A method, system, apparatus, and medium for ship impact risk of a radial gate

Abstract

The application provides a ship collision arc gate failure risk method, system, device and medium, and relates to the field of water conservancy engineering safety evaluation. The method comprises the following steps: constructing an index system for evaluating the failure risk of the arc gate; simulating the dynamic response of the arc gate after being hit by ships under different ship tonnages and ship speeds according to a finite element model, so as to extract actual values of various evaluation indexes; sending the actual values of the various evaluation indexes into a forward cloud generator model to generate corresponding cloud droplets, so as to calculate the membership degrees of the various evaluation indexes under different risk levels; calculating the combined weight values of the various evaluation indexes by using an entropy weight method and a variation coefficient method, and combining the membership degrees to obtain corresponding basic probability assignment functions; and fusing the basic probability assignment functions of the different evaluation indexes according to a Dempster combination rule, so as to determine the final failure risk evaluation grade of the arc gate under the ship collision. The technical scheme can accurately measure the failure risk of the gate under the dynamic load.

Description

Technical Field

[0001] This application relates to the field of safety assessment of water conservancy projects, and more specifically, to a method, system, equipment, and medium for assessing the risk of ship collision with an arc-shaped gate. Background Technology

[0002] The rapid development of inland waterway transportation has led to an increasing risk of ship collisions for various hydraulic structures, such as arched gates. Compared to large-volume water-retaining structures (such as dams), arched gates are structurally weaker and more susceptible to damage from ship impacts. Existing assessments of the dynamic response and safety of hydraulic gates to ship impacts largely focus on static conditions, lacking a comprehensive analysis of gate failure risks under dynamic loads. Furthermore, the assignment of indicator weights during the assessment process presents a challenge in balancing subjectivity and objectivity, making it difficult to accurately reflect the complex relationships between various indicators. In short, there is currently no mature method to accurately measure the risk of gate failure under dynamic loads. Summary of the Invention

[0003] The purpose of this application is to provide a method, system, device and medium for assessing the failure risk of a gate in the event of a ship collision, which can accurately measure the failure risk of the gate under dynamic loads.

[0004] This application is implemented as follows:

[0005] Firstly, this application provides a method for assessing the failure risk of an arc-shaped gate in a ship collision, comprising the following steps: constructing an index system for evaluating the failure risk of the arc-shaped gate, the index system including multiple evaluation indicators, each of which has upper and lower thresholds for a corresponding safety level classification standard; simulating the dynamic response of the arc-shaped gate after impact under different ship tonnage and speed conditions using a finite element model including the dam body, gate, water body, foundation, and ship, to extract the actual values ​​of each evaluation indicator; feeding the actual values ​​of each evaluation indicator into a forward cloud generator model to generate corresponding cloud droplets, to calculate the membership degree of each evaluation indicator under different risk levels; calculating the combined weight value of each evaluation indicator using the entropy weight method and the coefficient of variation method, and combining the membership degree of each evaluation indicator with the combined weight value to obtain the corresponding basic probability assignment function; and fusing the basic probability assignment functions of different evaluation indicators according to the Dempster combination rule to determine the final failure risk assessment level of the arc-shaped gate under ship impact.

[0006] In some implementations, the indicator system includes multiple evaluation indicators that characterize the strength, stiffness, and stability of the gate structure.

[0007] In some implementations, the index system includes at least the following indicators: the proportion of the overall plastic strain energy of the gate to the total strain energy, the MISES stress of each component, the maximum deformation value, and the overall stability coefficient of the support arm.

[0008] In some implementation methods, the risk level values ​​of each evaluation index are determined in the following ways: for stress-related indices, the allowable stress, yield strength, and ultimate tensile strength of the steel correspond to the threshold values ​​of each risk level; for deformation-related indices, the grade values ​​are determined by data obtained from numerical simulation through cluster analysis; for the overall stability coefficient of the outrigger, the grade value is determined by the ratio of the ultimate stress corresponding to the overall buckling of the outrigger to the yield strength of the steel through cluster analysis.

[0009] In some implementations, simulating the dynamic response of the arc gate after impact under different ship tonnage and speed conditions includes: using an acoustic-structure interaction algorithm to perform numerical simulation calculations of ship impact.

[0010] In some implementations, the step of sending the actual values ​​of each evaluation indicator into the positive cloud generator model to generate corresponding cloud droplets includes: setting upper and lower thresholds for the security level classification standards corresponding to each evaluation indicator to achieve quantitative processing of the membership degree of each evaluation indicator.

[0011] In some implementations, the method of using entropy weighting and coefficient of variation to calculate the combined weight value of each evaluation indicator includes: the entropy weighting method measures the uncertainty of the indicator data based on the information entropy, and determines the weight of each indicator accordingly; the coefficient of variation method determines the weight of the indicator by measuring the dispersion of each evaluation indicator value.

[0012] Secondly, this application provides a system for assessing the failure risk of a ship colliding with an arc-shaped gate, comprising: a system construction module configured to: construct an index system for evaluating the failure risk of the arc-shaped gate, wherein the index system includes multiple evaluation indicators, each of which has upper and lower threshold values ​​corresponding to safety level classification standards; a simulation extraction module configured to: simulate the dynamic response of the arc-shaped gate after impact under different ship tonnage and speed conditions based on a finite element model including the dam body, gate, water body, foundation, and ship, in order to extract the actual values ​​of each evaluation indicator; and a membership calculation module configured to: calculate the membership of each... The actual values ​​of the evaluation indicators are fed into the positive cloud generator model to generate corresponding cloud droplets, so as to calculate the membership degree of each evaluation indicator under different risk levels; the BPA generation module is configured to calculate the combined weight value of each evaluation indicator using the entropy weight method and the coefficient of variation method, and combine the membership degree of each evaluation indicator with the combined weight value to obtain the corresponding basic probability assignment function; the risk assessment module is configured to fuse the basic probability assignment functions of different evaluation indicators according to the Dempster combination rule to determine the final failure risk assessment level of the arc gate under ship impact.

[0013] Thirdly, this application provides an electronic device including a memory for storing one or more programs; a processor; and, when the one or more programs are executed by the processor, implementing the method as described in any one of the first aspects above.

[0014] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of the first aspects above.

[0015] Compared with the prior art, this application has at least the following advantages or beneficial effects:

[0016] This application proposes a method for assessing the failure risk of an arc-shaped gate under ship impact. By constructing a comprehensive evaluation index system, using a finite element model for simulation, and employing advanced methods such as cloud models and Dempster rules, the failure risk of the arc-shaped gate under ship impact can be more accurately evaluated. It considers not only the structural strength and material toughness of the arc-shaped gate itself, but also the influence of external conditions such as ship tonnage and speed, making the evaluation results more comprehensive and reliable. Furthermore, by using the entropy weight method and the coefficient of variation method to calculate the combined weight value, the problem of balancing subjectivity and objectivity in weight assignment can be avoided, making the evaluation results more objective and fair. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of an embodiment of a method for preventing ship collision with an arc-shaped gate according to this application;

[0019] Figure 2 This is a diagram illustrating the architecture of the indicator system for assessing the failure risk of an arc-shaped gate in one embodiment of this application.

[0020] Figure 3 This is a schematic diagram of a fully coupled finite element model in one embodiment of this application;

[0021] Figure 4 This is a schematic diagram of the finite element model of the arc-shaped gate in one embodiment of this application;

[0022] Figure 5 The interaction between ship tonnage and speed in one embodiment of this application affects the evaluation index D. 12 A schematic diagram of the affected contour lines;

[0023] Figure 6 The interaction between ship tonnage and speed in one embodiment of this application affects the evaluation index D. 13 A schematic diagram of the affected contour lines;

[0024] Figure 7 The interaction between ship tonnage and speed in one embodiment of this application affects the evaluation index D. 31 A schematic diagram of the affected contour lines;

[0025] Figure 8 The interaction between ship tonnage and speed in one embodiment of this application affects the evaluation index D. 32 A schematic diagram of the affected contour lines;

[0026] Figure 9 This is a normalized heatmap corresponding to one embodiment of this application;

[0027] Figure 10 This is a structural block diagram of an embodiment of a system for preventing ship collisions with arc-shaped gates, as described in this application.

[0028] Figure 11 This is a structural block diagram of an electronic device provided in an embodiment of this application.

[0029] Icons: 201, Processor; 202, Memory; 203, Communication Interface. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, 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. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0031] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the various embodiments and features described below can be combined with each other.

[0032] Example 1

[0033] The inventors observed that the rapid development of inland waterway transportation has increased the risk of ship collisions to hydraulic structures such as arc-shaped gates. Compared to large water-retaining structures, arc-shaped gates are structurally fragile and easily damaged by impacts. However, existing assessment methods mainly focus on static conditions, lacking a comprehensive analysis of gate failure risk under dynamic loads, and the assignment of index weights presents a problem of balancing subjectivity and objectivity. To address these issues, this application provides a method for assessing the failure risk of arc-shaped gates in the event of a ship collision, which can accurately measure the gate failure risk under dynamic loads.

[0034] Please see Figure 1 The method for mitigating the risk of ship collision failure with an arc-shaped gate includes the following steps:

[0035] Step S101: Construct an indicator system for assessing the failure risk of the arc gate. The indicator system includes multiple evaluation indicators, and each evaluation indicator has upper and lower thresholds for a corresponding safety level classification standard.

[0036] It should be noted that the indicator system includes several evaluation indicators closely related to the failure risk of arc-shaped gates, such as... Figure 2 As shown, the evaluation indicators can include the equivalent stress of the main beam, the equivalent stress of the secondary beam and diaphragm, the equivalent stress of the panel, the equivalent stress of the support arm, the ratio of plastic strain energy to total strain energy, the maximum deformation value of the main beam, the maximum deformation value of the support arm, and the overall stability coefficient of the support arm. Each indicator has corresponding upper and lower thresholds for safety level classification standards, which are used for subsequent risk level determination. That is, by constructing a failure risk assessment system for the arc-shaped gate that includes multiple evaluation indicators and setting upper and lower thresholds for the safety level classification standards corresponding to each indicator, it is convenient to comprehensively and systematically assess the failure risk of the arc-shaped gate under ship impact. This approach overcomes the limitations of existing technologies that only focus on static condition assessment and achieves a comprehensive analysis of the gate failure risk under dynamic loads.

[0037] Step S102: Based on the finite element model including the dam body, gate, water body, foundation and ship, simulate the dynamic response of the arc gate after impact under different ship tonnage and speed conditions, so as to extract the actual values ​​of each evaluation index.

[0038] By utilizing simulation, actual values ​​of various evaluation indicators can be extracted, such as the magnitude of the impact force and the degree of gate deformation. The simulation process accurately reflects the actual stress on the arc-shaped gate under ship impact, and the extracted actual values ​​provide crucial data support for subsequent risk assessment steps. Furthermore, by simulating impact responses under different conditions, the failure risk of the arc-shaped gate can be assessed more comprehensively.

[0039] Step S103: Input the actual values ​​of each evaluation indicator into the positive cloud generator model to generate corresponding cloud droplets, so as to calculate the membership degree of each evaluation indicator under different risk levels.

[0040] It should be noted that the forward cloud generator model is an effective tool for handling uncertainty issues. By generating and calculating cloud droplets, the membership degree of each evaluation indicator under different risk levels can be obtained. Using the forward cloud generator model to calculate membership degrees can fully account for the uncertainty of evaluation indicators, making the assessment results more accurate and reliable.

[0041] Step S104: Calculate the combined weight value of each evaluation index using the entropy weight method and the coefficient of variation method, and combine the membership degree of each evaluation index with the combined weight value to obtain the corresponding basic probability assignment function; for example, the entropy weight method measures the uncertainty of the index data based on the information entropy, and determines the weight of each index accordingly; the coefficient of variation method determines the weight of the index by measuring the dispersion of each evaluation index value.

[0042] It should be noted that by combining the entropy weight method and the coefficient of variation method, the weights of each evaluation indicator can be reasonably allocated, improving the objectivity and accuracy of the evaluation results and avoiding the problem of balancing subjectivity and objectivity in weight assignment.

[0043] Step S105: Based on the Dempster combination rule, the basic probability assignment functions of different evaluation indicators are fused to determine the final failure risk assessment level of the arc gate under ship impact.

[0044] It should be noted that by integrating Dempster's rules, the impact of various evaluation indicators on the failure risk of the arc gate can be comprehensively considered, resulting in a more accurate and comprehensive assessment. Furthermore, the determination of the final failure risk assessment level provides an important reference for the safe operation of the arc gate.

[0045] In summary, this application, by constructing a comprehensive evaluation index system, utilizing finite element modeling for simulation, and employing advanced methods such as cloud modeling and Dempster's rule, can more accurately assess the failure risk of arc-shaped gates under ship impact. It considers not only the structural strength and material toughness of the arc-shaped gate itself, but also the influence of external conditions such as ship tonnage and speed, making the evaluation results more comprehensive and reliable. Furthermore, by using the entropy weight method and the coefficient of variation method to calculate the combined weight values, it avoids the problem of balancing subjectivity and objectivity in weight assignment, making the evaluation results more objective and fair.

[0046] Based on the aforementioned scheme, in some implementations of this application, the index system includes multiple evaluation indicators characterizing the strength, stiffness, and stability of the gate structure. Among these, the gate structure strength evaluation indicators can be used to quantify the gate's ability to resist damage when subjected to ship impacts. For example, these may include the gate's yield strength, tensile strength, and shear strength, which reflect the ultimate bearing capacity of the gate material under stress. Stiffness refers to an object's ability to resist deformation under stress. For an arc-shaped gate, stiffness evaluation indicators may include the gate's deformation amount and deformation rate, which reflect the gate's deformation after being subjected to stress, thereby assessing its ability to maintain its original shape and size. Stability refers to an object's ability to maintain equilibrium under stress. For an arc-shaped gate, stability evaluation indicators may include the gate's overturning moment and sliding force, which reflect whether the gate is prone to losing balance or sliding under stress.

[0047] By introducing these specific evaluation indicators in the above implementation method, the stress and deformation of the gate under ship impact can be quantified more accurately, thus enabling a more precise assessment of its failure risk. Furthermore, in addition to considering the gate's structural characteristics, it also considers its deformation and stability under stress, making the evaluation more comprehensive and detailed. Moreover, these evaluation indicators can not only be used to assess the failure risk of existing gates but also provide important references for gate design and optimization. For example, adjusting the gate's structural parameters and material properties can improve its structural strength, stiffness, and stability, thereby reducing the failure risk.

[0048] Based on the aforementioned scheme, in some implementations of this application, the index system includes at least the following indicators: the proportion of the overall plastic strain energy of the gate to the total strain energy, the MISES stress of each component, the maximum deformation value, and the overall stability coefficient of the support arm.

[0049] It should be noted that plastic strain energy refers to the energy absorbed by a material during plastic deformation, while total strain energy includes both elastic strain energy and plastic strain energy. This index reflects the degree of plastic deformation of the gate during stress. Plastic deformation is the permanent deformation that occurs when a material exceeds its elastic limit. For curved gates, excessive plastic deformation may lead to structural failure. Therefore, this index is an important parameter for measuring the structural strength of the gate.

[0050] MISES stress (also known as von Mises stress or effective stress) is an equivalent stress used to predict whether a material will yield under complex multiaxial stress conditions. It is based on the principle of energy conservation, assuming that the stress components in all directions are equivalent when the material yields. In other words, MISES stress is an important indicator of a material's safety under complex stress conditions. For the components of an arc-shaped gate, understanding the MISES stress distribution during the stress process helps in assessing its structural strength and durability.

[0051] The maximum deformation value refers to the maximum displacement of a gate under stress. For curved gates, excessive deformation may cause them to malfunction or even fail. Therefore, the maximum deformation value is an important parameter for measuring the stiffness of a gate.

[0052] The overall stability coefficient of the outrigger reflects its ability to maintain equilibrium under stress. It is typically related to factors such as the outrigger's geometry, material properties, and stress conditions. The outrigger is a crucial component of an arc-shaped gate, and its stability directly impacts the overall gate's performance. The overall stability coefficient of the outrigger is a vital parameter for measuring gate stability. When the outrigger loses dynamic stability, it can lead to catastrophic damage to the entire gate.

[0053] In summary, the index system constructed in the above implementation method provides a comprehensive and accurate basis for evaluating the performance of the arc gate by comprehensively considering multiple aspects such as the proportion of the overall plastic strain energy of the gate to the total strain energy, the MISES stress of each component, the maximum deformation value, and the overall stability coefficient of the support arm.

[0054] Based on the aforementioned scheme, in some implementation methods of this application, the risk level values ​​of each evaluation index are determined in the following ways: for stress-related indices, the allowable stress, yield strength, and ultimate tensile strength of the steel correspond to the threshold values ​​of each risk level; for deformation-related indices, the grade values ​​are determined by data obtained from numerical simulation through cluster analysis; for the overall stability coefficient of the outrigger, the grade value is determined by the ratio of the ultimate stress corresponding to the overall buckling of the outrigger to the yield strength of the steel through cluster analysis.

[0055] It should be noted that for stress-related indicators, such as MISES stress, the risk level criteria are primarily determined based on the mechanical properties of the steel. Specifically, the allowable stress, yield strength, and ultimate tensile strength of the steel are used as references, and thresholds for different risk levels are established based on the values ​​of these parameters. Allowable stress is the maximum stress value permitted in the design; yield strength is the stress value at which the material begins to undergo plastic deformation; and ultimate tensile strength is the maximum stress value that the material can withstand in a tensile test. By comparing these values ​​with actual stress values, the risk level of the stress indicator can be determined.

[0056] For deformation-related indicators, such as maximum deformation values, the risk level thresholds are difficult to determine directly through theoretical calculations because deformation can vary depending on factors such as gate structure, stress mode, and material properties. In this application, cluster analysis is used to process the data obtained from numerical simulations. Cluster analysis is a data mining technique that divides a dataset into several groups, each with similar characteristics. By performing cluster analysis on the deformation data obtained from numerical simulations, the data distribution characteristics under different deformation degrees can be identified, thereby determining the value range of each risk level.

[0057] The determination of the risk level standard for the overall stability coefficient of the outrigger also requires comprehensive consideration of multiple factors. In this application, the ratio of the ultimate stress to the yield strength of the steel corresponding to the overall buckling of the outrigger is used as a reference. Outrigger buckling is the manifestation of the outrigger structure losing stability during the stress process, while the ultimate stress is the maximum stress value that the outrigger can withstand before buckling. By comparing the ratio of the ultimate stress to the yield strength with the actual stability coefficient, the stability status of the outrigger can be preliminarily judged. However, since the outrigger buckling process may involve complex mechanical behavior and multiple influencing factors, cluster analysis is also required to process the actual data to determine the value range of each risk level.

[0058] Based on the aforementioned scheme, in some implementations of this application, the simulation of the dynamic response of the arc gate after impact under different ship tonnage and speed conditions includes: using an acoustic-structure coupling algorithm to perform numerical simulation calculations of ship impact.

[0059] It should be noted that the acoustic-structure interaction algorithm considers the interaction between the fluid (in this case, water and air) and the solid (ship and gate). This interaction is particularly critical in the simulation of a ship impacting an arc-shaped gate, because the impact generates sound waves that propagate in the water and air and may further affect the structure of the gate and the ship. Using the acoustic-structure interaction algorithm for numerical simulation of ship impacts allows for a more comprehensive simulation of the complex phenomena during the impact process and improves the accuracy of the simulation results.

[0060] For example, the following steps may be included when using an acoustic-structure interaction algorithm to perform numerical simulation calculations of ship impacts:

[0061] 1) Model Building: First, accurate 3D models of the ship and the arc gate need to be built, including their geometry, material properties, and boundary conditions. These models should be as close to the actual situation as possible to ensure the accuracy of the simulation results.

[0062] 2) Mesh generation: The model is divided into small mesh cells to facilitate numerical calculations. The quality and density of the mesh generation have a significant impact on the accuracy of the simulation results.

[0063] 3) Parameter settings: Based on the ship's tonnage and speed, set the corresponding impact parameters, such as impact speed, impact angle, and impact location. Simultaneously, it is also necessary to set the relevant parameters for the acoustic-structure interaction algorithm, such as the propagation speed of sound waves, the density of the medium, and the elastic modulus.

[0064] 4) Calculation and Solution: Numerical calculations are performed using an acoustic-structure interaction algorithm to simulate the dynamic response process of a ship impacting an arc-shaped gate. This includes sound wave propagation, structural vibration, stress distribution, and deformation.

[0065] 5) Results Analysis: The simulation results are analyzed to evaluate the dynamic response characteristics of the arc gate under different ship tonnage and speed conditions, including stress distribution, deformation degree, and vibration frequency. These results can provide important references for the design and optimization of arc gates.

[0066] Based on the aforementioned scheme, in some implementations of this application, the step of sending the actual values ​​of each evaluation indicator into the positive cloud generator model to generate corresponding cloud droplets includes: setting upper and lower thresholds for the security level classification standards corresponding to each evaluation indicator to achieve quantitative processing of the membership degree of each evaluation indicator.

[0067] In the above implementation method, by setting reasonable upper and lower thresholds for security level classification standards and calculating membership degrees accordingly, the actual situation of evaluation indicators can be reflected more accurately. The upper and lower thresholds for security level classification standards can be adjusted according to actual needs, thus adapting to different application scenarios and evaluation requirements. Simultaneously, by generating cloud droplets and calculating cloud characteristic parameters, more information about the fuzziness and randomness of evaluation indicators can be obtained, providing stronger support for decision-making.

[0068] To provide a more intuitive understanding of this application for those skilled in the art, a specific example will be used here for illustration. The overall evaluation process includes steps 1-4:

[0069] Step 1: Establish an index system for assessing the failure risk of arc-shaped gates, select m evaluation indicators, and determine the safety level classification standards for each evaluation indicator according to the specifications. These are the upper and lower thresholds of the i-th (i = 1, 2, ..., m) evaluation index at the j-th (j = 1, 2, ..., n) rating level.

[0070] Step 2: By changing variables such as ship tonnage and speed, a numerical model is used to calculate the gate's response under different operating conditions. Based on the calculation results, the actual values ​​of each evaluation index are extracted as input parameters x for subsequent cloud model calculations, and the safety level values ​​of each evaluation index are adjusted accordingly.

[0071] Step 3: Determine the normal cloud model parameters corresponding to different evaluation levels based on the interval thresholds of the evaluation indicators. Generate cloud droplets using a forward cloud generator, setting the number of cloud droplets for each evaluation indicator to n = 500. Typically, the membership matrix U = [μ] for each evaluation level... ij (x)] mn Generally, this will not be satisfied. Furthermore, the membership matrices corresponding to different evaluation indicators are not comparable. Therefore, further analysis of U=[μ] is needed. ij (x)] mn Make corrections to ensure accuracy during subsequent evidence fusion calculations. Furthermore, the proportional relationships between the original membership matrices remain unchanged.

[0072] Step 4: Calculate the weight values ​​W(w1, w2, ..., w) of each evaluation index using the entropy weight method and the coefficient of variation method. m The basic probability assignment function (BPA) is generated by combining the BPA of different evaluation indicators with the standardized membership matrix. The Dempster combination rule is used to fuse the BPA of different evaluation indicators to determine the final risk assessment level (determine the final failure risk assessment level of the arc gate under ship impact).

[0073] It should be noted that the normal cloud model aims to address the limitations of traditional probability statistics and fuzzy theory in handling fuzzy problems, transforming qualitative concepts into quantitative expressions and achieving a quantitative description of qualitative concepts. Let V be a set of quantitative domains, T be a qualitative concept on V, and C be the membership degree of the quantitative value v to T. T (v)∈[0,1], the mapping is:

[0074]

[0075] In the formula, v represents cloud droplets of size V, and their distribution over the universe of discourse V constitutes the cloud model. The numerical characteristics of the cloud model include the expected value E. x Entropy E n and hyperentropy H e E x E represents the expectation of the distribution of cloud droplets in the domain space; n E represents the range of values ​​of cloud droplets that a qualitative concept can accept in the domain space. n The larger the value, the more macroscopic the qualitative concept, and the greater the fuzziness and randomness; H e For E n The uncertainty measure, H, reflects the cohesion of cloud droplets throughout the entire number domain space. e The larger the value, the greater the uncertainty of the model, and the greater the randomness of the membership degree.

[0076]

[0077] By combining the calculated results x of each indicator with the eigenvalues ​​of the cloud model, the membership degree μ of each evaluation indicator at each corresponding risk level can be calculated, i.e.:

[0078]

[0079] The workflow after inputting specific data in this example includes:

[0080] 1. Obtaining evaluation indicator data

[0081] (1) Finite element numerical fitting

[0082] In this example, numerical simulation is used to investigate the gate's response to ship impact. Specifically, calculations are performed using the ABAQUS explicit solver. A fully coupled model of the dam body, gate, water body, foundation, and ship hull is constructed, and an acoustic-structure interaction method is employed to conduct a numerical simulation experiment of the ship impact. Parameters for each component of the model are detailed in Table 1, and a schematic diagram of the fully coupled numerical model is shown below. Figure 3 As shown, the finite element model of the arc-shaped gate is as follows: Figure 4 As shown.

[0083]

[0084]

[0085] Table 1. Parameters of each component in the finite element model

[0086] Referring to the navigation standards for the Yangtze River main channel, the weight of inland dry bulk carriers should be controlled within the range of 1000-5000t. Upstream vessels should have a speed not lower than 4km / h (approximately 1.11m / s), and downstream vessels should have a speed not lower than 10km / h (approximately 2.78m / s), with a maximum speed not exceeding 15km / h (approximately 4.17m / s). Therefore, three weight classes—1000t, 2000t, and 3000t—were selected, with four speed levels of 1, 3, 5, and 7m / s respectively. Two additional speed levels of 2m / s and 4m / s were added for 1000t vessels, resulting in a total of 14 calculation scenarios.

[0087] The ratio of plastic strain energy to total strain, equivalent stress, and deformation data of each component in the overall model are extracted under each working condition. These correspond to the eight indicators in the ship collision arc gate failure risk assessment index system. Based on this, the finite element sample dataset A can be obtained. m×n m = 14, n = 8.

[0088] (2) Regression Fitting

[0089] Due to the limitations of the finite element numerical simulation conditions, in order to further analyze the statistical characteristics of the structural response under uncertain load parameters, enrich the key data of ship collision tests under different conditions, and improve the failure risk assessment system of the arc gate, ship tonnage m and ship speed v were selected as independent variables, and SPSS software was used to analyze dataset A. m×n Multiple regression fitting analysis was performed, and the predictive regression equations are shown in Table 2:

[0090]

[0091]

[0092] Table 2. Predictive Regression Equations for Evaluation Indicators

[0093] Evaluation index D 11 For example, the regression equation has an F-value of 109.350 and a P-value < 0.01, indicating that the regression model is highly significant and the regression equation is effective; the coefficient of determination R² = 0.9975, indicating that 99.75% of the data can be explained by this equation, and the interaction between ship tonnage and speed has a significant impact on the MISES stress of the main beam. The contour lines of the indicators for ship tonnage and speed are as follows: Figures 5-8 As shown, by Figures 5-8 It can be seen that when the ship's tonnage remains constant, the values ​​of each indicator show an increasing trend as the ship's speed increases; when the ship's speed is fixed, the above indicators increase as the ship's tonnage increases.

[0094] 2. Weight Calculation

[0095] Based on the fitted data, the objective weights of each evaluation index were calculated using the entropy weight method and the coefficient of variation method. The combined weight values ​​of the two methods were then integrated to determine the combined weight values ​​of the indicators. The specific results are shown in Table 3. Comparative analysis shows that the combined weights reduced the differences between the weighting results to a certain extent.

[0096] Indicator layer Entropy weighting method Weighting by coefficient of variation Combination empowerment <![CDATA[D 11 ]]> 0.089 0.150 0.148 <![CDATA[D 12 ]]> 0.246 0.140 0.238 <![CDATA[D 13 ]]> 0.145 0.067 0.126 <![CDATA[D 14 ]]> 0.020 0.223 0.085 <![CDATA[D 21 ]]> 0.151 0.011 0.052 <![CDATA[D 31 ]]> 0.193 0.060 0.138 <![CDATA[D 32 ]]> 0.006 0.239 0.048 <![CDATA[D 41 ]]> 0.150 0.109 0.164

[0097] Table 3. Combined Weights of Evaluation Indicators

[0098] 3. Determination of Membership Degree and Basic Credibility

[0099] The cloud model parameters (E) corresponding to different evaluation levels for each evaluation index are calculated using equation (1). x E n H e For ships with a tonnage range of 200-3000t and a speed range of 1-8m / s, based on the established regression equation, 500 fitted values ​​that meet the safety level standards are randomly selected for each evaluation index. The membership degree of each index at each level is calculated by equation (2) and shown in Table 8.

[0100]

[0101]

[0102] Table 4 Cloud Model Parameter Table for Gate Risk Assessment Indicators

[0103] index Ι Ⅱ Ⅲ Ⅳ <![CDATA[D 11 ]]> 0.2607 0.2988 0.3193 0 <![CDATA[D 12 ]]> 0 0.2210 0.2791 0.2815 <![CDATA[D 13 ]]> 0.1980 0.2562 0.2267 0.1831 <![CDATA[D 14 ]]> 0.3334 0.3571 0 0 <![CDATA[D 21 ]]> 0.2201 0.2419 0.2330 0.2517 <![CDATA[D 31 ]]> 0.1909 0.1946 0.1969 0.2126 <![CDATA[D 32 ]]> 0.4677 0.4560 0 0 <![CDATA[D 41 ]]> 0.2480 0.2499 0.2502 0.2502

[0104] Table 5 Membership Degree Table of Evaluation Indicators

[0105] By combining the combined weights, the basic probability assignment values ​​of the corresponding underlying indicators are obtained. These data demonstrate the relative importance of each underlying indicator in failure risk assessment and its contribution to the overall risk assessment. The corresponding heatmap after normalization is shown below. Figure 9 As shown, the largest number in each column is the darkest color, and the smallest number is the lightest color.

[0106] 4. Fusion and synthesis of DS evidence theory

[0107] According to Dempster's evidence synthesis rules, when the system contains n evaluation indicators, the basic probability assignment function needs to be fused n-1 times. In this example, a total of 7 fusions were performed: the four secondary indicators—main beam MISES stress D11, secondary beam and diaphragm MISES stress D12, panel MISES stress D13, and support arm MISES stress D14—were each treated as a separate evidence body and fused pairwise. The fused result served as the basic credibility allocation for the primary indicator evidence body. Similarly, the maximum deformation value of the main beam D31 and the maximum deformation value of the support arm D32 were fused, and the results are shown in Table 6.

[0108]

[0109] Table 6. Integration Process of Secondary Evaluation Indicators

[0110] The failure risk assessment results of the arc gate under ship impact were obtained by pairwise fusion of the primary indicators C1, C2, C3 and C4, as shown in Table 7.

[0111]

[0112] Table 7. Integration Process of Primary Evaluation Indicators

[0113] Based on the principle of maximum membership, the overall credibility allocation is determined as follows: Under the specified navigation requirements (ship tonnage 200–3000t, speed 1–8m / s), the maximum basic probability m(II) of the risk assessment fusion result after the gate is struck by a ship is 0.1632, and the risk level is determined to be "Level II and below". This means that after the impact, the gate has some quality defects that do not yet affect its operation. To ensure its long-term stability and safety, necessary maintenance is required. This aligns with the actual situation and accurately measures the gate failure risk under dynamic loads.

[0114] Example 2

[0115] Please see Figure 10 This application provides a system for assessing the failure risk of a ship colliding with an arc-shaped gate. The system includes: a system construction module configured to construct an index system for evaluating the failure risk of the arc-shaped gate, the index system including multiple evaluation indicators, each with corresponding upper and lower threshold values ​​for safety level classification; a simulation extraction module configured to simulate the dynamic response of the arc-shaped gate after impact under different ship tonnage and speed conditions using a finite element model including the dam body, gate, water body, foundation, and ship, to extract the actual values ​​of each evaluation indicator; and a membership calculation module configured to calculate the membership of each... The actual values ​​of the evaluation indicators are fed into the positive cloud generator model to generate corresponding cloud droplets, so as to calculate the membership degree of each evaluation indicator under different risk levels; the BPA generation module is configured to calculate the combined weight value of each evaluation indicator using the entropy weight method and the coefficient of variation method, and combine the membership degree of each evaluation indicator with the combined weight value to obtain the corresponding basic probability assignment function; the risk assessment module is configured to fuse the basic probability assignment functions of different evaluation indicators according to the Dempster combination rule to determine the final failure risk assessment level of the arc gate under ship impact.

[0116] For the specific implementation process of the above system, please refer to the method for preventing ship collision with arc-shaped gate failure provided in Example 1, which will not be repeated here.

[0117] Example 3

[0118] Please see Figure 11This application provides an electronic device including at least one processor 201 and at least one memory 202. The processor 201 and memory 202 are directly connected to each other, or communicate with each other through a communication interface 203, or are electrically connected through one or more communication buses or signal lines to achieve data transmission or interaction. The memory 202 stores program instructions executable by the processor 201, which calls the program instructions to execute a method for addressing the risk of ship collision with an arc-shaped gate failure. For example, it can be implemented as follows:

[0119] A failure risk assessment index system for arc-shaped gates is constructed, comprising multiple evaluation indicators, each with corresponding upper and lower thresholds for safety level classification. Using a finite element model encompassing the dam body, gate, water body, foundation, and ship, the dynamic response of the arc-shaped gate after impact is simulated under different ship tonnage and speed conditions to extract the actual values ​​of each evaluation indicator. These actual values ​​are then fed into a forward cloud generator model to generate corresponding cloud droplets, allowing for the calculation of the membership degree of each indicator at different risk levels. The entropy weight method and coefficient of variation method are used to calculate the combined weight values ​​of each evaluation indicator, and the membership degrees of each indicator are combined with the combined weight values ​​to obtain the corresponding basic probability assignment functions. Finally, the basic probability assignment functions of different evaluation indicators are fused according to the Dempster combination rule to determine the final failure risk assessment level of the arc-shaped gate under ship impact.

[0120] The memory 202 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0121] The processor 201 can be an integrated circuit chip with signal processing capabilities. The processor 201 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0122] Understandable. Figure 11 The structure shown is for illustrative purposes only; the electronic device may also include components that are more advanced than those shown. Figure 11 The more or fewer components shown, or having the same Figure 11 The different configurations shown. Figure 11 The components shown can be implemented using hardware, software, or a combination thereof.

[0123] Example 4

[0124] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor 201, implements a method for mitigating the risk of ship collision with an arc-shaped gate failure. For example, it implements:

[0125] A failure risk assessment index system for arc-shaped gates is constructed, comprising multiple evaluation indicators, each with corresponding upper and lower thresholds for safety level classification. Using a finite element model encompassing the dam body, gate, water body, foundation, and ship, the dynamic response of the arc-shaped gate after impact is simulated under different ship tonnage and speed conditions to extract the actual values ​​of each evaluation indicator. These actual values ​​are then fed into a forward cloud generator model to generate corresponding cloud droplets, allowing for the calculation of the membership degree of each indicator at different risk levels. The entropy weight method and coefficient of variation method are used to calculate the combined weight values ​​of each evaluation indicator, and the membership degrees of each indicator are combined with the combined weight values ​​to obtain the corresponding basic probability assignment functions. Finally, the basic probability assignment functions of different evaluation indicators are fused according to the Dempster combination rule to determine the final failure risk assessment level of the arc-shaped gate under ship impact.

[0126] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from the spirit or essential characteristics of this application. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of this application is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this application. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for mitigating the risk of ship collision failure with an arc-shaped gate, characterized in that, Includes the following steps: An indicator system for assessing the failure risk of arc-shaped gates is constructed. The indicator system includes multiple evaluation indicators, and each evaluation indicator is set with upper and lower thresholds for corresponding safety level classification standards. Based on the finite element model including the dam body, gate, water body, foundation and ship, the dynamic response of the arc gate after impact under different ship tonnage and speed conditions is simulated to extract the actual values ​​of each evaluation index. The actual values ​​of each evaluation indicator are fed into the positive cloud generator model to generate corresponding cloud droplets, so as to calculate the membership degree of each evaluation indicator under different risk levels. The combined weight values ​​of each evaluation index are calculated using the entropy weight method and the coefficient of variation method. The membership degree of each evaluation index is combined with the combined weight values ​​to obtain the corresponding basic probability assignment function. The basic probability assignment functions of different evaluation indicators are fused according to the Dempster combination rule to determine the final failure risk assessment level of the arc gate under ship impact. The risk level standards for each evaluation indicator are determined as follows: for stress-related indicators, the allowable stress, yield strength, and ultimate tensile strength of the steel correspond to the risk level thresholds; for deformation-related indicators, the grade values ​​are determined by data obtained from numerical simulation through cluster analysis; and for the overall stability coefficient of the outrigger, the grade value is determined by the ratio of the ultimate stress corresponding to the overall buckling of the outrigger to the yield strength of the steel through cluster analysis.

2. The method according to claim 1, characterized in that, The indicator system includes multiple evaluation indicators that characterize the strength, stiffness, and stability of the gate structure.

3. The method according to claim 1, characterized in that, The index system includes at least the following indicators: the proportion of the overall plastic strain energy of the gate to the total strain energy, the MISES stress of each component, the maximum deformation value, and the overall stability coefficient of the support arm.

4. The method according to claim 1, characterized in that, The simulation of the dynamic response of the arc gate after impact under different ship tonnage and speed conditions includes: numerical simulation calculation of ship impact using an acoustic-structure coupling algorithm.

5. The method according to claim 1, characterized in that, The step of sending the actual values ​​of each evaluation indicator into the positive cloud generator model to generate corresponding cloud droplets includes: setting upper and lower thresholds for the security level classification standards corresponding to each evaluation indicator to achieve quantitative processing of the membership degree of each evaluation indicator.

6. The method according to claim 1, characterized in that, The method of calculating the combined weight value of each evaluation indicator using the entropy weight method and the coefficient of variation method includes: the entropy weight method measures the uncertainty of the indicator data based on the information entropy, and determines the weight of each indicator accordingly; the coefficient of variation method determines the weight of the indicator by measuring the dispersion of each evaluation indicator value.

7. A system for mitigating the risk of ship collision failure with an arc-shaped gate, characterized in that, include: The system construction module is configured to: construct an indicator system for assessing the failure risk of arc gates, wherein the indicator system includes multiple evaluation indicators, and each evaluation indicator is set with upper and lower thresholds for corresponding safety level classification standards; The simulation extraction module is configured to: simulate the dynamic response of the arc gate after impact under different ship tonnage and speed conditions based on the finite element model including the dam body, gate, water body, foundation and ship, so as to extract the actual values ​​of each evaluation index. The membership calculation module is configured to: send the actual values ​​of each evaluation indicator into the positive cloud generator model to generate corresponding cloud droplets, so as to calculate the membership degree of each evaluation indicator under different risk levels. The BPA generation module is configured to: calculate the combined weight value of each evaluation index using the entropy weight method and the coefficient of variation method, and combine the membership degree of each evaluation index with the combined weight value to obtain the corresponding basic probability assignment function; The risk assessment module is configured to fuse the basic probability assignment functions of different evaluation indicators according to the Dempster combination rule in order to determine the final failure risk assessment level of the arc gate under ship impact. The risk level standards for each evaluation indicator are determined as follows: for stress-related indicators, the allowable stress, yield strength, and ultimate tensile strength of the steel correspond to the risk level thresholds; for deformation-related indicators, the grade values ​​are determined by data obtained from numerical simulation through cluster analysis; and for the overall stability coefficient of the outrigger, the grade value is determined by the ratio of the ultimate stress corresponding to the overall buckling of the outrigger to the yield strength of the steel through cluster analysis.

8. An electronic device, characterized in that, include: Memory, used to store one or more programs; processor; When the one or more programs are executed by the processor, the method as described in any one of claims 1-6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

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