A risk prevention and control information management method and system for water conservancy projects

Abstract

The present invention relates to the technical field of geographic information management, and particularly to a risk prevention and control information management method and system for water conservancy projects. The method includes the following steps: acquiring multi-source geographic information data; performing data preprocessing on the multi-source geographic information data to generate standard multi-source geographic information data; identifying potential hazard factors for the standard multi-source geographic information data to generate geographic information potential hazard factors; identifying protected objects for the standard multi-source geographic information data to generate geographic information protected object data; and performing spatial matching and association on the geographic information potential hazard factors and the geographic information protected object data to generate hazard factor-protected object association matrix data. The present invention improves the accuracy and real-time performance of geographic information data in risk prevention and control through systematic data integration, identification of potential hazard factors and protected objects, analysis of secondary disaster chains, and dynamic risk response mechanisms.

Description

Technical Field

[0001] The present invention relates to the technical field of geographic information management, and particularly to a risk prevention and control information management method and system for water conservancy projects. Background Art

[0002] In the early days, geographic information management relied on traditional paper maps and manual cartography. With the introduction of computer technology, the emergence of Geographic Information System (GIS) in the 1980s marked the entry of data management into the digital age. GIS technology made the storage, analysis, and visualization of geographic data more efficient, greatly improving the accuracy and speed of data processing. With the development of Global Positioning System (GPS) and remote sensing technology, the acquisition of geographic information data became more accurate and real-time. The combination of GIS and GPS not only improved the accuracy of spatial data but also expanded the scope of data applications, such as in the fields of environmental monitoring, urban planning, and disaster warning. In recent years, with the maturity of big data and cloud computing technologies, geographic information data management has entered the intelligent era. Big data technology can process and analyze massive amounts of geographic information data, providing more accurate risk assessment and prediction. Cloud computing provides strong support for the storage and sharing of geographic information data, making multi-party collaboration and real-time data update possible. However, currently, traditional risk prevention and control analysis of geographic information data usually ignores potential hazard factors and protected objects in geographic information. At the same time, for the modeling and simulation of secondary disasters, existing methods often have insufficient processing or lack dynamic response capabilities, resulting in lower efficiency and accuracy in risk identification, assessment, and response. Summary of the Invention

[0003] Based on this, it is necessary to provide a risk prevention and control information management method and system for water conservancy projects to solve at least one of the above technical problems.

[0004] To achieve the above object, a risk prevention and control information management method for water conservancy projects, the method includes the following steps:

[0005] A risk prevention and control information management method for water conservancy projects, characterized by including the following steps:

[0006] Step S1: Obtain multi-source geographic information data; perform data preprocessing on the multi-source geographic information data to generate standard multi-source geographic information data; perform identification of potential hazard factors on the standard multi-source geographic information data to generate geographic information potential hazard factors; perform identification of protected objects on the standard multi-source geographic information data to generate geographic information protected object data; perform spatial matching and association on the geographic information potential hazard factors and the geographic information protected object data to generate hazard factor - protected object association matrix data;

[0007] Step S2: Hierarchically decompose the risk factor - protection object association matrix data to generate secondary disaster decomposition data; conduct effect quantification analysis on the secondary disaster decomposition data to generate secondary disaster risk factors; perform secondary hazard factor disaster simulation on the standard multi - source geographic information data according to the secondary disaster risk factors to generate secondary hazard factor dynamic simulation data; construct a secondary disaster chain model for the secondary hazard factors based on the secondary hazard factor dynamic simulation data to generate a secondary disaster chain model;

[0008] Step S3: Based on the secondary disaster chain model, construct an initial risk module for the geographic information potential risk factors, secondary disaster risk factors, and geographic information protection object data to obtain geographic risk module data; perform a comprehensive risk transfer map conversion on the geographic risk module data according to the geographic information potential risk factors, secondary disaster risk factors, and geographic information protection object data to generate a comprehensive risk propagation map;

[0009] Step S4: Obtain historical risk prevention and control data; perform dynamic risk response decision - making on the comprehensive risk propagation map based on the historical risk prevention and control data to generate dynamic risk response decision data; record the decision response feedback for the dynamic risk response decision data to generate a real - time disaster chain feedback data set for implementing regional risk prevention and control management operations.

[0010] Through multi - source data acquisition and pre - processing, the present invention unifies the data format and standard, improving the data compatibility and reliability. Identifying potential risk factors and protection objects makes risk identification more comprehensive and accurate. Through spatial matching and correlation analysis, it provides a clear relationship between risk factors and protection objects, helping to identify specific risk areas and objects and supporting more targeted prevention and control measures. By constructing a secondary disaster chain model, it can simulate the development path of secondary disasters and help predict the cascading effects of complex disasters. The dynamic simulation of secondary risk factors provides in - depth understanding of the disaster evolution process, making risk prediction more accurate and real - time. Quantifying the effects of secondary disasters provides data support for risk assessment, improving the scientificity and reliability of risk assessment. The constructed geographic risk module provides basic data and models for subsequent risk management, making risk management evidence - based. The comprehensive risk propagation map provides a panoramic view, showing the propagation path and influence range of risks within the geographical area, facilitating the formulation of overall prevention and control strategies. Using historical data to dynamically adjust the risk propagation map can update risk prevention and control strategies in real - time and enhance the response ability. Recording and analyzing decision response feedback data helps to understand the effectiveness of prevention and control measures, timely adjust strategies, and enhance the flexibility and adaptability of regional risk management. Therefore, through systematic data integration, identification of potential risk factors and protection objects, secondary disaster chain analysis, and dynamic risk response mechanism, the present invention improves the accuracy and real - time performance of geographic information data in risk prevention and control.

[0011] Preferably, step S1 includes the following steps:

[0012] Step S11: Obtain multi-source geographic information data using sensors;

[0013] Step S12: Perform data preprocessing on the multi-source geographic information data to generate standard multi-source geographic information data, where data preprocessing includes data cleaning, data denoising, filling in missing data values, and data standardization;

[0014] Step S13: Identify potential hazard factors from the standard multi-source geographic information data to generate geographic information potential hazard factors; classify the standard multi-source geographic information data according to the geographic information potential hazard factors to generate a hazard factor classification data set;

[0015] Step S14: Identify protected objects from the standard multi-source geographic information data to generate geographic information protected object data; sort the geographic information protected object data in an orderly manner to generate a protected object priority data set; perform spatial matching and association on the hazard factor classification data set and the protected object priority data set to generate hazard factor - protected object association matrix data.

[0016] Through data preprocessing (including data cleaning, denoising, and standardization), the present invention can improve the quality of multi-source geographic information data, reduce the impact of noise and missing data, and thus obtain more accurate and reliable data. By identifying potential hazard factors in geographic information, it is possible to detect existing risk areas in advance and classify the data, which helps to better understand and manage these risks. By identifying and sorting protected objects in geographic information, important natural resources or infrastructure can be given priority consideration and protection to ensure their safety in a potential risk environment. Performing spatial matching and association on the hazard factor classification data set and the protected object priority data set can help identify the relationship between potential hazard factors and key protected objects, thereby supporting more accurate risk assessment and decision-making. Through the generated hazard factor - protected object association matrix data, decision-makers can be provided with clear association information between risks and protected objects, which helps to formulate more effective management strategies and emergency plans.

[0017] Preferably, identifying potential hazard factors from the standard multi-source geographic information data includes the following steps:

[0018] Calculate the terrain slope and aspect of the standard multi-source geographic information data through digital elevation technology to obtain regional geographic slope data and regional geographic aspect data; extract geological hazard risk factors from the standard multi-source geographic information data based on the regional geographic slope data and regional geographic aspect data to generate geological hazard risk factors;

[0019] Screen the regional meteorological data from the standard multi-source geographic information data according to the geological disaster risk factors to obtain regional rainfall observation data and regional wind speed observation; conduct regional wind field analysis on the regional wind speed observation data to generate regional wind field change data; extract meteorological disaster risk factors from the standard multi-source geographic information data according to the regional rainfall observation data and regional wind field change data to generate meteorological disaster risk factors.

[0020] Conduct regional geographical flood disaster simulation on the geological disaster risk factors and meteorological disaster risk factors to generate geological and meteorological composite risk factors; integrate the geological disaster risk factors, meteorological disaster risk factors and geological and meteorological composite risk factors to generate geographical information potential risk factors.

[0021] Through the multi-level analysis of geological disasters, meteorological disasters and compound disasters, the present invention can more comprehensively identify potential risk factors, thereby improving the accuracy and reliability of disaster risk management. Using digital elevation technology to calculate the slope and aspect of the region helps to accurately identify high-risk areas prone to geological disasters and provides a scientific basis for subsequent extraction of risk factors. By analyzing the regional rainfall observation data and wind field change data, the risk factors of meteorological disasters can be accurately extracted, which is of great significance for predicting and coping with meteorological disasters such as heavy rain and strong wind. By integrating geological and meteorological risk factors and conducting geographical flood disaster simulation, compound disasters caused by the combined action of multiple factors can be identified, which helps to prevent and reduce the losses caused by compound disasters. Integrating different types of risk factors to generate geographical information potential risk factors provides a unified data basis for subsequent spatial analysis and risk assessment, facilitating the comprehensive consideration of multiple disaster risks. The integrated geographical information potential risk factors provide a comprehensive view of disaster risks for decision-makers, helping to formulate more scientific and effective emergency plans and disaster management strategies and reducing the impact of disasters on society and the environment.

[0022] Preferably, step S2 includes the following steps:

[0023] Step S21: Conduct spatial risk analysis on the risk factor - protection object association matrix data to generate main disaster risk analysis data; conduct weight distribution impact identification on the main disaster risk analysis data to generate a basic secondary disaster chain data set.

[0024] Step S22: Conduct hierarchical decomposition on the basic secondary disaster chain data set to generate secondary disaster decomposition data; conduct disaster propagation path tracking on the secondary disaster decomposition data to generate secondary disaster chain structure data.

[0025] Step S23: Quantitatively analyze the effects of the secondary disaster chain structure data to generate secondary disaster risk factors; perform secondary risk factor disaster simulations on the standard multi-source geographic information data based on the secondary disaster risk factors to generate secondary risk factor dynamic simulation data;

[0026] Step S24: Conduct disaster chain coupling effect analysis on the secondary risk factor dynamic simulation data to generate secondary disaster coupling effect data; construct a secondary disaster chain model for the secondary risk factors based on the secondary disaster coupling effect data to generate a secondary disaster chain model.

[0027] Through the spatial risk analysis of the risk factor - protection object association matrix data, the present invention can identify and quantify the potential risks of the main disaster, provide a more accurate disaster assessment, and help prevent and manage the occurrence of the main disaster. Through the hierarchical decomposition of the secondary disaster chain and the tracking of the propagation path, it can systematically identify the structure and development path of the secondary disaster, provide a complete view of the secondary disaster chain, and provide comprehensive data support for subsequent disaster management. By quantitatively analyzing the effects of the secondary disaster chain structure data and performing secondary risk factor disaster simulations, it can dynamically predict and evaluate the potential impacts of secondary disasters, which helps to take measures in advance to reduce the destructive power of secondary disasters. Through the disaster chain coupling effect analysis, it can identify the mutual influences and superimposed effects between secondary disasters, which helps to understand the complexity of disasters and improve the ability to respond to multi-disaster situations. By constructing a secondary disaster chain model, it can systematize and model the complex secondary disaster process, enabling disaster managers to better predict, monitor, and respond to secondary disasters, and providing a scientific basis for disaster emergency response and long-term planning. The whole process systematically analyzes and manages the secondary disaster chain through data analysis, structure decomposition, effect quantification, dynamic simulation, and model construction, improving the scientificity and systematicness of disaster response and helping to reduce the overall impact of disasters.

[0028] Preferably, step S22 includes the following steps:

[0029] Step S221: Perform multi-level hierarchical decomposition on the basic secondary disaster chain data set to generate secondary disaster hierarchical data, where the multi-level hierarchical decomposition includes influence scope stratification, spatio-temporal characteristic stratification, and interrelationship stratification;

[0030] Step S222: Perform relationship mapping classification on the basic secondary disaster chain data set through the secondary disaster hierarchical data to generate secondary disaster mapping data;

[0031] Step S223: Conduct hierarchical causal analysis on the secondary disaster mapping data to generate secondary disaster decomposition data; conduct dynamic time evolution analysis on the secondary disaster decomposition data to generate secondary disaster time series evolution data;

[0032] Step S224: Use multi-path propagation simulation technology to track the disaster propagation paths of the secondary disaster time-series evolution data, generating secondary disaster chain propagation path tracking data; perform structured collation on the secondary disaster chain propagation path tracking data to generate secondary disaster chain structure data.

[0033] Through multi-level hierarchical deconstruction of the present invention (such as stratification by influence scope, spatio-temporal characteristics, and interrelationships), each dimension of secondary disasters can be analyzed more meticulously. This refined analysis helps to more deeply understand the complexity and diversity of secondary disasters, thereby improving the analysis accuracy. By performing relationship mapping classification on the secondary disaster hierarchical data, the relationships between secondary disasters can be identified and classified more clearly. This step can help establish the connections between disasters, laying a foundation for further causal analysis and enhancing the systematicness and effectiveness of disaster management. Hierarchical causal analysis can identify the causal chains between disaster events by deeply analyzing the causal relationships of the secondary disaster mapping data. This helps to find out the root causes and key triggering factors of disaster development, thereby formulating more targeted prevention measures. Through dynamic time evolution analysis, the changes and development trends of secondary disasters in the time dimension can be tracked, predicting the future disaster evolution paths and time nodes. This dynamic analysis can improve the forward-looking and flexibility in dealing with disasters. Using multi-path propagation simulation technology to track the time-series evolution data of secondary disasters can accurately simulate and predict the propagation paths of secondary disasters. This path tracking helps to identify high-risk areas in advance and take corresponding preventive measures to reduce the spread and expansion of secondary disasters. By performing structured collation on the secondary disaster chain propagation path tracking data, clear secondary disaster chain structure data can be generated. This structured data helps to better understand the overall architecture of the disaster chain, providing a solid data foundation for further disaster model construction and emergency management.

[0034] Preferably, step S3 includes the following steps:

[0035] Step S31: Based on the secondary disaster chain model, construct an initial risk module for the geographical information potential hazard factors, secondary disaster hazard factors, and geographical information protection object data to obtain geographical risk module data;

[0036] Step S32: Extract disaster risk characteristics from the geographical risk module data to generate geographical disaster risk characteristic data; perform association rule mining on the geographical disaster risk characteristic data to generate geographical disaster risk association data; perform dynamic risk assessment on the geographical disaster risk association data to generate geographical disaster comprehensive risk assessment data;

[0037] Step S33: Use the geographical disaster risk assessment data to verify the parsing units of the geographical risk module data, generating optimized data for the risk module parsing units;

[0038] Step S34: Perform multi-level risk transfer and diffusion analysis on the potential geographical information risk factors, secondary disaster risk factors, and geographical information protection object data to generate multi-level risk diffusion data; based on the multi-level risk diffusion data, perform comprehensive risk transfer map conversion on the geographical risk module data to generate a comprehensive risk propagation map.

[0039] The present invention constructs an initial risk module for the potential geographical information risk factors, secondary disaster risk factors, and geographical information protection object data to generate geographical risk module data. This process can accurately identify and quantify the potential risks in geographical information, providing an accurate data basis for subsequent risk assessment. Through disaster risk feature extraction and association rule mining, key risk features can be extracted from the geographical risk module data, and potential disaster risk patterns can be revealed through association rule mining. This analysis method helps to identify the relationships between complex disasters and improve the accuracy of risk prediction. Conducting dynamic risk assessment on the associated data of geographical disaster risks can monitor and evaluate the changing trends of risks in real time. This forward-looking assessment can help anticipate potential disaster risks in advance and provide strong support for the formulation of risk management and response strategies. By performing parsing unit verification on the geographical disaster risk assessment data, the accuracy and reliability of the risk module can be continuously optimized. This process helps to improve the data model of the initial risk module and ensure that the risk assessment results are more reliable and practical. The multi-level risk transfer and diffusion analysis can analyze in detail the diffusion and transfer process of potential risk factors at different levels to generate multi-level risk diffusion data. This comprehensive analysis method helps to identify the paths and intensities of risk diffusion, thereby enhancing the effectiveness of disaster prevention and mitigation measures. Through comprehensive risk transfer map conversion, the multi-level risk diffusion data is visualized as an intuitive propagation map. This graphical display method can help decision-makers better understand and respond to complex geographical disaster risks, improving the overall efficiency and scientific nature of risk management.

[0040] Preferably, step S34 includes the following steps:

[0041] Step S341: Perform dynamic change detection on the geographical information protection object data. When changes are detected in the geographical information protection object data, identify the damage status of the geographical information protection object data based on the potential geographical information risk factors and secondary disaster risk factors to generate damage status identification data;

[0042] Step S342: Match the damage status identification data with the potential geographical information risk factors and secondary disaster risk factors to generate new risk factors; based on the new risk factors, perform step-by-step overlap on the geographical risk module data to generate geographical risk overlap module data;

[0043] Step S343: Conduct risk diffusion analysis on the data of the geographical risk overlapping module to generate multi-level risk diffusion data; based on the multi-level risk diffusion data, perform comprehensive risk transfer map conversion on the data of the geographical risk module to generate a comprehensive risk propagation map.

[0044] Through dynamic change detection of the geographical information protection object data, the present invention can promptly capture any changes in the geographical information protection object. When a change is detected, damage status identification is immediately carried out, which helps to quickly understand and evaluate the potential impact of the change on the protection object, ensuring that corresponding measures can be taken in a timely manner. By using the potential geographical hazard factors and secondary disaster hazard factors to conduct damage status identification on the changed protection object data, the generated damage status identification data can accurately reflect the damaged degree and damage type of the protection object, providing accurate information support for subsequent risk assessment. Matching the identified damage status with the existing hazard factors and generating new hazard factors. This process can update the key factors in the risk assessment in real time, making the risk assessment more in line with the current actual situation and improving the accuracy and timeliness of the risk assessment. Through hierarchical overlapping analysis of the newly generated hazard factors, geographical risk overlapping module data is formed. This method can integrate multi-level risk information, thus providing a more comprehensive perspective for risk assessment and helping to identify existing compound risks. Conduct risk diffusion analysis on the geographical risk overlapping module data to generate multi-level risk diffusion data. This kind of analysis can reveal the propagation and diffusion paths of potential risks between different levels, help to identify the risk propagation mechanism, and provide key references for preventing secondary disasters. Based on the multi-level risk diffusion data, convert it into a comprehensive risk propagation map. The result of this mapping can visually display the risk transfer path and influence range, helping decision-makers to more clearly understand the complex risk structure, so as to formulate more effective disaster prevention and mitigation strategies.

[0045] Preferably, step S4 includes the following steps:

[0046] Step S41: Obtain historical risk prevention and control data;

[0047] Step S42: Based on the historical risk prevention and control data, make a dynamic risk response decision on the comprehensive risk propagation map to generate dynamic risk response decision data;

[0048] Step S43: Conduct real-time data flow monitoring on the dynamic risk response decision data to generate real-time decision monitoring data; conduct a comprehensive disaster chain status analysis on the real-time decision monitoring data to generate regional disaster chain status data;

[0049] Step S44: Adjust the decision response to the real-time decision monitoring data through the regional disaster chain status data to generate risk response decision adjustment data; record the feedback on the risk response decision adjustment data to generate a real-time disaster chain feedback data set for implementing regional risk prevention and control management operations.

[0050] Through obtaining historical risk prevention and control data, step S41 of the present invention can provide rich reference bases for current risk management. These data include past response measures and risk propagation situations, which help to make more scientific and reasonable dynamic risk response decisions on the existing comprehensive risk propagation map. The dynamic risk response decision generated based on historical data can quickly respond to current risk changes, improving the timeliness and flexibility of decision-making. Combined with real-time data stream monitoring, the evolution of risks can be grasped in real time to ensure the accurate implementation of risk management measures. By analyzing the overall disaster chain status of real-time decision monitoring data, the generated regional disaster chain status data provides comprehensive information support for identifying potential secondary disasters. This kind of analysis can reveal the vulnerable links in the disaster chain, helping to prevent the spread or escalation of disasters. By adjusting the real-time decision through the regional disaster chain status data, step S44 realizes the dynamic adjustment of risk response. This mechanism ensures that response measures can be continuously optimized as risks change, further improving the accuracy and effectiveness of risk prevention and control. Recording the adjustment data of the risk response decision as feedback to generate a real-time disaster chain feedback data set forms a closed-loop feedback mechanism. Through this mechanism, experience can be continuously accumulated, and future risk management strategies can be continuously improved and optimized. The finally generated real-time disaster chain feedback data set can guide regional risk prevention and control management operations to ensure the scientific and systematic implementation of prevention and control measures. This process significantly improves the overall effect of regional risk prevention and control, helping to reduce the losses and impacts of potential disasters.

[0051] Preferably, step S42 includes the following steps:

[0052] Step S421: Analyze the key propagation nodes of the comprehensive risk propagation map to generate comprehensive risk key propagation node data; extract the propagation characteristics from the comprehensive risk key propagation node data to obtain comprehensive risk key propagation characteristic data;

[0053] Step S422: Divide the historical risk prevention and control data into data sets to generate a model training set and a model test set; use the decision tree algorithm to train the model training set to generate a risk response decision pre-model; optimize and iterate the risk response decision pre-model through the model test set to generate a risk response decision model;

[0054] Step S423: Import the comprehensive risk key propagation characteristic data into the risk response decision model for dynamic risk response decision to generate dynamic risk response decision data.

[0055] By analyzing the key propagation nodes of the comprehensive risk propagation map, the present invention can identify the most influential nodes in the risk propagation process. The propagation characteristic data of these key nodes provide important reference bases for subsequent risk response decision-making, contributing to the realization of targeted risk prevention and control. Through the division and utilization of historical risk prevention and control data, high-quality model training sets and test sets are generated. Combining with the decision tree algorithm for model training and optimization iteration, the generated risk response decision model has powerful prediction capabilities and adaptability. This data-driven method can effectively improve the scientificity and reliability of decision-making. By importing the comprehensive risk key propagation characteristic data into the risk response decision model, the generation of dynamic risk response decisions is realized. This process ensures that the decision model can be adjusted and adapted to the changes in risks in real time, providing more intelligent countermeasures. Through the generation of dynamic risk response decision data, the countermeasures can be dynamically adjusted as the actual risks change, enhancing the flexibility and responsiveness of risk prevention and control. This dynamic decision-making mechanism can better handle complex risk scenarios and reduce potential losses. Through the division and application of the model training set and test set, the model training and optimization iteration process in step S422 ensures the continuous improvement of the risk response decision model. The continuous optimization of the model helps to provide more accurate decision support in different risk scenarios. Through the precise analysis of key nodes, intelligent dynamic decision-making, and continuously optimized decision model, the ability of comprehensive risk management is effectively enhanced. This process can significantly improve the pertinence and efficiency of prevention and control measures, ensuring the effective implementation of risk response measures.

[0056] In this specification, a risk prevention and control information management system for water conservancy projects is provided, which is used to execute the above-mentioned risk prevention and control information management method for water conservancy projects. The risk prevention and control information management system for water conservancy projects includes:

[0057] A disaster object identification module, which is used to obtain multi-source geographic information data; perform data preprocessing on the multi-source geographic information data to generate standard multi-source geographic information data; identify potential hazard factors from the standard multi-source geographic information data to generate geographic information potential hazard factors; identify protected objects from the standard multi-source geographic information data to generate geographic information protected object data; perform spatial matching and association on the geographic information potential hazard factors and the geographic information protected object data to generate hazard factor-protected object association matrix data;

[0058] The derivative disaster analysis module is used to hierarchically decompose the data of the hazard factor - protection object association matrix to generate secondary disaster decomposition data; perform effect quantification analysis on the secondary disaster decomposition data to generate secondary disaster hazard factors; conduct secondary hazard factor disaster simulations on the standard multi-source geographic information data based on the secondary disaster hazard factors to generate secondary hazard factor dynamic simulation data; construct a secondary disaster chain model for the secondary hazard factors based on the secondary hazard factor dynamic simulation data to generate a secondary disaster chain model.

[0059] The risk propagation analysis module is used to construct an initial risk module for the potential geographic information hazard factors, secondary disaster hazard factors, and geographic information protection object data based on the secondary disaster chain model to obtain geographic risk module data; perform a comprehensive risk transfer map conversion on the geographic risk module data according to the potential geographic information hazard factors, secondary disaster hazard factors, and geographic information protection object data to generate a comprehensive risk propagation map.

[0060] The decision-making response feedback module is used to obtain historical risk prevention and control data; make dynamic risk response decisions on the comprehensive risk propagation map based on the historical risk prevention and control data to generate dynamic risk response decision data; record the decision-making response feedback for the dynamic risk response decision data to generate a real-time disaster chain feedback data set for implementing regional risk prevention and control management operations.

[0061] The beneficial effects of the present invention are as follows: By obtaining geographical information data from multiple sources and performing preprocessing, a standardized data set is generated. This integration improves the quality and consistency of the data, providing a reliable basis for subsequent analysis. Through in-depth analysis of the standard data, potential risk factors in geographical information can be identified, revealing existing risk points. This helps in the early identification and assessment of potential disaster risks. Through hierarchical decomposition and effect quantification analysis, secondary disaster risk factors and secondary disaster chain models are generated. These models provide the detailed mechanisms and dynamic changes of secondary disasters, helping to understand the complexity of the disaster chain. The generated dynamic simulation data of secondary risk factors can provide predictions of future disaster development, providing forward-looking support for risk management and decision-making. Using the secondary disaster chain model to construct an initial risk module for potential risk factors, secondary disaster factors, and protected object data. This module integrates various types of data, laying a foundation for comprehensive risk assessment and management. By converting the risk module data into a comprehensive risk propagation map, the propagation path and influence range of risks can be visualized and analyzed, enhancing the comprehensiveness and systematicness of risk management. Based on historical risk prevention and control data, dynamic risk response decisions are made to ensure that the decision-making is based on real and reliable historical data. This approach enhances the scientificity and effectiveness of decision-making. Through the feedback record of dynamic risk response decision data, a real-time disaster chain feedback data set is generated, enabling timely adjustment of risk prevention and control measures. This process ensures the dynamic adaptation and adjustment of prevention and control strategies, enhancing the flexibility and adaptability of risk management. The entire process forms a systematic risk management framework through data integration, potential risk identification, secondary disaster model construction, risk propagation analysis, and dynamic response decision-making. This comprehensive method can better identify, assess, and respond to various risks, improving the overall efficiency and effectiveness of regional risk prevention and control. Therefore, the present invention improves the accuracy and real-time performance of geographical information data in risk prevention and control through systematic data integration, identification of potential risk factors and protected objects, secondary disaster chain analysis, and dynamic risk response mechanisms. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic diagram of the step process of a risk prevention and control information management method for water conservancy projects;

[0063] Figure 2 is Figure 1 a detailed implementation step process schematic diagram of step S2 in

[0064] Figure 3 is Figure 1 a detailed implementation step process schematic diagram of step S3 in

[0065] Figure 4 is Figure 1 a detailed implementation step process schematic diagram of step S4 in

[0066] The realization, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Specific embodiments

[0067] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0068] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0069] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0070] To achieve the above object, please refer to Figures 1 to 4 , a risk prevention and control information management method for water conservancy projects, the method comprising the following steps:

[0071] Step S1: Obtain multi-source geographic information data; perform data preprocessing on the multi-source geographic information data to generate standard multi-source geographic information data; perform identification of potential hazard factors on the standard multi-source geographic information data to generate geographic information potential hazard factors; perform identification of protected objects on the standard multi-source geographic information data to generate geographic information protected object data; perform spatial matching and association on the geographic information potential hazard factors and the geographic information protected object data to generate hazard factor-protected object association matrix data;

[0072] Step S2: Hierarchically decompose the data of the hazard factor - protection object association matrix to generate secondary disaster decomposition data; perform effect quantification analysis on the secondary disaster decomposition data to generate secondary disaster hazard factors; perform secondary hazard factor disaster simulation on the standard multi-source geographic information data according to the secondary disaster hazard factors to generate secondary hazard factor dynamic simulation data; construct a secondary disaster chain model for the secondary hazard factors based on the secondary hazard factor dynamic simulation data to generate a secondary disaster chain model;

[0073] Step S3: Based on the secondary disaster chain model, construct an initial risk module for the potential hazard factors of geographic information, secondary disaster hazard factors, and geographic information protection object data to obtain geographic risk module data; perform a comprehensive risk transfer map conversion on the geographic risk module data according to the potential hazard factors of geographic information, secondary disaster hazard factors, and geographic information protection object data to generate a comprehensive risk propagation map;

[0074] Step S4: Obtain historical risk prevention and control data; perform dynamic risk response decision-making on the comprehensive risk propagation map based on the historical risk prevention and control data to generate dynamic risk response decision data; record the decision response feedback of the dynamic risk response decision data to generate a real-time disaster chain feedback data set for implementing regional risk prevention and control management operations.

[0075] Through multi-source data acquisition and preprocessing, the present invention unifies the data format and standard, improving the compatibility and reliability of the data. Identifying potential hazard factors and protection objects makes risk identification more comprehensive and accurate. Through spatial matching and association analysis, it provides a clear relationship between hazard factors and protection objects, helping to identify specific risk areas and objects and supporting more targeted prevention and control measures. By constructing a secondary disaster chain model, it can simulate the development path of secondary disasters and help predict the cascading effects of complex disasters. The dynamic simulation of secondary hazard factors provides an in-depth understanding of the disaster evolution process, making risk prediction more accurate and real-time. Quantifying the effects of secondary disasters provides data support for risk assessment, improving the scientificity and reliability of risk assessment. The constructed geographic risk module provides basic data and models for subsequent risk management, making risk management well-founded. The comprehensive risk propagation map provides a panoramic view, showing the propagation path and influence range of risks within the geographic area, facilitating the formulation of overall prevention and control strategies. Using historical data to dynamically adjust the risk propagation map can update risk prevention and control strategies in real time and enhance the response ability. Recording and analyzing decision response feedback data helps to understand the effectiveness of prevention and control measures, timely adjust strategies, and enhance the flexibility and adaptability of regional risk management. Therefore, through systematic data integration, identification of potential hazard factors and protection objects, secondary disaster chain analysis, and dynamic risk response mechanism, the present invention improves the accuracy and real-time nature of geographic information data in risk prevention and control.

[0076] In the embodiments of the present invention, with reference to Figure 1 As described, it is a schematic diagram of the step flow of a risk prevention and control information management method for water conservancy projects according to the present invention. In this example, the risk prevention and control information management method for water conservancy projects includes the following steps:

[0077] Step S1: Obtain multi-source geographic information data; perform data preprocessing on the multi-source geographic information data to generate standard multi-source geographic information data; identify potential hazard factors from the standard multi-source geographic information data to generate geographic information potential hazard factors; identify protected objects from the standard multi-source geographic information data to generate geographic information protected object data; perform spatial matching and association on the geographic information potential hazard factors and the geographic information protected object data to generate hazard factor-protected object association matrix data;

[0078] In the embodiments of the present invention, geographic information data is obtained from different sources (such as satellite images, topographic maps, sensor data, measurement records, etc.). Ensure that the areas and types of data coverage (such as terrain, altitude, land use, etc.) meet the requirements. Use data fusion technology to integrate data from different sources into a unified geographic information dataset. Identify and correct errors or outliers in the data, such as null values, duplicate values, or inconsistent formats. Apply filters or smoothing algorithms to remove noise in the data to improve data quality. For missing geographic information data, use interpolation methods or statistical models to fill it. Convert the data into a standardized format to ensure data consistency and compatibility. Include unit conversion, coordinate system unification, etc. Through the above steps, a processed and standardized geographic information dataset is formed to ensure data quality and consistency. Extract features related to potential hazard factors (such as terrain undulation, wetland distribution, building density, etc.) from the standard multi-source geographic information data. Construct an identification model, such as a machine learning classifier, to detect potential hazard factors (such as landslide areas, flood areas, etc.). Based on the output of the model, generate potential hazard factor data, including the spatial distribution and intensity of the hazard factors. Extract features related to protected objects (such as historical relics, nature reserves, important infrastructure, etc.) from the standard multi-source geographic information data. Construct an identification model, such as an object detection algorithm, to identify and locate protected objects. Based on the output of the model, generate protected object data, including the spatial location and type of the protected objects. Ensure that the potential hazard factor data and the protected object data are aligned in the same coordinate system. Use spatial analysis techniques (such as spatial queries, buffer analysis) to perform spatial matching of potential hazard factors and protected objects. Create an association matrix to associate each potential hazard factor with the corresponding protected object to generate matrix data.

[0079] Step S2: Hierarchically decompose the risk factor - protected object association matrix data to generate secondary disaster decomposition data; perform effect quantification analysis on the secondary disaster decomposition data to generate secondary disaster risk factors; perform secondary risk factor disaster simulation on the standard multi - source geographic information data according to the secondary disaster risk factors to generate secondary risk factor dynamic simulation data; construct a secondary disaster chain model based on the secondary risk factor dynamic simulation data to generate a secondary disaster chain model;

[0080] In the embodiment of the present invention, through preliminary analysis of the risk factor - protected object association matrix data, the relationship between the main risk factors and protected objects is identified. Using hierarchical analysis methods (such as the Analytic Hierarchy Process AHP, decision trees, etc.), the association matrix data is decomposed into sub - matrices at multiple levels to identify potential triggering factors and influencing factors of secondary disasters. Based on the decomposition results, secondary disaster decomposition data is generated, including secondary disaster factors at different levels and their influence ranges. An effect quantification model (such as regression analysis, risk assessment model) is constructed to perform quantitative analysis on each factor in the secondary disaster decomposition data, and evaluate their influence degree and effect. Calculate the effect values of each secondary disaster factor to generate secondary disaster risk factor data. Use statistical analysis and risk assessment tools (such as GIS risk models, Monte Carlo simulations, etc.) for in - depth analysis. Based on the results of the effect quantification analysis, detailed secondary disaster risk factor data is generated, including the risk levels and spatial distributions of each factor. Use a disaster simulation model (such as simulation software, risk simulation tools) to establish a secondary risk factor disaster simulation model according to the characteristics of the secondary disaster risk factors. Input the secondary disaster risk factor data into the simulation model, run the simulation program, and generate secondary risk factor dynamic simulation data. Based on the simulation results, detailed secondary risk factor dynamic simulation data is generated, including the time series, spatial expansion, and influence range of the disaster development. Use the secondary risk factor dynamic simulation data to construct a secondary disaster chain model, identify and describe different stages and causal relationships in the disaster chain. Verify the secondary disaster chain model, compare it with actual data and simulation results, optimize the model parameters, and ensure the accuracy and reliability of the model. Complete the construction of the secondary disaster chain model to generate a comprehensive model describing the secondary disaster process, influence path, and cascade effect.

[0081] Step S3: Based on the secondary disaster chain model, construct an initial risk module for the geographic information potential risk factors, secondary disaster risk factors, and geographic information protected object data to obtain geographic risk module data; perform a comprehensive risk transfer map conversion on the geographic risk module data according to the geographic information potential risk factors, secondary disaster risk factors, and geographic information protected object data to generate a comprehensive risk propagation map;

[0082] In the embodiments of the present invention, data such as secondary disaster chain models, geographical information potential hazard factors, secondary disaster hazard factors, and geographical information protected object data are collected. The above data is standardized to ensure data consistency and compatibility. Based on the secondary disaster chain model, the main risk factors and influencing factors are identified, including potential hazard factors, secondary disaster factors, and protected objects. An initial risk module is constructed, and the risk factors and influencing factors are modularized according to their risk levels and relationships. This can be achieved through methods such as risk matrices and weighted scoring systems. The identified risk factors are assigned to different risk modules, considering spatial distribution and interactions. Based on the construction of risk modules, geographical risk module data is generated, including the risk assessment results and spatial distribution of each module. The framework of an integrated risk propagation map is designed, and the nodes (such as risk factors and protected objects) and edges (such as risk transmission paths) of the map are determined. The geographical risk module data is mapped into the map nodes and edges, and the paths and mechanisms of risk propagation are defined. Using the risk module data, a network model of risk propagation is constructed, including calculating the probability of risk transmission, the scope of influence, and the propagation path. According to the model results, an integrated risk propagation map is generated. The map should show the propagation paths and risk levels of risk factors, protected objects, and their interactions. Graphical tools (such as Gephi, Cytoscape, ArcGIS) are used to visualize the integrated risk propagation map, including the spatial distribution of risk factors, risk transmission paths, and their intensities. The dynamic characteristics of risk propagation are shown through the map to help users understand the risk propagation mechanism and key areas.

[0083] Step S4: Obtain historical risk prevention and control data; make dynamic risk response decisions on the integrated risk propagation map based on the historical risk prevention and control data to generate dynamic risk response decision data; record the decision response feedback for the dynamic risk response decision data to generate a real-time disaster chain feedback data set to perform regional risk prevention and control management operations.

[0084] In the embodiments of the present invention, historical risk prevention and control data is obtained from a historical disaster record library, etc., including previous risk prevention and control measures, disaster response records, accident reports, etc. The collected data may include the time, location, measure effects, resource usage, response time, etc. of risk prevention and control events. A suitable dynamic decision-making model (such as a decision tree, optimization model, machine learning model) is selected to process the historical data and the comprehensive risk propagation map. The historical risk prevention and control data is used to train the decision-making model to verify its accuracy and effectiveness. The historical risk prevention and control data and the comprehensive risk propagation map are input into the dynamic decision-making model. Dynamic risk response measures are calculated according to the model, such as adjusting resource allocation, formulating emergency response plans, and optimizing prevention and control strategies. Based on the results of the dynamic risk response analysis, decision-making data is generated, including recommended emergency measures, resource allocation suggestions, and response strategies. A feedback recording system is designed to record and store the implementation of decision responses, including measure execution effects, problem reports, adjustment suggestions, etc. The detailed information of the decision response is recorded, such as the implementation time, executing unit, effect evaluation, problems encountered, and solutions. During the implementation of emergency responses and risk prevention and control measures, feedback information is recorded in real time. The feedback data is sorted and classified to generate a real-time disaster chain feedback data set. The real-time disaster chain feedback data set is analyzed to evaluate the implementation effect, and the risk prevention and control strategies and measures are adjusted if necessary. Regional risk prevention and control management operations are performed based on the feedback data set, including implementing improvement measures, updating emergency response plans, and optimizing resource allocation.

[0085] Preferably, step S1 includes the following steps:

[0086] Step S11: Obtain multi-source geographic information data using sensors;

[0087] Step S12: Perform data preprocessing on the multi-source geographic information data to generate standard multi-source geographic information data, where the data preprocessing includes data cleaning, data denoising, filling of data missing values, and data standardization;

[0088] Step S13: Identify potential hazard factors for the standard multi-source geographic information data to generate geographic information potential hazard factors; classify the standard multi-source geographic information data according to the geographic information potential hazard factors to generate a hazard factor classification data set;

[0089] Step S14: Identify protected objects for the standard multi-source geographic information data to generate geographic information protected object data; sort the geographic information protected object data in an orderly manner to generate a protected object priority data set; perform spatial matching and association on the hazard factor classification data set and the protected object priority data set to generate hazard factor-protected object association matrix data.

[0090] In the embodiments of the present invention, multi-type sensors (such as satellite remote sensing, ground radar, drones, geographic information systems, etc.) deployed in the target area are used to obtain multi-source geographic information data in real time or periodically. These data include, but are not limited to, topographic data, land use data, hydrological data, meteorological data, biodiversity data, etc. A series of data preprocessing operations are performed on the obtained multi-source geographic information data to ensure the accuracy and consistency of the data. Specifically, it includes: removing or correcting noise, outliers, and incomplete records in the data. Using filtering, smoothing and other techniques to reduce random noise in the data. Filling in missing parts of the data through interpolation methods, statistical analysis, etc., and standardizing data from different sources and at different scales to make them have a unified format and measurement standard, generating standard multi-source geographic information data. Using pattern recognition, machine learning algorithms or rule-based analysis methods to identify potential hazard factors affecting regional security or the ecosystem from the standard multi-source geographic information data, such as geological disaster risks, highly polluted areas, high-incidence areas of forest fires, etc., and the generated result is a list of potential hazard factors of geographic information. Classifying the standard multi-source geographic information data according to the identified potential hazard factors and assigning the data to different hazard factor categories to form a hazard factor classification data set. This classification is based on criteria such as the type, influence range, and severity of the hazard factors. Through automated or semi-automated analysis means (such as deep learning, image recognition, etc.), natural or cultural objects that need to be protected are identified from the standard multi-source geographic information data, such as nature reserves, historical relics, densely populated areas, important infrastructure, etc., generating geographic information protection object data. According to factors such as the importance, vulnerability, and economic value of the protection objects, the geographic information protection object data is sorted by priority to generate a protection object priority data set. The hazard factor classification data set is spatially matched and associated with the protection object priority data set to generate hazard factor - protection object association matrix data. This matrix data can show the potential impact of hazard factors on each protection object and their spatial relationships.

[0091] Preferably, the identification of potential hazard factors from the standard multi-source geographic information data includes the following steps:

[0092] Calculating the topographic slope and aspect of the standard multi-source geographic information data through digital elevation technology to obtain regional geographic slope data and regional geographic aspect data; extracting geological disaster hazard factors from the standard multi-source geographic information data according to the regional geographic slope data and regional geographic aspect data to generate geological disaster hazard factors;

[0093] Screen the regional meteorological data from the standard multi-source geographic information data according to the geological disaster risk factors to obtain the regional rainfall observation data and regional wind speed observation; conduct regional wind field analysis on the regional wind speed observation data to generate regional wind field change data; extract the meteorological disaster risk factors from the standard multi-source geographic information data according to the regional rainfall observation data and regional wind field change data to generate meteorological disaster risk factors.

[0094] Conduct regional geographical flood disaster simulation on the geological disaster risk factors and meteorological disaster risk factors to generate geological and meteorological composite risk factors; integrate the geological disaster risk factors, meteorological disaster risk factors and geological and meteorological composite risk factors to generate geographical information potential risk factors.

[0095] In the embodiments of the present invention, elevation data of a target area is obtained by using a digital elevation model, and digital elevation technology (such as SRTM or LIDAR data) is applied to calculate the terrain slope. The slope represents the angle of ground inclination and is very important for predicting geological disasters such as landslides and debris flows. The formula S = arctan(Δz / Δd) is used, where S is the slope, Δz is the vertical height change, and Δd is the horizontal distance. The terrain aspect is calculated, that is, the direction of the slope surface, which is helpful for predicting the water flow direction and erosion. The formula Aspect = arctan(Δy / Δx) is used, where Aspect is the aspect, and Δy and Δx are the height changes in the north-south and east-west directions respectively. According to the calculated regional geographical slope data and aspect data, known geological disaster risk models (such as landslide stability models) are used to extract geological disaster hazard factors. For example, landslide stability factors such as the safety factor are used to identify high-risk areas. The safety factor is calculated as FS = c⋅L / W⋅tan(ϕ), where FS is the safety factor, c is the shear strength, L is the length of the sliding surface, W is the gravity, and ϕ is the internal friction angle. Regional rainfall data is screened and extracted from meteorological observation stations or satellite remote sensing data, which is crucial for predicting floods and debris flows. Regional wind speed observation data is obtained to analyze the impact of wind speed on meteorological disasters. Spatial analysis is performed on the regional wind speed observation data to generate regional wind field change data. The time series data of wind speed and wind direction are used to draw a wind field map to analyze the change trend of wind speed and the spatial distribution of the wind field. The wind field change formula is ΔV = (Vmax - Vmin) / T, where ΔV is the wind speed change, Vmax and Vmin are the maximum and minimum wind speeds respectively, and T is the time period. Combining the regional rainfall observation data and the regional wind field change data, meteorological disaster risk models (such as rainstorm flood models) are used to extract meteorological disaster hazard factors. For example, the impacts of rainfall amount and wind speed on floods and wind disasters are analyzed. The meteorological disaster risk index is calculated as R = α⋅Rprecip + β⋅Wwind, where R is the meteorological disaster risk index, Rprecip is the rainfall amount, Wwind is the wind speed, and α and β are weight coefficients. Based on the geological disaster hazard factors and meteorological disaster hazard factors, a combined geological and meteorological hazard factor is generated through a flood simulation model (such as a hydrological and hydrodynamic model). This will consider factors such as rainfall amount, slope, and soil type to predict the impact of floods. The geological disaster and meteorological disaster data are integrated to form a combined geological and meteorological hazard factor to identify the comprehensive risk area.

[0096] As an example of the present invention, with reference to Figure 2 shown, in this example, step S2 includes:

[0097] Step S21: Conduct spatial risk analysis on the risk factor - protection object association matrix data to generate main disaster risk analysis data; conduct weight distribution impact identification on the main disaster risk analysis data to generate a basic secondary disaster chain dataset;

[0098] Step S22: Conduct hierarchical decomposition on the basic secondary disaster chain dataset to generate secondary disaster decomposition data; conduct disaster propagation path tracking on the secondary disaster decomposition data to generate secondary disaster chain structure data;

[0099] Step S23: Conduct effect quantification analysis on the secondary disaster chain structure data to generate secondary disaster risk factors; conduct secondary hazard factor disaster simulation on the standard multi-source geographic information data according to the secondary disaster risk factors to generate secondary hazard factor dynamic simulation data;

[0100] Step S24: Conduct disaster chain coupling effect analysis on the secondary hazard factor dynamic simulation data to generate secondary disaster coupling effect data; construct a secondary disaster chain model for the secondary hazard factors according to the secondary disaster coupling effect data to generate a secondary disaster chain model.

[0101] In the embodiments of the present invention, spatial risk analysis is performed on the data of the association matrix of hazard factors - protected objects by applying spatial analysis techniques (such as spatial interpolation and hotspot analysis). High-risk areas and the distribution of main disaster risks are identified, and data on the main disaster risk areas, risk levels, and risk types are output, providing a spatial distribution map of disaster risks. Based on the data of the main disaster risk analysis, weight allocation is performed on the impacts of different hazard factors and protected objects. The weights can be allocated based on the severity, occurrence probability of the hazard factors, and the importance of the protected objects. Through weight allocation, potential secondary disaster chains are identified, and a dataset containing secondary disaster chains is generated. These chains demonstrate the chain reactions triggered by the main disaster. The basic secondary disaster chain dataset is hierarchically deconstructed to identify the main stages and key factors of each secondary disaster chain. Using graph theory or network analysis methods, the secondary disaster chains are decomposed into multiple levels to reveal their internal structures and relationships, and the hierarchical structure data of the secondary disaster chains are output, including detailed descriptions and key factors of each level. The disaster propagation model (such as diffusion model and network propagation model) is applied to trace the propagation paths of the secondary disaster deconstruction data. Simulate how secondary disasters spread in space and affect other regions, and output the propagation paths and structure data of the secondary disaster chains, including the diffusion directions, speeds, and affected areas of the secondary disasters. Quantitative analysis of the effects of the secondary disaster chain structure data is performed to evaluate the specific impacts and severity of the secondary disasters. Using statistical analysis and model prediction methods, the impacts of secondary disasters on the environment and society are quantified, and the secondary disaster hazard factor data are output, including the risk levels, affected ranges, and potential losses of each secondary disaster. Based on the secondary disaster hazard factors, simulations are performed on the standard multi-source geographic information data to predict the dynamic changes and impacts of secondary disasters, and the dynamic simulation data of secondary hazard factors at different time periods are output, including risk maps and disaster scenario analysis results. Coupling effect analysis is performed on the dynamic simulation data of secondary hazard factors to identify the interactions and linkage effects between secondary disaster chains. Using coupling models or multi-disaster coupling analysis methods, the comprehensive impacts of multiple secondary disasters on the overall risk are evaluated, and the coupling effect data of secondary disaster chains are output, including the interactions, common impacts, and comprehensive risk assessments between disaster chains.

[0102] Preferably, step S22 includes the following steps:

[0103] Step S221: Perform multi-level hierarchical deconstruction on the basic secondary disaster chain dataset to generate secondary disaster hierarchical data, where the multi-level hierarchy includes impact range hierarchy, spatio-temporal characteristic hierarchy, and interrelationship hierarchy;

[0104] Step S222: Perform relationship mapping classification on the basic secondary disaster chain dataset through the secondary disaster hierarchical data to generate secondary disaster mapping data;

[0105] Step S223: Conduct hierarchical causal analysis on the secondary disaster mapping data to generate secondary disaster deconstruction data; conduct dynamic time evolution analysis on the secondary disaster deconstruction data to generate secondary disaster time series evolution data;

[0106] Step S224: Use multi-path propagation simulation technology to track the disaster propagation path of the secondary disaster time series evolution data to generate secondary disaster chain propagation path tracking data; conduct structured collation on the secondary disaster chain propagation path tracking data to generate secondary disaster chain structure data.

[0107] In the embodiments of the present invention, by stratifying the influence scope of the basic secondary disaster chain dataset, the influence area and its spatial scope of each secondary disaster chain are determined. Using spatial analysis methods (such as buffer analysis, spatial interpolation), hierarchical data of different influence scopes are generated. Specifically, for example, different influence radii are set for each type of secondary disaster to generate a hierarchical map of the influence scope. According to the temporal and spatial characteristics, the secondary disaster chain dataset is stratified to determine the time period, frequency, and spatial distribution pattern of the disaster occurrence. Using spatio-temporal analysis tools (such as spatio-temporal data visualization), hierarchical data of spatio-temporal characteristics are generated. Specifically, for example, the occurrence time period and spatial distribution area of the secondary disaster are decomposed to generate hierarchical data of spatio-temporal characteristics. Analyze the interaction and relationship between different disasters in the secondary disaster chain, construct a relationship network diagram, identify the dependency relationship and interaction effect in the disaster chain, and generate hierarchical data of the mutual relationship. Specifically, for example, draw a causal relationship diagram of the secondary disaster to identify the connection and influence direction between disasters. Using the stratified data of secondary disasters, relationship mapping classification is performed on the basic secondary disaster chain dataset. The secondary disaster chains are classified according to their relationship patterns (such as causal relationship, temporal sequence relationship), and mapping data are generated. The secondary disaster chains are divided into types such as direct influence chains and indirect influence chains, and the corresponding relationship mapping data are generated. The secondary disaster chains of each classification and their mapping relationships, including the interaction and influence path between disasters, are output. Perform hierarchical causal analysis on the secondary disaster mapping data to determine the causal relationship and influence mechanism of each secondary disaster. Use a causal inference model (such as a structural equation model) to analyze the causal chain of the secondary disaster, construct a causal relationship diagram, determine the direct and indirect influences of each secondary disaster, and output the causal analysis results of each secondary disaster, including influence factors, causal chains, and their influence intensities. Perform dynamic time evolution analysis on the secondary disaster deconstruction data to analyze the change trend and evolution pattern of the secondary disaster over time. Using time series analysis methods (such as time series prediction, dynamic system modeling), time series evolution data of the secondary disaster are generated to simulate the evolution of the secondary disaster at different time points, generate a time series change diagram, and output the time series evolution data of the secondary disaster, including the time series of the disaster occurrence, intensity change, and dynamic change of the influence scope. Use multi-path propagation simulation technology to track the propagation path of the secondary disaster time series evolution data. Simulate how the secondary disaster spreads through different paths and generate propagation path tracking data. Use a propagation model (such as a diffusion model, propagation network model) to simulate the diffusion path of the disaster and generate propagation path data. Structurally organize the secondary disaster chain propagation path tracking data, identify the key nodes and propagation paths in the disaster chain, generate structured secondary disaster chain structure data, and organize and output the propagation structure diagram of the secondary disaster chain, including key path and node information.

[0108] As an example of the present invention, refer to Figure 3 shown, in this example, step S3 includes:

[0109] Step S31: Based on the secondary disaster chain model, construct the initial risk module for the geographical information potential hazard factors, secondary disaster hazard factors, and geographical information protection object data to obtain the geographical risk module data;

[0110] Step S32: Extract the disaster risk characteristics from the geographical risk module data to generate the geographical disaster risk characteristic data; perform association rule mining on the geographical disaster risk characteristic data to generate the geographical disaster risk association data; perform dynamic risk assessment on the geographical disaster risk association data to generate the comprehensive geographical disaster risk assessment data;

[0111] Step S33: Use the geographical disaster risk assessment data to verify the analysis unit of the geographical risk module data to generate the optimized data of the risk module analysis unit;

[0112] Step S34: Conduct multi-level risk transfer and diffusion analysis on the geographical information potential hazard factors, secondary disaster hazard factors, and geographical information protection object data to generate multi-level risk diffusion data; based on the multi-level risk diffusion data, perform comprehensive risk transfer map conversion on the geographical risk module data to generate the comprehensive risk propagation map.

[0113] In the embodiments of the present invention, by integrating the secondary disaster chain model, geographical information potential hazard factors, secondary disaster hazard factors, and geographical information protection object data, an initial risk data set is formed. Using data fusion technologies (such as data synthesis, data fusion algorithms), information from different data sources is combined to integrate potential hazard factors and protection object data, generating a preliminary geographical risk module. Based on the integrated data, an initial risk module is constructed. These modules can include the spatial distribution of hazard factors, the impact range of secondary disasters, and the distribution of protection objects, creating a risk module map, including the areas of hazard factors, the paths of secondary disaster chains, and the locations of protection objects, and outputting geographical risk module data, including the spatial distribution of various risk factors and protection objects and their potential impacts. Disaster risk feature extraction is performed on the geographical risk module data to identify key risk features, such as risk levels, occurrence probabilities, impact ranges, etc. Using feature engineering technologies (such as statistical analysis, feature selection) to extract these features, extracting the impact intensity, frequency, and risk categories of each disaster, and outputting a data set including various risk features for subsequent analysis and modeling. Applying association rule mining technologies (such as Apriori algorithm, FP-Growth algorithm) to analyze geographical disaster risk feature data to identify the association rules between risk factors. Searching for potential patterns and regularities, mining which risk factors often occur simultaneously to form association rules, and outputting a data set containing association rules to describe the relationships and dependencies between risk factors. Based on the geographical disaster risk association data, dynamic risk assessment is carried out. Using dynamic system modeling or simulation technologies (such as Monte Carlo simulation, time series analysis) to evaluate the change of risk over time and the comprehensive risk, simulating the changes of risk factors under different scenarios, generating a comprehensive risk assessment result, and outputting comprehensive risk assessment data, including the dynamic changes of risk, the comprehensive assessment result, and the risk prediction map. Based on the geographical disaster risk assessment data, parsing unit verification is performed on the geographical risk module data. Using verification technologies (such as cross-validation, model evaluation) to test the accuracy and effectiveness of each risk module, verifying the prediction ability of each risk module and the accuracy of risk assessment, and according to the verification results, adjusting and optimizing the risk module, improving the structure and parameters of the module, adjusting the weights of the risk assessment model, optimizing the performance of the risk module, and outputting the optimized risk module data, including the corrected risk assessment result and the improved model parameters. Applying a multi-level risk transfer and diffusion model (such as a propagation model, a diffusion model) to perform risk transfer and diffusion analysis on geographical information potential hazard factors, secondary disaster hazard factors, and protection object data. Simulating the transfer and diffusion process of risk at different levels, analyzing the transfer process of risk from high-risk areas to low-risk areas, generating multi-level risk diffusion data, and outputting risk diffusion data including different levels and stages to show the propagation path and diffusion range of risk. Based on the multi-level risk diffusion data, a comprehensive risk propagation map is constructed.Convert the diffusion data into a map form to display the overall spread of risks and the affected areas, draw a risk propagation map, identify different risk levels, propagation paths, and key nodes, and output a comprehensive risk propagation map, including the network structure of risk transmission, propagation paths, and diagrams of affected areas.

[0114] Preferably, step S34 includes the following steps:

[0115] Step S341: Perform dynamic change detection on the geographical information protection object data. When changes are detected in the geographical information protection object data, identify the damage status of the geographical information protection object data based on the geographical information potential hazard factors and secondary disaster hazard factors, and generate damage status identification data;

[0116] Step S342: Match the damage status identification data with the geographical information potential hazard factors and secondary disaster hazard factors to generate new hazard factors; based on the new hazard factors, perform hierarchical overlap on the geographical risk module data to generate geographical risk overlap module data;

[0117] Step S343: Conduct risk diffusion analysis on the geographical risk overlap module data to generate multi-level risk diffusion data; based on the multi-level risk diffusion data, perform conversion of the comprehensive risk transmission map on the geographical risk module data to generate a comprehensive risk propagation map.

[0118] In the embodiments of the present invention, real-time monitoring of geographical information protection object data is carried out by using a Geographic Information System (GIS) or other sensing technologies. The protection object data is updated and compared regularly to identify changes or anomalies. By comparing real-time data with historical data, displacements, deformations or other changes of the protection object are detected. Change detection algorithms (such as image difference detection, time series analysis) are used to identify changes in the protection object data. Displacements or structural damages of buildings in a certain area are identified. Based on geographical information potential risk factors and secondary disaster risk factors, the potential damages caused by changes in the protection object data are analyzed. Risk assessment models (such as multi-factor risk assessment, disaster impact model) are used to identify and evaluate the damage status. The impacts of factors such as earthquakes and floods on buildings are analyzed, their damage degrees are evaluated, and damage status identification data is output, including damage types, damage degrees and affected areas. The damage status identification data is matched with geographical information potential risk factors and secondary disaster risk factors. These data are integrated through factor association technologies (such as data fusion, factor matching algorithms) to identify new risk factors. The damage status is combined with potential risk factors (such as earthquakes, floods, etc.) to generate a new set of risk factors, and an updated data set containing the newly identified risk factors is output. Based on the new risk factors, a hierarchical overlapping analysis is carried out on the geographical risk module data to identify and record the overlapping areas and risk intersections in the new risk module, analyze the impacts of the new risk factors in the existing geographical risk modules, identify the overlapping risk areas, and output a data set including the overlapping areas and risk intersections. A diffusion model (such as diffusion equation, propagation model) is used to analyze the geographical risk overlapping module data, simulate the diffusion of risks at different levels and regions, analyze the diffusion paths and ranges of the overlapping risk areas, predict risk propagation, and output risk diffusion data at different levels and stages. Based on the multi-level risk diffusion data, a comprehensive risk transmission map is constructed. Map visualization technologies (such as graphic modeling, network diagram) are used to display the overall risk propagation situation, draw a comprehensive risk propagation map, mark the main risk transmission paths, key nodes and diffusion areas, and output a comprehensive risk propagation map, including the network structure of risk transmission, propagation paths and diagrams of affected areas.

[0119] As an example of the present invention, refer to Figure 4 shown. In this example, step S4 includes:

[0120] Step S41: Obtain historical risk prevention and control data;

[0121] Step S42: Based on the historical risk prevention and control data, make a dynamic risk response decision on the comprehensive risk propagation map to generate dynamic risk response decision data;

[0122] Step S43: Conduct real-time data stream monitoring on the dynamic risk response decision data to generate real-time decision monitoring data; conduct a comprehensive disaster chain status analysis on the real-time decision monitoring data to generate regional disaster chain status data;

[0123] Step S44: Adjust the decision response to the real-time decision monitoring data through the regional disaster chain status data to generate risk response decision adjustment data; record the feedback on the risk response decision adjustment data to generate a real-time disaster chain feedback data set for implementing regional risk prevention and control management operations.

[0124] In the embodiment of the present invention, historical risk prevention and control data are collected from emergency management agencies, etc. These data include past disaster records, risk management measures, emergency response situations, etc. Natural disaster data (such as earthquakes, floods) and related prevention and control measures and effectiveness evaluation reports in the past few years are collected. Data from different sources are integrated to establish a comprehensive historical risk prevention and control data set, and a data database containing disaster types, occurrence times, affected areas, and response measures is constructed. Using the historical risk prevention and control data, a dynamic risk response model is established. The model predicts the current risk situation based on the patterns and trends of historical data and formulates corresponding response strategies. A machine learning model trained with historical data is used to predict the current risk level and recommend corresponding response measures. According to the model prediction results and the current risk situation, dynamic risk response decision data are generated, an emergency plan and a resource allocation plan are formulated and recorded in the dynamic risk response decision data. According to new risk information and changes, the dynamic risk response decision data are updated in real time, and the emergency response plan and resource allocation are adjusted according to the real-time monitoring data. A real-time data stream monitoring system (such as a sensor network, satellite data, online monitoring platform) is used to collect the current disaster situation and the implementation of the emergency response. Real-time meteorological data, seismic activity data, etc. are obtained from the sensors. According to the real-time data stream monitoring results, real-time decision monitoring data are generated, and the disaster warning information and response progress are updated in real time. A comprehensive disaster chain status analysis is conducted on the real-time decision monitoring data to identify each link and status of the disaster chain, analyze the cause, development process, influence range of the disaster and its relationship with other disaster chains, and output a detailed data set containing the comprehensive disaster chain status. According to the regional disaster chain status data, the emergency response plan in the real-time decision monitoring data is adjusted to cope with new risk information and disaster situations, and the resource allocation, personnel deployment, and emergency response measures are updated according to the disaster chain status data. The adjustment of the risk response decision is recorded for feedback, the implementation situation and effects of the adjustment measures are collected and sorted out, a real-time disaster chain feedback data set is established for further optimizing the risk prevention and control management operations, a feedback report is generated, the effects of the adjustment measures are analyzed, and improvement suggestions are provided for future risk management.

[0125] Preferably, step S42 includes the following steps:

[0126] Step S421: Analyze the key propagation nodes of the comprehensive risk propagation map to generate comprehensive risk key propagation node data; extract the propagation characteristics from the comprehensive risk key propagation node data to obtain comprehensive risk key propagation characteristic data;

[0127] Step S422: Divide the historical risk prevention and control data into data sets to generate a model training set and a model test set; use the decision tree algorithm to train the model training set to generate a risk response decision pre-model; optimize and iterate the risk response decision pre-model through the model test set to generate a risk response decision model;

[0128] Step S423: Import the comprehensive risk key propagation characteristic data into the risk response decision model for dynamic risk response decision-making to generate dynamic risk response decision data.

[0129] In the embodiments of the present invention, the graph theory algorithms (such as centrality analysis and node importance analysis) are applied to the comprehensive risk propagation map to identify key propagation nodes. These nodes play a core role in the risk propagation process and have a significant impact on the spread of disasters. For example, algorithms such as PageRank and betweenness centrality are used to identify key nodes, generating a dataset containing the importance of each node in the propagation process and their mutual influence relationships, and identifying and recording the nodes with the greatest impact in the map (such as important infrastructure or high-risk areas). Feature extraction is performed on the data of key propagation nodes, and the characteristics of these nodes in the risk propagation process are analyzed, including propagation speed, influence range, propagation path, etc. Feature extraction techniques (such as feature selection algorithms and principal component analysis) are applied to extract the propagation characteristics of the nodes, generating comprehensive risk key propagation feature data describing the propagation characteristics of the nodes, and recording the propagation speed, influence magnitude of each key node and its relationship with other nodes. The historical risk prevention and control data is randomly divided into a model training set and a model test set, and the common ratio is 70% for training and 30% for testing. Methods such as K-fold cross-validation are used for data splitting to ensure the representativeness of the dataset and the generalization ability of the model, generating a training set and a test set. The decision tree algorithm is used to train the model training set to generate a risk response decision pre-model. The decision tree algorithm (such as CART and ID3) is applied for training, and specific parameters (such as the depth of the tree and the splitting criterion) are set to train a decision tree model for predicting the best response measures in different risk situations. The pre-model is optimized and iterated through the model test set to improve the accuracy and generalization ability of the model. Methods such as grid search and cross-validation are used to adjust the model parameters and improve the model performance, generating an optimized risk response decision model. The comprehensive risk key propagation feature data is imported into the optimized risk response decision model. The key propagation feature data (such as node influence and propagation speed) is converted into the model input format, and the risk response decision model is used to make dynamic risk response decisions on the imported data, outputting dynamic risk response decision data, including recommended emergency measures and resource allocation plans.

[0130] In this specification, a risk prevention and control information management system for a water conservancy project is provided, which is used to execute the above-mentioned risk prevention and control information management method for a water conservancy project. The risk prevention and control information management system for a water conservancy project includes:

[0131] The disaster object recognition module is used to obtain multi-source geographic information data; perform data preprocessing on the multi-source geographic information data to generate standard multi-source geographic information data; identify potential hazard factors from the standard multi-source geographic information data to generate geographic information potential hazard factors; identify protected object information from the standard multi-source geographic information data to generate geographic information protected object data; perform spatial matching and association on the geographic information potential hazard factors and the geographic information protected object data to generate hazard factor-protected object association matrix data;

[0132] The derivative disaster analysis module is used to perform hierarchical decomposition on the hazard factor-protected object association matrix data to generate secondary disaster decomposition data; perform effect quantification analysis on the secondary disaster decomposition data to generate secondary disaster hazard factors; perform secondary hazard factor disaster simulation on the standard multi-source geographic information data according to the secondary disaster hazard factors to generate secondary hazard factor dynamic simulation data; construct a secondary disaster chain model for the secondary hazard factors based on the secondary hazard factor dynamic simulation data to generate a secondary disaster chain model;

[0133] The risk propagation analysis module is used to construct an initial risk module for the geographic information potential hazard factors, secondary disaster hazard factors, and geographic information protected object data based on the secondary disaster chain model to obtain geographic risk module data; perform comprehensive risk transmission map conversion on the geographic risk module data according to the geographic information potential hazard factors, secondary disaster hazard factors, and geographic information protected object data to generate a comprehensive risk propagation map;

[0134] The decision-making response feedback module is used to obtain historical risk prevention and control data; perform dynamic risk response decision-making on the comprehensive risk propagation map based on the historical risk prevention and control data to generate dynamic risk response decision-making data; record the decision-making response feedback for the dynamic risk response decision-making data to generate a real-time disaster chain feedback data set to execute regional risk prevention and control management operations.

[0135] The beneficial effects of the present invention are as follows: By obtaining geographical information data from multiple sources and performing preprocessing, a standardized data set is generated. This integration improves the quality and consistency of the data, providing a reliable basis for subsequent analysis. Through in-depth analysis of the standard data, potential risk factors in geographical information can be identified, revealing existing risk points. This helps in the early identification and assessment of potential disaster risks. Through hierarchical decomposition and effect quantification analysis, secondary disaster risk factors and secondary disaster chain models are generated. These models provide the detailed mechanisms and dynamic changes of secondary disasters, helping to understand the complexity of the disaster chain. The generated dynamic simulation data of secondary risk factors can provide predictions of future disaster development, providing forward-looking support for risk management and decision-making. Using the secondary disaster chain model, an initial risk module is constructed for potential risk factors, secondary disaster factors, and protected object data. This module integrates various types of data, laying the foundation for comprehensive risk assessment and management. By converting the risk module data into a comprehensive risk propagation map, the propagation path and influence range of risks can be visualized and analyzed, enhancing the comprehensiveness and systematicness of risk management. Based on historical risk prevention and control data, dynamic risk response decisions are made to ensure that the decisions are based on true and reliable historical data. This approach enhances the scientificity and effectiveness of the decisions. Through the feedback record of dynamic risk response decision data, a real-time disaster chain feedback data set is generated, enabling timely adjustment of risk prevention and control measures. This process ensures the dynamic adaptation and adjustment of prevention and control strategies, enhancing the flexibility and adaptability of risk management. The entire process forms a systematic risk management framework through data integration, potential risk identification, secondary disaster model construction, risk propagation analysis, and dynamic response decision-making. This comprehensive approach can better identify, assess, and respond to various risks, improving the overall efficiency and effectiveness of regional risk prevention and control. Therefore, the present invention improves the accuracy and timeliness of geographical information data in risk prevention and control through systematic data integration, potential risk factor and protected object identification, secondary disaster chain analysis, and dynamic risk response mechanism.

[0136] Therefore, in any aspect, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to cover all changes falling within the meaning and scope of the equivalent elements of the application document within the present invention.

[0137] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A risk prevention and control information management method for water conservancy projects, characterized in that: The following steps are involved: Step S1: acquiring multi-source geographic information data; performing data preprocessing on the multi-source geographic information data to generate standard multi-source geographic information data; Identify potential risk factors for standard multi-source geographic information data and generate geographic information potential risk factors; Identify the protected objects of standard multi-source geographic information data and generate geographic information protected object data; spatially match and associate the potential risk factors of geographic information with the data of geographic information protected objects to generate risk factor-protected object association matrix data; Step S2: hierarchically deconstruct the risk factor-protection object association matrix data to generate secondary disaster deconstruction data; Conduct effect quantification analysis on secondary disaster deconstruction data to generate secondary disaster risk factors; conduct disaster simulation of secondary disaster risk factors on standard multi-source geographic information data based on secondary disaster risk factors to generate dynamic simulation data of secondary disaster risk factors; construct a secondary disaster chain model for secondary disaster risk factors based on the dynamic simulation data of secondary disaster risk factors to generate a secondary disaster chain model; Step S3: constructing an initial risk module based on the secondary disaster chain model for the potential risk factors of geographic information, the secondary disaster risk factors and the data of the objects to be protected by geographic information, and obtaining geographic risk module data; converting the geographic risk module data into a comprehensive risk transmission map based on the potential risk factors of geographic information, the secondary disaster risk factors and the data of the objects to be protected by geographic information, and generating a comprehensive risk transmission map; Step S4: Obtain historical risk prevention and control data; Make dynamic risk response decisions on the comprehensive risk propagation map based on historical risk prevention and control data to generate dynamic risk response decision data; Record the decision response feedback of dynamic risk response decision data and generate a real-time disaster chain feedback data set to perform regional risk prevention and control management operations.

2. The risk prevention and control information management method for water conservancy projects according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: using sensors to obtain multi-source geographic information data; Step S12: performing data preprocessing on the multi-source geographic information data to generate standard multi-source geographic information data, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization; Step S13: Identify potential risk factors of standard multi-source geographic information data to generate geographic information potential risk factors; classify the standard multi-source geographic information data according to the geographic information potential risk factors to generate a risk factor classification data set; Step S14: Identify the protected objects in the standard multi-source geographic information data to generate geographic information protected object data; sort the geographic information protected object data in an orderly manner to generate a protected object priority data set; spatially match and associate the hazard factor classification data set with the protected object priority data set to generate hazard factor-protected object association matrix data.

3. The risk prevention and control information management method for water conservancy projects according to claim 2 is characterized in that: Identification of potential risk factors using standard multi-source geographic information data includes the following steps: The terrain slope and aspect are calculated for standard multi-source geographic information data through digital elevation technology to obtain regional geographic slope data and regional geographic aspect data; geological hazard risk factors are extracted from standard multi-source geographic information data based on regional geographic slope data and regional geographic aspect data to generate geological hazard risk factors; According to the geological disaster risk factors, the standard multi-source geographic information data is screened for regional meteorological data to obtain regional rainfall observation data and regional wind speed observation; regional wind field analysis is performed on the regional wind speed observation data to generate regional wind field change data; meteorological disaster risk factors are extracted from the standard multi-source geographic information data based on the regional rainfall observation data and regional wind field change data to generate meteorological disaster risk factors; Conduct regional geographical flood disaster simulation on geological disaster risk factors and meteorological disaster risk factors to generate geological meteorological composite risk factors; integrate geological disaster risk factors, meteorological disaster risk factors and geological meteorological composite risk factors into potential risk factors to generate geographic information potential risk factors.

4. The risk prevention and control information management method for water conservancy projects according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: Perform spatial risk analysis on the risk factor-protection object association matrix data to generate primary disaster risk analysis data; perform weight allocation on the primary disaster risk analysis data to generate a basic secondary disaster chain data set; Step S22: hierarchically deconstruct the basic secondary disaster chain data set to generate secondary disaster deconstructed data; trace the disaster propagation path of the secondary disaster deconstructed data to generate secondary disaster chain structure data; Step S23: performing effect quantification analysis on the secondary disaster chain structure data to generate secondary disaster risk factors; performing secondary disaster risk factor disaster simulation on the standard multi-source geographic information data according to the secondary disaster risk factors to generate secondary disaster risk factor dynamic simulation data; Step S24: Analyze the disaster chain coupling effect of the dynamic simulation data of the secondary disaster risk factor to generate secondary disaster coupling effect data; construct a secondary disaster chain model for the secondary disaster risk factor based on the secondary disaster coupling effect data to generate a secondary disaster chain model.

5. The risk prevention and control information management method for water conservancy projects according to claim 4 is characterized in that: Step S22 includes the following steps: Step S221: Perform multi-level hierarchical deconstruction on the basic secondary disaster chain data set to generate secondary disaster hierarchical data, wherein the multi-level hierarchical deconstruction includes impact range hierarchical deconstruction, spatiotemporal characteristic hierarchical deconstruction and mutual relationship hierarchical deconstruction; Step S222: performing relationship mapping classification on the basic secondary disaster chain data set through the secondary disaster layered data to generate secondary disaster mapping data; Step S223: performing hierarchical causal analysis on the secondary disaster mapping data to generate secondary disaster deconstruction data; performing dynamic time evolution analysis on the secondary disaster deconstruction data to generate secondary disaster time series evolution data; Step S224: Utilize multi-path propagation simulation technology to track the disaster propagation path of the secondary disaster time series evolution data to generate secondary disaster chain propagation path tracking data; and structure the secondary disaster chain propagation path tracking data to generate secondary disaster chain structure data.

6. The risk prevention and control information management method for water conservancy projects according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: constructing an initial risk module for geographic information potential risk factors, secondary disaster risk factors and geographic information protection object data based on the secondary disaster chain model to obtain geographic risk module data; Step S32: extracting disaster risk characteristics from the geographic risk module data to generate geographic disaster risk characteristic data; performing association rule mining on the geographic disaster risk characteristic data to generate geographic disaster risk association data; performing dynamic risk assessment on the geographic disaster risk association data to generate geographic disaster comprehensive risk assessment data; Step S33: using the geographical disaster comprehensive risk assessment data to perform parsing unit verification on the geographical risk module data, and generating risk module parsing unit optimization data; Step S34: Conduct multi-level risk transfer and diffusion analysis on geographic information potential risk factors, secondary disaster risk factors and geographic information protection object data to generate multi-level risk diffusion data; convert the geographic risk module data into a comprehensive risk transfer map based on the multi-level risk diffusion data to generate a comprehensive risk propagation map.

7. The risk prevention and control information management method for water conservancy projects according to claim 6 is characterized in that: Step S34 includes the following steps: Step S341: Dynamically detect changes in the geographic information protection object data. When changes are detected in the geographic information protection object data, identify the damage status of the geographic information protection object data based on the geographic information potential risk factors and secondary disaster risk factors to generate damage status identification data; Step S342: matching the damage status identification data with the potential risk factors of geographic information and the secondary disaster risk factors to generate new risk factors; overlapping the geographic risk module data level by level based on the new risk factors to generate geographic risk overlapping module data; Step S343: Perform risk diffusion analysis on the geographic risk overlapping module data to generate multi-level risk diffusion data; perform comprehensive risk transfer map conversion on the geographic risk module data based on the multi-level risk diffusion data to generate a comprehensive risk transmission map.

8. The risk prevention and control information management method for water conservancy projects according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: Obtain historical risk prevention and control data; Step S42: making dynamic risk response decisions on the comprehensive risk propagation map based on historical risk prevention and control data to generate dynamic risk response decision data; Step S43: Perform real-time data flow monitoring on the dynamic risk response decision data to generate real-time decision monitoring data; perform global disaster chain status analysis on the real-time decision monitoring data to generate regional disaster chain status data; Step S44: Make decision response adjustments to the real-time decision monitoring data through the regional disaster chain status data to generate risk response decision adjustment data; record feedback on the risk response decision adjustment data to generate a real-time disaster chain feedback data set to execute regional risk prevention and control management operations.

9. The risk prevention and control information management method for water conservancy projects according to claim 8 is characterized in that: Step S42 includes the following steps: Step S421: performing key propagation node analysis on the comprehensive risk propagation graph to generate comprehensive risk key propagation node data; performing propagation feature extraction on the comprehensive risk key propagation node data to obtain comprehensive risk key propagation feature data; Step S422: divide the historical risk prevention and control data into data sets to generate a model training set and a model test set; use a decision tree algorithm to train the model training set to generate a risk response decision pre-model; use the model test set to perform model optimization iteration on the risk response decision pre-model to generate a risk response decision model; Step S423: Import the comprehensive risk key propagation feature data into the risk response decision model to make dynamic risk response decisions and generate dynamic risk response decision data.

10. A risk prevention and control information management system for water conservancy projects, characterized in that: Used to execute the risk prevention and control information management method for water conservancy projects as claimed in claim 1, the risk prevention and control information management system for water conservancy projects comprises: The disaster object identification module is used to obtain multi-source geographic information data; perform data preprocessing on the multi-source geographic information data to generate standard multi-source geographic information data; perform potential risk factor identification on the standard multi-source geographic information data to generate geographic information potential risk factors; perform protection object identification on the standard multi-source geographic information data to generate geographic information protection object data; perform spatial matching and association between geographic information potential risk factors and geographic information protection object data to generate risk factor-protection object association matrix data; The derivative disaster analysis module is used to hierarchically deconstruct the risk factor-protection object association matrix data to generate secondary disaster deconstruction data; to perform effect quantitative analysis on the secondary disaster deconstruction data to generate secondary disaster risk factors; to simulate the secondary disaster risk factors of the standard multi-source geographic information data according to the secondary disaster risk factors to generate dynamic simulation data of the secondary disaster risk factors; to construct a secondary disaster chain model for the secondary disaster risk factors based on the dynamic simulation data of the secondary disaster risk factors to generate a secondary disaster chain model; The risk propagation analysis module is used to construct an initial risk module based on the secondary disaster chain model for the potential risk factors of geographic information, secondary disaster risk factors and geographic information protection object data to obtain geographic risk module data; the geographic risk module data is converted into a comprehensive risk transmission map based on the potential risk factors of geographic information, secondary disaster risk factors and geographic information protection object data to generate a comprehensive risk propagation map; The decision response feedback module is used to obtain historical risk prevention and control data; make dynamic risk response decisions on the comprehensive risk propagation map based on historical risk prevention and control data to generate dynamic risk response decision data; record decision response feedback on the dynamic risk response decision data to generate a real-time disaster chain feedback data set to perform regional risk prevention and control management operations.

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