A port hydrodynamic risk assessment method, device, equipment, medium and product

Abstract

The application discloses a port hydrodynamic risk assessment method, device, equipment, medium and product, relates to the field of risk assessment, and comprises the following steps: carrying out storm surge-wave-tide multi-process coupling numerical model calculation to obtain a disaster factor space-time field; performing Bayesian prior-posterior updating on parameters of a function degradation model to obtain a parameter set; obtaining each risk index value according to the disaster factor space-time field, an asset ledger and the function degradation model corresponding to the parameter set; and summarizing each risk index value and performing hydrodynamic risk assessment according to each risk index value. The application can improve the accuracy and reliability of the assessment result.

Description

Technical Field

[0001] This application relates to the field of risk assessment, and in particular to a method, apparatus, equipment, medium and product for assessing port hydrodynamic risks. Background Technology

[0002] Currently, port hydrodynamic risk assessments mostly employ single-hazard or simple overlay methods, making it difficult to handle the combined effects of storm surges, long waves, and nearshore waves. Furthermore, at the asset level, there is a lack of updatable functional degradation models, making it difficult to quantify the impact of shutdowns. The assessment results are also fragmented, unauditable, and unable to support cross-departmental decision-making. These factors contribute to inaccurate and unreliable assessment results. Summary of the Invention

[0003] The purpose of this application is to provide a method, apparatus, equipment, medium, and product for port hydrodynamic risk assessment, which can improve the accuracy and reliability of assessment results.

[0004] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a port hydrodynamic risk assessment method, including: obtaining reanalysis data, observation data, and asset ledgers for the target area of ​​the port.

[0005] The values ​​of each hydrodynamic element in the reanalysis data are corrected based on the observation data. Then, the values ​​of each hydrodynamic element in the future are predicted based on the corrected values ​​of each hydrodynamic element. The values ​​of each hydrodynamic element in the future are then input into the dynamic lower-scale model to obtain the boundary condition library.

[0006] The marginal distribution of each hydrodynamic element is fitted by fitting the extreme values ​​of each hydrodynamic element in the boundary condition library. A joint probability model is constructed based on the marginal distribution of each hydrodynamic element. The environmental contour points are obtained by solving the joint probability model. The environmental contour points are each hydrodynamic element corresponding to a preset return period or a preset probability.

[0007] Based on scenario-driven files and boundary condition libraries, a coupled numerical model of storm surge, wave, and tide processes is used to calculate the spatiotemporal field of disaster-causing factors. The scenario-driven files include the extreme values ​​of each environmental profile point in the boundary condition library.

[0008] The asset ledger is mapped onto the target area of ​​the port to obtain the asset-business exposure layer.

[0009] Based on observational data and the asset-business exposure layer, the parameters of the functional degradation model are updated using Bayesian prior-posterior methods to obtain a posterior vulnerability and functional degradation parameter set.

[0010] Based on the spatiotemporal field of disaster-causing factors, asset ledgers, and the functional degradation model corresponding to the posterior vulnerability and functional degradation parameter set, the risk index values ​​corresponding to the target area of ​​the port are obtained.

[0011] The risk index values ​​corresponding to the target area of ​​the port are summarized, and a hydrodynamic risk assessment of the target area of ​​the port is conducted based on the risk index values ​​corresponding to the target area of ​​the port.

[0012] Secondly, this application provides a port hydrodynamic risk assessment device, including: a data acquisition module for acquiring reanalysis data, observation data, and asset ledgers of the target area of ​​the port.

[0013] The correction module is used to correct the values ​​of each hydrodynamic element in the reanalysis data based on the observation data. Then, it predicts the values ​​of each hydrodynamic element in the future based on the corrected values ​​of each hydrodynamic element. Finally, it inputs the values ​​of each hydrodynamic element in the future into the dynamic lower-scale model to obtain the boundary condition library.

[0014] The environmental contour point determination module is used to fit the edge distribution of each hydrodynamic element based on the extreme values ​​of each hydrodynamic element in the boundary condition library, construct a joint probability model based on the edge distribution of each hydrodynamic element, and solve the joint probability model to obtain the environmental contour points; the environmental contour points are each hydrodynamic element corresponding to a preset return period or a preset probability.

[0015] The module for determining the spatiotemporal field of disaster-causing factors is used to perform calculations of a coupled numerical model of storm surge, wave, and tide based on scenario-driven files and a boundary condition library, so as to obtain the spatiotemporal field of disaster-causing factors. The scenario-driven files include the extreme values ​​of each environmental profile point in the boundary condition library.

[0016] The Asset-Business Exposure Layer Determination Module is used to map the asset ledger onto the target area of ​​the port to obtain the Asset-Business Exposure Layer.

[0017] The vulnerability and functional degradation module is used to perform Bayesian prior-posterior updates on the parameters of the functional degradation model based on observation data and asset-business exposure layers, to obtain a posterior vulnerability and functional degradation parameter set.

[0018] The risk indicator value determination module is used to obtain the risk indicator values ​​corresponding to the port target area based on the spatiotemporal field of the disaster-causing factors, the asset ledger, and the functional degradation model corresponding to the posterior vulnerability and functional degradation parameter set.

[0019] The risk assessment module is used to summarize the risk indicator values ​​corresponding to the port target area and conduct a hydrodynamic risk assessment of the port target area based on the risk indicator values ​​corresponding to the port target area.

[0020] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the port hydrodynamic risk assessment method described above.

[0021] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the port hydrodynamic risk assessment method described above.

[0022] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the port hydrodynamic risk assessment method described above.

[0023] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method, apparatus, equipment, medium, and product for port hydrodynamic risk assessment. It addresses the challenge of handling the combined effects of storm surges, long waves, and nearshore waves using a multi-process coupled numerical model based on scenario-driven files and a boundary condition library. This model solves the problem of single-hazard or simple superposition methods failing to address the interconnected effects of storm surges, long waves, and nearshore waves. By updating the parameters of the functional degradation model using Bayesian prior-posterior updates based on observational data and asset-business exposure layers, it addresses the lack of updatable functional degradation models at the asset level, making it difficult to quantify the impact of shutdowns and supply chains, thus achieving updatable vulnerability assessment. Furthermore, by aggregating the risk indicator values ​​corresponding to the port target area and conducting hydrodynamic risk assessments based on these values, it resolves the issues of scattered and unauditable assessment results, hindering cross-departmental decision-making, and thus achieving auditability. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart illustrating a port hydrodynamic risk assessment method provided in one embodiment of this application.

[0026] Figure 2 A schematic diagram of the framework for generating scenario-driven files.

[0027] Figure 3 This is a schematic diagram of uncertainty propagation and sensitivity decomposition.

[0028] Figure 4 This is a schematic diagram of the functional modules of a port hydrodynamic risk assessment device provided in an embodiment of this application.

[0029] Figure 5This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0032] In one exemplary embodiment, such as Figure 1 As shown, a method for assessing the hydrodynamic risks of a port is provided, including: Step 201: Obtain reanalysis data, observation data, and asset ledgers for the target port area.

[0033] Step 202: Correct the values ​​of each hydrodynamic element in the reanalysis data based on the observation data. Then, predict the future values ​​of each hydrodynamic element based on the corrected values. Input the future values ​​of each hydrodynamic element into the dynamic downscale model to obtain the boundary condition library. The output data of the dynamic downscale model is the high-resolution future values ​​of each hydrodynamic element.

[0034] Step 203: Fit the marginal distribution of each hydrodynamic element to the extreme values ​​of each hydrodynamic element in the boundary condition library, construct a joint probability model based on the marginal distribution of each hydrodynamic element, and solve the joint probability model to obtain the environmental profile points; the environmental profile points are each hydrodynamic element corresponding to a preset return period (20 years, 50 years, 100 years, etc.) or a preset probability.

[0035] Step 204: Based on the scenario-driven file and boundary condition library, perform multi-process coupled numerical model calculations of storm surge, wave, and tide to obtain the spatiotemporal field of disaster-causing factors; the scenario-driven file includes the extreme values ​​of each environmental profile point in the boundary condition library.

[0036] Step 205: Map the asset ledger onto the target area of ​​the port to obtain the asset-business exposure layer.

[0037] Step 206: Based on the observation data and the asset-business exposure layer, perform Bayesian prior-posterior update on the parameters of the functional degradation model to obtain the posterior vulnerability and functional degradation parameter set.

[0038] Step 207: Obtain the risk index values ​​corresponding to the port target area based on the spatiotemporal field of the disaster-causing factors, the asset ledger, and the functional degradation model corresponding to the posterior vulnerability and functional degradation parameter set.

[0039] Step 208: Summarize the risk index values ​​corresponding to the port target area, and conduct a hydrodynamic risk assessment of the port target area based on the risk index values ​​corresponding to the port target area.

[0040] In another exemplary embodiment of this application, the value of each hydrodynamic element in the reanalysis data is corrected based on the observation data, specifically including: constructing a correction function for each hydrodynamic element based on the observation data and the reanalysis data.

[0041] The reanalysis data was verified based on the observation data, and the correction parameter values ​​of the correction functions for each hydrodynamic element were obtained.

[0042] For any hydrodynamic element, the correction parameter value of the correction function of the hydrodynamic element is substituted into the correction function of the hydrodynamic element, and the value of the hydrodynamic element in the reanalysis data is corrected using the correction function of the hydrodynamic element with the substituted correction parameter value.

[0043] In another exemplary embodiment of this application, the port hydrodynamic risk assessment method further includes: a preset adaptation and reinforcement scheme library; the adaptation and reinforcement scheme library includes multiple port reinforcement schemes.

[0044] For any port reinforcement scheme, calculate the risk index values ​​and confidence intervals of each risk index corresponding to the target area of ​​the port under the port reinforcement scheme.

[0045] The multi-objective comprehensive score of the port reinforcement scheme is calculated based on the target risk index value and the confidence interval of the target risk index corresponding to the target area of ​​the port under the port reinforcement scheme.

[0046] The optimal port reinforcement scheme is determined based on the multi-objective comprehensive score corresponding to each port reinforcement scheme.

[0047] The risk index values ​​corresponding to the target areas of each port under each port reinforcement scheme and the multi-objective comprehensive score corresponding to each port reinforcement scheme are summarized.

[0048] In another exemplary embodiment of this application, for any port reinforcement scheme, the calculation of each risk indicator value and the confidence interval of each risk indicator corresponding to the port target area under the port reinforcement scheme specifically includes: determining the spatiotemporal field of the disaster-causing factors and the posterior vulnerability and functional degradation parameter set under the port reinforcement scheme.

[0049] Based on the spatiotemporal field of the disaster-causing factors and the posterior vulnerability and functional degradation parameter set under the port reinforcement scheme, the risk index values ​​corresponding to the port target area under the port reinforcement scheme are obtained.

[0050] Using the boundary condition library, joint probability model, spatiotemporal field of disaster-causing factors under the port reinforcement scheme, and posterior vulnerability and functional degradation parameter set as sources of uncertainty, uncertainty propagation and sensitivity analysis are performed to obtain the confidence intervals of each risk indicator corresponding to the port target area under the port reinforcement scheme.

[0051] In practical applications, Bayesian prior-posterior updates are performed on the parameters of the functional degradation model based on observational data and the asset-business exposure layer to obtain the posterior vulnerability and functional degradation parameter set. Specifically, historical disaster-loss record data is obtained based on observational data; asset vulnerability curves are obtained based on the asset-business exposure layer; Bayesian prior-posterior updates are performed on the parameters of the functional degradation model based on historical disaster-loss record data and asset vulnerability curves; the asset vulnerability curves use the intensity of disaster-causing factors as the horizontal axis and the probability of asset structural damage or port shutdown as the vertical axis.

[0052] In another exemplary embodiment of this application, the risk index values ​​corresponding to the port target area are obtained based on the spatiotemporal field of the disaster-causing factor, the asset ledger, and the functional degradation model corresponding to the posterior vulnerability and functional degradation parameter set. Specifically, this includes coupling the spatiotemporal field of the disaster-causing factor, the asset ledger, and the functional degradation model corresponding to the posterior vulnerability and functional degradation parameter set to obtain the risk index values ​​corresponding to the port target area.

[0053] In another exemplary embodiment of this application, based on scenario-driven files and boundary condition libraries, a multi-process coupled numerical model calculation of storm surge, wave, and tide is carried out. Specifically, the target area of ​​the port is divided into grids according to the port area topography.

[0054] Using scenario-driven files and boundary condition libraries as input, a coupled numerical model of storm surge, wave, and tide is performed on the target port area after gridding.

[0055] In another exemplary embodiment of this application, the asset ledger is mapped onto the target area of ​​the port to obtain an asset-business exposure layer, specifically by establishing an asset-business exposure list based on the asset ledger.

[0056] The asset-business exposure list is mapped onto the port target area after grid division to obtain the asset-business exposure layer.

[0057] In practical applications, the risk indicator values ​​corresponding to the port target area are summarized to obtain the OU risk card corresponding to the port target area. OU refers to dividing the port into different OU areas (port target areas) according to function, and one OU area corresponds to one OU risk card.

[0058] In practical applications, hydrodynamic risk assessment of the port target area is conducted based on the risk index values ​​corresponding to the port target area. Specifically, for any risk index value corresponding to the port target area, the risk level of the risk index value corresponding to the port target area is determined based on the risk index value corresponding to the port target area, as well as the medium-risk threshold, high-risk threshold, and low-risk threshold.

[0059] Based on the risk level, the target areas of each port are marked on the high-resolution images of the port to obtain a graded risk map set for each target area of ​​the port under the risk index value.

[0060] This application also provides a more specific embodiment to describe the above method in detail, which specifically includes: S1: acquiring reanalysis data, observation data, port area topography and asset ledger of port waters, and performing consistent quality control according to time benchmark, spatial benchmark and unit system to form a quality-controlled basic database.

[0061] S2: Based on the basic database of S1, and using observational and reanalysis data as references, correction functions for various hydrodynamic elements such as wind, waves, and sea level are constructed (the independent variables of the correction functions are the original values ​​of each element output from the reanalysis data, and the dependent variables are the optimal estimates of each element after correction by the observational data); the reanalysis data is validated based on the observational data to obtain the correction parameter values ​​of the correction functions for each element; the correction parameter values ​​are substituted into the corresponding correction functions, and the values ​​of the corresponding elements in the reanalysis data are corrected for bias using the correction functions after substituting the values; the corrected element values ​​are input into the prediction model to output the values ​​of each hydrodynamic element at future times, obtaining the climate projection boundary conditions; the climate projection boundary conditions are processed using a dynamic lower-scale model to obtain high-resolution values ​​of each hydrodynamic element at future times, and the high-resolution values ​​of each hydrodynamic element at future times are determined as the boundary condition library for scenario generation and coupled calculation. Bias correction is necessary because the spatial resolution of reanalysis data is mostly tens to hundreds of kilometers, which cannot be directly adapted to the fine simulation requirements of port engineering at the meter to kilometer level. Therefore, hourly observation data from local port stations are used to correct the bias of the reanalysis data.

[0062] In practical applications, the S2 dynamic low-scale model comprises the sequentially connected WAVEWATCH III and SWAN models. WAVEWATCH III is a third-generation phase-averaged wave model and a mainstream tool for global ocean wave simulation. Its core is based on the wavenumber-direction spectrum spectral action balance equation, solving for the generation, propagation, evolution, and dissipation processes of waves in open sea areas, and it excels particularly in large-scale, global wave aftercasting / forecasting. Compared to WAVEWATCH III, the SWAN model can accurately characterize nearshore physical processes such as water depth changes, topographic refraction, wave diffraction, shallow water breaking, bottom friction, and wave reflection, making it a tool for nearshore wave simulation in coastal engineering. This application uses WAVEWATCH III to cover the deep-sea computational domain A related to the research port area to calculate large-scale waves in the deep-sea region. Climate projection boundary conditions are input as the dynamic driver to simulate the hydrodynamic elements of the deep-sea computational domain A. Subsequently, the SWAN model is used to cover the nearshore computational domain B near the port area to obtain high-resolution values ​​for each hydrodynamic element at future times. The boundary conditions are driven by the hydrodynamic elements output from the WAVEWATCH III model. The nearshore computational domain B of the SWAN model is smaller than the deep-sea computational domain A of WAVEWATCH III, while the spatial resolution of the nearshore computational domain B of the SWAN model is higher than that of the deep-sea computational domain A of WAVEWATCH III. Through this nested dynamic low-scale model, the SWAN model can capture the influence of local port topography on wave propagation paths, energy attenuation, and breaking characteristics in a more refined manner, significantly improving the simulation accuracy of nearshore wave height, period, and directional spectrum, and outputting high-precision hydrodynamic element conditions closer to the port area.

[0063] S3: Read the boundary condition library from S2, and combine it with the historical extreme samples obtained from S2 (extreme values ​​of wind, waves, and sea level in the boundary condition library). Fit the edge distributions of wind, waves, and sea level respectively, and establish a multivariate Copula or Vine-Copula joint probability model based on each edge distribution. Solve the joint probability model to obtain each hydrodynamic element corresponding to a given exceedance probability, called the environmental profile point. Then, through conditional sampling, extract the extreme values ​​of the environmental profile points from the extreme values ​​of each hydrodynamic element in the boundary condition library, generate a multi-factor time series consistent with the environmental profile points, and obtain the scenario-driven file, such as... Figure 2 As shown, the extreme values ​​of each environmental contour point in the scenario-driven file are called scenarios.

[0064] In practical applications, the annual maximum value method or the over-threshold method are used to perform semi-parametric extreme value fitting on the marginal distribution of each element. The joint probability model is selected based on the information criterion corresponding to the model (used to measure the overall fit of the model, the smaller the value, the better the overall model) and tail correlation diagnosis (used to assess the probability of concurrent extreme events (e.g., the probability of "extreme strong winds" and "extreme high tides" occurring at the same time)).

[0065] S4: Using the scenario-driven file generated in S3 and the boundary condition library obtained in S2 as input, perform calculations of a multi-process coupled numerical model of storm surge, wave and tide (e.g., nonlinear shallow water equation non-hydrostatic phase-resolved model) under a unified grid and coordinate system (determined according to the port area topography), and output the spatiotemporal field of disaster-causing factors such as water level, flow velocity, wave height, period, overtopping rate and inundation depth in the port basin, channel and wharf front.

[0066] In practical applications, the S4 storm surge-wave-tide multi-process coupled numerical model achieves two-way coupling of wind, waves and tides with a unified unstructured network, and defines the computational domain with land-water boundary conditions, thereby improving the accuracy and convergence of the two-way coupling of wind, waves and tides.

[0067] S5: Based on the asset ledger and business processes, establish an asset-business exposure list that includes asset geometry, elevation, replacement cost, redundancy relationships and key business nodes, and map it to the grid cells of S4 to obtain the asset-business exposure layer.

[0068] S6: Based on the historical disaster-loss record data in the observation data in S1 and the asset-business exposure layer obtained in S5, perform Bayesian prior-posterior update on the functional degradation model parameters to obtain the posterior vulnerability and functional degradation parameter set (values ​​of a and b).

[0069] The functional degradation model is based on the threshold formulas for port shutdown, breakwater damage, storage yard damage, and mechanical equipment damage in existing port specifications, and is divided into the following two sub-models: Structural damage vulnerability sub-model: The standard form of the log-normal cumulative distribution, well-known in coastal engineering risk assessment, is adopted to determine the intensity of disaster-causing factors. The x-axis represents the probability of asset structural damage. Construct for the ordinate: .

[0070] In the formula: The cumulative distribution function representing the standard normal distribution; Indicates the intensity of the disaster-causing factor (selected according to different asset types, such as significant wave height for breakwaters, inundation depth for storage yards, and overtopping volume for wharves). This represents the critical value for asset damage, which is the intensity of the hazard factor corresponding to a 50% probability of asset damage. It represents the shape parameter of the log-normal distribution and characterizes the degree of dispersion of the damage probability.

[0071] Specific asset function degradation mapping sub-model: Based on the probability of structural damage, a quantitative mapping is established with the degradation of port operational functions: .

[0072] In the formula: Indicates the rate of functional degradation. This indicates the threshold at which port functions begin to degrade. When the hazard factor exceeds this value, the port functions begin to degrade. The corresponding operational warning threshold is (e.g., if the wave height at the quay edge exceeds 0.6m, ships are prohibited from berthing and the port is shut down). This represents the threshold for complete functional failure. When the disaster-causing factor exceeds this value, the corresponding port function is completely lost and business is interrupted (such as breakwater damage, resulting in complete cessation of operations). and All represent shape parameters of functional degradation, parameters to be estimated. , Used to adjust the degradation rate. To accelerate degradation, To slow down degradation, it can be calibrated by asset type; This indicates the probability of damage to the asset structure.

[0073] In practical applications, the functional degradation model is used to output the probability of asset structural damage and the functional degradation rate. The parameters of the functional degradation model are updated using Bayesian posterior regression with asset vulnerability curves and historical disaster-loss record data. The asset vulnerability curve is obtained from the asset-business exposure layer, with the horizontal axis representing the intensity of the disaster-causing factor and the vertical axis representing the probability of asset structural damage or port shutdown. The historical disaster-loss record data comes from reports of ports actually experiencing historical disaster events or from satellite remote sensing imagery within the observational data.

[0074] S7: Couple the spatiotemporal field of the disaster-causing factors and the asset ledger obtained in S4 with the posterior vulnerability functional degradation model obtained in S6 (by inputting the posterior vulnerability and functional degradation parameter set into the functional degradation model) to calculate the initial port's expected annual damage (EAD), probable maximum loss (PML), annual inoperability rate, port operation service level, and expected life, among other risk indicators.

[0075] In practical applications, the expected annual loss is calculated as follows: The intensity of the disaster-causing factor is obtained from the spatiotemporal field of the disaster-causing factor (including the values ​​of the disaster-causing factor at multiple stages; the value of the stage to be calculated is used as the input). The intensity of the disaster-causing factor is then input into the posterior vulnerability functional degradation model obtained in S6 to obtain... and .

[0076] For the i-th asset, according to the formula Calculate the structural repair loss of the i-th asset. , This represents the full repair cost of the i-th asset within this unit in the asset ledger.

[0077] According to the formula Calculate inventory / equipment losses , This represents the total value of inventory and production equipment in this unit's asset ledger.

[0078] According to the formula Calculate the total direct loss per unit for the i-th asset. .

[0079] By grouping and summing the total direct losses of each asset in the port target area, the total direct economic loss under any scenario can be obtained. : .

[0080] Where N represents the total number of assets in the port target area.

[0081] According to the formula Calculate the expected annual loss (EAD) for the target area of ​​the port. This refers to the research period.

[0082] The given return period loss PML for the port target area is the sum of the total direct economic losses under each scenario in the scenario-driven file in S3.

[0083] Business interruption is a specific asset function degradation mapping sub-model, when Exceeding When a certain threshold is reached, a specific asset ceases operation, and the business interruption duration is: .

[0084] In the formula: This represents the business interruption time for the i-th asset; Indicates the total study period; Indicates the first The percentage of disaster-causing factors exceeding the threshold, This indicates the total number of disaster-causing factors.

[0085] The average downtime is calculated as follows: Based on the S5 asset-business exposure list, the business logic of the port's core assets is as follows: Single Link: Operational Flow: Outer Channel → Inner Channel → Wharf Front → Yard → Collection and Distribution Node. Failure of any node will result in the interruption of the entire link. Link Interruption Time... It is determined by the asset with the longest failure period.

[0086] .

[0087] Parallel links: Redundant facilities with the same function (such as multiple channels or berths). The function is interrupted only when all parallel links fail, thus reducing port downtime. This is the common failure duration for all parallel link assets.

[0088] .

[0089] Average downtime in the target port area According to the formula calculate.

[0090] Annual inoperability rate of the port target area The calculation formula is: .

[0091] Port operation service level in the target port area The calculation formula is: .

[0092] In the formula, , These are the weighting coefficients. ; Total replacement cost of all assets in Hong Kong; The value ranges from [0,1], with the closer to 1 representing a higher level of operational service.

[0093] The calculation process for the expected life of the port target area is as follows: The average annual structural failure rate of core assets is: .

[0094] In the formula, The annual average structural failure rate of the target core assets (breakwater, wharf) is the value. The higher the value, the higher the risk of structural failure in a given year. For the first case in the scenario-driven file In each scenario, the posterior structural damage probability of the target asset; For the first case in the scenario-driven file The annual exceedance probability for each scenario; This indicates the total number of scenarios in the scenario-driven file.

[0095] The formula for calculating the expected lifespan of the target area of ​​the port is: .

[0096] in, Indicates life expectancy; It represents the probability of structural failure within the design reference period, as specified in industry standards.

[0097] S8: As Figure 3 As shown, based on the uncertainty sources such as the boundary condition library obtained in S2, the joint probability model obtained in S3, the spatiotemporal field of disaster-causing factors obtained in S4, and the posterior vulnerability and functional degradation parameter set obtained in S6, Bayesian sampling or Bootstrap resampling is used to carry out uncertainty propagation and construct a sample set of risk indicators. On this basis, the Sobol index or hierarchical variance decomposition is used for sensitivity analysis to output the initial confidence intervals of each risk indicator of the port.

[0098] S9: Calculate the multi-objective comprehensive score for each solution in the adaptation and reinforcement solution library based on the objective function, and determine the optimal solution and its benefit-cost ratio based on the score. The adaptation and reinforcement solution library includes port reinforcement solutions, combining hardware engineering reinforcement and software operation and management strategies, including reinforcement solutions such as the increase in the height of dikes or breakwaters, the improvement in port drainage capacity, the wind resistance level of key equipment, and operational redundancy and scheduling strategies during business interruptions. In the benefit-cost ratio, the benefit refers to the economic losses successfully avoided after implementing a solution, i.e., the expected reduction in annual losses and the recovery value brought by the reduction in downtime. The cost refers to the engineering investment budget, reinforcement expenses, or asset replacement costs required to implement this solution.

[0099] For each solution t in the adaptation and hardening solution library i Repeat steps S4 through S8 to calculate the risk index values ​​R(t) for each plan. i The study also included the spatiotemporal field of the disaster-causing factors, the posterior vulnerability, and the functional degradation parameter set under this scheme. Uncertainty propagation and sensitivity analysis were performed to obtain the confidence intervals R for each risk indicator corresponding to this scheme. 95% (t i The initial port risk index value R0 is used as a benchmark.

[0100] According to industry standards, a representative risk indicator is selected as the target risk indicator. The score for each solution is calculated based on the value of the target risk indicator. The objective function is usually presented in the form of a multi-criteria analysis proxy (MCA). The multi-criteria function for a specific adaptive solution can be quantified by the following normalized formula: .

[0101] In the formula, t i This represents the i-th solution; The LAN(t) represents the multi-objective comprehensive score corresponding to the i-th scheme, and the disaster reduction benefit index is... i The target risk index value output by S7 is used to quantify the scheme t. i The risk reduction is calculated using the formula LAN(t). i)=(R0-R(t i )) / R0, where R0 is the initial target risk index value of the port, R(t) i ) represents scheme t i The target risk indicator value after implementation. If the target risk indicator is the expected annual loss, use (original expected annual loss - value adopted at time t) i The ratio of the expected annual loss of the optimized scheme to the original expected annual loss represents the percentage decrease in the expected annual loss risk factor.

[0102] Uncertainty robustness index RAP(t) i The confidence interval of the target risk index output by S8 is used to quantify the safety and reliability of the scheme under extreme uncertainty. The calculation formula is: when scheme t i 95% confidence ceiling risk value R95%(t) after implementation i When the risk level does not exceed the port's acceptable risk threshold Rth, RAP(t) i )=1, otherwise RAP(t) i )=0, where R95%(t) i Output from S8 uncertainty analysis.

[0103] The above two indicators and the benefit-cost ratio B / C(t) i Together, they form the multi-objective comprehensive evaluation function MCA(t). i The input is normalized and then summed to achieve a comprehensive evaluation of the merits of each adaptation scheme. In other words, the larger the sum, the better, providing a basis for selecting the Pareto optimal solution set. LAN represents the effect under the baseline scenario, and RAP represents the reliability under extreme uncertainty.

[0104] LAN reflects the extent to which the direct economic losses calculated by S7 and the downtime were reduced by the scheme. Scheme t i The disaster reduction benefit index represents the degree of risk reduction. LAN min and LAN max These represent the global minimum and global maximum values ​​of the disaster reduction benefit index among all alternative schemes.

[0105] RAP min and RAP max Let represent the global minimum and global maximum values ​​of the uncertainty robustness index in the set of all alternative solutions, respectively. Scheme t i The uncertainty robustness index requires that, at the upper limit of the 95% confidence interval (i.e., the worst combination of uncertainty), the EAD and PML of the optimized solution must be absolutely less than the set unacceptable risk threshold, representing the ability to still meet the risk threshold under extreme uncertainty.

[0106] Scheme t i The benefit-cost ratio represents the disaster reduction benefit per unit of cost input. This represents the global minimum of the benefit-cost ratio among all alternative solutions. The value of the benefit-cost ratio among all alternative solutions is represented by the global maximum value. This value is used for normalization to eliminate differences in the dimensions of different indicators, allowing the three objectives to be directly added together. To comprehensively evaluate the merits of each suitable solution, a multi-objective comprehensive evaluation function is constructed. This function is obtained by normalizing each indicator, eliminating differences in dimensions, and then summing the results. i The overall score is used to select the Pareto optimal solution set.

[0107] S10: Summarize the results obtained from S7 and S9 to generate OU risk cards and hierarchical risk maps, enabling traceable and auditable output of results. The OU risk card presents each risk indicator in four levels: red, orange, yellow, and green, as shown in Table 1. Based on the risk results presented in the OU risk card, combined with the high-definition port plan map, each area is visually labeled according to red, orange, yellow, and green, achieving visualized output of risks.

[0108] Table 1. OU Risk Card Illustration

[0109] The purpose of this application is to provide a port hydrodynamic risk assessment method for port planning, design, operation, maintenance, and emergency management. This method quantifies the direct losses and business interruption risks of ports under multi-hazard combined extreme events, and outputs traceable and auditable risk indicators and atlases. This application, while maintaining physical consistency, quantifies the direct losses and business interruptions of ports under multi-hazard combined extreme events, compares the disaster reduction benefits of different adaptation schemes, and outputs consistency indicators for planning, design, operation, maintenance, and emergency management. Compared with existing technologies, this application has the advantages of high efficiency, reproducibility, auditability, and strong adaptability.

[0110] This application uses joint probabilistic scenario generation coupled with multi-process calculations of wind, waves, and sea level to characterize complex extreme events while maintaining physical consistency, avoiding biases caused by the superposition of single disasters. It introduces an asset-business exposure list and an updatable functional degradation model, which can simultaneously assess direct losses and downtime impacts and dynamically update with monitoring evidence. By combining uncertainty propagation, sensitivity decomposition, and scheme combination optimization, it enables quantitative comparison and selection of adaptation and hardening schemes. Through OU risk cards and hierarchical risk atlas output, the results are reproducible, traceable, and auditable, facilitating planning, design, operation and maintenance, and emergency management applications.

[0111] This application also provides a more specific embodiment to describe the above method in detail. The specific steps are as follows: S1, collect reanalysis data of port waters, on-site observation data (wind, tide level, waves, etc.), climate model projection data, port area topography and asset ledger, and perform consistent quality control according to a unified time benchmark, spatial benchmark (coordinates) and unit system to form a basic database.

[0112] S2, Correction and Downscaling. Using the field observation and reanalysis data generated in S1 as a baseline, quantile mapping correction functions are established monthly or by weather pattern for wind (wind speed or direction), waves, and sea level (including astronomical tides and non-astronomical rises). The correction parameter values ​​are then verified to determine the corrected parameters. The correction parameter values ​​are substituted into the corresponding correction functions, and the resulting correction functions are used to correct the biases of the corresponding elements. The corrected element values ​​are then input into the prediction model to output the values ​​of each hydrodynamic element at future times, obtaining the climate projection boundary conditions. The climate projection boundary conditions are then input into the dynamic downscaling nested model to generate high-resolution values ​​of each hydrodynamic element at future times, forming a boundary condition library for use by S3 and S4.

[0113] S3, Composite Scenario. Based on the boundary condition library obtained in S2, extreme values ​​of wind, waves, and sea level at representative points or key sections of the port are selected, and marginal distributions are fitted respectively. A joint distribution is constructed using Vine-Copula. Multidimensional environmental contour points are solved under a given exceedance probability or return period, and sampling is performed using environmental contour points as conditions to generate a multi-factor time series scenario that satisfies the joint correlation structure, which is used to drive the coupled calculation of S4.

[0114] S4, Physical Coupling. Using the scenario-driven file generated in S3 and the boundary condition library obtained in S2 as input, and defining the computational domain with land and water boundary conditions, bidirectional coupling calculations of storm surge-wave-tide are carried out on a unified unstructured network to improve the accuracy and convergence of the wind-wave-tide bidirectional coupling.

[0115] S5, Exposure Mapping. Maps asset geometry, elevation, redundancy relationships, replacement costs, and business processes to grid or plot units.

[0116] S6, Vulnerability / Functionality. Select or build a functional degradation model and implement Bayesian updates using historical event monitoring data.

[0117] S7, Risk Assessment. Calculate the values ​​of each risk indicator.

[0118] S8, based on the boundary condition library of S2, the joint probability model of S3, the spatiotemporal field of the hazard factors of S4, and the posterior vulnerability and functional degradation parameter set of S6, employs Bayesian sampling or Bootstrap resampling to conduct uncertainty propagation and construct a sample set of risk indicators. On this basis, sensitivity analysis is performed using the Sobol index or hierarchical variance decomposition to output the confidence intervals for each risk indicator.

[0119] S9 constructs an adaptation and reinforcement scheme library, defines decision variables (such as the amount of dike or breakwater height increase, wind resistance level of key equipment, operational redundancy strategy, etc.) and engineering and budget constraints; with the risk index value output by S7 as the objective, a multi-objective genetic algorithm is used to search for scheme combinations, quickly evaluate the scheme effects, and obtain the Pareto front and recommended schemes.

[0120] S10, Output. Based on the risk indicators in S7, the uncertainty results in S8, and the Pareto scheme in S9, automatically generate OU risk cards and hierarchical risk atlases, and record the source of input data, parameters, and model version to achieve traceability and auditability.

[0121] This application also provides a specific embodiment of applying the above method to a port: Study area: The main operating area and main channel of a port are defined by a unified unstructured network analysis of nearshore islands and coastlines.

[0122] Boundary conditions: Local meteorological station, tide station and watershed inflow data were used for verification, and the analyzed data and observation data were corrected by quantile mapping and scaled down to about 500m-1000m.

[0123] Composite scenario: Construct a joint probability model of typhoon wind pressure field and astronomical tide, take environmental profile points with return periods of 20 years, 50 years and 100 years, and conditionally sample to generate time series of 72h-120h.

[0124] Coupled calculation: bidirectional coupling of storm surge and waves, output of overtopping rate, wave height, period, swell ratio and water level sequence of harbor basin and wharf front; exposure and vulnerability: the elevation of each wharf front, revetment type, yard equipment elevation and electrical facilities are stored in the database; the a priori vulnerability curve is updated with the a priori vulnerability curve and the storm event monitoring data of recent years.

[0125] Results: Output the direct economic losses, business interruption time and key performance indicators for each work area, and provide 3 Pareto adaptation combinations (e.g., "riverbank heightening + drainage capacity improvement + operational redundancy scheduling"), with B / C and risk levels "red / orange / yellow / green".

[0126] As can be seen from this embodiment, this application can be deployed in scenarios such as port planning and site selection, engineering design review, operation and maintenance management, insurance underwriting and emergency preparedness; it can run on regular workstations or cluster environments, supports proxy model acceleration and audit traceability, and has significant industrial application value.

[0127] Based on the same inventive concept, this application also provides a port hydrodynamic risk assessment device for implementing the port hydrodynamic risk assessment method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more port hydrodynamic risk assessment device embodiments provided below can be found in the limitations of the port hydrodynamic risk assessment method described above, and will not be repeated here.

[0128] In one exemplary embodiment, such as Figure 4 As shown, a port hydrodynamic risk assessment device is provided, comprising: The data acquisition module is used to acquire reanalysis data, observation data, and asset ledgers for the target area of ​​the port.

[0129] The correction module is used to correct the values ​​of each hydrodynamic element in the reanalysis data based on the observation data. Then, it predicts the values ​​of each hydrodynamic element in the future based on the corrected values ​​of each hydrodynamic element. Finally, it inputs the values ​​of each hydrodynamic element in the future into the dynamic lower-scale model to obtain the boundary condition library.

[0130] The environmental contour point determination module is used to fit the edge distribution of each hydrodynamic element based on the extreme values ​​of each hydrodynamic element in the boundary condition library, construct a joint probability model based on the edge distribution of each hydrodynamic element, and solve the joint probability model to obtain the environmental contour points; the environmental contour points are each hydrodynamic element corresponding to a preset return period or a preset probability.

[0131] The module for determining the spatiotemporal field of disaster-causing factors is used to perform calculations of a coupled numerical model of storm surge, wave, and tide based on scenario-driven files and a boundary condition library, so as to obtain the spatiotemporal field of disaster-causing factors. The scenario-driven files include the extreme values ​​of each environmental profile point in the boundary condition library.

[0132] The Asset-Business Exposure Layer Determination Module is used to map the asset ledger onto the target area of ​​the port to obtain the Asset-Business Exposure Layer.

[0133] The vulnerability and functional degradation module is used to perform Bayesian prior-posterior updates on the parameters of the functional degradation model based on observation data and asset-business exposure layers, to obtain a posterior vulnerability and functional degradation parameter set.

[0134] The risk indicator value determination module is used to obtain the risk indicator values ​​corresponding to the port target area based on the spatiotemporal field of the disaster-causing factors, the asset ledger, and the functional degradation model corresponding to the posterior vulnerability and functional degradation parameter set.

[0135] The risk assessment module is used to summarize the risk indicator values ​​corresponding to the port target area and conduct a hydrodynamic risk assessment of the port target area based on the risk indicator values ​​corresponding to the port target area.

[0136] In practical applications, the port hydrodynamic risk assessment device also includes a traceable visualization module, which is used to identify input data, parameters, model versions and output results with hash fingerprints and timestamps to form an audit chain.

[0137] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 5 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores port hydrodynamic risk assessment data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a port hydrodynamic risk assessment method.

[0138] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0139] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method embodiments.

[0140] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the above-described method embodiments.

[0141] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the above-described method embodiments.

[0142] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0143] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0144] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, etc., and are not limited to these.

[0145] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0146] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for assessing hydrodynamic risks in ports, characterized in that, The port hydrodynamic risk assessment method includes: Obtain reanalysis data, observation data, and asset ledgers for the target port area; The values ​​of each hydrodynamic element in the reanalysis data are corrected based on observational data. Then, the values ​​of each hydrodynamic element in the future are predicted based on the corrected values. The values ​​of each hydrodynamic element in the future are input into the dynamic low-scale model to obtain the boundary condition library. The correction of the values ​​of each hydrodynamic element in the reanalysis data based on observational data specifically includes: constructing a correction function for each hydrodynamic element based on the observational data and the reanalysis data; verifying the reanalysis data based on the observational data to obtain the correction parameter values ​​of the correction function for each hydrodynamic element; for any hydrodynamic element, substituting the correction parameter values ​​of the correction function of the hydrodynamic element into the correction function of the hydrodynamic element, and using the correction function of the hydrodynamic element with the substituted correction parameter values ​​to correct the value of the hydrodynamic element in the reanalysis data. The marginal distribution of each hydrodynamic element is fitted based on the extreme values ​​of each hydrodynamic element in the boundary condition library. A joint probability model is constructed based on the marginal distribution of each hydrodynamic element. The environmental profile points are obtained by solving the joint probability model. The environmental profile points are each hydrodynamic element corresponding to a preset return period or a preset probability. Based on scenario-driven files and boundary condition libraries, a coupled numerical model of storm surge, wave, and tide processes is used to calculate the spatiotemporal field of disaster-causing factors. The scenario-driven files include the extreme values ​​of each environmental profile point in the boundary condition library. Mapping the asset ledger to the target area of ​​the port yields the asset-business exposure layer; Based on observation data and the asset-business exposure layer, the parameters of the functional degradation model are updated using Bayesian prior-posterior methods to obtain the posterior vulnerability and functional degradation parameter set. Based on the spatiotemporal field of disaster-causing factors, asset ledgers, and the functional degradation model corresponding to the posterior vulnerability and functional degradation parameter set, the risk index values ​​corresponding to the target area of ​​the port are obtained. The risk index values ​​corresponding to the port target area are summarized, and a hydrodynamic risk assessment of the port target area is conducted based on the risk index values ​​corresponding to the port target area. A pre-defined adaptation and reinforcement solution library; the adaptation and reinforcement solution library includes multiple port reinforcement solutions; For any port reinforcement scheme, calculate the risk index values ​​and confidence intervals of each risk index corresponding to the port target area under the port reinforcement scheme. Calculate the multi-objective comprehensive score of the port reinforcement scheme based on the target risk index value and the confidence interval of the target risk index corresponding to the target area of ​​the port under the port reinforcement scheme. The optimal port reinforcement scheme is determined based on the multi-objective comprehensive score corresponding to each port reinforcement scheme. The risk index values ​​corresponding to the target areas of each port under each port reinforcement scheme and the multi-objective comprehensive score corresponding to each port reinforcement scheme are summarized.

2. The port hydrodynamic risk assessment method according to claim 1, characterized in that, Based on the spatiotemporal field of the disaster-causing factors, the asset ledger, and the functional degradation model corresponding to the posterior vulnerability and functional degradation parameter set, the risk index values ​​corresponding to the target area of ​​the port are obtained, specifically including: By coupling the spatiotemporal field of disaster-causing factors, asset ledgers, and the functional degradation model corresponding to the posterior vulnerability and functional degradation parameter set, the risk index values ​​corresponding to the target area of ​​the port are obtained.

3. The port hydrodynamic risk assessment method according to claim 1, characterized in that, Based on scenario-driven files and boundary condition libraries, a coupled numerical model of storm surge, wave, and tide processes is performed, specifically as follows: The target area of ​​the port is divided into grids based on the port area topography. Using scenario-driven files and boundary condition libraries as input, a coupled numerical model of storm surge, wave, and tide is performed on the target port area after gridding.

4. The port hydrodynamic risk assessment method according to claim 3, characterized in that, Mapping the asset ledger to the target area of ​​the port yields the asset-business exposure layer, specifically: Establish an asset-business exposure list based on the asset ledger; The asset-business exposure list is mapped onto the port target area after grid division to obtain the asset-business exposure layer.

5. A port hydrodynamic risk assessment device, characterized in that, The port hydrodynamic risk assessment device is used to perform the port hydrodynamic risk assessment method according to claim 1, and comprises: The data acquisition module is used to acquire reanalysis data, observation data, and asset ledgers for the target area of ​​the port. The correction module is used to correct the value of each hydrodynamic element in the reanalysis data based on the observation data, then predict the value of each hydrodynamic element in the future based on the corrected value of each hydrodynamic element, and input the value of each hydrodynamic element in the future into the dynamic lower-scale model to obtain the boundary condition library. The environmental contour point determination module is used to fit the edge distribution of each hydrodynamic element based on the extreme values ​​of each hydrodynamic element in the boundary condition library, construct a joint probability model based on the edge distribution of each hydrodynamic element, and solve the joint probability model to obtain the environmental contour points; the environmental contour points are each hydrodynamic element corresponding to a preset return period or a preset probability. The module for determining the spatiotemporal field of disaster-causing factors is used to perform calculations of a coupled numerical model of storm surge, wave, and tide based on scenario-driven files and a boundary condition library, and to obtain the spatiotemporal field of disaster-causing factors. The scenario-driven files include the extreme values ​​of each environmental profile point in the boundary condition library. The Asset-Business Exposure Layer Determination Module is used to map the asset ledger to the target area of ​​the port to obtain the asset-business exposure layer; The vulnerability and functional degradation module is used to perform Bayesian prior-posterior updates on the parameters of the functional degradation model based on observation data and asset-business exposure layers, to obtain a posterior vulnerability and functional degradation parameter set. The risk indicator value determination module is used to obtain the risk indicator values ​​corresponding to the port target area based on the spatiotemporal field of the disaster-causing factors, the asset ledger, and the functional degradation model corresponding to the posterior vulnerability and functional degradation parameter set. The risk assessment module is used to summarize the risk indicator values ​​corresponding to the port target area and conduct a hydrodynamic risk assessment of the port target area based on the risk indicator values ​​corresponding to the port target area.

6. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the port hydrodynamic risk assessment method according to any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the port hydrodynamic risk assessment method according to any one of claims 1-4.

8. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the port hydrodynamic risk assessment method according to any one of claims 1-4.

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