A method for tracing a disaster-causing path of a near-shore disaster-bearing body based on digital-analog experiments

Abstract

The application discloses a nearshore disaster-bearing body disaster-causing path tracing method based on digital-analog experiments, relates to the technical field of ocean dynamic disaster simulation, determines the ocean dynamic disaster characteristics of a nearshore research area, integrates multi-source historical disaster data, real-time monitoring data and disaster-bearing body background data, and constructs a time-space correlation database; based on a three-dimensional ocean and a third-generation sea wave model, a nearshore storm surge-sea wave coupling numerical model is established and verified. Through the construction of the storm surge-sea wave coupling numerical model, the present application comprehensively considers the nonlinear interaction of multiple disasters, introduces an energy conservation equation to quantify the generation, transmission and dissipation process of disaster energy, solves the limitations of traditional single-disaster linear models, and combines the real-time feedback of the three-dimensional dynamic model of the disaster-bearing body to form a closed energy-damage correlation mechanism, significantly improves the tracing accuracy of the disaster chain propagation path, and ensures that the identification of key nodes and dominant paths is more consistent with physical reality.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of marine dynamic disaster simulation, in particular to a nearshore hazard-recipient disaster path tracing method based on numerical simulation experiment. BACKGROUND

[0002] With the global climate change, the acceleration of urbanization process and the intensification of economic activities in coastal areas, the disaster risk in the coastal and nearshore areas is increasingly complex and serious. The nearshore area is usually densely populated and economically active, including nearshore hazard recipients such as population, buildings, infrastructure and environment, which are relatively high in natural disaster risk. Since marine disasters often do not occur independently but form a chain reaction, it is necessary to analyze and track the propagation path of disaster events to improve emergency management and early warning efficiency, so as to accurately issue disaster warnings and make disaster prevention preparations in advance.

[0003] In the prior art, the tracing of disaster path mostly adopts a single-disaster linear propagation model, which easily ignores the nonlinear coupling effect of multiple disasters and lacks hazard-recipient state feedback data, which easily leads to path tracing breakpoints. Therefore, how to construct a nearshore storm surge-sea wave coupling numerical model, introduce a disaster energy conservation equation, quantify the energy conversion efficiency at different disaster stages, and establish a hazard-recipient three-dimensional dynamic model for feedback loop of hazard-recipient state to ensure the accuracy of disaster path tracing management is a problem to be solved by the present application. Therefore, a nearshore hazard-recipient disaster path tracing method based on numerical simulation experiment is proposed. SUMMARY

[0004] The present application aims to provide a nearshore hazard-recipient disaster path tracing method based on numerical simulation experiment to solve the problems raised in the background.

[0005] To solve the above technical problems, the technical solution adopted by the present application is as follows:

[0006] A nearshore hazard-recipient disaster path tracing method based on numerical simulation experiment, comprising the following steps:

[0007] S1, determining the characteristics of marine dynamic disasters in the nearshore study area, and integrating multi-source historical disaster data, real-time monitoring data and hazard-recipient background data to construct a time-space correlation database to ensure that the data covers main disaster types and hazard-recipient types;

[0008] S2, based on a three-dimensional ocean and a third-generation sea wave model, establishing a nearshore storm surge-sea wave coupling numerical model and verifying it;

[0009] S3, introducing an energy conservation equation to analyze the generation, transmission and dissipation process of disaster energy under typhoon weather, quantifying the energy conversion efficiency between disasters, and outputting a dynamic evolution map of typhoon disaster energy;

[0010] S4, combine the output of the coupling numerical mode with the background data of the hazard- bearing body, evaluate the risk and vulnerability of the hazard- bearing body under the superposition of multiple disasters, and generate a dynamic risk zoning;

[0011] S5, a three-dimensional dynamic model of the hazard- bearing body is constructed to real- time feedback the state change of the hazard- bearing body in different disaster stages, and a closed loop feedback under the action of disasters is formed;

[0012] S6, based on the energy transmission chain and the state feedback of the hazard- bearing body, the disaster chain propagation path is traced back reversely, and the continuity of the tracing back result is verified by using historical disaster examples;

[0013] S7, the tracing back result is connected to the early warning platform with the risk evaluation data to monitor and early warn the disaster path coupled by multiple disasters.

[0014] Further improvement of the technical scheme of the application is that in S1, the process of constructing the space-time correlation database is:

[0015] Investigate the target nearshore research area, determine the marine dynamic disasters of the nearshore research area, determine the disaster types, formation mechanism and characteristic law, and define the main disaster species;

[0016] Collect multi-source historical disaster data, real-time monitoring data and hazard- bearing body background data of the nearshore research area, and perform preprocessing operations including cleaning, calibration and standardization on the data to eliminate the differences and errors between the data and ensure the data quality and availability;

[0017] According to the space-time correlation principle, the preprocessed data of various types is integrated according to the disaster occurrence time, spatial position and hazard- bearing body property, and a space-time correlation database covering the main disaster species and hazard- bearing body types is constructed.

[0018] Further improvement of the technical scheme of the application is that in S2, the process of establishing and verifying the nearshore storm surge-sea wave coupling numerical mode is:

[0019] Based on the three-dimensional ocean model and the third generation sea wave model, a two-way coupling interface is designed, a storm surge-sea wave coupling framework is constructed, and the calculation domain, grid resolution and time step are configured, and the initial field (temperature, salinity, flow rate) and boundary conditions (tide, wind field, air pressure) are set to ensure that the physical process parameterization scheme matches the regional marine dynamic characteristics, wherein the third generation sea wave model is Mazu·Sea Wave, which is a third generation sea wave spectrum numerical model independently developed by the National Marine Environmental Forecasting Center;

[0020] The two-way coupling of the ocean model and the sea wave model is carried out through OpenMP, and a nearshore storm surge-sea wave coupling numerical mode is established, wherein the ocean model provides the sea surface flow field and water level data to the sea wave model, and the sea wave model feeds back the radiation stress and wave-induced turbulence to the ocean model;

[0021] The observation data (tidal station, wave buoy) and reanalysis data (ERA5) are used for verifying the offshore storm surge-wave coupling numerical model, the initial field of the model is optimized through the ensemble Kalman filter, the simulation results are compared with the measured data, the tidal level error, the wave height correlation coefficient and the flow field consistency are quantitatively evaluated, and the simulation accuracy of the coupling model for key disaster elements such as storm surge water level increase, wave growth and wave-flow interaction is verified.

[0022] Further improvement of the technical scheme of the present application is that in S3, the process of outputting the typhoon disaster energy dynamic evolution graph is:

[0023] Based on the offshore storm surge-wave coupling numerical model, the energy conservation framework of the typhoon system is constructed to define the energy input term, the transfer term and the dissipation term, the exchange efficiency of energy at the ocean-atmosphere interface is quantified through the analysis of eddy kinetic energy budget, and the energy flow control equation is established;

[0024] The spectral analysis method is used to decompose the energy components of each disaster, the conversion rate between storm surge potential energy, wave mechanical energy and current kinetic energy is calculated, the wave-flow interaction is described by introducing the nonlinear coupling term, the cross-scale energy transfer path is analyzed through the energy flux divergence, and the influence weight of the typhoon parameters on the energy distribution is determined through the sensitivity experiment;

[0025] Based on the closed error analysis, the balance of the energy conservation equation is verified, the energy conversion efficiency of each disaster is calculated, the energy level string process is tested through the energy spectrum density function, the dissipation parameters are calibrated combined with the observation data, and the typhoon disaster energy dynamic evolution graph is output.

[0026] Further improvement of the technical scheme of the present application is that in S4, the process of evaluating the risk under the superposition of multiple disasters and the vulnerability of disaster bearing body and generating the dynamic risk zoning is:

[0027] Based on the output results of the coupling numerical model, combined with the typhoon path and intensity forecast data extracted from the spatio-temporal correlation database, the extreme value combination of multiple disasters is analyzed, the joint occurrence probability of different intensity disasters is quantified through the probability statistical method, the risk probability is calculated, the low risk level, the medium risk level and the high risk level are divided, the dynamic risk distribution graph is generated, and the risk level of the region under the synergistic action of multiple disasters is represented;

[0028] The multi-parameter vulnerability function library is constructed by integrating the background data of the disaster bearing body, the loss rate of the disaster bearing body under different disaster scenarios is analyzed combined with the real-time disaster element field, the vulnerability classification index is calculated, the low vulnerability level, the medium vulnerability level and the high vulnerability level are divided, and the vulnerability spatio-temporal distribution graph layer is formed;

[0029] The dynamic risk evolution atlas under the superposition of multiple disasters is generated by spatially superimposing the dynamic hazard distribution and the vulnerability distribution, dividing the risk levels through the risk matrix method, combining real-time monitoring data and model prediction results, and updating the risk zoning according to the set time step.

[0030] The further improvement of the technical scheme of the present application is that the process of generating the dynamic risk evolution atlas under the superposition of multiple disasters comprises the following steps:

[0031] The dynamic hazard distribution data is extracted, covering the hazard level information of the spatial superposition of multiple disasters, and the vulnerability spatial distribution data is obtained, containing the vulnerability level of the disaster-bearing body in different regions, and the dynamic hazard distribution and the vulnerability distribution data are superimposed in the spatial dimension by using the geographic information system technology, so that each region corresponds to the corresponding hazard and vulnerability combination;

[0032] According to the risk matrix method, a two-dimensional risk matrix is constructed by taking the superposition result of the dynamic hazard distribution and the vulnerability distribution as the input, the horizontal axis represents the vulnerability level, and the vertical axis represents the hazard level, different hazard and vulnerability combinations in the two-dimensional risk matrix are divided into different risk levels, including low risk level, medium risk level and high risk level, wherein the combination of low hazard and low vulnerability corresponds to the low risk level, the combination of high hazard and high vulnerability corresponds to the high risk level, and the combination of single high hazard or single high vulnerability and medium hazard and medium vulnerability is divided into the medium risk level;

[0033] The preliminary divided risk zoning is verified and corrected in combination with real-time monitoring data, and whether there is inconsistency is checked, if inconsistency is found, the risk zoning is corrected according to the actual situation of the monitoring data, and the model prediction result is referred to, the risk zoning is updated according to the set time step, and a preliminary dynamic risk evolution atlas is formed;

[0034] The quality of the preliminary generated dynamic risk evolution atlas is checked and optimized to ensure the accuracy, integrity and visualization effect of the atlas, and real-time monitoring data and the latest model prediction results are continuously accessed, and the dynamic risk evolution atlas is updated in real time according to the set time step, so as to reflect the dynamic change of the regional risk under the superposition of multiple disasters.

[0035] The further improvement of the technical scheme of the present application is that the S5 specifically comprises:

[0036] Based on the background data of the disaster-bearing body, the three-dimensional dynamic model of the disaster-bearing body is constructed by combining the building BIM model and the infrastructure GIS data, the dynamic characteristics of the material properties and mechanical parameters are embedded, the digital mapping with the physical world is established through real-time monitoring data, and the consistency of the model geometry and physical characteristics is ensured;

[0037] Based on the coupling of numerical mode and disaster prediction data, the action process of different disaster stages on the disaster-bearing body is simulated, the simulation results are fed back to the three-dimensional dynamic model of the disaster-bearing body in real time, and the state change information of the disaster-bearing body in each disaster stage, i.e. real-time damage data, is dynamically updated;

[0038] By comparing the real-time monitoring data with the model feedback results, the difference reasons are analyzed, the model parameters are adjusted according to the difference, the model precision is optimized, and the process is continuously cycled to form a closed-loop feedback mechanism of the state change of the disaster-bearing body under the action of disasters.

[0039] The further improvement of the technical scheme of the application is that the S6 specifically comprises:

[0040] Based on the output of the coupling numerical mode, the time and space distribution of disaster energy is extracted, an energy flow network model is constructed, the key energy nodes and propagation paths are identified by graph theory method, the trigger sequence and interaction relationship of each disaster are inversely solved by combining the energy conservation equation, the energy conversion efficiency and loss are quantified, and a physical mechanism tracing framework of disaster chain is formed;

[0041] The real-time damage data of the three-dimensional dynamic model of the disaster-bearing body are used to inversely deduce the disaster action intensity and space-time range, the consistency of the disaster chain propagation process and the response of the disaster-bearing body is ensured by comparing the simulation state with the actual monitoring data, calibrating the energy transmission path and correcting the intermediate nodes of the inverse tracing;

[0042] The historical compound disaster events are selected, the tracing results are compared with the field investigation data, the path continuity, i.e. the node missing rate is less than 5%, is evaluated, the energy transmission probability is updated by using the Bayesian method, the inverse tracing algorithm is optimized, and a reusable disaster chain knowledge base is formed.

[0043] The further improvement of the technical scheme of the application is that in the S7, the process of monitoring and early warning of the disaster-causing path of multiple disasters is:

[0044] The disaster chain tracing results and risk assessment data are subjected to space-time benchmarking and format standardization operations to ensure data compatibility, are integrated according to the unified format required by the early warning platform, are packaged in JSON-LD format, and complete semantic association and space-time attributes are reserved;

[0045] The energy transmission path in the tracing results is superimposed with the dynamic risk zoning of risk assessment, the danger level and the vulnerability level of the disaster-bearing body are matched through a spatial correlation algorithm, the risk level is determined, and the early warning signal of the multiple disaster-causing events is generated;

[0046] According to the early warning results of the multiple disaster-causing events, a machine-readable CAP format message is generated through the early warning engine, the disaster-causing path and the risk level are marked, a multi-channel distribution strategy is adopted, and the target area is pushed directionally to establish a feedback mechanism and verify the early warning accuracy through actual disaster data.

[0047] Due to the adoption of the above technical scheme, the technical progress achieved by the present application relative to the prior art is:

[0048] The present application provides a nearshore hazard-bearing body disaster-causing path tracing method based on digital-analog experiment, which builds a storm surge-sea wave coupling numerical model, comprehensively considers the nonlinear interaction of multiple disasters, introduces an energy conservation equation to quantify the generation, transmission and dissipation process of disaster energy, solves the limitations of traditional single-disaster linear model, and combines the real-time feedback of the three-dimensional dynamic model of the hazard-bearing body to form a closed energy-damage correlation mechanism, significantly improves the tracing accuracy of the disaster chain propagation path, and ensures that the identification of key nodes and dominant paths is more consistent with physical reality.

[0049] The present application provides a nearshore hazard-bearing body disaster-causing path tracing method based on digital-analog experiment, which integrates numerical model output and hazard-bearing body background data to establish a dynamic hazard-vulnerability coupling assessment framework, uses Copula function to quantify the joint probability of multiple disasters, and generates a time and space continuous risk zoning based on the risk matrix method, which is updated by hour step combined with real-time monitoring data to realize dynamic tracking of risk evolution, and improve the accuracy and reliability of precise early warning and emergency decision-making. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0051] Figure 1 The working flowchart of the present application, a nearshore hazard-bearing body disaster-causing path tracing method based on digital-analog experiment, is shown in the figure.

[0052] Figure 2 The method flowchart of the present application, a nearshore hazard-bearing body disaster-causing path tracing method based on digital-analog experiment, is shown in the figure. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0054] Embodiment 1, as Figure 1 , Figure 2As shown, the present application provides a nearshore disaster-bearing body disaster-causing path tracing method based on digital-analog experiments, comprising the following steps:

[0055] S1, determine the characteristics of the marine dynamic disaster of the target nearshore study area, and integrate multi-source historical disaster data, real-time monitoring data and disaster-bearing body background data to build a time-space correlation database, ensure that the data cover the main disaster types and disaster-bearing body types, investigate the target nearshore study area, and determine the marine dynamic disaster of the nearshore study area, determine the disaster type, formation mechanism and characteristic law, define the main disaster, collect multi-source historical disaster data, real-time monitoring data and disaster-bearing body background data of the nearshore study area, and perform preprocessing operations including cleaning, calibration and standardization on the data, eliminate the differences and errors between the data, and ensure the data quality and availability, according to the time-space correlation principle, integrate the preprocessed data according to the disaster occurrence time, spatial position and disaster-bearing body attribute, and build a time-space correlation database covering the main disaster types and disaster-bearing body types;

[0056] The specific work content is: determine the range of the target nearshore study area and conduct the marine dynamic disaster research of the nearshore study area, systematically analyze the marine dynamic disaster type, formation mechanism and evolution law of the region, define the main disaster including storm surge, disastrous sea wave, tsunami, etc. through literature review, historical disaster statistics and field observation, analyze its driving factors (typhoon path, cold wave intensity, topographic effect, etc.), study the spatio-temporal distribution characteristics of the disaster including occurrence frequency, intensity change and seasonal regularity, establish a disaster classification system based on physical mechanism and statistical law; collect multi-source historical disaster data (typhoon trajectory, tide level record, wave observation, etc.), real-time monitoring data (buoy, satellite remote sensing, shore-based radar, etc.) and disaster-bearing body background data (population distribution, building structure, infrastructure, etc.) for the nearshore study area, and clean the original data, eliminate outliers and missing items, calibrate (unify time and space reference, correct sensor bias) and standardize (normalization processing and format conversion), eliminate systematic errors and scale differences between the data; based on the preprocessed data, integrate according to the time-space correlation principle, take the disaster occurrence time, spatial coordinates and disaster-bearing body attribute as the key fields, wherein, according to the time sequence of disaster occurrence, sort out the development state of disasters at different times; according to the spatial position information, clarify the distribution range and influence area of the disaster in the nearshore study area; combined with the disaster-bearing body attribute, define the influence degree of the disaster on various disaster-bearing bodies, and then build a time-space correlation database covering the main disaster types and disaster-bearing body types;

[0057] S2, based on three-dimensional ocean and third-generation sea wave model, establish nearshore storm surge-sea wave coupling numerical model and verify, based on three-dimensional ocean model and third-generation sea wave model, design two-way coupling interface, build storm surge-sea wave coupling framework, and configure calculation domain, grid resolution and time step, at the same time, set initial field (temperature, salinity, flow rate) and boundary condition (tide, wind field, air pressure), ensure that the parameterization scheme of physical process matches the regional ocean dynamic characteristics, wherein, the third-generation sea wave model is Mazu-sea wave, which is a third-generation sea wave spectrum numerical model independently developed by the State Oceanic and Atmospheric Administration, the two-way coupling of ocean model and sea wave model is realized through OpenMP, and the nearshore storm surge-sea wave coupling numerical model is established, wherein, the ocean model provides sea surface flow field and water level data to the sea wave model, and the sea wave model feeds back radiation stress and wave-induced turbulence to the ocean model, asynchronous communication strategy is adopted to optimize data exchange efficiency, GPU acceleration is combined to improve large-scale parallel computing performance, ensure the real-time and stability of high-resolution simulation, observation data (tidal station, wave buoy) and reanalysis data (ERA5) are used to verify the nearshore storm surge-sea wave coupling numerical model, the initial field of the model is optimized by ensemble Kalman filter, the simulated results are compared with the measured data, the tidal level error, wave height correlation coefficient and flow field consistency are quantitatively evaluated, and the simulation accuracy of the coupled model on key disaster elements such as storm surge water level increase, sea wave growth and wave flow interaction is verified;

[0058] The specific work content is: based on the three-dimensional ocean model and the third generation wave model (Mazu wave), a two-way coupling interface is designed to build a two-way coupling numerical framework to realize the dynamic interaction of storm surge and wave, wherein the ocean model adopts a three-dimensional circulation model based on FVCOM, the time step of the ocean model is set to 10-30 seconds, the wave model step is 1-5 minutes, ensuring that the time scale of physical processes matches, the calculation domain covers the target nearshore area, the horizontal grid resolution is set to 100-500 meters, the vertical layering is not less than 10 layers, and the boundary conditions include tidal forcing, wind field and pressure field; through OpenMP parallel programming, the two-way coupling of the ocean model and the Mazu wave model is realized, and the asynchronous communication strategy is adopted to reduce the data exchange delay: after the ocean model completes the calculation at each time step, the sea surface flow rate and water level variables are transmitted to the wave model through shared memory; the wave model updates the radiation stress and wave-induced turbulence based on the latest sea state, and feeds back to the ocean model to correct the momentum equation, combines GPU acceleration technology to optimize wave spectrum calculation and radiation stress solution, uses CUDA kernel function to realize thread-level parallelism, and through dynamic load balancing to allocate computing tasks, avoiding waste of computing resources caused by grid resolution differences; using the observation data of tidal station, wave buoy measured data and ERA5 reanalysis data, the nearshore storm surge-wave coupling model is verified in multiple dimensions, the initial field of the model is optimized through ensemble Kalman filter assimilation technology to reduce the influence of initial error on the simulation results, and the root mean square error is calculated to evaluate the simulation accuracy of storm surge water level; analyze the correlation coefficient and scatter plot distribution of wave height time series to verify the reliability of the wave growth process; combined with ADCP flow observation data, the simulation effect of wave-flow interaction on nearshore flow field reconstruction is quantitatively evaluated;

[0059] S3, introduce energy conservation equation, analyze the generation, transmission and dissipation process of disaster energy under typhoon weather, quantify the energy conversion efficiency between disasters, and output the dynamic evolution atlas of typhoon disaster energy, based on the nearshore storm surge-wave coupling numerical model, construct the energy conservation framework of typhoon system to define the energy input, transmission and dissipation, through the analysis of eddy kinetic energy budget, quantify the energy exchange efficiency at the ocean-atmosphere interface, establish the energy flow control equation, use spectral analysis method to decompose the energy components of each disaster, calculate the conversion rate between storm surge potential energy, wave mechanical energy and current kinetic energy, and introduce nonlinear coupling term to describe the wave-flow interaction, analyze the cross-scale energy transmission path through energy flux divergence, use sensitivity experiment to determine the influence weight of typhoon parameters on energy distribution, verify the balance of energy conservation equation based on closure error analysis, calculate the energy conversion efficiency of each disaster, test the energy level string process through energy spectrum density function, calibrate the dissipation parameters combined with observation data, and output the dynamic evolution atlas of typhoon disaster energy;

[0060] The specific work content is: based on the offshore storm surge-wave coupling numerical model, the energy conservation framework of the typhoon system is constructed, and the definition and quantification method of energy input, transmission and dissipation term are clarified, among them, the energy input term includes typhoon wind stress work, atmospheric pressure disturbance potential energy injection and tidal forced energy; the transmission term covers the contribution of wave radiation stress to the kinetic energy of the sea current, the vertical energy mixing caused by wave-induced turbulence and the conversion of storm surge potential energy to kinetic energy; the dissipation term involves wave breaking energy loss, bottom friction dissipation and turbulent viscosity dissipation, and through the analysis of eddy kinetic energy budget, the energy exchange efficiency of the ocean-atmosphere interface is quantified, and the energy flow control equation containing nonlinear interaction term is established;

[0061] The energy conversion rate between storm surge potential energy, wave mechanical energy and kinetic energy of sea current is calculated by using spectral analysis method, the nonlinear coupling term is introduced to describe the wave-current interaction, including the influence of wave modulation sea surface roughness on wind energy input and the change of wave spectrum evolution caused by sea current shear, the energy transfer path in spatial scale is clarified through energy flux divergence analysis, the influence weight of typhoon intensity, moving speed and spatial scale on energy distribution is quantified by parameter sensitivity experiment, the key physical process of dominant energy transfer is determined, the physical consistency of energy conversion term is ensured in the process of numerical discretization, and the false energy source or sink is avoided; Based on the balance analysis of closing error, the numerical closing degree of energy conversion efficiency of each disaster type is calculated to ensure that the residual term is controlled within an acceptable range (<5%), and the energy level cascade process is tested by energy spectrum density function, the cascade transfer characteristics of energy from low-frequency storm surge to high-frequency wave are analyzed, the dissipation parameters are calibrated combined with field observation data, the accuracy of parameterization scheme such as wave breaking and bottom friction is optimized, and the energy dynamic evolution atlas of typhoon disaster is output, which shows the distribution, conversion and dissipation process of energy in the life cycle of typhoon in the form of space-time matrix.

[0062] The expression of the conversion rate between storm surge potential energy and kinetic energy of sea current is as follows:

[0063] ;

[0064] The expression of the conversion rate between wave mechanical energy and kinetic energy of sea current is as follows:

[0065] ;

[0066] The expression of the conversion rate between wave mechanical energy and storm surge potential energy (if exists, usually small) is as follows:

[0067] ;

[0068] In the formula: The efficiency of storm surge potential energy conversion to kinetic energy of sea current is affected by bottom friction, topography, etc. Wave energy converted from wave radiation stress, etc., with high efficiency; Wave energy converted from wave radiation stress, etc., with high efficiency; Storm surge potential energy, caused by abnormal water level, unit: J (Joule); Wave mechanical energy, including kinetic energy and potential energy, unit: J (Joule); Ocean current kinetic energy, caused by flow velocity, unit: J (Joule); Energy change rate with time, unit: W (Watt); Energy conversion efficiency;

[0069] The expression of the numerical closure of the energy conversion efficiency of each disaster type is as follows:

[0070] ;

[0071] ;

[0072] In the formula: The numerical closure of the energy conversion efficiency of each disaster type, representing the balance error rate of the energy conservation equation; Total energy input, the total energy input from the atmosphere to the ocean-tropical cyclone system within the calculation period, mainly including: the work done by the wind stress of the typhoon on the sea surface and the potential energy input by the atmospheric pressure disturbance; Total energy transfer, the total energy converted between different disaster types within the system, mainly including: the contribution of wave radiation stress to ocean current kinetic energy, the energy consumed by vertical mixing caused by wave-induced turbulence, and the conversion of storm surge potential energy to ocean current kinetic energy; Total energy dissipation, the energy irreversibly lost in the form of heat energy, etc. in the process of energy transfer, mainly including: wave breaking energy loss, bottom friction dissipation, and turbulent viscosity dissipation; System energy storage change, the net change of energy stored in the system within the calculation period; Residual term, the unbalanced amount of energy budget; Ideally, it should be 0, but due to model parameterization, numerical discretization, etc. there is always a residual error; the ideal value is 0% (complete conservation, which is impossible in reality), the acceptable range is <5%, and the unacceptable range is >5%, indicating that the model has significant errors, and the parameterization scheme or initial conditions need to be calibrated; The better the trend is, the more accurate the calculation of energy input, transfer, dissipation, and storage is, the higher the balance of the energy conservation equation is, and the higher the physical credibility of the simulation results is;

[0073] S4, coupling the output of the numerical model with the background data of the hazard- bearing body, evaluating the risk and vulnerability of the hazard- bearing body under the superposition of multiple disasters, generating a dynamic risk zoning map, based on the output results of the coupled numerical model, combining the typhoon path and intensity forecast data extracted from the spatio-temporal correlation database, analyzing the extreme value combination of the spatio-temporal superposition of multiple disasters, and quantifying the joint occurrence probability of different intensity disasters through probability statistics method, calculating the risk probability, dividing into low risk level, medium risk level and high risk level, generating a dynamic risk distribution map, representing the risk level of the region under the synergistic effect of multiple disasters, integrating the background data of the hazard- bearing body to construct a multi-parameter vulnerability function library, combining the real-time disaster element field to analyze the loss rate of the hazard- bearing body under different disaster scenarios, calculating the vulnerability classification index, dividing into low vulnerability level, medium vulnerability level and high vulnerability level, forming a vulnerability spatio-temporal distribution layer, superimposing the dynamic risk distribution and vulnerability distribution in space, dividing the risk level through the risk matrix method, combining real-time monitoring data and model prediction results, updating the risk zoning according to the set time step, and generating a dynamic risk evolution atlas under the superposition of multiple disasters;

[0074] The calculation expression of the risk probability is as follows:

[0075] ;

[0076] Wherein, is the risk probability, which comprehensively measures the possibility of the region under the synergistic effect of multiple disasters; is the probability of the occurrence of the i-th disaster, which is calculated based on historical data and real-time forecast information through probability statistics method; is the weight coefficient of the i-th disaster intensity on the risk, which is determined according to the influence degree of different disasters on the risk level of the region, and the greater the influence, the higher the weight; The value of is closer to 1, indicating that the possibility of the region under the synergistic effect of multiple disasters is higher, The closer to 0, the lower the possibility of the occurrence of the risk;

[0077] The calculation expression of the vulnerability classification index is as follows:

[0078] ;

[0079] ;

[0080] Wherein, is the vulnerability classification index, which is used to divide the vulnerability level of the hazard- bearing body under different disaster scenarios; is the weight of the j-th influencing factor, which is determined according to the importance of each factor on the vulnerability of the hazard- bearing body, and the more important the influence, the greater the weight; is the vulnerability function value corresponding to the jth influence factor, used to quantify the vulnerability degree of the hazard-bearing body under the action of the influence factor, and is constructed by integrating the background data of the hazard-bearing body, The closer the value of is to 1, the more vulnerable the building is under the wind disaster, and the greater the possible loss, The closer the value of is to 0, the lower the vulnerability of the building is; is the actual intensity value of the jth influence factor in the real-time disaster element field, reflecting the influence degree of the current disaster on the hazard-bearing body; m is the number of vulnerability influence factors of the hazard-bearing body, that is, the total number of factors influencing the vulnerability of the hazard-bearing body participating in the analysis; is the threshold intensity of the jth influence factor on the hazard-bearing body, is the intensity value of the jth influence factor reaching the complete destruction of the hazard-bearing body, The closer the value of is to 1, the more vulnerable the hazard-bearing body is under the disaster scenario, and the higher the loss rate, The closer the value of is to 0, the lower the vulnerability of the hazard-bearing body is, and the smaller the loss rate is;

[0081] In addition, the process of generating a dynamic risk evolution map under the superposition of multiple disasters is as follows:

[0082] Extract dynamic hazard distribution data, covering hazard level information under the spatio-temporal superposition of multiple disasters, and obtain vulnerability spatio-temporal distribution data, containing vulnerability level information of hazard-bearing bodies in different regions, and use geographic information system technology to superimpose dynamic hazard distribution and vulnerability distribution data in the spatial dimension, so that each region corresponds to the corresponding hazard and vulnerability combination; according to the risk matrix method, taking the superposition result of dynamic hazard distribution and vulnerability distribution as input, a two-dimensional risk matrix is constructed, the horizontal axis represents the vulnerability level, and the vertical axis represents the hazard level, according to the position of different hazard and vulnerability combinations in the two-dimensional risk matrix, the region is divided into different risk levels, including low risk level, medium risk level and high risk level, wherein, the combination of low hazard and low vulnerability corresponds to the low risk level; the combination of high hazard and high vulnerability corresponds to the high risk level;

[0083] The single high risk or single high vulnerability, and the combination of medium risk and medium vulnerability are divided into medium risk level; the preliminary classified risk zoning is checked and corrected combined with real-time monitoring data, whether there is inconsistency is checked, if inconsistency is found, the risk zoning is corrected according to the actual situation of the monitoring data, and the mode prediction result is referred to, and the risk zoning is updated according to the set time step, and a preliminary dynamic risk evolution map is formed; the quality of the preliminary generated dynamic risk evolution map is checked and optimized to ensure the accuracy, integrity and visualization effect of the map, and the real-time monitoring data and the latest mode prediction result are continuously accessed, and the dynamic risk evolution map is updated in real time according to the set time step, so as to reflect the dynamic change of regional risk under the superposition of multiple disasters;

[0084] The specific work content is: based on the output results of the coupled numerical model, the time and space sequence of key disaster elements including storm surge, wave growth and wave current interaction are extracted, and the typhoon path and intensity prediction data extracted from the time and space correlation database are combined, the extreme value combination of each disaster in the time and space grid is extracted through the extreme value statistical method, the joint occurrence probability of disasters of different intensities is quantified by using the Copula function, and then the hazard probability is calculated, the low, medium and high hazard levels are divided based on the probability threshold of the hazard probability, and the dynamic hazard distribution map of the synergistic effect of multiple disasters is generated, and the hazard level of the region affected by the superposition of extreme disasters is represented in the form of probability density.

[0085] The expression of the Copula function quantifying the joint occurrence probability of disasters of different intensities is as follows:

[0086] ;

[0087] ;

[0088] In the formula: is the joint exceedance probability, which represents the probability that o kinds of disasters exceed their respective preset intensity thresholds at the same time V is the Copula function, a mathematical function that connects the marginal distribution to form a joint distribution, which describes the dependence structure between multiple disasters is the marginal distribution function value, the probability that the intensity of the s-th disaster exceeds the threshold is the cumulative distribution function (CDF), which represents the probability that the intensity of the s-th disaster is less than or equal to the threshold , is the disaster variable and the threshold , which represents the intensity of the s-th disaster The larger the value is, the higher the considered disaster intensity threshold​​​​ The lower the value, the more likely the event is to occur, and the larger the V value tends to be;

[0089] Among them, the low-risk level corresponds to the disaster intensity under common meteorological conditions, the medium-risk level reflects the compound event caused by a moderate-intensity typhoon or cold wave, and the high-risk level characterizes the rare scenario of extreme typhoon and astronomical high tide superimposed. The system integrates the baseline data of disaster-bearing bodies, including building structures and infrastructure, to establish a multi-parameter vulnerability function library, including the relationship curve between building damage rate and wave height-current velocity and the response function of inundation depth. Combined with real-time disaster element fields (wind speed, wave height, and water rise), the loss rate of each disaster-bearing body is calculated through vulnerability functions. Cluster analysis is used to classify the low-vulnerability level, medium-vulnerability level, and high-vulnerability level. Based on the geographic information system platform, a spatiotemporal distribution layer of vulnerability is generated to dynamically reflect the vulnerability changes of disaster-bearing bodies under different disaster scenarios.

[0090] Low vulnerability level corresponds to areas with strong disaster resistance or low exposure, medium vulnerability level reflects built-up areas with moderate sensitivity, and high vulnerability level identifies critical infrastructure. By spatially overlaying dynamic hazard distribution and spatiotemporal vulnerability distribution layers, the risk matrix method is used to map hazard level and vulnerability level to risk level, dividing the risk into low risk level, medium risk level and high risk level. Combined with real-time monitoring data and short-term forecast results, the risk zoning is updated at a preset 1-hour time step to generate a spatiotemporally continuous multi-hazard risk evolution map. Among them, the low risk level is an area with low hazard and vulnerability, the medium risk level reflects a transitional zone with a prominent single element (high hazard or high vulnerability or both elements in a moderate state), and the high risk level identifies key prevention and control areas with both high hazard and vulnerability.

[0091] S5. Construct a three-dimensional dynamic model of the disaster-bearing body, and provide real-time feedback on the changes in the state of the disaster-bearing body at different stages of disaster to form a closed-loop feedback under the action of disaster.

[0092] S6. Based on the energy transfer chain and the status feedback of the disaster-bearing body, trace the disaster chain propagation path in reverse and use historical disaster cases to verify the continuity of the tracing results;

[0093] S7. Connect the tracing results with the risk assessment data to the early warning platform to monitor and warn of multi-hazard coupled disaster-causing paths.

[0094] Example 2, as Figure 1 , Figure 2 As shown, based on Embodiment 1, the present invention provides a technical solution: Preferably, S5 specifically includes:

[0095] Based on the background data of the hazard-affected body, combined with the building BIM model and infrastructure GIS data, a three-dimensional dynamic model of the hazard-affected body is constructed, the dynamic characteristics of material properties and mechanical parameters are embedded, the digital mapping with the physical world is established through real-time monitoring data, the consistency of the geometric and physical characteristics of the model is ensured, based on the coupling of numerical mode and disaster prediction data, the action process of different disaster stages on the hazard-affected body is simulated, the simulation results are fed back to the three-dimensional dynamic model of the hazard-affected body in real time, the state change information of the hazard-affected body in each disaster stage, i.e. real-time damage data, is dynamically updated, the difference between the real-time monitoring data and the model feedback results is compared, the reasons for the difference are analyzed, the model parameters are adjusted according to the analysis results, the model accuracy is optimized, and the process is continuously cycled to form a closed-loop feedback mechanism of the state change of the hazard-affected body under the action of disasters;

[0096] The specific work content is: based on the background data of the hazard-affected body, combined with the building BIM model and infrastructure GIS data, the building BIM model provides detailed geometric and structural information of the building, and the GIS data presents the spatial distribution and correlation of the infrastructure, and then a three-dimensional dynamic model of the hazard-affected body is constructed to present the appearance and layout of the disaster body and embed the dynamic characteristics of material properties and mechanical parameters, combined with real-time monitoring data, a digital mapping with the physical world is established, and then the physical world changes are captured in real time and synchronized to the three-dimensional dynamic model of the hazard-affected body, ensuring that the geometric shape and physical characteristics of the three-dimensional dynamic model of the hazard-affected body are consistent with the actual situation;

[0097] Based on the coupling of numerical mode and disaster prediction data, the action process of different disaster stages on the hazard-affected body is simulated, the coupling numerical mode comprehensively considers the interaction of multiple disaster factors, the disaster prediction data provides the disaster development trend and intensity information, the simulation presents the influence mode and degree of the disaster on the hazard-affected body in each stage, the simulation results are fed back to the three-dimensional dynamic model of the hazard-affected body in real time, the model dynamically updates the real-time damage data in each disaster stage according to the simulation situation, and the real state of the hazard-affected body in the disaster is reflected in real time; by comparing the real-time monitoring data with the model feedback results, the reasons for the difference between the two are analyzed, the model parameters are adjusted according to the analysis results, the model accuracy is optimized, the model is more consistent with the actual situation, the comparison, analysis and adjustment process is continuously cycled, a closed-loop feedback mechanism of the state change of the hazard-affected body under the action of disasters is formed, and through continuous iteration and optimization, the prediction ability of the model for the state change of the hazard-affected body under the action of disasters is improved;

[0098] S6 specifically includes:

[0099] Based on the output of the coupled numerical model, the spatiotemporal distribution of disaster energy is extracted, and an energy flow network model is constructed. The key energy nodes and propagation paths are identified by graph theory method. The trigger sequence and interaction relationship of each disaster are inversely solved by combining the energy conservation equation, the energy conversion efficiency and loss are quantified, and the physical mechanism tracing framework of disaster chain is formed. The disaster action intensity and spatiotemporal range are inversely deduced by using the real-time damage data of the three-dimensional dynamic model of the hazard-affected body. By comparing the simulation state with the actual monitoring data, the energy transfer path is calibrated, the intermediate nodes of the inverse tracing are corrected, and the consistency of the disaster chain propagation process and the response of the hazard-affected body is ensured. The historical compound disaster events are selected, the tracing results are compared with the field investigation data, the path continuity is evaluated, i.e. the node missing rate is less than 5%, and the energy transfer probability is updated by using the Bayesian method to optimize the inverse tracing algorithm and form a reusable disaster chain knowledge base.

[0100] Among them, the betweenness centrality is used to identify the key energy nodes, and its expression is as follows:

[0101] ;

[0102] In the formula: is the betweenness centrality of the key energy node , which measures the pivotal degree of the key energy node in the network; is the total number of shortest paths between the key energy node and ; is the number of paths passing through the key energy node in all shortest paths from to ; The larger the value of , the more the key energy node plays the role of “bridge” in energy transfer;

[0103] The node energy balance equation is used as the basis for inverse solution, and its expression is as follows:

[0104] ;

[0105] In the formula: is the total energy of node b; is the rate of change of energy of node b with time, which can be 0 if it is steady-state or specific time analysis; is the input energy, which is the energy transferred from the upstream node to node b, is the conversion efficiency from to b; is the sum of all incoming energy to node b; is the output energy, which is the energy transferred from node b to the downstream node c, is the conversion efficiency from b to c; is the sum of all energy out of node b; is the energy loss, energy irreversibly dissipated inside node b; is the energy source term, energy generated inside node b; energy loss The larger the value, the more the node (region or process) is a major "sink" of energy, where energy is largely consumed;

[0106] The specific work is: Based on the output data of the coupled numerical model, the spatio-temporal distribution characteristics of disaster energy such as storm surge and sea wave are extracted, a directed and weighted network model is constructed with energy nodes and transmission paths as the core, the node represents the energy aggregation area, and the edge weight represents the energy conversion efficiency. Through the betweenness centrality analysis and the shortest path algorithm in graph theory, the key energy hubs and dominant transmission paths are identified, combined with the energy conservation equation, the trigger sequence and coupling relationship between each disaster are inversely solved, the proportion of energy input (wind stress work), transmission (radiation stress) and dissipation (wave breaking) is quantified, and the physical mechanism explanation framework of disaster chain is established.

[0107] Using the real-time damage data provided by the three-dimensional dynamic model of the disaster-bearing body, the intensity and influence range of disaster action are inversely engineered, the disaster energy field simulated by numerical simulation is spatio-temporally aligned with the actual response of the disaster-bearing body, the key parameters in the energy transmission path are fitted by the least squares method, for the nodes that do not match the energy transmission path and the damage of the disaster-bearing body, the energy distribution weight is dynamically adjusted to ensure the consistency of the energy network model and the physical observation. Through iterative optimization, eliminate breakpoints or contradictory nodes in path tracing, form a closed energy-damage feedback loop, and improve the physical credibility of disaster chain tracing; select historical compound disaster events, compare the disaster chain path traced in reverse with the disaster development process of field investigation data, evaluate the path continuity index, and require the node loss rate to be <5%, use Bayesian probability method, update the conditional probability of energy transmission path based on historical compound disaster events, optimize the prior parameters of the reverse tracing algorithm, and form a structured disaster chain knowledge base.

[0108] In S7, the process of monitoring and early warning of multi-disaster coupling disaster-causing path is:

[0109] The operation of spatiotemporal reference unification and format standardization of the disaster chain tracing result and risk assessment data ensures data compatibility, integrates according to the unified format required by the early warning platform, encapsulates data in JSON-LD format, retains complete semantic association and spatiotemporal attributes, superimposes the energy transfer path in the tracing result on the dynamic risk zoning of risk assessment, matches the hazard level and the vulnerability level of the hazard-affected body through a spatial correlation algorithm, determines the risk level, generates early warning signals of multi-disaster coupling disaster events, generates machine-readable CAP format messages through the early warning engine according to the early warning results of multi-disaster coupling disaster events, labels the disaster-causing path and risk level, adopts a multi-channel distribution strategy, and pushes to the target area. A feedback mechanism is established to verify the accuracy of the early warning through actual disaster data.

[0110] The specific work content is: implementing spatiotemporal reference unification and format standardization of disaster chain tracing results and risk assessment data, correcting the time and space information of both according to the unified spatiotemporal reference system, ensuring accurate correspondence of data in the spatiotemporal dimension, and converting data into a unified format according to the requirements of the early warning platform to ensure data compatibility. Data is encapsulated in JSON-LD format to retain complete semantic association and spatiotemporal attributes.

[0111] The energy transfer path in the disaster chain tracing result is spatially superimposed on the dynamic risk zoning, the hazard level and the vulnerability level of the hazard-affected body are matched through a correlation algorithm, the risk level of each geographic unit is determined by the hazard and vulnerability through a spatial connection method, based on the matching result, early warning signals of multi-disaster coupling disaster events are generated, including disaster-causing type, risk level and expected duration; according to the early warning results of multi-disaster coupling disaster events, machine-readable CAP format messages are generated through the early warning engine, the messages detail the disaster-causing path and risk level and other key information, a multi-channel distribution strategy is adopted, the early warning messages are pushed to the target area according to its characteristics and needs, ensuring timely and accurate information transmission, and a feedback mechanism is established to collect actual disaster data and compare it with the early warning results for analysis to verify the accuracy of the early warning.

[0112] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for tracing a disaster path of a near-shore disaster-bearing body based on a digital-analog experiment, characterized in that, Comprise the following steps: S1, determine the characteristics of marine dynamic disasters in the nearshore study area, and integrate multi-source historical disaster data, real-time monitoring data and background data of disaster-bearing bodies to build a spatio-temporal correlation database; S2, based on three-dimensional ocean and third-generation sea wave model, establish nearshore storm surge-sea wave coupling numerical model and verify; S3, introduce the energy conservation equation, analyze the generation, transmission and dissipation process of disaster energy under typhoon weather, quantify the energy conversion efficiency between disasters, and output the dynamic evolution atlas of typhoon disaster energy; S4, combined with the output of the coupling numerical model and the background data of the disaster-bearing body, assess the risk and vulnerability of the disaster-bearing body under the superposition of multiple disasters, and generate dynamic risk zoning, the specific process is: Based on the output results of the coupling numerical model, combined with the typhoon path and intensity forecast data extracted from the spatio-temporal correlation database, analyze the extreme value combination of multi-disaster spatio-temporal superposition, and quantify the joint occurrence probability of different intensity disasters through probability statistical method, calculate the risk probability, divide into low risk level, medium risk level and high risk level, generate dynamic risk distribution map, which represents the risk level of the region affected by the synergistic effect of multiple disasters; Integrate the background data of the disaster-bearing body to build a multi-parameter vulnerability function library, analyze the loss rate of the disaster-bearing body under different disaster scenarios combined with real-time disaster element field, calculate the vulnerability classification index, divide into low vulnerability level, medium vulnerability level and high vulnerability level, and form the vulnerability spatio-temporal distribution map layer; Superimpose the dynamic risk distribution and vulnerability distribution in space, divide the risk level through the risk matrix method, update the risk zoning according to the set time step combined with real-time monitoring data and model prediction results, and generate the dynamic risk evolution atlas under the superposition of multiple disasters; The calculation expression of risk probability is as follows: ； wherein, is the probability of danger; n is the number of disaster types, i.e. the total number of disaster types participating in the analysis; is the probability of occurrence of the i-th disaster; is the weight coefficient of the i-th disaster intensity on the impact of danger. The calculation expression of vulnerability classification index is as follows: ； ； wherein, is a vulnerability classification index; is a weight of the jth influence factor; is a vulnerability function value corresponding to the jth influence factor; is an actual intensity value of the jth influence factor in the real-time disaster element field; m is the number of vulnerability influence factors of the hazard- bearing body, i.e., the total number of factors influencing the vulnerability of the hazard-bearing body participating in the analysis; is a threshold intensity at which the jth influence factor begins to affect the hazard-bearing body, is an intensity value at which the jth influence factor reaches the complete destruction of the hazard-bearing body; S5, build a three-dimensional dynamic model of the disaster-bearing body, real-time feedback of the state change of the disaster-bearing body in different disaster stages, and form a closed loop feedback under the action of disaster; S6, based on the energy transfer chain and the state feedback of the disaster-bearing body, trace back the disaster chain propagation path in reverse, and verify the continuity of the tracing result by using historical disaster examples; S7, connect the tracing result with the risk assessment data to the early warning platform, and monitor and warn the disaster-causing path of multiple disasters.

2. The method of claim 1, wherein the method is based on a digital model experiment. In the S1, the process of building the spatio-temporal correlation database is: Investigate the target nearshore study area, determine the marine dynamic disasters in the nearshore study area, determine the disaster type, formation mechanism and characteristic rule, and define the main disaster; Collect multi-source historical disaster data, real-time monitoring data and background data of disaster-bearing bodies in the nearshore study area, and perform preprocessing operations including cleaning, calibration and standardization on the data; According to the spatio-temporal correlation principle, integrate the preprocessed data according to the disaster occurrence time, spatial location and disaster-bearing body attribute, and build a spatio-temporal correlation database covering the main disaster and disaster-bearing body type.

3. The method of claim 1, wherein the method is based on a digital model experiment. In the S2, the process of establishing nearshore storm surge-sea wave coupling numerical model and verification is: Based on the three-dimensional ocean model and the third generation wave model, a two-way coupling interface is designed to build a storm surge-wave coupling framework, and the calculation domain, grid resolution and time step are configured, and the initial field and boundary conditions are set. The third generation wave model is Mazu·Wave, which is a third generation wave spectrum numerical model. Through OpenMP, the two-way coupling of the ocean model and the wave model is established to build a nearshore storm surge-wave coupling numerical model. The ocean model provides the sea surface flow field and water level data to the wave model, and the wave model feeds back the radiation stress and wave-induced turbulence to the ocean model. The observed data and reanalysis data are used to verify the nearshore storm surge-wave coupling numerical model. The ensemble Kalman filter is used to optimize the model initial field. By comparing the simulation results with the measured data, the tidal level error, wave height correlation coefficient and flow field consistency are quantitatively evaluated to verify the simulation accuracy of the coupling model for key disaster elements such as storm surge water level increase, wave growth and wave-flow interaction.

4. The method of claim 1, wherein the method is based on a digital model experiment. In the S3, the process of outputting the typhoon disaster energy dynamic evolution map is: Based on the nearshore storm surge-wave coupling numerical model, an energy conservation framework of the typhoon system is built to define the energy input, transfer and dissipation. Through the analysis of the eddy kinetic energy budget, the exchange efficiency of energy at the ocean-atmosphere interface is quantified, and the energy flow control equation is established. The spectral analysis method is used to decompose the energy components of each disaster, calculate the conversion rate between storm surge potential energy, wave mechanical energy and sea current kinetic energy, and introduce a nonlinear coupling term to describe the wave-flow interaction. Through the analysis of energy flux divergence, the cross-scale energy transfer path is analyzed, and the influence weight of typhoon parameters on energy distribution is determined through sensitivity experiments. Based on the closed error analysis, the balance of the energy conservation equation is verified, the energy conversion efficiency of each disaster is calculated, the energy level string process is tested through the energy spectrum density function, the dissipation parameters are calibrated combined with the observed data, and the typhoon disaster energy dynamic evolution map is output.

5. The method of claim 1, wherein the method is based on a digital model experiment of a coastal hazard-bearing body disaster-causing path tracing method. The process of generating a dynamic risk evolution map under the superposition of multiple disasters is: Extract the dynamic risk distribution data, which covers the risk level information of the spatial and temporal superposition of multiple disasters, and obtain the vulnerability spatial and temporal distribution data, which contains the vulnerability level of different regional disaster bearing bodies. Using geographic information system technology, the dynamic risk distribution and vulnerability distribution data are superimposed in the spatial dimension to make each region correspond to the corresponding risk and vulnerability combination; According to the risk matrix method, a two-dimensional risk matrix is constructed by taking the superposition results of dynamic risk distribution and vulnerability distribution as input. The horizontal axis represents the vulnerability level, and the vertical axis represents the risk level. According to the position of different risk and vulnerability combinations in the two-dimensional risk matrix, the region is divided into different risk levels, including low risk level, medium risk level and high risk level. The combination of low risk and low vulnerability corresponds to the low risk level. The combination of high risk and high vulnerability corresponds to the high risk level. Single high risk or single high vulnerability, as well as the combination of medium risk and medium vulnerability, is divided into the medium risk level. The preliminary risk zoning is checked and corrected in combination with real-time monitoring data, and whether there is inconsistency is checked. If inconsistency is found, the risk zoning is corrected according to the actual situation of the monitoring data, and the risk zoning is updated according to the set time step, to form a preliminary dynamic risk evolution map; The quality of the preliminary dynamic risk evolution map is checked and optimized, and real-time monitoring data and the latest model prediction results are continuously inputted, and the dynamic risk evolution map is updated in real time according to the set time step.

6. The method of claim 1, wherein the method is based on a digital model experiment. The S5 specifically includes: Based on the background data of the disaster-bearing body, the three-dimensional dynamic model of the disaster-bearing body is constructed by combining the building BIM model and the infrastructure GIS data, the dynamic characteristics of the material properties and mechanical parameters are embedded, and the digital mapping with the physical world is established through real-time monitoring data; Based on the coupling of numerical models and disaster prediction data, the action process of different disasters on the disaster-bearing body is simulated, the simulation results are fed back to the three-dimensional dynamic model of the disaster-bearing body in real time, and the state change information of the disaster-bearing body in each disaster stage is dynamically updated, that is, real-time damage data; The real-time monitoring data and the model feedback results are compared, the difference reasons are analyzed, the model parameters are adjusted according to the difference, the model accuracy is optimized, and the process is continuously cycled to form a closed-loop feedback mechanism of the state change of the disaster-bearing body under the action of disasters.

7. The method of claim 1, wherein the method is based on a digital model experiment. The S6 specifically includes: Based on the output of the coupled numerical model, the spatio-temporal distribution of disaster energy is extracted, an energy flow network model is constructed, key energy nodes and propagation paths are identified by graph theory method, the triggering order and interaction relationship of each disaster are inversely solved by combining the energy conservation equation, the energy conversion efficiency and loss are quantified, and a physical mechanism tracing framework of disaster chain is formed; The real-time damage data of the three-dimensional dynamic model of the disaster-bearing body are used to back-propagate the disaster action intensity and spatio-temporal range, and the energy transfer path is calibrated by comparing the simulation state with the actual monitoring data, and the intermediate nodes of the reverse tracing are corrected; The historical compound disaster events are selected, the tracing results are compared with the field investigation data, the path continuity is evaluated, that is, the node missing rate is less than 5%, and the energy transfer probability is updated by using the Bayesian method, the reverse tracing algorithm is optimized, and a reusable disaster chain knowledge base is formed.

8. The method of claim 1, wherein the method is based on a digital model experiment of a coastal hazard-bearing body disaster-causing path tracing method. In the S7, the process of monitoring and early warning of the disaster-causing path of multiple disasters is as follows: The spatio-temporal benchmarking and format standardization of the disaster chain tracing results and risk assessment data are performed, the data are integrated in the unified format required by the early warning platform, the data are packaged in JSON-LD format, and the complete semantic association and spatio-temporal attributes are retained; The energy transfer path in the tracing results and the dynamic risk zoning in the risk assessment are superimposed, the hazard level and the vulnerability level of the disaster-bearing body are matched by the spatial correlation algorithm, the risk level is determined, and the early warning signal of the multiple disaster-causing events is generated; According to the early warning results of the multiple disaster-causing events, the machine-readable CAP format message is generated by the early warning engine, the disaster-causing path and the risk level are marked, the multi-channel distribution strategy is adopted, and the target area is pushed directionally, a feedback mechanism is established, and the early warning accuracy is verified by the actual disaster data.

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