Method and system for regulating water level of a water conservancy project

Abstract

The application provides a water level regulation method and system for a water conservancy hub. The method includes the following steps: collecting data by underwater robots and satellite remote sensing data simultaneously, constructing a sediment deposition space-time atlas containing a deposition gradient, and periodically correcting the space-time atlas. A radar array is deployed in the curved section of the spillway, and a multi-frequency pulse signal is used to analyze the three-dimensional sediment flow velocity field vector distribution. Through the correlation analysis of the dynamic coupling of the deposition gradient and the flow velocity field, the correlation area of the flow velocity mutation and the sediment deposition is accurately identified, and a constraint condition set containing coordinate positioning and flow-silt matching rules is established. Combined with real-time water level monitoring data, the constraint condition set is used to calculate and generate a dynamic adjustment parameter sequence of the spillway gate opening, and the water level is accurately regulated. The technical scheme provided by the application can accurately regulate the water level of the water conservancy hub by intelligently optimizing the spillway parameters, effectively alleviate the sediment deposition problem in the high-sediment water area, and ensure the safe operation and flood control efficiency of the hub.

Description

Technical Field

[0001] This application relates to the field of automated control technology for water conservancy projects, and in particular to a method and system for regulating the water level of a water conservancy hub. Background Technology

[0002] In the operation of water conservancy projects on rivers with high sediment content, the ability of water flow to carry sediment is closely related to dynamic changes in water level. Precise regulation is needed to achieve a synergistic goal of controlling sediment deposition in the reservoir area and maintaining water level stability. These scenarios require water conservancy projects to ensure flood control safety, power generation efficiency, and ecological needs, while simultaneously monitoring sediment transport in real time and dynamically adjusting discharge strategies to avoid problems such as siltation in front of the gates, reservoir capacity loss, and severe scouring of the downstream riverbed.

[0003] Currently, the main technical solution to this need is an automated control system that combines hydrodynamic models with real-time monitoring data. This system continuously collects water level, flow velocity, and sediment concentration data by deploying underwater sensors, uses a numerical model coupled with hydrodynamic and sediment transport mechanisms to predict short-term sedimentation trends, and generates optimized instructions for gate opening and closing or unit operation based on the prediction results.

[0004] However, existing solutions still face some bottlenecks in practical applications. The accuracy of the sand coupling model is limited by the ability to obtain parameters such as sediment particle size distribution in real time. Especially under extreme sediment concentration conditions, the model is prone to deviation, leading to a mismatch between control commands and actual operating conditions. Secondly, sensors are prone to signal attenuation or data drift in high-turbidity water bodies, and the lack of key parameters may cause control logic failure. Summary of the Invention

[0005] This application provides a method and system for regulating the water level of a water conservancy hub, in order to solve the problem that limits the robustness and universality of the system in high sediment content scenarios in the prior art.

[0006] Firstly, this application provides a method for regulating the water level of a water conservancy project, comprising:

[0007] For waters with high sediment content in the reservoir area of ​​a water conservancy hub and the downstream section of the flood discharge channel, multi-dimensional scanning data of the riverbed topography by an underwater robot and full-area coverage data of satellite remote sensing are acquired simultaneously to generate a spatiotemporal map of sediment deposition containing sediment gradients, and the spatiotemporal map of sediment deposition is periodically corrected based on the changes in the sediment gradients.

[0008] A radar array is deployed at the bend transition section of the spillway outlet. Based on the corrected spatiotemporal map of sediment deposition, the corresponding three-dimensional sediment velocity field vector distribution is calculated in real time by the difference in scattering intensity and phase shift trajectory of multi-frequency pulse signals.

[0009] The deposition gradient is dynamically coupled with a three-dimensional sediment flow velocity field vector distribution to analyze a correlation region of flow velocity mutation and sediment deposition in the transition section of the bend, and a constraint condition set including coordinates of the correlation region and matching rules of flow velocity and deposition is generated;

[0010] According to the constraint condition set, real-time water level monitoring data and a target water level interval of a water conservancy hub reservoir are combined to calculate a dynamic adjustment parameter sequence of a flood discharge gate opening degree.

[0011] Optionally, time series data of the deposition gradient and the three-dimensional sediment flow velocity field vector distribution are spatially superimposed based on a geographic coordinate system of the bend transition section to generate a synchronously updated deposition flow velocity coupling data set;

[0012] In the deposition flow velocity coupling data set, a flow velocity vector module length mutation region that meets the deposition amount matching rule in the three-dimensional sediment flow velocity field is detected, and the flow velocity vector module length mutation region is marked as a primary flow velocity mutation zone;

[0013] The second derivative distribution of the deposition gradient is superimposed on the primary flow velocity mutation zone to screen out a flow velocity deposition correlation region that simultaneously meets an extreme value judgment rule and has a mutation in the deposition gradient;

[0014] A three-dimensional geographic coordinate set of the flow velocity deposition correlation region is extracted, and a numerical correspondence between the deposition gradient and the flow velocity vector module length in the flow velocity deposition correlation region is counted, and a constraint condition set including coordinates of the correlation region and matching rules of flow velocity and deposition is generated according to the numerical correspondence.

[0015] Optionally, based on the spatial unit flow velocity vector length distribution of the primary flow velocity mutation zone, a historical reference and a current period reference of the flow velocity vector length are set, and a primary screening region is defined in a spatial unit that simultaneously meets the historical reference and the current period reference;

[0016] Deposition gradient data of each position point in the primary screening region is extracted, and the second derivative distribution of the deposition gradient in adjacent time periods is superimposed and calculated for each position point;

[0017] Parallelly distributed virtual measurement lines are set in the primary screening region along the direction of the water flow, a discrete point sequence is generated for each virtual measurement line at an array interval, and the second derivative distribution calculation result corresponding to the discrete point sequence is mapped as a second derivative intensity distribution;

[0018] Based on the overall distribution state of the second derivative intensity distribution, an extreme value judgment rule is set, and adjacent discrete point regions that continuously meet the extreme value judgment rule are marked as deposition gradient mutation regions;

[0019] Superimpose the sedimentation gradient mutation zone along the vertical direction of the virtual measurement line in three-dimensional space, fuse the spatial coordinates of the primary screening area and the distribution range of the sedimentation gradient mutation zone, and define a set of superimposed space units that simultaneously satisfy the flow velocity vector length reference condition and the extreme value judgment rule as the flow velocity sedimentation correlation area.

[0020] Optionally, a multi-band pulse signal containing a low-frequency penetration signal and a high-frequency tracking signal is synchronously transmitted, and after receiving the reflected signal, a sediment density distribution map penetrating to the riverbed bottom layer and a surface sediment motion trajectory map are generated by separation;

[0021] The sediment accumulation density variation rate of each depth layer in the vertical direction is extracted from the sediment density distribution map, and the sedimentation gradient at the corresponding position in the corrected sediment deposition space-time map is combined to generate a vertical density gradient correlation profile.

[0022] The surface sediment motion trajectory map is decomposed in three-dimensional space, and a surface particle motion direction angle distribution map is established according to the spatial position offset of the reflected signal feature points in the continuous transmission period.

[0023] The vertical density gradient correlation profile and the surface particle motion direction angle distribution map are matched in space position, the surface motion direction angle data at the corresponding position of the vertical density gradient mutation zone is superimposed, and the corresponding three-dimensional sediment flow velocity field vector distribution is generated by vector direction transmission.

[0024] Optionally, the depth layer data of the vertical density gradient correlation profile and the plane coordinates of the surface particle motion direction angle distribution map are matched in space position, and the vertical density gradient mutation zone corresponding to the plane coordinates is screened out.

[0025] The surface motion direction angle data corresponding to the plane coordinates of the vertical density gradient mutation zone is extracted, and the horizontal motion vector of each plane coordinate point is calculated according to the surface motion direction angle data.

[0026] The gradient direction variation amount of each position point in the vertical density gradient mutation zone is determined, and the corresponding vertical direction correction amount value is calculated based on the gradient direction variation amount.

[0027] After superimposing the horizontal motion vector and the vertical direction correction amount value, the gradient direction is transmitted layer by layer, and the vector direction is corrected based on the gradient direction and the surface direction angle offset angle.

[0028] The corrected motion vectors of all depth layers in the vertical density gradient mutation zone are superimposed in space to generate a three-dimensional sediment flow velocity field vector distribution covering the corresponding plane coordinates.

[0029] Optionally, the underwater robot is configured to set a scanning path for a riverbed terrain, synchronously acquire sediment accumulation thickness and multi-dimensional scanning data of each scanning point, and synchronously acquire global coverage data of satellite remote sensing within a robot scanning interval;

[0030] The sediment accumulation thickness of each scanning point is calculated for a thickness change difference in a continuous scanning period, the thickness change difference and a horizontal distance ratio of adjacent scanning points are taken as a deposition gradient, the multi-dimensional scanning data and the global coverage data are combined to generate a sediment deposition space-time atlas containing a gradient direction and a deposition gradient;

[0031] When the direction of the deposition gradient of any scanning point continuously deviates from an initial direction by more than a set angle, or the numerical value of the deposition gradient fluctuates by more than a set proportion, the scanning path of the region where the point is located is encrypted;

[0032] The sediment accumulation thickness is reacquired in the encrypted scanning region, the numerical value and direction of the deposition gradient are corrected based on the new sediment accumulation thickness data, and the data of the corresponding region in the original space-time atlas is covered.

[0033] Optionally, real-time water level monitoring data of a water conservancy hub reservoir area is acquired, a deviation amount of a current water level from a median value in a target water level interval is calculated, and a water level change trend strength in a future period is predicted based on water level change history data;

[0034] The flow rate threshold and the deposition gradient threshold of each associated region in the constraint condition set are analyzed, a ratio relationship between a current measured flow rate and the flow rate threshold is converted into a flow rate adjustment parameter, and a ratio relationship between a current deposition gradient and the deposition gradient threshold is converted into a deposition gradient adjustment parameter;

[0035] The flow rate adjustment parameter and the deposition gradient adjustment parameter are weighted and fused to generate an initial adjustment parameter value of each associated region, and the initial adjustment parameter value is directionally corrected based on a water level deviation direction and a trend strength;

[0036] The corrected adjustment parameter values of each region are arranged in a time sequence, it is ensured that a parameter change amplitude of adjacent periods does not exceed a gate mechanical action safety threshold, and a parameter difference of spatially adjacent regions is smoothed to generate a dynamic adjustment parameter sequence of a flood discharge gate opening degree.

[0037] In a second aspect, the application provides a water level regulation system of a water conservancy hub, comprising:

[0038] The acquisition module synchronously acquires multi-dimensional scanning data of a riverbed terrain by an underwater robot and global coverage data by satellite remote sensing for a high-silt-content water area of a reservoir area of a water conservancy hub and a downstream river section of a spillway, to generate a silt deposition space-time atlas containing a deposition amount gradient, and periodically correct the silt deposition space-time atlas based on a change of the deposition amount gradient;

[0039] The solving module arranges a radar array at a bend transition section of an outlet of the spillway, and solves a corresponding three-dimensional silt flow velocity field vector distribution in real time based on the corrected silt deposition space-time atlas, through a difference in scattering intensity and a phase shift track of multi-frequency band pulse signals.

[0040] The generating module dynamically couples and analyzes the deposition amount gradient and the three-dimensional silt flow velocity field vector distribution, identifies a correlation area of flow velocity mutation and silt deposition in the bend transition section, and generates a constraint condition set containing coordinates of the correlation area and a matching rule of flow velocity and deposition amount.

[0041] The computing module calculates a dynamic adjustment parameter sequence of a spillway gate opening degree according to the constraint condition set, in combination with real-time water level monitoring data and a target water level interval of the reservoir area of the water conservancy hub.

[0042] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component, to implement the water level adjustment method of the water conservancy hub as described in the first aspect.

[0043] In a fourth aspect, an embodiment of the present application provides a computer storage medium, which stores a computer program; when the computer program is executed by a computer, a water level adjustment method of a water conservancy hub as described in the first aspect is implemented.

[0044] The embodiment of the application is aimed at high-sediment-content water areas in the reservoir area of a water conservancy hub and the downstream river section of a spillway. Multi-dimensional scanning data of the riverbed terrain by an underwater robot and global coverage data by satellite remote sensing are synchronously acquired to generate a sediment deposition space-time atlas containing a deposition gradient. The sediment deposition space-time atlas is periodically corrected based on changes in the deposition gradient. A radar array is arranged at a transition section of a bend at the outlet of the spillway. Based on the corrected sediment deposition space-time atlas, a three-dimensional sediment flow velocity field vector distribution is solved in real time by differences in scattering intensity and phase shift trajectories of multi-frequency band pulse signals. The deposition gradient and the three-dimensional sediment flow velocity field vector distribution are dynamically coupled and analyzed to identify a correlation area of flow velocity mutation and sediment deposition in the transition section of the bend, and a constraint condition set containing coordinates of the correlation area and matching rules of flow velocity and deposition amount is generated. According to the constraint condition set, real-time water level monitoring data and a target water level interval of the reservoir area of the water conservancy hub are combined to calculate a dynamic adjustment parameter sequence of the opening degree of the spillway gate.

[0045] The application has the following beneficial effects:

[0046] The application constructs a high-precision sediment deposition space-time atlas through multi-source data fusion (underwater robot scanning and satellite remote sensing), analyzes a three-dimensional sediment flow velocity field by a radar array with multi-frequency band signals, dynamically couples a deposition gradient and a flow velocity field vector, accurately identifies a correlation area of flow velocity mutation and sediment deposition in a bend section, intelligently calculates a spillway gate control parameter sequence based on a constraint rule set and real-time water level data, and finally realizes accurate prevention and control of sediment deposition in a high-sediment-content water area and dynamic optimization of the water level, significantly improving the flood control safety, reservoir stability and sediment discharge efficiency of the water conservancy hub, while taking into account the automation decision-making and multi-target collaborative control capability.

[0047] Further, by spatially superimposing time series data of the deposition gradient and the three-dimensional sediment flow velocity field vector based on a geographical coordinate system of the transition section of the bend, a dynamically updated deposition-flow velocity coupling data set is generated. Based on the deposition amount matching rule, a flow velocity vector length mutation area in the data set is detected and marked as a primary flow velocity mutation area. A deposition gradient second derivative distribution is further superimposed to filter out a deposition gradient mutation overlapping area that simultaneously satisfies the extreme value condition, and finally the three-dimensional coordinates and the deposition-flow velocity numerical relationship thereof are extracted to generate a constraint condition set containing the matching rules. The spatio-temporal resolution and decision reliability of sediment deposition risk positioning are significantly improved, thereby optimizing the targeted sediment discharge control capability of the spillway gate and ensuring the accuracy of water level control and the safety of hub operation.

[0048] These and other aspects of the application will become more apparent from the following description of embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0050] Figure 1 A flow chart of a water level regulation method of a water conservancy hub provided by the present application is shown;

[0051] Figure 2 A structural schematic diagram of a water level regulation system of a water conservancy hub provided by the present application is shown;

[0052] Figure 3 A structural schematic diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0053] In order to enable personnel in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0054] In some of the processes described in the specification and claims of the present application and the above-described drawings, a plurality of operations appear in a specific order, but it should be clearly understood that these operations can be executed or performed in parallel or in a different order from that in which they appear in this text. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes can include more or fewer operations, and the operations can be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this text are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence. Also, "first" and "second" are not of different types.

[0055] Researchers found that due to the coupling effect of sediment dynamic deposition and complex flow field, the traditional water level regulation method of water conservancy hub in high-sand water area has problems such as insufficient sediment monitoring accuracy, lack of flow rate-deposition correlation modeling and gate regulation lag, resulting in loss of reservoir capacity, low sediment discharge efficiency and increased flood control risk. Based on this, a multi-source data driven intelligent water level regulation method of water conservancy hub is provided. The method can construct a dynamic sediment deposition atlas by underwater robot scanning and satellite remote sensing fusion, combine radar array to solve three-dimensional flow rate field in real time, and generate targeted constraint rules based on deposition-flow rate dynamic coupling analysis, and finally realize precise timing regulation of gate opening.

[0056] The technical solution of the present application can be applied to the management of water conservancy hubs of high-sand-content rivers, and is particularly suitable for the prevention and control of sediment and the optimization of water level control in complex flow state scenarios such as the transition section of the spillway bend and the sediment accumulation sensitive area in the reservoir area.

[0057] The technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0058] Figure 1 A flowchart of a water level regulation method for a water conservancy hub is provided for the embodiments of the present application, as shown in Figure 1 The method comprises:

[0059] 101. For the high-sand-content water area of the reservoir area of the water conservancy hub and the downstream river section of the spillway, multi-dimensional scanning data of the riverbed topography by an underwater robot and global coverage data by satellite remote sensing are synchronously acquired to generate a sediment deposition space-time graph containing a deposition gradient, and the sediment deposition space-time graph is periodically corrected based on the change of the deposition gradient;

[0060] In step 101, the reservoir area of the water conservancy hub refers to the water storage area formed by the interception of the dam; the downstream river section of the spillway refers to the transition area from the outlet of the spillway gate to the flat section of the downstream river; the high-sand-content water area refers to water with a suspended sediment concentration of more than 50 kilograms per cubic meter; the multi-dimensional scanning data of the underwater robot refers to the riverbed topography data collected by the autonomous underwater vehicle equipped with a multi-beam sonar and a three-dimensional laser scanner; the satellite remote sensing global coverage data refers to the watershed monitoring data fused by high-resolution synthetic aperture radar and multispectral images; the deposition gradient refers to the rate of change of the thickness of sediment deposition per unit distance; and the sediment deposition space-time graph refers to a three-dimensional model of the deposition distribution containing the time dimension and the space dimension.

[0061] In the embodiments of the present application, first, underwater robots equipped with 1.5 megahertz multi-beam sonar and 532 nanometer three-dimensional laser scanners are deployed in high-silt water areas of the reservoir area and the downstream river section of the spillway to scan the reservoir area and the downstream river section at a 0.5-meter grid resolution, generate riverbed topography point cloud data, for example, a certain area is scanned to obtain a sediment thickness of 1.2 meters, the adjacent grid has a sediment thickness of 1.5 meters, and the sedimentation gradient is calculated to be 0.3 meters per meter. Satellite remote sensing global coverage data including satellite multispectral images and synthetic aperture radar data are received synchronously, and point cloud registration algorithms are used to fuse the satellite data and underwater scanning data, for example, the 10-meter resolution grid of the satellite image is aligned with the 0.5-meter grid of the underwater scanning. Secondly, based on the grid alignment, a three-dimensional sediment deposition space-time atlas is constructed based on a three-dimensional interpolation algorithm, each grid element in the model contains a sedimentation gradient parameter, for example, the sedimentation gradient of a certain grid element is 0.05 meters per day. Finally, a filtering algorithm is used to periodically correct the atlas, and the correction period is set to 24 hours. When the sedimentation gradient change rate of the key area is detected to exceed 0.01 meters per hour, real-time updating is triggered.

[0062] In the flood control of a certain water conservancy hub in the middle reaches of a river, a cluster of underwater robots equipped with three-dimensional terrain scanning systems are deployed in the high-silt river section of the reservoir area and the downstream of the spillway. These robots conduct high-precision topographic mapping along the longitudinal section of the river, and simultaneously obtain the global surface suspended sediment concentration distribution in real time by combining high-resolution satellite synthetic aperture radar data. By fusing the terrain point cloud data of the underwater robots and the satellite-inverted sediment dynamic information, the system constructs a three-dimensional sediment deposition space-time atlas containing sedimentation gradient. Every 6 hours, the system automatically introduces the latest collected riverbed sediment particle size analysis data to dynamically correct the prediction model of the deposition rate in the atlas, so that the calculation error of the reservoir sediment accumulation is controlled within 8%.

[0063] 102. A radar array is arranged at the transition section of the bend at the outlet of the spillway. Based on the corrected sediment deposition space-time atlas, the three-dimensional sediment flow velocity field vector distribution is calculated in real time by the difference in scattering intensity and the phase shift trajectory of multi-frequency pulse signals.

[0064] In step 102, the transition section of the bend at the outlet of the spillway refers to the arc-shaped curved section where the spillway connects with the downstream river; the radar array refers to a monitoring system composed of multi-frequency radars; the difference in scattering intensity of multi-frequency pulse signals refers to the difference in return power after different frequency electromagnetic waves interact with sediment particles; the phase shift trajectory refers to the spatial distribution characteristics of the phase change of the echo signal; and the three-dimensional sediment flow velocity field vector distribution refers to a three-dimensional space vector field containing flow velocity and direction.

[0065] In this embodiment, a radar array is first deployed at the transition section of the bend at the outlet of the spillway. For example, the node spacing of the radar array is 5 meters, covering a fan-shaped monitoring area of ​​120 degrees. Each radar array node alternately transmits multi-band pulse signals, such as an X-band pulse signal with a frequency of 9.5 GHz and a Ka-band pulse signal with a frequency of 35 GHz. The receiving end collects data on the differences in scattering intensity and phase shift trajectory of the multi-band pulse signals. Secondly, frequency domain transformation and Doppler frequency shift calculation algorithms are used, for example, with a time resolution of 0.1 seconds, to reconstruct the three-dimensional sediment velocity field vector distribution in real time, generating a velocity vector distribution matrix with a grid accuracy of 0.1 meters per second.

[0066] At the S-shaped bend transition section of the spillway outlet, a multi-band phased array radar monitoring array was deployed along both banks of the dike. Based on the real-time updated spatiotemporal map of sediment deposition, the system transmits frequency-modulated continuous wave signals of different frequency bands and uses the difference in scattering intensity of the radar echoes to invert the surface water flow velocity. At the same time, it analyzes the phase shift trajectory to capture the characteristics of sediment transport in the lower layers. When the flow velocity at the bend apex suddenly increases to 3.5 meters per second, the system uses Doppler effect analysis technology to generate a three-dimensional sediment velocity field vector distribution in real time, accurately identifying the secondary suspension phenomenon of sediment caused by helical flow in the outer bend area, providing key hydrodynamic parameters for gate control.

[0067] 103. Perform dynamic coupling analysis on the sedimentation gradient and the three-dimensional sediment velocity field vector distribution to identify the associated region of velocity change and sediment deposition in the bend transition section, and generate a set of constraints containing the coordinates of the associated region and the matching rules of velocity and sedimentation.

[0068] In step 103, dynamic coupling analysis refers to modeling the correlation between sedimentation and velocity fields based on time series; velocity abrupt change region refers to the region where the local velocity change rate exceeds the threshold; sediment deposition correlation region refers to the superposition region of velocity abrupt change and abnormal growth in sedimentation; constraint condition set refers to the rule base containing spatial coordinate range, velocity threshold and sedimentation limit.

[0069] In this embodiment, the sedimentation gradient data of the spatiotemporal map of sediment deposition is first dynamically coupled with the three-dimensional sediment velocity field vector distribution for analysis, for example, with a time window of 10 minutes, and the correlation coefficient matrix is ​​calculated. When a region of abrupt velocity change is detected, for example, a velocity change rate threshold of 0.5 meters per second, and the sedimentation gradient growth rate exceeds 0.1 centimeters per hour, it is determined to be a sediment deposition associated region. Secondly, a set of constraints is generated through machine learning algorithms, for example, the spatial coordinate range of the sediment deposition associated region is defined as 20 meters to 30 meters on the X-axis, and the maximum allowable sedimentation growth rate is 0.08 centimeters per hour.

[0070] By dynamically coupling the sediment deposition gradient model of the reservoir area with the three-dimensional flow velocity field data of the curved channel, the system finds that when the longitudinal flow velocity exceeds a certain threshold and the intensity of the lateral circulation reaches a critical value, a rapid deposition area of coarse-grained sediment will be formed at the base of the outer bend revetment. Based on this correlation rule, the system automatically divides 12 key coordinate areas and establishes dynamic matching rules between flow velocity and sediment deposition: when the relationship between the measured flow velocity and sediment deposition in a certain area breaks through the preset threshold, a constraint condition set containing spatial coordinates, upper and lower limits of flow velocity, and sediment warning level is immediately generated, providing spatialized decision-making basis for gate control.

[0071] 104、According to the constraint condition set, combined with the real-time water level monitoring data and the target water level interval of the water conservancy hub reservoir area, the dynamic adjustment parameter sequence of the flood discharge gate opening degree is calculated.

[0072] In step 104, the real-time water level monitoring data refers to the reservoir water level value collected by the sensor array; the target water level interval refers to the safe water level range specified in the water conservancy scheduling regulations; and the dynamic adjustment parameter sequence of the flood discharge gate opening degree refers to a set of gate control instructions sorted by time.

[0073] In the embodiments of the present application, first, real-time water level monitoring data is collected, for example, the sensor accuracy is ±1 centimeter, and the difference from the target water level interval is calculated. Second, based on the minimum flow velocity threshold of the sediment deposition correlation area in the constraint condition set, for example 1.2 meters per second, the required flood discharge flow is inverted. Then, the flood discharge flow is calculated through the hydraulic model, for example, the current inflow is 800 cubic meters per second, and the flood discharge amount needs to be increased by 400 cubic meters per second. Finally, the dynamic adjustment parameter sequence of the flood discharge gate opening degree is generated, for example, the gate opening adjustment step is 5%, and the time interval is 5 minutes.

[0074] Combined with the real-time water level monitoring data and the target water level interval of the water conservancy hub reservoir area, the system automatically calculates the dynamic control scheme of the flood discharge gate based on the constraint condition set generated in the previous steps. When the upstream inflow reaches 3850 cubic meters per second, the system generates phased opening instructions for gates 7 to 9, dynamically balances water level fluctuations and curved channel sediment transport requirements, stabilizes the reservoir water level within the target value of ±0.15 meters, and reduces the daily average deposition thickness of the curved channel transition section from 12 centimeters to 4 centimeters, significantly improving the coordinated control capability of reservoir capacity retention and flood discharge safety during high-sediment floods.

[0075] In summary, the intelligent closed-loop regulation and control technology for sediment transport in a water conservancy project is achieved through steps 101 to 104. The dynamic sedimentation gradient map is constructed by fusing underwater topography and remote sensing cross-domain data. The three-dimensional sediment flow velocity field in the curved region is analyzed by combining multi-frequency radar scattering inversion technology. Based on the spatiotemporal coupling relationship between the deposition gradient and the flow velocity vector, the flow-deposition synergy scope is located, and the self-adaptive decision model of the flood discharge gate opening is generated by the flow-deposition coupling rule set. The closed-loop optimization chain of sediment transport state perception, multi-parameter dynamic correlation, and gate control strategy is formed, which overcomes the spatiotemporal decoupling problem of traditional single-point regulation and provides an integrated solution for global dynamic perception and intelligent collaborative regulation in high-sediment water areas.

[0076] To solve the technical problems of the dynamic coupling relationship between flow velocity mutation and sediment deposition in the transition section of the river bend being difficult to quantify and analyze, and the lack of adaptability of the regulation rules, in some embodiments, the deposition gradient and the three-dimensional sediment flow velocity field vector distribution are dynamically coupled and analyzed in step 103 to identify the associated area of flow velocity mutation and sediment deposition in the transition section of the curved channel, and a constraint condition set containing the coordinates of the associated area and the matching rules of flow velocity and sediment deposition is generated, including:

[0077] 201. The time series data of the deposition gradient and the three-dimensional sediment flow velocity field vector distribution are spatially superimposed based on the geographic coordinate system of the transition section of the curved channel, and a synchronously updated deposition flow velocity coupling data set is generated.

[0078] In step 201, the time series data of the deposition gradient refers to the sediment deposition thickness rate data set recorded in the time dimension; the three-dimensional sediment flow velocity field vector distribution refers to the three-dimensional spatial vector field containing the flow velocity vector size and direction; the geographic coordinate system of the transition section of the curved channel refers to the local spatial coordinate system established with the center point of the flood discharge channel outlet as the origin; and the deposition flow velocity coupling data set refers to the fusion data generated by spatial superposition, which synchronously reflects the dynamic relationship between deposition and flow velocity.

[0079] In the embodiments of the present application, firstly, the time series data of the deposition gradient is uniformly processed in space coordinates, for example, the UTM projection conversion algorithm is used to convert the deposition gradient data from the geographic coordinate system to the local coordinate system with the center point of the spillway outlet as the origin, and the conversion parameters include an east offset of 500000 meters, a north offset of 0 meters, and a central meridian longitude of 120 degrees. Then, the spatial resolution of the three-dimensional sediment flow velocity field vector distribution is adjusted to 0.5 meter grid consistent with the deposition gradient data. Then, data superposition is performed through a bilinear interpolation algorithm, for example, the interpolation result of the center point is calculated based on the deposition gradient values and the flow velocity vector components of the four adjacent grids in each grid cell, and a synchronously updated deposition flow velocity coupling data set is generated. The data synchronization mechanism uses timestamp alignment technology, for example, the time consistency of the deposition and flow velocity data is checked at a frequency of 10 times per second, and data compensation interpolation is triggered when the time deviation exceeds 50 milliseconds.

[0080] 202、In the deposition flow velocity coupling data set, detect the flow velocity vector length mutation area in the three-dimensional sediment flow velocity field that meets the deposition amount matching rule, and mark the flow velocity vector length mutation area as a primary flow velocity mutation area;

[0081] In step 202, the deposition amount matching rule refers to the empirical correlation model between the flow velocity vector length and the deposition gradient; the flow velocity vector length mutation area refers to a local area where the flow velocity vector length change rate exceeds a set threshold; and the primary flow velocity mutation area refers to an abnormal flow velocity area marked by preliminary screening.

[0082] In the embodiments of the present application, firstly, a mathematical model of the deposition amount matching rule is established, for example, an exponential decay relationship between the flow velocity vector length and the deposition gradient is obtained by fitting historical data, when the flow velocity length increases by 1 meter per second, the deposition gradient decreases by 0.05 centimeters per hour. Then, a sliding window detection method is used to identify the flow velocity vector length mutation area, for example, a 3 by 3 grid cell is used as a detection window to calculate the standard deviation of the flow velocity length in the window, and when the standard deviation exceeds the threshold value of 0.6 meters per second, it is determined as a mutation area. Subsequently, morphological dilation processing is performed on each mutation area, the structure element is a 3 by 3 square kernel, and discrete noise points are eliminated and adjacent areas are merged. Finally, the processed area is marked as a primary flow velocity mutation area, a binary mask matrix is generated and stored in association with the deposition flow velocity coupling data set.

[0083] 203、Superimpose the second derivative distribution of the deposition gradient on the primary flow velocity mutation area to screen out the area that meets the extreme value judgment rule and the deposition gradient mutation as the flow velocity deposition correlation area;

[0084] In step 203, the second derivative distribution of the deposition gradient refers to the curvature variation characteristics of the deposition gradient in space; the extreme value determination rule refers to the screening condition of the spatial overlap of the maximum value of the second derivative and the flow velocity sudden change area; and the flow deposition correlation area refers to the superimposed area that simultaneously satisfies the flow velocity sudden change and the deposition curvature anomaly.

[0085] In the embodiment of the present application, first, the second derivative distribution of the deposition gradient is calculated, for example, the Sobel operator is used to perform spatial differential operation on the deposition gradient data, the second partial derivative in the X direction and the Y direction is calculated respectively, and the curvature distribution map is generated by merging. Then, the extreme value determination rule is set, for example, the area with a curvature value greater than the threshold value of 0.1 cm / m2 is screened, and the spatial logical AND operation is performed with the binary mask of the primary flow velocity sudden change area. Then, the connected component analysis is performed on the overlapping area, and the continuous area with an area greater than 2 m2 is extracted as the candidate correlation area. Finally, the boundary optimization algorithm is used to smooth the edge of the candidate area, for example, the Active Contour model is used to generate a closed polygon to describe the flow deposition correlation area, and the region vertex coordinates are stored in the spatial database.

[0086] 204, extract a set of three-dimensional geographic coordinates of the flow deposition correlation area, and count the numerical correspondence between the deposition gradient and the flow velocity vector length in the flow deposition correlation area, and generate a constraint condition set containing the coordinates of the correlation area and the matching rule of the flow velocity and the deposition amount according to the numerical correspondence.

[0087] In step 204, the set of three-dimensional geographic coordinates refers to the set of spatial position data of the correlation area; the numerical correspondence refers to the statistical correlation between the deposition gradient and the flow velocity vector length in the area; and the constraint condition set refers to the control rule library containing the coordinate boundary and the parameter threshold.

[0088] In the embodiment of the present application, first, the set of three-dimensional geographic coordinates of the flow deposition correlation area is exported from the spatial database, in the format of a GeoJSON file containing XYZ coordinates and time stamps. Then, the statistical correlation analysis is performed, for example, the deposition gradient and the flow velocity length of all grid cells in each correlation area are extracted, the Pearson correlation coefficient of the two is calculated, and when the correlation coefficient is lower than-0.6, it is determined as strong negative correlation. Then, the constraint condition set is constructed by the decision tree algorithm, for example, the flow velocity length of 1.2 m / s is taken as the split threshold to generate the segmented rule: when the flow velocity length is not less than 1.5 m / s, the upper limit of the deposition gradient is set to 0.05 cm / h. Finally, the rule is encoded into a constraint condition set in JSON format, containing the matching rule of the area coordinate boundary and the flow velocity and the deposition amount.

[0089] In summary, the multi-dimensional association rule dynamic generation technology of sediment transport state is realized through steps 201 to 204, the spatio-temporal coupling model is constructed through the spatial synchronous fusion of the sediment gradient time series data and the three-dimensional flow velocity field, and the primary abnormal area of the flow velocity vector module length mutation is automatically detected in the bend geographic coordinate system; combined with the second derivative distribution characteristics of the sediment gradient, the double threshold joint criterion of the extreme gradient and the flow velocity mutation is used to screen the spatio-temporal overlapping area, and the strong correlation scope of the flow velocity distortion and the sediment anomaly is accurately located; based on the dynamic mapping relationship between the sediment gradient extreme value and the flow velocity module length in the correlation domain, the dynamic constraint set containing the three-dimensional geographic coordinates and the flow-silt parameter matching rule is generated, the spatio-temporal decoupling defects of the traditional single criterion threshold segmentation are broken through, the high confidence recognition of the sediment transport abnormal area and the adaptive generation of the regulation rule are realized, and the multi-parameter collaborative decision benchmark is provided for the flow pattern regulation of the water conservancy hub.

[0090] To solve the technical problems of insufficient recognition accuracy of the flow velocity and sediment dynamic coupling area in the transition section of the river bend and the difficulty of multi-dimensional spatio-temporal feature association, in some embodiments, step 203 includes:

[0091] 301、based on the spatial unit flow velocity vector length distribution of the primary flow velocity mutation area, setting the historical reference and the current period reference of the flow velocity vector length, and delimiting the primary screening area in the spatial unit that meets the historical reference and the current period reference at the same time;

[0092] In step 301, the primary flow velocity mutation area refers to the abnormal flow velocity area marked through preliminary screening; the historical reference refers to the flow velocity vector length reference value based on historical data statistics; the current period reference refers to the statistical reference value of the flow velocity vector length in the current monitoring period; and the primary screening area refers to the spatial unit set that meets the historical and current flow velocity references at the same time.

[0093] In the embodiments of the present application, first, based on the spatial unit flow velocity vector length distribution of the primary flow velocity mutation area, the historical reference of the flow velocity vector length is set, for example, the 75th percentile of the flow velocity module length in the past 30 days is taken as the threshold value, and the historical reference value is 1.8 meters per second. Then, the current period reference is calculated, for example, the sliding window mean value of the current 24-hour flow velocity module length is taken as the reference, and the current reference value is 1.5 meters per second. Subsequently, through spatial logical and operation, the spatial unit whose flow velocity vector length is higher than the historical reference and the current reference at the same time is screened out, for example, the flow velocity module length of a certain area is 2.1 meters per second, which is higher than the historical reference 1.8 meters per second and the current reference 1.5 meters per second, and the unit is divided into the primary screening area.

[0094] 302、extracting the deposition amount gradient data of each position point in the primary screening area, and superimposedly calculating the second derivative distribution of the deposition amount gradient in the adjacent time period for each position point;

[0095] In step 302, the deposition amount gradient data refers to the change rate of the sediment deposition thickness per unit distance; the adjacent time period refers to a fixed time window before and after the current time; and the second derivative distribution refers to the curvature change characteristics of the deposition amount gradient in the time dimension.

[0096] In the embodiment of the present application, firstly, the deposition amount gradient data of each position point in the primary screening area is extracted, for example, the current deposition amount gradient at the coordinate X=25 meters is 0.12 cm / m. Then, the second derivative in the adjacent time period is calculated for each position point, for example, using the central difference method to calculate the curvature of the deposition amount gradient with respect to time with a time window of 6 hours, the formula is described as: the current gradient value minus the gradient value 3 hours ago, minus the difference between the gradient value 3 hours later minus the current gradient value, and finally divided by the square of the length of the time window.

[0097] 303、along the water flow direction, a plurality of virtual measurement lines are arranged in the primary screening area, a discrete point sequence is generated at an array interval for each virtual measurement line, and the second derivative distribution calculation result corresponding to the discrete point sequence is mapped as a second derivative intensity distribution;

[0098] In step 303, the water flow direction refers to the main flow direction of the downstream river channel of the spillway; the virtual measurement line refers to an artificial monitoring line parallel to the water flow direction; the array interval refers to a fixed interval between discrete points on the measurement line; and the second derivative intensity distribution refers to the quantitative mapping result of the second derivative at the discrete point.

[0099] In the embodiment of the present application, firstly, the virtual measurement lines are arranged along the water flow direction, for example, 5 parallel lines with an interval of 2 meters are arranged in the primary screening area. Then, a discrete point sequence is generated at an array interval, for example, discrete points are generated at an interval of 0.5 meters on each measurement line, and there are 40 points on a single line. Then, the second derivative calculation result corresponding to each discrete point is mapped as an intensity value, for example, the second derivative range of 0 to 0.2 cm / hour² is mapped as an intensity value of 0 to 100 by using normalization processing, and an intensity distribution curve along the measurement line is generated.

[0100] 304、based on the overall distribution state of the second derivative intensity distribution, an extreme value determination rule is set, and an adjacent discrete point area continuously satisfying the extreme value determination rule is marked as a deposition amount gradient mutation area;

[0101] In step 304, the extreme value determination rule refers to the abnormal region screening conditions set based on the intensity distribution of the second derivative; continuous satisfaction means that the intensity values ​​of adjacent discrete points continuously exceed the threshold; the sedimentation gradient abrupt change zone refers to a continuous spatial region that meets the extreme value determination.

[0102] In this embodiment, an extreme value determination rule is first set for the overall distribution state of the second derivative intensity distribution, such as an intensity value exceeding 80 and three or more consecutive discrete points satisfying the condition. Next, the discrete point sequence is scanned along a virtual measurement line. For example, if the intensity values ​​of points 10 to 15 on measurement line L1 are detected to be 85, 89, 92, 88, 84, and 81 respectively, they are determined to be continuously satisfied regions. Finally, adjacent abrupt change regions are merged through morphological closing operations, for example, regions with a spacing of less than 1 meter are merged into a single sedimentary gradient abrupt change region.

[0103] 305. The sediment gradient abrupt change zone is superimposed in three-dimensional space along the vertical direction of the virtual measurement line. The spatial coordinates of the primary screening area and the distribution range of the sediment gradient abrupt change zone are integrated. The set of superimposed spatial units that simultaneously meet the velocity vector length benchmark condition and the extreme value determination rule is defined as the velocity deposition correlation region.

[0104] In step 305, three-dimensional spatial overlay refers to the fusion of lateral data along the direction perpendicular to the water flow; the velocity vector length reference condition refers to the dual velocity threshold defined in step 301; the velocity-deposition correlation region refers to the set of spatial units that simultaneously satisfy the velocity and deposition anomaly conditions.

[0105] In this embodiment, the sediment gradient abrupt change zones on each virtual measurement line are first spatially interpolated along the direction perpendicular to the water flow, for example, by using an inverse distance weighting method to generate a horizontally continuous three-dimensional abrupt change zone distribution. Next, a logical AND operation is performed between the spatial coordinates of the initially screened area and the abrupt change zone distribution, for example, selecting areas within the coordinate range of X=24-26 meters and Y=3-5 meters that simultaneously satisfy a velocity modulus ≥1.5 m / s and a sediment gradient abrupt change intensity ≥80. Finally, the superimposed result is defined as a velocity-deposition correlation region, and its boundary coordinate set is output.

[0106] In summary, the multi-reference spatio-temporal joint positioning technology of sediment transport associated areas is realized by steps 301 to 305, the preliminary abnormal space unit is constructed based on the historical and current double-period reference screening of the flow velocity vector length, the virtual measurement line is deployed along the flow direction to grid discrete analysis system combining the spatio-temporal evolution characteristics of the sedimentation gradient second derivative, and the sedimentation gradient extreme value distribution zone is automatically calibrated by the dynamic threshold criterion; the three-dimensional space superposition fusion algorithm of the flow velocity reference and the gradient extreme value is adopted to realize the multi-dimensional parameter coupling verification of the preliminary flow velocity abnormal area and the sedimentation mutation zone, and the composite associated area meeting the flow velocity vector length constraint and the second derivative extreme value condition is accurately locked. The method breaks through the limitation of single-dimensional static threshold judgment, enhances the recognition robustness of the flow-silt associated area through the multi-reference spatio-temporal joint analysis mechanism, and provides a decision basis with spatio-temporal continuity and parameter synergy for the regulation and control of the sediment transport of the water conservancy hub.

[0107] In order to solve the technical problems of insufficient fusion of vertical and surface layer data and poor real-time performance in dynamic analysis of three-dimensional sediment flow field, in some embodiments, based on the modified sediment accumulation spatio-temporal atlas in step 102, the three-dimensional sediment flow field vector distribution is solved in real time by the scattering intensity difference and phase shift trajectory of multi-band pulse signals, including:

[0108] 401, synchronously emit multi-band pulse signals containing low-frequency penetrating signals and high-frequency tracking signals, and after receiving the reflected signals, separate to generate a sediment density distribution atlas penetrating to the riverbed bottom layer and a surface sediment motion trajectory atlas;

[0109] In step 401, the low-frequency penetrating signal refers to a long-wavelength electromagnetic wave or acoustic wave signal that can penetrate to the riverbed bottom layer; the high-frequency tracking signal refers to a short-wavelength signal used to capture the motion of surface sediment particles; the sediment density distribution atlas refers to tomographic imaging data reflecting the sediment accumulation density in the vertical direction of the riverbed; and the surface sediment motion trajectory atlas refers to the surface particle displacement trajectory reconstructed by the reflection characteristics of the high-frequency signal.

[0110] In the embodiments of the present application, multi-band pulse signals are first synchronously emitted, for example, a low-frequency signal uses a 10-kilohertz acoustic wave with a penetration depth of 5 meters to generate riverbed bottom layer density data; a high-frequency signal uses a 100-megahertz radar wave with a resolution of 0.1 meters to track the motion of sediment within 0.5 meters of the surface. After receiving the reflected signals, the low-frequency and high-frequency components are separated by frequency domain filtering, for example, the 10-kilohertz to 100-kilohertz low-frequency band is extracted by Fourier transform to generate a sediment density distribution atlas; the 1-megahertz to 100-megahertz high-frequency band is extracted to generate a surface sediment motion trajectory atlas by a feature point matching algorithm.

[0111] 402、extracting the change rate of the sediment accumulation density of each depth layer in the vertical direction from the sediment density distribution atlas, combining the deposition gradient of the corresponding position in the corrected sediment accumulation space-time atlas, to generate a vertical density gradient correlation profile;

[0112] In step 402, the change rate of the sediment accumulation density of each depth layer in the vertical direction refers to the increase or decrease rate of the density value per unit depth; the vertical density gradient correlation profile refers to the vertical section model of the space correlation of the density change rate and the deposition gradient.

[0113] In the embodiments of the present application, first, the vertical density change rate is extracted from the sediment density distribution atlas, for example, the density is 1.8 grams per cubic centimeter at a depth of 2 meters, and the density is 2.1 grams per cubic centimeter at a depth of 2.5 meters, and the change rate is calculated as 0.6 grams per cubic centimeter per meter. Then, the deposition gradient of the same position is obtained from the corrected sediment accumulation space-time atlas, for example, the deposition gradient of the position is 0.15 centimeters per meter. Through a spatial interpolation algorithm, the two are superimposed, for example, in the depth interval of 2 to 3 meters, a correlation profile is generated with the density change rate as the horizontal axis and the deposition gradient as the vertical axis, and the density gradient mutation area is marked, for example, the area where the change rate exceeds 0.5 grams per cubic centimeter per meter.

[0114] 403、performing three-dimensional spatial decomposition on the surface sediment movement trajectory atlas, and establishing a surface particle movement direction angle distribution atlas according to the spatial position offset amount of the characteristic points of the reflected signals in a continuous emission period;

[0115] In step 403, three-dimensional spatial decomposition refers to expanding two-dimensional surface movement trajectory data to three-dimensional space; the reflected signal characteristic point refers to the high-frequency echo scattering center of the surface sediment particle; the spatial position offset amount refers to the coordinate change amount of the characteristic point in a continuous signal period; and the surface particle movement direction angle distribution atlas refers to a spatial distribution model reflecting the deflection angle of the movement direction of the surface particle.

[0116] In the embodiments of the present application, first, the surface sediment movement trajectory atlas is subjected to three-dimensional decomposition, for example, the two-dimensional plane coordinates X and Y are superimposed with the water depth data Z to reconstruct the three-dimensional movement path of the particle. Then, the offset amount of the characteristic points in a continuous emission period is extracted, for example, a certain characteristic point moves from the coordinates of time t1, for example, 10 meters, 5 meters, 0.2 meters, to t2, for example, 10.3 meters, 5.1 meters, 0.2 meters, and the displacement vector is calculated, for example, ΔX=0.3 meters, ΔY=0.1 meters. Then, the direction angle is calculated by the vector direction angle calculation formula, for example, the direction angle θ=arctan(ΔY / ΔX), and the movement direction angle of the point is obtained as 18.4 degrees. Finally, the direction angle distribution atlas is generated by binning at intervals of 5 degrees by statistical analysis of all characteristic points.

[0117] 404. Spatially matching the vertical density gradient correlation profile with the surface particle motion direction angle distribution map, superimposing the surface motion direction angle data at the corresponding position in the vertical density gradient mutation zone, and generating the corresponding three-dimensional sediment flow velocity field vector distribution through vector direction transmission.

[0118] In step 404, spatial matching refers to aligning the vertical profile data with the geographic coordinates of the surface direction angle data; the vertical density gradient mutation zone refers to the depth interval where the density change rate exceeds the threshold; and vector direction transmission refers to an algorithm for calculating the bottom flow velocity vector according to the surface motion direction.

[0119] In the embodiments of the present application, the vertical density gradient correlation profile is first matched with the surface direction angle distribution map in coordinates, for example, at the plane coordinates X = 20 meters, Y = 10 meters, the vertical profile covers the depth of 0 to 5 meters, and the surface direction angle data corresponds to the water depth of 0 to 0.5 meters. Then, the surface direction angle is superimposed in the density gradient mutation zone, such as the depth of 2-3 meters, for example, the average surface direction angle in this area is 20 degrees. Through the direction transmission algorithm, it is assumed that there is a linear correlation between the bottom flow direction and the surface, for example, the bottom direction angle = surface direction angle x 0.8, and the flow direction at the depth of 2 meters is generated as 16 degrees. Finally, the direction and density gradient data of each depth layer are fused to generate the three-dimensional sediment flow velocity field vector distribution.

[0120] In summary, the multi-band collaborative inversion technology of riverbed sediment motion state is realized through steps 401 to 404, the vertical density distribution of the riverbed and the surface particle motion trajectory are synchronously acquired through the composite detection mechanism of low-frequency penetration signals and high-frequency tracking signals; the vertical density-sediment correlation model is constructed based on the spatial mapping of the vertical density gradient change rate and the sedimentation gradient, and the surface particle motion direction angle distribution is inversely calculated through the three-dimensional decomposition technology of the motion trajectory; the vertical-surface motion vector transfer function is established through the spatial matching of the vertical density mutation zone and the surface direction angle, and the three-dimensional sediment flow velocity field dynamic distribution model is generated by fusing multi-source data. This method breaks through the sensing limitations of traditional single-dimensional detection, realizes the cross-scale dynamic correlation analysis of the vertical structure evolution of the riverbed and the surface flow state motion, and provides the three-dimensional vector inversion accuracy guarantee under the vertical density gradient constraint for the flow velocity field reconstruction in high-sediment water area.

[0121] In order to solve the problems of fusion missing of vertical and horizontal motion data and vector direction transmission distortion in three-dimensional sediment flow velocity field reconstruction, in some embodiments, the spatial position matching of the vertical density gradient correlation profile and the surface particle motion direction angle distribution map in step 404 superimposes the surface motion direction angle data at the corresponding position in the vertical density gradient mutation zone, and generates the corresponding three-dimensional sediment flow velocity field vector distribution through vector direction transmission, which includes:

[0122] 501. Spatial position matching is performed between the data of each depth layer of the vertical density gradient correlation profile and the planar coordinates of the surface particle motion direction angle distribution map, and the vertical density gradient abrupt change region corresponding to the planar coordinates is selected.

[0123] In step 501, the vertical density gradient correlation profile refers to the tomographic data reflecting the correlation between the vertical density change of the riverbed and the sediment gradient; the surface particle movement direction angle distribution map refers to the planar distribution model of the deflection angle of the surface sediment particle movement direction; the vertical density gradient abrupt change zone refers to the vertical depth range where the density change rate exceeds a set threshold; and the planar coordinates refer to the X-axis and Y-axis positions in a two-dimensional geographic coordinate system.

[0124] In this embodiment, the data of each depth layer of the vertical density gradient correlation profile is first spatially matched with the planar coordinates of the surface particle motion direction angle distribution map. For example, a georeferencing algorithm is used to align the grid coordinate system of the vertical profile with that of the surface direction angle data. Next, the vertical density gradient abrupt change regions corresponding to the planar coordinates are selected. For example, at coordinates X = 20 meters and Y = 5 meters, if the vertical profile shows a density change rate of 0.8 grams per cubic centimeter at a depth of 2 to 3 meters, exceeding the threshold of 0.5 grams per cubic centimeter per meter, this region is marked as a vertical density gradient abrupt change region.

[0125] 502. Extract the surface motion direction angle data of the plane coordinates corresponding to the vertical density gradient abrupt change region, and calculate the horizontal motion vector of each plane coordinate point based on the surface motion direction angle data;

[0126] In step 502, the surface motion direction angle data refers to the horizontal motion direction angle of the surface sediment particles at the plane coordinate point; the horizontal motion vector refers to the two-dimensional plane vector synthesized by the direction angle and the velocity modulus; the plane coordinate point refers to the specific position of the X-axis and Y-axis in the two-dimensional geographic coordinate system.

[0127] In this embodiment, the surface motion direction angle data corresponding to the vertical density gradient abrupt change region is first extracted. For example, the surface direction angle is 25 degrees when the X coordinate is 20 meters and the Y coordinate is 5 meters. Then, the horizontal motion vector of each plane coordinate point is calculated based on the surface motion direction angle data. For example, if the surface velocity modulus is 1.2 meters per second, the X component of the horizontal vector is 1.2 multiplied by the cosine of 25 degrees, and the Y component is 1.2 multiplied by the sine of 25 degrees, generating a horizontal motion vector with an X component of 1.09 meters per second and a Y component of 0.51 meters per second.

[0128] 503. Determine the gradient direction change at each location point in the vertical density gradient abrupt change region, and calculate the corresponding vertical direction correction value based on the gradient direction change.

[0129] In step 503, the gradient direction change amount refers to the deflection angle of the density change direction of different depth layers in the vertical density gradient mutation zone; the vertical direction correction amount value refers to the adjustment value of the vertical component of the vector based on the gradient direction change; the position point refers to the X-axis, Y-axis and Z-axis coordinate points in the three-dimensional space.

[0130] In the embodiments of the present application, first, the gradient direction change amount of each position point in the vertical density gradient mutation zone is determined, for example, the density gradient direction at a depth of 2 meters is downward deflection of 30 degrees, and at a depth of 3 meters, it becomes downward deflection of 45 degrees, and the gradient direction change amount is calculated as 15 degrees. Then, the corresponding vertical direction correction amount value is calculated based on the gradient direction change amount, for example, the correction coefficient of the vertical component is 0.1 meters per second for every 5 degrees of depth direction change, and the vertical direction correction amount value from a depth of 2 meters to 3 meters is 0.3 meters per second.

[0131] 504, after superimposing the horizontal motion vector on the vertical direction correction amount value, the vector is transmitted layer by layer along the vertical density gradient change direction, and the vector direction is corrected based on the deflection angle of the gradient direction and the surface layer direction angle;

[0132] In step 504, the deflection angle of the gradient direction and the surface layer direction angle refers to the included angle between the vertical density gradient direction and the surface layer motion direction; the vector direction correction refers to adjusting the three-dimensional direction of the motion vector according to the deflection angle; the gradient direction change direction refers to the deflection trend of the vertical density gradient with the increase of the depth.

[0133] In the embodiments of the present application, first, the horizontal motion vector is superimposed on the vertical direction correction amount value, for example, the horizontal vector X component 1.09 meters per second and Y component 0.51 meters per second at the coordinate X equal to 20 meters and Y equal to 5 meters, and the vertical direction correction amount 0.3 meters per second is superimposed to generate the initial three-dimensional vector X component 1.09 meters per second, Y component 0.51 meters per second and Z component 0.3 meters per second. Then, the deflection angle of the gradient direction and the surface layer direction angle is calculated, for example, the horizontal deflection angle of the gradient direction downward deflection of 35 degrees and the surface layer direction of 25 degrees is 10 degrees. Based on the deflection angle, the vector direction is corrected, for example, the three-dimensional vector is rotated by 10 degrees around the Z-axis to obtain the corrected vector X component 1.05 meters per second, Y component 0.61 meters per second and Z component 0.3 meters per second.

[0134] 505, spatially superimposing the corrected motion vectors of all depth layers in the vertical density gradient mutation zone to generate a three-dimensional sediment flow velocity field vector distribution covering the corresponding plane coordinates.

[0135] In step 505, the corrected motion vector refers to the three-dimensional flow velocity vector after direction correction; the corresponding plane coordinates covered refer to the X-axis and Y-axis range of the three-dimensional space model covering the target area; the three-dimensional sediment flow velocity field vector distribution refers to the three-dimensional continuous vector field containing the flow velocity and direction.

[0136] In the embodiments of the present application, first, the modified motion vectors of all depth layers in the vertical density gradient mutation zone are spatially superimposed, for example, the modified vectors at depths of 2 meters, 2.5 meters and 3 meters are linearly interpolated respectively. Then, a three-dimensional sediment flow velocity field vector distribution covering the corresponding plane coordinates is generated, for example, within the range of plane coordinates X equal to 18 meters to 22 meters and Y equal to 4 meters to 6 meters, the three-dimensional flow velocity vector of each grid cell is output.

[0137] In summary, the cross-dimension dynamic vector reconstruction technology of the riverbed sediment flow velocity field is realized through steps 501 to 505. The vertical mutation zone is located by spatial matching of the vertical density gradient correlation profile and the surface layer motion direction angle distribution. The recursive expansion of the horizontal motion component to the vertical direction is completed by using the gradient direction driven vector dynamic transfer algorithm based on the multi-direction coupling calculation of the surface layer horizontal motion vector and the vertical gradient correction amount. The spatial pointing correction of the motion vector is realized by the offset angle compensation mechanism of the vertical gradient change direction and the surface layer direction angle. Finally, the three-dimensional sediment flow velocity field distribution model is constructed by fusing the modified vectors of each depth layer. This method breaks through the dimensional limitations of traditional two-dimensional flow field extrapolation, realizes the cross-scale dynamic correlation of the vertical density gradient mutation and the surface layer motion vector, and provides the three-dimensional flow velocity field reconstruction capability under the vertical structure constraint for high-sediment water areas.

[0138] To solve the problem of insufficient multi-source data fusion and lagging atlas correction in dynamic monitoring of riverbed sediment deposition, in some embodiments, the multi-dimensional scanning data of the underwater robot on the riverbed topography and the global coverage data of satellite remote sensing are synchronously acquired in step 101 to generate a sediment deposition spatiotemporal atlas containing a deposition gradient, and the sediment deposition spatiotemporal atlas is periodically corrected based on the change of the deposition gradient, including:

[0139] 601. Set the scanning path of the underwater robot on the riverbed topography, synchronously acquire the deposition accumulation thickness and multi-dimensional scanning data of each scanning point, and synchronously acquire the global coverage data of satellite remote sensing within the scanning interval of the robot;

[0140] In step 601, the scanning path of the underwater robot refers to the pre-planned riverbed topography detection trajectory; the deposition accumulation thickness refers to the vertical height of the sediment from the riverbed surface to the underlying stable layer; the multi-dimensional scanning data refers to the comprehensive detection data including the sonar reflection intensity, terrain undulation and bottom hardness; the scanning interval refers to the moving waiting time after the robot completes a single scan; and the satellite remote sensing global coverage data refers to the surface coverage information of the river basin range acquired by optical or radar satellites.

[0141] In this embodiment, the underwater robot's scanning path is first set, for example, using a spiral path with 20-meter intervals to cover the reservoir area. It stays at each scanning point for 10 minutes to acquire sediment thickness data; for example, the thickness at a certain point is measured to be 2.3 meters. Simultaneously, sediment thickness and multi-dimensional scanning data are acquired at each scanning point, such as a sonar reflection intensity value of -30 dB and a bottom hardness of medium sand. During the interval between robot movements to the next scanning point, satellite remote sensing data covering the entire area is received, for example, multispectral imagery from Sentinel-2 satellite and synthetic aperture radar data from Sentinel-1 satellite.

[0142] 602. Calculate the thickness variation difference of the sediment accumulation thickness at each scanning point according to the continuous scanning cycle, and use the ratio of the thickness variation difference to the horizontal distance between adjacent scanning points as the sedimentation gradient. Combine the multi-dimensional scanning data and the full-area coverage data to generate a spatiotemporal map of sediment deposition that includes gradient direction and sedimentation gradient.

[0143] In step 602, the thickness variation difference refers to the difference in sediment thickness between adjacent scanning cycles at the same scanning point; the horizontal spacing ratio refers to the straight-line distance between two adjacent scanning points; the sedimentation gradient refers to the amount of change in sediment thickness within a unit horizontal distance; the gradient direction refers to the spatial direction of the maximum rate of change of sedimentation gradient; and the sedimentation spatiotemporal map refers to a sedimentary dynamic model that integrates time, space, and multi-dimensional attributes.

[0144] In this embodiment, the thickness variation of sediment accumulation at the scanning points is first calculated over consecutive periods. For example, if the thickness was 2.3 meters in the previous period and 2.5 meters in the current period, the thickness variation difference is 0.2 meters. Next, the horizontal spacing ratio between adjacent scanning points is calculated. This thickness variation difference is used as the sedimentation gradient. For example, if the distance between point A and point B is 10 meters, the sedimentation gradient is 0.2 meters divided by 10 meters, which equals 0.02 meters per meter. Combining multi-dimensional scanning data and full-area coverage data, for example, if the sonar reflection intensity at point A is -30 dB, corresponding to a fine sand substrate type, and satellite imagery shows that the vegetation coverage in this area is less than 5%, a spatiotemporal map of sediment deposition with a gradient direction of 15 degrees southeast is finally generated.

[0145] 603. When the direction of the deposition gradient at any scanning point continuously deviates from the initial direction by more than a set angle, or the fluctuation range of the deposition gradient value exceeds a set proportion, the scanning path of the area where the point is located is encrypted.

[0146] In step 603, the initial direction refers to the spatial orientation of the deposition gradient when first scanned; the set angle refers to the maximum threshold value allowed for the gradient direction deviation; the set proportion refers to the maximum allowed range of the deposition gradient value fluctuation; and the scan path encryption refers to increasing the density of scan points in the abnormal area.

[0147] In the embodiments of the present application, the gradient direction change of the scan points is first monitored. For example, the initial direction is southeast by east 15 degrees, and the current period detects that the direction deviates to due east 30 degrees, with a deviation angle of 15 degrees exceeding the set threshold value of 10 degrees. Or the deposition gradient value fluctuation is detected, for example, from 0.02 meters per meter to 0.03 meters per meter, with a fluctuation amplitude of 50% exceeding the set proportion of 30%. When any condition is met, the path encryption of the area is triggered, for example, the original 20-meter scan interval is shortened to 5 meters, and a ring-shaped supplementary scan path is added in the area.

[0148] 604, In the encrypted scan area, the thickness of the sediment accumulation is reacquired, the value and direction of the deposition gradient are corrected based on the new sediment accumulation thickness data, and the data of the corresponding area in the original space-time map is covered.

[0149] In step 604, the encrypted scan area refers to the newly added high-density detection range after path adjustment; the corrected deposition gradient refers to updating the original gradient parameters based on new data; and the data coverage refers to replacing the corresponding part of the original space-time map with the new calculation result.

[0150] In the embodiments of the present application, the thickness of the sediment accumulation in the encrypted scan area is first reacquired, for example, three sub-points are added to the original abnormal point, and the thicknesses measured are 2.6 meters, 2.55 meters and 2.58 meters. The deposition gradient is calculated based on the new data, for example, the thickness change difference of the horizontal interval of 5 meters is 0.05 meters, the gradient is corrected to 0.01 meters per meter, and the direction is adjusted to southeast by east 10 degrees. Finally, the corrected gradient value and direction are covered in the original space-time map, for example, replacing the old data in the range of coordinates X=100-105 meters and Y=50-55 meters.

[0151] In summary, steps 601 to 604 implemented an adaptive dynamic optimization technique for riverbed sediment deposition monitoring. A gradient-driven spatiotemporal evolution analysis model was constructed using a collaborative acquisition mechanism of multi-dimensional underwater robot scanning and full-domain satellite remote sensing data. Based on the difference in sediment thickness variation and spatial gradient calculation within continuous scanning cycles, a spatiotemporal map containing gradient direction and intensity information was dynamically generated. A dual-modal anomaly detection mechanism for gradient direction deviation and fluctuation amplitude was introduced, triggering an autonomous encryption strategy for the scanning path. Local rescan data was used to correct gradient parameters and map spatial distribution in real time. This method overcomes the response hysteresis defect of fixed-path scanning, achieving a balance between monitoring accuracy and resource efficiency through a dynamic feedback mechanism triggered by gradient anomalies, forming a closed loop of dynamic riverbed topography perception: "full-domain coverage - anomaly localization - local enhancement."

[0152] To address the technical challenges of insufficient dynamic coupling of multiple parameters (water level, flow velocity, and sedimentation) and inadequate safety of adjustment parameters in reservoir floodgate regulation, some embodiments include, in step 104, calculating the dynamic adjustment parameter sequence for the floodgate opening based on the set of constraints, combined with real-time water level monitoring data and the target water level range of the reservoir area, including:

[0153] 701. Obtain real-time water level monitoring data of the reservoir area of ​​the water conservancy hub, calculate the deviation between the current water level and the median of the target water level range, and predict the intensity of water level change trend in the future period based on historical water level change data;

[0154] In step 701, real-time water level monitoring data refers to the current water level value of the reservoir area collected by the pressure sensor array; the median of the target water level interval refers to the midpoint value of the safe water level range specified in the water conservancy dispatching regulations; the water level deviation refers to the algebraic difference between the current water level and the target median; historical water level change data refers to the water level fluctuation records within a set time period in the past; and the intensity of the water level change trend in the future period refers to the quantitative index of the rate of rise and fall of water level predicted based on historical data.

[0155] In this embodiment, data is first collected through a real-time water level monitoring network of six pressure sensors deployed in the reservoir area. For example, sensor nodes upload water level values ​​at a sampling frequency of once per second. The system performs redundancy verification and mean filtering on the six sets of data, removes outliers, and takes the arithmetic mean as the current water level value. If the current water level is 100.6 meters above sea level and the median of the target water level range is 100.75 meters, the calculated water level deviation is -0.15 meters. Next, water level data for the past three hours is extracted from the historical database, for example, hourly records of 100.70 meters, 100.65 meters, and 100.62 meters. The rate of water level change is calculated using the first-order difference method as a decrease of 0.03 meters per hour, and the trend intensity for the next two hours is predicted to be a decrease of 0.025 meters per hour based on the autoregressive moving average model. The prediction result is marked with a quantification level of "high".

[0156] 702、analyzing the flow rate threshold and the deposition gradient threshold of each associated region in the constraint condition set, converting the ratio relationship between the current measured flow rate and the flow rate threshold into a flow rate adjustment parameter, and converting the ratio relationship between the current deposition gradient and the deposition gradient threshold into a deposition gradient adjustment parameter;

[0157] In step 702, the constraint condition set refers to a rule base containing the three-dimensional geographic coordinates of the associated region, the flow rate threshold and the deposition gradient threshold; the flow rate adjustment parameter refers to the ratio of the current measured flow rate to the flow rate threshold; and the deposition gradient adjustment parameter refers to the ratio of the current deposition gradient to the deposition gradient threshold.

[0158] In the embodiment of the application, first, the rule parameters of the associated region R001 are extracted from the constraint condition set, for example, the coordinate range of the region is X equal to 20 to 25 meters, Y equal to 5 to 10 meters, and Z equal to 0 to 5 meters, the flow rate threshold is 1.5 meters per second, and the deposition gradient threshold is 0.08 centimeters per hour. The current measured flow rate is 1.2 meters per second obtained by the underwater acoustic Doppler current profiler deployed in the region, and the current deposition gradient is 0.1 centimeters per hour extracted from the sediment accumulation space-time graph. When calculating the flow rate adjustment parameter, the measured flow rate 1.2 meters per second is divided by the flow rate threshold 1.5 meters per second to obtain a ratio of 0.8; the deposition gradient adjustment parameter is the measured gradient 0.1 centimeters per hour divided by the threshold 0.08 centimeters per hour to obtain a ratio of 1.25. If the measured value exceeds the threshold, the parameter value is marked as a red warning state.

[0159] 703、weighting and fusing the flow rate adjustment parameter and the deposition gradient adjustment parameter to generate an initial adjustment parameter value of each associated region, and applying directional correction to the initial adjustment parameter value based on the water level deviation direction and the trend intensity;

[0160] In step 703, weighting and fusing refers to linear combination of the flow rate adjustment parameter and the deposition gradient adjustment parameter according to a preset weight; the initial adjustment parameter value refers to the comprehensive adjustment coefficient after fusion; and the directional correction refers to adjustment of the sign or amplitude of the parameter value according to the water level deviation direction and the trend intensity.

[0161] In this embodiment, weighting coefficients are first set according to the water conservancy scheduling strategy: the flow velocity adjustment parameter has a weight of 0.6, and the sedimentation gradient adjustment parameter has a weight of 0.4. The adjustment parameters for region R001 are weighted and fused, and the initial adjustment parameter value is calculated as 0.8 multiplied by 0.6 plus 1.25 multiplied by 0.4, resulting in 0.98. Next, the parameter sign is corrected according to the direction of water level deviation: because the current water level is below the target median and the predicted trend is downward, the flood discharge needs to be reduced, so the parameter value 0.98 is multiplied by -1, resulting in -0.98. Simultaneously, based on the trend intensity level of "high," a gain is applied to the parameter amplitude, for example, multiplying -0.98 by 1.2, resulting in a final corrected parameter value of -1.176. This value indicates that the flood discharge needs to be reduced to below 1.176 times the current flow rate.

[0162] 704. Arrange the corrected adjustment parameter values ​​for each region in a time sequence to ensure that the parameter change range between adjacent time periods does not exceed the safety threshold of the gate's mechanical action, and smooth the parameter differences between spatially adjacent regions to generate a dynamic adjustment parameter sequence for the flood discharge gate opening.

[0163] In step 704, time series arrangement refers to arranging parameter values ​​in chronological order; mechanical action safety threshold refers to the maximum allowable range of a single adjustment of the gate opening; spatially adjacent regions refer to related regions with adjacent geographical coordinates; and smoothing processing refers to reducing parameter abrupt changes through interpolation or filtering algorithms.

[0164] In this embodiment, the corrected parameter values ​​for each region are first divided into time windows, for example, the next two hours are divided into eight 15-minute time slots, generating an initial sequence: -1.176, -1.128, -1.080, -1.032, -0.984, etc. Next, the magnitude of parameter changes between adjacent time slots is checked. For example, the change from -1.176 in the first time slot to -1.128 in the second time slot is 4.1%, while the change from the second time slot to the third time slot is 4.2%, both within the allowable range. If a parameter jump in a certain time slot exceeds a threshold, for example, jumping directly from -1.176 to -1.032, intermediate parameters -1.104 and -1.068 are inserted for a smooth transition. Finally, Gaussian filtering is applied to the parameter differences between adjacent spatial regions R001 and R002: if the parameter of R001 is -1.080 and that of R002 is -0.950, with a difference of 13%, then a weighted average is used to adjust R001 to -1.015 and R002 to -0.985, reducing the difference to 3%. The final output parameter sequence is then sent to the gate actuator through the control loop, driving the hydraulic system to adjust the opening in steps.

[0165] In summary, steps 701 to 704 implemented a multimodal adaptive decision-making technology for flood discharge gates. By dynamically sensing real-time water level deviation and trend intensity, and combining this with a dual-threshold parameter analysis mechanism for flow-deposition correlation regions, a multi-objective collaborative optimization model was constructed for flow velocity adjustment parameters and sedimentation gradient adjustment parameters. A water level-driven directional correction algorithm was used to dynamically calibrate the optimization parameters, and a spatiotemporal smoothing constraint mechanism ensured the mechanical safety and regional flow regime coordination of the gate control sequence, forming a closed-loop control strategy linking water level, flow regime, and sedimentation. This method breaks through the rigid constraints of traditional single-threshold control, achieving dynamic optimization of gate opening within a safe boundary and collaborative adaptation of multi-regional parameters, providing intelligent decision support for high-sediment-laden water areas that balances flood control scheduling and sedimentation prevention.

[0166] Figure 2 This application provides a schematic diagram of the structure of a water level regulation system for a hydraulic engineering project, as shown in the embodiment of the present application. Figure 2 As shown, the system includes:

[0167] The acquisition module 21 acquires multi-dimensional scanning data of the riverbed topography by an underwater robot and full-area coverage data of satellite remote sensing for high sediment content water areas in the reservoir area of ​​the water conservancy hub and the downstream section of the flood discharge channel, so as to generate a spatiotemporal map of sediment deposition containing sediment gradient, and periodically corrects the spatiotemporal map of sediment deposition based on the changes in the sediment gradient.

[0168] The calculation module 22 deploys a radar array at the bend transition section of the spillway outlet. Based on the corrected spatiotemporal map of sediment deposition, it calculates the corresponding three-dimensional sediment velocity field vector distribution in real time by using the scattering intensity difference and phase shift trajectory of multi-frequency pulse signals.

[0169] The generation module 23 performs dynamic coupling analysis on the sedimentation gradient and the three-dimensional sediment velocity field vector distribution, identifies the correlation region between the velocity change and sediment deposition in the bend transition section, and generates a set of constraints containing the coordinates of the correlation region and the velocity-sedimentation matching rules.

[0170] The calculation module 24 calculates the dynamic adjustment parameter sequence of the flood discharge gate opening based on the set of constraints and the real-time water level monitoring data and target water level range of the water conservancy hub reservoir area.

[0171] Figure 2 The aforementioned water level regulation system for a water conservancy hub can perform... Figure 1 The implementation principle and technical effects of the water level regulation method for a water conservancy hub described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the water level regulation system of the above embodiment have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0172] In one possible design, Figure 2 A water level regulating system of a water conservancy project shown in the embodiment can be implemented as a computing device, such as a server. Figure 3 As shown, the computing device can include a storage component 31 and a processing component 32.

[0173] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0174] The processing component 32 is configured to perform the above Figure 1 A water level regulating method of a water conservancy project shown in the embodiment.

[0175] The processing component 32 can include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component can also be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, configured to execute the above method.

[0176] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0177] Of course, the computing device can also include other components, such as an input / output interface, a display component, a communication component, etc.

[0178] The input / output interface provides an interface between the processing component and peripheral interface modules, which can be output devices, input devices, etc.

[0179] The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, etc.

[0180] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform, and the computing device can be a cloud server, and the processing component, the storage component, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0181] The embodiment of the application further provides a computer storage medium, which stores a computer program. Figure 1 The application further provides a water level regulating method of a water conservancy hub.

[0182] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the system, device and unit described above can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0183] The device embodiments described above are only schematic, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, that is, can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment scheme. Those skilled in the art can understand and implement without creative labor.

[0184] Through the foregoing description of the embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and a necessary general hardware platform, and of course, can also be realized by hardware. Based on such understanding, the foregoing technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0185] Finally, it should be noted that: the foregoing embodiments are only used to illustrate the technical solutions of the application, rather than limit them; although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the application.

Claims

1. A method for regulating the water level of a water conservancy project, characterized in that, Comprise: For the high-silt water area of the reservoir area and the downstream river section of the spillway of the water conservancy hub, the multi-dimensional scanning data of the riverbed terrain by the underwater robot and the global coverage data by satellite remote sensing are simultaneously obtained to generate a sediment deposition space-time atlas containing a sediment deposition gradient, and the sediment deposition space-time atlas is periodically corrected based on the change of the sediment deposition gradient; A radar array is arranged at the transition section of the bend at the outlet of the spillway, and based on the corrected sediment deposition space-time atlas, the corresponding three-dimensional sediment flow velocity field vector distribution is calculated in real time through the scattering intensity difference and phase shift trajectory of multi-frequency band pulse signals; The sediment deposition gradient and the three-dimensional sediment flow velocity field vector distribution are dynamically coupled and analyzed to identify the correlation area of flow velocity mutation and sediment deposition in the transition section of the bend, and a constraint condition set containing the coordinates of the correlation area and the matching rules of flow velocity and sediment deposition is generated; According to the constraint condition set, combined with the real-time water level monitoring data of the reservoir area of the water conservancy hub and the target water level interval, the dynamic adjustment parameter sequence of the spillway gate opening degree is calculated.

2. The method of claim 1, wherein, The sediment deposition gradient and the three-dimensional sediment flow velocity field vector distribution are dynamically coupled and analyzed to identify the correlation area of flow velocity mutation and sediment deposition in the transition section of the bend, and a constraint condition set containing the coordinates of the correlation area and the matching rules of flow velocity and sediment deposition is generated, comprising: The time series data of the sediment deposition gradient and the three-dimensional sediment flow velocity field vector distribution are spatially superimposed based on the geographic coordinate system of the transition section of the bend to generate a synchronously updated sediment flow velocity coupling data set; In the sediment flow velocity coupling data set, the flow velocity vector length mutation area that meets the sediment deposition matching rule in the three-dimensional sediment flow velocity field is detected, and the flow velocity vector length mutation area is marked as a primary flow velocity mutation area; The second derivative distribution of the sediment deposition gradient is superimposed on the primary flow velocity mutation area to screen out the area that meets the extreme value judgment rule and the sediment deposition gradient occurs mutation as a flow velocity deposition correlation area; The three-dimensional geographic coordinate set of the flow velocity deposition correlation area is extracted, and the numerical correspondence relationship between the sediment deposition gradient and the flow velocity vector length in the flow velocity deposition correlation area is counted, and a constraint condition set containing the coordinates of the correlation area and the matching rules of flow velocity and sediment deposition is generated according to the numerical correspondence relationship.

3. The method of claim 2, wherein, The second derivative distribution of the sediment deposition gradient is superimposed on the primary flow velocity mutation area to screen out the area that meets the extreme value judgment rule and the sediment deposition gradient occurs mutation as a flow velocity deposition correlation area, comprising: Based on the spatial unit flow velocity vector length distribution of the primary flow velocity mutation area, the historical reference and the current period reference of the flow velocity vector length are set, and the primary screening area is determined in the spatial unit that meets the historical reference and the current period reference at the same time; The sediment deposition gradient data of each position point in the primary screening area is extracted, and the second derivative distribution of the sediment deposition gradient in the adjacent time period is calculated for each position point; Parallelly distributed virtual measuring lines are arranged in the primary screening area along the water flow direction, a discrete point sequence is generated for each virtual measuring line at an array interval, and a second derivative distribution corresponding to the discrete point sequence is calculated and mapped as a second derivative intensity distribution; Based on the overall distribution state of the second derivative intensity distribution, an extreme value judgment rule is set, and adjacent discrete point areas that continuously satisfy the extreme value judgment rule are marked as sediment deposition gradient mutation areas; The sediment deposition gradient mutation areas are superimposed in the vertical direction of the virtual measuring lines in three-dimensional space, the spatial coordinates of the primary screening area and the distribution range of the sediment deposition gradient mutation areas are fused, and a superimposed space unit set that simultaneously satisfies the flow velocity vector length reference condition and the extreme value judgment rule is defined as a flow velocity and sediment deposition correlation area.

4. The method of claim 1, wherein, Based on the corrected sediment deposition space-time spectrum, the three-dimensional sediment flow velocity field vector distribution is calculated in real time through the scattering intensity difference and phase offset trajectory of multi-frequency pulse signals, including: Synchronous transmission of multi-frequency pulse signals containing low-frequency penetration signals and high-frequency tracking signals, separation of reflected signals to generate sediment density distribution spectrum penetrating to the riverbed bottom layer and surface sediment motion trajectory spectrum; The sediment deposition gradient of the corresponding position in the corrected sediment deposition space-time spectrum is combined with the sediment deposition gradient of the corresponding position in the corrected sediment deposition space-time spectrum to generate a vertical density gradient correlation profile; The surface sediment motion trajectory spectrum is decomposed in three-dimensional space, and a surface particle motion direction angle distribution spectrum is established according to the spatial position offset of the reflected signal feature points in the continuous transmission period; The vertical density gradient correlation profile and the surface particle motion direction angle distribution spectrum are matched in space position, the surface motion direction angle data of the corresponding position in the vertical density gradient mutation area are superimposed, and the corresponding three-dimensional sediment flow velocity field vector distribution is generated through vector direction transmission.

5. The method of claim 4, wherein, The vertical density gradient correlation profile and the surface particle motion direction angle distribution spectrum are matched in space position, the surface motion direction angle data of the corresponding position in the vertical density gradient mutation area are superimposed, and the corresponding three-dimensional sediment flow velocity field vector distribution is generated through vector direction transmission, including: The vertical density gradient correlation profile and the surface particle motion direction angle distribution spectrum are matched in space position, the surface motion direction angle data of the corresponding position in the vertical density gradient mutation area are superimposed, and the corresponding three-dimensional sediment flow velocity field vector distribution is generated through vector direction transmission. The vertical density gradient correlation profile and the surface particle motion direction angle distribution spectrum are matched in space position, the surface motion direction angle data of the corresponding position in the vertical density gradient mutation area are superimposed, and the corresponding three-dimensional sediment flow velocity field vector distribution is generated through vector direction transmission. The vertical density gradient correlation profile and the surface particle motion direction angle distribution spectrum are matched in space position, the surface motion direction angle data of the corresponding position in the vertical density gradient mutation area are superimposed, and the corresponding three-dimensional sediment flow velocity field vector distribution is generated through vector direction transmission. ​ The modified motion vectors of all depth layers in the vertical density gradient mutation region are spatially superimposed to generate a three-dimensional sediment flow velocity field vector distribution covering the corresponding plane coordinates.

6. The method of claim 1, wherein, Synchronously acquiring multi-dimensional scanning data of riverbed topography by an underwater robot and global coverage data by satellite remote sensing to generate a sediment deposition space-time atlas containing a deposition gradient, and periodically correcting the sediment deposition space-time atlas based on changes in the deposition gradient, including: Setting a scanning path of the riverbed topography by the underwater robot, synchronously acquiring sediment accumulation thickness and multi-dimensional scanning data of each scanning point, and synchronously acquiring global coverage data by satellite remote sensing within the robot scanning interval; Calculating the thickness change difference of the sediment accumulation thickness of each scanning point according to the continuous scanning period, taking the thickness change difference and the horizontal distance ratio of adjacent scanning points as the deposition gradient, and combining the multi-dimensional scanning data and the global coverage data to generate a sediment deposition space-time atlas containing a gradient direction and a deposition gradient; When the direction of the deposition gradient of any scanning point continuously deviates from the initial direction by more than a set angle, or the numerical fluctuation amplitude of the deposition gradient exceeds a set proportion, triggering the encryption of the scanning path in the region where the point is located; Reacquiring the sediment accumulation thickness in the encrypted scanning region, correcting the numerical value and direction of the deposition gradient based on the new sediment accumulation thickness data, and covering the data of the corresponding region in the original space-time atlas.

7. The method of claim 1, wherein, According to the constraint condition set, combining real-time water level monitoring data and target water level interval of the reservoir area of the water conservancy hub, calculating a dynamic adjustment parameter sequence of the flood discharge gate opening, including: Obtaining real-time water level monitoring data of the reservoir area of the water conservancy hub, calculating the deviation of the current water level from the median value of the target water level interval, and predicting the water level change trend strength in the future period based on the water level change history data; Analyzing the flow velocity threshold and deposition gradient threshold of each associated region in the constraint condition set, converting the ratio relationship between the current measured flow velocity and the flow velocity threshold into a flow velocity adjustment parameter, and converting the ratio relationship between the current deposition gradient and the deposition gradient threshold into a deposition gradient adjustment parameter; Weighted fusion of the flow velocity adjustment parameter and the deposition gradient adjustment parameter generates an initial adjustment parameter value for each associated region, and directional correction is applied to the initial adjustment parameter value based on the water level deviation direction and trend strength; Arranging the corrected adjustment parameter values of each region in time sequence to ensure that the parameter change amplitude of adjacent time periods does not exceed the mechanical action safety threshold of the gate, and smoothing the parameter difference between spatially adjacent regions to generate a dynamic adjustment parameter sequence of the flood discharge gate opening.

8. A water level regulating system for a water conservancy project, characterized in that, including: An acquisition module, for high-sediment-content water areas of the reservoir area of the water conservancy hub and the downstream river section of the flood discharge channel, synchronously acquiring multi-dimensional scanning data of riverbed topography by an underwater robot and global coverage data by satellite remote sensing to generate a sediment deposition space-time atlas containing a deposition gradient, and periodically correcting the sediment deposition space-time atlas based on changes in the deposition gradient; The solving module is arranged with a radar array at a bend transition section of the spillway outlet, and based on the corrected spatiotemporal sediment accumulation pattern, the three-dimensional sediment flow velocity field vector distribution is solved in real time through the scattering intensity difference and phase offset trajectory of multi-frequency band pulse signals; The generating module dynamically couples and analyzes the sediment accumulation gradient and the three-dimensional sediment flow velocity field vector distribution, identifies the correlation area of flow velocity mutation and sediment accumulation in the bend transition section, and generates a constraint condition set containing the coordinates of the correlation area and the matching rules of flow velocity and sediment accumulation; The computing module calculates the dynamic adjustment parameter sequence of the spillway gate opening degree according to the constraint condition set, in combination with the real-time water level monitoring data and the target water level interval of the water conservancy hub reservoir area.

9. A computing device, comprising: The storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to realize the water level adjustment method of the water conservancy hub as claimed in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer program is stored in the computer, and when the computer program is executed by the computer, the water level adjustment method of the water conservancy hub as claimed in any one of claims 1-7 is realized.

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