Intelligent deduction and submerging probability visualization system and method for dike breach collapse potential and medium

Abstract

The invention belongs to the technical field of dike breach emergency rescue, and discloses a dike breach breach potential intelligent deduction and submerging probability visualization system and method and a medium. The engineering intervention deduction engine is constructed on the basis of a physically guided neural operator, takes a two-dimensional multi-physical reconstruction field containing pile body positions, material distribution, a flow velocity field and terrains as input, and directly outputs a water flow velocity-water depth physical field time history in a future time period; the online self-evolution engine is used for triggering fine adjustment of the model by comparing a deduction result with the deviation of real-time monitoring data; and the uncertainty quantification visual interface is used for dynamically displaying the deduction result in the form of a probabilistic submerging thermodynamic diagram. According to the method, a deduction framework integrating physical constraints and neural operators is created for the first time, the technical problems that traditional numerical simulation calculation is low in efficiency and pure data driving methods are physically inconsistent are solved, the unification of minute-level deduction speed and physical rule following is achieved, and real-time and reliable scientific decision support is provided for dike breach emergency rescue.

Description

Technical Field

[0001] This invention relates to the field of dike breach emergency repair technology, and in particular to a system, method and medium for intelligent prediction of dike breach and visualization of inundation probability. Background Technology

[0002] my country experiences frequent floods, and emergency response decisions regarding dike breaches heavily rely on expert experience and traditional numerical simulation methods, presenting significant technical bottlenecks. While traditional hydrodynamic simulations have clear mechanisms, they consume tens of hours of computation, failing to meet minute-level decision-making needs. Furthermore, they struggle to integrate real-time monitoring data with engineering intervention measures and are prone to convergence issues under complex boundary conditions. Purely data-driven machine learning methods, although computationally fast, are prone to violating physical laws, exhibiting poor generalization ability, and potentially producing absurd predictions such as "water flows uphill" when data is sparse. The main shortcomings of existing technologies are: difficulty in balancing computational efficiency and extrapolation accuracy; insufficient integration of multi-source data with physical mechanisms; a lack of dynamic response simulation capabilities for engineering interventions; and a lack of uncertainty quantification in prediction results.

[0003] This invention achieves an organic integration of physical laws and data-driven approaches through an engineering intervention inference engine using physically guided neural operators; it ensures the model dynamically adapts to the disaster relief process through an online self-evolutionary engine; and it provides risk perception capabilities through uncertainty quantification and visualization. While maintaining physical consistency, it increases the inference speed by two orders of magnitude, effectively solving key challenges of timeliness, accuracy, and practicality in traditional methods. Summary of the Invention

[0004] The purpose of this invention is to provide a system, method, and medium for intelligent prediction of dike breach and visualization of inundation probability, in order to solve the problems existing in the prior art and realize the functions of intelligent prediction of dike breach and visualization of dike inundation probability under high-speed water flow environment and engineering intervention.

[0005] The technical solution of the present invention is as follows: A system for intelligent prediction of dike breach and visualization of inundation probability, comprising:

[0006] The engineering intervention simulation engine is based on the construction of a physically guided neural operator. It takes a two-dimensional multi-physics reconstruction field as input and directly outputs the time history of the water velocity-depth physical field and the breach shape, which includes the water velocity field and the water depth field, for a specified future time period.

[0007] The online self-evolution engine automatically compares the predictions of the engineering intervention simulation engine with the actual monitoring data. When the deviation between the two exceeds a threshold, the fine-tuning system of the online self-evolution engine is triggered.

[0008] The uncertainty quantification visualization interface displays the water flow velocity-depth physical field time history and breach morphology output by the engineering intervention simulation engine on the real-world map in the form of a probabilistic inundation heat map, where color saturation represents the probability of inundation.

[0009] The two-dimensional multiphysics reconstruction field includes the number, diameter, pile location and depth of the sealing piles, size and weight of the sealing material, PIV water velocity field, and CAD point cloud terrain field.

[0010] The physical-guided neural operator is derived by a multidimensional neural operator convolutional layer, and the loss function includes data-driven loss and physical constraint loss.

[0011] The engineering intervention simulation engine is trained during the training phase using numerical simulations based on different two-dimensional multiphysics reconstruction fields, as well as the simulated water velocity-depth physical field time histories and breach morphologies. It learns the nonlinear mapping relationship between the two-dimensional multiphysics reconstruction fields and the water velocity-depth physical field time histories and breach morphologies. The physical-guided neural operator functional expression is as follows.

[0012] ;

[0013] For the solution operator to be trained, For projection operators, Let be the activation function of the t-th layer. For the linear operator at level t, used to provide linear transformations, For the integral kernel of the t-th layer, Let be the bias tensor of the t-th layer. The activation function for layer 1. This is the last layer of a multidimensional Fourier convolutional layer; This is the first-level lifting operator;

[0014] , The initial hidden feature function is... for The domain, for The range of values; For the input function, Represented as ,in For the domain, The range; For the integral kernel operator of the i-th layer, embed a kernel function, expressed as follows:

[0015] ;

[0016] ;

[0017] in, As an integral operator, it provides nonlocal information interaction, allowing information transmission between distant points; for The Borel measure, where, It is a space of arbitrary dimensions; It is a nonlinear kernel function; The hidden characteristic function of the i-th integral kernel operator, Let be the cumulative space of the first i arbitrary-dimensional spaces; For any function, Let be the dimension of the value space corresponding to the velocity field of the i-th sample;

[0018] The total loss function consists of data-driven loss and physical constraint loss, and its formula is as follows:

[0019] ;

[0020] For data-driven loss, Weights for data-driven loss; For physical constraint loss, The weights for the physical constraint loss;

[0021] The data-driven loss is calculated from the root mean square error, and the formula is as follows.

[0022] ;

[0023] For sample size, The predicted velocity field value of the i-th sample, The observed velocity field value of the i-th sample, The predicted water depth field value of the i-th sample The water depth field observation value of the i-th sample;

[0024] The physical constraint loss consists of multiple loss values, including the continuity equation constraint of the two-dimensional shallow water equation, the momentum equation constraint of the two-dimensional shallow water equation, the pile-flow interaction constraint, the improved material deposition constraint, and the improved sediment transport equation constraint, the formulas of which are as follows.

[0025] Physical constraint loss ;

[0026] Continuity equation constraints : ;

[0027] Momentum equation constraints :

[0028] ;

[0029] Pile-flow interaction constraint : ;

[0030] Improved material deposition constraints :

[0031] ;

[0032] Improved sediment transport equation constraints :

[0033] ;

[0034] Where g is the acceleration due to gravity, z i Let S be the riverbed elevation field for the i-th sample. vis S is a viscous source term. fri For the friction source term, S ine For sediment inertial source term, Let be the circulation area ratio of the i-th sample. Let be the obstruction area of ​​the pile for the i-th sample. For the area of ​​the grid cell, Let be the flow velocity correction coefficient for the i-th sample. Let be the effective diffusion coefficient of the i-th sample. For flow rate correction adjustment coefficient, The initial diffusion coefficient is . The diffusion regulation coefficient is... For sediment quality, The saturated sand content, For the sediment concentration field, The characteristic velocity of material settling. Let be the critical starting flow velocity for the i-th sample. Let be the sediment diffusion coefficient of the i-th sample. The erosion rate coefficient is... This is the deposition rate coefficient; For the i-th water depth field, For sample size, For the divergence sign, It is the Hadamard product of vectors.

[0035] The online self-evolutionary engine is implemented by deploying an intelligent simulation and inundation probability visualization system for dike breaches. Then, the deviation between the water flow velocity-depth physical field simulated by the engineering intervention simulation engine at the final moment and the updated two-dimensional multi-physics reconstruction field is calculated. This deviation includes velocity deviation and depth deviation, calculated as follows:

[0036] Flow rate deviation:

[0037] Water depth deviation:

[0038] deviation:

[0039] Triggering conditions:

[0040] in, To deduce the velocity vector at the i-th grid point at the final moment, To monitor the velocity vector of the i-th grid point after the update, To deduce the water depth at the i-th grid point at the final moment, To monitor the water depth at the i-th grid point after the update, The weight corresponding to the flow velocity deviation. The weights corresponding to the deviations in water depth are: The deviation threshold, For flow rate threshold, The water depth threshold is used; the fine-tuning system is triggered if any condition in the triggering condition formula is met.

[0041] The specific implementation of the fine-tuning system is as follows: based on the updated two-dimensional multi-physics reconstruction field as training samples, the weights, biases, and kernel hyperparameters of all layers except the last multi-dimensional Fourier convolutional layer are frozen, and only the weights, biases, and kernel hyperparameters of the last multi-dimensional Fourier convolutional layer are adjusted; the residuals of the training history are recorded during the adjustment iteration process, and an early stopping mechanism is introduced to avoid overfitting.

[0042] The specific implementation of the uncertainty quantification visualization interface is as follows: based on the water flow velocity-depth physical field time history and breach morphology output by the engineering intervention simulation engine, the reliability analysis method is used to calculate the inundation probability of the real-scene map at each future time point, and the real-scene map is rendered with a transparency gradient color layer.

[0043] The reliability analysis method obtains the water velocity field and water depth field respectively based on the time history of the water velocity-depth physical field output by the engineering intervention simulation engine; and calculates the water depth gradient characteristics, kinetic energy density characteristics, potential energy characteristics, flow field divergence characteristics, and cumulative time effect characteristics based on the water velocity field and water depth field, with the following calculation formulas.

[0044] Water depth gradient characteristics:

[0045] Kinetic energy density characteristics:

[0046] Potential energy characteristics:

[0047] Flow field divergence characteristics:

[0048] Cumulative time effect characteristics:

[0049] Among them, h threshoold K is the safety threshold for the water depth gradient. ed E is the kinetic energy density coefficient. v,threshoold Where P is the kinetic energy density safety threshold, and E is the potential energy coefficient. g,threshoold For the potential energy safety threshold, t measure For cumulative time, v ed The water flow velocity field is used; the risk value is calculated based on five characteristics, as follows.

[0050] Risk value:

[0051] Where sigmoid is the sigmoid function, w i The weight corresponding to the i-th feature is obtained; an improved diffusion reaction equation is constructed based on the risk value, as follows.

[0052] Improved diffusion reaction equation:

[0053] Where P is the flooding probability distribution, and D ed λ is the terrain-adaptive diffusion coefficient, λ is the probability decay rate, P0 is the initial probability condition, and 0 is the zero vector. The flooding probability distribution is obtained by solving the improved diffusion reaction equation, and the color saturation is adjusted according to the probability value of each plane position at each time.

[0054] A method for intelligent prediction and visualization of dike breach potential and inundation probability is proposed, which is implemented using a system for intelligent prediction and visualization of dike breach potential and inundation probability. The method includes the following steps: inputting a two-dimensional multi-physics reconstruction field, which is a fusion of the number, diameter, location and depth of the sealing piles, the size and weight of the sealing material, the PIV water velocity field, and the CAD point cloud topographic field, into a pre-trained engineering intervention prediction engine. This engine quickly predicts the time history of the water velocity-depth physical field and the breach morphology for a specified future time period, including the water velocity field and the water depth field. The prediction results are dynamically displayed in the form of a probabilistic inundation heat map through an uncertainty quantification visualization interface. During system operation, the engineering intervention prediction engine is continuously fine-tuned using real-time updated monitoring data through an online self-evolution engine.

[0055] During the simulation, the coupling relationship between the velocity field and the water depth field is strictly maintained. The coupling is ensured by the following means: when training the engineering intervention simulation engine, its loss function simultaneously includes the continuity equation constraint of the two-dimensional shallow water equation, the momentum equation constraint of the two-dimensional shallow water equation, the pile-flow interaction constraint, the improved material deposition constraint, and the improved sediment transport equation constraint.

[0056] The pre-training method of the engineering intervention simulation engine includes: numerical simulation based on different two-dimensional multi-physics reconstruction fields, simulating the time history of the water velocity-depth physical field and the breach shape, and using the two-dimensional multi-physics reconstruction field and the corresponding water velocity-depth physical field time history and breach shape as training samples; training the physically guided neural operator based on physical constraint loss, so that it learns the nonlinear mapping relationship between the two-dimensional multi-physics reconstruction field and the water velocity-depth physical field time history and breach shape.

[0057] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for intelligent prediction of dike breach and visualization of inundation probability.

[0058] The beneficial effects of this invention are as follows: Compared with existing technologies, this invention pioneers an intelligent inference architecture that integrates a physics-guided neural operator with an online self-evolutionary mechanism, achieving deep coupling between physical laws and data-driven approaches; it develops a multi-physics field coupling constraint system considering engineering interventions involving piles, water flow, and sedimentary materials; and it establishes a complete closed-loop system from two-dimensional multi-physics reconstructed monitoring data to the visualization of dike inundation probability. The inference speed is 100 times faster than traditional numerical simulations, achieving minute-level response; it significantly improves prediction accuracy while ensuring physical consistency; it adapts to the dynamic process of emergency rescue through real-time self-evolution; and it provides probabilistic risk assessment, offering a more comprehensive scientific basis for emergency rescue decisions. Attached Figure Description

[0059] Figure 1 This is a flowchart of the training process of the present invention;

[0060] Figure 2 This is a diagram showing the internal structure of the engineering intervention simulation engine of this invention;

[0061] Figure 3 This is a flowchart illustrating the uncertainty quantification visualization process of the present invention.

[0062] Figure 4 The following are time histories of the water velocity-depth physical field, breach morphology, and flooding probability distribution diagrams of the present invention: (a) is the time histories of the water velocity-depth physical field, and (b) is the breach morphology and flooding probability distribution diagram.

[0063] Figure 5The following are time histories of the average time history of the water velocity field, the average time history of the water depth field, the time history of the flood probability, and the time history of the flood area in this invention: (a) is the average time history of the water velocity field, (b) is the average time history of the water depth field, (c) is the time history of the flood probability, and (d) is the time history of the flood area.

[0064] Figure 6 This is a frequency diagram of the flooding probability of the present invention. Detailed Implementation

[0065] To facilitate the application of the technical solutions, some concepts involved in this application are first illustrated with examples. The technical solutions of this invention are clearly and completely described in conjunction with the accompanying drawings of the embodiments. Obviously, the described embodiments are only a part of this invention, not all of it. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0066] Example

[0067] The following is a detailed description of the invention using the emergency repair and sealing operation of the dike breach at Xinhuawan in Huarong County, Yueyang City, Hunan Province on July 10, 2016 as an example. The water level at the Xinhuawan dike breach reached 35.15 meters, exceeding the guaranteed water level by 0.15 meters. The piping flow increased from 1 m³ / s to 10 m³ / s within half an hour, ultimately leading to the dike cracking and breach.

[0068] See Figure 1 In this embodiment, a smart prediction and inundation probability visualization system for dike breaches includes an engineering intervention prediction engine, constructed based on a physics-guided neural operator, configured to: take a two-dimensional multi-physics reconstruction field as input and directly output the time history of the water flow velocity-depth physical field and the breach morphology within a specified future time period, including the water flow velocity field and the water depth field; an online self-evolution engine, which automatically compares the prediction with subsequent actual monitoring data during system operation, and triggers the fine-tuning system of the online self-evolution engine when the deviation between the two exceeds a threshold; and an uncertainty quantification visualization interface, which displays the time history of the water flow velocity-depth physical field and the breach morphology output by the engineering intervention prediction engine in the form of a probabilistic inundation heat map on a real-world map, where color saturation represents the probability of inundation.

[0069] See Figure 2In this embodiment, the physics-guided neural operator is derived by a multidimensional neural operator convolutional layer, and the loss function includes data-driven loss and physical constraint loss. The engineering intervention derivation engine is trained during the training phase using numerical simulations based on different two-dimensional multiphysics reconstruction fields, as well as the simulated water velocity-depth physical field time histories and breach morphologies, thereby learning the nonlinear mapping relationship between the two-dimensional multiphysics reconstruction field and the water velocity-depth physical field time histories and breach morphologies. The functionalized expression of the physics-guided neural operator is as follows:

[0070]

[0071] in, For the solution operator to be trained, For projection operators, Let be the activation function of the t-th layer. For the linear operator at level t, used to provide linear transformations, For the integral kernel of the t-th layer, Let be the bias tensor of the t-th layer. The activation function for layer 1. The last layer is a multidimensional Fourier convolutional layer; the first layer's lifting operator is... This maps the input function to a high-dimensional feature space. The initial hidden feature function is... Let be the activation function of the i-th layer. For the i-th layer, there is a linear operator used to provide linear transformations. Let be the bias function of the i-th layer, and construct a function similar to the input function and the hidden feature function. Let i be the integral kernel operator of the i-th layer, which embeds a kernel function, expressed as follows:

[0072]

[0073]

[0074] in, As an integral operator, it provides nonlocal information exchange, allowing information to be transmitted between distant points. for Borel measure, where In the formula It is a nonlinear kernel function.

[0075] The physical constraint loss consists of multiple loss values, including the continuity equation constraint of the two-dimensional shallow water equation, the momentum equation constraint of the two-dimensional shallow water equation, the pile-flow interaction constraint, the improved material deposition constraint, and the improved sediment transport equation constraint. The formulas are as follows.

[0076] Physical constraints: ;

[0077] Continuity equation constraints: ;

[0078] Momentum equation constraints:

[0079]

[0080] Pile-water flow interaction constraints:

[0081] Improved material deposition constraints:

[0082] ;

[0083] Improved sediment transport equation constraints:

[0084] ;

[0085] Where g is the acceleration due to gravity, z i Let S be the riverbed elevation field for the i-th sample. vis S is a viscous source term. fri For the friction source term, S ine For sediment inertial source term, Let be the circulation area ratio of the i-th sample. Let be the obstruction area of ​​the pile for the i-th sample. For the area of ​​the grid cell, Let be the flow velocity correction coefficient for the i-th sample. Let be the effective diffusion coefficient of the i-th sample. For flow rate correction adjustment coefficient, The initial diffusion coefficient is . The diffusion regulation coefficient is... For sediment quality, The saturated sand content, For the sediment concentration field, The characteristic velocity of material settling. Let be the critical starting flow velocity for the i-th sample. Let be the sediment diffusion coefficient of the i-th sample. The erosion rate coefficient is... is the deposition rate coefficient.

[0086] In this embodiment, the online self-evolutionary engine is implemented by automatically calculating the deviation between the water flow velocity-depth physical field at the last moment calculated by the engineering intervention simulation engine and the updated two-dimensional multiphysics reconstruction field after system deployment. The deviation includes flow velocity deviation and water depth deviation, and the calculation formula is as follows.

[0087] Flow rate deviation:

[0088] Water depth deviation:

[0089] deviation:

[0090] Triggering conditions:

[0091] in, To deduce the velocity vector at the i-th grid point at the final moment, To monitor the velocity vector of the i-th grid point after the update, To deduce the water depth at the i-th grid point at the final moment, To monitor the water depth at the i-th grid point after the update, The weight corresponding to the flow velocity deviation. The weights corresponding to the deviations in water depth are: The deviation threshold, For flow rate threshold, The water depth threshold is used; the fine-tuning system is triggered if any condition in the triggering condition formula is met.

[0092] In this embodiment, the fine-tuning system is implemented as follows: based on the updated two-dimensional multi-physics reconstruction field as the training sample, the weights, biases, and kernel hyperparameters of all layers except the last multi-dimensional Fourier convolutional layer are frozen, and only the weights, biases, and kernel hyperparameters of the last multi-dimensional Fourier convolutional layer are adjusted; the residuals of the training history are recorded during the adjustment iteration process, and an early stopping mechanism is introduced to avoid overfitting.

[0093] See Figure 3 and Figure 4 In this embodiment, the uncertainty quantification visualization interface is specifically implemented by using the water flow velocity-depth physical field time history and breach morphology output by the engineering intervention simulation engine, employing reliability analysis methods to calculate the inundation probability of the real-world map at each future time point, and rendering the real-world map with a transparency gradient color layer.

[0094] In this embodiment, the reliability analysis method obtains the water velocity field and water depth field respectively based on the time history of the water velocity-depth physical field output by the engineering intervention simulation engine; and calculates the water depth gradient characteristics, kinetic energy density characteristics, potential energy characteristics, flow field divergence characteristics, and cumulative time effect characteristics based on the water velocity field and water depth field, as shown in the following formulas.

[0095] Water depth gradient characteristics:

[0096] Kinetic energy density characteristics:

[0097] Potential energy characteristics:

[0098] Flow field divergence characteristics:

[0099] Cumulative time effect characteristics:

[0100] Among them, h threshoold K is the safety threshold for the water depth gradient. ed E is the kinetic energy density coefficient. v,threshoold Where P is the kinetic energy density safety threshold, and E is the potential energy coefficient. g,threshoold For the potential energy safety threshold, t measure For cumulative time, v ed This represents the water flow velocity field. The risk value is calculated based on five characteristics, as follows:

[0101] Risk value:

[0102] Where sigmoid is the sigmoid function, w i Let be the weight corresponding to the i-th feature. An improved diffusion reaction equation is constructed based on the risk value, as follows:

[0103] Improved diffusion reaction equation:

[0104] Where P is the flooding probability distribution, and D ed Let λ be the terrain-adaptive diffusion coefficient, λ be the probability decay rate, P0 be the initial probability condition, and 0 be the zero vector. The flooding probability distribution is obtained by solving the improved diffusion reaction equation, and the color saturation is adjusted according to the probability value of each planar location at each time step.

[0105] See Figure 5 and Figure 6 In this embodiment, the average time history of water flow velocity field, the average time history of water depth field, the breach morphology, and the flooding probability time history and flooding area time history plot calculated by the reliability analysis method are output by the engineering intervention simulation engine.

[0106] Compared to existing technologies, this embodiment pioneers an intelligent inference architecture that integrates a physics-guided neural operator with an online self-evolutionary mechanism, achieving deep coupling between physical laws and data-driven approaches. It develops a multi-physics field coupling constraint system considering engineering interventions involving piles, water flow, and sedimentary materials; and establishes a complete closed-loop system from two-dimensional multi-physics reconstructed monitoring data to a visualized probability of dike inundation. The inference speed is 100 times faster than traditional numerical simulations, achieving minute-level response; prediction accuracy is significantly improved while ensuring physical consistency; it adapts to the dynamic process of emergency response through real-time self-evolution; and it provides probabilistic risk assessment, offering a more comprehensive scientific basis for emergency response decisions.

Claims

1. A system for intelligent prediction of dike breach and flood probability visualization, characterized in that, include: The engineering intervention simulation engine is based on the construction of a physically guided neural operator. It takes a two-dimensional multi-physics reconstruction field as input and directly outputs the time history of the water velocity-depth physical field and the breach shape, which includes the water velocity field and the water depth field, for a specified future time period. The online self-evolution engine automatically compares the predictions of the engineering intervention simulation engine with the actual monitoring data. When the deviation between the two exceeds a threshold, the fine-tuning system of the online self-evolution engine is triggered. The uncertainty quantification visualization interface displays the water flow velocity-depth physical field time history and breach morphology output by the engineering intervention simulation engine on the real-world map in the form of a probabilistic inundation heat map, where color saturation represents the probability of inundation.

2. The intelligent simulation and flood probability visualization system for dike breach and collapse as described in claim 1, characterized in that, The two-dimensional multiphysics reconstruction field includes the number, diameter, pile location and depth of the sealing piles, size and weight of the sealing material, PIV water velocity field, and CAD point cloud terrain field.

3. The intelligent simulation and flood probability visualization system for dike breach and collapse as described in claim 1, characterized in that, The physical-guided neural operator is derived by a multidimensional neural operator convolutional layer, and the loss function includes data-driven loss and physical constraint loss. The engineering intervention simulation engine is trained during the training phase using numerical simulations based on different two-dimensional multiphysics reconstruction fields, as well as the simulated water velocity-depth physical field time histories and breach morphologies. It learns the nonlinear mapping relationship between the two-dimensional multiphysics reconstruction fields and the water velocity-depth physical field time histories and breach morphologies. The physical-guided neural operator functional expression is as follows. ； For the solution operator to be trained, For projection operators, Let be the activation function of the t-th layer. For the linear operator at level t, used to provide linear transformations, For the integral kernel of the t-th layer, Let be the bias tensor of the t-th layer. The activation function for layer 1. This is the last layer of a multidimensional Fourier convolutional layer; This is the first-level lifting operator; , The initial hidden feature function is... for The domain, for The range of values; For the input function, Represented as ,in For the domain, The range; For the integral kernel operator of the i-th layer, embed a kernel function, expressed as follows: ； ； in, As an integral operator, it provides nonlocal information interaction, allowing information transmission between distant points; for The Borel measure, where, It is a space of arbitrary dimensions; It is a nonlinear kernel function; The hidden characteristic function of the i-th integral kernel operator, Let be the cumulative space of the first i arbitrary-dimensional spaces; For any function, Let be the dimension of the value space corresponding to the velocity field of the i-th sample; The total loss function consists of data-driven loss and physical constraint loss, and its formula is as follows: ； For data-driven loss, Weights for data-driven loss; For physical constraint loss, The weights for the physical constraint loss; The data-driven loss is calculated from the root mean square error, and the formula is as follows. ； For sample size, The predicted velocity field value of the i-th sample, The observed velocity field value of the i-th sample, The predicted water depth field value of the i-th sample The water depth field observation value of the i-th sample; The physical constraint loss consists of multiple loss values, including the continuity equation constraint of the two-dimensional shallow water equation, the momentum equation constraint of the two-dimensional shallow water equation, the pile-flow interaction constraint, the improved material deposition constraint, and the improved sediment transport equation constraint, the formulas of which are as follows. Physical constraint loss ; Continuity equation constraints : ; Momentum equation constraints : ； Pile-flow interaction constraint : ; Improved material deposition constraints : ； Improved sediment transport equation constraints : ； Where g is the acceleration due to gravity, z i Let S be the riverbed elevation field for the i-th sample. vis S is a viscous source term. fri For the friction source term, S ine For sediment inertial source term, Let be the circulation area ratio of the i-th sample. Let be the obstruction area of ​​the pile for the i-th sample. For the area of ​​the grid cell, Let be the flow velocity correction coefficient for the i-th sample. Let be the effective diffusion coefficient of the i-th sample. For flow rate correction adjustment coefficient, The initial diffusion coefficient is . The diffusion regulation coefficient is... For sediment quality, The saturated sand content, For the sediment concentration field, The characteristic velocity of material settling. Let be the critical starting flow velocity for the i-th sample. Let be the sediment diffusion coefficient of the i-th sample. The erosion rate coefficient is... This is the deposition rate coefficient; For the i-th water depth field, For sample size, For the divergence sign, It is the Hadamard product of vectors.

4. The intelligent simulation and flood probability visualization system for dike breach and collapse as described in claim 1, characterized in that, The online self-evolutionary engine is implemented by deploying an intelligent simulation and inundation probability visualization system for dike breaches. Then, the deviation between the water flow velocity-depth physical field simulated by the engineering intervention simulation engine at the final moment and the updated two-dimensional multi-physics reconstruction field is calculated. This deviation includes velocity deviation and depth deviation, calculated as follows: Flow rate deviation: Water depth deviation: deviation: Triggering conditions: in, To deduce the velocity vector at the i-th grid point at the final moment, To monitor the velocity vector of the i-th grid point after the update, To deduce the water depth at the i-th grid point at the final moment, To monitor the water depth at the i-th grid point after the update, The weight corresponding to the flow velocity deviation. The weights corresponding to the deviations in water depth are: The deviation threshold, For flow rate threshold, The water depth threshold is used; the fine-tuning system is triggered if any condition in the triggering condition formula is met.

5. The intelligent simulation and flood probability visualization system for dike breach and collapse as described in claim 1, characterized in that, The specific implementation of the fine-tuning system is as follows: based on the monitored and updated two-dimensional multi-physics reconstruction field as training samples, the weights, biases, and kernel function hyperparameters of all layers except the last multi-dimensional Fourier convolutional layer are frozen, and only the weights, biases, and kernel function hyperparameters of the last multi-dimensional Fourier convolutional layer are adjusted. Adjust the residuals recorded during the iteration process and introduce an early stopping mechanism to avoid overfitting.

6. The intelligent simulation and flood probability visualization system for dike breach and collapse as described in claim 1, characterized in that, The specific implementation of the uncertainty quantification visualization interface is as follows: based on the water flow velocity-depth physical field time history and breach morphology output by the engineering intervention simulation engine, the reliability analysis method is used to calculate the inundation probability of the real-scene map at each future time point, and the real-scene map is rendered with a transparency gradient color layer.

7. The intelligent simulation and flood probability visualization system for dike breach and collapse as described in claim 6, characterized in that, The reliability analysis method obtains the water velocity field and water depth field respectively based on the time history of the water velocity-depth physical field output by the engineering intervention simulation engine; and calculates the water depth gradient characteristics, kinetic energy density characteristics, potential energy characteristics, flow field divergence characteristics, and cumulative time effect characteristics based on the water velocity field and water depth field, with the following calculation formulas. Water depth gradient characteristics: Kinetic energy density characteristics: Potential energy characteristics: Flow field divergence characteristics: Cumulative time effect characteristics: Among them, h threshoold K is the safety threshold for the water depth gradient. ed E is the kinetic energy density coefficient. v,threshoold Where P is the kinetic energy density safety threshold, and E is the potential energy coefficient. g,threshoold For the potential energy safety threshold, t measure For cumulative time, v ed The water flow velocity field is used; the risk value is calculated based on five characteristics, as follows. Risk value: Where sigmoid is the sigmoid function, w i Let be the weight corresponding to the i-th feature; an improved diffusion reaction equation is constructed based on the risk value, as follows. Improved diffusion reaction equation: Where P is the flooding probability distribution, and D ed λ is the terrain-adaptive diffusion coefficient, λ is the probability decay rate, P0 is the initial probability condition, and 0 is the zero vector. The flooding probability distribution is obtained by solving the improved diffusion reaction equation, and the color saturation is adjusted according to the probability value of each plane position at each time.

8. A method for intelligent prediction of dike breach and visualization of inundation probability, characterized in that, The system, described in any one of claims 1-7, employs an intelligent simulation and inundation probability visualization system for dike breaches. The system comprises the following steps: inputting a two-dimensional multi-physics reconstruction field, fused from the number, diameter, location, and depth of the sealing piles, the size and weight of the sealing material, the PIV water velocity field, and the CAD point cloud topographic field, into a pre-trained engineering intervention simulation engine. This rapidly simulates the time history of the water velocity-depth physical field and the breach morphology within a specified future time period, including both the water velocity and depth fields. The simulation results are dynamically displayed as a probabilistic inundation heatmap through an uncertainty quantification visualization interface. During system operation, the engineering intervention simulation engine is continuously fine-tuned using real-time updated monitoring data via an online self-evolutionary engine.

9. The method for intelligent prediction of dike breach and flood probability visualization according to claim 8, characterized in that, The pre-training method of the engineering intervention simulation engine includes: numerical simulation based on different two-dimensional multi-physics reconstruction fields, simulating the time history of the water velocity-depth physical field and the breach shape, and using the two-dimensional multi-physics reconstruction field and the corresponding water velocity-depth physical field time history and breach shape as training samples; training the physically guided neural operator based on physical constraint loss, so that it learns the nonlinear mapping relationship between the two-dimensional multi-physics reconstruction field and the water velocity-depth physical field time history and breach shape.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the method for intelligent simulation of dike breach and flood probability visualization as described in claim 8 or 9.

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