31 results about "Probabilistic inference" patented technology

The present invention discloses a system and method for secure artificial intelligence-based financial technology governance and risk management, designed to provide real-time, autonomous, and verifiable compliance assurance within digital financial ecosystems. The invention integrates a secure artificial intelligence processing unit, a governance control processor, a cryptographically anchored storage unit, a federated learning coordination processor, and a quantum-resistant communication interface enclosed within a tamper-proof hardware structure. The system performs encrypted machine learning computations on financial transaction data using homomorphic encryption and trusted execution environments to preserve confidentiality during analysis. It computes a governance risk index based on probabilistic inference and anomaly detection to identify regulatory deviations, applies adaptive compliance reasoning across multi-jurisdictional frameworks, and automatically enforces governance actions through secure decision logic.

The invention discloses an industrial product quality risk association method based on a graph neural network, and relates to the field of industrial manufacturing, and the method comprises the steps: collecting multi-source industrial data, carrying out the preprocessing of the multi-source industrial data, obtaining a standardized data table, and constructing an industrial element heterogeneous graph based on the standardized data table; inputting the industrial element heterogeneous graph into a graph attention network basic model for node representation learning, and setting a supervised learning task taking a product batch node in the industrial element heterogeneous graph as a prediction target and taking a historical quality detection result as a supervised label; and introducing a meta-path self-learning layer in a message passing process of the graph attention network basic model. According to the method, deterministic prediction is upgraded into probabilistic inference, internal confidence degree measurement is provided for each risk prediction value, and automatic, accurate and interpretable correlation analysis and traceability positioning of the industrial quality risk are realized.

The invention discloses an unplanned secondary operation decision support method and system based on EMR analysis. According to the method, firstly, multi-source EMR data are collected in real time through an HL7 FHIR standardized interface, differential analysis is conducted through a time sequence analysis algorithm and a BioBERT model fused with a medical knowledge graph, and standardized clinical indexes are generated; secondly, the indexes are matched with an unplanned secondary surgery thematic knowledge graph in real time, multi-path probabilistic reasoning is carried out based on a Bayesian network, and a risk conclusion with confidence and a reasoning chain are output; and generating a structured report including risk early warning, core basis, possible reasons and processing suggestions, and pushing the structured report in multiple channels. And finally, collecting clinical feedback, optimizing the knowledge graph and the analytical model, and forming a closed loop. According to the invention, dependence on experience of doctors is reduced, and accuracy and timeliness of risk research and judgment are improved.

The invention relates to the technical field of model simulation, and discloses a precise traceability method and system for underground water organic pollutants, and the method comprises the steps: building a space-time feature set based on monitoring well concentration data and geological parameters, and generating a preliminary continuous concentration field through Gaussian process regression; secondly, extracting geometric features of a concentration field, establishing a flow field model in combination with an underground water level gradient, simulating a pollutant transport process, screening a potential source position set according with a time deviation threshold value, and constructing a source strong release model to invert optimal release parameters; a migration path is generated, and an effective path is screened according to the cumulative flux coverage degree to reconstruct a global concentration field; and finally, determining a final pollution source coordinate through concentration field gradient rotation analysis and isoline geometric continuity evaluation in combination with Bayesian probabilistic reasoning. According to the method, the traceability accuracy can be improved.

This invention discloses a method and system for controlling an automotive electronic vacuum pump based on braking intention recognition, belonging to the field of intention recognition. The method includes: acquiring vehicle status information, driver operation information, and environmental perception information from an advanced driver assistance module; calculating environmental risk levels based on environmental perception information and vehicle status information, including a forward collision risk level based on collision time and a downhill risk level based on road gradient; extracting current pedal feature parameters based on operation information, and inputting them along with the environmental risk level and historical braking tendency quantification values ​​into a Bayesian network to calculate the posterior probability distribution of braking intention at the current moment through probabilistic inference; generating pre-scheduled control commands for the electronic vacuum pump based on the posterior probability distribution of braking intention and a preset probability threshold, and sending them to the electronic vacuum pump controller for execution. This application solves the technical problem of existing automotive electronic vacuum pumps having slow response and difficulty in handling complex operating conditions, resulting in insufficient braking safety.

The invention provides a control method for accurate blanking of a hopper scale, and relates to the technical field of industrial automation and process control, and the method comprises the steps: obtaining the internal density distribution data of a material in a hopper through at least one electrical capacitance tomography sensor; obtaining inter-particle stress data of the material through at least one acoustic emission sensor; taking the internal density distribution data and the inter-particle stress data as observation variables, and inputting the observation variables into a dynamic Bayesian network DBN model; utilizing a DBN model to carry out probabilistic inference on a hidden material state of the material, the hidden material state at least comprising a flow failure probability; when the flow failure probability exceeds a preset threshold value, generating a preventive control signal; a preventative control signal is applied to a flow actuator to correct the flow condition of the material prior to a macroscopic flow failure of the material. The blocking risk can be reduced to the germination state as much as possible in the early stage of formation of the pre-bridging microstructure.

This application relates to the fields of artificial intelligence and computer technology, specifically providing a risk decision-making method and system for the entire lifecycle of power data. The method includes: acquiring multi-source heterogeneous log data from the entire lifecycle of a power information system; using an unsupervised feature extraction module based on a variational autoencoder to extract latent feature vectors and determine the VAE reconstruction error; performing spatiotemporal feature extraction on the latent feature vectors to obtain the temporal behavior probability from the time-series analysis stream and the spatial graph embedding distance from the spatial analysis stream; and constructing a dynamic decision-making module based on a Bayesian network, using the VAE reconstruction error, temporal behavior probability, and spatial graph embedding distance as multi-source evidence nodes for the dynamic decision-making module to calculate the posterior probability of risk events. This application, through the collaborative fusion of spatiotemporal features and causal probabilistic inference, can significantly improve the safety risk perception and dynamic control capabilities of power data throughout the entire process of acquisition, transmission, and processing.

The application discloses an industrial product quality risk correlation method based on a graph neural network, relates to the field of industrial manufacturing, and comprises the following steps: collecting multi-source industrial data, preprocessing the multi-source industrial data, obtaining a standardized data table, constructing an industrial element heterogeneous graph based on the standardized data table, inputting the industrial element heterogeneous graph into a graph attention network base model for node representation learning, setting a product batch node in the industrial element heterogeneous graph as a prediction target, and setting a historical quality detection result as a supervised label of a supervised learning task, and introducing a meta-path self-learning layer in a message passing process of the graph attention network base model. The application upgrades deterministic prediction to probabilistic inference, provides an inherent confidence measure for each risk prediction value, and realizes automatic, accurate and interpretable correlation analysis and traceability positioning of industrial quality risks.

This invention discloses an epidural anesthesia assistance method and system based on pressure curve characteristics, including a data acquisition and preprocessing module, an anatomical layer segmentation module, a multidimensional feature extraction module, a template matching and initial classification module, a Bayesian probabilistic inference and fusion decision-making module, a risk assessment and feedback output module, and an online adaptive update module. By utilizing real-time pressure monitoring, negative pressure recognition, and biometric frequency extraction technologies, it assists in determining the position of the epidural puncture needle, significantly reducing the rate of epidural puncture mispuncture and improving clinical safety. Through a Bayesian online learning update mechanism, the model parameters can be continuously iterated based on clinically labeled data, adapting to different operator habits and sensor drift, achieving long-term stable operation and individualized calibration. This invention is easy to integrate and has low hardware costs, facilitating large-scale clinical deployment.

This invention discloses a smart home central control system and method based on multimodal fusion. The control system includes a multimodal interaction interface layer, a device execution layer, and a central controller. The central controller is communicatively connected to both the multimodal interaction interface layer and the device execution layer. The central controller includes an instruction receiving and parsing unit, a context awareness unit, a multimodal fusion decision engine, a scheduling and execution unit, and a feedback unit. The instruction receiving and parsing unit receives and parses interactive instructions; the context awareness unit acquires various dynamic data to provide the context information required for decision-making; the multimodal fusion decision engine is trained based on a hierarchical hybrid decision architecture, which includes a rule engine layer, a probabilistic inference layer, and a reinforcement learning layer. This invention achieves a more accurate, robust, and personalized smart home control experience through a dedicated fusion algorithm, a hierarchical conflict arbitration strategy, hardware and software co-optimization, and an adaptive feedback mechanism.

This invention discloses a machine learning-based method for predicting the compressive strength of geopolymer-stabilized soil, belonging to the field of building material prediction technology. The method includes: constructing a Bayesian prediction model for geopolymer properties based on theoretical strength values, combining a physical constraint layer and a Bayesian probabilistic inference layer; training the Bayesian prediction model to generate a trained model; inputting the mix proportion parameter vector of the geopolymer-stabilized soil into the trained model to perform multiple Monte Carlo sampling predictions to obtain predicted strength values ​​and physical constraint strength values; and performing statistical analysis on the predicted strength values ​​to generate prediction confidence intervals. This invention, by generating prediction confidence intervals and physical contribution parameters, achieves a simultaneous characterization of the distribution characteristics of predicted strength and the degree of mechanistic influence, and realizes a unified expression of uncertainty quantification and mechanistic contribution within a machine learning framework.

The invention relates to the technical field of task scheduling, in particular to a store patrol task dynamic allocation method based on load balancing. Comprising the steps of generating a task vector through a shop patrol task processing request; generating a node load vector through the mobile terminal state data; constructing a directed attribute graph, calculating transfer parameters, generating a space transfer matrix, and constructing a feature tensor; inputting the feature tensor into an evaluation network for processing, and outputting a score matrix; outputting candidate sites through a search algorithm; calculating a load variance variable quantity and an out-of-limit probability according to the candidate sites; inputting the load variance variable quantity and the out-of-limit probability into a multi-objective optimization function for calculation, and outputting a distribution strategy; and analyzing the distribution strategy, and generating a scheduling instruction containing a node routing sequence. According to the method, chain structured fusion is carried out through probability deduction of high-dimensional data, search pruning of physical boundaries and multi-target game of global dimensions, and long-term stability and dynamic balance of cluster load distribution are facilitated.

A system and method for implementing artificial intelligence using deterministic wave interference and collapse logic derived from the Total Wave Modified Schrödinger Equation (TWMSE). An artificial agent is modeled as a system wavefunction interacting with one or more observer wavefunctions representing electromagnetic, gravitational, weak, or strong fields. A collapse function determines when interference exceeds a threshold, producing deterministic action or comprehension. Field parameters adapt through feedback to enable learning, residual interference forms resonant memory, and computation is performed directly on optical, electromagnetic, or neuromorphic hardware. The invention provides a unified framework—Total Wave Artificial Intelligence (TWAI)—that integrates action, understanding, learning, memory, and physical embodiment through field-based collapse rather than probabilistic inference.

This invention discloses a method for tracing the source of construction power supply accidents based on causal inference, comprising the following steps: collecting data on current, voltage, switch status, and equipment operation events at the construction site, and performing time synchronization processing to generate a construction power supply state sequence; dividing the state sequence into time slices according to preset intervals to form a time window data set; constructing a dynamic Bayesian network model based on the time window data, establishing time slice nodes and conditional probability connections; assigning conditional probabilities to nodes and determining accident constraint nodes; performing reverse probabilistic inference starting from the accident constraint nodes, calculating the conditional probabilities of upstream nodes and generating a propagation path set; sorting the path probabilities, determining the target tracing path, selecting the node with the highest probability in the path as the root cause node, and outputting the root cause node and its corresponding path. This invention achieves causal source tracing analysis of construction power supply accidents through a time-sliced ​​dynamic Bayesian network and reverse probabilistic inference.

The invention discloses a method and system for establishing a nuclear power plant accident risk assessment model, and relates to the technical field of nuclear power plant accident management. Comprising the following steps: constructing a topological structure of a Bayesian network based on a power plant probabilistic safety analysis model; converting a logic relation in the model into a probability relation of a Bayesian network; assigning probability parameters to all nodes in the Bayesian network to complete quantification of the model; and carrying out sensitivity analysis on the quantized model and carrying out simplified verification based on an analysis result. According to the method, a static probabilistic safety analysis model is converted into a dynamic Bayesian network, a probabilistic reasoning framework capable of fusing real-time monitoring data is constructed, and sensitivity analysis is adopted to optimize the model, so that the calculation efficiency is improved while the precision is ensured; therefore, the problem of dynamic risk assessment caused by limited, uncertain and missing parameters under a serious accident is solved, the accident process and the radioactive release consequence are quickly and accurately predicted, and the emergency response capability of the nuclear power plant is remarkably improved.

The present application relates to the technical field of image node classification, and especially relates to a semi-supervised node classification method based on a graph convolution network and a probabilistic inference model, solves the technical problems in the background art, the method utilizes an edge complete tree to divide a graph, and lets a message propagation mechanism of the graph convolution network interactively execute inside and between subgraphs, integrates the similarity between nodes into the graph convolution network, so that the node state matrix in each layer satisfies the similarity constraint, in order to perform node classification, the method utilizes a conditional random field to model the correlation between node labels, and deeply fuses it with the graph convolution network fusing the node similarity. The method uses a kind of local first-order approximation in frequency domain graph convolution to realize convolution architecture, can learn the information of graph on hidden layer, also uses fast approximate convolution, can quickly, scalablely complete semi-supervised classification task based on point, can efficiently semi-supervised learning on large-scale graph data.

The embodiment of the invention relates to the technical field of plant management, and discloses an unmanned aerial vehicle-based vegetation AI identification management method, which comprises the following steps of: identifying wide-area remote sensing data to obtain all abnormal sub-areas and window detection areas in a target detection area; executing a second acquisition task on all abnormal sub-regions and window detection regions to obtain corresponding fine remote sensing data; determining vegetation state information of the abnormal sub-region according to the fine remote sensing data, determining a canopy shielding relationship in the window detection region according to the fine remote sensing data, and identifying and determining a potential under-forest region under canopy coverage; probabilistic inference is carried out on the vegetation state of the potential under-forest region through fusion of multi-source evidence, and an under-forest vegetation probability distribution diagram containing inferred vegetation categories and corresponding confidence coefficients is generated; and generating a comprehensive vegetation state report of the target detection area. And the overall identification management efficiency is improved through a layered acquisition mode.

The present application relates to resource scheduling technical field, specifically to the innovative resource self-organizing scheduling method based on ant colony cooperation and bayesian learning, including the following steps: S1, factor space state variable initialization; S2, structured reading of public clues; S3, probabilistic inference of opportunity credibility.The present application combines the decision-making and collaborative behavior of individual innovation subjects, uses dynamically updated public clues, opportunity credibility inference, subjective value evaluation and dynamic depiction of collaboration threshold, effectively optimizes resource allocation and collaboration mode, improves decision-making efficiency through bayesian inference in practical application, reduces the negative impact of information overload, and ensures efficient collaboration when the project scale expands through the introduction of synergistic effect and immediate adjustment mechanism, enhances the adaptability and continuous optimization ability of the system through the endogenous decay of feedback mechanism and environmental memory, thereby improving the utilization efficiency of innovative resources and the success rate of project execution.

The invention provides a model-based offset sensing agent offline reinforcement learning method and device. The method comprises the steps of obtaining an agent offline data set; training an integrated model according to the offline data set; replacing the environment where the intelligent agent is located according to the integrated model, and generating synthetic data; extracting samples from the offline data set and the synthetic data, and determining an offset perception reward according to a state transition classifier and a strategy classifier; wherein the state transition classifier is used for compensating difference between prediction of the integrated model and a real environment, and the strategy classifier is used for compensating distribution offset caused by strategy updating; and according to a sample corresponding to the offset perception reward, updating an intelligent agent operation strategy by adopting an SAC algorithm. According to the method, the problem of data distribution mismatching in a real environment and a model is solved by uniformly processing the offline data set and the synthetic data from a probabilistic reasoning framework.

The invention discloses an unmanned aerial vehicle radio interference suppression method and system based on causal reasoning, and relates to the technical field of radio direction finding and electromagnetic environment monitoring. The method comprises the following steps: carrying out probabilistic inference on interference causes under a structured generative causal model framework; and selecting a plurality of candidate communication actions in a preset intervention scheme library, evaluating the improvement effect on the link performance under each candidate communication action through anti-fact reasoning, selecting a target anti-interference action, and issuing and executing the target anti-interference action. According to the scheme, a systematic engineering solution from interference detection, cause diagnosis to strategy optimization and model updating is achieved, accurate identification, real-time anti-interference decision and long-term adaptive optimization of radio interference of the unmanned aerial vehicle in a complex electromagnetic environment are achieved, and the method has remarkable engineering application value and popularization significance.

A method and system for analysing and mitigating security risks in open innovation ecosystem is disclosed. The system comprises detects potential vulnerabilities in open innovation activities, including intellectual property exchanges, research data handling, and partner collaboration processes to provide identified threat data as input. The system represents interactions between an innovator and an adversary as a two-player zero-sum game. The system computes Nash equilibrium from the payoff matrix. The Nash equilibrium represents optimal defensive investments under adversarial conditions. The system also models adversary uncertainty using probability distributions, and further updates the equilibrium strategies based on incomplete or dynamic information. The system, thereafter, evaluates adversary uncertainty using Entropy-based risk assessment to determine levels of security investment resources responsive to the quantified uncertainty. Finally, the system integrates results of the equilibrium analysis, probabilistic inference, and uncertainty quantification to generate actionable security recommendations and guidelines for mitigation strategies.

This application discloses a method and system for document review based on rule engine retrieval enhancement generation, relating to the field of document review. The method includes: initializing the review task's operating environment; performing structured processing and metadata binding on the document opening review rules corresponding to the target review task to construct a rule knowledge base; acquiring the target document text and performing preprocessing and updating; extracting factual information from the target document text and mapping the factual information to rule matching parameters; constructing a rule matching object based on the target document text, and using the rule matching parameters to call the rule engine retrieval enhancement generation mechanism to review the rule matching object and obtain the review explanation results; and summarizing and outputting a document opening review analysis report. This application avoids the rule omission problem caused by similarity retrieval, improves the reliability, consistency, and traceability of document compliance review, and reduces the fluctuations caused by probabilistic inference of the model.

This invention provides an intelligent anchor bolt support system for phosphate mines based on multi-source data fusion, belonging to the field of intelligent anchor bolt support technology for phosphate mines. The intelligent anchor bolt support system includes: a data acquisition module that collects multi-source monitoring signals to obtain basic data sources; a data preprocessing module that extracts core feature parameters to generate a standardized feature dataset; a data fusion module that constructs a Bayesian weighted fusion model, performs probabilistic inference, and obtains a comprehensive evaluation result; a decision generation module that constructs a multi-agent reinforcement learning model to generate an optimal distributed control instruction set; an execution control module that performs instruction parsing and target value calibration, and uses a magnetorheological damper to drive anchor bolt stress adjustment; and a feedback iteration module that optimizes the parameters corresponding to the Bayesian weighted fusion model and the multi-agent reinforcement learning model based on the evaluated control effect. This invention significantly reduces the misjudgment rate of rock mass stability levels, achieves global collaborative bearing capacity of the anchor bolt group, and enables the control strategy to adapt to dynamic changes in phosphate mine geology.

The invention discloses a nuclear power plant originating event diagnosis method and device, electronic equipment and a storage medium, and relates to the technical field of nuclear power safety and artificial intelligence. Comprising the following steps: constructing an originating event-oriented hierarchical causal diagnosis network structure; fusing the multi-source data to construct a conditional probability table of the diagnosis network; collecting real-time state data of the nuclear power plant and carrying out robustness preprocessing to generate an evidence set; executing backward probabilistic reasoning based on the conditional probability table and the evidence set, and calculating the posterior probability of the originating event; and visually outputting a diagnosis result, and triggering closed-loop feedback according to an error so as to iteratively update the model. According to the method, through the accident-oriented hierarchical causal network and the multi-source fusion conditional probability table, quantitative diagnosis of the originating event is realized based on Bayesian backward reasoning, and compared with a traditional method, the accuracy is remarkably improved, and false alarms are reduced; meanwhile, the anti-interference capability is enhanced through robust preprocessing; the diagnosis decision time is shortened through interactive visualization; and a closed-loop feedback mechanism continuously improves the model adaptability.

This invention belongs to the field of geological information and artificial intelligence technology, and discloses a method and system for automatically generating geological map descriptions. The method includes: acquiring multimodal geological data and assigning initial weights to it; constructing a probabilistic geological knowledge graph; employing a multi-agent collaborative framework, where a planning agent decomposes the description generation task into sub-tasks; the planning agent scheduling working agents to execute tasks, including using a probabilistic inference engine to infer multiple possible tectonic evolution sequences and their probability distributions on the probabilistic geological knowledge graph; the working agents generating a draft text based on the inference results; a critique agent evaluating the quality of the draft, and providing feedback for revisions if it does not meet the standards; the planning agent generating new correction or refinement tasks based on the feedback, initiating a new round of iterations until the quality requirements are met; and integrating the various parts to generate the final description. This invention improves the accuracy of the generated content by introducing probabilistic inference and a multi-agent iterative correction mechanism.

This invention discloses a method and system for determining the commissioning boundary during substation expansion and renovation. The method includes: acquiring the entire substation configuration file; mapping intelligent devices and virtual loops to nodes and directed edges in a directed graph; calculating the sub-cyclic redundancy check (CRC) code corresponding to each directed edge; dividing nodes into expansion domains, directly associated domains, and indirectly associated domains based on configuration change identifiers and topological relationships; performing sub-CRC comparison and constructing a state transition matrix based on the dataset priority attributes; starting with high-risk seed nodes, calculating the attenuation probability of configuration change risk propagating outwards based on the state transition matrix; iteratively adding nodes with probabilities greater than the safety confidence threshold to the target test boundary set until convergence. This invention integrates underlying communication semantics with graph theory probabilistic inference to define the minimum safety test boundary, avoiding unnecessary shutdowns of primary equipment on-site, and improving the safety and efficiency of power grid commissioning.

The invention provides a robot navigation system based on a probabilistic knowledge graph, and a navigation robot realizes robust navigation by modeling objects in an environment and relationships thereof into a probabilistic knowledge graph. Each node in the map represents an environment object and comprises probability distribution of attributes such as poses and functions of the environment object; and a planning module of the navigation robot performs Bayesian reasoning based on the map to generate an action sequence capable of maximizing the task success probability. Compared with a traditional grid or semantic map, the probability map can convert a navigation task into a probabilistic reasoning problem in an uncertain environment to adapt to a dynamic change scene. Object attributes and relations in an environment are represented through a probability model, and robust navigation in an uncertain environment is realized by combining Bayesian reasoning and a partial observable Markov decision process (POMDP). The system not only solves the limitation (such as lack of semantics and poor robustness) of the traditional navigation system, but also has expandability and adaptability.

The invention discloses a PCB production line fault processing method and system, and the method comprises the steps: collecting multi-process technological parameters in real time, and adding a high-precision timestamp based on an IEEE 1588 protocol; aligning the cross-process parameter time sequence of the same PCB according to the timestamp, and packaging the cross-process parameter time sequence into a full-link process parameter record; adding a quality label containing a sensor health state, timestamp credibility and data integrity to each parameter; inputting the record as an evidence variable into a preset causal reasoning model; probabilistic reasoning is carried out based on the evidence variable and a quality label, the contribution degree of each upstream process parameter to the current defect is calculated, and the quality label serves as a weight factor to participate in calculation; and determining root cause parameters causing the defects according to the contribution degree. The system comprises a sensor array, a clock synchronization module, a central process database, a data processing module and a root cause positioning engine. According to the method, high-precision alignment and credible fusion of cross-process data are realized, and the accuracy and interpretability of fault root cause positioning are improved.

This invention relates to the field of power system operation and control, and discloses a collaborative governance method for three-phase random imbalance in low-voltage distribution networks for distributed photovoltaic (PV) access scenarios. The method includes the following steps: collecting and preprocessing multi-temporal-scale operational measurement data of the low-voltage distribution network; constructing a PV-load-voltage joint dataset; using an improved Gaussian mixture model to perform multimodal fitting of the node voltage probability density in the joint dataset; and simultaneously performing singular value screening and low-rank reconstruction on the covariance matrix of the Gaussian components in the model. By constructing an improved Gaussian mixture model, high-precision multimodal fitting of the non-Gaussian voltage distribution under PV fluctuations is achieved. Combined with singular value screening and low-rank reconstruction, dimensionality reduction of the covariance matrix is ​​completed. Furthermore, based on the block characteristics of the admittance matrix, a real isomorphic affine quadratic mapping model is established, realizing a linear analytical transformation from voltage to power distribution. This effectively solves the problems of nonlinear probabilistic inference and low computational efficiency in traditional methods.

This invention relates to a method for assessing the disaster evolution and risk classification of estuarine channel regulation structures, comprising: acquiring foundation state and hydrodynamic boundary environment data of the structures; performing nested disaster evolution calculations, iterating through inner-layer state feedback within extreme event steps, interactively updating the foundation scour, wave load, and structural deformation states until convergence; performing multi-factor cross-coupling judgment based on the convergence state to identify interactive instability states; fusing the information from each state to perform probabilistic inference and outputting a comprehensive risk level. This invention can capture the positive feedback amplification mechanism between scour and structural response, solving the problem of risk underestimation caused by multi-factor coupling in static analysis, and improving the accuracy of damage assessment and risk warning for estuarine regulation structures throughout their entire life cycle.