Rapid probability forecasting method for safety risk of offshore engineering in typhoon process

Abstract

The invention belongs to the technical field of marine engineering safety operation, and discloses a rapid probability forecasting method for marine engineering safety risks in a typhoon process, which comprises the following steps: S1, collecting historical typhoon and associated disaster data, and reconstructing a typhoon-storm surge-high current-storm wave combined process to obtain historical record information; s2, performing dimension reduction and clustering analysis on historical record information to obtain hydrological element occurrence probability distribution, and performing correlation analysis on a current typhoon event and historical typhoons to obtain an ocean dynamic factor ensemble forecast; according to the method, the risk probability of the offshore engineering under the typhoon event can be quickly calculated, and a scientific basis is provided for emergency management of the offshore engineering; a new typhoon event can be quickly responded, and timely risk information is provided for a decision maker.

Description

Technical Field

[0001] The present invention relates to the technical field of safe operation of ocean engineering. More specifically, the present invention relates to a method for rapidly predicting the probability of safety risks of offshore projects during typhoon processes. Background Art

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

[0003] However, due to the complexity of typhoon processes, there are certain uncertainties in the prediction and forecasting of typhoon development and evolution processes, as well as the prediction and forecasting of huge waves, storm surges, and strong currents during the processes. Traditional deterministic forecasting has drawbacks in judging the safety of offshore projects, mainly manifested in its inability to effectively reflect the uncertainty and error range of forecasting results; in contrast, the advantage of ensemble forecasting is that it gives a series of possible forecasting results after considering various errors in the forecasting process, so as to more comprehensively evaluate disaster risks. However, ensemble forecasting directly based on large-scale numerical simulation requires a large amount of computing resources and professional personnel support, and sometimes it is difficult to meet the needs of rapid assessment and decision-making in ocean engineering practice.

[0004] In view of this, the present invention proposes a method for rapidly predicting the probability of safety risks of offshore projects during typhoon processes to solve the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art and achieve the above object, the present invention provides the following technical solution: A method for rapidly predicting the probability of safety risks of offshore projects during typhoon processes, including the following steps:

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

[0007] S2. Perform dimensionality reduction and clustering analysis on the historical record information to obtain the probability distribution of the occurrence of hydrological elements, and perform correlation analysis between the current typhoon event and historical typhoons to obtain the ensemble forecast of ocean dynamic factors;

[0008] S3. Collect the structural design indicators of ocean environmental engineering, and set the structural failure threshold based on the structural design indicators of ocean environmental engineering;

[0009] S4. Combine the ensemble forecast of ocean dynamic factors and the structural failure threshold to calculate the risk probability of offshore projects under the current typhoon event.

[0010] Further, the steps of collecting historical typhoon and associated disaster data, reconstructing the typhoon-storm surge-strong current-storm wave joint process, and then obtaining the historical record information include:

[0011] S101. Collect historical typhoon and associated disaster data, establish a model wind field of the typhoon cyclone center according to a mathematical model, calculate the rotational wind gradient velocity at a distance r from the cyclone center, and fuse the model wind field with the velocity of the observed wind field 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, V ERA5 is the velocity of the observed wind field, V model is the velocity of the model wind field, and α(r) is a transition function;

[0014] S102: Simulate the water level rise and ocean current events in the ocean hydrodynamic process: Calculate by driving the FVCOM or ADCIRC ocean dynamics respectively with the fused wind field formed in the above S101, that is, obtain the water level change process on the sea surface during the typhoon process and the change processes of the flow velocity, flow direction, and vertical distribution of the flow velocity of the ocean current;

[0015] S103: Simulate the wave events in the ocean hydrodynamic process: Calculate by driving the third-generation wave model SWAN with the fused wind field formed in the above S101, that is, obtain the time-varying processes of the wave height, wave direction, period, and wavelength of the ocean waves;

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

[0017] Further, the expression for the rotational wind gradient velocity at a distance r from the cyclone center is:

[0018]

[0019] where V g is the rotational wind gradient velocity at a distance r from the cyclone center, and V max is the maximum wind speed of the typhoon.

[0020] Further, the expression for the transition function is:

[0021]

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

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

[0024] S201: Discretize the calculation sea area by dividing it into blocks with a unit of 0.1°×0.1°, statistically analyze typhoon information for each block. The typhoon information includes typhoon path, wind speed, air pressure, moving speed, and direction. Perform event clustering on the typhoons in the block through the weighted Fuzzy C-means clustering algorithm, extract the typhoon clustering characteristics of several types of typhoons through event clustering, and then obtain the occurrence frequencies of different types of typhoons.

[0025] S202: Based on the wind-tide-current-wave joint data of the historical record information and the typhoon clustering characteristics of S201, statistically analyze the tide-current-wave during different types of typhoon processes in each block respectively, to obtain the probability distribution characteristics of the tide-current-wave under different types of typhoons. By combining the occurrence frequencies of different types of typhoons, obtain the conditional probability distribution dataset of the wind-tide-current-wave joint data, that is, the hydrological event conditional probability distribution dataset.

[0026] S203: Furthermore, obtain the probability distribution of the occurrence 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, judge the correlation between the current typhoon event and historical typhoons in the historical record information through Pearson correlation analysis, and then combine with the hydrological event conditional probability distribution dataset formed in S202 to obtain the ensemble forecast of ocean dynamic factors.

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

[0029] S301: Collect the structural design indicators of different types of ocean environmental engineering, and store the structural design indicators of different types of ocean environmental engineering as the corresponding structural failure thresholds of different types of ocean environmental engineering in the structural risk probability assessment database.

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

[0031] Further, the steps of calculating the risk probability of the offshore project under the current typhoon event by combining the ensemble prediction of ocean dynamic factors and the structural failure threshold include:

[0032] S401: Obtain the structural design indexes corresponding to the offshore project, and obtain the structural failure threshold corresponding to the offshore project according to the structural design indexes corresponding to the offshore project;

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

[0034] The technical effects and advantages of a rapid probability prediction method for the safety risk of offshore projects during typhoon processes according to the present invention:

[0035] 1. The present invention can quickly calculate the risk probability of offshore projects under typhoon events, providing a scientific basis for the emergency management of offshore projects; it can significantly reduce the calculation complexity and improve the efficiency of risk assessment, enabling it to quickly respond to new typhoon events and provide timely risk information for decision-makers; it can provide customized risk assessments for various ocean engineering structures such as offshore platforms, breakwaters, and bridges, enhancing the versatility and applicability of the method. Description of the Drawings

[0036] Figure 1 is a schematic flow chart of a rapid probability prediction method for the safety risk of offshore projects during typhoon processes according to the present invention;

[0037] Figure 2 is a schematic diagram for calculating the risk probability in S4 of the present invention. Detailed Embodiments

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0039] Embodiment 1

[0040] Please refer to Figure 1 and Figure 2 As shown, a rapid probability prediction method for the safety risk of offshore projects during typhoon processes in this embodiment includes the following steps:

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

[0042] S2. Perform dimensionality reduction and clustering analysis on the historical record information to obtain the probability distribution of the occurrence of hydrological elements, and perform correlation analysis between the current typhoon event and historical typhoons to obtain the ensemble forecast of ocean dynamic factors.

[0043] S3. Collect the structural design indicators of ocean environmental engineering and set the structural failure threshold based on the structural design indicators of ocean environmental engineering.

[0044] S4. Combine the ensemble forecast of ocean dynamic factors and the structural failure threshold to calculate the risk probability of offshore engineering under the current typhoon event.

[0045] Furthermore, the step of collecting historical typhoon and associated disaster data, reconstructing the typhoon-storm surge-strong current-storm wave joint process, and then obtaining the historical record information includes:

[0046] S101. Collect historical typhoon and associated disaster data (the typhoon-storm surge-strong current-storm wave joint process from 1949 to the present), establish a model wind field of the typhoon (i.e., historical typhoon) cyclone center according to a mathematical model, calculate the rotational wind gradient velocity at a distance r from the cyclone center, and fuse the velocity of the model wind field with the velocity of the observed wind field (low resolution) to obtain the 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, V ERA5 is the velocity of the observed wind field (ERA5 reanalysis wind field), V model is the velocity of the model wind field, and α(r) is the transition function.

[0049] S102: Simulate the water level rise and ocean current events in the ocean hydrodynamic process: Use the fused wind field formed in S101 above to drive ocean dynamic models such as FVCOM or ADCIRC for calculation to obtain the water level change process on the sea surface during the typhoon process and the change process of the flow velocity, flow direction, and vertical distribution of the flow velocity of the ocean current.

[0050] S103: Simulate the wave events in the ocean hydrodynamic process: Drive the third-generation wave model SWAN or WaveWatch-III model for calculation according to the wind field information during the typhoon process to obtain the time-varying process of the wave height, wave direction, period, and wavelength of the waves.

[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 water level change process of the sea surface; the flow velocity, flow direction of the ocean current, and the change process of the vertical distribution of the flow velocity; the time - varying process of the wave height, wave direction, period, and wavelength of the ocean waves.

[0052] Specifically, taking the ECMWF wind field dataset ERA5 as an example for the observed wind field, a smooth transition is adopted from the model wind field to the ERA5 re - analyzed wind field data. ERA5 is the fifth - generation global climate re - analysis dataset developed by the European Centre for Medium - Range Weather Forecasts (ECMWF). Among them, the re - analyzed wind field is the global spatio - temporal change data that simulates and assimilates the observed data and serves as the background field. Generally, the data of the anemometer tower are only site data and cannot cover the area required for analysis.

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

[0054]

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

[0056] Specifically, its expression is given by Young's empirical formula, and it should be noted that corrections should be made for the bias, asymmetry, and wind circle radius of the wind field.

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

[0058]

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

[0060] Specifically, this transition function makes the wind field mainly based on the speed of the model wind field at the position of 1 times the wind circle radius from the cyclone center, and the proportion of the model wind field is 90%; when the position is more than 4 times the wind circle radius from the cyclone center, the wind field is mainly based on the ERA5 wind, and the proportion of the model wind field does not exceed 10%;

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

[0062] S201: Divide the calculation sea area into blocks with a unit of 0.1°×0.1° for discretization, and count the typhoon information in each block. The typhoon information includes the typhoon path, wind speed, air pressure, moving speed and direction. The typhoons in the block are clustered by the weighted Fuzzy C-means clustering algorithm. The typhoon clustering features of several types of typhoons are extracted by clustering (the typhoon clustering features are the process features of the clustering center, such as the central longitude and latitude, maximum wind speed, minimum air pressure, moving speed, etc.), and then the occurrence frequency of different types of typhoons is obtained;

[0063] S202: Based on the wind-tide-current-wave joint data of historical record information 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 frequencies of different types of typhoons, the conditional probability distribution data set of the wind-tide-current-wave joint data is obtained, that is, the conditional probability distribution data set of hydrological events;

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

[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 typhoon 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 ensemble forecast of ocean dynamic factors (the ensemble forecast of ocean dynamic factors includes waves, water surge, ocean currents, etc.), where the forecast value of the ensemble forecast of ocean dynamic factors 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 determined by using the Pearson correlation coefficient, which ranges from -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 to calculate the probability of occurrence of hydrological events under the current ensemble forecast (current typhoon conditions) in combination with the characteristics of the current typhoon event. 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 the

[0067] Furthermore, the step of collecting the structural design indicators of the marine environment engineering and setting the structural failure threshold based on the structural design indicators of the marine environment engineering includes:

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

[0069] S302. Obtain the hydrological working conditions corresponding to the 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 working conditions.

[0070] Specifically, in the design stage of marine engineering, it is necessary to master environmental information, and usually ensure the disaster-bearing capacity of the structure under the combined action of extreme working conditions. This design extreme working condition can be used as the threshold for structural failure; this method does not consider the problem of structural material uncertainty in the assessment of structural safety, and only takes the design standard as a reference. If the environmental working condition is weaker than the design extreme working condition, the structure is absolutely safe, and conversely, if the environmental working condition is stronger than the design working condition, the structure fails. Save the design extreme working condition of the structure to the database for searching. The extreme working condition is as shown by the orange dotted line in Figure 2 to provide a threshold for the forecast and serve as the basis for risk assessment.

[0071] Furthermore, the steps of calculating the risk probability of the offshore project under the current typhoon event by combining the ensemble forecast of marine dynamic factors and the structural failure threshold include:

[0072] S401: Obtain the structural design indicators corresponding to the offshore project, and obtain the corresponding structural failure threshold of the offshore project according to the structural design indicators corresponding to the offshore project;

[0073] S402: Calculate the exceedance cumulative probability (which can be calculated using the joint probability formula) between the structural failure threshold corresponding to the offshore project and the ensemble forecast of marine 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;

[0074] Specifically, taking the once-in-a-century typhoon event as an example, if the recurrence period of the design extreme working condition (structural design indicator) of the structure (offshore project) is 50 years, then use the extreme working condition with a 50-year recurrence period as the threshold; in the once-in-a-century typhoon event, if the calculated probability of the meteorological and hydrological elements exceeding this threshold is 92%, then the potential failure probability of the structure is evaluated as 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 2is similar to the situation indicated by the red area in [reference]; conversely, if the same structure encounters a weaker tropical depression and the calculated exceedance cumulative probability is only 5.2%, then 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 the situation indicated by the blue area in [reference]). Figure 2 is similar to the situation indicated by the blue area in [reference].

[0075] In this embodiment, by combining historical typhoon data, numerical simulation, and real-time ensemble forecasting, the risk probability of offshore engineering under typhoon events can be quickly calculated. Through cluster analysis and correlation assessment, the accuracy and timeliness of risk assessment are improved, providing a scientific basis for the emergency management of offshore engineering; by discretely analyzing the calculation sea area divided into 0.1°×0.1° blocks, refined risk assessment of different regions can be achieved. By using dimensionality reduction and cluster analysis techniques to process historical data, extract key features, and form a conditional probability distribution data set, the computational complexity can be significantly reduced, and the efficiency of risk assessment can be improved, enabling it to quickly respond to new typhoon events and provide timely risk information for decision-makers; by considering the design indicators of different types of offshore engineering structures and incorporating them as failure thresholds into the risk assessment system, and by associating the structural design indicators with hydrological conditions, customized risk assessment can be provided for various offshore 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, the failure risk of offshore engineering under the current typhoon event can be intuitively reflected. Furthermore, it can help managers take measures such as personnel evacuation and reinforcement protection in advance, effectively reducing disaster losses.

[0076] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0077] In 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 way, and there can be other division methods in actual implementation. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0078] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention.

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

Claims

1. A rapid probability forecasting method for offshore engineering safety risks during a typhoon, characterized in that: The following steps are involved: S1. Collect historical typhoon and associated disaster data, and reconstruct the typhoon-storm surge-strong current-storm wave joint 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 center of the typhoon cyclone according to a mathematical model, calculate the rotating wind gradient velocity at a certain distance r from the center of the cyclone, and fuse the model wind field with the observed wind field velocity to obtain a fused wind field. The fused expression is: V=α(r)V ERA5 +[1-α(r)]V model , Where V is the velocity of the fused wind field, r is the distance from any point in space to the center of the cyclone, and V ERA5 is the speed of the observed wind field, V model is the speed of the model wind field, α(r) is the transition function; S102: Simulate the water surge and ocean current events in the ocean hydrodynamic process: Use the fused wind field formed in S101 to drive FVCOM or ADCIRC ocean dynamics to calculate, that is, obtain the change process of sea surface water level and the change process of ocean current velocity, flow direction and vertical distribution of velocity during the typhoon; S103: Simulate the wave events in the ocean hydrodynamic process: Drive the third-generation wave model SWAN according to the fused wind field formed in S101 to perform calculations, that is, obtain the time-varying process of the wave height, wave 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, 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; the time-varying process of the wave height, direction, period, and wavelength; S2. Perform dimensionality reduction and cluster analysis on historical record information to obtain the probability distribution of hydrological elements, and perform correlation analysis on current typhoon events and historical typhoons to obtain an ensemble forecast of ocean dynamic factors; S3. Collect structural design indicators of marine environmental engineering and set structural failure thresholds based on the structural design indicators of marine environmental engineering; S4. Combine the ensemble forecast of ocean dynamic factors and the structural failure threshold to calculate the risk probability of offshore engineering under the current typhoon event.

2. The rapid probability forecasting method for offshore engineering safety risks during a typhoon according to claim 1 is 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 rapid probability forecasting method for offshore engineering safety risks during a typhoon according to claim 2 is characterized in that: The expression of the transition function is: In the formula, R mw is the radius of maximum wind speed, a=4.3944, b=2.

5.

4. The rapid probability forecasting method for offshore engineering safety risks during a typhoon according to claim 1 is characterized in that: The steps of performing dimensionality reduction and cluster analysis on the historical record information, i.e. obtaining the probability distribution of hydrological elements, and performing correlation analysis on the current typhoon event and the historical typhoons, i.e. obtaining the ensemble forecast of the ocean dynamic factors, include: S201: Divide the calculation sea area into blocks with a unit of 0.1°×0.1° for discretization, and collect statistics on typhoon information in each block. The typhoon information includes typhoon path, wind speed, air pressure, movement speed and direction. Use the weighted Fuzzy C-means clustering algorithm to cluster the typhoons in the block, extract the typhoon clustering features of several types of typhoons through clustering, and then obtain the occurrence frequency of different types of typhoons; S202: Based on the wind-tide-current-wave joint data of historical record information 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 frequencies of different types of typhoons, the conditional probability distribution data set of the wind-tide-current-wave joint data is obtained, that is, the conditional probability distribution data set of hydrological events; S203: Then, the probability distribution of hydrological elements corresponding to different types of typhoons in several sea areas is obtained; 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 conditional probability distribution data set of the hydrological events formed in S202 to obtain the ensemble forecast of the ocean dynamic factors.

5. The rapid probability forecasting method for offshore engineering safety risks during a typhoon according to claim 1 is characterized in that: The steps of collecting the structural design indicators of the marine environment project and setting the structural failure threshold based on the structural design indicators of the marine environment project include: S301, collecting structural design indicators of different types of marine environmental projects, using the structural design indicators of different types of marine environmental projects as structural failure thresholds corresponding to different types of marine environmental projects and storing them in a structural risk probability assessment database; S302, obtaining hydrological conditions corresponding to structural design indicators of different types of marine environmental projects, and associating structural design indicators of the same type of marine environmental projects with corresponding hydrological conditions.

6. The rapid probability forecasting method for offshore engineering safety risks during a typhoon according to claim 5 is characterized in that: The step 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 comprises: S401: Obtaining a structural design index corresponding to the offshore project, and obtaining a structural failure threshold corresponding to the offshore project according to the structural design index corresponding to the offshore project; 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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