A rapid probabilistic forecast method for offshore engineering safety risks during typhoons

Abstract

The present invention belongs to the technical field of safe operation of marine engineering. The present invention discloses a method for rapid probabilistic forecasting of marine engineering safety risks during typhoons, comprising the following steps: S1. collecting historical typhoon and associated disaster data, and reconstructing the typhoon-storm surge-strong current-storm wave combined process to obtain historical record information; S2. performing dimensionality reduction and cluster analysis on the historical record information to obtain the probability distribution of hydrological elements, and performing correlation analysis between the current typhoon event and historical typhoons to obtain an ensemble forecast of marine dynamic factors. The present invention can rapidly calculate the risk probability of marine engineering under typhoon events, providing a scientific basis for emergency management of marine engineering; and can rapidly respond to new typhoon events to provide decision makers with timely risk information.

Description

Technical Field

[0001] The present invention relates to the technical field of safe operation of marine engineering, and more specifically, to a method for rapid probabilistic forecasting of marine engineering safety risks during a typhoon. Background Art

[0002] Strong winds, huge waves, and strong currents during typhoons are the main disaster-causing factors for coastal and offshore projects. Therefore, it is very necessary to understand typhoon information and potential disaster risks in the sea area. Typhoon wind-wave-current forecasts based on high-precision numerical simulations can provide a good basis for decision-making.

[0003] However, due to the complexity of typhoon processes, there is a certain degree of uncertainty in the prediction and forecast of typhoon development and evolution, as well as the prediction and forecast of large waves, surges, and strong currents during the process. Traditional deterministic forecasts have drawbacks in assessing the safety of offshore engineering projects, primarily due to their inability to effectively reflect the uncertainty and error range of forecast results. In contrast, the advantage of ensemble forecasts lies in considering various errors in the forecast process to provide a range of possible forecast results, thereby more comprehensively assessing disaster risks. However, ensemble forecasts based directly on large-scale numerical simulations require a large amount of computing resources and professional support, and sometimes cannot meet the needs of rapid assessment and decision-making in offshore engineering practice.

[0004] In view of this, the present invention proposes a method for rapid probabilistic forecasting of offshore engineering safety risks during typhoons to solve the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for rapid probabilistic forecasting of offshore engineering safety risks during a typhoon, comprising the following steps:

[0006] S1. Collect historical typhoon and associated disaster data, and reconstruct the typhoon-storm surge-strong current-storm wave combined process to obtain historical record information;

[0007] S2. Perform dimensionality reduction and cluster analysis on historical records to obtain the probability distribution of hydrological elements, and perform correlation analysis between current typhoon events and historical typhoons to obtain an ensemble forecast of ocean dynamic factors;

[0008] S3. Collect structural design indicators of marine environmental projects and set structural failure thresholds based on the structural design indicators of marine environmental projects;

[0009] S4. Calculate the risk probability of offshore engineering projects under the current typhoon event by combining the ensemble forecast of ocean dynamic factors and the structural failure threshold.

[0010] Furthermore, the steps of collecting historical typhoon and associated disaster data and reconstructing the typhoon-storm surge-strong current-storm wave combined process to obtain historical record information include:

[0011] S101. Collect historical typhoon and associated disaster data, establish a model wind field at the typhoon cyclone center based on a mathematical model, calculate the rotating wind gradient velocity at a certain distance r from the cyclone center, and fuse the model wind field with the observed wind field velocity to obtain a fused wind field. The fusion expression is:

[0012] V=α(r)V ERA5 +[1-α(r)]V model ,

[0013] Where V is the velocity of the fused wind field, r is the distance from any point in space to the cyclone center, and V ERA5 is the speed of the observed wind field, V model is the velocity of the model wind field, α(r) is the transition function;

[0014] S102: Simulate the surge and ocean current events in the ocean hydrodynamic process: Use the fused wind field generated in S101 to drive the FVCOM or ADCIRC ocean dynamics calculations, thereby obtaining the sea level changes during the typhoon and the changes in the velocity, direction, and vertical distribution of the ocean currents.

[0015] S103: Simulate ocean wave events in the ocean hydrodynamic process: Calculate the third-generation wave model SWAN based on the fused wind field generated in S101 above, and obtain the time-varying process of the wave height, direction, period, and wavelength.

[0016] S104: Integrate the above S101-S103 to obtain historical record information, which includes: the longitude and latitude of the typhoon center, the maximum wind speed, the minimum air pressure, and the radius of the wind circle; the change process of the sea level; the change process of the velocity, direction, and vertical distribution of the ocean current; and the time-varying process of the wave height, direction, period, and wavelength.

[0017] Furthermore, the expression for the rotating wind gradient velocity at a certain distance r from the cyclone center is:

[0018]

[0019] Where V g is the rotating wind gradient velocity at a certain distance r from the cyclone center, V max The maximum wind speed of a typhoon.

[0020] Furthermore, the expression of the transition function is:

[0021]

[0022] Where R mw is the radius of maximum wind speed, a=4.3944, b=2.5.

[0023] Furthermore, the steps of performing dimensionality reduction and cluster analysis on historical record information to obtain the probability distribution of hydrological elements, and performing correlation analysis on the current typhoon event and historical typhoons to obtain an ensemble forecast of ocean dynamic factors include:

[0024] S201: The calculation sea area is discretized into blocks with a unit of 0.1°×0.1°. Typhoon information is collected for each block. The typhoon information includes typhoon path, wind speed, air pressure, movement speed and direction. The typhoon events within the block are clustered using the weighted Fuzzy C-means clustering algorithm. The typhoon clustering characteristics of several types of typhoons are extracted through the clustering, and the occurrence frequency of different types of typhoons is obtained.

[0025] S202: Based on the wind-tide-current-wave joint data of historical records and the typhoon clustering characteristics of S201, statistics are collected on the tides, currents, and waves during different types of typhoons in each block, that is, the probability distribution characteristics of tides, currents, and waves under different types of typhoons are obtained. By combining the occurrence frequency of different types of typhoons, a conditional probability distribution dataset of the wind-tide-current-wave joint data is obtained, that is, the conditional probability distribution dataset of hydrological events;

[0026] S203: Then, obtaining the probability distribution of hydrological elements corresponding to different types of typhoons in several sea areas;

[0027] S204: Obtain the current ensemble forecast of the current typhoon event through the meteorological station, determine the correlation between the current typhoon event and the historical typhoons in the historical record information through Pearson correlation analysis, and then combine the hydrological event conditional probability distribution data set formed in S202 to obtain the ocean dynamic factor ensemble forecast.

[0028] Furthermore, the steps of collecting structural design indicators of the marine environmental engineering and setting a structural failure threshold based on the structural design indicators of the marine environmental engineering include:

[0029] S301. Collect structural design indicators of different types of marine environmental projects, use the structural design indicators of different types of marine environmental projects as structural failure thresholds corresponding to the different types of marine environmental projects, and store them in a structural risk probability assessment database;

[0030] S302: Obtain hydrological conditions corresponding to structural design indicators of different types of marine environmental projects, and associate the structural design indicators of the same type of marine environmental projects with the corresponding hydrological conditions.

[0031] Furthermore, the step of calculating the risk probability of offshore engineering under the current typhoon event by combining the ocean dynamic factor ensemble forecast and the structural failure threshold includes:

[0032] S401: Obtaining a structural design index corresponding to the offshore project, and obtaining a structural failure threshold corresponding to the offshore project based on the structural design index;

[0033] S402: Calculate the cumulative probability of exceeding the structural failure threshold corresponding to the offshore project and the ensemble forecast of the ocean dynamic factors of the current typhoon event, and use the cumulative probability value as the risk probability of the offshore project under the current typhoon event.

[0034] The technical effects and advantages of the method for rapid probabilistic forecasting of offshore engineering safety risks during typhoons provided by the present invention are as follows:

[0035] 1. The present invention can quickly calculate the risk probability of offshore projects under typhoon events, providing a scientific basis for emergency management of offshore projects. It can significantly reduce computational complexity and improve the efficiency of risk assessment, enabling rapid response to new typhoon events and providing timely risk information to decision makers. It can also provide customized risk assessments for various offshore engineering structures such as offshore platforms, breakwaters, and bridges, enhancing the versatility and applicability of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of a method for rapid probabilistic forecasting of offshore engineering safety risks during a typhoon according to the present invention;

[0037] Figure 2 Schematic diagram of calculating risk probability in S4 of the present invention. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0039] Example 1

[0040] See also Figure 1 and Figure 2 As shown, the method for rapid probabilistic forecasting of offshore engineering safety risks during a typhoon described in this embodiment includes the following steps:

[0041] S1. Collect historical typhoon and associated disaster data, and reconstruct the typhoon-storm surge-strong current-storm wave combined process to obtain historical record information;

[0042] S2. Perform dimensionality reduction and cluster analysis on historical records to obtain the probability distribution of hydrological elements, and perform correlation analysis between current typhoon events and historical typhoons to obtain an ensemble forecast of ocean dynamic factors;

[0043] S3. Collect structural design indicators of marine environmental projects and set structural failure thresholds based on the structural design indicators of marine environmental projects;

[0044] S4. Calculate the risk probability of offshore engineering projects under the current typhoon event by combining the ensemble forecast of ocean dynamic factors and the structural failure threshold.

[0045] Furthermore, the steps of collecting historical typhoon and associated disaster data and reconstructing the typhoon-storm surge-strong current-storm wave combined process to obtain historical record information include:

[0046] S101. Collect historical typhoon and associated disaster data (the combined process of typhoons, storm surges, strong currents, and storm waves from 1949 to the present). Establish a model wind field at the center of the cyclone (i.e., historical typhoons) based on a mathematical model. Calculate the rotational wind gradient velocity at a certain distance r from the cyclone center. Fuse the model wind field with the observed wind field (low resolution) to obtain a fused wind field. The fusion expression is:

[0047] V=α(r)V ERA5 +[1-α(r)]V model ,

[0048] Where V is the velocity of the fused wind field, r is the distance from any point in space to the cyclone center, and V ERA5 is the speed of the observed wind field (ERA5 reanalysis wind field), V model is the velocity of the model wind field, α(r) is the transition function;

[0049] S102: Simulate the surge and ocean current events in the ocean hydrodynamic process: Use the fused wind field generated in S101 to drive ocean dynamic models such as FVCOM or ADCIRC to perform calculations, thereby obtaining the changes in sea surface water level and the changes in ocean current velocity, direction, and vertical distribution during the typhoon.

[0050] S103: Simulate ocean wave events during ocean hydrodynamics: Calculate the time-varying process of the wave height, direction, period, and wavelength using the third-generation wave model SWAN or WaveWatch-III, driven by wind field information during typhoons.

[0051] S104: Integrate the above S101-S103 to obtain historical record information, which includes: the longitude and latitude of the typhoon center, the maximum wind speed, the minimum air pressure, and the radius of the wind circle; the change process of the sea level; the change process of the velocity, direction, and vertical distribution of the ocean current; and the time-varying process of the wave height, direction, period, and wavelength.

[0052] Specifically, the observed wind field takes the ECMWF wind field dataset ERA5 as an example, and a smooth transition is adopted between the model wind field and the ERA5 reanalysis wind field data. ERA5 is the fifth-generation global climate reanalysis dataset developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). The reanalysis wind field is a global spatiotemporal variation data that simulates and assimilates the observation data. As the background field, the general wind tower data is only the site data and cannot cover the area required for analysis.

[0053] Furthermore, the expression for the rotating wind gradient velocity at a certain distance r from the cyclone center is:

[0054]

[0055] Where V g is the rotating wind gradient velocity at a certain distance r from the cyclone center, V max is the maximum wind speed of the typhoon;

[0056] Specifically, its expression is given by Young's empirical formula, and attention should be paid to corrections for the wind field's directionality, asymmetry, and wind circle radius.

[0057] Furthermore, the expression of the transition function is:

[0058]

[0059] Where R mw is the radius of maximum wind speed, a=4.3944, b=2.5;

[0060] Specifically, the transition function makes the wind field dominated by the model wind field speed at a distance of 1 times the wind circle radius from the cyclone center, and the model wind field accounts for 90%; when the wind field is more than 4 times the wind circle radius from the cyclone center, the wind field is dominated by ERA5 wind, and the model wind field accounts for no more than 10%;

[0061] Furthermore, the steps of performing dimensionality reduction and cluster analysis on historical record information to obtain the probability distribution of hydrological elements, and performing correlation analysis on the current typhoon event and historical typhoons to obtain an ensemble forecast of ocean dynamic factors include:

[0062] S201: The calculation sea area is discretized into blocks of 0.1°×0.1°. Typhoon information is collected for each block. The typhoon information includes typhoon path, wind speed, air pressure, movement speed, and direction. Typhoon events within the block are clustered using a weighted Fuzzy C-means clustering algorithm. Typhoon clustering features of several types of typhoons are extracted through event clustering (typhoon clustering features are process features of cluster centers, such as central longitude and latitude, maximum wind speed, minimum air pressure, movement speed, etc.), thereby obtaining the occurrence frequency of different types of typhoons.

[0063] S202: Based on the wind-tide-current-wave joint data of historical records and the typhoon clustering characteristics of S201, statistics are collected on the tides, currents, and waves during different types of typhoons in each block, that is, the probability distribution characteristics of tides, currents, and waves under different types of typhoons are obtained. By combining the occurrence frequency of different types of typhoons, a conditional probability distribution dataset of the wind-tide-current-wave joint data is obtained, that is, the conditional probability distribution dataset of hydrological events;

[0064] S203: Then, obtaining the probability distribution of hydrological elements corresponding to different types of typhoons in several sea areas;

[0065] S204: Obtain the current ensemble forecast of the current typhoon event (new typhoon event) through the meteorological station, determine the correlation between the current typhoon event and the historical typhoons in the historical record information through Pearson correlation analysis, and then combine the hydrological event conditional probability distribution data set formed in S202 to obtain the ocean dynamic factor ensemble forecast (the ocean dynamic factor ensemble forecast includes waves, water surge, ocean current, etc.), where the forecast value of the ocean dynamic factor ensemble forecast is the probability of occurrence of the hydrological event under the current typhoon event, and obtain the following: Figure 2 The blue or red curves indicate the environmental ensemble forecast results;

[0066] Specifically, the correlation between the current typhoon event and the historical typhoon event is judged by using the Pearson correlation coefficient. The value range of the Pearson correlation coefficient is -1 to 1. The closer the value is to ±1, the stronger the correlation is. The conditional probability distribution data set of hydrological events (such as the probability distribution of wave height, water level, flow velocity, etc.) is used, combined with the characteristics of the current typhoon event, to calculate the probability of occurrence of hydrological events under the current ensemble forecast (current typhoon conditions). The final forecast result is the probability of occurrence of hydrological events (such as storm surges, waves, etc.) under the current ensemble forecast, which is usually displayed in the form of a probability curve, such as Figure 2 The blue or red curve in .

[0067] Furthermore, the steps of collecting structural design indicators of the marine environmental engineering and setting a structural failure threshold based on the structural design indicators of the marine environmental engineering include:

[0068] S301. Collect structural design indicators (design extreme working conditions) of different types of marine environmental projects, use the structural design indicators of different types of marine environmental projects as structural failure thresholds corresponding to the different types of marine environmental projects, and store them in a structural risk probability assessment database (the structural risk probability assessment database facilitates rapid retrieval of structural design indicators when a new typhoon approaches);

[0069] S302: Obtain hydrological conditions corresponding to structural design indicators of different types of marine environmental projects, and associate the structural design indicators of the same type of marine environmental projects with the corresponding hydrological conditions.

[0070] Specifically, marine engineering requires environmental information in the design phase, and usually the disaster-bearing capacity of the structure under the combined action of extreme working conditions must be guaranteed. The designed extreme working conditions can be used as the threshold for structural failure. This method does not consider the uncertainty of structural materials in the assessment of structural safety, but only uses the design standards as a reference. If the environmental working conditions are weaker than the designed extreme working conditions, the structure is absolutely safe. On the contrary, if the environmental working conditions are stronger than the designed working conditions, the structure will fail. The designed extreme working conditions of the structure are saved in the database for search. Extreme conditions such as Figure 2 The orange dotted line in the middle provides a threshold for forecasting as a basis for risk assessment.

[0071] Furthermore, the step of calculating the risk probability of offshore engineering under the current typhoon event by combining the ocean dynamic factor ensemble forecast and the structural failure threshold includes:

[0072] S401: Obtaining a structural design index corresponding to the offshore project, and obtaining a structural failure threshold corresponding to the offshore project based on the structural design index;

[0073] S402: Calculate the cumulative probability of exceeding the structural failure threshold corresponding to the offshore project and the ensemble forecast of the ocean dynamic factors of the current typhoon event (which can be calculated using a joint probability formula), and use the cumulative probability value as the risk probability of the offshore project under the current typhoon event;

[0074] Specifically, taking a 100-year typhoon event as an example, if the recurrence period of the design extreme working condition (structural design index) of the structure (offshore engineering) is 50 years, the extreme working condition with a 50-year recurrence period is used as the threshold; in a 100-year typhoon event, if the probability calculation result of the meteorological and hydrological elements exceeding this threshold is 92%, then the potential failure probability of the structure is assessed to be 92%. In this case, on-site personnel need to take disaster prevention and control measures according to the safety plan, including but not limited to emergency tasks such as personnel evacuation (this situation may be related to Figure 2On the contrary, if the same structure encounters a weaker tropical depression, the calculated cumulative probability of exceedance is only 5.2%, so the failure probability of the structure is relatively low; in this case, only necessary protective measures need to be taken (this situation is similar to Figure 2 The situation is similar for the middle blue area).

[0075] In this embodiment, by combining historical typhoon data, numerical simulation and real-time ensemble forecast, the risk probability of offshore engineering under typhoon events can be quickly calculated. Through cluster analysis and correlation evaluation, the accuracy and timeliness of risk assessment are improved, providing a scientific basis for emergency management of offshore engineering. By dividing the calculation sea area into 0.1°×0.1° blocks for discrete analysis, it is possible to achieve refined risk assessment of different regions. By using dimensionality reduction and cluster analysis technology to process historical data, extract key features and form a conditional probability distribution data set, it can significantly reduce the computational complexity and improve the efficiency of risk assessment. It can quickly respond to new typhoon events and provide timely risk information to decision makers; by considering the design indicators of different types of marine engineering structures and incorporating them into the risk assessment system as failure thresholds, and by correlating structural design indicators with hydrological conditions, it can provide customized risk assessments for various marine engineering structures such as offshore platforms, breakwaters, and bridges, enhancing the versatility and applicability of the method; by calculating the cumulative probability of environmental conditions exceeding the structural failure threshold, it can intuitively reflect the failure risk of marine engineering under the current typhoon event, and thus help managers take measures such as personnel evacuation and reinforcement and protection in advance to effectively reduce disaster losses.

[0076] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0077] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only one type. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0078] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

[0079] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A rapid probabilistic forecasting method for offshore engineering safety risks during a typhoon, characterized by: The following steps are involved: S1. Collect historical typhoon and associated disaster data, and reconstruct the typhoon-storm surge-strong current-storm wave combined process to obtain historical record information. The specific steps of S1 are: S101. Collect historical typhoon and associated disaster data, establish a model wind field at the typhoon cyclone center based on a mathematical model, calculate the rotating wind gradient velocity at a certain distance r from the cyclone center, and fuse the model wind field with the observed wind field velocity to obtain a fused wind field. The fusion expression is: ， Where V is the velocity of the fused wind field, r is the distance from any point in space to the cyclone center, and V ERA5 is the speed of the observed wind field, V model is the velocity of the model wind field, α(r) is the transition function; S102: Simulate the surge and ocean current events in the ocean hydrodynamic process: Use the fused wind field generated in S101 to drive the FVCOM or ADCIRC ocean dynamics calculations, thereby obtaining the sea level changes during the typhoon and the changes in the velocity, direction, and vertical distribution of the ocean currents. S103: Simulate ocean wave events in the ocean hydrodynamic process: Calculate the third-generation wave model SWAN based on the fused wind field generated in S101 above, and obtain the time-varying process of the wave height, direction, period, and wavelength. S104: Integrate the above S101-S103 to obtain historical record information, which includes: the longitude and latitude of the typhoon center, maximum wind speed, minimum air pressure, and wind circle radius; the change process of the sea surface water level; the change process of the velocity, direction, and vertical distribution of the ocean current; and the time-varying process of the wave height, direction, period, and wavelength; S2. Perform dimensionality reduction and cluster analysis on historical records to obtain the probability distribution of hydrological elements, and perform correlation analysis between current typhoon events and historical typhoons to obtain an ensemble forecast of ocean dynamic factors; The steps of performing dimensionality reduction and cluster analysis on historical record information to obtain the probability distribution of hydrological elements, and performing correlation analysis on the current typhoon event and historical typhoons to obtain an ensemble forecast of ocean dynamic factors include: S201: The calculation sea area is discretized into blocks with a unit of 0.1°×0.1°. Typhoon information is collected for each block. The typhoon information includes typhoon path, wind speed, air pressure, movement speed and direction. The typhoon events within the block are clustered using the weighted Fuzzy C-means clustering algorithm. The typhoon clustering characteristics of several types of typhoons are extracted through the clustering, and the occurrence frequency of different types of typhoons is obtained. S202: Based on the wind-tide-current-wave joint data of historical records and the typhoon clustering characteristics of S201, statistics are collected on the tides, currents, and waves during different types of typhoons in each block, that is, the probability distribution characteristics of tides, currents, and waves under different types of typhoons are obtained. By combining the occurrence frequency of different types of typhoons, a conditional probability distribution dataset of the wind-tide-current-wave joint data is obtained, that is, the conditional probability distribution dataset of hydrological events; S203: Then, obtaining the probability distribution of hydrological elements corresponding to different types of typhoons in several sea areas; S204: Obtain the current ensemble forecast of the current typhoon event from the meteorological station, determine the correlation between the current typhoon event and the historical typhoons in the historical record information through Pearson correlation analysis, and then combine the hydrological event conditional probability distribution data set formed in S202 to obtain the ocean dynamic factor ensemble forecast; S3. Collect structural design indicators of marine environmental projects and set structural failure thresholds based on the structural design indicators of marine environmental projects; S4. Calculate the risk probability of offshore engineering projects under the current typhoon event by combining the ensemble forecast of ocean dynamic factors and the structural failure threshold.

2. The method for rapid probabilistic prediction of offshore engineering safety risks during a typhoon according to claim 1, characterized in that: The expression of the rotating wind gradient velocity at a certain distance r from the cyclone center is: ， Where V g is the rotating wind gradient velocity at a certain distance r from the cyclone center, V max The maximum wind speed of a typhoon.

3. The method for rapid probabilistic prediction of offshore engineering safety risks during a typhoon according to claim 2, characterized in that: The expression of the transition function is: ， Where R mw is the radius of maximum wind speed, a=4.3944, b=2.

5.

4. The method for rapid probabilistic prediction of offshore engineering safety risks during a typhoon according to claim 1, characterized in that: The steps of collecting structural design indicators of the marine environmental engineering and setting a structural failure threshold based on the structural design indicators of the marine environmental engineering include: S301. Collect structural design indicators of different types of marine environmental projects, use the structural design indicators of different types of marine environmental projects as structural failure thresholds corresponding to the different types of marine environmental projects, and store them in a structural risk probability assessment database; S302: Obtain hydrological conditions corresponding to structural design indicators of different types of marine environmental projects, and associate the structural design indicators of the same type of marine environmental projects with the corresponding hydrological conditions.

5. The method for rapid probabilistic prediction of offshore engineering safety risks during a typhoon according to claim 4, characterized in that: The steps of calculating the risk probability of offshore engineering under the current typhoon event by combining the ensemble forecast of ocean dynamic factors and the structural failure threshold include: S401: Obtaining a structural design index corresponding to the offshore project, and obtaining a structural failure threshold corresponding to the offshore project based on the structural design index; S402: Calculate the cumulative probability of exceeding the structural failure threshold corresponding to the offshore project and the ensemble forecast of the ocean dynamic factors of the current typhoon event, and use the cumulative probability value as the risk probability of the offshore project under the current typhoon event.

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