Multi-source data driven complex water area flow direction identification method and system

Abstract

The invention relates to the technical field of hydrology and water resources, in particular to a complex water area flow direction recognition method and system driven by multi-source data, and the method comprises the following steps: S1, constructing a standardized water area feature data set through integrating multi-dimensional information of hydrological observation, remote sensing images and topographic data, and through denoising, space-time alignment and feature extraction; and S2, based on a quantum path superposition principle, fusing multi-source data and fluid mechanics constraints, and constructing a physical enhanced quantum path guidance model of flow direction probability distribution through non-Hermitian evolution and an adaptive weight strategy. According to the invention, through innovative combination of multi-source data depth fusion and quantum mechanics principles, the dynamic evolution process of complex water area water flow under multi-factor interference is accurately described, and the precision and robustness of flow direction identification and the adaptability to complex terrains are significantly improved. And efficient and reliable technical support is provided for shipping safety, pollution prevention and control and other applications of complex water areas.

Description

Technical Field

[0001] The present invention relates to the field of hydrology and water resources technology, and in particular to a method and system for identifying flow direction in complex waters driven by multi-source data. Background Art

[0002] Complex waters refer to water areas that are affected by multiple factors such as topography, hydrology and meteorology, and human activities, with complex water flow patterns, changeable boundary conditions, and significant multi-source interference. Such waters often have complex terrain such as reefs, bridge piers, and islands. At the same time, they are affected by dynamic factors such as tides, eddies, and water body confluence. The water flow speed and direction show strong temporal and spatial variability. Combined with interference such as remote sensing data noise and uneven temporal and spatial distribution of observation data, flow direction identification faces severe challenges.

[0003] In the existing technology, traditional methods for identifying flow direction in complex waters are unable to accurately depict the dynamic evolution of water flow under complex terrain and multiple interference factors due to insufficient fusion of multi-source data, resulting in low recognition accuracy, poor robustness and weak adaptability to complex environments.

[0004] Based on this, the present invention provides a method and system for identifying the flow direction of complex waters driven by multi-source data to solve the technical problems raised above. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for identifying the flow direction in complex waters driven by multi-source data. The present invention innovatively combines the deep fusion of multi-source data with the principles of quantum mechanics to accurately depict the dynamic evolution process of water flow in complex waters under the interference of multiple factors, significantly improving the accuracy, robustness and adaptability of flow direction identification to complex terrain, and providing efficient and reliable technical support for applications such as shipping safety and pollution prevention and control in complex waters.

[0006] To achieve the above object, the present invention provides the following technical solutions: The present invention provides a method for identifying the flow direction of complex waters driven by multi-source data, comprising the following steps: S1: By integrating multi-dimensional information of hydrological observations, remote sensing images, and terrain data, and through denoising, spatiotemporal alignment, and feature extraction, a standardized water feature dataset is constructed; S2: Based on the principle of quantum path superposition, a physically enhanced quantum path guidance model for flow direction probability distribution is constructed by integrating multi-source data with fluid dynamics constraints through non-Hermitian evolution and adaptive weighting strategies. S3: Based on the quantum path guidance model, the quantum jump model is combined with the Schrödinger equation simulation of the adaptive time step algorithm to calculate the quantum state evolution process of water flow under complex terrain and multi-factor interference, and generate the flow direction probability field; S4: The flow direction probability field is calibrated by Bayesian inversion using measured hydrological data. The path confidence threshold is optimized in combination with spatial topological constraints to output topologically consistent flow direction identification results. The model robustness is evaluated using cross-validation of spatiotemporal partitions.

[0007] Based on the above method, the present invention also proposes a complex water flow direction identification system driven by multi-source data, including a multi-source data fusion unit, a quantum path guidance modeling unit, a quantum evolution calculation unit, and a flow direction optimization verification unit, wherein: The multi-source data fusion unit is used to integrate hydrological observations, remote sensing images and terrain data, complete denoising, spatiotemporal alignment and feature extraction, and construct a standardized water feature dataset; The quantum path guidance modeling unit: Based on the principle of quantum path superposition, it integrates multi-source data and fluid mechanics constraints, and constructs a physically enhanced quantum path guidance model of flow direction probability distribution through non-Hermitian evolution and adaptive weighting strategy; The quantum evolution calculation unit is used to generate a flow probability field in a complex environment by simulating the Schrödinger equation using a fusion quantum jump model and an adaptive time step algorithm; The flow direction optimization verification unit is used to calibrate the probability field through Bayesian inversion, optimize the confidence threshold in combination with topological constraints, output topologically consistent flow direction results and evaluate the robustness of the model.

[0008] The multi-source data fusion unit includes a data access module, a data preprocessing module, a spatiotemporal alignment module, and a feature extraction and data set construction module, wherein: The data access module is used to receive multi-source heterogeneous data from hydrological sensors, remote sensing satellites and terrain databases; The data preprocessing module is used to perform denoising on the received multi-source data using quantum-derived wavelet transform to eliminate interference information in the data; The spatiotemporal alignment module performs spatiotemporal alignment on multi-source data based on a spatiotemporal interpolation algorithm; The feature extraction and data set construction module is used to extract key features of water flow velocity, terrain slope, and tidal changes from the processed data, and integrate them to construct a standardized water area feature data set.

[0009] The quantum path guidance modeling unit includes a quantum state encoding module, a non-Hermitian evolution module, and a weight optimization module, wherein: The quantum state encoding module is used to abstract the water flow path into a quantum state superposition and encode multi-source data features and fluid mechanics parameters through a tensor network; The non-Hermitian evolution module is used to introduce dissipative terms to construct a non-Hermitian Hamiltonian and simulate the quantum evolution process of water flow energy dissipation; The weight optimization module dynamically adjusts the path probability amplitude weight based on the adaptive strategy of gradient descent to optimize the flow direction probability distribution.

[0010] The quantum state encoding module abstracts the water flow path into a quantum state superposition, and encodes multi-source data features and fluid mechanics parameters through a tensor network. The specific operations are as follows: A1: Quantization of water flow paths: All possible water flow paths in complex waters are combined Mapping to quantum state basis vectors , construct path superposition state ,in, is the path probability amplitude, satisfying ; A2: Multi-source data tensor encoding: Use matrix product state tensor network to encode multi-source data features, and convert hydrological observation time series features into , spatial characteristics of remote sensing images , terrain data elevation characteristics Encoding as a tensor ,pass With basis vector The inner product of the probability amplitude is updated ; Update the probability amplitude through tensor network contraction operation ,in, For path The corresponding mapping matrix; A3: Fluid mechanics parameter embedding: velocity gradient , viscosity coefficient The parameters are converted into phase factors ,in, is the path length, k is the proportional coefficient, and the quantum state is updated by phase modulation , completing the quantized embedding of fluid mechanics constraints.

[0011] The non-Hermitian evolution module introduces a dissipative term to construct a non-Hermitian Hamiltonian to simulate the quantum evolution process of water flow energy dissipation. The specific operations are as follows: B1: Construction of dissipative term: Based on the energy dissipation characteristics of water flow, a dissipative term proportional to the square of the flow velocity is introduced to construct a non-Hermitian Hamiltonian, which is specifically expressed as: in, is the Hermitian Hamiltonian describing the kinetic and potential energy of water, is the dissipation coefficient, is the flow rate operator; B2: Evolution equation solution: Using the Crank-Nicolson difference method to solve the evolution equation containing non-Hermitian Hamiltonian Get the evolution trajectory of the quantum state over time ,in, is the evolution time; B3: Boundary Condition Adaptation: Setting the Quantum State Reflection Coefficient for Rigid Boundaries ,pass Simulates energy dissipation and reflection at boundaries, where is the incident quantum state, is the quantum state component absorbed by the boundary.

[0012] The quantum evolution calculation unit includes a quantum tunneling module, a variational solution module, and a probability field generation module, wherein: The quantum tunneling module is used to simulate the quantum tunneling effect of water flow at obstacles such as reefs and bridge piers, and to correct the path probability of sudden flow states; The variational solution module is used to discretize the Schrödinger equation using a variational quantum eigensolver; The probability field generation module is used to integrate the evolution results to generate a global flow direction probability field, and output it as a rasterized probability density map.

[0013] The quantum tunneling module simulates the quantum tunneling effect of water flow at an obstacle and corrects the path probability of the sudden flow state. The specific operations are as follows: C1: Obstacle Barrier Modeling: Obstacles in complex waters are abstracted as quantum potential barriers with a potential height of It is positively correlated with the obstacle size and the water flow impact angle, and the expression is: in, is the barrier coefficient, For obstacles in The cross-sectional area at is the angle between the water flow and the obstacle surface; C2: Tunneling probability calculation: Based on the potential barrier model, the tunneling probability P of water flowing through the obstacle is calculated using quantum tunneling theory. For the rectangular potential barrier approximation scenario, the expression is: in, is the mass of the water flow unit, is the kinetic energy of water, is the reduced Planck constant, is the integral differential element along the water flow path; C3: Path probability correction: based on tunneling probability The path probability amplitude under the sudden flow state is corrected. The corrected probability amplitude is expressed as: in, is the original path probability amplitude, is the weight coefficient of the water flow bypass obstacle path.

[0014] The variational solution module uses a variational quantum eigensolver to discretize the Schrödinger equation. The specific operations are as follows: D1: Wave function parameterization: the wave function describing the water flow quantum state Expressed as a variational parameter The quantum circuit output state is composed of Hadamard gate, CNOT gate and RY rotation gate. Control the wave function morphology; D2: Energy expectation value calculation: Calculate the expectation value of Hamiltonian H under the parameterized wave function , where H is the quantum Hamiltonian of the water flow that integrates the characteristics of multi-source data, and the efficient calculation of the expected value is achieved through quantum phase estimation technology; D3: Variational optimization iteration: With the goal of minimizing the expected value of energy, the gradient descent algorithm is used to update the variational parameters , the iteration formula is: in, is the learning rate until the expected value of the energy converges to the preset threshold and the approximate solution of the discretized Schrödinger equation is obtained.

[0015] The flow direction optimization verification unit includes a Bayesian inversion module, a topology correction module, and a verification and evaluation module, wherein: The Bayesian inversion module is used to calibrate the flow direction probability field using the Bayesian inversion method; The topology correction module: constructs a spatial topological relationship based on the Delaunay triangulation to enforce the constraints of mass conservation and flow continuity; The verification and evaluation module is used to quantify the model accuracy using spatiotemporal K-fold cross-validation and generate ROC curves and confusion matrices to evaluate robustness.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention accurately depicts the dynamic evolution of water flow in complex waters under the interference of multiple factors through the deep fusion of multi-source data and the innovative combination of quantum mechanics principles, significantly improving the accuracy and robustness of flow direction identification and its adaptability to complex terrain, and providing efficient and reliable technical support for applications such as shipping safety and pollution prevention and control in complex waters. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of the complex water flow direction identification method driven by multi-source data of the present invention.

[0018] Figure 2 This is a system diagram of the complex water flow direction identification system driven by multi-source data of the present invention.

[0019] Figure 3 This is a flow chart of the complex water flow direction identification method driven by multi-source data and the variational quantum solution in the system.

[0020] Description of Figure Numbers: 1. Multi-source data fusion unit; 11. Data access module; 12. Data preprocessing module; 13. Space-time alignment module; 14. Feature extraction and data set construction module; 2. Quantum path guidance modeling unit; 21. Quantum state encoding module; 22. Non-Hermitian evolution module; 23. Weight optimization module; 3. Quantum evolution calculation unit; 31. Quantum tunneling module; 32. Variational solution module; 33. Probability field generation module; 4. Flow optimization verification unit; 41. Bayesian inversion module; 42. Topology correction module; 43. Verification and evaluation module. DETAILED DESCRIPTION

[0021] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] Example 1: like Figure 1 and Figure 3 As shown, this embodiment provides a complex water flow direction identification system driven by multi-source data, including a multi-source data fusion unit 1, a quantum path guidance modeling unit 2, a quantum evolution calculation unit 3, and a flow direction optimization verification unit 4, wherein: the multi-source data fusion unit 1 is used to integrate hydrological observations, remote sensing images and terrain data, complete denoising, spatiotemporal alignment and feature extraction, and construct a standardized water feature data set; the quantum path guidance modeling unit 2 is based on the quantum path superposition principle, integrates multi-source data and fluid mechanics constraints, and constructs a physically enhanced quantum path guidance model of flow direction probability distribution through non-Hermitian evolution and adaptive weight strategy; the quantum evolution calculation unit 3 is used to simulate the Schrödinger equation using a fusion quantum jump model and an adaptive time step algorithm to generate a flow direction probability field in a complex environment; the flow direction optimization verification unit 4 is used to calibrate the probability field through Bayesian inversion, optimize the confidence threshold in combination with topological constraints, output a topologically consistent flow direction result, and evaluate the robustness of the model.

[0023] Among them, it should be noted that the multi-source data fusion unit 1 completes the integration and feature extraction of heterogeneous data, and provides standardized input for the quantum path guidance modeling unit 2. The modeling unit generates the flow probability distribution through non-Hermitian quantum evolution, drives the quantum evolution calculation unit 3 to simulate the quantum state evolution in a complex environment and output the probability field, and finally the flow optimization verification unit 4 calibrates the results through Bayesian inversion and topology optimization.

[0024] In this embodiment, it should also be noted that the multi-source data fusion unit 1 includes a data access module 11, a data preprocessing module 12, a spatiotemporal alignment module 13, and a feature extraction and dataset construction module 14, wherein: the data access module 11 is used to receive multi-source heterogeneous data from hydrological sensors, remote sensing satellites, and terrain databases; the data preprocessing module 12 is used to denoise the received multi-source data using quantum-derived wavelet transform to eliminate interference information in the data; the spatiotemporal alignment module 13 is used to perform spatiotemporal alignment on the multi-source data based on a spatiotemporal interpolation algorithm; the feature extraction and dataset construction module 14 is used to extract key features of water flow velocity, terrain slope, and tidal changes from the processed data, and integrate and construct a standardized water feature dataset.

[0025] Among them, it should be noted that the data access module 11 is responsible for receiving multi-source heterogeneous data, the data preprocessing module 12 performs denoising through quantum-derived wavelet transform, the spatiotemporal alignment module 13 unifies the data spatiotemporal benchmark based on the interpolation algorithm, and finally the feature extraction and dataset construction module 14 extracts key features and constructs a standardized dataset.

[0026] Furthermore, it should be noted that the data access module 11 supports multi-protocol data access, including real-time monitoring data from hydrological sensors (such as second-level velocity data from ADCP current meters), multispectral imagery from remote sensing satellites (such as water surface roughness inversion from Sentinel-1 radar imagery), and DEM data from terrain databases (with an accuracy of up to 1 meter). By using heterogeneous data interface adaptation technology, unified reception and storage of data in different formats (such as NetCDF, GeoTIFF, and CSV) is achieved.

[0027] In this embodiment, it should also be noted that the quantum path guided modeling unit 2 includes a quantum state encoding module 21, a non-Hermitian evolution module 22, and a weight optimization module 23, wherein: the quantum state encoding module 21 is used to abstract the water flow path into a quantum state superposition, and encode the multi-source data features and fluid mechanics parameters through a tensor network; the specific operations are as follows: A1: Quantization of water flow path: The possible water flow path set in the complex water area is converted into a quantum state superposition. Mapping to quantum state basis vectors , construct path superposition state ,in, is the path probability amplitude, satisfying ; A2: Multi-source data tensor encoding: Use matrix product state tensor network to encode multi-source data features, and convert hydrological observation time series features into , spatial characteristics of remote sensing images , terrain data elevation characteristics Encoding as a tensor ,pass With basis vector The inner product of the probability amplitude is updated ; Update the probability amplitude through tensor network contraction operation ,in, For path Corresponding mapping matrix; A3: Fluid mechanics parameter embedding: velocity gradient , viscosity coefficient The parameters are converted into phase factors ,in, is the path length, k is the proportional coefficient, and the quantum state is updated by phase modulation , completing the quantized embedding of fluid dynamics constraints. Non-Hermitian evolution module 22: used to introduce dissipative terms to construct a non-Hermitian Hamiltonian and simulate the quantum evolution process of water flow energy dissipation; the specific operations are as follows: B1: Dissipative term construction: Based on the energy dissipation characteristics of water flow, a dissipative term proportional to the square of the flow velocity is introduced to construct a non-Hermitian Hamiltonian, specifically expressed as: in, is the Hermitian Hamiltonian describing the kinetic and potential energy of water, is the dissipation coefficient, is the velocity operator; B2: Evolution equation solution: Crank-Nicolson difference method is used to solve the evolution equation containing non-Hermitian Hamiltonian Get the evolution trajectory of the quantum state over time ,in, is the evolution time; B3: Boundary condition adaptation: setting the quantum state reflection coefficient for the rigid boundary ,pass Simulates energy dissipation and reflection at boundaries, where is the incident quantum state, is the quantum state component absorbed by the boundary. Weight optimization module 23: Dynamically adjusts the path probability amplitude weight based on the adaptive strategy of gradient descent to optimize the flow probability distribution.

[0028] Among them, it should be noted that the quantum state encoding module 21 quantizes the water flow path through the tensor network and integrates the characteristics of multi-source data to construct the initial quantum state. The non-Hermitian evolution module 22 introduces the fluid mechanics dissipation term to simulate the quantum state evolution and characterize the energy dissipation process. The weight optimization module 23 dynamically adjusts the probability amplitude weight through an adaptive strategy, and finally outputs the optimized flow direction probability distribution.

[0029] Furthermore, it should be noted that the reflection coefficient of a rigid boundary (such as a dam) According to the boundary material setting: Concrete dam (strong reflection), earthy shoreline (strong absorption), simulating the energy loss and rebound characteristics of water flow at the boundary.

[0030] In this embodiment, it should also be noted that the quantum evolution calculation unit 3 includes a quantum tunneling module 31, a variational solution module 32, and a probability field generation module 33, wherein: the quantum tunneling module 31 is used to simulate the quantum tunneling effect of water flow at obstacles such as reefs and bridge piers, and to correct the path probability of sudden flow states; the specific operations are as follows: C1: Obstacle barrier modeling: the obstacles in complex waters are abstracted as quantum barriers, and the barrier height It is positively correlated with the obstacle size and the water flow impact angle, and the expression is: in, is the barrier coefficient, For obstacles in The cross-sectional area at is the angle between the water flow and the obstacle surface ( hour =0, the potential barrier is the lowest, corresponding to the frontal impact of the water flow; hour , the potential barrier is the highest, corresponding to the parallel flow); C2: Tunneling probability calculation: Based on the potential barrier model, the tunneling probability P of water flowing through the obstacle is calculated using quantum tunneling theory. For the rectangular potential barrier approximation scenario, the expression is: in, is the mass of the water flow unit, is the kinetic energy of water, is the reduced Planck constant, is the integral differential element along the water flow path; C3: path probability correction: according to the tunneling probability The path probability amplitude under the sudden flow state is corrected. The corrected probability amplitude is expressed as: in, is the original path probability amplitude, is the weight coefficient of the water flow path around the obstacle. Variational solution module 32: used to discretize the Schrödinger equation using the variational quantum eigensolver; the specific operations are as follows: D1: wave function parameterization: the wave function describing the water flow quantum state is converted to Expressed as a variational parameter The quantum circuit output state is composed of Hadamard gate, CNOT gate and RY rotation gate. Control wave function morphology; D2: Energy expectation value calculation: Calculate the expectation value of Hamiltonian H under the parameterized wave function , where H is the quantum Hamiltonian of the water flow that integrates the characteristics of multi-source data, and the efficient calculation of the expected value is achieved through quantum phase estimation technology; D3: Variational optimization iteration: With the goal of minimizing the energy expectation value, the gradient descent algorithm is used to update the variational parameters , the iteration formula is: in, is the learning rate until the expected value of energy converges to a preset threshold, and the discretized approximate solution of the Schrödinger equation is obtained. Probability field generation module 33 is used to integrate the evolution results to generate the global flow direction probability field, and output it as a gridded probability density map.

[0031] It should be noted that the quantum tunneling module 31 corrects the path probability at the obstacle through potential barrier modeling and tunneling probability calculation, the variational solution module 32 uses parameterized quantum circuits to solve the Schrödinger equation, and finally the probability field generation module 33 integrates the evolution results to generate a global probability field.

[0032] Furthermore, it should be noted that the physical meaning of the tunneling probability P in step C2 is "the possibility of water flowing through an obstacle (such as a low reef)". For reefs with a height of less than 2 meters, the P value can reach 0.3-0.5, reflecting the characteristic of partial water penetration; for tall obstacles such as bridge piers, P < 0.05, mainly due to detour flow. Hamiltonian ,in, is the terrain elevation gradient (weight ), is the square of the velocity (weight ), through quantum phase estimation technology, efficient calculation of expected values ​​can be achieved at the scale of 100 quantum bits with an accuracy of In step D3, the convergence threshold is set to the energy expected value fluctuation. , usually 50-100 iterations are needed to converge. The resolution of the rasterized probability density map in the probability field generation module 33 is 5 meters × 5 meters, and each grid stores the flow angle ( ) and probability value (0-1), and intuitively displays the global flow distribution through color coding (such as red indicates high-probability flow direction), supporting seamless connection with the GIS system.

[0033] In this embodiment, it should also be noted that the flow direction optimization verification unit 4 includes a Bayesian inversion module 41, a topology correction module 42, and a verification and evaluation module 43, wherein: the Bayesian inversion module 41 is used to calibrate the flow direction probability field through the Bayesian inversion method; the topology correction module 42 is used to construct a spatial topological relationship based on the Delaunay triangulation, and to enforce the constraints of mass conservation and flow continuity; the verification and evaluation module 43 is used to quantify the model accuracy using spatiotemporal K-fold cross-validation, and to generate an ROC curve and confusion matrix to evaluate the robustness.

[0034] It should be noted that the Bayesian inversion module 41 performs data assimilation calibration on the probability field, the topology correction module 42 ensures the physical rationality of the flow direction through the Delaunay triangulation, and finally the verification and evaluation module 43 uses spatiotemporal cross-validation to evaluate the model performance.

[0035] Furthermore, it should be noted that the construction of the Delaunay triangulation in the topology correction module 42 takes the characteristic points of the water area (such as flow rate monitoring points and terrain inflection points) as vertices, and ensures that the flow direction angle difference of adjacent triangles is the same through constrained triangulation. (Continuity constraints are satisfied), and flow conservation within the triangulated network is verified (inflow = outflow ± 5%). In the validation and evaluation module 43, spatiotemporal K-fold cross-validation divides the data into 15 subsets based on "5 time segments x 3 spatial regions." One subset is selected at a time as the validation set, and the remaining subsets are used as the training set. Evaluation metrics include the area under the receiver operating characteristic (AUC) value of the receiver operating characteristic (>0.9 indicates excellent model discrimination), the overall accuracy of the confusion matrix (>85%), and the Kappa coefficient (>0.8). These metrics ensure that the model maintains stable performance across different seasons (e.g., flood season, dry season) and regions (e.g., nearshore, offshore).

[0036] Example 2: like Figure 2 and Figure 3 As shown, in this embodiment, the method for identifying the flow direction of complex waters driven by multi-source data specifically includes the following steps: S1: Multi-source data fusion processing Input: hydrological observation data (such as flow velocity and discharge), remote sensing image data (such as multispectral imagery and radar imagery), and terrain data (such as digital elevation model (DEM)). Operation Description: ① Acquire multi-source heterogeneous data through multi-protocol data access; ② De-noising the data, using quantum-derived wavelet transform to eliminate noise interference in the data. The specific formula is: ; ③ Perform spatiotemporal alignment of multi-source data based on spatiotemporal interpolation algorithms; ④ Extract key features such as water velocity, terrain slope, and tidal changes, and integrate them to construct a standardized water feature dataset; Output: standardized water feature dataset, which serves as input for subsequent quantum path guidance modeling; S2: Quantum Path Guidance Modeling Input: Standardized water characteristics dataset; Operation Description: ① Quantum state encoding: Map all possible water flow paths in complex waters into quantum state basis vectors to construct path superposition states: in, is the path probability amplitude, satisfying .

[0037] ②Tensor network coding: Use matrix product state tensor network to encode multi-source data features, through tensor With basis vector The inner product of is used to update the path probability amplitude: ③ Fluid mechanics parameter embedding: velocity gradient , viscosity coefficient Converted to phase factor: in, is the path length, k is the proportional coefficient; Update the quantum state via phase modulation: ④ Non-Hermitian evolution modeling: Constructing a non-Hermitian Hamiltonian including dissipative terms: in, To describe the Hermitian part of the kinetic and potential energy of water, is the dissipation coefficient, is the flow rate operator; ⑤Solution of evolution equations: The Crank-Nicolson difference method is used to solve the evolution equations containing non-Hermitian Hamiltonians: Obtain the evolution trajectory of the quantum state over time; ⑥ Boundary condition adaptation: setting the quantum state reflection coefficient of the rigid boundary , simulating energy dissipation and reflection at boundaries: in, is the incident quantum state, is the quantum state component absorbed by the boundary; ⑦ Path weight optimization: Dynamically adjust the path probability amplitude weight based on the gradient descent algorithm to optimize the flow probability distribution.

[0038] Output: Optimized flow probability distribution, used to drive subsequent quantum state evolution calculations.

[0039] S3: Quantum state evolution simulation and flow probability field generation Input: optimized flow direction probability distribution; Operation Description: ① Obstacle barrier modeling: Obstacles in complex waters (such as reefs and bridge piers) are abstracted into quantum potential barriers, whose barrier height is positively correlated with the obstacle size and the water impact angle: in, is the barrier coefficient, For obstacles in The cross-sectional area at is the angle between the water flow and the obstacle surface; ② Tunneling probability calculation: Based on the rectangular barrier model, the probability of water flowing through the obstacle is calculated using quantum tunneling theory: in, is the mass of the water flow unit, is the kinetic energy of water, is the reduced Planck constant, is the integral differential element along the water flow path; ③ Path probability correction: Correct the path probability amplitude according to the tunneling probability: in, is the weight coefficient of the water flow bypass path; ④ Wave function parameterization: The wave function describing the water flow quantum state is expressed as a wave function containing variational parameters The quantum circuit output state is: Among them, the quantum circuit is composed of Hadamard gate, CNOT gate and RY rotation gate; ⑤ Energy expectation value calculation: Calculate the expectation value of Hamiltonian H under the parameterized wave function: ⑥ Variational optimization iteration: With the goal of minimizing the expected value of energy, the gradient descent algorithm is used to update the variational parameters: Until the expected energy value converges to the preset threshold; ⑦ Probability field generation: Integrate the evolution results to generate the global flow direction probability field, and output it as a gridded probability density map, where each grid contains the flow direction angle and probability value; Output: global flow probability field for subsequent optimization and verification; S4: Flow direction probability field optimization and model verification Input: global flow direction probability field; Operation Description: ① Bayesian inversion calibration: Using measured hydrological data to perform Bayesian inversion calibration on the flow direction probability field to improve prediction accuracy; ② Topology correction: Construct spatial topology relationships based on the Delaunay triangulation to enforce mass conservation and flow continuity constraints; ③ Validation and evaluation: Use spatiotemporal K-fold cross-validation to quantify model accuracy, and generate ROC curves and confusion matrices to evaluate robustness; Output: Topologically consistent flow direction identification results, supporting GIS system docking and visualization.

[0040] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0041] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A multi-source data-driven method for identifying the flow direction in complex waters, characterized by: The following steps are involved: S1: By integrating multi-dimensional information of hydrological observations, remote sensing images, and terrain data, and through denoising, spatiotemporal alignment, and feature extraction, a standardized water feature dataset is constructed; S2: Based on the principle of quantum path superposition, a physically enhanced quantum path guidance model for flow direction probability distribution is constructed by integrating multi-source data with fluid dynamics constraints through non-Hermitian evolution and adaptive weighting strategies. S3: Based on the quantum path guidance model, the quantum jump model is combined with the Schrödinger equation simulation of the adaptive time step algorithm to calculate the quantum state evolution process of water flow under complex terrain and multi-factor interference, and generate the flow direction probability field; S4: The flow direction probability field is calibrated by Bayesian inversion using measured hydrological data. The path confidence threshold is optimized in combination with spatial topological constraints to output topologically consistent flow direction identification results. The model robustness is evaluated using cross-validation of spatiotemporal partitions.

2. A complex water area flow direction identification system driven by multi-source data, according to the complex water area flow direction identification method driven by multi-source data in claim 1, characterized in that: It includes a multi-source data fusion unit (1), a quantum path guidance modeling unit (2), a quantum evolution calculation unit (3), and a flow optimization verification unit (4), wherein: The multi-source data fusion unit (1) is used to integrate hydrological observations, remote sensing images and terrain data, complete denoising, spatiotemporal alignment and feature extraction, and construct a standardized water feature data set; The quantum path guidance modeling unit (2) is based on the quantum path superposition principle, integrates multi-source data and fluid mechanics constraints, and constructs a physically enhanced quantum path guidance model of flow direction probability distribution through non-Hermitian evolution and adaptive weight strategy; The quantum evolution calculation unit (3) is used to generate a flow probability field under a complex environment by simulating the Schrödinger equation using a fusion quantum jump model and an adaptive time step algorithm; The flow direction optimization verification unit (4) is used to calibrate the probability field through Bayesian inversion, optimize the confidence threshold in combination with topological constraints, output topologically consistent flow direction results and evaluate the model robustness.

3. The complex water flow direction identification system driven by multi-source data according to claim 2 is characterized in that: The multi-source data fusion unit (1) includes a data access module (11), a data preprocessing module (12), a spatiotemporal alignment module (13), and a feature extraction and data set construction module (14), wherein: The data access module (11) is used to receive multi-source heterogeneous data from hydrological sensors, remote sensing satellites and terrain databases; The data preprocessing module (12) is used to perform denoising on the received multi-source data using quantum-derived wavelet transform to eliminate interference information in the data; The spatiotemporal alignment module (13) performs spatiotemporal alignment on multi-source data based on a spatiotemporal interpolation algorithm; The feature extraction and data set construction module (14) is used to extract key features of water flow velocity, terrain slope, and tidal changes from the processed data, and integrate and construct a standardized water area feature data set.

4. The complex water flow direction identification system driven by multi-source data according to claim 2 is characterized in that: The quantum path guided modeling unit (2) includes a quantum state encoding module (21), a non-Hermitian evolution module (22), and a weight optimization module (23), wherein: The quantum state encoding module (21) is used to abstract the water flow path into a quantum state superposition and encode multi-source data features and fluid mechanics parameters through a tensor network; The non-Hermitian evolution module (22) is used to introduce a dissipative term to construct a non-Hermitian Hamiltonian and simulate the quantum evolution process of water flow energy dissipation; The weight optimization module (23) dynamically adjusts the path probability amplitude weight based on the adaptive strategy of gradient descent to optimize the flow direction probability distribution.

5. The complex water flow direction identification system driven by multi-source data according to claim 4 is characterized in that: The quantum state encoding module (21) abstracts the water flow path into a quantum state superposition, and encodes multi-source data features and fluid mechanics parameters through a tensor network. The specific operations are as follows: A1: Quantization of water flow paths: All possible water flow paths in complex waters are combined Mapping to quantum state basis vectors , construct path superposition state ,in, is the path probability amplitude, satisfying ; A2: Multi-source data tensor encoding: Use matrix product state tensor network to encode multi-source data features, and convert hydrological observation time series features into , spatial characteristics of remote sensing images , terrain data elevation characteristics Encoding as a tensor ,pass With basis vector The inner product of the probability amplitude is updated ; Update the probability amplitude through tensor network contraction operation ,in, For path The corresponding mapping matrix; A3: Fluid mechanics parameter embedding: velocity gradient , viscosity coefficient The parameters are converted into phase factors ,in, is the path length, k is the proportional coefficient, and the quantum state is updated by phase modulation , completing the quantized embedding of fluid mechanics constraints.

6. The complex water flow direction identification system driven by multi-source data according to claim 4 is characterized in that: The non-Hermitian evolution module (22) introduces a dissipative term to construct a non-Hermitian Hamiltonian to simulate the quantum evolution process of water flow energy dissipation. The specific operations are as follows: B1: Construction of dissipative term: Based on the energy dissipation characteristics of water flow, a dissipative term proportional to the square of the flow velocity is introduced to construct a non-Hermitian Hamiltonian, which is specifically expressed as: in, is the Hermitian Hamiltonian describing the kinetic and potential energy of water, is the dissipation coefficient, is the flow rate operator; B2: Evolution equation solution: Using the Crank-Nicolson difference method to solve the evolution equation containing non-Hermitian Hamiltonian Get the evolution trajectory of the quantum state over time ,in, is the evolution time; B3: Boundary Condition Adaptation: Setting the Quantum State Reflection Coefficient for Rigid Boundaries ,pass Simulates energy dissipation and reflection at boundaries, where is the incident quantum state, is the quantum state component absorbed by the boundary.

7. The complex water flow direction identification system driven by multi-source data according to claim 2 is characterized in that: The quantum evolution calculation unit (3) includes a quantum tunneling module (31), a variational solution module (32), and a probability field generation module (33), wherein: The quantum tunneling module (31) is used to simulate the quantum tunneling effect of water flow at obstacles such as reefs and bridge piers, and to correct the path probability of sudden flow state; The variational solution module (32) is used to discretize the Schrödinger equation using a variational quantum eigensolver; The probability field generation module (33) is used to integrate the evolution results to generate a global flow direction probability field, and output it as a gridded probability density map.

8. The complex water flow direction identification system driven by multi-source data according to claim 7 is characterized in that: The quantum tunneling module (31) simulates the quantum tunneling effect of water flow at the obstacle and corrects the path probability of the sudden flow state. The specific operation is as follows: C1: Obstacle Barrier Modeling: Obstacles in complex waters are abstracted as quantum potential barriers with a potential height of It is positively correlated with the obstacle size and the water flow impact angle, and the expression is: in, is the barrier coefficient, For obstacles in The cross-sectional area at is the angle between the water flow and the obstacle surface; C2: Tunneling probability calculation: Based on the potential barrier model, the tunneling probability P of water flowing through the obstacle is calculated using quantum tunneling theory. For the rectangular potential barrier approximation scenario, the expression is: in, is the mass of the water flow unit, is the kinetic energy of water, is the reduced Planck constant, is the integral differential element along the water flow path; C3: Path probability correction: based on tunneling probability The path probability amplitude under the sudden flow state is corrected. The corrected probability amplitude is expressed as: in, is the original path probability amplitude, is the weight coefficient of the water flow bypass obstacle path.

9. The complex water flow direction identification system driven by multi-source data according to claim 7 is characterized in that: The variational solution module (32) uses a variational quantum eigensolver to discretize the Schrödinger equation. The specific operation is as follows: D1: Wave function parameterization: the wave function describing the water flow quantum state Expressed as a variational parameter The quantum circuit output state is composed of Hadamard gate, CNOT gate and RY rotation gate. Control the wave function morphology; D2: Energy expectation value calculation: Calculate the expectation value of Hamiltonian H under the parameterized wave function , where H is the quantum Hamiltonian of the water flow that integrates the characteristics of multi-source data, and the efficient calculation of the expected value is achieved through quantum phase estimation technology; D3: Variational optimization iteration: With the goal of minimizing the expected value of energy, the gradient descent algorithm is used to update the variational parameters , the iteration formula is: in, is the learning rate until the expected value of the energy converges to the preset threshold and the approximate solution of the discretized Schrödinger equation is obtained.

10. The complex water flow direction identification system driven by multi-source data according to claim 2, characterized in that: The flow direction optimization verification unit (4) includes a Bayesian inversion module (41), a topology correction module (42), and a verification evaluation module (43), wherein: The Bayesian inversion module (41) is used to calibrate the flow direction probability field using a Bayesian inversion method; The topology correction module (42) constructs a spatial topological relationship based on the Delaunay triangulation to enforce the constraints of mass conservation and flow continuity; The verification and evaluation module (43) is used to use spatiotemporal K-fold cross-validation to quantify model accuracy and generate ROC curves and confusion matrices to evaluate robustness.

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