Embedded hydraulic engineering risk intelligent regulation and control system based on knowledge graph

Abstract

The invention discloses an embedded hydraulic engineering risk intelligent regulation and control system based on a knowledge graph, and belongs to the technical field of hydraulic engineering. The system comprises a multi-level knowledge graph construction module for constructing a multi-level knowledge graph and forming a dynamically updated knowledge network; the atlas embedding expression module is used for encoding entity attributes, relation characteristics and time sequence evolution characteristics thereof in real time; the risk identification and evaluation module is used for integrating atlas embedded features and expert knowledge rules and analyzing hydraulic engineering risk features and spatial propagation modes thereof so as to realize accurate identification and quantitative evaluation of risks; the reinforcement learning regulation and control decision module encodes the risk state and the regulation and control target into a state-action space based on a deep reinforcement learning framework; the dynamic feedback optimization module is used for automatically updating the entity state and the relationship strength so as to form a closed loop of continuous optimization and knowledge accumulation; and the visual interaction module is used for visually displaying the risk situation, the regulation and control effect and the propagation path of the water conservancy project.

Description

Technical Field

[0001] The present application relates to the technical field of water conservancy engineering, and more specifically, to an intelligent risk control system for water conservancy engineering based on embedded knowledge graph. Background Art

[0002] As one of the important infrastructures, the safety and stability of water conservancy projects are of vital importance to social and economic development, the safety of people's lives and property, and the protection of the ecological environment. As the scale of water conservancy projects continues to expand, the risk factors they face are becoming increasingly complex and diverse, including natural disasters, equipment failures, environmental changes, etc. These risks are not only highly uncertain, but may also have dynamic characteristics of space and time, which poses a great challenge to traditional risk management methods.

[0003] At present, risk management of water conservancy projects mainly relies on expert experience and traditional monitoring technology. Although some intelligent methods have been introduced, traditional analysis methods are often difficult to conduct comprehensive and accurate risk identification and prediction due to the scattered data sources, huge amount of information and spatiotemporal variation. Existing systems usually find it difficult to effectively integrate large amounts of multi-dimensional data, historical knowledge and real-time monitoring data, which makes the accuracy and real-time nature of risk assessment and control decisions insufficient, affecting the safety assurance capabilities of water conservancy projects.

[0004] In recent years, with the rapid development of artificial intelligence, the Internet of Things, and big data technologies, intelligent methods based on knowledge graphs have gradually been applied to the risk management of complex systems. As a graph structure that can effectively express entities and their relationships, knowledge graphs can map high-dimensional complex data into low-dimensional vector spaces through graph embedding technology, thereby extracting potential knowledge features. However, how to combine the real-time monitoring data of water conservancy projects, expert control strategies, and the potential complex relationships in water conservancy projects to build a dynamically updated knowledge graph, and on this basis, achieve accurate risk identification, assessment, and control, is still a technical problem that needs to be solved urgently.

[0005] To sum up, how to effectively build a dynamic knowledge graph that integrates real-time data, historical knowledge and expert experience in water conservancy projects, and perform efficient risk identification, quantitative assessment and intelligent regulation based on this graph has become a technical problem that needs to be solved urgently. Summary of the invention

[0006] In order to overcome a series of defects in the prior art, the purpose of this application is to provide a water conservancy project risk intelligent control system based on knowledge graph embedded in order to solve the above problems, including the following modules:

[0007] Multi-level Knowledge Graph Construction Module, which constructs a multi-level knowledge graph. By integrating the infrastructure of water conservancy projects, real-time monitoring data, risk assessment indicators, and expert control strategies, a dynamically updated knowledge network is formed to support intelligent management;

[0008] Graph Embedding Representation Module, which adopts a multi-task learning framework and attention mechanism to map high-dimensional knowledge graph information to a low-dimensional vector space to real-time encode entity attributes, relationship features, and their temporal evolution characteristics;

[0009] Risk Identification and Assessment Module, which integrates graph embedding features and expert knowledge rules to analyze the risk characteristics of water conservancy projects and their spatial propagation patterns to achieve accurate risk identification and quantitative assessment;

[0010] Reinforcement Learning Regulation Decision-making Module, based on the deep reinforcement learning framework, encodes the risk state and regulation objectives into the state-action space, generates an optimized regulation plan through the policy network, and evaluates the long-term benefits of the regulation plan to achieve dynamic regulation decisions with multiple objectives;

[0011] Dynamic Feedback Optimization Module, by real-time monitoring the regulation effect and risk changes, feeds back the decision execution result to the knowledge graph, automatically updates the entity state and relationship strength, thus forming a closed-loop of continuous optimization and knowledge accumulation;

[0012] Visualization and Interaction Module, which intuitively displays the risk situation, regulation effect, and propagation path of water conservancy projects, and provides an expert intervention interface to support interactive decision-making.

[0013] Furthermore, the multi-level knowledge graph construction module includes the following components:

[0014] Data Acquisition Unit, which collects the infrastructure data, monitoring information, and environmental variables of water conservancy projects in real-time to provide raw data support for the knowledge graph;

[0015] Data Preprocessing Unit, which cleans, standardizes, and formats the collected data to ensure the data quality and consistency of the input knowledge graph;

[0016] Knowledge Extraction Unit, which identifies key entities, relationships, and attributes from the preprocessed data to construct the nodes and edges of the knowledge graph;

[0017] Graph Construction Unit, which organizes the extracted knowledge information into a multi-level structure to form the basic framework of the knowledge graph;

[0018] Risk Assessment Integration Unit, which embeds risk assessment indicators into the knowledge graph to enhance the ability to identify and warn of potential risks and provide a more comprehensive risk view;

[0019] The expert control strategy integration unit integrates expert control strategies and experiences to enhance the intelligent management ability of the knowledge graph and achieve effective decision support.

[0020] Furthermore, the graph embedding representation module includes the following components:

[0021] The data input unit receives high-dimensional knowledge graph information from the multi-level knowledge graph construction module, including entities, relationships, and their attributes;

[0022] The feature extraction unit extracts meaningful features from the input data, including entity attributes and relationship features, providing a basis for subsequent embedding generation;

[0023] The multi-task learning framework designs and implements multi-task learning strategies, using shared representations to train multiple related tasks simultaneously to improve the effect and generalization ability of the embedding representation;

[0024] The attention mechanism unit applies the attention mechanism to automatically assign weights, highlighting important entity and relationship features, thereby enhancing the expressive ability of the graph information;

[0025] The vector mapping unit converts the extracted high-dimensional features into low-dimensional vectors through a mapping function, reducing the data dimension while retaining important information;

[0026] The embedding optimization unit optimizes the generated low-dimensional vectors, adjusting the vector representation through a loss function to improve the performance and accuracy of downstream tasks;

[0027] The temporal evolution encoding unit processes and encodes temporal information to capture the dynamic changes of entities and relationships, providing temporal context for subsequent tasks.

[0028] Furthermore, the risk identification and assessment module includes the following components:

[0029] The risk feature integration and analysis unit integrates the feature vectors in the graph embedding representation module and expert knowledge rules and extracts key risk features;

[0030] The spatial propagation pattern recognition unit analyzes the propagation pattern of risks in the geographical space, determines the risk correlation between regions to support regional risk assessment;

[0031] The risk quantification unit quantitatively evaluates the identified risk features, calculates the probability and impact degree of the risk occurrence;

[0032] The dynamic monitoring and updating unit monitors the changes of relevant parameters in the water conservancy project in real time, updates the risk assessment results in a timely manner to ensure the timeliness of the assessment;

[0033] The expert knowledge base unit stores expert knowledge and experience related to the risk management of water conservancy projects, and enhances the depth and reliability of risk assessment through knowledge reasoning;

[0034] The evaluation result output unit visualizes the quantitative evaluation results and the output information of risk characteristic analysis, and provides an intuitive report to support the decision-makers' countermeasures.

[0035] Furthermore, the reinforcement learning regulation decision-making module includes the following components:

[0036] The state encoding unit encodes the real-time risk state and environmental parameters of the water conservancy project into the state vector of the reinforcement learning model, providing input to reflect the current risk situation;

[0037] The action space construction unit defines the set of possible regulation actions and generates adaptive actions according to the regulation objectives;

[0038] The policy network unit generates a regulation policy for the current state based on the deep reinforcement learning framework to optimize the safety and benefits of the water conservancy project;

[0039] The value evaluation network estimates the cumulative benefits of each regulation plan in the future time series, providing benefit estimates to support the long-term effectiveness of decision-making;

[0040] The reward function and feedback unit designs and optimizes the reward function, gives feedback according to the execution effect of the regulation plan, and dynamically balances the exploration of the regulation plan and the utilization of known strategies to drive the reinforcement learning model to optimize towards the optimal decision-making direction;

[0041] The regulation plan output unit: outputs the optimized regulation plan, including the corresponding regulation measures and their expected effects, to support the dynamic regulation decision-making of multiple objectives.

[0042] Furthermore, the dynamic feedback optimization module includes the following steps:

[0043] The regulation effect and risk dynamic monitoring unit monitors the execution results of the regulation plan and the changes in the risk state in the water conservancy project in real time, quantifies the impact of the regulation measures on the risk indicators, and ensures the timeliness of risk assessment and decision-making;

[0044] The feedback information integration unit integrates the processed regulation effect and risk change data, and converts them into the entity attribute and relationship update information required in the knowledge graph;

[0045] The knowledge graph update unit applies the feedback information to the knowledge graph, automatically updates the states and relationship strengths of relevant entities to maintain the timeliness and accuracy of the graph;

[0046] The regulation strategy adaptive adjustment unit optimizes the regulation strategy parameters according to the feedback information and dynamically adjusts the strategy to adapt to new risk characteristics and environmental changes;

[0047] The closed-loop feedback management unit realizes the closed-loop management of decision feedback, ensures that each feedback is used for improvement and accumulation, and continuously enhances the intelligence and adaptability of the system.

[0048] Compared with the prior art, the present application has the following beneficial effects:

[0049] The present application constructs a multi-level knowledge graph, combines graph embedding and deep reinforcement learning to realize the intelligent identification, quantitative evaluation and optimal regulation of water conservancy project risks, and continuously optimizes the regulation strategy through dynamic feedback, improving the intelligence and security of water conservancy project management. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 FIG. is a schematic structural diagram of an intelligent regulation system for water conservancy project risks based on knowledge graph embedding disclosed in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, of the embodiments of the present invention.

[0052] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0053] The embodiments described below with reference to the accompanying drawings and directional terms are exemplary only and are intended to explain the present invention and should not be construed as limiting the present invention.

[0054] As Figure 1 shown, an intelligent regulation system for water conservancy project risks based on knowledge graph embedding includes the following modules:

[0055] The multi-level knowledge graph construction module constructs a multi-level knowledge graph, and forms a dynamically updated knowledge network by integrating the infrastructure of water conservancy projects, real-time monitoring data, risk assessment indicators and expert regulation strategies to support intelligent management;

[0056] The graph embedding representation module uses a multi-task learning framework and an attention mechanism to map high-dimensional knowledge graph information to a low-dimensional vector space to real-time encode entity attributes, relationship features and their temporal evolution characteristics;

[0057] A risk identification and assessment module that integrates graph embedding features and expert knowledge rules to analyze the risk characteristics of water conservancy projects and their spatial propagation patterns, so as to achieve accurate risk identification and quantitative assessment;

[0058] A reinforcement learning-based regulation and decision-making module that encodes the risk state and regulation objectives into a state-action space based on a deep reinforcement learning framework, generates an optimized regulation plan through a policy network, and evaluates the long-term benefits of the regulation plan to achieve dynamic regulation and decision-making with multiple objectives;

[0059] A dynamic feedback optimization module that feeds back the decision execution results to the knowledge graph by monitoring the regulation effect and risk changes in real time, automatically updates the entity state and relationship strength, thus forming a closed loop of continuous optimization and knowledge accumulation;

[0060] A visualization and interaction module that intuitively displays the risk situation, regulation effect and propagation path of water conservancy projects, and provides an expert intervention interface to support interactive decision-making.

[0061] The core technical effect of the multi-level knowledge graph construction module is to build a comprehensive and dynamically updated knowledge network by integrating multiple levels of knowledge sources (such as water conservancy project infrastructure, real-time monitoring data, risk assessment indicators, expert regulation strategies, etc.). This knowledge graph can cover all key factors and dynamic information of water conservancy projects, providing strong knowledge support for subsequent intelligent regulation. Water conservancy projects usually involve complex equipment, environmental changes and management decisions. The multi-level structure based on graph construction can achieve comprehensive coverage from macro to micro, supporting accurate risk identification and assessment. Through dynamic update, it can reflect the changes in the project status, fluctuations in risk factors and strategy adjustments of relevant experts in real time, enabling intelligent regulation to be adjusted in a timely manner with data changes, improving the flexibility and adaptability of the system. In addition, the multi-level design of the graph can achieve knowledge expression at different granularities, supporting both large-scale macro decisions and providing detailed-level data support, greatly enhancing the scalability and adaptability of the system.

[0062] The graph embedding representation module is one of the core technologies of the system, aiming to transform the complex high-dimensional knowledge graph in water conservancy projects into low-dimensional vector representations for subsequent machine learning and reasoning operations. By adopting a multi-task learning framework and attention mechanism, it can efficiently learn the entity attributes, relationship features in the graph, and their evolution characteristics at different time periods. Through graph embedding, multiple entities (such as devices, sensors, risk indicators, etc.) and their complex relationships can be transformed into operable numerical vectors, facilitating the processing of machine learning models. The attention mechanism can adaptively focus on more important graph elements during this process, thereby enhancing the model's sensitivity and accuracy to key factors. This low-dimensional representation can not only reduce the consumption of computing resources but also more accurately reflect the risk evolution and state changes of water conservancy projects at different time periods by capturing the time-series change characteristics, improving the timeliness and accuracy of risk identification and decision-making.

[0063] The innovation of the risk identification and assessment module lies in its ability to comprehensively analyze the risk factors and their spatial propagation patterns in water conservancy projects. Risk identification combines real-time monitoring data (such as water level, pressure, flow rate, etc.) with historical data and conducts comprehensive analysis using the entity relationships and time-series characteristics in the graph to accurately identify potential risks and their development trends. The analysis of the spatial propagation pattern can reveal the propagation paths of risks at different locations and time periods, predicting possible accident occurrence points in advance. This precise risk identification and quantitative assessment not only improve the accuracy of early warnings but also provide more scientific data support for subsequent regulation decisions, enabling the system to intervene at the initial stage of risks and reducing catastrophic consequences caused by slow responses.

[0064] The reinforcement learning regulation decision-making module is based on the deep reinforcement learning (DRL) framework. By encoding the risk state and regulation objectives into the state-action space and generating an optimized regulation plan through the policy network, it realizes dynamic regulation decision-making for multiple objectives. The reinforcement learning algorithm adjusts the decision-making strategy according to the long-term rewards by simulating the execution effects of different regulation measures, thereby finding the optimal regulation path. This module can automatically optimize the regulation strategy and improve the regulation efficiency by continuously learning and exploring in the face of complex and changing risk states. Its technical effect is reflected in the ability to achieve dynamic adjustment and real-time optimization. Even in the face of uncertain or sudden situations, the system can still generate optimal or sub-optimal decisions. In addition, the application of deep reinforcement learning can gradually improve the strategy, enhancing the regulation effect and decision-making quality through continuous learning and experience accumulation.

[0065] The dynamic feedback optimization module forms a closed-loop for continuous optimization and knowledge accumulation by monitoring the regulation effect and risk changes in real time and feeding back the decision execution results to the knowledge graph. Through continuous monitoring and evaluation, the effect changes after the implementation of regulation measures can be captured, and subsequent decisions can be adjusted accordingly. This feedback mechanism not only helps to adjust the regulation plan that does not meet expectations, but also can dynamically update the entity status and relationship strength in the knowledge graph, keeping the knowledge graph up-to-date and most accurate. Over time and with the accumulation of more experience, risks can be identified and optimization plans can be formulated more precisely, enhancing the self-optimization ability of the system. The innovation of this module is reflected in realizing an adaptive regulation process that can cope with complex environmental changes and risk fluctuations, continuously improving system performance and maintaining efficient operation.

[0066] The innovation of the visual interaction module lies in presenting the risk situation, regulation effect and propagation path of the water conservancy project through an intuitive graphical interface and providing an expert intervention interface. Through real-time data visualization, complex risk information, decision results and project status are presented to users, helping users quickly grasp the current situation and make appropriate decisions. The visual interface supports multi-dimensional display, which can not only present the general overview of the project risks on a large scale, but also display the specific risk dynamics in a local area in detail. At the same time, the design of the expert intervention interface enables experts to adjust the regulation strategy or manually intervene in the decision-making process according to the intuitive data, improving the flexibility and adaptability of the system. The innovation of this module lies in improving the transparency and interactivity of decision-making, supporting multi-faceted and multi-level decision optimization, and realizing an intelligent decision-making process of human-machine collaboration.

[0067] In summary, the intelligent risk regulation system for water conservancy projects based on knowledge graph embedding combines modules such as multi-level knowledge graph, graph embedding, reinforcement learning, dynamic feedback optimization and visual interaction. By deeply integrating various types of data and intelligent algorithms, it not only improves the accuracy of risk identification and assessment for water conservancy projects, but also provides strong support for regulation decisions. It can perform dynamic adjustment and real-time optimization in a complex and changing environment, and ultimately achieve the efficient and safe operation of water conservancy projects.

[0068] Furthermore, the multi-level knowledge graph construction module includes the following components:

[0069] The data collection unit collects the infrastructure data, monitoring information and environmental variables of the water conservancy project in real time, providing raw data support for the knowledge graph;

[0070] The data preprocessing unit cleans, standardizes and formats the collected data to ensure the data quality and consistency of the input to the knowledge graph;

[0071] The knowledge extraction unit identifies key entities, relationships, and attributes from the preprocessed data to construct the nodes and edges of the knowledge graph;

[0072] The graph construction unit organizes the extracted knowledge information into a multi-level structure to form the basic framework of the knowledge graph;

[0073] The risk assessment integration unit embeds risk assessment indicators into the knowledge graph to enhance the ability to identify and warn of potential risks and provide a more comprehensive risk view;

[0074] The expert regulation strategy integration unit integrates expert regulation strategies and experiences to improve the intelligent management ability of the knowledge graph and achieve effective decision support.

[0075] Generally speaking, the multi-level knowledge graph construction module provides a powerful knowledge foundation for the risk intelligent regulation system of water conservancy projects by integrating multiple units such as data collection, data preprocessing, knowledge extraction, graph construction, risk assessment, and expert regulation strategies. Through real-time data collection, standardized processing, and intelligent knowledge extraction, it can dynamically construct a detailed and hierarchical knowledge graph that accurately reflects the actual situation and potential risks of water conservancy projects. The knowledge graph can not only support accurate risk identification and assessment but also integrate expert knowledge to generate optimized regulation strategies, realizing intelligent decision support and management. With the continuous improvement of the module, the entire system will be able to provide efficient and flexible risk management and regulation capabilities in the complex and changing water conservancy project environment.

[0076] Furthermore, the graph embedding representation module includes the following components:

[0077] The data input unit receives high-dimensional knowledge graph information from the multi-level knowledge graph construction module, including entities, relationships, and their attributes;

[0078] The feature extraction unit extracts meaningful features from the input data, including entity attributes and relationship features, providing a basis for subsequent embedding generation;

[0079] The multi-task learning framework designs and implements multi-task learning strategies, using shared representations to simultaneously train multiple related tasks to improve the effect and generalization ability of the embedding representation;

[0080] The attention mechanism unit applies the attention mechanism to automatically assign weights to highlight important entity and relationship features, thereby enhancing the expressive ability of the graph information;

[0081] The vector mapping unit converts the extracted high-dimensional features into low-dimensional vectors through a mapping function, reducing the data dimension while retaining important information;

[0082] Embedding optimization unit, which optimizes the generated low-dimensional vectors, adjusts the vector representation through the loss function to improve the performance and accuracy of downstream tasks;

[0083] Temporal evolution encoding unit, which processes and encodes temporal information to capture the dynamic changes of entities and relationships, providing temporal context for subsequent tasks.

[0084] In summary, the components of the graph embedding representation module cooperate with each other to jointly promote the generation of efficient and accurate knowledge representation. The information of the multi-level knowledge graph is obtained through the data input unit, the key features are extracted by the feature extraction unit, the multi-task learning framework enhances the generalization ability, the attention mechanism highlights the key features, the vector mapping unit realizes dimensionality reduction and information retention, the embedding optimization unit improves the quality of the vector representation, and the temporal evolution encoding unit captures the dynamic temporal information. The combination of all these functions can provide powerful data representation and processing capabilities in a complex water conservancy project environment, not only improving the accuracy of graph information, but also enhancing the intelligence and refinement of decision support.

[0085] Furthermore, the loss function L is: where N is the number of samples in the dataset, which is represented as low-dimensional vectors in the graph embedding representation module; v i is the low-dimensional vector representation of the i-th entity; is the target vector of the i-th entity; α is the weight hyperparameter of the downstream task loss; y i is the true label of the i-th sample; is the predicted label of the i-th sample; λ represents the weight of the regularization term in the total loss function, which is used to control the complexity of the embedding.

[0086] In the graph embedding representation module, the design goal of the loss function is to optimize the low-dimensional vector v i to make it as close as possible to the target vector while balancing the loss of downstream tasks and the complexity of the embedding. Through the weight hyperparameters λ and α, the dependence of the model on different tasks and regularization can be flexibly adjusted, so that the embedding representation can not only optimize practical application tasks (such as risk assessment), but also avoid overfitting or overly complex representation forms.

[0087] Furthermore, the risk identification and assessment module includes the following components:

[0088] Risk feature integration and analysis unit, which integrates the feature vectors and expert knowledge rules in the graph embedding representation module and extracts key risk features;

[0089] Spatial propagation pattern recognition unit, which analyzes the propagation pattern of risks in the geographical space, determines the risk correlation between regions to support regional risk assessment;

[0090] A risk quantification unit that quantitatively evaluates the identified risk characteristics and calculates the probability and impact degree of risk occurrence;

[0091] A dynamic monitoring and updating unit that real-time monitors the changes in relevant parameters in the water conservancy project, timely updates the risk assessment results, and ensures the timeliness of the assessment;

[0092] An expert knowledge base unit that stores expert knowledge and experience related to water conservancy project risk management, and enhances the depth and reliability of risk assessment through knowledge reasoning;

[0093] An assessment result output unit that visualizes the quantitative assessment results and the output information of risk characteristic analysis, and provides an intuitive report to support the decision-makers' response measures.

[0094] As can be seen above, the risk identification and assessment module, through the collaboration of multiple refined units, jointly improves the intelligence and accuracy of water conservancy project risk management. The risk characteristic integration and analysis unit integrates the embedded characteristics and expert knowledge, the spatial propagation pattern recognition unit reveals the propagation law of risks in the geographical space, the risk quantification unit provides quantitative assessment indicators for risks, the dynamic monitoring and updating unit ensures real-time assessment results, the expert knowledge base unit provides profound domain experience for assessment, and the assessment result output unit helps decision-makers better understand and respond to risks through visualization technology. The design of the entire module ensures that the risk assessment of water conservancy projects in a dynamic environment can be timely and accurate, and supports complex decision-making processes, thus greatly improving the efficiency and intelligence level of water conservancy project risk management.

[0095] Furthermore, the formula for integrating the feature vectors and expert knowledge rules in the graph embedding representation module and extracting key risk characteristics is: where K is the probability distribution vector of key risk factors; Softmax is a normalization function used to convert feature scores into probability distributions, making the sum of the weights of all features equal to 1, facilitating the determination of the importance of each risk characteristic; n is the number of graph embedding feature vectors, that is, the number of risk characteristics extracted from the knowledge graph embedding representation module; w k is the weight of the k-th graph embedding feature vector, representing the importance of this feature in the identification of potential risk characteristics; e k is the k-th graph embedding feature vector, generated by the graph embedding representation module, representing the risk characteristic information of an entity or relationship in the water conservancy project; m is the number of expert knowledge rules, that is, the total number of expert knowledge rules used for risk assessment; a j is the weight of the j-th expert knowledge rule, representing the contribution degree of this rule in the identification of potential risk characteristics; r jIt represents the value of the j-th expert knowledge rule, which is a risk characteristic judgment set by experts and is used to identify and evaluate the potential risks of water conservancy projects.

[0096] As can be seen from the above, by combining the graph embedding feature vectors and expert knowledge rules, the process of integrating risk characteristics and extracting key risk factors realizes the intelligence and comprehensiveness of water conservancy project risk assessment. This formula not only effectively combines data-driven features with the experience of domain experts, but also improves the accuracy and reliability of feature extraction through weighted and normalization processing. This method provides precise feature support for risk quantification, monitoring update, and dynamic regulation decision-making, and ultimately provides a scientific and practical tool for the risk management of water conservancy projects.

[0097] Furthermore, the reinforcement learning regulation decision-making module includes the following components:

[0098] The state encoding unit encodes the real-time risk state and environmental parameters of the water conservancy project into the state vector of the reinforcement learning model, providing input to reflect the current risk situation;

[0099] The action space construction unit defines the set of possible regulation actions and generates adaptive actions according to the regulation objectives;

[0100] The policy network unit generates regulation policies for the current state based on the deep reinforcement learning framework to optimize the safety and benefits of the water conservancy project;

[0101] The value evaluation network estimates the cumulative benefits of each regulation plan in the future time series, providing benefit estimates to support the long-term effectiveness of decision-making;

[0102] The reward function and feedback unit designs and optimizes the reward function, provides feedback according to the execution effect of the regulation plan, and dynamically balances the exploration of the regulation plan and the utilization of known strategies to drive the reinforcement learning model to optimize towards the optimal decision-making direction;

[0103] The regulation plan output unit: outputs the optimized regulation plan, including the corresponding regulation measures and their expected effects, to support multi-objective dynamic regulation decision-making.

[0104] In summary, the reinforcement learning regulation decision-making module realizes dynamic and precise risk regulation decision-making in water conservancy projects by integrating deep reinforcement learning methods. Each component in the module complements each other, providing a complete decision-making framework from state perception to action selection, enabling the water conservancy project to make optimal regulation decisions in the face of complex environments and changing risks. This not only improves the intelligence level of water conservancy project management, but also provides an efficient support tool for decision-makers to help them achieve more secure, reliable, and economical water conservancy project management.

[0105] Further, the state vector of the reinforcement learning model is represented as: Among them, S T represents the state vector at time T; r g,T represents the value of the g-th risk factor at time T; w g is the weight coefficient of the g-th risk factor; P T represents the state vector of the water conservancy facility at time T; A T represents the regional risk correlation at time T; n g represents the total number of risk factors; d 1,T represents the value of the first environmental parameter at time T; μ d1 represents the mean of the first environmental parameter; σ d1 represents the standard deviation of the first environmental parameter, which is used to measure the fluctuation range of this parameter; represents the value of the n d -th environmental parameter at time T; represents the mean of the n d -th environmental parameter; represents the standard deviation of the n d -th environmental parameter, which is used to measure the fluctuation range of this parameter; n d represents the total number of environmental parameters.

[0106] Further, the dynamic feedback optimization module includes the following steps:

[0107] Regulation effect and risk dynamic monitoring unit, which monitors the execution results of the regulation plan and the changes in the risk status in the water conservancy project in real time, quantifies the impact of the regulation measures on the risk indicators, and ensures the timeliness of risk assessment and decision-making;

[0108] Feedback information integration unit, which integrates the processed regulation effect and risk change data and converts them into the entity attributes and relationship update information required in the knowledge graph;

[0109] Knowledge graph update unit, which applies the feedback information to the knowledge graph and automatically updates the status and relationship strength of relevant entities to maintain the real-time and accuracy of the graph;

[0110] Regulation strategy adaptive adjustment unit, which optimizes the regulation strategy parameters according to the feedback information and dynamically adjusts the strategy to adapt to the new risk characteristics and environmental changes;

[0111] Closed-loop feedback management unit, which realizes the closed-loop management of decision feedback, ensures that each feedback is used for improvement and accumulation, and continuously enhances the intelligence and adaptability of the system.

[0112] As can be seen from the above, the dynamic feedback optimization module forms a closed-loop feedback management mechanism by monitoring the regulation effect and risk status in real time, promptly feeding back the feedback information into the knowledge graph, and optimizing and adjusting the regulation strategy. The core advantage of this module lies in its ability to dynamically adjust the regulation strategy according to real-time data, ensuring that each decision can effectively respond to the current and future possible risk changes. Through this continuous learning and optimization, its intelligent level can be gradually improved, and the ability to cope with the uncertainties in the complex water conservancy project environment can be enhanced. This closed-loop feedback mechanism not only improves the scientificity and accuracy of decision-making but also provides a strong technical guarantee for the long-term sustainable management of water conservancy projects.

[0113] Furthermore, the influence of the regulation measures on the risk indicators is expressed by the following comprehensive formula: Among them, R(p + Δp) represents the risk status at time p + Δp, that is, after a time step Δp, the risk status of the water conservancy project; R(p) represents the risk status at time p; h(C(p)) is the influence of the regulation measures on the risk status; Δp represents the time step, describing the time interval from time p to p + Δp; represents the change rate of the risk status R(p) with respect to time p, describing the change speed of the risk status over time; dp represents a small increment of time, which is a very small change amount.

[0114] In summary, the formula for the influence of the regulation measures on the risk indicators provides a comprehensive mathematical model. By considering the current risk status, the time change rate, and the role of the regulation measures, it can accurately calculate the change of the future risk status. This formula provides a quantitative basis for the intelligent regulation decision-making of water conservancy projects, enabling real-time monitoring and adjustment. By optimizing the implementation of the regulation measures, the risk can be effectively reduced, the safety and stability of water conservancy projects can be improved, and strong support can be provided for long-term risk management and decision-making.

[0115] Finally, it should be pointed out that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting it. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A water conservancy project risk intelligent control system based on knowledge graph embedded, characterized in that: Includes the following modules: Multi-level knowledge graph construction module: construct a multi-level knowledge graph to form a dynamically updated knowledge network to support intelligent management by integrating the infrastructure of water conservancy projects, real-time monitoring data, risk assessment indicators and expert control strategies; The graph embedding representation module uses a multi-task learning framework and attention mechanism to map high-dimensional knowledge graph information into a low-dimensional vector space to encode entity attributes, relationship features, and their temporal evolution characteristics in real time; The risk identification and assessment module integrates graph embedding features and expert knowledge rules to analyze the risk characteristics of water conservancy projects and their spatial propagation patterns to achieve accurate identification and quantitative assessment of risks; The reinforcement learning control decision module, based on the deep reinforcement learning framework, encodes the risk state and control objectives into a state-action space, generates an optimized control plan through a policy network, and evaluates the long-term benefits of the control plan to achieve dynamic control decisions with multiple objectives; The dynamic feedback optimization module monitors the control effects and risk changes in real time, feeds back the decision-making execution results to the knowledge graph, and automatically updates the entity status and relationship strength, thus forming a closed loop of continuous optimization and knowledge accumulation; The visual interaction module intuitively displays the risk situation, regulation effects and transmission paths of water conservancy projects, and provides an expert intervention interface to support interactive decision-making.

2. According to claim 1, a water conservancy project risk intelligent control system based on knowledge graph embeddedness is characterized in that: The multi-level knowledge graph building module includes the following components: The data collection unit collects infrastructure data, monitoring information and environmental variables of water conservancy projects in real time, providing raw data support for the knowledge graph; The data preprocessing unit cleans, standardizes and formats the collected data to ensure the quality and consistency of the data input into the knowledge graph; The knowledge extraction unit identifies key entities, relations, and attributes from the preprocessed data in order to construct nodes and edges of the knowledge graph; The graph construction unit organizes the extracted knowledge information into a multi-level structure to form the basic framework of the knowledge graph; The risk assessment integration unit embeds risk assessment indicators into the knowledge graph to enhance the ability to identify and warn of potential risks and provide a more comprehensive risk view; The expert control strategy integration unit integrates expert control strategies and experience to enhance the intelligent management capabilities of the knowledge graph and achieve effective decision support.

3. According to claim 1, a water conservancy project risk intelligent control system based on knowledge graph embeddedness is characterized in that: The graph embedding representation module consists of the following components: A data input unit receives high-dimensional knowledge graph information from a multi-level knowledge graph building module, including entities, relationships and their attributes; Feature extraction unit, which extracts meaningful features from the input data, including entity attributes and relationship features, providing a basis for subsequent embedding generation; Multi-task learning framework: design and implement multi-task learning strategies, using shared representations to train multiple related tasks simultaneously to improve the effect and generalization ability of embedded representations; Attention mechanism unit: Apply attention mechanism to automatically assign weights and highlight important entity and relationship features, thereby improving the expressiveness of graph information. The vector mapping unit converts the extracted high-dimensional features into low-dimensional vectors through a mapping function, reducing the data dimension while retaining important information; The embedding optimization unit optimizes the generated low-dimensional vectors and adjusts the vector representation through the loss function to improve the performance and accuracy of downstream tasks; The temporal evolution encoding unit processes and encodes temporal information to capture the dynamic changes of entities and relations and provide temporal context for subsequent tasks.

4. According to claim 1, a water conservancy project risk intelligent control system based on knowledge graph embeddedness is characterized in that: The risk identification and assessment module includes the following components: The risk feature integration and analysis unit integrates the feature vectors and expert knowledge rules in the graph embedding representation module and extracts key risk features; Spatial propagation pattern identification unit, which analyzes the propagation pattern of risks in geographic space and determines the risk correlation between regions to support regional risk assessment; The risk quantification unit conducts quantitative assessment of the identified risk characteristics and calculates the probability of risk occurrence and the degree of impact; Dynamic monitoring and updating unit monitors the changes of relevant parameters in water conservancy projects in real time, updates risk assessment results in a timely manner, and ensures the timeliness of the assessment; Expert knowledge base unit, which stores expert knowledge and experience related to water conservancy project risk management and enhances the depth and reliability of risk assessment through knowledge reasoning; The assessment result output unit visualizes the output information of quantitative assessment results and risk characteristic analysis, and provides intuitive reports to support decision makers' response measures.

5. According to claim 1, a water conservancy project risk intelligent control system based on knowledge graph embeddedness is characterized in that: The reinforcement learning control decision module includes the following components: The state encoding unit encodes the real-time risk status and environmental parameters of the water conservancy project into the state vector of the reinforcement learning model, providing input to reflect the current risk situation; The action space construction unit defines the set of possible control actions and generates adaptive actions based on the control objectives; The policy network unit generates a control strategy for the current state based on a deep reinforcement learning framework to optimize the safety and benefits of water conservancy projects; The value assessment network estimates the cumulative benefits of each regulatory option in the future time series and provides benefit estimates to support the long-term effectiveness of decision-making; Reward function and feedback unit, which designs and optimizes the reward function, provides feedback based on the execution effect of the control scheme, and dynamically balances the exploration of control schemes and the use of known strategies to drive the reinforcement learning model to optimize towards the optimal decision direction; Control scheme output unit: outputs the optimized control scheme, including corresponding control measures and their expected effects, to support multi-objective dynamic control decisions.

6. According to claim 1, a water conservancy project risk intelligent control system based on knowledge graph embeddedness is characterized in that: The dynamic feedback optimization module includes the following steps: The regulation effect and risk dynamic monitoring unit monitors the implementation results of the regulation plan and the risk status changes in the water conservancy project in real time, quantifies the impact of regulation measures on risk indicators, and ensures the timeliness of risk assessment and decision-making; Feedback information integration unit, which integrates the processed regulatory effect and risk change data and converts them into entity attributes and relationship update information required in the knowledge graph; The knowledge graph update unit applies feedback information to the knowledge graph and automatically updates the status and relationship strength of related entities to maintain the real-time and accuracy of the graph; The control strategy adaptive adjustment unit optimizes the control strategy parameters according to feedback information and dynamically adjusts the strategy to adapt to new risk characteristics and environmental changes; The closed-loop feedback management unit realizes closed-loop management of decision feedback, ensuring that each feedback is used for improvement and accumulation, and continuously enhancing the intelligence and adaptability of the system.

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