A dam safety decision-making system based on OODA loop theory

Abstract

The present invention discloses a dam safety decision-making system based on OODA cycle theory, which relates to the field of dam safety management in water conservancy construction. It includes: a human-computer interaction module, a data perception module, a performance diagnosis module, an intelligent decision-making module and a management and control response module; the human-computer interaction module is used to provide visual display data when observing various links of dam management; the data perception module is used for data processing in the process of dam safety management; the performance diagnosis module is used to provide a data-mechanism-knowledge ternary data-driven model, and construct a dam physical state model by combining a water conservancy model with machine learning; the intelligent decision-making module is used to provide an intelligent decision-making target model and evaluation method for the safe operation of hydropower projects, and provide the optimal evaluation rules for plan samples; the management and control response module is used to provide multi-level and multi-departmental task responsibilities and collaborative action planning. The dam safety decision-making system of the present invention can provide effective support for intelligent dam management.

Description

Technical Field

[0001] The present invention relates to the field of dam safety management in water conservancy construction, and more particularly to a dam safety decision-making system based on OODA cycle theory. Background Art

[0002] Dam safety decision-making involves multiple processes, including instrument monitoring, data mining, analytical decision-making, and feedback adjustment. Dam safety also involves social, economic, and ecological benefits. The decision-making function of dam safety management can provide managers with effective decision-making recommendations and establish a sound long-term service guarantee for dam projects. Furthermore, given the complex service environment of dams and the multidisciplinary nature of dam safety dynamics, proposing a comprehensive, closed-loop dam safety decision-making system and establishing an efficient and intelligent dam safety decision-making system is crucial for improving the efficiency of intelligent dam management and decision-making capabilities.

[0003] Dam safety decision-making systems are a crucial component of water project construction and management, serving as a key strategic measure for addressing challenges such as the normalization of climate change and the risks and accidents caused by aging projects. However, current engineering safety decision-making and management systems lack a comprehensive and universal framework. Therefore, researchers in this field urgently need to investigate how to integrate the OODA loop theory with safety decision-making and management to clarify the order and standardize the decision-making and management processes, promote orderly, high-quality, and efficient decision-making and management, and achieve high-quality development in water conservancy. Summary of the Invention

[0004] In view of this, the present invention provides a dam safety decision-making system based on the OODA loop theory to solve the problems existing in the background technology.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A dam safety decision-making system based on the OODA loop theory, including: a human-computer interaction module, a data perception module, a performance diagnosis module, an intelligent decision-making module, and a control and response module;

[0007] The human-computer interaction module is used to provide visual display data when observing various aspects of dam management;

[0008] The data perception module is used for data processing in the dam safety management process;

[0009] The performance diagnosis module is used to provide a data-mechanism-knowledge ternary data-driven model, and to build a dam physical status model by combining hydraulic models with machine learning.

[0010] The intelligent decision-making module is used to provide a target model and evaluation method for intelligent decision-making on safe operation of hydropower projects, and provides optimization evaluation rules for emergency plan samples;

[0011] The management and response module is used to provide multi-level and multi-departmental task responsibilities and collaborative action planning.

[0012] Optionally, the human-computer interaction module is provided with a water conservancy data base unit, which constructs a digital twin high-precision data base based on a multi-dimensional stereoscopic perception system and uses an all-round stereoscopic data acquisition system to realize the spatiotemporal fusion of multi-source heterogeneous data.

[0013] Optionally, the human-computer interaction module is provided with a data lineage relationship unit, which is used to display data source nodes and conversion processes, locate data problems, analyze the causes of abnormal data, and automatically calculate a global data lineage map to display data link relationships.

[0014] Optionally, the human-computer interaction module is provided with a risk one-picture unit, and the risk one-picture unit is used for visual display based on the information provided by the data perception module.

[0015] Optionally, the human-computer interaction module is provided with a collaborative management and control unit, which is used to manage information from the management and response module, and provide a display of the responsibilities of the emergency organization system, the emergency measures taken, and the mutual relationships and collaborative actions.

[0016] Optionally, the data perception module includes a data aggregation unit, a data governance unit and a data service unit; the data aggregation unit is used to provide unified aggregation management rules for multi-source data for water conservancy business applications; the data governance unit is used to provide unified management of data rights and responsibilities, database standards, data cleaning and transformation, data fusion and data lineage analysis rules for the aggregated multi-source data; the data service unit is used to provide directory services, query analysis services, visualization services and shared service rules for the data base.

[0017] Optionally, the performance diagnosis module includes a safety status prediction unit, a risk factor identification unit and a risk grading assessment unit; the safety status prediction unit is used to provide a method for monitoring the engineering safety status represented by physical elements and its changing trends; the risk factor identification unit is used to provide actual risk factor items of the dam based on the dam safety evaluation content and the actual operating conditions of the dam; the risk grading assessment unit is used to provide a risk grading assessment method.

[0018] Optionally, the intelligent decision-making module includes a plan-making sample unit and a plan-optimizing sample unit; the plan-making sample unit is used to provide a plan sample method based on the historical dangerous situations and their handling processes output by the dam safety decision-making system; the plan-optimizing sample unit is used to provide the optimal plan based on the multiple plan samples and optimization method output by the dam safety decision-making system.

[0019] Optionally, the management and control response module includes an emergency process triggering unit and a response event management unit; the emergency process triggering unit is used to digitize the plan, automatically analyze the emergency response, and generate and publish emergency tasks according to the action plan of the plan; the response event management unit is used to monitor emergency tasks, provide response status feedback, and provide disaster feedback.

[0020] Through the above technical solutions, it can be seen that compared with the existing technology, the present invention provides a dam safety decision-making system based on the OODA cycle theory. Based on a comprehensive analysis of the scenarios and requirements of the observation-judgment-decision-action links in the entire life cycle of the dam, a functional sub-module corresponding to the decision support provided by the link is established in each link. Not only the data correlation relationship between modules is considered, but also from an overall perspective, intelligent collaboration is carried out based on the data interaction between modules. The decision-making system is supported by multi-source data and multiple driving criteria such as knowledge and mechanism, and then analyzes the dam safety status and the best plan, and coordinates and links various management departments to make scientific decisions. The system has opened up the full chain path from terminal perception to scientific research and analysis to top-level decision-making. The standardized, normalized and traceable dam safety decision-making system can provide effective support for smart dam management. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0022] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] The embodiment of the present invention discloses a dam safety decision-making system based on the OODA loop, wherein the observation link, judgment link, decision link, and action link of the OODA loop theory are as follows:

[0025] 1) The "observe" process is abstracted into event collection. Event collection is the process of sensing various situations. Sources include active IoT sensing, such as video and various sensor information, and passive data collection, such as the internet or government networks. Event collection is conducted during monitoring, including meteorological monitoring, runoff monitoring, pollutant monitoring, internet monitoring, and public monitoring. Event collection permeates the entire lifecycle of project operations and management. Comprehensive, detailed, and real-time information tracking and collection provides strong data support for subsequent event orientation, decision-making, and correction.

[0026] 2) Abstract the "orient" process into incident assessment. By analyzing the type, time, location, cause, process, scope, and associated impacts of the collected information, the severity of the incident can be comprehensively assessed to identify key issues that need to be addressed.

[0027] 3) The "decide" process is abstracted into event decision-making. Under the new decision-making and management model, the core of decision-making is the contingency plan. A contingency plan is a pre-planned plan to ensure the prompt, orderly, and effective handling of incidents. It is a specific arrangement made in advance for each department's responsibilities, objectives, staffing, technical equipment, facilities, materials, and command actions. All previous incidents and the processes by which they were handled serve as samples for contingency plan development. Events are aggregated through the previous step's event analysis. Subsequently, the contingency plan samples are comprehensively evaluated and ranked based on indicators such as linkage, operability, cost-effectiveness, and pertinence to identify the optimal overall solution. In this step, the relevant contingency plans are selected and deployed.

[0028] 4) The "act" process is abstracted into incident handling, that is, plan execution. During the execution process, various management departments achieve mutual cooperation, information sharing, and functional complementarity through collaboration and linkage, thus forming a joint response force and effectively improving the incident handling effect.

[0029] The next OODA process is carried out in real time after the previous OODA process's plan is executed, forming an OODA loop. The "observe" event collection phase promptly tracks and provides feedback on incident status and changing requirements. The "orient" event analysis phase considers unresolved issues and emerging new ones, determines the next course of action based on the plan, and executes it again. This iterative cycle of project safety incident collection, analysis, decision-making, and resolution forms a new model for intelligent decision-making and management of hydropower project safety operations, facilitating event resolution.

[0030] The present invention provides a dam safety decision-making system based on the OODA loop, wherein the interactive process of the observation link, judgment link, decision link, and action link of the OODA loop theory can be described in detail as follows:

[0031] 1) Observation and Adjustment: Observation is the only source of information during the adjustment phase, and therefore crucial. This involves determining research objectives and clarifying the problem to be solved. Data, a digital representation of the physical watershed and its impact area, forms the foundation for building a digital twin of the watershed. This includes information on physical geography, main and tributary river systems, water conservancy projects, and the economy and society. By building an integrated air-ground water conservancy perception network, we can comprehensively capture standardized, refined, and diverse multi-source, heterogeneous data, laying the foundation for the construction of a "big perception" network for water conservancy data.

[0032] 2) Observational Information Input: Observation constantly interacts with the external environment, generating unfolding events, external information, feedback from decisions and actions, and implicit guidance and control from adjustments. This involves acquiring data and resolving data source issues. Data extraction, cleaning, conversion, normalization, and connection are determined to identify valid and usable data, forming core data layers that can be integrated into the analysis and judgment phase. Furthermore, data updates should be strengthened, and mechanisms for updating basic data such as physical watersheds and water conservancy projects should be improved.

[0033] 3) Implicit guidance and control adapted to observation: New mental models are used to directly guide and control observation, enabling faster adjustments to the way the external environment is observed. Specifically, within the digital twin project, data analysis dimensions are constructed, key feature knowledge is collected, and a knowledge graph and knowledge base are constructed. This allows data to be classified and processed based on individual analysis dimensions, including static base data and dynamic process data, and assigned corresponding classification labels. Algorithms, which are mathematical expressions of the natural laws of the physical watershed, are key to building a digital twin watershed. These algorithms include specialized water conservancy models, intelligent analysis models, and simulation visualization models. By establishing a data-driven, mechanism-driven, and knowledge-driven framework, high fidelity is ensured for both the digital twin watershed simulation process and the physical processes within the watershed, ensuring authenticity and accuracy. Simultaneously, a knowledge graph encompassing laws, regulations, standards, plans, scheduling rules, and historical cases is constructed to promote the integrated intelligentization of forecasting and scheduling, and the synchronized interactive scheduling of physical projects and digital twins.

[0034] 4) Adjust to implicit guidance and control of action: skip the decision-making step and act directly. This means that professionals proficient in a specific field can rely on subjective experience to take action directly, reducing the waiting time and speeding up decision-making.

[0035] 5) Feedback route from decision-making to observation: Some decisions do not require or are not convenient to take action for the time being. At this time, corresponding feedback information must be given to the observer.

[0036] 6) Two feedback paths between action and observation: one without interaction with the environment, and one requiring interaction with the environment. The former directly feeds the results of the action back to the observation; the latter requires interaction with the environment before feeding the results back to the observation.

[0037] Through the OODA loop principle, the intelligent decision-making framework of hydropower projects is connected in series, and continuous efforts are made in the intelligent management and emergency decision-making of water conservancy projects.

[0038] See also Figure 1 The present invention discloses a dam safety decision-making system based on the OODA cycle theory, including: a human-computer interaction module, a data perception module, a performance diagnosis module, an intelligent decision-making module and a management and control response module;

[0039] in:

[0040] The human-computer interaction module is used to provide visual display data in the dam management and observation phase, provide a visual display of the dam's safety status based on the received diagnostic information in the analysis phase, receive multiple plan samples and optimal sample feature data in the decision-making phase, and provide a visual display of corresponding decision-making suggestions in the daily operation and scheduling of the dam, hazard removal and reinforcement, flood prevention and rescue, etc.; in the action phase, directly obtain dam warning level information, and determine the visual display method of the alarm information based on the current hazard mode, warning level and the effectiveness of the plan set;

[0041] The data perception module is used to provide the management rules and data integration model required by the "air-space-ground-interior" integrated monitoring model during the observation phase of the dam safety management process. It extracts, transforms, and refines the initially integrated rule information. In the assessment phase, it provides rule information for dam safety status diagnosis, as well as rule information for decision-making and management.

[0042] The performance diagnosis module is used to provide a data-mechanism-knowledge ternary data-driven model in the research and judgment phase of the dam safety management process, combining professional water conservancy models with machine learning to construct a dam physical status model;

[0043] The intelligent decision-making module is used to provide a target model and evaluation method for intelligent decision-making on the safe operation of hydropower projects during the decision-making process of dam safety management, and provides optimization evaluation rules for emergency plan samples;

[0044] The management and response module is used to provide multi-level and multi-departmental task responsibilities and collaborative action planning in the action links of the dam safety management process.

[0045] Furthermore, the human-computer interaction module uses Internet of Things technology to build a decision-making exclusive network, sharing event information, disaster consequence information, and decision-making effect simulation information within the scope of multiple emergency departments, and can update emergency status information in real time, completing the human-computer interaction link, and thus assisting decision-making.

[0046] The human-computer interaction module includes the following sub-modules:

[0047] The water conservancy data base unit constructs a visual water conservancy data base based on a multi-dimensional "sky-air-ground-interior" perception system. Reservoir visualization scene modeling utilizes drone oblique photography and BIM modeling to provide a three-dimensional, visual model of complex spatial data, equipment information, monitoring information, video information, and engineering maintenance and inspection information, providing model support for the integrated display of spatial attributes. Oblique photography technology uses multiple sensors on a single flight platform to simultaneously capture imagery from five different angles: vertical, forward, left, right, and rearward. Vertical imagery is processed using traditional aerial photogrammetry techniques to produce 4D products (DEM, DOM, DLG, and DRG). Oblique images from forward, left, right, and rearward, with angles ranging from 15° to 45°, are used to capture rich texture information from the sides of features. Through efficient and automated 3D modeling technology, a true 3D spatial scene with accurate feature location information is rapidly constructed, intuitively capturing the detailed features of the topography and all buildings within the target area.

[0048] Data lineage refers to the relationships between data. Lineage maps provide a hierarchical display of data, with arrows pointing to downstream data. Lineage maps cover the source of a data table, recording its data source, the mapping between the source and target tables, and the data volume. They also provide a search function to quickly locate the upstream and downstream lineage relationships of a data table and view detailed information about the table.

[0049] The "One Risk Map" unit, based on a 3D digital scene of the dam project, integrates project safety analysis and early warning, flood control and dispatch, inspection and maintenance scenarios, and integrates information such as hydrological conditions, flood forecasts, project safety, dam monitoring, and video surveillance to simulate the project status. Specifically, the system provides comprehensive display capabilities for monitoring results, including project images, data change process lines, characteristic value tables, distribution maps, layout diagrams, and project animations, enabling 3D visualization of monitoring data. Based on the determination of rehearsal nodes, boundary conditions, and other information, this information is input into a rehearsal scenario construction template to construct the rehearsal scenario. Based on the requirements for rehearsing the dam's safety status, a rehearsal scenario construction template is provided, allowing staff to input relevant information based on actual rehearsal requirements. Leveraging water conservancy digital twin spatial analysis and professional model simulation technology, it integrates results from dam safety finite element analysis, enabling safety and 3D visualization. This supports visualization of safety factors such as temperature, deformation, stress and strain, and seepage at dam sections and cross-sections using cloud maps, contour lines, shading maps, and deformation exaggeration. Furthermore, the system also displays the effects of flood control and dispatch plans. In a digital scenario, combined with the generation and changes of digital flow fields, the formed flood control scheduling scheme set is displayed through the application of three-dimensional visualization technology. The scheme scheduling effects are vividly displayed, including the evolution process of floods during the scheduling process, the inundation range, and the impact of floods on the downstream and surrounding areas of the main river channels. At the same time, corresponding forecast data is given to provide support for the optimization of the scheme.

[0050] The control network unit's visualization displays, specifically for operational scheduling, utilize visualization models and digital simulation engines to conduct 3D visualization simulations of the evolution of water disasters or risk events, as well as the operational process of water conservancy project scheduling, enabling intuitive, real-time, and dynamic display of water safety elements. This visualization allows for a 3D flood preview within the digital flow field, tailored to a specific scheduling scenario and based on the type of visualization interface, showcasing the dynamic evolution of flooding within the downstream basin.

[0051] In the collaborative control unit, emergency action management information comes from the control and response module, which provides the responsibilities of the emergency organization system, the emergency measures taken, and the mutual connections and collaborative actions; various departments work together to conduct rapid and optimal scheduling, and at the same time obtain feedback information in a timely manner to provide a basis for plan adjustments.

[0052] Furthermore, the data perception module is used to provide the management rules and data integration model required by the "air-space-ground-interior" integrated monitoring model during the observation phase of the dam safety management process. It extracts, transforms and refines the initially integrated rule information, and provides rule information for dam safety status diagnosis, decision-making and management during the assessment phase.

[0053] The data perception module includes the following submodules:

[0054] The data collection unit is used to provide management rules for basic data of hydropower projects, dynamic monitoring data and cross-departmental shared data according to the needs of dam project scenarios and scheduling tasks; on the basis of the existing data sharing system, it collects basic water conservancy data, business management data, dynamic monitoring data, geographic spatial data and cross-industry shared data in the basin, and uses the data model as the core to associate and integrate data, forming a data base with unified basic data, collection of monitoring data, two-dimensional and three-dimensional integration, and cross-level and cross-business, to achieve digital mapping of all factors, and integrate with the model platform and knowledge platform to achieve business data standardization, forming a data support system with continuous updating capabilities.

[0055] The data governance unit is used to provide unified management of data rights and responsibilities, database standards, data cleaning and transformation, data fusion, and data lineage analysis rules for the collected multi-source data;

[0056] When data governance is completed and standard data assets are formed, the corresponding responsible departments should be designated to take the lead in formulating data asset management policies, standards, rules, processes, etc. to ensure the standardization of data asset management; the construction of basic data, monitoring data, business management data, geospatial data and metadata standards should comply with the relevant technical standards and specifications of the water conservancy industry and information construction for standardization and standardization design; data cleaning rules should be formulated for the collected data in accordance with the construction data standards, and the scattered basic data, spatial data, business data, etc. should be filtered, deduplicated, converted, and verified through manual cleaning, ETL task cleaning, etc., and data traceability and data quality control should be carried out at the same time to ensure the consistency, authority, integrity, legality and uniqueness of the data; fusion modes and processing algorithms should be selected for multidimensional data. The data fusion methods include structured data access, unstructured data access, API data access, streaming data access, streaming data import, etc.; the link relationship generated by data is generated through data lineage analysis, and the lineage map provides the ability to display data in layers, with lines with arrows reflecting data associations, and the arrows pointing to downstream data.

[0057] The data service unit is used to provide directory services, query and analysis services, visualization services and sharing service rules for the data base; the data resource directory sorts, encodes and describes the information resources that have been managed to facilitate the retrieval, positioning and acquisition of information resources; the data query and analysis service constructs the water conservancy field ontology by using common terms in water conservancy informatization, and integrates the semantics of the HowNet to formally describe the association between water conservancy data, define semantic reasoning rules, and build an inference engine based on fuzzy semantics to conduct technical exploration on the semantic relationship discovery of water conservancy big data and realize data query and analysis; data visualization services are carried out through information visualization technology; service encapsulation and combination technology are used to publish data access functions as data services, build a water conservancy data resource service system, and provide users with multi-mode data sharing services such as service aggregation, publish and subscribe, and portal access through API interface sharing and front-end sharing.

[0058] Furthermore, the performance diagnosis module is used to provide a data-mechanism-knowledge ternary data-driven model in the analysis and judgment phase of the dam safety management process, combining professional water conservancy models with machine learning to construct a dam physical status model.

[0059] The morphological diagnosis module includes the following submodules:

[0060] The safety status prediction unit is used to provide a method for representing the engineering safety status and its changing trends represented by important monitoring physical elements; based on the calculation results of the safety monitoring analysis model, using the changing law of the monitoring effect quantity, combined with the environmental quantity prediction time series data, it represents the prediction and forecast of key physical quantities of the engineering safety status.

[0061] The risk factor identification unit is a method used to analyze the potential safety risks of the project based on the dam safety evaluation content and the actual operation status of the dam. Based on the actual risk factors of the reservoir dam, through the actual operation status of the reservoir dam and on-site inspections, the project safety risks including flood control safety risks, structural safety risks, seepage safety risks, operation and management safety risks, and earthquake safety risks are analyzed.

[0062] The risk grading assessment unit is used to provide a risk grading assessment model based on the risk assessment model when a dangerous situation occurs in the reservoir dam and the risk level of the impact consequences on the downstream; and analyze the impact on the downstream when a dangerous situation occurs in the reservoir dam.

[0063] Furthermore, the intelligent decision-making module is used to provide an intelligent decision-making target model and evaluation method for the safe operation of hydropower projects in the decision-making process during the dam safety management process, and to provide the optimal evaluation rules for the plan samples.

[0064] The intelligent decision-making module includes the following sub-modules:

[0065] Formulate a plan sample unit to provide rules for plan samples based on historical dangerous situations and their handling processes output by the dam safety decision-making system; provide plan samples by building a forecast scheme library, expert experience library, historical scenario model library, business rule library, reservoir knowledge graph library and plan knowledge library. Build a reservoir forecast and dispatching plan database, including: forecast and dispatching plans for previous years, forecast and dispatching plans under extreme climate conditions, forecast and dispatching plans under special engineering conditions and dangerous situations, etc. Build a reservoir expert experience database, including expert-recommended plans for reservoir dangerous situations under different circumstances, expert-recommended plans for dam break evacuation, expert-recommended plans for dam classification and disposal, etc. Build a reservoir historical scenario model database, including: historical typical scenario models, special engineering conditions and dangerous situation scenario models, etc. Build a reservoir business rule database, including: relevant national, provincial, municipal, and industry policies and technical standards; reservoir operation and management rules; integrate and transform existing basic geography, economic and social, engineering management, equipment management, hydrology and meteorology, settlement observation, seepage monitoring, government data and other data resource types, classify and associate knowledge according to needs, and build a reservoir knowledge graph database; the plan knowledge base includes reservoir excessive flood defense plans, reservoir dam safety management emergency plans, reservoir flood control emergency plans, reservoir dam emergency plans, etc.

[0066] The optimal plan sample unit is used to output multiple plan samples and optimization rules based on the dam safety decision-making system, providing the optimal plan. The optimal scheduling plan is primarily based on the effectiveness demonstration and benefit evaluation results of the scheduling plan set, combined with flood control scheduling rules and dam safety rehearsal status to formulate the final flood control scheduling plan. This process is divided into two modes: human-computer interaction and preset rule tree mode. Optimized plans are stored in the knowledge base and are valuable reference plan samples for responding to different types of safety risk events. These samples are compiled through careful analysis of past implementation experience, expert experience, and auxiliary optimization calculations, based on historical typical reservoir and dam safety risk events or hypothetical typical scenarios. The optimal plan can be automatically matched based on current safety risk information.

[0067] The management and response module is used to provide multi-level and multi-departmental task responsibilities and collaborative action planning in the action links of the dam safety management process.

[0068] The control and response module includes the following submodules:

[0069] The emergency process trigger unit is used to digitize the plan, automatically analyze the emergency response, and generate and release the emergency tasks according to the action plan of the plan; the emergency process trigger is to digitize the plan and automatically analyze the emergency response according to the response process of the plan; the emergency task generation and release is to generate emergency response tasks according to the action plan of the plan and the real-time emergency scenario, and release them to the corresponding responsible persons.

[0070] The response event management unit is used for emergency task monitoring, response situation feedback and disaster situation feedback; emergency task monitoring is the online monitoring and management of the execution of emergency tasks; response situation feedback is the emergency task leader uploading and feedback of the response situation; response event management is the query, deletion and modification of response time.

[0071] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0072] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A dam safety decision-making system based on OODA loop theory, characterized in that: include: Human-computer interaction module, data perception module, performance diagnosis module, intelligent decision-making module and management and control response module; The human-computer interaction module is used to provide visual display data when observing various aspects of dam management; The data perception module is used for data processing in the dam safety management process; it is also used to provide the management rules required by the "air-space-ground-interior" integrated monitoring model in the observation stage of the dam safety management process; it also includes a data integration model, which is used to extract, transform and refine the rule information of the initial fusion integration, and provide rule information for dam safety status diagnosis in the research and judgment stage, as well as rule information for decision-making and control; The performance diagnosis module is used to provide a data-mechanism-knowledge ternary data-driven model, and to build a dam physical status model by combining a hydraulic model with machine learning; The smart decision-making module is used to provide a smart decision-making target model and evaluation method for safe operation of hydropower projects, and to provide the optimal evaluation rules for emergency plan samples; The control and response module is used to provide multi-level and multi-departmental task responsibilities and collaborative action planning; The human-computer interaction module is provided with a data lineage unit, which is used to display data source nodes and conversion processes, locate data problems, analyze the causes of abnormal data, and automatically calculate a global data lineage map to display data link relationships; wherein the lineage map is used to display data in layers, with arrows showing data association relationships, and arrows pointing to downstream data; the lineage map also covers the source of the data table, recording the data source to which the data table belongs, the mapping relationship between the source and the target table, and the data volume; The human-computer interaction module is provided with a collaborative control unit, which is used to manage information from the control and response module, and provide the responsibilities of the emergency organization system, the emergency measures taken, and the display of the mutual association and collaborative actions; The data perception module includes a data collection unit, a data governance unit and a data service unit; the data collection unit is used to provide unified aggregation management rules for multi-source data of water conservancy business applications; the data governance unit is used to provide unified management of data rights and responsibilities, database standards, data cleaning and transformation, data fusion and data lineage analysis rules for the aggregated multi-source data; the data service unit is used to provide directory services, query analysis services, visualization services and shared service rules for the data backplane.

2. The dam safety decision-making system based on OODA cycle theory according to claim 1 is characterized in that: The human-computer interaction module is provided with a water conservancy data base unit. The water conservancy data base unit constructs a digital twin high-precision data base based on a multi-dimensional stereoscopic perception system, and uses an all-round stereoscopic data acquisition system to realize the spatiotemporal fusion of multi-source heterogeneous data.

3. The dam safety decision-making system based on OODA cycle theory according to claim 1 is characterized in that: The human-computer interaction module is provided with a risk-one-picture unit, and the risk-one-picture unit is used for visual display according to the information provided by the data perception module.

4. The dam safety decision-making system based on OODA cycle theory according to claim 1 is characterized in that: The performance diagnosis module includes a safety status prediction unit, a risk factor identification unit and a risk classification assessment unit; the safety status prediction unit is used to provide a method for monitoring the engineering safety status represented by physical elements and its change trend; the risk factor identification unit is used to provide the actual risk factor items of the dam according to the dam safety evaluation content and the actual operation status of the dam; The risk grading assessment unit is used to provide a risk grading assessment method.

5. The dam safety decision-making system based on OODA cycle theory according to claim 1 is characterized in that: The intelligent decision-making module includes a plan-making sample unit and a plan-optimizing sample unit; the plan-making sample unit is used to provide a method for plan samples based on historical dangerous situations and their handling processes output by the dam safety decision-making system; the plan-optimizing sample unit is used to provide the optimal plan based on multiple plan samples and optimization methods output by the dam safety decision-making system.

6. The dam safety decision-making system based on OODA cycle theory according to claim 1 is characterized in that: The management and response module includes an emergency process trigger unit and a response event management unit; the emergency process trigger unit is used to digitize the plan, automatically analyze the emergency response, and generate and publish emergency tasks according to the action plan of the plan; the response event management unit is used to monitor emergency tasks, provide response status feedback and disaster feedback.

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