A typhoon storm surge disaster risk assessment method and system for water engineering safety

Abstract

The present invention relates to the field of disaster identification and assessment technology, and in particular to a typhoon storm surge disaster risk assessment method and system for water engineering safety. The method comprises the following steps: obtaining typhoon real-time monitoring data; analyzing the typhoon impact range of the typhoon real-time monitoring data to generate typhoon impact range data; predicting the typhoon movement path of the typhoon real-time monitoring data based on the typhoon impact range data to generate typhoon movement path prediction data; obtaining geographic space data; performing preliminary disaster-affected area division on the geographic space data based on the typhoon movement path prediction data and the typhoon impact range data to generate preliminary disaster-affected area data. The present invention improves the real-time, accuracy and flexibility in typhoon storm surge disaster risk identification and assessment through wind-rainstorm-storm-wave comprehensive risk data analysis, precise geographic space assessment, dynamic emergency response optimization and full life cycle management.

Description

Technical Field

[0001] The present invention relates to the technical field of disaster identification, and in particular to a typhoon storm surge disaster risk assessment method and system for water engineering safety. Background Art

[0002] Early typhoon forecasts relied on limited meteorological observations and simple meteorological models. Scientists mainly relied on ship and land observation data, combined with basic statistical methods, to estimate the path and intensity of typhoons. The technology of this period limited the accuracy and timeliness of risk identification. Entering the mid-20th century, the application of satellite technology brought a major breakthrough. Meteorological satellites provide a large amount of real-time typhoon data, allowing meteorologists to track typhoon paths and intensities more accurately. In addition, the introduction of numerical weather forecast models, combined with advances in computer technology, has significantly improved the accuracy and timeliness of typhoon forecasts. With the development of remote sensing technology and big data analysis, the typhoon storm surge disaster risk assessment method for water engineering safety has been further optimized. The combination of high-resolution satellite images, meteorological radar data, and geographic information systems (GIS) has made risk assessment not limited to the typhoon itself, but also able to analyze its impact on different regions. The introduction of machine learning and artificial intelligence has made data processing and pattern recognition more intelligent, and can more accurately predict the potential impact area and intensity of typhoons. However, the current traditional division of disaster-stricken areas lacks detailed and precise analysis, while the emergency response strategies lack flexibility and adaptability, which results in low real-time, accuracy and flexibility in typhoon storm surge disaster risk assessment for water engineering safety. Summary of the invention

[0003] Based on this, it is necessary to provide a typhoon storm surge disaster risk assessment method and system for water engineering safety to solve at least one of the above technical problems.

[0004] To achieve the above object, a typhoon storm surge disaster risk assessment method for water engineering safety is provided, the method comprising the following steps:

[0005] Step S1: Acquire typhoon real-time monitoring data; perform typhoon impact range analysis on the typhoon real-time monitoring data to generate typhoon impact range data; perform typhoon movement path prediction on the typhoon real-time monitoring data according to the typhoon impact range data to generate typhoon movement path prediction data;

[0006] Step S2: Acquire geographic spatial data; perform preliminary disaster-affected area division on the geographic spatial data according to the typhoon movement path prediction data and the typhoon impact range data, and generate preliminary disaster-affected area data; perform network connectivity analysis on the preliminary disaster-affected area data, and generate preliminary disaster-affected area connectivity analysis data; perform detailed zoning on the preliminary disaster-affected area data through the preliminary disaster-affected area connectivity analysis data, and generate typhoon-affected detailed affected area data; perform regional risk map construction on the typhoon-affected detailed affected area data, and generate a comprehensive risk map of the affected area;

[0007] Step S3: Perform matching analysis on the risk distribution map data with the historical emergency response strategy library to generate real-time emergency strategy adjustment data; perform resource allocation and emergency measures optimization processing on the real-time emergency strategy adjustment data to generate dynamic emergency response matrix data; perform typhoon disaster aftereffect evaluation on the real-time emergency strategy adjustment data through the dynamic emergency response matrix data to generate typhoon disaster aftereffect evaluation data;

[0008] Step S4: Formulate a disaster strategy for the entire process based on the disaster post-effect assessment report data to generate pre-disaster prevention strategy data, disaster emergency response optimization data, and post-disaster recovery and reconstruction data; integrate the full life cycle management framework through the pre-disaster prevention strategy data, disaster emergency response optimization data, and post-disaster recovery and reconstruction data to generate a full life cycle management database to perform typhoon disaster risk identification and assessment operations.

[0009] The present invention can quickly identify the latest dynamics of a typhoon, including its location, intensity and development trend, by acquiring typhoon monitoring data in real time. This real-time monitoring can timely update the typhoon impact range data, making subsequent analysis and decision-making more accurate. According to the typhoon impact range data, the moving path prediction can be carried out to estimate the future path and intensity changes of the typhoon. This prediction helps to identify the affected areas in advance and provide data support for emergency preparedness. The preliminary disaster-affected area division of geographic spatial data using typhoon moving path prediction data and impact range data can identify the preliminary scope of the disaster-affected area. This helps to quickly determine the areas that need attention and make targeted emergency preparations. Network connectivity analysis can reveal the infrastructure and transportation network conditions in the disaster-affected area and identify isolated or restricted areas. This analysis helps to optimize resource allocation and ensure that emergency resources can effectively cover the disaster-affected area. By refining the zoning, more accurate disaster-affected area data can be generated. This refinement can improve the accuracy of disaster assessment and make the risk assessment of a specific area more detailed and reliable. The construction of a regional risk map helps to comprehensively assess the risk situation of the disaster-affected area, provide an intuitive risk distribution map, and provide visual support for disaster management and emergency decision-making. Through the matching analysis of the historical emergency response strategy library, a real-time emergency strategy suitable for the current typhoon disaster can be generated. This adjustment can quickly adjust emergency measures according to the actual situation of the typhoon, improve the pertinence and effectiveness of the response. The data processing of the dynamic emergency response matrix can optimize resource allocation, ensure that resources can be effectively deployed to the most needed areas, and improve the efficiency and effectiveness of the emergency response. Through the dynamic emergency response matrix, the actual impact of the typhoon disaster can be analyzed, and data support can be provided for subsequent recovery work, which helps to evaluate and improve the emergency response strategy. The formulation of strategies for pre-disaster prevention, emergency response during disasters, and post-disaster recovery and reconstruction helps to establish a comprehensive disaster management system covering the entire process of typhoon disasters. This comprehensive strategy formulation can improve the systematicness and efficiency of disaster management. Through the integration of the full life cycle management framework, all aspects of prevention, emergency response, and recovery and reconstruction can be systematically managed. This integrated management ensures a comprehensive response to typhoon disasters and improves the overall ability of disaster risk identification and assessment. Therefore, the present invention improves the real-time, accuracy, and flexibility of typhoon storm surge disaster risk assessment for water engineering safety through real-time data analysis, precise geospatial assessment, dynamic emergency response optimization, and full life cycle management.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Acquire typhoon real-time monitoring data;

[0012] Step S12: preprocessing the typhoon real-time monitoring data to generate standard typhoon real-time monitoring data, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization;

[0013] Step S13: extracting the typhoon center wind force from the standard typhoon real-time monitoring data to generate typhoon eye wind force data; analyzing the typhoon impact range of the typhoon real-time monitoring data based on the typhoon risk wind force data to generate typhoon impact range data;

[0014] Step S14: predicting the typhoon's moving path based on the typhoon's real-time monitoring data according to the typhoon's impact range data, and generating typhoon moving path prediction data.

[0015] The present invention ensures the timeliness of typhoon movement path prediction by acquiring real-time monitoring data of typhoons. At the same time, data preprocessing steps such as data cleaning, denoising and missing value filling further improve the accuracy of the data and reduce the prediction error. The standard typhoon real-time monitoring data generated by preprocessing can eliminate the data deviation between different monitoring sources, making subsequent analysis and prediction more consistent and reliable. Extracting typhoon center wind data helps to accurately locate the eye of the typhoon, and by analyzing the typhoon impact range, the potential risks of typhoons to different regions can be better assessed, which is convenient for decision makers to take preventive measures. Using typhoon impact range data to predict the movement path of the typhoon can more accurately estimate the future movement trajectory of the typhoon, provide more valuable prediction information, and help emergency response and resource allocation. This step covers everything from data acquisition, preprocessing, feature extraction to final path prediction, making full use of the multi-dimensional information of typhoon monitoring data, making typhoon analysis more comprehensive, and helping to formulate more scientific disaster prevention and mitigation strategies.

[0016] Preferably, step S14 comprises the following steps:

[0017] Step S141: Detecting the typhoon wind field using meteorological radar technology based on the typhoon impact range data and the real-time typhoon monitoring data to generate typhoon wind field detection data;

[0018] Step S142: extracting key features from the typhoon wind field detection data to obtain key feature data of the typhoon wind field, wherein the key feature data of the typhoon wind field includes wind field vorticity data, wind field divergence data and wind field streamline data; performing clustering pattern recognition on the wind field vorticity data, wind field divergence data and wind field streamline data to generate a wind field feature matrix;

[0019] Step S143: Calculate the typhoon wind field guide flow on the wind field characteristic matrix to obtain typhoon wind field guide flow data; predict the overall movement trend of the typhoon on the typhoon real-time monitoring data through the typhoon wind field guide flow data to generate typhoon overall movement trend prediction data;

[0020] Step S144: extracting the edge atmospheric pressure of the typhoon real-time monitoring data based on the typhoon overall movement trend prediction data to generate typhoon edge atmospheric pressure data; converting the typhoon edge atmospheric pressure data into a gradient map to generate a typhoon edge atmospheric pressure gradient map;

[0021] Step S145: Calculate the pressure difference of the atmospheric pressure gradient map at the edge of the typhoon to generate the atmospheric pressure difference data at the edge of the typhoon; perform regional trough analysis on the atmospheric pressure gradient map at the edge of the typhoon using the atmospheric pressure difference data at the edge of the typhoon to generate the typhoon movement trough data; use the typhoon movement trough data to perform local movement prediction on the typhoon's overall movement trend prediction data to generate the typhoon movement path prediction data.

[0022] The present invention detects the typhoon wind field by using meteorological radar technology to generate accurate wind field detection data, which helps to more comprehensively grasp the real-time dynamics of the typhoon, especially the structure and changes of the wind field. By extracting the key features of the wind field, such as vorticity, divergence and streamline data, and performing clustering pattern recognition to generate a wind field feature matrix, this in-depth analysis makes the understanding of the wind field more accurate and helps to identify the intensity and development trend of the typhoon. The wind field feature matrix is ​​guided by flow calculation, and the overall movement trend of the typhoon is predicted based on this. This process can capture the dominant movement trend of the typhoon, provide more accurate path prediction, and help take preventive measures in time. By extracting the edge atmospheric pressure and generating a gradient map, the pressure changes at the edge of the typhoon can be more clearly observed, especially the distribution of the pressure gradient, which provides important data support for subsequent regional trough analysis and path prediction. Through regional trough analysis, combined with the atmospheric pressure difference data at the edge of the typhoon, the local movement trend of the typhoon can be more accurately predicted, further improving the accuracy of path prediction, especially in complex terrain or climate environment. Combining overall trends and local path predictions, this method can provide more comprehensive and detailed typhoon path prediction data, which helps to improve the scientificity and timeliness of disaster prevention and mitigation decisions.

[0023] Preferably, step S2 comprises the following steps:

[0024] Step S21: Acquire geographic spatial data;

[0025] Step S22: Calculate the path influence of the geographic space data according to the typhoon movement path prediction data and the typhoon impact range data to generate typhoon influence data; use the typhoon influence data to perform preliminary disaster-affected area division on the geographic space data to generate preliminary disaster-affected area data;

[0026] Step S23: Identify the network key feature points of the preliminary disaster-affected area data to generate the key feature point data of the disaster-affected area, wherein the network key feature point identification includes city key points, transportation hub key points and important facility key points;

[0027] Step S24: using the key feature point data of the disaster-affected area as network nodes to perform network connectivity analysis, and generating preliminary disaster-affected area connectivity analysis data; using the preliminary disaster-affected area connectivity analysis data to refine the preliminary disaster-affected area data, and generating typhoon-affected refined affected area data;

[0028] Step S25: Perform multi-dimensional risk distribution risk assessment on the typhoon disaster-affected detailed area data to generate multi-dimensional risk distribution data of the disaster-affected area; perform dynamic risk propagation simulation on the typhoon disaster-affected detailed area data according to the multi-dimensional risk distribution data of the disaster-affected area to generate risk propagation path data of the disaster-affected area;

[0029] Step S26: Construct a regional risk map based on the risk propagation path data of the disaster-stricken area to generate a comprehensive risk map of the disaster-stricken area.

[0030] The present invention calculates the path influence of geographic space data by combining typhoon movement path prediction data and impact range data, thereby generating more accurate typhoon influence data. This step helps to preliminarily divide the affected areas and ensure the accuracy of subsequent analysis. The network key feature points of the preliminary affected areas are identified, such as urban key points, transportation hubs and important facilities, which can effectively identify the key nodes in the affected areas and help to give priority to protecting important resources and infrastructure. Connectivity analysis of the key feature points of the affected areas as network nodes helps to understand and evaluate the potential impact of typhoons on regional transportation, communication and infrastructure networks, further refine the division of affected areas, and enhance the connectivity and integrity of disaster response. By conducting a multi-dimensional risk distribution assessment on the typhoon-affected and refined affected areas, risks in different dimensions, such as geography, population, economy, etc., can be identified. This comprehensive risk analysis helps to formulate a more comprehensive emergency response plan and disaster prevention and mitigation strategy. Dynamic risk propagation simulation based on multi-dimensional risk distribution data can predict the propagation path of risks in the affected areas, identify high-risk areas in advance, and provide decision makers with accurate risk warning information, so as to facilitate timely and effective response measures. Finally, a regional risk map is constructed based on the risk propagation path data, which can not only comprehensively display the comprehensive risk distribution of the disaster-stricken areas, but also help plan emergency rescue and post-disaster recovery work, and improve the scientificity and effectiveness of disaster management.

[0031] Preferably, step S25 comprises the following steps:

[0032] Step S251: Perform terrain analysis on the typhoon-affected detailed area data to generate typhoon-affected terrain feature data; divide the typhoon-affected detailed area data into coastal and inland areas based on the typhoon-affected terrain feature data to generate typhoon-affected coastal area data and typhoon-affected inland area data;

[0033] Step S252: Performing a huge wave risk assessment on the data of the coastal areas affected by the typhoon to generate the huge wave risk assessment data of the coastal areas affected by the typhoon; performing a strong wind and heavy rain risk assessment on the data of the inland areas affected by the typhoon to generate the strong wind risk assessment data of the inland areas affected by the typhoon and the heavy rain risk assessment data of the inland areas affected by the typhoon; performing a storm surge risk assessment on the data of the coastal areas affected by the typhoon to generate the typhoon storm surge risk assessment data;

[0034] Step S253: superimposing and weighting the risk factors of the typhoon-affected coastal giant wave risk assessment data, the typhoon-affected inland strong wind risk assessment data, the typhoon storm surge risk assessment data, and the typhoon-affected inland rainstorm risk assessment data to generate multi-dimensional risk distribution data for the affected area;

[0035] Step S254: Perform dynamic risk propagation simulation on the typhoon disaster-affected area data according to the multi-dimensional risk distribution data of the disaster-affected area to generate risk propagation path data for the disaster-affected area.

[0036] The present invention refines the typhoon-affected areas into coastal and inland areas through terrain analysis, and considers the impact of terrain on different types of risks (such as huge waves, strong winds, heavy rains, and storm surges), making risk assessment more targeted. The coastal areas are assessed for huge wave risks, and the inland areas are assessed for strong winds, heavy rains, and storm surges. In this way, more accurate risk predictions can be provided for different regions to ensure that the risk assessment covers all possible types of disasters. Different risk factors are superimposed and weighted to generate multidimensional risk distribution data for the affected areas. This comprehensive assessment method takes into account the interaction between multiple risk factors, provides a more comprehensive risk distribution map, and provides decision makers with richer information. Based on the multidimensional risk distribution data, dynamic risk propagation simulation is performed, which can foresee the spread and change trend of risks and identify high-risk areas in advance. This simulation provides forward-looking support for disaster prevention and mitigation decisions and helps to optimize emergency response measures. From terrain analysis to multidimensional risk superposition, and then to dynamic risk propagation simulation, the entire process covers all aspects from regional division to risk assessment to risk propagation, providing a systematic solution for disaster management and improving the ability to respond to typhoon disasters. Through detailed regional risk assessment, the generated multi-dimensional risk data can help formulate differentiated response strategies for coastal and inland areas, ensuring the effective allocation of resources and the precise implementation of post-disaster recovery work.

[0037] Preferably, step S254 includes the following steps:

[0038] Step S2541: Performing a wind dynamic propagation simulation on the multi-dimensional risk distribution data of the disaster-stricken area to generate wind risk propagation path data;

[0039] Step S2542: Perform rainstorm impact analysis on the wind risk propagation path data to generate wind-rainstorm superposition risk propagation data;

[0040] Step S2543: Perform coastal storm surge impact analysis on the wind-rainstorm superposition risk propagation data to generate wind-storm surge superposition risk propagation data;

[0041] Step S2544: performing huge wave risk diffusion calculation on the wind-storm surge superposition risk propagation data and the wind-rainstorm superposition risk propagation data to generate wind-rainstorm-huge wave comprehensive risk propagation path data;

[0042] Step S2545: Conduct a regional risk increment assessment on the wind-rainstorm-huge wave comprehensive risk propagation path data to generate final risk propagation path data for the affected area.

[0043] The present invention can accurately predict the diffusion path of wind risk in the disaster-stricken area by simulating the dynamic propagation of wind on multi-dimensional risk distribution data. This provides a scientific basis for identifying potential high-risk areas and helps to take preventive measures in a timely manner. On the basis of the wind risk propagation path, the impact of heavy rain on wind propagation is further analyzed to generate wind-heavy rain superposition risk propagation data. This analysis method combines two common typhoon disaster factors, provides a more comprehensive risk prediction, and provides more powerful support for coping with complex disasters. By performing a huge wave risk diffusion calculation on the wind-heavy rain superposition risk propagation data, wind-heavy rain-huge wave comprehensive risk propagation path data is generated. By performing a storm surge risk diffusion calculation on the wind-tide superposition risk propagation data, wind-tide comprehensive risk propagation path data is generated. This step combines the three main disaster factors, making the risk propagation prediction more comprehensive and three-dimensional, and providing a more complete risk panorama for disaster management. By performing a regional risk increment assessment on the wind-heavy rain-huge wave comprehensive risk propagation path data, the risk increase points in the region can be accurately identified, and the final risk propagation path data of the disaster-stricken area can be generated. This incremental assessment helps to accurately locate the hotspots of risk diffusion, facilitate the priority allocation of rescue resources, and reduce the impact of disasters. Based on the final risk propagation path data, the emergency response strategy can be dynamically adjusted to ensure the efficient use of resources and the timely update of response measures, thereby improving the flexibility and effectiveness of disaster response. By comprehensively considering the interaction of wind, heavy rain and huge waves and their propagation paths, a more accurate early warning mechanism can be provided for the disaster management system, helping disaster prevention and mitigation departments to deploy emergency measures in advance and reduce casualties and property losses. The risk propagation path data finally generated provides decision makers with scientific and detailed reference information, making disaster response measures more targeted and effective. This data-driven decision support system can significantly improve the efficiency and effectiveness of disaster management.

[0044] Preferably, step S3 comprises the following steps:

[0045] Step S31: Perform matching analysis on the risk distribution map data with the historical emergency response strategy library to generate preliminary emergency response strategy data; perform real-time monitoring data fusion processing on the preliminary emergency response strategy data to generate real-time emergency strategy adjustment data;

[0046] Step S32: performing strategy optimization iteration on the real-time emergency strategy adjustment data to generate emergency response strategy optimization data; performing resource allocation and emergency measures optimization processing on the emergency response strategy optimization data to generate dynamic emergency response matrix data;

[0047] Step S33: Perform disaster simulation on the real-time emergency strategy adjustment data through dynamic emergency response matrix data, and collect post-disaster impact data to obtain a typhoon post-disaster simulation data set; perform typhoon disaster aftereffect evaluation on the typhoon post-disaster simulation data set to generate typhoon disaster aftereffect evaluation data.

[0048] The present invention generates preliminary emergency response strategy data by matching and analyzing the risk distribution map data with the historical emergency response strategy library, and fuses it with the real-time monitoring data to generate real-time emergency strategy adjustment data. This fusion processing method combines historical experience and real-time information to ensure that the emergency strategy has both the guidance of historical experience and can be adjusted in real time according to the current situation, thereby improving the accuracy and timeliness of the response. The real-time emergency strategy adjustment data is subjected to strategy optimization iteration to generate emergency response strategy optimization data, so that the emergency response strategy can be continuously improved and improved in practical applications. Through this iterative optimization, the efficiency and effect of the emergency response can be continuously improved, making the strategy more adaptable and operable. By performing resource allocation and emergency measures optimization processing on the optimized emergency response strategy data, dynamic emergency response matrix data is generated. This matrix data integrates multi-dimensional emergency resources and measures, so that resources can be flexibly allocated in the emergency response process, the optimal emergency measures can be implemented, and the efficiency and order of the emergency response can be ensured. The real-time emergency strategy is simulated by using the dynamic emergency response matrix data for disaster simulation, and the post-disaster impact data is collected to obtain a typhoon post-disaster simulation data set. Through this simulation, not only can the effectiveness of emergency response strategies be evaluated in advance, but also preliminary assessment data of disaster impacts can be quickly obtained after a disaster occurs, supporting timely post-disaster rescue and recovery work. The typhoon disaster after-effect evaluation is performed on the typhoon post-disaster simulation data set to generate typhoon disaster after-effect evaluation data. This step provides a scientific post-disaster evaluation mechanism that can quantitatively evaluate the long-term impact of disasters, provide data support for post-disaster recovery work, and help formulate more targeted recovery and reconstruction strategies. The entire step makes the typhoon emergency management process more intelligent and systematic through automated data analysis, strategy optimization, simulation and evaluation, reduces the blindness and subjectivity of human decision-making, and greatly improves the overall level of emergency management.

[0049] Preferably, step S33 includes the following steps:

[0050] Step S331: performing Monte Carlo simulation on the real-time emergency strategy adjustment data through dynamic emergency response matrix data to generate typhoon disaster simulation data; collecting disaster characteristic data on the typhoon disaster simulation data to generate a typhoon post-disaster simulation data set;

[0051] Step S332: dividing the typhoon disaster post-simulation data set into a data set to generate a model training set and a model test set; using the after-effect evaluation algorithm to perform model training on the model training set to generate a typhoon disaster after-effect evaluation pre-model; based on the model test set, performing model optimization iteration on the typhoon disaster after-effect evaluation pre-model to generate a typhoon disaster after-effect evaluation model;

[0052] Step S333: importing the typhoon disaster post-simulation data set into the typhoon disaster after-effect assessment model to perform typhoon disaster after-effect assessment and generate typhoon disaster after-effect assessment data.

[0053] The present invention uses the Monte Carlo simulation method in step S331 to perform a large number of randomized experiments on the real-time emergency strategy adjustment data to generate typhoon disaster simulation data. This simulation method can handle uncertainty and generate a variety of possible disaster scenarios, so that the simulation results are closer to reality and the accuracy and reliability of disaster assessment are improved. In step S332, the typhoon disaster post-simulation data set is divided into data sets to generate a model training set and a model test set respectively to ensure the reasonable distribution and utilization of data. Subsequently, the model training and optimization iteration are performed through the after-effect evaluation algorithm to generate a more accurate typhoon disaster after-effect evaluation model. This process improves the accuracy of the evaluation results by continuously optimizing the model, ensuring that the final typhoon disaster after-effect evaluation model has good generalization ability. By importing the typhoon disaster post-simulation data set into the optimized evaluation model, the after-effect of the typhoon disaster can be scientifically evaluated. The typhoon disaster after-effect evaluation data generated in this step provides strong data support for post-disaster recovery work, allowing decision makers to more accurately evaluate the impact range, severity and long-term consequences of the disaster, thereby formulating more reasonable recovery and reconstruction strategies. The model optimization iteration in step S332 enables the typhoon disaster after-effect assessment model to better adapt to different disaster scenarios and improves the model's predictive ability and stability. This makes the assessment results not only applicable to the current disaster situation, but also can provide a reliable reference for similar disasters in the future. The entire step relies on large-scale data simulation and model training to ensure that the assessment results are based on rich data and powerful algorithm support. This data-driven decision-making mechanism reduces the impact of human factors, enhances the scientificity and objectivity of decision-making, and thus provides more accurate guidance in post-disaster recovery and resource allocation. By using dynamic emergency response matrix data for disaster simulation and combining the typhoon disaster after-effect assessment model for post-disaster assessment, a closed-loop disaster emergency management process is formed. This process not only focuses on pre-disaster emergency response, but also provides a complete solution for post-disaster recovery and assessment, improving the efficiency and effectiveness of overall emergency management.

[0054] Preferably, step S4 comprises the following steps:

[0055] Step S41: performing pre-disaster risk identification on the disaster post-effect assessment report data to generate pre-disaster risk identification data; performing pre-disaster prevention strategy formulation on the pre-disaster risk identification data to generate pre-disaster prevention strategy data;

[0056] Step S42: Optimize the emergency response strategy during the disaster based on the pre-disaster prevention strategy data to generate optimized emergency response data during the disaster; formulate a post-disaster recovery and reconstruction plan based on the optimized emergency response data during the disaster to generate post-disaster recovery and reconstruction data;

[0057] Step S43: construct a full life cycle management framework for post-disaster recovery and reconstruction data to generate full life cycle management framework data; perform technical integration and application on the full life cycle management framework data to generate a full life cycle management database to perform typhoon disaster risk identification and assessment operations.

[0058] The present invention generates pre-disaster risk identification data by performing pre-disaster risk identification on the disaster post-effect evaluation report data. This pre-disaster risk identification method based on post-disaster evaluation makes pre-disaster prevention more scientific and accurate, helps to identify potential risks early and formulate targeted prevention strategies, reduce the possibility of disasters or reduce the losses caused by disasters. Pre-disaster prevention strategy data is used to optimize the emergency response strategy during the disaster. Through in-depth analysis of pre-disaster prevention strategy data, emergency response measures can be effectively optimized to make them more adaptable to actual conditions, improve the efficiency and effectiveness of emergency response, so as to respond quickly and effectively when disasters occur and reduce the direct impact of disasters. Analysis of the optimization data of emergency response during the disaster is used to formulate a post-disaster recovery and reconstruction plan. This process ensures the systematic and comprehensive nature of post-disaster recovery and reconstruction work, helps to quickly restore the normal order of the disaster area, and promotes the long-term sustainable development of the disaster area through reasonable resource allocation and planning. By constructing a full life cycle management framework for post-disaster recovery and reconstruction data, a complete management path from pre-disaster prevention to post-disaster recovery is provided. This framework management method not only covers all stages of disaster management, but also ensures the organic connection of each stage, thereby improving the overall efficiency and effectiveness of disaster management. The data of the life cycle management framework is further integrated into the life cycle management database. Through the application of technology integration, the seamless connection of data and technology in the disaster management process is achieved. This integrated application method greatly improves the efficiency of data utilization and makes disaster management more systematic and forward-looking. The establishment of the life cycle management database provides a rich data foundation and experience accumulation for future disaster management. By continuously analyzing and optimizing these data, disaster management strategies can be continuously improved, making future disaster prevention, disaster reduction, emergency response and post-disaster reconstruction more scientific and effective.

[0059] In this specification, a typhoon storm surge disaster risk assessment system for water engineering safety is provided, which is used to execute the above-mentioned typhoon storm surge disaster risk assessment method for water engineering safety. The typhoon storm surge disaster risk assessment system for water engineering safety includes:

[0060] A typhoon movement identification module is used to obtain typhoon real-time monitoring data; analyze the typhoon impact range of the typhoon real-time monitoring data to generate typhoon impact range data; predict the typhoon movement path of the typhoon real-time monitoring data based on the typhoon impact range data to generate typhoon movement path prediction data;

[0061] The disaster-affected area analysis module is used to obtain geographic spatial data; perform preliminary disaster-affected area division on the geographic spatial data according to the typhoon movement path prediction data and the typhoon impact range data, and generate preliminary disaster-affected area data; perform network connectivity analysis on the preliminary disaster-affected area data, and generate preliminary disaster-affected area connectivity analysis data; refine the preliminary disaster-affected area data through the preliminary disaster-affected area connectivity analysis data, and generate typhoon-affected refined affected area data; construct a regional risk map for the typhoon-affected refined affected area data, and generate a comprehensive risk map for the disaster-affected area;

[0062] The after-effect evaluation module is used to match and analyze the historical emergency response strategy library with the risk distribution map data to generate real-time emergency strategy adjustment data; optimize resource allocation and emergency measures for the real-time emergency strategy adjustment data to generate dynamic emergency response matrix data; and evaluate the after-effect of typhoon disasters on the real-time emergency strategy adjustment data through the dynamic emergency response matrix data to generate typhoon disaster after-effect evaluation data;

[0063] The whole process management module is used to formulate disaster strategies for the whole process based on the disaster post-effect assessment report data, generate pre-disaster prevention strategy data, disaster emergency response optimization data and post-disaster recovery and reconstruction data; integrate the whole life cycle management framework through the pre-disaster prevention strategy data, disaster emergency response optimization data and post-disaster recovery and reconstruction data, and generate a whole life cycle management database to perform typhoon disaster risk identification and assessment operations.

[0064] The beneficial effect of the present invention is that by obtaining real-time typhoon monitoring data and performing impact range analysis and movement path prediction, the early warning of typhoons is made more timely and accurate. This can identify the typhoon-affected areas and movement trends in advance, thereby providing reliable data support for prevention and emergency response. By dividing the affected areas and analyzing the network connectivity of geographic spatial data, detailed disaster-affected area data and risk maps are generated. This process helps to identify the key network points and risk distribution in the disaster-affected areas, ensure that the assessment of the disaster area is more comprehensive and detailed, and help to formulate more effective rescue and recovery plans. By matching the emergency strategy library and optimizing resource allocation for the risk distribution map data, dynamic emergency response matrix data is generated. This step can improve the accuracy and efficiency of emergency response, ensure that resources are reasonably allocated and utilized, so that rescue and post-disaster assessment can be carried out quickly and effectively when a typhoon occurs. By formulating a full-process strategy for the disaster post-effect assessment report data, it covers all stages before, during, and after the disaster, and integrates it into a full life cycle management framework. This comprehensive management approach ensures that every stage of disaster management is fully considered, and through the establishment of a full life cycle management database, systematic risk identification and assessment are achieved, optimizing the overall efficiency of disaster management. The overall coordination of disaster management is improved by systematically integrating monitoring data, risk assessment, emergency response and recovery planning. Each step supports and complements each other, ensuring the smooth and efficient work of disaster management, so that better prevention, response and recovery can be achieved in the face of natural disasters such as typhoons. The implementation of the entire process relies on precise data analysis and model optimization, providing decision makers with data-driven decision support. This can reduce the bias of human judgment, improve the scientificity and accuracy of decision-making, and provide a solid data foundation for disaster response and recovery. Through modern data processing and technology integration, step S4 incorporates the full life cycle management framework into the full life cycle management database, realizing the intelligence of disaster management. This not only improves the efficiency of disaster management, but also enhances its flexibility and adaptability, making disaster management more in line with the current technological development trend. Therefore, the present invention improves the real-time, accuracy and flexibility of typhoon storm surge disaster risk assessment for water engineering safety through real-time data analysis, precise geospatial assessment, dynamic emergency response optimization and full life cycle management. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A schematic diagram of the steps of a typhoon storm surge disaster risk assessment method for water engineering safety;

[0066] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0067] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0068] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0069] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0070] 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 figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying 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 implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0071] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0072] To achieve this, please refer to Figures 1 to 3 , a typhoon storm surge disaster risk assessment method for water engineering safety, the method comprising the following steps:

[0073] Step S1: Acquire typhoon real-time monitoring data; perform typhoon impact range analysis on the typhoon real-time monitoring data to generate typhoon impact range data; perform typhoon movement path prediction on the typhoon real-time monitoring data according to the typhoon impact range data to generate typhoon movement path prediction data;

[0074] Step S2: Acquire geographic spatial data; perform preliminary disaster-affected area division on the geographic spatial data according to the typhoon movement path prediction data and the typhoon impact range data, and generate preliminary disaster-affected area data; perform network connectivity analysis on the preliminary disaster-affected area data, and generate preliminary disaster-affected area connectivity analysis data; perform detailed zoning on the preliminary disaster-affected area data through the preliminary disaster-affected area connectivity analysis data, and generate typhoon-affected detailed affected area data; perform regional risk map construction on the typhoon-affected detailed affected area data, and generate a comprehensive risk map of the affected area;

[0075] Step S3: Perform matching analysis on the risk distribution map data with the historical emergency response strategy library to generate real-time emergency strategy adjustment data; perform resource allocation and emergency measures optimization processing on the real-time emergency strategy adjustment data to generate dynamic emergency response matrix data; perform typhoon disaster aftereffect evaluation on the real-time emergency strategy adjustment data through the dynamic emergency response matrix data to generate typhoon disaster aftereffect evaluation data;

[0076] Step S4: Formulate a disaster strategy for the entire process based on the disaster post-effect assessment report data to generate pre-disaster prevention strategy data, disaster emergency response optimization data, and post-disaster recovery and reconstruction data; integrate the full life cycle management framework through the pre-disaster prevention strategy data, disaster emergency response optimization data, and post-disaster recovery and reconstruction data to generate a full life cycle management database to perform typhoon disaster risk identification and assessment operations.

[0077] The present invention can quickly identify the latest dynamics of a typhoon, including its location, intensity and development trend, by acquiring typhoon monitoring data in real time. This real-time monitoring can timely update the typhoon impact range data, making subsequent analysis and decision-making more accurate. According to the typhoon impact range data, the moving path prediction can be carried out to estimate the future path and intensity changes of the typhoon. This prediction helps to identify the affected areas in advance and provide data support for emergency preparedness. The preliminary disaster-affected area division of geographic spatial data using typhoon moving path prediction data and impact range data can identify the preliminary scope of the disaster-affected area. This helps to quickly determine the areas that need attention and make targeted emergency preparations. Network connectivity analysis can reveal the infrastructure and transportation network conditions in the disaster-affected area and identify isolated or restricted areas. This analysis helps to optimize resource allocation and ensure that emergency resources can effectively cover the disaster-affected area. By refining the zoning, more accurate disaster-affected area data can be generated. This refinement can improve the accuracy of disaster assessment and make the risk assessment of a specific area more detailed and reliable. The construction of a regional risk map helps to comprehensively assess the risk situation of the disaster-affected area, provide an intuitive risk distribution map, and provide visual support for disaster management and emergency decision-making. Through the matching analysis of the historical emergency response strategy library, a real-time emergency strategy suitable for the current typhoon disaster can be generated. This adjustment can quickly adjust emergency measures according to the actual situation of the typhoon, improve the pertinence and effectiveness of the response. The data processing of the dynamic emergency response matrix can optimize resource allocation, ensure that resources can be effectively deployed to the most needed areas, and improve the efficiency and effectiveness of the emergency response. Through the dynamic emergency response matrix, the actual impact of the typhoon disaster can be analyzed, and data support can be provided for subsequent recovery work, which helps to evaluate and improve the emergency response strategy. The formulation of strategies for pre-disaster prevention, emergency response during disasters, and post-disaster recovery and reconstruction helps to establish a comprehensive disaster management system covering the entire process of typhoon disasters. This comprehensive strategy formulation can improve the systematicness and efficiency of disaster management. Through the integration of the full life cycle management framework, all aspects of prevention, emergency response, and recovery and reconstruction can be systematically managed. This integrated management ensures a comprehensive response to typhoon disasters and improves the overall ability of disaster risk identification and assessment. Therefore, the present invention improves the real-time, accuracy, and flexibility of typhoon storm surge disaster risk assessment for water engineering safety through real-time data analysis, precise geospatial assessment, dynamic emergency response optimization, and full life cycle management.

[0078] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a typhoon storm surge disaster risk assessment method for water engineering safety of the present invention. In this example, the typhoon storm surge disaster risk assessment method for water engineering safety includes the following steps:

[0079] Step S1: Acquire typhoon real-time monitoring data; perform typhoon impact range analysis on the typhoon real-time monitoring data to generate typhoon impact range data; perform typhoon movement path prediction on the typhoon real-time monitoring data according to the typhoon impact range data to generate typhoon movement path prediction data;

[0080] In an embodiment of the present invention, real-time data of a typhoon is obtained from meteorological satellites, radars, weather stations, and ocean observation equipment. These data include the location, wind speed, air pressure, humidity, and other meteorological variables of the typhoon. Ensure that the acquired data format is unified, including satellite images, radar echo maps, meteorological records, etc. Clean and standardize the data to remove noise and outliers. Process the satellite images to extract the cloud range and intensity distribution of the typhoon. Use a meteorological model or machine learning algorithm to analyze the impact range of the typhoon. Common methods include wind field simulation, intensity distribution analysis, and impact radius calculation to generate typhoon impact range data and identify the areas affected by the typhoon, including strong wind areas, rainstorm areas, etc. Use the historical trajectory and impact range data of the typhoon to establish a prediction model. Time series analysis, regression analysis, or deep learning models can be used. Train and optimize the prediction model based on the typhoon impact range data, taking into account factors such as the typhoon's moving speed, direction, and meteorological changes. Apply the trained model to predict the future path of the typhoon and generate typhoon movement path prediction data. This includes predicting the movement trajectory, expected arrival time, and change trend of the typhoon. Visualize typhoon impact area data and typhoon movement path forecast data to generate charts or maps for easier understanding by decision makers and the public.

[0081] Step S2: Acquire geographic spatial data; perform preliminary disaster-affected area division on the geographic spatial data according to the typhoon movement path prediction data and the typhoon impact range data, and generate preliminary disaster-affected area data; perform network connectivity analysis on the preliminary disaster-affected area data, and generate preliminary disaster-affected area connectivity analysis data; perform detailed zoning on the preliminary disaster-affected area data through the preliminary disaster-affected area connectivity analysis data, and generate typhoon-affected detailed affected area data; perform regional risk map construction on the typhoon-affected detailed affected area data, and generate a comprehensive risk map of the affected area;

[0082] In an embodiment of the present invention, geospatial data including topography, infrastructure, land use, population distribution and other information are obtained from a geographic information system (GIS), satellite images, topographic maps and map services. Ensure that the format of geospatial data is unified, and common formats include vector data (such as shapefile, GeoJSON) and raster data (such as GeoTIFF). Integrate typhoon movement path prediction data and typhoon impact range data with geospatial data. Match the typhoon impact range with geographic data through spatial overlay analysis. Based on the typhoon impact range data, divide the disaster-affected area on the geospatial data. Use spatial analysis tools such as buffer analysis, overlay analysis and network analysis to generate preliminary disaster-affected area data. Obtain infrastructure network data in the area, including road networks, bridges, public transportation, etc. Use network analysis tools to evaluate the network connectivity of the disaster-affected area. Analyze whether the disaster-affected area is affected by network interruption, such as whether the road is blocked and whether public facilities are accessible, generate preliminary disaster-affected area connectivity analysis data, and identify the network interruption area and the main channels affected. According to the results of the network connectivity analysis, the preliminary disaster-affected area is refined. Taking into account factors such as road interruptions and bridge damage, further divide the disaster severity areas. Revise the preliminary disaster-affected area data, update the boundaries and classifications of the disaster-affected areas, and generate detailed typhoon disaster-affected area data. Integrate other risk factor data, such as geological disaster risks, building disaster resistance, population density, etc. Use spatial analysis tools and risk assessment models to conduct a comprehensive risk assessment of the detailed impact area and generate a comprehensive risk map of the disaster-affected area, including the risk level of the area, potential loss estimates, and emergency response recommendations. Visualize the comprehensive risk map of the disaster-affected area in the form of charts or maps to facilitate analysis and planning by decision makers and rescue personnel.

[0083] Step S3: Perform matching analysis on the risk distribution map data with the historical emergency response strategy library to generate real-time emergency strategy adjustment data; perform resource allocation and emergency measures optimization processing on the real-time emergency strategy adjustment data to generate dynamic emergency response matrix data; perform typhoon disaster aftereffect evaluation on the real-time emergency strategy adjustment data through the dynamic emergency response matrix data to generate typhoon disaster aftereffect evaluation data;

[0084] In an embodiment of the present invention, by collecting and establishing a historical emergency response strategy library, emergency response strategies, resource scheduling schemes and emergency measures for similar disasters in the past are included. The current risk distribution map data is matched with the strategies in the historical emergency response strategy library. Data mining technology and pattern recognition methods are used to identify historical cases similar to the current risk situation. The matching results are analyzed to generate real-time emergency strategy adjustment data. The emergency strategy is adjusted to adapt to the current risk distribution situation, taking into account the changing risk factors and new needs. Based on the real-time emergency strategy adjustment data, the required resource types and quantities are evaluated, including personnel, materials, equipment, etc. Optimization algorithms (such as linear programming, integer programming, etc.) are used to reasonably allocate resources to ensure the effective use of resources and maximize the emergency response effect. According to the optimized resource allocation scheme, the emergency measures are adjusted and optimized, including the priority of the rescue operation, the adjustment of the on-site command, the scheduling of resources, etc. Dynamic emergency response matrix data is formed, including detailed information on the resource allocation scheme, emergency measures and action plans. Actual data after the typhoon disaster is collected, including loss assessment, recovery status, resource usage, etc. Compare and analyze the dynamic emergency response matrix data with the post-disaster effect data to evaluate the effectiveness and after-effects of the emergency response strategy, including response time, resource consumption, recovery effect, etc. Based on the analysis results, generate typhoon disaster after-effect evaluation data, including strategy effectiveness evaluation, resource utilization efficiency, post-disaster recovery progress and other information. Visualize the real-time emergency strategy adjustment data and typhoon disaster after-effect evaluation data for easy understanding and decision-making. Use charts, heat maps or maps to display the effects of emergency response and after-effect evaluation results.

[0085] Step S4: Formulate a disaster strategy for the entire process based on the disaster post-effect assessment report data to generate pre-disaster prevention strategy data, disaster emergency response optimization data, and post-disaster recovery and reconstruction data; integrate the full life cycle management framework through the pre-disaster prevention strategy data, disaster emergency response optimization data, and post-disaster recovery and reconstruction data to generate a full life cycle management database to perform typhoon disaster risk identification and assessment operations.

[0086] In the embodiment of the present invention, by analyzing the disaster after-effect assessment report data in detail, the key issues and risk points in the disaster process are identified. This includes the occurrence of disasters, emergency response, and problems and challenges in the recovery process. According to the assessment data, an effective pre-disaster prevention strategy is formulated, including the establishment of an early warning system, risk assessment, improvement of emergency plans, and public education. An optimized emergency response strategy during the disaster is formulated, including emergency command, resource scheduling, on-site emergency measures, and coordination mechanisms for emergency response. A post-disaster recovery and reconstruction strategy is formulated, including post-disaster assessment, recovery plan, reconstruction work arrangements, and allocation of recovery resources. The above strategies are integrated into a comprehensive disaster full-process strategy document to ensure that the strategies of each stage are coordinated with each other and cover the entire life cycle of the disaster. The pre-disaster prevention strategy, the disaster emergency response optimization strategy, and the post-disaster recovery and reconstruction strategy are included in the full life cycle management framework. The framework structure is designed to ensure the seamless connection of the strategies at each stage. A data model is created to integrate all strategy data into a unified database. The data model should include information such as risk identification, assessment, response, and recovery. The structure of the full life cycle management database is designed, including data tables, fields, and relationships. Ensure that the database can effectively store and manage strategy data at different stages. Input pre-disaster prevention strategy data, disaster emergency response optimization data, and post-disaster recovery and reconstruction data into the database. Clean, standardize, and integrate data to ensure data accuracy and consistency. Test and verify the database to ensure data integrity, accuracy, and availability. Generate a full life cycle management database based on the design and input data. Ensure that the database can support query, analysis, and reporting functions. Implement database functions, including data storage, retrieval, analysis, and report generation. Ensure that it can support the needs of typhoon disaster risk identification and assessment operations. Use the data in the full life cycle management database to identify typhoon disaster risks, including analyzing disaster influencing factors, assessing risk levels, and identifying potential risk areas. Generate a typhoon disaster risk assessment report based on risk identification results. The report should include detailed information on risk identification, assessment results, and recommended measures. Based on the risk assessment report, adjust pre-disaster prevention strategies, disaster emergency response optimization strategies, and post-disaster recovery and reconstruction strategies. Ensure that the strategies can effectively respond to identified risks.

[0087] Preferably, step S1 comprises the following steps:

[0088] Step S11: Acquire typhoon real-time monitoring data;

[0089] Step S12: preprocessing the typhoon real-time monitoring data to generate standard typhoon real-time monitoring data, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization;

[0090] Step S13: extracting the typhoon center wind force from the standard typhoon real-time monitoring data to generate typhoon eye wind force data; analyzing the typhoon impact range of the typhoon real-time monitoring data based on the typhoon risk wind force data to generate typhoon impact range data;

[0091] Step S14: predicting the typhoon's moving path based on the typhoon's real-time monitoring data according to the typhoon's impact range data, and generating typhoon moving path prediction data.

[0092] In an embodiment of the present invention, typhoon monitoring data is obtained in real time through various means such as meteorological satellites, radars, ocean buoys and ground observation stations. These data generally include key parameters such as air pressure, wind speed, wind direction, rainfall and temperature. The monitoring data from different sources are integrated to ensure the comprehensiveness and accuracy of the data. Outliers and noise in the typhoon monitoring data are removed to ensure the quality of the data. For example, erroneous data generated by equipment failure or communication problems are excluded. A filter or other denoising algorithm, such as wavelet transform or Kalman filter, is applied to remove random noise in the data to make the signal clearer. For missing values ​​in the typhoon monitoring data, interpolation, nearest neighbor filling or a prediction model based on machine learning is used to fill in the missing values ​​to ensure the integrity of the data. The typhoon monitoring data is standardized to make data of different dimensions comparable, such as normalizing data such as wind speed and air pressure to the same scale. By analyzing the changes in wind speed and wind direction in the typhoon monitoring data, the wind data of the typhoon center (eye) is extracted to generate "typhoon eye wind data". Typically, this involves identifying areas with the highest wind speed and the smallest wind speed gradient. Based on the extracted typhoon eye wind data and typhoon periphery wind data, algorithms (such as pressure gradient models or wind field analysis models) are used to analyze the typhoon's impact range and generate "typhoon impact range data". This analysis can reveal the potential threat level of the typhoon to the surrounding areas. Based on the typhoon impact range data, the future movement path of the typhoon is predicted using machine learning algorithms (such as convolutional neural networks or LSTM) or physical models (such as dynamic equations and vortex dynamics models). The typhoon's movement path prediction results are used to generate "typhoon movement path prediction data", which are then verified and corrected in combination with historical data to improve the accuracy of the prediction.

[0093] Preferably, step S14 comprises the following steps:

[0094] Step S141: Detecting the typhoon wind field using meteorological radar technology based on the typhoon impact range data and the real-time typhoon monitoring data to generate typhoon wind field detection data;

[0095] Step S142: extracting key features from the typhoon wind field detection data to obtain key feature data of the typhoon wind field, wherein the key feature data of the typhoon wind field includes wind field vorticity data, wind field divergence data and wind field streamline data; performing clustering pattern recognition on the wind field vorticity data, wind field divergence data and wind field streamline data to generate a wind field feature matrix;

[0096] Step S143: Calculate the typhoon wind field guide flow on the wind field characteristic matrix to obtain typhoon wind field guide flow data; predict the overall movement trend of the typhoon on the typhoon real-time monitoring data through the typhoon wind field guide flow data to generate typhoon overall movement trend prediction data;

[0097] Step S144: extracting the edge atmospheric pressure of the typhoon real-time monitoring data based on the typhoon overall movement trend prediction data to generate typhoon edge atmospheric pressure data; converting the typhoon edge atmospheric pressure data into a gradient map to generate a typhoon edge atmospheric pressure gradient map;

[0098] Step S145: Calculate the pressure difference of the atmospheric pressure gradient map at the edge of the typhoon to generate the atmospheric pressure difference data at the edge of the typhoon; perform regional trough analysis on the atmospheric pressure gradient map at the edge of the typhoon using the atmospheric pressure difference data at the edge of the typhoon to generate the typhoon movement trough data; use the typhoon movement trough data to perform local movement prediction on the typhoon's overall movement trend prediction data to generate the typhoon movement path prediction data.

[0099] In an embodiment of the present invention, a detailed wind field detection is performed on the real-time monitoring data of a typhoon by using meteorological radar technology. The radar can provide high-resolution wind field data to capture the spatial changes in wind speed and direction. "Typhoon wind field detection data" is generated according to the radar detection results, including the three-dimensional distribution of the wind field. The rotation or vortex intensity in the wind field is calculated to reflect the rotation structure of the typhoon center. The diffusion or contraction of the airflow in the wind field is calculated to indicate the divergence and convergence characteristics of the wind field, and a streamline diagram describing the direction of the wind flow is generated to show the movement pattern of the wind field. The wind field vorticity, divergence and streamline data are clustered and analyzed to generate a wind field feature matrix. This step uses a clustering algorithm (such as K-means clustering or DBSCAN) to identify the main patterns and features in the wind field. The guide flow is calculated through the wind field feature matrix to obtain the main flow direction and intensity of the wind field. The guide flow represents the main power direction in the wind field, affects the movement path of the typhoon, and obtains the "typhoon wind field guide flow data". The overall movement trend of the typhoon is predicted based on the guide flow data. Use models (such as wind field guiding flow models or fluid dynamics models) to generate "typhoon overall movement trend forecast data". Extract the air pressure data at the edge of the typhoon based on the real-time typhoon monitoring data to generate "typhoon edge atmospheric pressure data". Perform gradient map conversion on the atmospheric pressure data at the edge of the typhoon to generate "typhoon edge atmospheric pressure gradient map". The gradient map shows the spatial distribution of pressure changes and is used to analyze the impact range and intensity of the typhoon. Perform pressure difference calculation on the atmospheric pressure gradient map at the edge of the typhoon to obtain "typhoon edge atmospheric pressure difference data". The pressure difference indicates the intensity of air pressure changes in the typhoon edge area. Use the pressure difference data to perform regional trough analysis on the gradient map to generate "typhoon movement trough data". Trough analysis identifies the distribution of low-pressure areas and high-pressure areas in the typhoon movement path. According to the typhoon movement trough data, the typhoon overall movement trend forecast data is locally adjusted to generate "typhoon movement path forecast data". This step takes into account the impact of local air pressure changes on the typhoon path and provides a more accurate path prediction.

[0100] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0101] Step S21: Acquire geographic spatial data;

[0102] Step S22: Calculate the path influence of the geographic space data according to the typhoon movement path prediction data and the typhoon impact range data to generate typhoon influence data; use the typhoon influence data to perform preliminary disaster-affected area division on the geographic space data to generate preliminary disaster-affected area data;

[0103] Step S23: Identify the network key feature points of the preliminary disaster-affected area data to generate the key feature point data of the disaster-affected area, wherein the network key feature point identification includes city key points, transportation hub key points and important facility key points;

[0104] Step S24: using the key feature point data of the disaster-affected area as network nodes to perform network connectivity analysis, and generating preliminary disaster-affected area connectivity analysis data; using the preliminary disaster-affected area connectivity analysis data to refine the preliminary disaster-affected area data, and generating typhoon-affected refined affected area data;

[0105] Step S25: Perform multi-dimensional risk distribution risk assessment on the typhoon disaster-affected detailed area data to generate multi-dimensional risk distribution data of the disaster-affected area; perform dynamic risk propagation simulation on the typhoon disaster-affected detailed area data according to the multi-dimensional risk distribution data of the disaster-affected area to generate risk propagation path data of the disaster-affected area;

[0106] Step S26: Construct a regional risk map based on the risk propagation path data of the disaster-stricken area to generate a comprehensive risk map of the disaster-stricken area.

[0107] In an embodiment of the present invention, high-resolution geospatial data including satellite images, terrain data, land use data, population distribution data, etc. are collected. Geospatial data from different sources are integrated to ensure the integrity and consistency of the data. The influence of the typhoon on the geospatial data is calculated based on the "typhoon movement path prediction data" and the "typhoon impact range data". This can be achieved through an influence function or model to evaluate the potential impact of the typhoon path and impact range on each region to obtain "typhoon influence data". Using the typhoon influence data, the geospatial data is analyzed to divide the preliminary disaster-affected areas to obtain "preliminary disaster-affected area data". Identify important areas in the city, such as the city center, residential areas, etc. Identify transportation hubs, such as railway stations, airports, major intersections, etc. Identify important facilities, such as hospitals, schools, power stations, etc. Use geographic information systems (GIS) and image recognition technology to identify and mark these key points to obtain "key feature point data of the disaster-affected area". The key feature point data of the disaster-affected area is used as network nodes for connectivity analysis. Use the connectivity algorithms in graph theory (such as the shortest path algorithm, connected component analysis, etc.) to evaluate the connectivity between nodes and obtain the "preliminary connectivity analysis data of the affected areas". Based on the preliminary connectivity analysis data of the affected areas, the preliminary affected area data is refined and divided, taking into account the connectivity of the affected areas and the impact of key nodes, to obtain the "typhoon affected area data". Perform a multi-dimensional risk assessment on the "typhoon affected area data", including risk assessment of personnel, property, infrastructure and other aspects, to obtain the "multi-dimensional risk distribution data of the affected areas". Based on the multi-dimensional risk distribution data, a dynamic risk propagation simulation is performed. This can use epidemiological models or diffusion models to simulate the spread of risks in the affected areas and obtain the "risk propagation path data of the affected areas". Based on the "risk propagation path data of the affected areas", a comprehensive risk map is created. This includes visualizing the risk data, showing the risk distribution and risk propagation path of the affected areas, and obtaining the "comprehensive risk map of the affected areas".

[0108] Preferably, step S25 comprises the following steps:

[0109] Step S2541: Performing a wind dynamic propagation simulation on the multi-dimensional risk distribution data of the disaster-stricken area to generate wind risk propagation path data;

[0110] Step S2542: Perform rainstorm impact analysis on the wind risk propagation path data to generate wind-rainstorm superposition risk propagation data;

[0111] Step S2543: Perform coastal storm surge impact analysis on the wind-rainstorm superposition risk propagation data to generate wind-storm surge superposition risk propagation data;

[0112] Step S2544: performing huge wave risk diffusion calculation on the wind-storm surge superposition risk propagation data and the wind-rainstorm superposition risk propagation data to generate wind-rainstorm-huge wave comprehensive risk propagation path data;

[0113] Step S2545: Conduct a regional risk increment assessment on the wind-rainstorm-huge wave comprehensive risk propagation path data to generate final risk propagation path data for the affected area.

[0114] In an embodiment of the present invention, data of the affected area of ​​the typhoon disaster is collected, including elevation data, land use data, etc. A geographic information system (GIS) tool is used to perform terrain analysis, and terrain feature data of the typhoon-affected area, such as altitude, slope, terrain undulation, etc., are extracted. Based on the extracted terrain feature data, a spatial analysis method is applied to divide the area into coastal and inland areas. Models such as terrain segmentation models and threshold segmentation methods can be used to generate typhoon-affected coastal area data and typhoon-affected inland area data. These data will be used for subsequent risk assessment. Marine data of coastal areas are obtained, including sea level height, tidal information, wind speed data, etc. A huge wave risk assessment model, such as a fluctuation model, a tidal prediction model, etc., is used to perform a huge wave risk assessment on coastal areas, generate typhoon-affected coastal huge wave risk assessment data, and describe the possibility and intensity of huge waves. Meteorological data of inland areas are obtained, including wind speed, wind direction, etc. A strong wind risk model is used to perform a strong wind risk assessment on inland areas, generate typhoon-affected inland strong wind risk assessment data, and describe the possibility and intensity of strong winds. Obtain precipitation data for inland areas, including rainfall, rainfall intensity, etc. Use the rainstorm risk model to conduct rainstorm risk assessment for inland areas, generate typhoon-affected inland rainstorm risk assessment data, and describe the possibility and intensity of rainstorms. Integrate the typhoon-affected coastal giant wave risk assessment data, typhoon-affected inland strong wind risk assessment data, and typhoon-affected inland rainstorm risk assessment data. Use the weighted superposition method to assign weights according to the weights of different risk factors. The weights can be determined based on historical data, expert opinions, or model calculations to generate multidimensional risk distribution data for the affected area to describe the comprehensive impact of each risk factor. Select a suitable dynamic risk propagation model, such as a propagation model, a dynamic system model, etc. Input the multidimensional risk distribution data of the affected area into the model. Conduct a dynamic risk propagation simulation, analyze the risk propagation path in time and space, generate risk propagation path data for the affected area, and describe the direction, speed, and scope of risk propagation.

[0115] Preferably, step S254 includes the following steps:

[0116] Step S2541: Perform wind dynamic propagation simulation on the multi-dimensional risk distribution data of the disaster-stricken area to generate wind risk propagation path data;

[0117] Step S2542: Perform rainstorm impact analysis on the wind risk propagation path data to generate wind-rainstorm superposition risk propagation data;

[0118] Step S2543: performing huge wave risk diffusion calculation on the wind-rainstorm superposition risk propagation data to generate wind-rainstorm-huge wave comprehensive risk propagation path data;

[0119] Step S2544: performing huge wave risk diffusion calculation on the wind-storm surge superposition risk propagation data and the wind-rainstorm superposition risk propagation data to generate wind-rainstorm-huge wave comprehensive risk propagation path data;

[0120] Step S2545: Conduct a regional risk increment assessment on the wind-rainstorm-huge wave comprehensive risk propagation path data to generate final risk propagation path data for the affected area.

[0121] In an embodiment of the present invention, wind data of the disaster-stricken area, including wind speed, wind direction and typhoon path, are obtained from a meteorological model. Initial simulation conditions are set, such as wind intensity, wind direction distribution, etc. A suitable wind propagation model is selected, such as a wind field simulation model, a CFD (computational fluid dynamics) model, etc. The selected wind propagation model is used to simulate the dynamic propagation of wind in the disaster-stricken area, generate wind risk propagation path data, and describe the propagation trajectory and intensity changes of wind in the area. Heavy rain data, including rainfall, rainfall intensity and heavy rain distribution, are obtained from the meteorological model. The wind risk propagation path data is integrated with the heavy rain data. A heavy rain impact analysis model is used, such as a rainfall-runoff model, a heavy rain superposition impact model, etc. The wind risk propagation path data is analyzed for the impact of heavy rain, the superposition effect of heavy rain on wind risk is identified, and wind-heavy rain superposition risk propagation data is generated to describe the risk propagation path and its changes under the joint action of wind and heavy rain. Giant wave data, including giant wave height, propagation speed, etc., are obtained from the ocean model. The wind-heavy rain superposition risk propagation data is integrated with the giant wave data. Use giant wave diffusion calculation models, such as wave propagation models, ocean wave models, etc. Perform giant wave risk diffusion calculations on wind-rainstorm superimposed risk propagation data, consider the impact of giant waves on wind and rainstorm risks, generate wind-rainstorm-giant wave comprehensive risk propagation path data, and describe the risk propagation path and its changes under the combined effects of wind, rainstorm and giant waves. Collect wind-rainstorm-giant wave comprehensive risk propagation path data and regional risk background data. Use regional risk assessment models, such as incremental risk models, regional risk analysis models, etc. Perform regional risk incremental assessment on wind-rainstorm-giant wave comprehensive risk propagation path data, consider the overall impact of different risk factors on the region, generate final risk propagation path data for the affected area, and describe the final risk propagation path and its possible risk points under the combined influence of wind, rainstorm and giant waves.

[0122] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0123] Step S31: Perform matching analysis on the risk distribution map data with the historical emergency response strategy library to generate preliminary emergency response strategy data; perform real-time monitoring data fusion processing on the preliminary emergency response strategy data to generate real-time emergency strategy adjustment data;

[0124] Step S32: performing strategy optimization iteration on the real-time emergency strategy adjustment data to generate emergency response strategy optimization data; performing resource allocation and emergency measures optimization processing on the emergency response strategy optimization data to generate dynamic emergency response matrix data;

[0125] Step S33: Perform disaster simulation on the real-time emergency strategy adjustment data through dynamic emergency response matrix data, and collect post-disaster impact data to obtain a typhoon post-disaster simulation data set; perform typhoon disaster aftereffect evaluation on the typhoon post-disaster simulation data set to generate typhoon disaster aftereffect evaluation data.

[0126] In the embodiment of the present invention, risk distribution map data is collected, including the spatial distribution and intensity of various risk factors. A data warehouse containing historical emergency response strategies is established, covering different types of disasters and emergency response methods. Matching algorithms, such as similarity calculation, pattern matching, etc., are used to match the risk distribution map data with the strategies in the historical emergency response strategy library. The matching results are analyzed to generate preliminary emergency response strategy data that are compatible with the current risk distribution, and preliminary emergency response strategy data are generated, including preset emergency measures and strategies for different risk factors. Real-time monitoring data, such as real-time weather data, disaster data, emergency response conditions, etc. are collected. Real-time monitoring data is integrated with preliminary emergency response strategy data. Data fusion techniques, such as weighted average, fusion algorithm, etc., are used to process real-time monitoring data and preliminary strategy data. Real-time emergency strategy adjustment data is generated based on the fusion results to reflect the current actual situation and risk changes, and dynamic emergency strategy data reflecting the current situation is generated for further optimization and adjustment. Real-time emergency strategy adjustment data and historical emergency response effect data are collected. Optimization algorithms, such as genetic algorithms, simulated annealing, linear programming, etc., are used to iteratively optimize the strategy. Through multiple rounds of iterations, optimize emergency response strategies, improve response efficiency and effectiveness, generate optimized emergency response strategy data, and improve emergency response measures. Collect resource allocation data, emergency measures data, and optimization strategy data. Use resource optimization allocation methods, such as optimal resource allocation algorithm, linear programming, etc. to determine resource allocation plans. According to the optimization strategy data, adjust emergency measures and resource allocation to ensure the efficiency of emergency response, and generate dynamic emergency response matrix data that reflects resource allocation and emergency measures optimization for actual operation and monitoring. Collect dynamic emergency response matrix data and disaster simulation model data. Use disaster simulation models, such as meteorological models and disaster diffusion models, to simulate dynamic emergency response matrix data. Simulate the impact of disasters and the effects of emergency response, generate post-disaster impact data, and generate simulation data sets to describe the situation after typhoon disasters and the effects of emergency response. Collect typhoon post-disaster simulation data sets and historical post-disaster effect evaluation data. Use disaster post-effect evaluation models, such as loss assessment models and impact assessment models, to perform post-effect evaluation on simulation data sets. Analyze post-disaster impacts, evaluate the long-term effects and impacts of typhoon disasters, generate typhoon disaster after-effect assessment data, and provide comprehensive assessment results of disaster aftereffects to guide subsequent recovery and reconstruction work.

[0127] Preferably, step S33 includes the following steps:

[0128] Step S331: Monte Carlo simulation is performed on the real-time emergency strategy adjustment data through dynamic emergency response matrix data to generate typhoon disaster simulation data; disaster characteristic data is collected on the typhoon disaster simulation data to generate a typhoon post-disaster simulation data set;

[0129] Step S332: dividing the typhoon disaster post-simulation data set into a data set to generate a model training set and a model test set; using the after-effect evaluation algorithm to perform model training on the model training set to generate a typhoon disaster after-effect evaluation pre-model; based on the model test set, performing model optimization iteration on the typhoon disaster after-effect evaluation pre-model to generate a typhoon disaster after-effect evaluation model;

[0130] Step S333: importing the typhoon disaster post-simulation data set into the typhoon disaster after-effect assessment model to perform typhoon disaster after-effect assessment and generate typhoon disaster after-effect assessment data.

[0131] In an embodiment of the present invention, dynamic emergency response matrix data and real-time emergency strategy adjustment data are collected. The initial conditions of the Monte Carlo simulation are set, including the number of simulations, the range of random variables, etc. A large number of possible disaster scenarios are generated by random sampling and probability distribution using the Monte Carlo simulation method. A Monte Carlo simulation is run to generate multiple typhoon disaster simulation data, describe the disaster impacts under different circumstances, and generate a large amount of simulation data, covering the risk performance and emergency response effects under different disaster scenarios. The typhoon disaster simulation data are analyzed to extract the main disaster characteristics, such as wind speed, rainfall, and degree of loss. Disaster feature data are extracted from the simulation data using statistical analysis or machine learning methods to generate a simulation data set containing disaster feature data for subsequent model training and evaluation. A typhoon post-disaster simulation data set is collected. The data set partitioning method is used to divide the data set into a model training set and a model test set, usually divided at a ratio of 70%-30% or 80%-20%. A data set for training the model. A data set for testing and verifying model performance. Select a suitable post-effect evaluation algorithm, such as regression analysis, classification model, deep learning model, etc. Use the model training set for training, adjust the model parameters, and generate a typhoon disaster after-effect assessment pre-model. Use the model test set to optimize and iterate the pre-model to improve the accuracy and generalization ability of the model. Use evaluation indicators (such as accuracy, recall, F1 score, etc.) to evaluate the model performance, and make necessary adjustments and improvements to generate an optimized typhoon disaster after-effect assessment model that can accurately assess the after-effect impact of typhoon disasters. Import the typhoon disaster simulation data set into the trained typhoon disaster after-effect assessment model. Use the typhoon disaster after-effect assessment model to evaluate the typhoon disaster simulation data set. Analyze the model output results and generate typhoon disaster after-effect assessment data to describe the after-effect impact of the disaster, including economic losses, casualties, infrastructure damage, etc. Provide detailed typhoon disaster after-effect assessment results for decision support for post-disaster recovery and emergency response.

[0132] Preferably, step S4 comprises the following steps:

[0133] Step S41: performing pre-disaster risk identification on the disaster post-effect assessment report data to generate pre-disaster risk identification data; performing pre-disaster prevention strategy formulation on the pre-disaster risk identification data to generate pre-disaster prevention strategy data;

[0134] Step S42: Optimize the emergency response strategy during the disaster based on the pre-disaster prevention strategy data to generate optimized emergency response data during the disaster; formulate a post-disaster recovery and reconstruction plan based on the optimized emergency response data during the disaster to generate post-disaster recovery and reconstruction data;

[0135] Step S43: construct a full life cycle management framework for post-disaster recovery and reconstruction data to generate full life cycle management framework data; perform technical integration and application on the full life cycle management framework data to generate a full life cycle management database to perform typhoon disaster risk identification and assessment operations.

[0136] In the embodiment of the present invention, by collecting disaster post-effect evaluation report data, the disaster impact and risk characteristics are analyzed. Key indicators for pre-disaster risk identification, such as disaster type, impact range, and affected population, are determined. Data analysis methods, such as risk assessment models and statistical analysis, are used to identify potential pre-disaster risks. Pre-disaster risk identification data is generated based on the analysis results, including risk information of different types and levels. A detailed pre-disaster risk identification report is provided to guide the formulation of prevention strategies. Based on the pre-disaster risk identification data, the response measures and prevention strategies of different risk types are analyzed. Prevention strategies are formulated using decision support systems, optimization algorithms, etc., and pre-disaster prevention strategy data for different risk types are generated, including early warning systems, public education, infrastructure improvements, etc. Detailed plans and implementation plans for pre-disaster prevention measures are provided. Based on the pre-disaster prevention strategy data, actual emergency response data during the disaster, such as response speed, resource allocation, and emergency measures effects, are collected. Existing emergency response strategies are optimized using optimization algorithms, simulation models, etc. Emergency response measures and resource allocation are adjusted to generate optimized emergency response strategy data. Optimized emergency response strategies are provided, including improvement measures and adjustment plans. Analyze post-disaster recovery needs and reconstruction tasks based on disaster emergency response optimization data. Use recovery planning models, resource allocation algorithms, etc. to develop recovery and reconstruction plans, and generate post-disaster recovery and reconstruction plan data, covering recovery priorities, reconstruction steps, resource allocation, etc. Provide detailed recovery and reconstruction plans to guide the implementation of post-disaster work. Collect post-disaster recovery and reconstruction data and data on existing management frameworks. Use framework construction methods, such as life cycle management models and system engineering methods, to design a full life cycle management framework, and generate full life cycle management framework data including pre-disaster, disaster, and post-disaster stages, covering risk management, emergency response, recovery and reconstruction, etc. Provide a detailed description and implementation plan of the full life cycle management framework. Based on the full life cycle management framework data, collect data on related technologies and systems. Use technical integration methods, such as data integration and system integration, to apply the management framework to technical systems, generate a full life cycle management database, integrate data and technology at each stage, provide comprehensive management support, and generate a database containing the full life cycle management framework and technical applications for actual operation and management.

[0137] In this specification, a typhoon storm surge disaster risk assessment system for water engineering safety is provided, which is used to execute the above-mentioned typhoon storm surge disaster risk assessment method for water engineering safety. The typhoon storm surge disaster risk assessment system for water engineering safety includes:

[0138] A typhoon movement identification module is used to obtain typhoon real-time monitoring data; analyze the typhoon impact range of the typhoon real-time monitoring data to generate typhoon impact range data; predict the typhoon movement path of the typhoon real-time monitoring data based on the typhoon impact range data to generate typhoon movement path prediction data;

[0139] The disaster-affected area analysis module is used to obtain geographic spatial data; perform preliminary disaster-affected area division on the geographic spatial data according to the typhoon movement path prediction data and the typhoon impact range data, and generate preliminary disaster-affected area data; perform network connectivity analysis on the preliminary disaster-affected area data, and generate preliminary disaster-affected area connectivity analysis data; refine the preliminary disaster-affected area data through the preliminary disaster-affected area connectivity analysis data, and generate typhoon-affected refined affected area data; construct a regional risk map for the typhoon-affected refined affected area data, and generate a comprehensive risk map for the disaster-affected area;

[0140] The after-effect evaluation module is used to match and analyze the historical emergency response strategy library with the risk distribution map data to generate real-time emergency strategy adjustment data; optimize resource allocation and emergency measures for the real-time emergency strategy adjustment data to generate dynamic emergency response matrix data; and evaluate the after-effect of typhoon disasters on the real-time emergency strategy adjustment data through the dynamic emergency response matrix data to generate typhoon disaster after-effect evaluation data;

[0141] The whole process management module is used to formulate disaster strategies for the whole process based on the disaster post-effect assessment report data, generate pre-disaster prevention strategy data, disaster emergency response optimization data and post-disaster recovery and reconstruction data; integrate the whole life cycle management framework through the pre-disaster prevention strategy data, disaster emergency response optimization data and post-disaster recovery and reconstruction data, and generate a whole life cycle management database to perform typhoon disaster risk identification and assessment operations.

[0142] The beneficial effect of the present invention is that by obtaining real-time typhoon monitoring data and performing impact range analysis and movement path prediction, the early warning of typhoons is made more timely and accurate. This can identify the typhoon-affected areas and movement trends in advance, thereby providing reliable data support for prevention and emergency response. By dividing the affected areas and analyzing the network connectivity of geographic spatial data, detailed disaster-affected area data and risk maps are generated. This process helps to identify the key network points and risk distribution in the disaster-affected areas, ensure that the assessment of the disaster area is more comprehensive and detailed, and help to formulate more effective rescue and recovery plans. By matching the emergency strategy library and optimizing resource allocation for the risk distribution map data, dynamic emergency response matrix data is generated. This step can improve the accuracy and efficiency of emergency response, ensure that resources are reasonably allocated and utilized, so that rescue and post-disaster assessment can be carried out quickly and effectively when a typhoon occurs. By formulating a full-process strategy for the disaster post-effect assessment report data, it covers all stages before, during, and after the disaster, and integrates it into a full life cycle management framework. This comprehensive management approach ensures that every stage of disaster management is fully considered, and through the establishment of a full life cycle management database, systematic risk identification and assessment are achieved, optimizing the overall efficiency of disaster management. The overall coordination of disaster management is improved by systematically integrating monitoring data, risk assessment, emergency response and recovery planning. Each step supports and complements each other, ensuring the smooth and efficient work of disaster management, so that better prevention, response and recovery can be achieved in the face of natural disasters such as typhoons. The implementation of the entire process relies on precise data analysis and model optimization, providing decision makers with data-driven decision support. This can reduce the bias of human judgment, improve the scientificity and accuracy of decision-making, and provide a solid data foundation for disaster response and recovery. Through modern data processing and technology integration, step S4 incorporates the full life cycle management framework into the full life cycle management database, realizing the intelligence of disaster management. This not only improves the efficiency of disaster management, but also enhances its flexibility and adaptability, making disaster management more in line with the current technological development trend. Therefore, the present invention improves the real-time, accuracy and flexibility of typhoon storm surge disaster risk assessment for water engineering safety through real-time data analysis, precise geospatial assessment, dynamic emergency response optimization and full life cycle management.

[0143] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0144] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A typhoon storm surge disaster risk assessment method for water engineering safety, characterized in that: The following steps are involved: Step S1: Obtaining real-time typhoon monitoring data; Analyze the typhoon impact range based on the real-time typhoon monitoring data to generate typhoon impact range data; predict the typhoon movement path based on the real-time typhoon monitoring data to generate typhoon movement path prediction data; Step S2: Obtaining geospatial data; Based on the typhoon movement path prediction data and typhoon impact range data, the geographic space data is divided into preliminary disaster-affected areas to generate preliminary disaster-affected area data; network connectivity analysis is conducted on the preliminary disaster-affected area data to generate preliminary disaster-affected area connectivity analysis data; The preliminary disaster-affected area data is refined through the connectivity analysis data of the preliminary disaster-affected area to generate the typhoon disaster-affected area data; Constructing a regional risk map for the typhoon-affected area data to generate a comprehensive risk map for the affected area; step S2 includes the following steps: Step S21: Acquire geographic spatial data; Step S22: Calculate the path influence of the geographic space data according to the typhoon movement path prediction data and the typhoon impact range data to generate typhoon influence data; use the typhoon influence data to perform preliminary disaster-affected area division on the geographic space data to generate preliminary disaster-affected area data; Step S23: Identify the network key feature points of the preliminary disaster-affected area data to generate the key feature point data of the disaster-affected area, wherein the network key feature point identification includes city key points, transportation hub key points and important facility key points; Step S24: using the key feature point data of the disaster-affected area as network nodes to perform network connectivity analysis, and generating preliminary disaster-affected area connectivity analysis data; using the preliminary disaster-affected area connectivity analysis data to refine the preliminary disaster-affected area data, and generating typhoon-affected refined affected area data; Step S25: Perform multi-dimensional risk distribution risk assessment on the typhoon disaster-affected detailed area data to generate multi-dimensional risk distribution data of the disaster-affected area; perform dynamic risk propagation simulation on the typhoon disaster-affected detailed area data according to the multi-dimensional risk distribution data of the disaster-affected area to generate risk propagation path data of the disaster-affected area; Step S26: constructing a regional risk map based on the risk propagation path data of the disaster-affected area to generate a comprehensive risk map of the disaster-affected area; Step S3: Perform matching analysis on the risk distribution map data with the historical emergency response strategy library to generate real-time emergency strategy adjustment data; perform resource allocation and emergency measures optimization processing on the real-time emergency strategy adjustment data to generate dynamic emergency response matrix data; perform typhoon disaster aftereffect evaluation on the real-time emergency strategy adjustment data through the dynamic emergency response matrix data to generate typhoon disaster aftereffect evaluation data; Step S4: Formulate a disaster strategy for the entire process based on the disaster post-effect assessment report data to generate pre-disaster prevention strategy data, disaster emergency response optimization data, and post-disaster recovery and reconstruction data; integrate the full life cycle management framework through the pre-disaster prevention strategy data, disaster emergency response optimization data, and post-disaster recovery and reconstruction data to generate a full life cycle management database to perform typhoon disaster risk identification and assessment operations.

2. The typhoon storm surge disaster risk assessment method for water engineering safety according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire typhoon real-time monitoring data; Step S12: preprocessing the typhoon real-time monitoring data to generate standard typhoon real-time monitoring data, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization; Step S13: extracting the typhoon center wind force from the standard typhoon real-time monitoring data to generate typhoon eye wind force data; analyzing the typhoon impact range of the typhoon real-time monitoring data based on the typhoon risk wind force data to generate typhoon impact range data; Step S14: predicting the typhoon's moving path based on the typhoon's real-time monitoring data according to the typhoon's impact range data, and generating typhoon moving path prediction data.

3. The typhoon storm surge disaster risk assessment method for water engineering safety according to claim 2 is characterized in that: Step S14 includes the following steps: Step S141: Detecting the typhoon wind field using meteorological radar technology based on the typhoon real-time monitoring data according to the typhoon impact range data, and generating typhoon wind field detection data; Step S142: extracting key features from the typhoon wind field detection data to obtain key feature data of the typhoon wind field, wherein the key feature data of the typhoon wind field includes wind field vorticity data, wind field divergence data and wind field streamline data; performing clustering pattern recognition on the wind field vorticity data, wind field divergence data and wind field streamline data to generate a wind field feature matrix; Step S143: Calculate the typhoon wind field guide flow on the wind field characteristic matrix to obtain typhoon wind field guide flow data; predict the overall movement trend of the typhoon on the typhoon real-time monitoring data through the typhoon wind field guide flow data to generate typhoon overall movement trend prediction data; Step S144: extracting the edge atmospheric pressure of the typhoon real-time monitoring data based on the typhoon overall movement trend prediction data to generate typhoon edge atmospheric pressure data; converting the typhoon edge atmospheric pressure data into a gradient map to generate a typhoon edge atmospheric pressure gradient map; Step S145: Calculate the pressure difference of the atmospheric pressure gradient map at the edge of the typhoon to generate the atmospheric pressure difference data at the edge of the typhoon; perform regional trough analysis on the atmospheric pressure gradient map at the edge of the typhoon using the atmospheric pressure difference data at the edge of the typhoon to generate the typhoon movement trough data; use the typhoon movement trough data to perform local movement prediction on the typhoon's overall movement trend prediction data to generate the typhoon movement path prediction data.

4. The typhoon storm surge disaster risk assessment method for water engineering safety according to claim 1 is characterized in that: Step S25 includes the following steps: Step S251: Perform terrain analysis on the typhoon-affected detailed area data to generate typhoon-affected terrain feature data; divide the typhoon-affected detailed area data into coastal and inland areas based on the typhoon-affected terrain feature data to generate typhoon-affected coastal area data and typhoon-affected inland area data; Step S252: Performing a huge wave risk assessment on the data of the coastal areas affected by the typhoon to generate the huge wave risk assessment data of the coastal areas affected by the typhoon; performing a strong wind and heavy rain risk assessment on the data of the inland areas affected by the typhoon to generate the strong wind risk assessment data of the inland areas affected by the typhoon and the heavy rain risk assessment data of the inland areas affected by the typhoon; performing a storm surge risk assessment on the data of the coastal areas affected by the typhoon to generate the typhoon storm surge risk assessment data; Step S253: superimposing and weighting the risk factors of the typhoon-affected coastal giant wave risk assessment data, the typhoon-affected inland strong wind risk assessment data, the typhoon storm surge risk assessment data, and the typhoon-affected inland rainstorm risk assessment data to generate multi-dimensional risk distribution data for the affected area; Step S254: Perform dynamic risk propagation simulation on the typhoon disaster-affected area data according to the multi-dimensional risk distribution data of the disaster-affected area to generate risk propagation path data for the disaster-affected area.

5. The typhoon storm surge disaster risk assessment method for water engineering safety according to claim 4 is characterized in that: Step S254 includes the following steps: Step S2541: Performing a wind dynamic propagation simulation on the multi-dimensional risk distribution data of the disaster-stricken area to generate wind risk propagation path data; Step S2542: Perform rainstorm impact analysis on the wind risk propagation path data to generate wind-rainstorm superposition risk propagation data; Step S2543: Perform coastal storm surge impact analysis on the wind-rainstorm superposition risk propagation data to generate wind-storm surge superposition risk propagation data; Step S2544: performing huge wave risk diffusion calculation on the wind-storm surge superposition risk propagation data and the wind-rainstorm superposition risk propagation data to generate wind-rainstorm-huge wave comprehensive risk propagation path data; Step S2545: Conduct a regional risk increment assessment on the wind-rainstorm-huge wave comprehensive risk propagation path data to generate final risk propagation path data for the affected area.

6. The typhoon storm surge disaster risk assessment method for water engineering safety according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Perform matching analysis on the risk distribution map data with the historical emergency response strategy library to generate preliminary emergency response strategy data; perform real-time monitoring data fusion processing on the preliminary emergency response strategy data to generate real-time emergency strategy adjustment data; Step S32: performing strategy optimization iteration on the real-time emergency strategy adjustment data to generate emergency response strategy optimization data; performing resource allocation and emergency measures optimization processing on the emergency response strategy optimization data to generate dynamic emergency response matrix data; Step S33: Perform disaster simulation on the real-time emergency strategy adjustment data through dynamic emergency response matrix data, and collect post-disaster impact data to obtain a typhoon post-disaster simulation data set; perform typhoon disaster aftereffect evaluation on the typhoon post-disaster simulation data set to generate typhoon disaster aftereffect evaluation data.

7. The typhoon storm surge disaster risk assessment method for water engineering safety according to claim 6 is characterized in that: Step S33 includes the following steps: Step S331: Monte Carlo simulation is performed on the real-time emergency strategy adjustment data through dynamic emergency response matrix data to generate typhoon disaster simulation data; disaster characteristic data is collected on the typhoon disaster simulation data to generate a typhoon post-disaster simulation data set; Step S332: dividing the typhoon disaster post-simulation data set into a data set to generate a model training set and a model test set; using the after-effect evaluation algorithm to perform model training on the model training set to generate a typhoon disaster after-effect evaluation pre-model; based on the model test set, performing model optimization iteration on the typhoon disaster after-effect evaluation pre-model to generate a typhoon disaster after-effect evaluation model; Step S333: importing the typhoon disaster post-simulation data set into the typhoon disaster after-effect assessment model to perform typhoon disaster after-effect assessment and generate typhoon disaster after-effect assessment data.

8. The typhoon storm surge disaster risk assessment method for water engineering safety according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: performing pre-disaster risk identification on the disaster post-effect assessment report data to generate pre-disaster risk identification data; performing pre-disaster prevention strategy formulation on the pre-disaster risk identification data to generate pre-disaster prevention strategy data; Step S42: Optimize the emergency response strategy during the disaster based on the pre-disaster prevention strategy data to generate optimized emergency response data during the disaster; formulate a post-disaster recovery and reconstruction plan based on the optimized emergency response data during the disaster to generate post-disaster recovery and reconstruction data; Step S43: construct a full life cycle management framework for post-disaster recovery and reconstruction data to generate full life cycle management framework data; perform technical integration and application on the full life cycle management framework data to generate a full life cycle management database to perform typhoon disaster risk identification and assessment operations.

9. A typhoon storm surge disaster risk assessment system for water engineering safety, characterized in that: Used to execute the typhoon storm surge disaster risk assessment method for water engineering safety as claimed in claim 1, the typhoon storm surge disaster risk assessment system for water engineering safety comprises: A typhoon movement identification module is used to obtain typhoon real-time monitoring data; analyze the typhoon impact range of the typhoon real-time monitoring data to generate typhoon impact range data; predict the typhoon movement path of the typhoon real-time monitoring data based on the typhoon impact range data to generate typhoon movement path prediction data; The disaster-affected area analysis module is used to obtain geographic spatial data; perform preliminary disaster-affected area division on the geographic spatial data according to the typhoon movement path prediction data and the typhoon impact range data, and generate preliminary disaster-affected area data; perform network connectivity analysis on the preliminary disaster-affected area data, and generate preliminary disaster-affected area connectivity analysis data; refine the preliminary disaster-affected area data through the preliminary disaster-affected area connectivity analysis data, and generate typhoon-affected refined affected area data; construct a regional risk map for the typhoon-affected refined affected area data, and generate a comprehensive risk map for the disaster-affected area; The after-effect evaluation module is used to match and analyze the historical emergency response strategy library with the risk distribution map data to generate real-time emergency strategy adjustment data; optimize resource allocation and emergency measures for the real-time emergency strategy adjustment data to generate dynamic emergency response matrix data; and evaluate the after-effect of typhoon disasters on the real-time emergency strategy adjustment data through the dynamic emergency response matrix data to generate typhoon disaster after-effect evaluation data; The whole process management module is used to formulate disaster strategies for the whole process based on the disaster post-effect assessment report data, generate pre-disaster prevention strategy data, disaster emergency response optimization data and post-disaster recovery and reconstruction data; integrate the whole life cycle management framework through the pre-disaster prevention strategy data, disaster emergency response optimization data and post-disaster recovery and reconstruction data, and generate a whole life cycle management database to perform typhoon disaster risk identification and assessment operations.

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