow data of each measurement point within the target section is adapted to its local environment. The final total flow of the section can more accurately reflect the hydrodynamic characteristics of the tidal channel, providing high-precision data support for flood control scheduling and water resource management.

[0201] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for determining the flow rate of a tidal river, characterized in that, A control device applied in an underwater robot, the method comprising: Obtain the initial flow rate at each measurement point in the target cross-section of the tidal channel; Obtain water temperature data, salinity data, and depth data corresponding to each of the measured points; Based on the water temperature data, the salinity data, and the depth data, the initial flow rate corresponding to each of the measured points is corrected to obtain the target flow rate corresponding to each of the measured points in the target cross section. The step of correcting the initial flow rate corresponding to each of the measured points based on the water temperature data, the salinity data, and the depth data to obtain the target flow rate corresponding to each of the measured points in the target cross-section includes: Obtain the original sound velocity corresponding to each of the measured points; Based on the water temperature data, salinity data and depth data corresponding to each of the measured points, the true speed of sound corresponding to each of the measured points is calculated. Based on the relationship between the original sound velocity and the true sound velocity, the initial flow rate corresponding to each of the test points is corrected to obtain the candidate flow rate corresponding to each of the test points. Target test points located in the mixing layer are determined from each of the test points; the mixing layer represents the cross-sectional layer in the target section of the tidal channel where river water and seawater mix; The candidate flow rate corresponding to each target test point is corrected to obtain the target flow rate corresponding to each target test point; The candidate flows corresponding to other test points besides the target test point are determined as the target flows corresponding to the other test points; The step of determining the target test point in the mixing layer from each of the test points includes: Based on the salinity data corresponding to each of the measured points, calculate the first vertical salinity gradient data corresponding to each of the measured points; Based on the first vertical salinity gradient data corresponding to each of the test points, the mixing layer in the target section is determined; The target test point located in the hybrid layer is determined from each of the test points.

2. The method according to claim 1, characterized in that, The step of determining the mixing layer in the target section based on the first vertical salinity gradient data corresponding to each of the measured points includes: The first vertical salinity gradient data corresponding to each of the test points is compared with a preset dynamic gradient threshold; the preset dynamic gradient threshold is related to the mean and standard deviation of the first salinity gradient corresponding to each of the test points. If the first vertical salinity gradient data corresponding to the test point is greater than the preset dynamic gradient threshold, then the test point is determined as a candidate halocline point, and the preset area where the candidate halocline point is located is determined as the initial salinity anomaly area. If the first vertical salinity gradient data corresponding to the test point is less than or equal to the preset dynamic gradient threshold, and the salinity data corresponding to the test point changes abruptly by more than the preset change threshold, then the test point is determined as a potential disturbance point, and the preset region where the potential disturbance point is located is determined as a potential disturbance region. The first vertical salinity gradient data of multiple consecutive test points are extracted from the initial salinity anomaly region and the potential disturbance region, respectively. If the first vertical salinity gradient data of multiple consecutive test points in the initial salinity anomaly region and / or the potential disturbance region are in the same direction and all exceed the preset dynamic gradient threshold, then the initial salinity anomaly region and / or the potential disturbance region are determined to be time-stable regions. If the direction of the first vertical salinity gradient data of multiple consecutive test points in the initial salinity anomaly region and / or the potential disturbance region changes abruptly, or if the first vertical salinity gradient data corresponding to a test point is less than or equal to the preset dynamic gradient threshold, then the initial salinity anomaly region and / or the potential disturbance region are determined to be time-unstable regions. Spatial verification is performed on the time-stable region and / or the time-unstable region to determine the hybrid layer in the target section.

3. The method according to claim 2, characterized in that, The step of spatially verifying the time-stable region and / or the time-instantaneous region to determine the mixing layer in the target section includes: For the time-stable region and / or the time-unstable region, calculate the absolute value of the salinity difference between the current survey line and the adjacent survey line at the target depth; If the absolute value of the salinity difference is greater than a preset salinity difference threshold, then the time-stable region and / or the time-unstable region are determined as candidate regions for the mixing layer. If the absolute value of the salinity difference is less than or equal to a preset salinity difference threshold, then the time-stable region and / or the time-unstable region will be removed from the candidate region of the mixing layer. The candidate regions of the hybrid layer are identified to determine the hybrid layer in the target cross section.

4. The method according to claim 3, characterized in that, The step of identifying the candidate regions of the hybrid layer and determining the hybrid layer in the target cross-section includes: Detect whether the second vertical salinity gradient data corresponding to the current measurement line at the target depth in the candidate region of the mixed layer is greater than the preset dynamic gradient threshold; If the second vertical salinity gradient data is greater than the preset dynamic gradient threshold, then it is detected whether the third vertical salinity gradient data of the adjacent survey line corresponding to the current survey line corresponding to the target depth in the mixed layer candidate region is greater than the preset dynamic gradient threshold. If the third vertical salinity gradient data is greater than the preset dynamic gradient threshold, and the target depth is the first depth detected from the water surface downwards in the candidate region of the mixing layer that satisfies the following conditions: the second vertical salinity gradient data is greater than the preset dynamic gradient threshold, the third vertical salinity gradient data is greater than the preset dynamic gradient threshold, and the absolute value of the salinity difference is greater than the preset salinity difference threshold, then the target depth is determined as the upper layer of the mixing layer. If the target depth is the first depth detected from the bottom of the water body upward in the candidate region of the mixing layer that satisfies the following conditions: the second vertical salinity gradient data is greater than the preset dynamic gradient threshold, the third vertical salinity gradient data is greater than the preset dynamic gradient threshold, and the absolute value of the salinity difference is greater than the preset salinity difference threshold, then the target depth is determined as the lower layer of the mixing layer.

5. The method according to claim 1, characterized in that, The step of correcting the candidate flow corresponding to each of the target test points to obtain the target flow corresponding to each of the target test points includes: Determine the thickness of the mixing layer and the mean value of the second salinity gradient corresponding to the mixing layer; The thickness threshold corresponding to the mixing layer is determined based on the average value of the second salinity gradient. The thickness corresponding to the hybrid layer is compared with the thickness threshold. Based on the comparison results, the candidate flow rates corresponding to each target test point are corrected to obtain the target flow rates corresponding to each target test point.

6. The method according to claim 5, characterized in that, The step of correcting the candidate flow corresponding to each target test point based on the comparison results to obtain the target flow corresponding to each target test point includes: If the thickness of the hybrid layer is less than the thickness threshold, then the upper boundary flow and lower boundary flow of the hybrid layer are obtained. Obtain the first pose of the underwater robot device when collecting the upper boundary flow rate and the second pose when collecting the lower boundary flow rate; The first weight corresponding to the upper boundary flow is determined based on the first pose. The second weight corresponding to the lower boundary flow is determined based on the second pose. Based on the upper boundary flow, the lower boundary flow, the first weight, and the second weight, the candidate flow corresponding to each target test point is corrected to obtain the target flow corresponding to each target test point.

7. The method according to claim 5, characterized in that, The step of correcting the candidate flow corresponding to each target test point based on the comparison results to obtain the target flow corresponding to each target test point includes: If the thickness of the hybrid layer is greater than or equal to the thickness threshold, then the upper boundary momentum flux and lower boundary momentum flux of the hybrid layer are obtained. Based on the upper boundary momentum flux and the lower boundary momentum flux, the initial momentum flux corresponding to each of the target test points is corrected to obtain the target momentum flux; For each target measurement point, the target flow rate corresponding to the target measurement point is calculated based on the target momentum flux.

8. An underwater robot device, characterized in that, The underwater robot equipment includes: an underwater robot body, an acoustic Doppler flow profiler, an integrated conductivity-temperature-depth meter, and a control device; wherein, the acoustic Doppler flow profiler, the integrated conductivity-temperature-depth meter, and the control device are all mounted on the underwater robot body, and the acoustic Doppler flow profiler and the integrated conductivity-temperature-depth meter are communicatively connected to the control device; wherein: The acoustic Doppler flow profiler is used to detect the initial flow rate at each test point in the target section of a tidal channel. The integrated conductivity-temperature-depth meter is used to monitor the water temperature, salinity and depth data corresponding to each of the test points; The control device is used to perform the method for determining the tidal channel flow rate according to any one of claims 1-7.

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