Method, system and equipment for preventing failure risk of ship colliding with radial gate and medium

Abstract

The invention provides a ship collision radial gate failure risk method, system and device and a medium, and relates to the field of hydraulic engineering safety assessment. The method comprises the following steps: constructing an index system for radial gate failure risk evaluation; according to the finite element model, simulating the dynamic response after the radial gate is impacted under the conditions of different ship tonnages and ship speeds so as to extract the actual value of each evaluation index; sending the actual value of each evaluation index into a forward cloud generator model to generate a corresponding cloud droplet so as to calculate the membership degree of each evaluation index under different risk levels; adopting an entropy weight method and a variable coefficient method to calculate a combined weight value of each evaluation index, and combining the membership degree to obtain a corresponding basic probability assignment function; and fusing the basic probability assignment functions of different evaluation indexes according to a Dempster combination rule so as to determine a final failure risk evaluation grade of the radial gate under ship collision. According to the technical scheme, the gate failure risk under the dynamic load can be accurately measured.

Description

Technical Field

[0001] The present application relates to the field of safety assessment of hydraulic engineering. Specifically, it relates to a method, system, device and medium for the failure risk of a ship-collided radial gate. Background Art

[0002] The rapid development of inland waterway shipping has led to an increasing risk of various hydraulic structures, such as radial gates, being impacted by ships. Compared with large-volume water retaining structures (such as dams), the structure of radial gates is relatively weak and is vulnerable to damage caused by ship impacts. Existing dynamic responses and safety assessments of hydraulic gates under ship impacts mostly focus on assessments under static conditions and lack a comprehensive analysis of the failure risk of gates under dynamic loads. In addition, there is a balance problem between subjectivity and objectivity in the assignment of index weights during the assessment process, making it difficult to accurately reflect the complex relationships between various indicators. That is, there is currently no mature method to accurately measure the failure risk of gates under dynamic loads. Summary of the Invention

[0003] The purpose of the present application is to provide a method, system, device and medium for the failure risk of a ship-collided radial gate, which can accurately measure the failure risk of the gate under dynamic loads.

[0004] The present application is implemented as follows:

[0005] In a first aspect, the present application provides a method for the failure risk of a ship-collided radial gate, including the following steps: constructing an index system for the failure risk assessment of the radial gate, where the index system includes multiple evaluation indicators, and upper and lower thresholds of the corresponding safety level division criteria are set for each evaluation indicator; according to a finite element model including a dam body, a gate, water body, foundation and a ship, simulating the dynamic response of the radial gate after being impacted under different ship tonnages and ship speeds to extract the actual values of each evaluation indicator; sending the actual values of each evaluation indicator into a forward cloud generator model to generate corresponding cloud droplets to calculate the membership degrees of each evaluation indicator at different risk levels; using the entropy weight method and the coefficient of variation method to calculate the combined weight values of each evaluation indicator, and combining the membership degrees of each evaluation indicator with the combined weight values to obtain the corresponding basic probability assignment function; 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 radial gate under ship impact.

[0006] In some implementation manners, the index system includes multiple evaluation indicators characterizing the structural strength, stiffness and stability of the gate.

[0007] In some implementation manners, the index system at least includes the following indicators: the proportion of the overall plastic strain energy of the gate in 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 implementations, the risk level value-taking criteria for each evaluation index are determined in the following manner: for the index related to stress, the allowable stress, yield strength, and ultimate tensile strength of the steel are used to correspond to the risk level thresholds; for the index related to deformation, the hierarchical value-taking is determined by clustering and analyzing the data obtained from numerical simulation; for the overall stability coefficient of the boom, the ratio of the ultimate stress corresponding to the overall buckling of the boom to the yield strength of the steel is used, and its level value-taking is determined by clustering analysis.

[0009] In some implementations, the simulation of the dynamic response of the arc gate after being impacted under different ship tonnages and ship speeds includes: using the acoustic-solid coupling algorithm to perform numerical simulation calculations of ship impact.

[0010] In some implementations, when sending the actual values of each evaluation index into the forward cloud generator model to generate corresponding cloud droplets, it includes: setting the upper and lower thresholds of the safety level division criteria corresponding to each evaluation index to achieve the quantification process of the membership degree of each evaluation index.

[0011] In some implementations, the calculation of the combined weight values of each evaluation index using the entropy weight method and the coefficient of variation method includes: the entropy weight method measures the uncertainty based on the information entropy of the index data and determines the weights of each index accordingly; the coefficient of variation method determines the weights of the indexes by measuring the dispersion degree of the values of each evaluation index.

[0012] In a second aspect, the present application provides a system for the failure risk of a ship-impacted arc gate, which includes: a system construction module configured to: construct an index system for the failure risk evaluation of the arc gate, the index system including multiple evaluation indexes, and the upper and lower thresholds of the safety level division criteria corresponding to each evaluation index are set; a simulation extraction module configured to: according to a finite element model including a dam body, a gate, a water body, a foundation, and a ship, simulate the dynamic response of the arc gate after being impacted under different ship tonnages and ship speeds to extract the actual values of each evaluation index; a membership degree calculation module configured to: send the actual values of each evaluation index into the forward cloud generator model to generate corresponding cloud droplets to calculate the membership degree of each evaluation index at different risk levels; a BPA generation module configured to: calculate the combined weight values 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 values to obtain the corresponding basic probability assignment function; a risk evaluation module configured to: fuse the basic probability assignment functions of different evaluation indexes according to the Dempster combination rule to determine the final failure risk evaluation level of the arc gate under ship impact.

[0013] In a third aspect, the present application provides an electronic device, which includes a memory for storing one or more programs; a processor; when the above one or more programs are executed by the above processor, the method described in any one of the above first aspects is implemented.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method described in any one of the above first aspects is implemented.

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

[0016] The present application proposes a method for the failure risk of a ship hitting an arc-shaped gate. By constructing a comprehensive evaluation index system, using a finite element model for simulation, and adopting advanced methods such as cloud models and Dempster rules, the failure risk of the arc-shaped gate under ship impact can be evaluated more accurately. It not only considers factors such as the structural strength and material toughness of the arc-shaped gate itself, but also considers the influence of external conditions such as ship tonnage and ship speed, making the evaluation results more comprehensive and reliable. And by calculating the combined weight value using the entropy weight method and the coefficient of variation method, the subjectivity and objectivity balance problem of weight assignment can be avoided, making the evaluation results more objective and fair. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a flowchart of an embodiment of a method for the failure risk of a ship hitting an arc-shaped gate according to the present application;

[0019] Figure 2 It is an architecture diagram of an index system for the failure risk evaluation of an arc-shaped gate in an embodiment of the present application;

[0020] Figure 3 It is a schematic diagram of a fully coupled finite element model in an embodiment of the present application;

[0021] Figure 4 It is a schematic diagram of a finite element model diagram of an arc-shaped gate in an embodiment of the present application;

[0022] Figure 5 It is for the interaction between ship tonnage and ship speed on evaluation index D 12 A schematic diagram of the contour line of the influence;

[0023] Figure 6 Schematic diagram of contour lines showing the influence of the interaction between ship tonnage and ship speed on evaluation index D in an embodiment of the present application 13 ;

[0024] Figure 7 Schematic diagram of contour lines showing the influence of the interaction between ship tonnage and ship speed on evaluation index D in an embodiment of the present application 31 ;

[0025] Figure 8 Schematic diagram of contour lines showing the influence of the interaction between ship tonnage and ship speed on evaluation index D in an embodiment of the present application 32 ;

[0026] Figure 9 Heat map corresponding to normalization in an embodiment of the present application

[0027] Figure 10 Structural block diagram of an embodiment of a system for the failure risk of a ship hitting an arc gate in the present application

[0028] Figure 11 Structural block diagram of an electronic device provided in an embodiment of the present application

[0029] Icons: 201, processor; 202, memory; 203, communication interface Detailed implementation manners

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Usually, the components of the embodiments of the present application described and illustrated herein can be arranged and designed in various different configurations

[0031] The following will describe in detail some embodiments of the present application with reference to the accompanying drawings. Without conflict, the various embodiments and the various features in the embodiments below can be combined with each other

[0032] Embodiment 1

[0033] The inventor observed that the rapid development of inland waterway shipping has increased the risk of ship collisions with hydraulic structures such as arc gates. Compared with large water retaining structures, arc gates have a fragile structure and are vulnerable to impact damage. However, existing assessment methods mainly focus on static conditions, lack a comprehensive analysis of the failure risk of gates under dynamic loads, and there are problems in the balance between subjectivity and objectivity in the assignment of index weights. To solve the above problems, the embodiments of the present application provide a method for the failure risk of a ship hitting an arc gate, which can accurately measure the failure risk of the gate under dynamic loads

[0034] Please refer to Figure 1 , a method for the failure risk of an arc gate impacted by a ship includes the following steps:

[0035] Step S101: Construct an index system for evaluating the failure risk of the arc gate. The index system includes multiple evaluation indexes, and upper and lower thresholds corresponding to the safety level division criteria are set for each evaluation index;

[0036] It should be noted that the index system includes multiple evaluation indexes closely related to the failure risk of the arc gate. As Figure 2 shown, the evaluation indexes may include the equivalent stress of the main beam, the equivalent stress of the secondary beam and the diaphragm, the equivalent stress of the panel, the equivalent stress of the support arm, the ratio of the plastic strain energy to the 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. Corresponding upper and lower thresholds of the safety level division criteria are set for each index, and these thresholds are used for subsequent risk level judgment. That is, by constructing an evaluation system for the failure risk of the arc gate including multiple evaluation indexes and setting the upper and lower thresholds of the safety level division criteria corresponding to each index, it is convenient to comprehensively and systematically evaluate the failure risk of the arc gate under ship impact in the subsequent process. This approach overcomes the limitation of only focusing on static condition evaluation in the prior art and realizes a comprehensive analysis of the failure risk of the gate under dynamic loads.

[0037] Step S102: According to the finite element model including the dam body, the gate, the water body, the foundation and the ship, simulate the dynamic response of the arc gate after being impacted under different ship tonnages and ship speeds to extract the actual values of each evaluation index;

[0038] Through simulation, the actual values of each evaluation index can be extracted, such as the magnitude of the impact force and the degree of gate deformation. The simulation process can accurately reflect the actual stress situation of the arc gate under ship impact, and the extracted actual values provide key data support for the subsequent risk assessment. At the same time, by simulating the impact response under different conditions, the failure risk of the arc gate can be evaluated more comprehensively.

[0039] Step S103: Send the actual values of each evaluation index into the forward cloud generator model to generate corresponding cloud droplets, and calculate the membership degrees of each evaluation index at different risk levels;

[0040] It should be noted that the forward cloud generator model is an effective tool for dealing with uncertainty problems. Through the generation and calculation of cloud droplets, the membership degrees of each evaluation index at different risk levels can be obtained. Using the forward cloud generator model to calculate the membership degrees can fully consider the uncertainty of the evaluation index and make the evaluation result more accurate and reliable.

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

[0042] It should be noted that by using the combination of the entropy weight method and the coefficient of variation method, the weights of each evaluation index can be reasonably allocated, the objectivity and accuracy of the evaluation results are improved, and the subjectivity and objectivity balance problem of weight assignment is avoided.

[0043] Step S105: Integrate the basic probability assignment functions of different evaluation indexes according to the Dempster combination rule to determine the final failure risk evaluation level of the radial gate under ship impact.

[0044] It should be noted that through the integration of the Dempster rule, the influence of each evaluation index on the failure risk of the radial gate can be comprehensively considered, and a more accurate and comprehensive evaluation result can be obtained. At the same time, the determination of the final failure risk evaluation level provides an important reference for the safe operation of the radial gate.

[0045] In summary, through constructing a comprehensive evaluation index system, using the finite element model for simulation, and adopting advanced methods such as the cloud model and the Dempster rule, this application can more accurately evaluate the failure risk of the radial gate under ship impact. It not only considers factors such as the structural strength and material toughness of the radial gate itself, but also considers the influence of external conditions such as ship tonnage and ship speed, making the evaluation results more comprehensive and reliable. And by using the entropy weight method and the coefficient of variation method to calculate the combined weight values, the subjectivity and objectivity balance problem of weight assignment can be avoided, making the evaluation results more objective and fair.

[0046] Based on the foregoing solutions, in some implementation manners of the present application, the index system includes multiple evaluation indexes characterizing the structural strength, stiffness, and stability of the gate. Among them, the evaluation indexes of the gate structural strength can be used to quantify the ability of the gate to resist damage when subjected to ship impact. For example, it can include the yield strength, tensile strength, shear strength, etc. of the gate, and these parameters can reflect the ultimate bearing capacity of the gate material when stressed. In addition, stiffness refers to the ability of an object to resist deformation when stressed. For an arc gate, the stiffness evaluation indexes can include the deformation amount, deformation rate, etc. of the gate, and these parameters can reflect the deformation situation of the gate after being stressed, so as to evaluate its ability to maintain its original shape and size. And stability refers to the ability of an object to maintain a balanced state when stressed. For an arc gate, the stability evaluation indexes can include the overturning moment, sliding force, etc. of the gate, and these parameters can reflect whether the gate is prone to losing balance or slipping when stressed.

[0047] In the above implementation manners, by introducing these specific evaluation indexes, the stress situation and deformation degree of the gate under ship impact can be more accurately quantified, so as to more accurately evaluate its failure risk. And in addition to considering the structural characteristics of the gate itself, it also considers its deformation and stability situations when stressed, making the evaluation more comprehensive and detailed. In addition, these evaluation indexes can not only be used to evaluate the failure risk of existing gates, but also provide important references for the design and optimization of gates. For example, the structural strength, stiffness, and stability of the gate can be improved by adjusting the structural parameters and material properties of the gate, so as to reduce the failure risk.

[0048] Based on the foregoing solutions, in some implementation manners of the present application, the index system at least includes the following indexes: the proportion of the overall plastic strain energy of the gate in 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 the material during plastic deformation, and the total strain energy includes both elastic strain energy and plastic strain energy. This index reflects the degree of plastic deformation of the gate during the stress process. Plastic deformation is the permanent deformation that occurs when the material exceeds the elastic limit. For an arc gate, 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 called von Mises stress or effective stress) is an equivalent stress used to predict whether a material will yield under a complex multiaxial stress state. It is based on the principle of energy conservation and assumes that the stress components in all directions are equivalent when the material yields. In other words, MISES stress is an important indicator for measuring the safety of materials under complex stress states. For each component of the radial gate, understanding its MISES stress distribution during the stress process helps to evaluate its structural strength and durability.

[0051] The maximum deformation value refers to the maximum displacement of the gate during the force application process. For radial gates, excessive deformation may cause them to not work properly or even fail. Therefore, the maximum deformation value is an important parameter to measure the rigidity of the gate.

[0052] The overall stability coefficient of the support arm is an indicator of the ability of the support arm to maintain a balanced state during the stress process. It is usually related to factors such as the geometric dimensions, material properties and stress conditions of the support arm. The support arm is an important part of the radial gate, and its stability directly affects the performance of the entire gate. The overall stability coefficient of the support arm is an important parameter to measure the stability of the gate. When the support arm loses its power stability, it may cause devastating 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 radial gates 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 arm.

[0054] Based on the aforementioned scheme, in some implementations of the present application, the risk level value standard for each evaluation indicator is determined in the following manner: for indicators related to stress, the allowable stress, yield strength and ultimate tensile strength of steel correspond to each risk level threshold; for indicators related to deformation, the graded value is determined by clustering analysis of the data obtained from numerical simulation; for the overall stability coefficient of the arm, the grade value is determined by clustering analysis based on the ratio of the ultimate stress corresponding to the overall buckling of the arm to the yield strength of the steel.

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

[0056] For deformation-related indicators such as the maximum deformation value, it is difficult to directly determine the risk level threshold through theoretical calculations because the deformation conditions may vary due to various factors such as the gate structure, loading method, and material properties. In this application, a clustering analysis method is used to process the data obtained from numerical simulations. Clustering analysis is a data mining technique that can divide a data set into several groups, and the data within each group has similar characteristics. By performing clustering analysis on the deformation data obtained from numerical simulations, the data distribution characteristics under different deformation degrees can be identified, thereby determining the value ranges of each risk level.

[0057] For the indicator of the overall stability coefficient of the boom, the determination of the risk level value standard also needs to comprehensively consider various factors. In this application, the ratio of the ultimate stress corresponding to the overall buckling of the boom to the yield strength of the steel is used as a reference. Boom buckling is the manifestation of the loss of stability of the boom structure during the loading process, and the ultimate stress is the maximum stress value that the boom 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 boom can be preliminarily judged. However, since the boom buckling process may involve complex mechanical behaviors and various influencing factors, it is also necessary to use the clustering analysis method to process the actual data to determine the value ranges of each risk level.

[0058] Based on the foregoing solutions, in some implementation manners of this application, the simulation of the dynamic response of the radial gate after being impacted under different ship tonnages and ship speeds includes: using the acoustic-structure coupling algorithm to perform numerical simulation calculations of ship impact.

[0059] It should be noted that the acoustic-structure coupling algorithm takes into account the interaction between the fluid (here water and air) and the solid (ship and gate). In the simulation of ship impact on the radial gate, this interaction is particularly crucial because the impact will generate sound waves, which will propagate in water and air and may have further effects on the structures of the gate and the ship. Performing numerical simulation calculations of ship impact through the acoustic-structure coupling algorithm can more comprehensively simulate the complex phenomena during the impact process and improve the accuracy of the simulation results, etc.

[0060] Exemplarily, when using the acoustic-structure coupling algorithm to perform numerical simulation calculations of ship impact, the following steps may be included:

[0061] 1) Model establishment: First, an accurate three-dimensional model of the ship and the radial gate needs to be established, including their geometric shapes, material properties, and boundary conditions, etc. 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 elements for numerical calculations. The quality and density of the mesh division have a great impact on the accuracy of the simulation results.

[0063] 3) Parameter setting: According to the tonnage and speed of the ship, set the corresponding impact parameters, such as impact speed, impact angle, and impact position, etc. At the same time, relevant parameters of the acoustic-solid coupling algorithm also need to be set, such as the propagation speed of sound waves, the density and elastic modulus of the medium, etc.

[0064] 4) Calculation and solution: Use the acoustic-solid coupling algorithm for numerical calculation to simulate the dynamic response process after the ship impacts the radial gate. This includes the propagation of sound waves, the vibration of the structure, the distribution of stress, and deformation, etc.

[0065] 5) Result analysis: Analyze the simulation results to evaluate the dynamic response characteristics of the radial gate under different ship tonnages and speeds, including stress distribution, deformation degree, vibration frequency, etc. These results can provide important references for the design and optimization of the radial gate.

[0066] Based on the foregoing solution, in some implementation manners of the present application, when sending the actual values of each evaluation index into the forward cloud generator model to generate corresponding cloud droplets, it includes: by setting the upper and lower thresholds of the safety level division criteria corresponding to each evaluation index, so as to realize the quantization process of the membership degree of each evaluation index.

[0067] In the above implementation manner, by setting reasonable upper and lower thresholds of the safety level division criteria and calculating the membership degree accordingly, the actual situation of the evaluation index can be more accurately reflected. Among them, the upper and lower thresholds of the safety level division criteria can be adjusted according to actual needs, so as to adapt to different application scenarios and evaluation requirements. At the same time, by generating cloud droplets and calculating cloud characteristic parameters, more information about the fuzziness and randomness of the evaluation index can be obtained, providing more powerful support for decision-making.

[0068] To enable those skilled in the art to more intuitively understand the present application, a specific example will be used to illustrate here. Among them, the total evaluation steps include steps 1-4:

[0069] Step 1: Establish an index system for evaluating the failure risk of the radial gate, select m evaluation indexes, and determine the safety level division criteria of each evaluation index according to the specifications They are respectively 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, use the numerical model to calculate the response of the gate under different working conditions. According to the calculation results, extract the actual values of each evaluation index as the input parameter x for subsequent cloud model calculation, and appropriately adjust the safety level values of each evaluation index accordingly.

[0071] Step 3: Determine the normal cloud model parameters corresponding to different evaluation levels according to the interval thresholds of the evaluation indicators. Generate cloud droplets through the forward cloud generator, and set the number of cloud droplets n = 500 for each evaluation indicator. Generally, the membership degree matrix U = [μ ij (x)] mn generally does not satisfy and there is no comparability between the membership degree matrices corresponding to different evaluation indicators. Therefore, it is necessary to further correct U = [μ ij (x)] mn to ensure that during subsequent evidence fusion operations and the proportional relationship between the original membership degree matrices remains unchanged.

[0072] Step 4: Calculate the weight values W(w 1 , w 2 , …, w m ) of each evaluation indicator by using the entropy weight method and the coefficient of variation method, and combine them with the standardized membership degree matrix to generate the basic probability assignment function (BPA). The BPA of different evaluation indicators is fused by the Dempster combination rule to determine the final risk evaluation level (determine the final failure risk evaluation level of the radial gate under ship impact).

[0073] It should be noted that the normal cloud model aims to solve the limitations of traditional probability statistics and fuzzy theory in dealing with fuzzy problems, and can transform qualitative concepts into quantitative expressions to achieve quantitative descriptions of qualitative concepts. Let V be a set quantitative domain, T be a qualitative concept on V, and the membership degree C T (v) of the quantitative value v to T belongs to [0, 1], and the mapping is:

[0074]

[0075] In the formula, v is the cloud droplet of V, and its distribution on the domain of discourse V is the cloud model. The digital characteristics of the cloud model include the expected value E x , entropy E n and hyperentropy H e . E x represents the expectation of the cloud droplet distribution in the domain of discourse space; E n represents the value range of the cloud droplets that can be received by the qualitative concept in the domain of discourse space. The larger E n , the more macroscopic the qualitative concept, and the greater the fuzziness and randomness; H e is the uncertainty measure of E n , reflecting the condensation degree of the cloud droplets in the entire number domain space. The larger H e , the greater the uncertainty of the model, and the corresponding increase in the randomness of the membership degree.

[0076]

[0077] From the calculation results \(x\) of each index and the characteristic values of the cloud model, the membership degree \(\mu\) of each evaluation index under the corresponding risk levels can be calculated, that is:

[0078]

[0079] The process after substituting specific data into this example includes:

[0080] 1. Acquisition of evaluation index data

[0081] (1) Finite element numerical fitting

[0082] In this example, numerical simulation means are adopted to explore the response of the gate under ship impact, that is, calculations are carried out based on the ABAQUS explicit solver. By constructing a full-coupling model of "dam body - gate - water body - foundation - hull", the acoustic-solid coupling method is used for ship impact numerical simulation tests. The parameters of each component of the model are shown in Table 1, and the schematic diagram of the full-coupling numerical model is as Figure 3 shown, and the finite element model diagram of the radial gate is as Figure 4 shown.

[0083]

[0084]

[0085] Table 1 Parameter table of each component of the finite element model

[0086] Referring to the navigation standards regulations of the main line of the Yangtze River, the ship weight of inland river bulk carriers should be controlled within the range of 1000 - 5000t. The upstream ship speed should not be lower than 4 km / h (about 1.11 m / s), and the downstream ship speed should not be lower than 10 km / h (about 2.78 m / s), and the maximum should not exceed 15 km / h (about 4.17 m / s). Therefore, three ship weight levels of 1000t, 2000t, and 3000t are selected, and four speed levels of 1, 3, 5, and 7 m / s are set respectively. For the 1000t ship, two additional levels of 2 m / s and 4 m / s are added, totaling 14 calculation conditions.

[0087] Extract the ratio of the plastic strain energy of the overall model to the total strain, the equivalent stress and deformation data of each component under each condition, corresponding to the 8 indicators in the ship impact radial gate failure risk evaluation index system, and thus the finite element sample data set \(A\) m×n , \(m = 14, n = 8\).

[0088] (2) Regression fitting

[0089] Due to the limitations of the finite element numerical simulation working condition volume, 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 radial gates, the ship tonnage m and ship speed v are selected as independent variables, and the SPSS software is used for the dataset A m×n to conduct a multiple regression fitting analysis, and the prediction regression equation is established as shown in Table 2:

[0090]

[0091]

[0092] Table 2 Prediction Regression Equation Table of Evaluation Indexes

[0093] Taking the evaluation index D 11 as an example, the F value of the regression equation is 109.350, and the P value < 0.01, indicating that the regression model is extremely significant and the regression equation is valid; the determination coefficient R2 = 0.9975, indicating that 99.75% of the data can be explained by this equation, and the interaction between the ship tonnage and the ship speed has a significant impact on the MISES stress of the main girder. The contour lines of the ship tonnage and the ship speed for the index are as Figures 5 - 8 shown. It can be seen from Figures 5 - 8 that when the ship tonnage remains unchanged, with the increase of the ship speed, the values of each index show an increasing trend; when the ship speed is fixed, the above indexes increase with the increase of the ship tonnage.

[0094] 2. Weight Calculation

[0095] Based on the fitted data, the objective weights of each evaluation index are obtained by using the entropy weight method and the coefficient of variation method respectively, and the combined weight value of the index is determined by fusing the two weight values. The specific results are shown in Table 3. Through comparative analysis, it can be obtained that the combined weight reduces the difference between the weighted results to a certain extent.

[0096] Index layer Entropy weight method for weighting Coefficient of variation method for weighting Combined weighting <![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 Weight Table of Evaluation Indexes

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

[0099] The cloud model parameters (E x , E n , H e ) corresponding to different evaluation levels of each evaluation index are calculated from Equation (1). For the conditions where the ship tonnage ranges from 200 to 3000 t and the ship speed ranges from 1 to 8 m / s, according to the established regression equation, 500 fitting values that meet the safety level standards of each evaluation index are randomly selected for each evaluation index, and the membership degrees of each index at each level are calculated from Equation (2) and shown in Table 8.

[0100]

[0101]

[0102] Table 4 Cloud model parameter table of gate risk evaluation indexes

[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 indexes

[0105] Combined with the combined weight values to obtain the basic probability assignment values of the corresponding underlying indexes. These data show the relative importance of each underlying index in the failure risk evaluation and its contribution degree to the overall risk assessment. The corresponding heat map after normalization is as Figure 9 shown. The number with the largest value in each column has the darkest color, and the number with the smallest value has the lightest color.

[0106] 4. D-S evidence theory fusion and synthesis

[0107] According to Dempster's evidence combination rule, when the system contains n evaluation indexes, the basic probability assignment function needs to be fused n - 1 times. In this example, a total of 7 fusions are carried out: the four secondary indexes of the main girder MISES stress D11, the secondary girder and diaphragm MISES stress D12, the panel MISES stress D13, and the support arm MISES stress D14 are used as separate evidence bodies for pairwise fusion respectively, and the results after fusion are used as the basic credibility assignment of the first-level index evidence body. Similarly, the maximum deformation value D31 of the main girder and the maximum deformation value D32 of the support arm are fused, and the results are shown in Table 6.

[0108]

[0109] Table 6 Fusion process table of secondary evaluation indexes

[0110] Pairwise fusion of the first-level indexes C1, C2, C3, and C4 to obtain the failure risk evaluation results of the arc gate under ship impact, as shown in Table 7.

[0111]

[0112] Table 7 Fusion process table of first-level evaluation indexes

[0113] According to the principle of maximum membership degree, it can be determined from the credibility assignment of the whole that under the restricted conditions of the specified ship navigation requirements, that is, the ship tonnage is 200 - 3000t and the ship speed is in the range of 1 - 8m / s. Through the research of this example, the maximum basic probability m(ΙΙ) of the risk assessment fusion result after the current gate is impacted by a ship is 0.1632, and the risk level is determined to be "Level II and below Level II". This means that there are some quality hidden dangers that do not affect the operation after the impact. To ensure its long-term stability and safety, it is necessary to carry out necessary maintenance on the gate, which is in line with the actual situation and can accurately measure the failure risk of the gate under dynamic loads.

[0114] Example 2

[0115] Please refer to Figure 10 , this embodiment of the present application provides a system for the failure risk of a ship-collided arc gate, which includes: a system construction module configured to construct an index system for evaluating the failure risk of an arc gate, the index system includes multiple evaluation indicators, and upper and lower thresholds of corresponding safety level division criteria are set for each evaluation indicator; a simulation extraction module configured to simulate the dynamic response of the arc gate after being impacted under different ship tonnages and ship speeds according to a finite element model including a dam body, a gate, a water body, a foundation, and a ship, so as to extract the actual values of each evaluation indicator; a membership degree calculation module configured to send the actual values of each evaluation indicator into a forward cloud generator model to generate corresponding cloud droplets, so as to calculate the membership degree of each evaluation indicator at different risk levels; a BPA generation module configured to calculate the combined weight values of each evaluation indicator by using the entropy weight method and the coefficient of variation method, and combine the membership degrees of each evaluation indicator with the combined weight values to obtain corresponding basic probability assignment functions; a risk evaluation module configured to fuse the basic probability assignment functions of different evaluation indicators according to the Dempster combination rule to determine the final failure risk evaluation level of the arc gate under ship impact.

[0116] For the specific implementation process of the above system, please refer to the method for the failure risk of a ship-collided arc gate provided in Example 1, which will not be elaborated here.

[0117] Example 3

[0118] Please refer to Figure 11, an embodiment of the present application provides an electronic device, which includes at least one processor 201 and at least one memory 202; wherein, the processor 201 is directly connected to the memory 202, or communicates with each other through a communication interface 203, or is 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, and the processor 201 calls the program instructions to execute a method for the failure risk of a ship hitting an arc gate. For example, it can achieve:

[0119] Construct an index system for evaluating the failure risk of the arc gate. The index system includes multiple evaluation indexes, and corresponding upper and lower thresholds of the safety level division standard are set for each evaluation index; according to the finite element model including the dam body, gate, water body, foundation and ship, simulate the dynamic response of the arc gate after being hit under different ship tonnages and ship speeds to extract the actual values of each evaluation index; send the actual values of each evaluation index into the forward cloud generator model to generate corresponding cloud droplets to calculate the membership degrees of each evaluation index under different risk levels; use the entropy weight method and the coefficient of variation method to calculate the combined weight values of each evaluation index, and combine the membership degrees of each evaluation index with the combined weight values to obtain the corresponding basic probability assignment function; fuse the basic probability assignment functions of different evaluation indexes according to the Dempster combination rule to determine the final failure risk evaluation level of the arc gate under ship impact.

[0120] Among them, the memory 202 can be, but is not limited to, random access memory (Random Access Memory, RAM), read only memory (Read Only Memory, ROM), programmable read only memory (Programmable Read-Only Memory, PROM), erasable programmable read only memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable programmable read only memory (Electric 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 Processing (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0122] It can be understood that Figure 11 The structure shown is only schematic, and the electronic device may also include more or fewer components than those shown Figure 11 in it, or have a different configuration from that shown Figure 11 in it. Figure 11 Each component shown in it can be implemented by hardware, software, or a combination thereof.

[0123] Embodiment 4

[0124] This application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by the processor 201, it implements a method for the failure risk of a ship hitting an arc gate. For example, it implements:

[0125] Construct an index system for evaluating the failure risk of the arc gate. The index system includes multiple evaluation indexes, and corresponding upper and lower thresholds of the safety level division standard are set for each evaluation index; according to the finite element model including the dam body, the gate, the water body, the foundation, and the ship, simulate the dynamic response of the arc gate after being hit under different ship tonnages and ship speeds to extract the actual values of each evaluation index; send the actual values of each evaluation index into the forward cloud generator model to generate corresponding cloud droplets to calculate the membership degrees of each evaluation index at different risk levels; use the entropy weight method and the coefficient of variation method to calculate the combined weight values of each evaluation index, and combine the membership degrees of each evaluation index with the combined weight values to obtain the corresponding basic probability assignment function; fuse the basic probability assignment functions of different evaluation indexes according to the Dempster combination rule to determine the final failure risk evaluation level of the arc gate under ship impact.

[0126] When the above functions are implemented in the form of software function 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 part of this 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 enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0127] For those skilled in the art, it is obvious that this application is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of this application, this application can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of this application is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in this application. Any reference signs in the claims should not be regarded as limiting the claimed rights.

Claims

1. A method for reducing the risk of failure of a ship hitting a radial gate, characterized in that: The following steps are involved: Constructing an index system for the risk assessment of radial gate failure, the index system comprising a plurality of evaluation indicators, each of which is set with an upper and lower threshold value of a corresponding safety level classification standard; Based on the finite element model including the dam body, gate, water body, foundation and ship, the dynamic response of the radial gate after being hit under different ship tonnage and ship speed conditions is simulated to extract the actual value of each evaluation index; The actual values ​​of each evaluation index are sent to the forward cloud generator model to generate corresponding cloud droplets, so as to calculate the membership degree of each evaluation index under different risk levels; The entropy weight method and the coefficient of variation method are used to calculate the combined weight value of each evaluation index, and the membership degree of each evaluation index is combined with the combined weight value 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 radial gate under ship impact.

2. The method according to claim 1, characterized in that The index system includes multiple evaluation indicators that characterize the strength, rigidity 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 any one of claims 1 to 3, characterized in that: The risk level value standard for each evaluation index is determined in the following way: for the indexes related to stress, the allowable stress, yield strength and ultimate tensile strength of steel correspond to the threshold value of each risk level; for the indexes related to deformation, the graded value is determined by cluster analysis of the data obtained from numerical simulation; for the overall stability coefficient of the arm, the grade value is determined by cluster analysis based on the ratio of the ultimate stress corresponding to the overall buckling of the arm to the yield strength of the steel.

5. The method according to claim 1, characterized in that The method of simulating the dynamic response of the radial gate after being hit under conditions of different ship tonnages and ship speeds includes: using an acoustic-solid coupling algorithm to perform numerical simulation calculations of ship impact.

6. The method according to claim 1, characterized in that The sending of the actual values ​​of the various evaluation indicators into the forward cloud generator model to generate corresponding cloud droplets includes: setting upper and lower thresholds of the safety level classification standard corresponding to each evaluation indicator to achieve quantitative processing of the membership of each evaluation indicator.

7. The method according to claim 1, characterized in that The entropy weight method and the coefficient of variation method are used to calculate the combined weight value of each evaluation index, including: 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 degree of dispersion of each evaluation index value.

8. A system for preventing the risk of failure of a ship hitting a radial gate, characterized in that: include: The system construction module is configured to: construct an index system for radial gate failure risk assessment, wherein the index system includes a plurality of evaluation indicators, and each evaluation indicator is set with an upper and lower threshold value of a corresponding safety level classification standard; The simulation extraction module is configured to: simulate the dynamic response of the radial gate after being hit under different ship tonnages and ship speeds according to the finite element model including the dam body, the gate, the water body, the foundation and the ship, so as to extract the actual value of each evaluation index; The membership calculation module is configured to: send the actual value of each evaluation index into the forward cloud generator model to generate corresponding cloud droplets, so as to calculate the membership of each evaluation index at different risk levels; The BPA generation module is configured to: calculate the combined weight value of each evaluation index by 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 to determine the final failure risk assessment level of the radial gate under the impact of the ship.

9. An electronic device, characterized in that: include: A memory for storing one or more programs; processor; When the one or more programs are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images