R-KGCN emergency disposal method for silt danger of floating wing suspension door in storm surge period of oversize tide gate

Abstract

The invention provides an R-KGCN emergency disposal method for the silt danger of a floating wing suspension door in the storm surge period of a super-huge tide gate. According to the method, a system framework covering knowledge modeling, reasoning definition and reasoning optimization is constructed on the basis of a relationship-enhanced knowledge graph convolutional network, and structured expression and intelligent reasoning of sediment disaster emergency disposal knowledge are realized. The method comprises the following main technical links: (1) constructing a knowledge graph mode layer and a data layer oriented to a sediment disaster scene, and establishing a'feature-event-disposal 'ternary model and a hierarchical relationship; (2) proposing a structured reasoning problem definition framework of an emergency processing task, and supporting modeling requirements of multi-source, multi-scale and multi-relation semantics; (3) designing an R-KGCN inference algorithm integrated with a semantic relationship enhancement module, and improving the accuracy and interpretability of inference; and (4) forming a knowledge reasoning and intelligent recommendation method oriented to sediment dangerous cases, and providing decision support and emergency response guarantee for operation scheduling of the oversize tide gate.

Description

Technical Field

[0001] This invention relates to the intersection of water conservancy engineering safety and artificial intelligence technology, specifically to an R-KGCN emergency response method for siltation hazards at floating wing suspended gates during storm surges of extra-large tidal barrier gates. It is applicable to auxiliary scheduling, risk warning, and emergency response scenarios in tidal barrier gate areas under sudden siltation situations, and belongs to the technical fields of disaster prevention and mitigation, water conservancy informatization, and smart water safety. Background Technology

[0002] In recent years, influenced by global climate change and human activities, the uncertainty and complexity of estuarine water and sediment conditions have significantly increased. The frequency and intensity of typhoons and storm surges have continued to rise, posing a risk of overlapping with astronomical tide levels, heavy rainfall, and upstream water inflows, potentially leading to rapid tidal rises and sudden hydrodynamic shifts. Estuarine sediment transport processes have become more complex, with diverse sources, significant particle size variations, and variable transport paths, posing greater risks to flood control, tide prevention, and navigation projects. With the increase in extreme events and the development of coastal urban clusters, the strategic importance of high-level flood control facilities in regional water security is becoming increasingly prominent, leading to a continuous growth in demand for engineering construction and operation management.

[0003] Large-scale tidal gates are a crucial component of high-level flood control systems in coastal areas, and the safe opening and closing of the gate area during storm surges directly impacts overall flood control safety. Among these, the "floating wing suspended gate" is a novel structural form, employing a large-span rotatable swing-opening steel arm and a multi-panel combined suspended sub-gate system, with opening and closing operations achieved via a heavy-duty mobile locomotive. This helps improve the hydrodynamic environment of the estuary and enhance urban water security. However, under extreme hydrodynamic conditions such as storm surges, this structure still faces technical challenges. Due to the large gate span and unique gate leaf suspension arrangement, local flow patterns are prone to turbulence and strong shear under forced conditions, potentially leading to asymmetrical sediment erosion and deposition in the gate slot and threshold areas, affecting smooth opening and closing and structural stress. Therefore, there is an urgent need to establish an intelligent identification and decision-making response technology system for sediment hazards in floating wing suspended gates during storm surges, enabling rapid assessment and scientific handling of local sediment disasters within the gate area, ensuring the safe and stable operation of large-scale tidal gates under extreme conditions.

[0004] Research on sediment problems during the operation of storm surge barriers mainly includes two typical approaches: First, prediction methods based on physical models or numerical simulations: These methods, using physical flume experiments or numerical techniques such as CFD, can reflect the flow field and sediment transport characteristics under specific operating conditions in detail. However, these methods generally suffer from poor real-time performance, long calculation cycles, and sensitivity to parameters, making it difficult to provide real-time or near-real-time response support in the event of sudden storm surge emergencies. Second, emergency response methods based on expert knowledge and rule-based reasoning: These methods rely on engineering operation experience and fuzzy logic rules, which can support on-site response to some extent. However, their knowledge organization is fragmented, lacking a unified knowledge system, and they are difficult to generalize effectively when knowledge is incomplete, thus limiting their application effectiveness.

[0005] In recent years, with the development of artificial intelligence and knowledge graph technologies, related research has begun to explore the introduction of knowledge-driven methods into emergency scenarios in water conservancy projects. These methods can integrate multi-source data and expert experience to achieve semantic modeling and reasoning for complex events, possessing advantages such as intuitive knowledge representation and strong logical interpretability, demonstrating good development potential. However, their practical implementation in the emergency management of complex hydraulic structures still faces the following three key challenges:

[0006] (1) There is a lack of a specialized modeling system for the local hydrodynamic characteristics of floating wing suspended doors during storm surges. The existing model layer and data layer have not yet formed a standardized process, and the knowledge ontology classification, attribute modeling and relation abstraction methods lack unified specifications, making it difficult to accurately describe the sudden siltation or scouring problems that may occur in the door slot and threshold area; (2) A systematic reasoning framework for emergency response tasks has not yet been formed. The existing methods lack a knowledge unit modeling path with "feature-event-response" as the core, making it difficult to transform complex scenarios into formal reasoning problems, thus restricting the intelligent identification and response to mud and sand hazards of floating wing suspended doors; (3) Existing knowledge graph neural networks such as KGCN have weak dynamic adaptability in emergency scenarios and limited ability to express complex relationships, making it difficult to effectively support the emergency response reasoning and rapid decision-making needs driven by multi-source heterogeneous knowledge. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of existing emergency systems in large-scale tidal gates during storm surges and under turbulent water and sediment conditions. These systems are prone to localized siltation and scouring in the gate slot and surrounding areas, leading to obstructed opening and closing, abnormal structural stress, and other problems. These shortcomings include fragmented knowledge organization, ambiguous correlations between features and hazards, and opaque reasoning logic, making it difficult to quickly identify and accurately handle localized hazards. This invention proposes an emergency response method that integrates relationship-enhanced knowledge graph reasoning. By constructing a structured knowledge representation system of "monitoring features - hazard type - handling measures" and introducing a relationship enhancement mechanism to strengthen the reasoning ability between multi-source hydrodynamic features and hazard patterns, this method enables real-time intelligent identification and decision support for handling localized silt disasters at the gate, thereby improving the emergency response efficiency and operational safety of large-scale tidal gates under complex hydrodynamic conditions.

[0008] The method of this invention is based on relation-enhanced knowledge graph convolutional network (R-KGCN) to construct a system framework covering knowledge modeling, reasoning definition, and reasoning optimization, thereby realizing the structured expression and intelligent reasoning of knowledge for emergency response to sediment disasters. Its main technical aspects include: (1) constructing a knowledge graph pattern layer and data layer for sediment disaster scenarios, and establishing a ternary model and hierarchical relationship of "feature-event-response"; (2) proposing a structured reasoning problem definition framework for emergency response tasks to support the modeling requirements of multi-source, multi-scale, and multi-relational semantics; (3) designing an R-KGCN reasoning algorithm that integrates semantic relationship enhancement modules to improve the accuracy and interpretability of reasoning; and (4) forming a knowledge reasoning and intelligent recommendation method for sediment hazards to provide decision support and emergency response guarantee for the operation and scheduling of extra-large tide gates.

[0009] To achieve the objectives of this invention, the following technical solution is adopted:

[0010] An R-KGCN emergency response method for siltation hazards at suspended gates during storm surges of extra-large tidal gates includes:

[0011] (1) Modeling with the knowledge-based modeling method of siltation hazards in the floating wing suspended gate during storm surge: Semantic modeling is carried out for possible siltation, gate flow state and hazard evolution during storm surge, and the ontology classification, attribute definition and relation structure are clarified.

[0012] (2) Method for constructing a knowledge dataset on mud and sand hazards of floating wing suspended gates during storm surge: Collect and integrate measured monitoring data, engineering operation records and numerical simulation results to construct a knowledge data layer covering semantic units of “feature-event-disposal”, and realize the standardization and instantiation of knowledge instances.

[0013] (3) Establish a knowledge reasoning problem definition framework in emergency response tasks: In response to the emergency response needs of mud and sand hazards in hanging doors, a formal modeling method suitable for structured reasoning is adopted to clarify the semantic mapping and logical constraints between “event-response measures”. This modeling process provides a formal problem definition and input framework for the subsequent reasoning algorithm of relation-enhanced knowledge graph convolutional network (R-KGCN).

[0014] (4) Establishing an R-KGCN inference model with enhanced fusion relationship mechanism: In response to the complex water-sand coupling relationship in the hanging gate area under storm surge conditions, a relationship attention mechanism is introduced to improve the modeling and reasoning ability of key semantic relationships.

[0015] (5) Conduct emergency response link prediction and intelligent recommendation: Based on the R-KGCN model, realize the semantic association prediction of "sediment hazard - emergency measures", form a visualized reasoning result and recommendation scheme, and provide intelligent decision support for engineering operation scheduling and emergency response.

[0016] Based on the above technical solutions, the present invention may also employ the following further technical solutions, or combine these further technical solutions:

[0017] (1) Knowledge-based modeling method for silt hazards during storm surge and floating wing suspended doors

[0018] This invention addresses the shortcomings of existing emergency knowledge graphs for water conservancy projects, which are mostly general models lacking support for specific operating conditions and emergency scenarios. It proposes a scenario-based knowledge modeling method for sediment hazard situations at suspended gates during storm surges in extra-large tide barriers. This method introduces operating condition constraints and risk feature representations into the knowledge graph's schema layer, enabling direct integration with actual operational data. This differs from existing modeling methods that rely solely on static knowledge entries, thus improving the model's relevance and operability. It includes:

[0019] 1) Knowledge Logical Structure

[0020] Emergency knowledge is organized using entity-predicate triples (subject-predicate-object), and a composite graph structure supporting multiple node types and semantic relationship edges is constructed. By introducing a logical chain of "operating condition parameters - emergency response measures," a structured model of siltation hazards at hanging gates during storm surges is achieved, ensuring that it can be directly encoded and reasoned using adjacency matrix in subsequent graph neural networks.

[0021] 2) Entity type design

[0022] Based on the actual emergency needs in the scenario of mud and sand hazards at suspended doors, three types of entities are defined:

[0023] Event characteristic entities include event time, duration, event type, triggering mechanism, event level, occurrence area, event impact, monitoring indicators, and gate operating status; the set of event characteristic entities is represented as f. j ∈F.

[0024] Emergency event entities: encompassing typical sediment risks that may occur in the suspended gate area, such as siltation at the lower edge or gate slot of the main gate and side gates, sediment accumulation in the barge storage, localized scouring and exposure of the gate area, and localized scour instability of the gate area; the set of emergency event entities is represented by c. i ∈C.

[0025] Emergency response measures entity: Based on the operating conditions of the suspended gate, a multi-level system of measures is designed, including monitoring and early warning, emergency reporting, engineering dredging, gate scheduling, facility emergency repair and maintenance, and organizational coordination. The emergency response measures entity is represented by t. k ∈T.

[0026] 3) Relationship type design

[0027] To support knowledge reasoning and link prediction, the following relationship is defined:

[0028] Has-feature relationship: connects an emergency event with its corresponding feature information, such as event type, location, and dispatch mode, e.g., "foreign object blockage in the main gate – has – trigger mechanism". The set of has-feature relationships is represented as R. has_feat When a sudden mudslide disaster occurs... i Having characteristic f j When, it is represented as r has_feat (c i ,f j )∈R has_feat .

[0029] Need-task relationship: connects an event with a response strategy, such as "sand accumulation in a pontoon dock – need – dredging". The set of need-task relationships is represented as R. need_task When a water supply emergency occurs i Required measures k When, it is represented as r has_feat (c i ,f j )∈R has_feat .

[0030] Inheritance: Used to describe hierarchical or compositional relationships, such as "sand accumulation in the pontoon warehouse – belongs to – siltation in the lock area". It can be represented as r. kind_of (“siltation in the barge warehouse”, “siltation in the lock area”)∈R inheritance .

[0031] 4) Knowledge Model Structure

[0032] Through the aforementioned entity and relationship sets, a complete scenario-based knowledge pattern graph is constructed. The knowledge structure pattern for emergency response to sudden events is as follows: Figure 3 As shown. Structurally, event feature entities are located at the upper layer of the graph, emergency event entities are in the middle, and response measure entities are located at the lower layer. Nodes are connected by semantic relationship edges, forming a "feature-event-measure" reasoning chain, which can be directly converted into an adjacency matrix input, providing structured support for the subsequent R-KGCN reasoning model.

[0033] Compared with existing technologies, this invention not only has the ability to express general emergency knowledge, but also achieves scenario-based constraints by introducing hanging gate operating parameters and risk characteristics. This ensures that the knowledge model can dynamically reflect the evolution of sediment hazards during storm surges, improves the expressibility, relevance, and reasoning basis of knowledge about sudden sediment disasters at storm surge gates, and provides a unified conceptual support framework for the subsequent construction of knowledge graph data layers and graph neural network reasoning models.

[0034] (2) Method for constructing a knowledge dataset on mud and sand hazards during storm surge periods using floating wing suspended doors

[0035] Based on the established schema layer, this invention proposes a method for constructing a knowledge dataset on siltation hazards in suspended gates. This method primarily employs existing entity and relation extraction techniques to transform information such as the operating conditions of suspended gates, siltation disaster events, and emergency response measures into structured triplet data. For semi-structured or structured text and tabular data, conventional rule-based extraction methods can be used, such as text preprocessing, lexicon construction, word segmentation and part-of-speech tagging, extraction rule setting, information extraction, and manual verification. The method's workflow includes steps such as text preprocessing, lexicon construction, rule setting, information extraction, and manual verification. Figure 4 As shown. The above method is a common technical approach in this field, and the specific implementation process can be found in existing published literature or related patents. Its function is to ensure the integrity of the knowledge graph data layer and provide basic data support for subsequent knowledge reasoning and emergency decision-making.

[0036] (3) Definition of the problem in reasoning about the dangers of mud and sand in floating wing suspended doors during storm surges.

[0037] This invention proposes an intelligent knowledge reasoning method for dealing with sediment hazards at suspended gates during storm surges. It aims to address the emergency response needs of extra-large storm gates in complex hydrodynamic and sediment environments by intelligently matching hazard characteristics with appropriate response measures and recommending strategies. The core idea of ​​this method is to infer and generate optimal emergency response measures based on known sediment hazard event characteristics, suspended gate operating conditions, and storm surge constraints, thus supporting rapid response and scientific decision-making in engineering projects.

[0038] To achieve computation and knowledge completion for complex semantic relationships, this invention formalizes the emergency response reasoning problem as a knowledge graph link prediction problem. Link prediction is one of the core tasks in knowledge reasoning, which can infer the potential association paths between event nodes and measure nodes based on the constructed knowledge graph structure, through deep learning or embedded representations, thereby enabling automatic recommendation and optimization of response strategies.

[0039] In the inference task modeling process, factors such as storm surge triggering conditions (water level, tidal range, flow velocity, sediment content, scouring and silting rate) and applicable measures (equipment accessibility, operation window, structural stress limitations, dredging operation safety, energy consumption constraints, etc.) are integrated to form an entity pair prediction problem oriented towards scenario constraints. By judging the correlation between event nodes and measure nodes, the demand-measure relationship R in the emergency knowledge graph is predicted. need_task This is essentially a typical binary classification problem, determining whether a connection exists between nodes, such as... Figure 5 As shown.

[0040] By identifying the structure and modeling the relationships of the "demand-measure" path, this invention can effectively support emergency response reasoning under the danger of mud and sand in the hanging gate of a super-large storm surge gate during storm surge, and improve the interpretability and response timeliness of the knowledge model in the actual engineering environment.

[0041] The mathematical definition of a knowledge-based reasoning problem concerning emergency response measures is as follows:

[0042] 1) Let the set of emergency events c be C = {c1, c2, ..., c I}, where I represents the number of emergency events c;

[0043] 2) Let the set of treatment measures t be T = {t1, t2, ..., t3}. M}, where M represents the number of treatment measures t;

[0044] 3) Let Y∈R be the interaction matrix between emergency event c and response measure t. I×M , where y ct =1 indicates that emergency event c has an interactive relationship with response measure t; otherwise, y = 1. ct =0;

[0045] 4) Knowledge graph G, which consists of triples (hd, rp, tl), where hd∈E represents the head of the triple, tl∈E represents the tail of the triple, rp∈R represents the relationship between the head entity hd and the tail event tl in the triple, and E and R represent the entity set and the relation set, respectively.

[0046] For entity c, entity t, interaction matrix Y and knowledge graph G, the problem of reasoning about emergency response measures can be transformed into reasoning whether entity c is associated with entity t. That is, the goal of the model is to learn the prediction function, as shown in equation (1).

[0047]

[0048] In the formula: Θ represents the probability that entity c will be associated with entity t, and Θ represents the model parameters of function F.

[0049] (4) Design of R-KGCN inference model for relationship enhancement mechanism

[0050] This invention proposes a Relation-Enhanced Knowledge Graph Convolutional Network (R-KGCN) method to improve the accuracy and reasoning ability of entity relationship modeling in knowledge graphs. This method introduces a relation-level enhancement mechanism based on traditional KGCN, starting from entity-relation-entity triples and combining relational semantic weights to guide adjacency sampling and weight calculation during graph convolution aggregation. It is particularly suitable for handling sudden event scenarios with complex logical relationships, such as large-span tidal barrier silt disasters. Figure 6 As shown, the core processing steps are as follows:

[0051] 2) Neighbor Sampling

[0052] To avoid the overhead of full-graph computation, this model adopts a layer-by-layer neighbor sampling strategy. Let the number of sampled neighbors at each layer be K. For any central node v, its outgoing adjacent nodes are grouped by relation type, and samples are taken from its adjacent entity set N(v) to generate the adjacency set N. K (v) The sampling mechanism combines the edge weights between entities, semantic similarity, and relation frequency to make a weighted selection, ensuring that the sampled neighbors are structurally and semantically representative.

[0053] 2) Relation-aware Weighting

[0054] To emphasize the importance of different semantic relations in learning the representation of the central node, a relational attention mechanism is introduced, assigning dynamic weights to adjacent edges. Specifically, for any pair of adjacent nodes (v, u) and their connection relation r, the relational attention weights are calculated as follows:

[0055]

[0056] In the formula, This represents the attention weight from node h to its neighboring node t (via relation r); e v Represents the embedding representation of node v; e v Represents the embedding representation of node v; e u represents the embedding representation of node u; LeakyReLU(·) represents the non-linear activation function selected by the attention mechanism method; a represents the attention scoring vector, which is used as a training parameter to calculate the weight score of the concatenated vector; || represents vector concatenation; r represents the relation vector between nodes, representing "suggested action" or other semantic relationships.

[0057] 3) Relation-aware Aggregation

[0058] In each convolutional layer, the representation vector of the target entity v The updated result is a weighted aggregation of its neighborhood features, calculated using the following formula:

[0059]

[0060] In the formula, W (l) Let σ be the weight matrix of the l-th layer network. Raa The nonlinear activation function used in the neighborhood feature aggregation method. This is the embedded representation of the neighbor node v in the previous layer.

[0061] (5) Design of a prediction method for emergency response link for mud and sand hazards during storm surge wing-mounted gates

[0062] This invention addresses the complex evolution and emergency response needs of sediment hazards at the floating wing suspension gates of extra-large storm surge barriers during storm surges. It proposes a scenario-based link prediction method based on the R-KGCN model to achieve intelligent matching and dynamic recommendation between hazard events and response measures. This method fully considers typical scenario characteristics during storm surges, such as sudden tide rises, surges in sediment content, and gate confinement. It introduces operational constraints and semantic weight distribution mechanisms at the knowledge graph structure level to enhance the model's ability to recognize time-varying features and multi-dimensional semantics. The method includes key steps such as positive and negative sample construction, prediction logic modeling, and recommendation strategy formulation.

[0063] During model construction, by combining monitoring data from the operation of the tide gate (sand content, flow velocity, tide level, sediment content, scouring and silting volume, etc.) and historical emergency response records, the state vectors of event nodes and execution constraint parameters of response nodes (equipment accessibility, operation window, structural stress limitations, dredging operation safety, energy consumption constraints, etc.) are defined to achieve an engineering-based expression of sample semantics. The model enhances semantic interaction between nodes through relational convolution and attention mechanisms, outputting the demand-response link probability values ​​of event nodes and response nodes under different emergency scenarios.

[0064] By combining a Top-K recommendation mechanism with scenario-based context filtering and similarity ranking strategies, the predicted results are screened and optimized to output the optimal emergency response strategy that meets the current tide level, structural status, and engineering safety constraints. This method enables rapid response and strategy recommendation for siltation hazards at suspended gates, improving the accuracy and timeliness of engineering emergency decision-making.

[0065] (6) Parameters and training of R-KGCN knowledge reasoning model

[0066] The R-KGCN (Relation-Enhanced Knowledge Graph Convolutional Network) model weights are initialized using the Xavier initialization method to ensure feature propagation stability, and the Adam optimizer is used for parameter updates to improve training efficiency. R-KGCN introduces a relation-level modeling mechanism, requiring additional settings for parameters such as the relation embedding matrix, relation transformation weights, and relation filtering thresholds, thereby enhancing the model's ability to express diverse relational semantics. During training, to improve generalization performance and suppress overfitting, Dropout and early stopping strategies are employed, using a cross-entropy loss function combined with L2 regularization. Simultaneously, grid search is used to optimize hyperparameters such as learning rate, embedding dimension, number of neighbor samples, and number of relation types, improving the model's inference accuracy and robustness in complex, sudden event semantic environments.

[0067] Link prediction between nodes is a binary classification problem, which determines whether a connection is established between nodes. To evaluate the accuracy of the prediction results, this invention uses AUC, ACC, Recall, and F-score to assess the model's prediction results and verify its accuracy and practicality in the emergency decision-making scenario of the tide gate.

[0068] (7) Knowledge Reasoning and Intelligent Decision-Making for Sediment Hazards of Floating Wing Suspended Doors During Storm Surges

[0069] Based on the established knowledge graph structure of sediment hazards in suspended gate systems and the trained and optimized R-KGCN inference model, and combined with information such as event type, operational scenario, triggering mechanism, and affected area, the system performs link prediction and knowledge inference for emergency response measures under typical sediment hazards. Emergency response measures include monitoring and early warning, emergency response reporting, engineering dredging, emergency dispatch, facility repair, and organizational management and coordination.

[0070] The model comprehensively evaluates the matching degree, rationality, and feasibility of measures, enabling automatic recommendation and scenario-based simulation of response plans. This method possesses rapid response capabilities to emergencies and can be embedded in floodgate scheduling or emergency command systems, providing data-driven intelligent decision support and dynamic optimization capabilities for engineering management.

[0071] Compared with the prior art, the beneficial effects of the present invention are:

[0072] (1) Significantly improved emergency intelligent decision-making capability for suspended gate areas during storm surge: This invention addresses the sudden mud and sand hazards (such as gate blockage, sand accumulation, local scouring, etc.) in suspended gate areas during storm surge. Based on the constructed emergency knowledge graph covering the three-layer structure of "feature-event-disposal", combined with the Relationship-Aware Graph Convolutional Network (R-KGCN) with semantic enhancement mechanism, it realizes accurate reasoning and automatic recommendation of disposal measures in disaster scenarios.

[0073] Compared with traditional methods that rely on human experience, fixed rules, or single monitoring indicators, this invention can achieve minute-level response and automatic comparison of multiple schemes in complex hydrodynamic environments, significantly improving the safety and intelligence level of gate area operation during storm surge.

[0074] (2) Enhancing Complex Relationship Modeling Capabilities, Semantic Reasoning Depth, and Interpretability: This invention proposes a relationship enhancement weighting mechanism to fully explore the semantic dependencies between event features and response plans. Compared to existing graph convolution methods (such as KGCN, RGCN, etc.) that only support structural connections or weak relationship modeling, this invention can not only identify explicit associations between high-frequency events and measures, but also capture low-frequency, implicit relationship chains, achieving deep completion and dynamic updating of the emergency knowledge graph. This effectively reduces the misjudgment rate in link prediction tasks and improves the accuracy and interpretability of response measure recommendations.

[0075] (3) Strong adaptability to engineering scenarios: This invention closely adheres to the engineering constraints of operating extra-large storm surge gates, combining the stress on the suspended gate structure, opening and closing conditions, operating time windows, and sediment accumulation patterns to construct a knowledge reasoning model with scenario adaptability. Compared to general emergency decision-making systems, this invention forms an intelligent decision-making mechanism specifically for storm surge-induced sediment disaster scenarios involving suspended gates. The model can be dynamically adjusted according to different gate layouts and operating states, significantly improving the model's applicability in terms of structural constraints, operational safety, and response timeliness.

[0076] (4) Constructing an efficient and scalable knowledge-driven emergency reasoning system: This invention, through training and optimizing the R-KGCN model and combining it with the structural features of the knowledge graph, realizes a closed-loop knowledge decision-making system from sediment disaster identification and relational reasoning to measure recommendation. This system has cross-basin and cross-condition portability and scalability, and can be quickly deployed and reused in different tide gates or hydraulic structures. It breaks through the bottlenecks of existing emergency systems such as "static model", "reaction lag" and "shallow semantics", and provides data-driven technical support for tide control scheduling and sediment emergency management. Attached Figure Description

[0077] Figure 1This is a flowchart illustrating the emergency response process for mud and sand hazards during storm surges using the R-KGCN method.

[0078] Figure 2 This is a schematic diagram of the knowledge graph model layer for emergency response to mud and sand hazards during storm surge.

[0079] Figure 3 This is the R-KGCN emergency response knowledge structure model for siltation hazards at floating wing suspended gates during storm surges of extra-large tide barriers. Here, E = {C, F} refers to the set of emergency event entities, representing the set of emergency event nodes and event characteristic nodes; G... ACF (E, R) refers to the set of characteristic maps of sudden sediment disasters, representing event characteristics, emergency event nodes, and their related relationships; Y I×K The relationship between emergency events and response measures. T This refers to the set of relationships between nodes of the response measures. (G) T (T, R) T A set of ).

[0080] Figure 4 It is a rule-based knowledge extraction and identification process for emergency response to sediment disasters at tide gates.

[0081] Figure 5 This is a schematic diagram illustrating the prediction of mud and sand hazards associated with the floating wing suspension gate during storm surges of an extra-large tide gate.

[0082] Figure 6 This is a schematic diagram of the R-KGCN knowledge graph convolutional network. Detailed Implementation

[0083] This invention proposes an R-KGCN emergency response method for the siltation hazard of suspended gates during storm surges of extra-large tide gates. The main steps are as follows:

[0084] 1. Knowledge-based modeling method for siltation hazards in floating wing suspended gates during storm surge: Semantic modeling is carried out for possible siltation, gate flow patterns and hazard evolution during storm surge, clarifying ontology classification, attribute definition and relation structure.

[0085] 2. Method for constructing a knowledge dataset on sediment hazards during storm surge: Collect and integrate measured monitoring data, engineering operation records and numerical simulation results to construct a knowledge data layer covering semantic units of "feature-event-disposal" to achieve standardized and instantiated expression of knowledge instances.

[0086] 3. Establish a knowledge reasoning problem definition framework in emergency response tasks: In response to the emergency response needs of mud and sand hazards in hanging doors, a formal modeling method suitable for structured reasoning is proposed to clarify the semantic mapping and logical constraints between "event-response measures". This modeling process provides a formal problem definition and input framework for the subsequent reasoning algorithm of relation-enhanced knowledge graph convolutional network (R-KGCN).

[0087] 4. Establish an R-KGCN inference model with an enhanced fusion relationship mechanism: In response to the complex water-sediment coupling relationship in the hanging gate area under storm surge conditions, a relational attention mechanism is introduced to improve the modeling and inference capabilities of key semantic relationships.

[0088] 5. Conduct emergency response link prediction and intelligent recommendation: Based on the R-KGCN model, realize the semantic association prediction of "sediment hazard - emergency measures", form a visualized reasoning result and recommended solution, and provide intelligent decision support for engineering operation scheduling and emergency response.

[0089] Detailed explanation:

[0090] (1) Knowledge-based modeling method for silt hazards during storm surge and floating wing suspended doors

[0091] This invention addresses the problems of frequent sediment disasters, complex hazard evolution mechanisms, and static traditional emergency knowledge structures in the hanging gate areas of extra-large storm surge gates during storm surges. It proposes a scenario-based knowledge modeling method for sediment hazards at the hanging gates of extra-large storm surge gates during storm surges. This method uses the operating conditions of the hanging gates during storm surges as the core semantic scenario, integrating operating parameters, sediment evolution characteristics, and emergency response knowledge to achieve intelligent expression and reasoning support for the entire process of sediment hazards.

[0092] 1) Logical Representation of Knowledge Graph

[0093] Emergency knowledge is organized using entity-predicate triples (subject-predicate-object), and a composite graph structure supporting multiple node types and semantic relationship edges is constructed. By introducing a logical chain of "operating parameters - hazardous events - response measures," a structured model of siltation hazards at hanging gates during storm surges is achieved, ensuring that it can be directly encoded and reasoned using adjacency matrix in subsequent graph neural networks.

[0094] 2) Entity type design

[0095] Based on the actual emergency needs in the scenario of a suspended door sludge and sand hazard, three types of entities are defined, such as... Figure 2 As shown:

[0096] Event characteristic entities: These are multi-dimensional operational attributes used to characterize sediment hazards at floating sluice gates, including event time, duration, event type, triggering mechanism, event level, occurrence area, event impact, monitoring indicators, and gate operating status. The occurrence area includes typical structural units such as the main gate opening, side openings, pontoon storage, and gate slots. Monitoring indicators include sediment concentration, flow velocity, tide level, sediment content, and scouring / deposition. Gate operating status includes opening angle, opening / closing frequency, and scheduling method. The set of event characteristic entities is represented as f. j ∈F.

[0097] Emergency event entities: encompassing typical sediment risks that may occur in the floating gate area, such as siltation at the lower edge or gate slot of the main gate and side gates, sediment accumulation in the pontoon warehouse, localized scouring and exposure of the gate area, and localized scour instability of the gate area; the set of emergency event entities is represented by c. i ∈C.

[0098] Emergency response measures entity: Based on the operating conditions of the suspended gate, a multi-level system of measures is designed, including monitoring and early warning, emergency reporting, engineering dredging, gate scheduling, facility emergency repair and maintenance, and organizational coordination. The emergency response measures entity is represented by t. k ∈T.

[0099] Emergency response measures entity: Based on the operating conditions of the suspended gate, a multi-level system of measures is designed, including monitoring and early warning, emergency reporting, engineering dredging, gate scheduling, facility emergency repair and maintenance, and organizational coordination. The emergency response measures entity is represented by t. k ∈T.

[0100] 3) Relationship type design

[0101] To support knowledge reasoning and link prediction, the following relationship is defined:

[0102] Has-feature relationship: connects an emergency event with its corresponding feature information, such as event type, location, and dispatch mode, e.g., "foreign object blockage in the main gate – has – trigger mechanism". The set of has-feature relationships is represented as R. has_feat When a sudden mudslide disaster occurs... i Having characteristic f j When, it is represented as r has_feat (c i ,f j )∈R has_feat .

[0103] Need-task relationship: connects an event with a response strategy, such as "sand accumulation in a pontoon dock – need – dredging". The set of need-task relationships is represented as R. need_task When a water supply emergency occurs i Required measures kWhen, it is represented as r has_feat (c i f i )∈R has_feat .

[0104] Inheritance: Used to describe hierarchical or compositional relationships, such as "sand accumulation in the pontoon warehouse – belongs to – siltation in the lock area". It can be represented as r. kind_of (“siltation in the barge warehouse”, “siltation in the lock area”)∈R inheritance .

[0105] 4) Knowledge Model Structure

[0106] Through the aforementioned entity and relationship sets, a complete scenario-based knowledge pattern graph is constructed. The knowledge structure pattern for emergency response to sudden events is as follows: Figure 3 As shown. Structurally, event feature entities are located at the upper layer of the graph, emergency event entities are in the middle, and response measure entities are located at the lower layer. Nodes are connected by semantic relationship edges, forming a "feature-event-measure" reasoning chain, which can be directly converted into an adjacency matrix input, providing structured support for the subsequent R-KGCN reasoning model.

[0107] Compared with existing technologies, this invention not only has the ability to express general emergency knowledge, but also achieves scenario-based constraints by introducing hanging gate operating parameters and risk characteristics. This ensures that the knowledge model can dynamically reflect the evolution of sediment hazards during storm surges, improves the expressibility, relevance, and reasoning basis of knowledge about sudden sediment disasters at storm surge gates, and provides a unified conceptual support framework for the subsequent construction of knowledge graph data layers and graph neural network reasoning models.

[0108] (2) Method for constructing a knowledge dataset on mud and sand hazards during storm surge periods using floating wing suspended doors

[0109] Based on the established schema layer, this invention proposes a method for constructing a knowledge dataset on siltation hazards in suspended gates. This method primarily employs existing entity and relation extraction techniques to transform information such as the operating conditions of suspended gates, siltation disaster events, and emergency response measures into structured triplet data. For semi-structured or structured text and tabular data, conventional rule-based extraction methods can be used, such as text preprocessing, lexicon construction, word segmentation and part-of-speech tagging, extraction rule setting, information extraction, and manual verification. The method's workflow includes steps such as text preprocessing, lexicon construction, rule setting, information extraction, and manual verification. Figure 4 As shown. The above method is a common technical means in this field. For specific implementation processes, please refer to existing public literature or relevant patents, and will not be elaborated further. Its function is to ensure the integrity of the knowledge graph data layer and provide basic data support for subsequent knowledge reasoning and emergency decision-making.

[0110] (3) Definition of the problem in reasoning about the dangers of mud and sand in floating wing suspended doors during storm surges.

[0111] This invention proposes an intelligent knowledge reasoning method for dealing with sediment hazards at suspended gates during storm surges. It aims to address the emergency response needs of extra-large storm gates in complex hydrodynamic and sediment environments by intelligently matching hazard characteristics with appropriate response measures and recommending strategies. The core idea of ​​this method is to infer and generate optimal emergency response measures based on known sediment hazard event characteristics, suspended gate operating conditions, and storm surge constraints, thus supporting rapid response and scientific decision-making in engineering projects.

[0112] To achieve computation and knowledge completion for complex semantic relationships, this invention formalizes the emergency response reasoning problem as a knowledge graph link prediction problem. Link prediction is one of the core tasks in knowledge reasoning, which can infer the potential association paths between event nodes and measure nodes based on the constructed knowledge graph structure, through deep learning or embedded representations, thereby enabling automatic recommendation and optimization of response strategies.

[0113] In the inference task modeling process, factors such as storm surge triggering conditions (water level, tidal range, flow velocity, sediment content, scouring and silting rate) and applicable measures (equipment accessibility, operation window, structural stress limitations, dredging operation safety, energy consumption constraints, etc.) are integrated to form an entity pair prediction problem oriented towards scenario constraints. By judging the correlation between event nodes and measure nodes, the demand-measure relationship R in the emergency knowledge graph is predicted. need_task This is essentially a typical binary classification problem, determining whether a connection exists between nodes, such as... Figure 5 As shown.

[0114] By identifying the structure and modeling the relationships of the "demand-measure" path, this invention can effectively support emergency response reasoning under the danger of mud and sand in the hanging gate of a super-large storm surge gate during storm surge, and improve the interpretability and response timeliness of the knowledge model in the actual engineering environment.

[0115] The mathematical definition of a knowledge-based reasoning problem concerning emergency response measures is as follows:

[0116] 1) Let the set of emergency events c be C = {c1, c2, ..., c3}. I}, where I represents the number of emergency events c;

[0117] 2) Let the set of treatment measures t be T = {t1, t2, ..., t3}. M}, where M represents the number of treatment measures t;

[0118] 3) Let Y∈R be the interaction matrix between emergency event c and response measure t. I×M , where y ct =1 indicates that emergency event c has an interactive relationship with response measure t; otherwise, y = 1. ct=0;

[0119] 4) Knowledge graph G, which consists of triples (hd, rp, tl), where hd∈E represents the head of the triple, tl∈E represents the tail of the triple, rp∈R represents the relationship between the head entity hd and the tail event tl in the triple, and E and R represent the entity set and the relation set, respectively.

[0120] For entity c, entity t, interaction matrix Y and knowledge graph G, the problem of reasoning about emergency response measures can be transformed into reasoning whether entity c is associated with entity t. That is, the goal of the model is to learn the prediction function, as shown in equation (1).

[0121]

[0122] In the formula: Θ represents the probability that entity c will be associated with entity t, and Θ represents the model parameters of function F.

[0123] (4) Design of R-KGCN inference model for relationship enhancement mechanism

[0124] This invention proposes a Relation-Enhanced Knowledge Graph Convolutional Network (R-KGCN) method to improve the accuracy and reasoning ability of entity relationship modeling in knowledge graphs. This method introduces a relation-level enhancement mechanism based on traditional KGCN, starting from entity-relation-entity triples and combining relational semantic weights to guide adjacency sampling and weight calculation during graph convolution aggregation. It is particularly suitable for handling sudden event scenarios with complex logical relationships, such as large-span tidal barrier silt disasters. Figure 6 As shown, the core processing steps are as follows:

[0125] 1) Neighbor Sampling

[0126] To avoid the overhead of full-graph computation, this model adopts a layer-by-layer neighbor sampling strategy. Let the number of sampled neighbors at each layer be K. For any central node v, its outgoing adjacent nodes are grouped by relation type, and samples are taken from its adjacent entity set N(v) to generate the adjacency set N. K (v) The sampling mechanism combines the edge weights between entities, semantic similarity, and relation frequency to make a weighted selection, ensuring that the sampled neighbors are structurally and semantically representative.

[0127] 2) Relation-aware Weighting

[0128] To emphasize the importance of different semantic relations in learning the representation of the central node, a relational attention mechanism is introduced, assigning dynamic weights to adjacent edges. Specifically, for any pair of adjacent nodes (v, u) and their connection relation r, the relational attention weights are calculated as follows:

[0129]

[0130] In the formula, This represents the attention weight from node h to its neighboring node t (via relation r); e v Represents the embedding representation of node v; e v Represents the embedding representation of node v; e u represents the embedding representation of node u; LeakyReLU(·) represents the non-linear activation function selected by the attention mechanism method; a represents the attention scoring vector, which is used as a training parameter to calculate the weight score of the concatenated vector; || represents vector concatenation; r represents the relation vector between nodes, representing "suggested action" or other semantic relationships.

[0131] 3) Relation-aware Aggregation

[0132] After obtaining the attention weights of all neighbors, a weighted feature aggregation operation is performed. In each convolutional layer, the representation vector of the target entity v is... The updated result is a weighted aggregation of its neighborhood features, calculated using the following formula:

[0133]

[0134] In the formula, W (l) Let σ be the weight matrix of the l-th layer network. Raa The nonlinear activation function used in the neighborhood feature aggregation method. This is the embedded representation of the neighbor node v in the previous layer.

[0135] (5) Design of a prediction method for emergency response link for mud and sand hazards during storm surge wing-mounted gates

[0136] This invention addresses the complex evolution and emergency response needs of sediment hazards at the floating wing suspension gates of extra-large storm surge barriers during storm surges. It proposes a scenario-based link prediction method based on the R-KGCN model to achieve intelligent matching and dynamic recommendation between hazard events and response measures. This method fully considers typical scenario characteristics during storm surges, such as sudden tide rises, surges in sediment content, and gate confinement. It introduces operational constraints and semantic weight distribution mechanisms at the knowledge graph structure level to enhance the model's ability to recognize time-varying features and multi-dimensional semantics. The method includes key steps such as positive and negative sample construction, prediction logic modeling, and recommendation strategy formulation, as detailed below:

[0137] 1) Positive and negative sample generation strategy

[0138] Link prediction, as a binary classification task, requires constructing positive and negative sample sets for model training. This invention employs the following generation strategy: positive samples are extracted from existing "event-response measure" triples (hd, rp, tl) in the constructed emergency knowledge graph, where the relation is a "suggested response" semantic relation; these triples are directly used as positive sample input. Negative samples are constructed based on the negative sampling approach, keeping the head entity hd and relation rp unchanged, and randomly replacing the tail entity tl from the entity set to generate semantically reasonable but actually non-existent negative triples (hd, rp, tl′), thus enhancing the model's discriminative ability. To ensure the balance and generalization of model training, the ratio of positive to negative samples is typically set to 1:2 or 1:3, and can be dynamically adjusted according to the entity distribution characteristics in the knowledge graph.

[0139] 2) Recommended strategies for handling the situation

[0140] To enhance the system's practicality and intelligence, this invention designs diverse recommendation strategies based on the link prediction results, specifically including: ① Top-K ranking recommendation: All candidate treatment measures are sorted in descending order of prediction scores, and the top K highest-scoring measures are selected as the recommendation results; ② Context-based filtering based on scenarios: Rule constraints are applied based on event context elements (such as tide level, water-sediment combination, time window, etc.) to recommend only treatment measures that are feasible and timely in the current scenario; ③ Similar event memory recommendation: An event similarity matching mechanism is introduced, drawing on treatment strategies from similar historical events to enhance and supplement the prediction results, improving the stability and interpretability of the recommendations.

[0141] (6) Parameters and training of R-KGCN knowledge reasoning model

[0142] The R-KGCN (Relation-Enhanced Knowledge Graph Convolutional Network) model weights are initialized using the Xavier initialization method to ensure feature propagation stability, and the Adam optimizer is used for parameter updates to improve training efficiency. R-KGCN introduces a relation-level modeling mechanism, requiring additional settings for parameters such as the relation embedding matrix, relation transformation weights, and relation filtering thresholds, thereby enhancing the model's ability to express diverse relational semantics. During training, to improve generalization performance and suppress overfitting, Dropout and early stopping strategies are employed, using a cross-entropy loss function combined with L2 regularization. Simultaneously, grid search is used to optimize hyperparameters such as learning rate, embedding dimension, number of neighbor samples, and number of relation types, improving the model's inference accuracy and robustness in complex, sudden event semantic environments.

[0143] The R-KGCN model employs supervised learning during training, with the following details: A binary cross-entropy loss function is used to predict links between connected samples (positive samples) and unconnected samples (negative samples). The Adam optimizer is used for parameter updates, with a learning rate of 0.001, β1 = 0.9, and β2 = 0.999. A dynamic negative sampling strategy is employed, randomly generating a certain number of negative samples (1:3) for each positive sample to improve training stability. The training epochs are 300, stopping early based on the convergence trend of the validation set; the batch size is 64; the embedding size is 32; and the dropout rate is 0.5 to prevent overfitting.

[0144] Link prediction between nodes is a binary classification problem, that is, determining whether a connection is formed between nodes. In order to evaluate the accuracy of the prediction results, this paper uses AUC, ACC, Recall and F-score to evaluate the model prediction results, as shown in Equations (7) to (10).

[0145]

[0146]

[0147] In the formula: TP is the true positive of the correct class sample; FP is the false positive of the incorrect class sample; TN is the true negative of the correct class sample; FN is the false negative of the incorrect class sample.

[0148] (7) Conduct knowledge reasoning and intelligent decision-making for silt hazards caused by floating wing suspended doors during storm surges.

[0149] Based on the established knowledge graph structure of sediment hazards in suspended gate systems and the trained and optimized R-KGCN inference model, and combined with information such as event type, operational scenario, triggering mechanism, and affected area, the system performs link prediction and knowledge inference for emergency response measures under typical sediment hazards. Emergency response measures include monitoring and early warning, emergency response reporting, engineering dredging, emergency dispatch, facility repair, and organizational management and coordination.

[0150] The model comprehensively evaluates the matching degree, rationality, and feasibility of measures, enabling automatic recommendation and scenario-based simulation of response plans. This method possesses rapid response capabilities to emergencies and can be embedded in floodgate scheduling or emergency command systems, providing data-driven intelligent decision support and dynamic optimization capabilities for engineering management.

Claims

1. R-KGCN Emergency Response Method for Sediment Hazards of Floating Wing Suspended Gates during Storm Surges of Extra-Large Tidal Barriers, characterized in that... include: (1) The modeling method of knowledge-based scenario modeling of mud and sand hazards during storm surge is adopted; Semantic modeling is conducted to address potential sediment deposition, gate flow patterns, and hazard evolution during storm surges, clarifying ontology classification, attribute definitions, and relational structures. (2) Construction of knowledge dataset for mud and sand hazards of floating wing suspended gate during storm surge: Collect and integrate measured monitoring data, engineering operation records and numerical simulation results, construct a knowledge data layer covering the semantic units of "feature-event-disposal", and realize the standardization and instantiation of knowledge instances; (3) Establish a knowledge reasoning problem definition framework in emergency response tasks: In response to the emergency response needs of mud and sand hazards in hanging doors, a formal modeling method suitable for structured reasoning is adopted to clarify the semantic mapping and logical constraints between "event-response measures". This modeling process provides a formal problem definition and input framework for the subsequent reasoning algorithm of relation-enhanced knowledge graph convolutional network (R-KGCN). (4) Establishing an R-KGCN inference model with an enhanced fusion relationship mechanism: In response to the complex water-sediment coupling relationship in the hanging gate area under storm surge conditions, a relationship attention mechanism is introduced to improve the modeling and inference capabilities of key semantic relationships; (5) Conduct emergency response link prediction and intelligent recommendation: Based on the R-KGCN model, realize the semantic association prediction of "sediment hazard - emergency measures", form a visualized reasoning result and recommendation scheme, and provide intelligent decision support for engineering operation scheduling and emergency response.

2. The R-KGCN emergency response method for silt hazards at the floating wing suspended gate of an extra-large tide gate during storm surges, as described in claim 1, is characterized in that... The method for scenario-based knowledge modeling of silt hazards during storm surge periods using floating wing suspended doors includes: 1) Knowledge Logical Structure Emergency knowledge is organized in the form of entity-relation triples, and a composite graph structure supporting multiple types of nodes and multiple semantic relation edges is constructed. By introducing the logical chain of "operating condition parameters-hazard events-response measures", a structured model of the siltation hazard of hanging gates during storm surge is realized, ensuring that it can be directly encoded and reasoned in the adjacency matrix in the subsequent graph neural network. 2) Entity type design Based on the actual emergency needs in the scenario of mud and sand hazards at suspended doors, three types of entities are defined: Event characteristic entities include event time, duration, event type, triggering mechanism, event level, occurrence area, event impact, monitoring indicators, and gate operating status; the set of event characteristic entities is represented as f. j ∈F; Emergency event entities: encompassing typical sediment risks that may occur in the suspended gate area, such as siltation at the lower edge or gate slot of the main gate and side gates, sediment accumulation in the barge storage, localized scouring and exposure of the gate area, and localized scour instability of the gate area; the set of emergency event entities is represented by c. i ∈C; Emergency response measures entity: Based on the operating conditions of the suspended gate, a multi-level measure is designed, including monitoring and early warning, emergency reporting, engineering dredging, gate scheduling, facility emergency repair and maintenance, and organizational coordination; the emergency response measures entity is represented by t. k ∈T; 3) Relationship type design To support knowledge reasoning and link prediction, the following relationship is defined: Possession-feature relationship: Connecting emergency events with their corresponding feature information, the set of possession-feature relationships is represented as R. has_feat ; Demand-Response Relationship: Connecting events with response strategies, the set of demand-response relationships is represented as R. need_task ; Inheritance: Used to describe hierarchical or compositional relationships; 4) Knowledge Model Structure Event feature entities are located at the upper layer of the graph, emergency event entities are in the middle, and response measure entities are located at the lower layer. Nodes are connected by semantic relationship edges to form a "feature-event-measure" reasoning chain, which is directly converted into an adjacency matrix input, providing structured support for the subsequent R-KGCN reasoning model.

3. The R-KGCN emergency response method for silt hazards at the floating wing suspended gate of an extra-large tide gate during storm surges, as described in claim 1, is characterized in that... For the construction of a knowledge dataset on sediment hazards during storm surge, based on the established schema layer, entity and relation extraction techniques are used to transform the operating conditions of the suspended gate, sediment disaster events, and emergency response measures into structured triplet data.

4. The R-KGCN emergency response method for silt hazards during storm surges at extra-large storm gates, as described in claim 1, is characterized in that... A framework for defining the knowledge-based reasoning problem regarding the siltation hazard of floating wing suspended doors during storm surges: In the inference task modeling process, storm surge triggering conditions and measure applicability conditions are integrated to form an entity pair prediction problem oriented towards scenario constraints; by judging the correlation between event nodes and measure nodes, the demand-measure relationship R in the emergency knowledge graph is predicted. need_task This is essentially a typical binary classification problem, determining whether a connection exists between nodes; By identifying the structure and modeling the relationships within the "demand-measure" path; The mathematical definition of a knowledge-based reasoning problem concerning emergency response measures is as follows: 1) Let the set of emergency events c be C = <c1,c2,…,c I }, where I represents the number of emergency events c; 2) Let the set of treatment measures t be T = {t1, t2, ..., t3}. M }, where M represents the number of treatment measures t; 3) Let Y∈R be the interaction matrix between emergency event c and response measure t. I×M , where y ct =1 indicates that emergency event c has an interactive relationship with response measure t; otherwise, y = 1. ct =0; 4) Knowledge graph G, which consists of triples (hd, rp, tl), where hd∈E represents the head of the triple, tl∈E represents the tail of the triple, rp∈R represents the relationship between the head entity hd and the tail event tl in the triple, and E and R represent the entity set and the relation set, respectively. For entity c, entity t, interaction matrix Y and knowledge graph G, the problem of emergency response measures knowledge reasoning can be transformed into reasoning whether entity c is associated with entity t. That is, the goal of the model is to learn the prediction function, as shown in equation (1). In the formula: Θ represents the probability that entity c will be associated with entity t, and Θ represents the model parameters of function F.

5. The R-KGCN emergency response method for silt hazards during storm surges at extra-large storm gates, as described in claim 1, is characterized in that... The R-KGCN inference model for relation enhancement introduces a relation-level enhancement mechanism based on KGCN. Starting from the entity-relation-entity triple, it combines relation semantic weights to guide adjacency sampling and weight calculation in the graph convolution aggregation process. The core processing steps are as follows: 1) Neighbor sampling To avoid the overhead of full-graph computation, the R-KGCN inference model employs a layer-by-layer neighbor sampling strategy. Let K be the number of neighbors sampled at each layer. For any central node v, its outgoing adjacent nodes are grouped by relation type, and samples are taken from its adjacent entity set N(v) to generate the adjacency set N. K (v) The sampling mechanism combines the edge weights between entities, semantic similarity, and relation frequency to make a weighted selection, ensuring that the sampled neighbors are structurally and semantically representative. 2) Calculation of Relationship Enhancement Weights For any pair of adjacent nodes (v, u) and their connection relationship r, calculate the relation attention weights: In the formula, This represents the attention weight from node h to its neighboring node t (via relation r); e v Represents the embedding representation of node v; e v Represents the embedding representation of node v; e u represents the embedding representation of node u; LeakyReLU(·) represents the non-linear activation function selected by the attention mechanism method; a represents the attention scoring vector, which is used as a training parameter to calculate the weight score of the concatenated vector; || represents vector concatenation; r represents the relation vector between nodes, representing "suggested action" or other semantic relationships; 3) Neighborhood feature aggregation In each convolutional layer, the representation vector of the target entity v The updated result is a weighted aggregation of its neighborhood features, calculated using the following formula: In the formula, W (l) Let σ be the weight matrix of the l-th layer network. Raa The nonlinear activation function used in the neighborhood feature aggregation method. This is the embedded representation of the neighbor node v in the previous layer.

6. The R-KGCN emergency response method for silt hazards during storm surges at extra-large storm gates, as described in claim 1, is characterized in that... Predicted emergency response plan for silt hazards at floating wing suspended doors during storm surge: A working condition constraint and semantic weight distribution mechanism are introduced at the knowledge graph structure level to enhance the model's ability to recognize time-varying features and multi-dimensional semantics; including positive and negative sample construction, prediction logic modeling, and recommendation strategy formulation steps; During the model construction process, the state vector of the event node and the execution constraint parameters of the disposal node are defined by combining the operation monitoring data of the tide gate and the historical emergency response records, so as to realize the engineering expression of the sample semantics. The model enhances the semantic interaction between nodes through relational convolution and attention mechanism, and outputs the demand-response link probability value of event nodes and disposal nodes under different emergency scenarios. By combining the Top-K recommendation mechanism with scenario-based context filtering and similarity ranking strategies, the prediction results are screened and optimized to output the optimal emergency response strategy that meets the current tide level, structural status and engineering safety constraints.

7. The R-KGCN emergency response method for silt hazards during storm surges at extra-large storm gates, as described in claim 1, is characterized in that... Regarding the parameters and training of the R-KGCN knowledge reasoning model: The R-KGCN model weights are initialized using the Xavier initialization method to ensure feature propagation stability, and the Adam optimizer is used for parameter updates to improve training efficiency. During training, to improve generalization performance and suppress overfitting, Dropout and early stopping strategies are adopted, and the cross-entropy loss function is combined with L2 regularization. At the same time, the hyperparameters such as learning rate, embedding dimension, number of neighbor samples, and number of relation types are optimized through grid search to improve the inference accuracy and robustness of the model in complex sudden event semantic environments. The AUC, ACC, Recall, and F-score metrics were used to evaluate the model's prediction results and verify its accuracy and practicality in emergency decision-making scenarios for tide gates.

8. The R-KGCN emergency response method for mud and sand hazards during storm surges at extra-large storm gates, as described in claim 1, is characterized in that... For knowledge reasoning and intelligent decision-making regarding sediment hazards during storm surge, based on the constructed knowledge graph structure of sediment hazards and the trained and optimized R-KGCN reasoning model, and combined with event type, operational scenario, triggering mechanism and affected area, the system implements link prediction and knowledge reasoning for emergency response measures under typical sediment hazards. Emergency response measures include monitoring and early warning, emergency response reporting, engineering dredging, emergency dispatch, facility repair and organization and management coordination. The model comprehensively evaluates the matching degree, rationality and feasibility of measures, and realizes automatic recommendation and scenario simulation of disposal plans.

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