Mineral lifting cable parameter double-layer optimization method and system under complex sea conditions

Abstract

The application provides a mineral lifting cable parameter double-layer optimization method and system under complex sea conditions, and belongs to the technical field of deep sea mining. The method comprises the following steps: obtaining cable tension data under multiple sea condition combinations, determining a target sea condition combination that maximizes the tension, a limit load, and a range of each sea condition parameter; determining a dominant sea condition parameter and an associated cable structure parameter according to the range; scaling a reference value of the cable structure parameter according to the range proportion of the dominant sea condition parameter to obtain a target value range; selecting a target structure parameter in the range to perform structure optimization solving to obtain an optimal cable parameter combination. The application directly converts the environmental influence intensity into a limiting boundary of the structure design space, makes the parameter value range free from subjective experience dependence, realizes coupled optimization of the environmental load and the structure parameter, and finally outputs an optimal cable design scheme that meets the safety and lightweight requirements.

Description

Technical Field

[0001] This invention relates to the field of deep-sea mining technology, and in particular to a two-layer optimization method and system for mineral hoisting cable parameters under complex sea conditions. Background Technology

[0002] In deep-sea mining operations, the safe and efficient lifting of seabed minerals to the surface mining vessel is a core aspect of the project. The lifting cables must withstand the dynamic loads of the complex deep-sea environment over extended periods; therefore, the cable parameters need to be scientifically selected and designed to ensure the overall safety and economy of deep-sea lifting operations.

[0003] Existing methods for optimizing hoisting cable parameters typically treat environmental load analysis and cable structural parameter optimization as two separate steps. Specifically, this method first calculates the stress on the cable under marine environmental factors. Then, when entering the structural parameter optimization stage, it directly relies on the engineer's subjective engineering experience to artificially define the upper and lower limits of the values ​​for various cable structural parameters (such as elastic modulus, diameter, number of strands, etc.), and optimizes the cable structural scheme within this subjective experience range.

[0004] Existing technologies rely on subjective experience to define the range of values ​​for cable structure parameters, which leads to a disconnect between the actual impact of environmental factors and the structural design space. This results in a lack of clear physical calculation basis for setting the range of values ​​for cable structure parameters, ultimately making it impossible for the cable parameter design scheme to achieve the coupling optimization of environmental load and structural parameters, and making it difficult to meet the safety and lightweight requirements under complex sea conditions. Summary of the Invention

[0005] This invention provides a two-layer optimization method and system for mineral hoisting cable parameters under complex sea conditions, which can solve the defects in the prior art, realize the coupled optimization of environmental load and structural parameters, and thus output the optimal cable design scheme that meets the requirements of safety and lightweighting.

[0006] This invention provides a two-layer optimization method for mineral hoisting cable parameters under complex sea conditions, comprising the following steps: Acquire cable tension data of the cable hoisting system under various preset sea state parameter combinations; Based on the cable tension data, determine the target sea state parameter combination that maximizes the cable tension, the ultimate load of the cable under the target sea state parameter combination, and the range of the influence of each target sea state parameter on the cable tension in the target sea state parameter combination. Based on the magnitude of the range of each of the target sea state parameters, at least one dominant sea state parameter is determined from the combination of the target sea state parameters; For any of the dominant sea state parameters, determine the cable structure parameters associated with the dominant sea state parameters, and scale the reference values ​​of the cable structure parameters according to the range ratio of the dominant sea state parameters to obtain the target value range of the cable structure parameters; the range ratio is the ratio of the range of the dominant sea state parameters to the sum of the ranges of all dominant sea state parameters. From the cable structure parameters, a target structural parameter is selected as the optimization target. Within the target value range of each cable structure parameter, structural optimization is performed based on the ultimate load to obtain the optimal combination of cable parameters that satisfies the preset constraints.

[0007] According to the present invention, a two-layer optimization method for mineral lifting cable parameters under complex sea conditions is provided, wherein the dominant sea state parameters include wave height, current velocity, and wind speed. For any of the dominant sea state parameters, determining the cable structure parameters associated with the dominant sea state parameter includes: The equivalent elastic modulus of the cable is determined as a cable structural parameter associated with the wave height. The diameter of the cable is determined as a cable structure parameter associated with the flow velocity; The number of strands in the cable is determined as a cable structure parameter associated with the wind speed.

[0008] According to the present invention, a two-layer optimization method for mineral hoisting cable parameters under complex sea conditions is provided, wherein scaling the baseline values ​​of the cable structure parameters according to the range ratio to obtain the target value range of the cable structure parameters includes: The target value range for each of the cable structure parameters is calculated using the following formula: ; ; ; in, This is the lower limit boundary of the equivalent elastic modulus of the cable. This represents the upper limit boundary of the equivalent elastic modulus of the cable. This is the reference value for the equivalent elastic modulus. The range ratio of the wave height; This is the lower limit boundary of the diameter of the cable. This is the upper limit boundary of the diameter of the cable. This is the reference value for the diameter. This represents the range ratio of the flow rates; This represents the lower limit boundary of the number of strands in the cable. This represents the upper limit boundary of the number of strands in the cable. This is the baseline value for the number of shares. This represents the range ratio of the wind speeds. round This is the integer division operator.

[0009] According to the present invention, a two-layer optimization method for mineral hoisting cable parameters under complex sea conditions is provided to determine the ultimate load of the cable under the target sea state parameter combination, including: Extract the maximum effective tension during the simulation of the target sea state parameter combination; The ultimate load is obtained by multiplying the maximum effective tension by a preset environmental safety factor.

[0010] According to the present invention, a two-layer optimization method for mineral hoisting cable parameters under complex sea conditions is provided. The method involves performing structural optimization based on the ultimate load within the target value range of each cable structural parameter to obtain the optimal combination of cable parameters that satisfies preset constraints. This includes: Within the target value range of each of the cable structure parameters, a test sample set containing multiple sets of structural parameters is generated; For each set of structural parameters in the test sample set, a cable solid model corresponding to the structural parameter is established, and the maximum equivalent stress of the cable solid model under the ultimate load is taken as the sample equivalent stress corresponding to the set of structural parameters. Using the structural parameters of each group and the corresponding sample equivalent stress, a quadratic polynomial regression model with equivalent stress as the dependent variable and each of the cable structural parameters as the independent variable is fitted and constructed. Numerical optimization is performed based on the quadratic polynomial regression model to obtain the optimal combination of cable parameters that satisfies the preset constraints.

[0011] According to the present invention, a two-layer optimization method for mineral hoisting cable parameters under complex sea conditions is provided. The step of numerical optimization based on the quadratic polynomial regression model to obtain the optimal combination of cable parameters that satisfies the preset constraints includes: The objective function is set according to the target structural parameters, with the constraint that the predicted equivalent stress output by the quadratic polynomial regression model is less than or equal to the allowable stress. Solve the quadratic polynomial regression model within the target value range of each of the cable structure parameters to obtain the combination of independent variables that satisfies the constraints and minimizes the result of the optimization objective function. Use the combination of independent variables as the optimal combination of cable parameters.

[0012] The present invention provides a two-layer optimization method for mineral hoisting cable parameters under complex sea conditions, which, after obtaining the optimal combination of cable parameters that satisfies preset constraints, further includes: The optimal cable parameter combination is input into a preset three-dimensional solid finite element model of the cable, and the ultimate load is applied to perform finite element solution calculation to obtain the finite element verification equivalent stress. Calculate the relative error between the finite element verification equivalent stress and the predicted equivalent stress output by the quadratic polynomial regression model for the optimal cable parameter combination; When the relative error is greater than the preset error threshold, the optimal cable parameter combination and its corresponding finite element verification equivalent stress are added as new sample points to the original sample set to obtain an updated sample set. Based on the updated sample set, the parameter prediction model is reconstructed, and the structural optimization solution is re-solved in the reconstructed parameter prediction model based on the ultimate load until the relative error obtained by the re-solution is less than or equal to the preset error threshold.

[0013] According to the present invention, a two-layer optimization method for mineral hoisting cable parameters under complex sea conditions further includes, in the process of re-solving the structural optimization in the reconstructed parameter prediction model based on the ultimate load, the following steps are also included: Monitor the number of iterations and the rate of change of the relative error between two consecutive iterations; When the number of iterations reaches the preset maximum number of iterations, or when the rate of change of the relative error between two consecutive iterations is less than a preset rate of change threshold and the current relative error is still greater than the preset error threshold, the iteration calculation is stopped and the optimal cable parameter combination obtained from the last iteration calculation is output.

[0014] According to the present invention, a two-layer optimization method for mineral hoisting cable parameters under complex sea conditions is provided, wherein obtaining cable tension data of the cable hoisting system under multiple preset sea condition parameter combinations includes: Extract multiple preset environmental factors and multiple numerical levels corresponding to each environmental factor; The multiple environmental factors and their corresponding numerical levels are cross-combined using orthogonal experimental design to generate the various preset sea state parameter combinations; The preset sea state parameters are sequentially input into a pre-established system dynamics simulation model containing the mining vessel, cable, and ore collection bag for simulation and solution. The maximum effective tension value of the cable within the preset simulation period is extracted as the cable tension data.

[0015] This invention also provides a two-layer optimization system for mineral hoisting cable parameters under complex sea conditions, comprising the following modules: The data acquisition module is used to acquire cable tension data of the cable hoisting system under various preset sea state parameter combinations; The data analysis module is used to determine, based on the cable tension data, the target sea state parameter combination that maximizes the cable tension, the ultimate load of the cable under the target sea state parameter combination, and the range of the influence of each target sea state parameter on the cable tension. The parameter filtering module is used to determine at least one dominant sea state parameter from the combination of target sea state parameters based on the magnitude of the range of each target sea state parameter. The data processing module is used to determine the cable structure parameters associated with any of the dominant sea state parameters, and to scale the reference values ​​of the cable structure parameters according to the range ratio of the dominant sea state parameters to obtain the target value range of the cable structure parameters; the range ratio is the ratio of the range of the dominant sea state parameters to the sum of the ranges of all dominant sea state parameters. The optimization solution module is used to select target structural parameters from the cable structure parameters as optimization targets, and perform structural optimization solution based on the ultimate load within the target value range of each cable structure parameter to obtain the optimal combination of cable parameters that meets the preset constraints.

[0016] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the two-layer optimization method for mineral hoisting cable parameters under complex sea conditions as described above.

[0017] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a two-layer optimization method for mineral hoisting cable parameters under complex sea conditions as described above.

[0018] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements a two-layer optimization method for mineral hoisting cable parameters under complex sea conditions as described above.

[0019] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: By acquiring cable tension data for the cable hoisting system under various preset sea state parameter combinations, a foundation is provided for quantitatively assessing the impact of different environmental factors on cable stress. Based on the cable tension data, the target sea state parameter combination that maximizes cable tension, the ultimate load of the cable under the target sea state parameter combination, and the range of the influence of each target sea state parameter in the target sea state parameter combination on cable tension are determined. This data foundation is used to pinpoint extreme service conditions and obtain quantitative range values ​​characterizing the strength of the influence of each environmental factor. Based on the magnitude of the range of each target sea state parameter, at least one dominant sea state parameter is determined from the target sea state parameter combination. This allows for the elimination of secondary environmental factors based on the quantitative range value, focusing on the core environmental factors that decisively affect cable stress. For any dominant sea state parameter, the associated cable structure parameters are determined. The baseline values ​​of these parameters are scaled according to the range ratio of the dominant sea state parameter to obtain the target range for the cable structure parameters. The range ratio is the ratio of the range of the dominant sea state parameter to the sum of the ranges of all dominant sea state parameters. This directly transforms the influence intensity of core environmental factors into the limiting boundary of the structural design space, freeing the subsequent delineation of the structural parameter range from subjective experience and providing a clear physical calculation basis. By selecting target structural parameters from among the various cable structure parameters as optimization objectives, and within the target range of each cable structure parameter, structural optimization is performed based on the ultimate load to obtain the optimal combination of cable parameters that satisfies the preset constraints. This completes the optimization of environmental loads and structural parameters within the value range, ultimately outputting a cable parameter design scheme that meets the safety and lightweight requirements of complex sea conditions. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the dual-layer optimization method for mineral lifting cable parameters under complex sea conditions provided by the present invention.

[0022] Figure 2 This is a schematic diagram of the dynamic model of the deep-sea mining hoisting system provided by the present invention.

[0023] Figure 3 This is a comparison chart of the fitting effects of the multiple linear regression equation provided by this invention.

[0024] Figure 4 This is the main effect diagram of the various factor levels on the maximum effective tension of the cable provided by the present invention.

[0025] Figure 5 This is a schematic diagram of the finite element mesh generation of the cable structure provided by the present invention.

[0026] Figure 6 This is the equivalent stress cloud diagram of the cable under ultimate tensile load provided by the present invention.

[0027] Figure 7 This is a schematic diagram of the structure of the dual-layer optimization system for mineral lifting cable parameters under complex sea conditions provided by the present invention.

[0028] Figure 8 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0030] It should be noted that in the description of this invention, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. The terms "upper," "lower," etc., indicating orientation or positional relationships according to the accompanying drawings, are only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the system or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0031] The terms "first," "second," etc., used in this invention are used to distinguish similar objects, not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, without limiting the number of objects; for example, a first object can be one or more. Furthermore, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0032] The following is combined with Figure 1 to Figure 8 This invention describes the dual-layer optimization method, system, electronic device, storage medium, and computer program product for mineral hoisting cable parameters under complex sea conditions.

[0033] The execution entities of the method described in this invention encompass various forms, including but not limited to engineering analysis workstations integrating dynamic simulation and finite element calculation modules, intelligent deep-sea mining design platforms with multi-software collaborative calling capabilities, and dedicated cable parameter optimization processing systems driven collaboratively by a central processing unit and a high-performance graphics processing unit. In the detailed descriptions of the following embodiments, the deep-sea mineral hoisting cable parameter dual-layer optimization processing system (hereinafter referred to as the system) is uniformly used as the execution entity. This system integrates the Orcaflex dynamic simulation environment, the ABAQUS finite element calculation environment, and the Design-Expert response surface numerical optimization environment, and is responsible for driving the execution and data flow of all method steps.

[0034] Reference Figure 1 , Figure 1 This is a flowchart illustrating the dual-layer optimization method for mineral hoisting cable parameters under complex sea conditions provided by the present invention, as shown below. Figure 1 As shown, steps 110 to 150 are included: Step 110: Obtain cable tension data of the cable lifting system under various preset sea state parameter combinations.

[0035] The system is designed for deep-sea mineral hoisting operations. It pre-sets multiple sets of sea state parameters, encompassing various environmental factors such as wind speed, current velocity, significant wave height, and wave incidence angle. Through system dynamics simulation, it solves the dynamic stress response of the cable under each sea state parameter combination, extracting the maximum effective tension borne by the cable during the simulation for each sea state parameter combination, thus forming a complete cable tension dataset. Each record in the cable tension dataset corresponds to a specific set of sea state parameter values ​​and the corresponding maximum effective tension value, providing a sufficient data foundation for subsequent range analysis.

[0036] In one possible implementation, the cable tension data of the cable lifting system under various preset sea state parameter combinations is obtained through the following steps.

[0037] Step 111: Extract multiple preset environmental factors and multiple numerical levels corresponding to each environmental factor.

[0038] Based on historical sea state statistics of the target operating area, the system selects multiple environmental factors that have a significant physical impact on cable tension and sets multiple representative numerical levels for each environmental factor. The values ​​for each numerical level cover a wide range, from normal operating sea states to complex sea states occurring once every 100 years, ensuring that subsequent range analysis can fully capture the influence patterns of each environmental factor under extreme operating conditions. Taking an operating scenario with a water depth of 3000m in a certain sea area as an example, the four selected environmental factors and their corresponding numerical levels are shown in Table 1: Step 112: Use orthogonal experimental design to cross-combine multiple environmental factors and their corresponding numerical levels to generate a variety of preset sea state parameter combinations.

[0039] The system employs a standard L16(4^5) orthogonal experimental design table to systematically combine the four numerical levels of four environmental factors, generating 16 sets of preset sea state parameter combinations, with the fifth column as an empty column. The orthogonal experimental design method arranges the experimental schemes according to the principle of balanced distribution. The 16 sets of preset sea state parameter combinations uniformly cover the combination space of each factor and each level with the fewest number of experiments, minimizing the total amount of simulation calculations while ensuring the reliability of the analysis conclusions.

[0040] Step 113: Input the preset sea state parameter combinations into the pre-established system dynamics simulation model containing the mining vessel, cable, and ore collection bag for simulation and solution, and extract the maximum effective tension value of the cable within the preset simulation period as the cable tension data.

[0041] The system pre-builds a complete hoisting system dynamics simulation model in the Orcaflex dynamics simulation environment, including the mining vessel, fiber optic cables, and ore collection sack (containing minerals). The specific form of the system dynamics simulation model is referenced. Figure 2 , Figure 2 This is a schematic diagram of the dynamic model of the deep-sea mining hoisting system provided by the present invention. The main parameter settings of the system dynamic simulation model include: the displacement, length and dynamic positioning system configuration of the mining vessel; the initial geometric parameters of the fiber optic cable (diameter, dry weight, weight in water, axial stiffness); the total mass of the ore collection bag and the minerals; the hoisting speed; the wave spectrum type and peak factor; and the current velocity profile distribution that varies with water depth.

[0042] The system inputs the 16 sets of preset sea state parameter combinations generated in the above steps into the system dynamics simulation model one by one, simulating the complete hoisting process of the mineral from the seabed to the deck of the mining vessel on the surface. The simulation time covers the entire hoisting cycle. The maximum effective tension value borne by the cable during the entire hoisting process is extracted from the simulation results of each set, and used as the cable tension data corresponding to each set of preset sea state parameter combinations. Taking a sea area with a water depth of 3000m as an example, the simulation calculation results of the 16 sets of preset sea state parameter combinations are shown in Table 2: Step 120: Based on the cable tension data, determine the target sea state parameter combination that maximizes the cable tension, the ultimate load of the cable under the target sea state parameter combination, and the range of the influence of each target sea state parameter on the cable tension in the target sea state parameter combination.

[0043] Based on the obtained cable tension dataset, the system performs range analysis on each environmental factor, calculates the mean tension of each environmental factor at each numerical level and the range of each environmental factor, and the range quantifies the magnitude of the influence of each environmental factor on the maximum effective tension of the cable.

[0044] Based on the arrangement pattern of the average tension values ​​of various environmental factors, the system determines the target sea state parameter combination that will maximize the cable tension from various preset sea state parameter combinations, and determines the ultimate load that the cable must withstand under the target sea state parameter combination by surrogate model prediction or simulation extraction.

[0045] In one possible implementation, based on the 16 sets of cable tension data in Table 2 of the aforementioned implementation, and from the arrangement pattern of the average values ​​of each factor, it can be seen that the target sea state parameter combination that maximizes the maximum effective cable tension is: wind speed at level 3 (48.73 m / s), current speed at level 3 (1.86 m / s), significant wave height at level 3 (12.3 m), and wave incidence angle at level 1 (0°), corresponding to a maximum effective tension of 2065.52 kN.

[0046] Furthermore, the system calculates the mean stress value of each environmental factor at each numerical level according to the range analysis calculation rules shown in Table 3. who / 4 and the range of each environmental factor R : The range analysis results show that the order of influence of various environmental factors on the maximum effective tension of the cable is: effective wave height ( R C =762.64kN) > Wave incident angle ( R D =489.62kN) > Flow velocity ( RB =401.23kN) > Wind speed ( R A =321.57kN).

[0047] Furthermore, the system determines the ultimate load of the cable under the target sea state parameter combination according to the following steps.

[0048] Step 121: Extract the maximum effective tension during the simulation of the target sea state parameter combination.

[0049] After determining the target sea state parameter combination in step 120, the system substitutes the target sea state parameter combination into the multiple linear regression surrogate model for prediction, or directly extracts the maximum effective tension value corresponding to the target sea state parameter combination from the system dynamics simulation result dataset. The multiple linear regression surrogate model is established by fitting simulation data of all preset sea state parameter combinations, with wind speed, current speed, significant wave height, and wave incidence angle as independent variables, and the maximum effective tension of the cable as the dependent variable, to establish a linear regression equation: Y = a 1 A + a 2 B + a 3 C + a 4 D + a 0; in, Y This is the predicted value of the maximum effective tension. A , B , C , D These are the values ​​for wind speed, current velocity, significant wave height, and wave incidence angle, respectively. a 0 is the regression constant. a 1. a 2. a 3. a 4 represents the regression coefficients for each environmental factor. (Refer to...) Figure 3 , Figure 3 This is a comparison chart of the fitting effects of the multiple linear regression equation provided by this invention. When the coefficient of determination of the multiple linear regression surrogate model... R 2 When the value is not lower than 0.95, the system determines that the prediction accuracy of the multiple linear regression surrogate model meets the engineering requirements, and uses the predicted value output by the multiple linear regression surrogate model as the maximum effective tension under the target sea state parameter combination.

[0050] Taking a sea area with a water depth of 3000m as an example, the multiple linear regression equation obtained by fitting 16 sets of orthogonal experimental data is as follows: Y =4.87A +436.65 B +96.38 C -3.61 D -303.87.

[0051] Multiple linear regression equation R 2 =0.962, after adjustment R 2 =0.948. Substituting the target sea state parameter combination (wind speed 48.73 m / s, current velocity 1.86 m / s, wave height 12.3 m, wave direction angle 0°) into the multiple linear regression equation, the predicted value of the maximum effective tension is obtained. Ymax The relative error between the value of approximately 2063.2 kN and the direct calculation result of 2065.52 kN from the Orcaflex dynamic simulation is only 0.11%, which verifies the prediction accuracy of the multiple linear regression surrogate model.

[0052] Step 420: Multiply the maximum effective tension by the preset environmental safety factor to obtain the ultimate load.

[0053] The system multiplies the maximum effective tension extracted in step 410 by a preset environmental safety factor to obtain the ultimate load applied to the finite element model of the cable structure. F ult : F ult = Ymax × k env ; in, k env The environmental safety factor is set to cover the uncertainty of the probability of complex sea conditions, the errors introduced by the simplification of the simulation model, and the time-related random fluctuations of environmental loads. It is a specific amplification of the uncertainty at the load end. The environmental safety factor is independent of the structural safety factor used in the structural optimization stage to obtain the allowable stress from the reduction of the tensile strength of the cable material. The two safety factors act on the load end and the strength end respectively, playing their independent safety protection roles in the entire design chain.

[0054] Taking an operation scenario with a water depth of 3000m in a certain sea area as an example, and taking the environmental safety factor... k env =6, then the ultimate load is: F ult =2063.2×6=12379.2kN≈12380kN.

[0055] Step 130: Based on the range of each target sea state parameter, determine at least one dominant sea state parameter from the combination of target sea state parameters.

[0056] The system sorts the range values ​​of each target sea state parameter in descending order, and identifies the target sea state parameters with the highest range values ​​that significantly contribute to changes in cable tension as the dominant sea state parameters. There must be at least one dominant sea state parameter, the specific number determined by both engineering design requirements and the distribution characteristics of the range values. By selecting the dominant sea state parameters, the system can focus on the physical factors that contribute most significantly to the structural response, eliminating interference from secondary factors in setting the range of subsequent structural parameters.

[0057] In one possible implementation, the dominant sea state parameters include wave height, current velocity, and wind speed.

[0058] Specifically, based on range analysis results, the system identifies wave height, current velocity, and wind speed—three environmental factors with significant range differences—as the dominant sea state parameters. (Refer to...) Figure 4 , Figure 4 This invention provides a main effect diagram of the various factor levels on the maximum effective tension of the cable. Wave height has the greatest influence on cable tension, followed by current velocity, and then wind speed. The range values ​​of the three are all significantly higher than the wave incident angle. Therefore, wave height, current velocity, and wind speed are determined as the dominant sea state parameters.

[0059] Step 140: For any dominant sea state parameter, determine the cable structure parameters associated with the dominant sea state parameter, and scale the baseline values ​​of the cable structure parameters according to the range ratio of the dominant sea state parameter to obtain the target value range of the cable structure parameters; the range ratio is the ratio of the range of the dominant sea state parameter to the sum of the ranges of all dominant sea state parameters.

[0060] The system first determines the cable structure parameters that are most closely related to each dominant sea state parameter in terms of physical mechanism, and establishes a one-to-one mapping relationship between the dominant sea state parameters and the cable structure parameters.

[0061] After establishing the mapping relationship, the system calculates the range proportions of each dominant sea state parameter. Let the ranges of all dominant sea state parameters be respectively... Then the first i Range ratio of the dominant sea state parameters The calculation method is as follows: ; Range Ratio The numerical value objectively reflects the first i The actual contribution of the dominant sea state parameters to the total change in cable tension serves as a quantitative bridge connecting the environmental layer analysis results with the structural layer parameter design.

[0062] The system starts with the benchmark values ​​of various cable structure parameters widely used in engineering practice. Using the range ratio of the dominant sea state parameters associated with each cable structure parameter as a scaling factor, it expands symmetrically to both sides of the benchmark values ​​to obtain the target value range for each cable structure parameter—that is, the upper and lower limits of the target value range. Through this quantitative transfer rule, the width of the target value range for each cable structure parameter is directly determined by the actual contribution intensity of the corresponding dominant sea state parameter, rather than by subjective human experience. This fundamentally eliminates the deficiency in traditional methods where the setting of structural parameter value ranges lacks a physical basis.

[0063] In one possible implementation, this embodiment further discloses the specific mapping relationship between the dominant sea state parameters and the cable structure parameters, and proceeds to the subsequent structural parameter mapping stage.

[0064] Furthermore, determining the cable structure parameters associated with the dominant sea state parameters specifically includes the following steps: The equivalent elastic modulus of the cable is determined as a cable structural parameter related to wave height. The diameter of the cable is determined as a cable structure parameter related to the flow velocity; The number of strands in the cable is determined as a cable structure parameter related to wind speed.

[0065] Specifically, the system establishes a correlation between wave height and the equivalent elastic modulus of the cable based on physical mechanism analysis. Wave height is the most dominant environmental factor causing changes in the dynamic tensile amplitude of the cable, while the equivalent elastic modulus directly determines the deformation stiffness response characteristics of the cable under axial dynamic tensile load. The two are closely related in terms of physical mechanism.

[0066] The system uses the wave height range ratio to scale the equivalent elastic modulus benchmark value, and the resulting target value range of the equivalent elastic modulus covers a wide range of material selection space from flexible polyester materials to high-modulus polyethylene materials, providing sufficient design freedom for subsequent structural optimization.

[0067] The system establishes the correlation between flow velocity and cable diameter, as well as the correlation between wind speed and the number of cable strands. Flow velocity affects the lateral stress state of the cable by increasing the hydrodynamic drag load, thus influencing the effective working area requirement of the cable's axial load-bearing cross-section. Since the cable diameter directly determines the cross-sectional area, the range of flow velocities is used to scale the diameter baseline value. Wind speed applies cyclic dynamic loads to the cable through the combined action of ship motion response and waves. The number of cable strands determines the overall stiffness characteristics and cyclic fatigue resistance of the cable stranded structure; therefore, the range of wind speeds is used to scale the strand number baseline value.

[0068] Based on this, the system calculates the target range of equivalent elastic modulus, diameter, and number of strands according to the following formulas, based on the established mapping relationship between the dominant sea state parameters and the cable structure parameters, as well as the determined range ratio of each dominant sea state parameter.

[0069] For the equivalent elastic modulus of the cable associated with wave height, the system uses the following formula to determine the lower limit of the target range of the equivalent elastic modulus. and upper limit boundary : ; in, The equivalent elastic modulus of the cable is a pre-set reference value. This represents the range ratio of wave height. The value is equal to the range of wave height. Divide by wave height range Extremely poor flow rate Extremely poor wind speed The sum of the three is: ; For the cable diameter associated with flow velocity, the system uses the following formula to determine the lower limit of the target range for the cable diameter. and upper limit boundary : ; in, The cable diameter is a pre-set reference value. This represents the range ratio of the flow velocities, i.e.: ; For the number of cable strands associated with wind speed, the system uses the following formula to determine the lower limit of the target range for the number of cable strands. and upper limit boundary : ; in, The preset cable strand count is the baseline value. This represents the range ratio of wind speeds, i.e.: ; The round operation rounds the result to the nearest integer to ensure that the number of shares is a positive integer that has practical significance in engineering.

[0070] Taking an operational scenario at a water depth of 3000m in a certain sea area as an example, the results obtained from range analysis are as follows: R C =762.64kN, R B =401.23kN,R A =321.57kN, the sum of the three is 1485.44kN. The corresponding range ratios are: l C =762.64 / 1485.44≈0.513; l B =401.23 / 1485.44≈0.270; l A =321.57 / 1485.44≈0.217.

[0071] Using aramid cable as the base material, E 0 = 130 GPa, substituting into the formula, we get: E min =130×(1-0.513)≈63.3GPa; E max =130×(1+0.513)≈196.7Gpa.

[0072] After taking the integer part of the engineering model, the target value range of the equivalent elastic modulus is expanded to 60~200GPa, covering the complete spectrum of commonly used fiber cable materials from polyester (about 60GPa) to high modulus polyethylene (about 200GPa).

[0073] Pick D 0 = 138mm, substituting into the formula, we get: D min =138×(1-0.270)≈100.7mm; D max =138×(1+0.270)≈175.3mm.

[0074] To cover more engineering possibilities, the target diameter value range after rounding and expansion is determined to be 88~188mm.

[0075] Pick N 0 = 9 strands, substituting into the formula, we get: N min =round(9×(1-0.217))=round(7.047)=7 shares; N max =round(9×(1+0.217))=round(10.953)=11 shares.

[0076] Based on a comprehensive analysis of the commonly used number of shares in engineering projects, the target range for the number of shares is determined to be 6 to 12 shares.

[0077] Using the three scaling formulas described above, the upper and lower limits of the target value range for each cable structure parameter are precisely calculated using objectively quantified range ratios. The width of the value range is directly proportional to the actual influence intensity of the corresponding dominant sea state parameter. The greater the influence intensity of the dominant sea state parameter, the wider the value range of the corresponding cable structure parameter, and the larger the optimization search space.

[0078] Step 150: Select the target structural parameter from the various cable structural parameters as the optimization target. Within the target value range of each cable structural parameter, perform structural optimization solution based on the ultimate load to obtain the optimal combination of cable parameters that satisfies the preset constraints.

[0079] Within the target value range of each cable structure parameter, the system uses the ultimate load as the external load applied to the cable, takes the cable structure parameter that most directly reflects the cable's economy and lightweight level in engineering applications as the optimization target, and takes the equivalent stress of the cable under the ultimate load not exceeding the allowable stress as the preset constraint condition. The system performs structural optimization within the target value range to obtain the optimal combination of cable parameters that minimizes the optimization target while satisfying the strength constraint.

[0080] In this step, the allowable stress is obtained by dividing the tensile strength of the cable material by the preset structural safety factor. The structural safety factor is independent of the environmental safety factor used in step 120 to determine the ultimate load. The two correspond to the material uncertainty at the strength end and the environmental uncertainty at the load end, respectively.

[0081] In one possible implementation, step 150 is specifically achieved through the following steps: Step 151: Within the target value range of each cable structure parameter, generate a test sample set containing multiple sets of structural parameters.

[0082] Within the target value range of the three cable structure parameters—equivalent elastic modulus, diameter, and number of strands—the system uses the Box-Behnken experimental design method to generate a test sample set. The Box-Behnken experimental design method is based on the corresponding encoded values ​​(-1, 0, +1) of the lower boundary, median, and upper boundary of each of the three cable structure parameters. It generates test schemes according to the geometric relationship between the vertices and center point of a regular dodecahedron. In this embodiment, a total of 17 test schemes are generated, including 5 center point repeat test schemes. Taking a sea area with a water depth of 3000m as an example, the correspondence between the encoded values ​​and actual values ​​of each cable structure parameter in the 17 test schemes is shown in Table 4. Step 152: For each set of structural parameters in the test sample set, establish a solid model of the cable corresponding to the structural parameter, and take the maximum equivalent stress of the cable solid model under the ultimate load as the sample equivalent stress of the corresponding structural parameter.

[0083] The system established three-dimensional solid models of the cables in the ABAQUS finite element analysis environment for 17 sets of structural parameters in the test sample set. Key settings for each cable 3D solid model included: meshing the cable body using hexahedral elements, localizing the mesh in the twisted contact area, with a mesh size ranging from 2 to 5 mm. Specific meshing methods were described in [reference needed]. Figure 5 , Figure 5 This is a schematic diagram of the finite element mesh generation of the cable structure provided by the present invention; the cable strands adopt a universal contact mode, and the friction coefficient is set to 0.25; one end of the cable is subjected to a completely fixed constraint, and the other end is subjected to the ultimate load determined in step 420 along the axial direction. Fult The material constitutive relation adopts a linear elastic model, with Poisson's ratio set to 0.3, and the elastic modulus input according to the values ​​of each group of test schemes. After completing the finite element solution of each cable's three-dimensional solid model, the maximum equivalent stress (Mises stress) in the model is extracted as the sample equivalent stress of the corresponding group of structural parameters. The specific equivalent stress distribution pattern refers to... Figure 6 , Figure 6 This is the equivalent stress cloud diagram of the cable under ultimate tensile load provided by the present invention.

[0084] Taking a sea area with a water depth of 3000m as an example, the mean of the maximum equivalent stress of the five center point repeated test schemes is 1808.22MPa, the standard deviation is 3.48MPa, and the coefficient of variation is only 0.19%, which shows that the finite element simulation results have good repeatability and stability and meet the data quality requirements for subsequent response surface model fitting.

[0085] Step 153: Using the structural parameters of each group and the corresponding sample equivalent stress, fit and construct a quadratic polynomial regression model with equivalent stress as the dependent variable and each cable structural parameter as the independent variable.

[0086] The system utilizes 17 sets of structural parameters and corresponding sample equivalent stresses, and employs the least squares method to fit and establish the equivalent stress. s (Unit: MPa) About the encoded variable X 1 (equivalent elastic modulus) X 2 (Cable diameter) X 3. Quadratic polynomial regression model for (number of cable strands): s = β 0+ β 1 X 1+ β 2 X2+ β 3 X 3+ β 12 X 1 X 2+ β 13 X 1 X 3+ β 23 X 2 X 3+ β 11 X 1 2 + β 22 X 2 2 + β 33 X 3 2 ; in, β 0 represents the intercept term. β 1. β 2. β 3 is the coefficient of the linear term. β 12 , β 1. β 23 The coefficient of the interaction term. β 11 , β 22 , β 33 Here are the coefficients of the quadratic term. Taking an operation scenario with a water depth of 3000m in a certain sea area as an example, the fitted quadratic polynomial regression model is as follows: σ= 1808.22 - 48.36 X 1 - 742.97 X 2 + 16.69 X 3 + 5.04 X 1 X 2 - 19.62 X 1 X 3 - 1.26 X 2 X3 + 48.62 X 1 2 + 287.81 X 2 2 + 19.74 X 3 2 .

[0087] The system performed an analysis of variance on the quadratic polynomial regression model to verify its significance and goodness of fit. The results of the analysis of variance are shown in Table 5. Coefficient of determination of a quadratic polynomial regression model R 2 =0.9854, adjusted coefficient of determination Adj R 2 =0.9666, coefficient of determination for prediction Pred R 2 =0.8451, signal-to-noise ratio Adeq Precision =20.45, coefficient of variation C . V .%=5.26. The p-value of the F-test for the model is less than 0.0001, indicating that the quadratic polynomial regression model is highly significant overall; the p-value for the lack-of-fit term is 0.2270, which is greater than 0.05, indicating that the quadratic polynomial regression model does not exhibit any lack of fit. R 2 and Adj R 2 All are greater than 0.96. C . V .% less than 10, Adeq Precision The values ​​are much greater than 4, which together indicate that the quadratic polynomial regression model has high fitting accuracy and good prediction reliability.

[0088] Step 154: Perform numerical optimization based on the quadratic polynomial regression model to obtain the optimal combination of cable parameters that satisfies the preset constraints.

[0089] The system calls the Design-Expert numerical optimization solution environment. Within the target value range of each cable structure parameter, it uses a quadratic polynomial regression model as a prediction tool to perform numerical optimization according to the preset optimization objective function and constraints, and obtains the optimal combination of cable parameters that satisfies the preset constraints and minimizes the optimization objective.

[0090] In one possible implementation, this embodiment further discloses a specific method for numerical optimization based on a quadratic polynomial regression model to obtain the optimal combination of cable parameters.

[0091] Step 1541: Set the optimization objective function according to the target structural parameters, with the constraint that the predicted equivalent stress output by the quadratic polynomial regression model is less than or equal to the allowable stress.

[0092] The system selects the cable diameter from various cable structure parameters. X 2. As the target structural parameter for optimization, minimizing the actual value of the cable diameter is taken as the optimization objective function. The cable diameter is a core parameter that determines the cable cross-sectional area, self-weight, manufacturing cost, and specifications of the supporting equipment of the operating system. Minimizing the cable diameter can maximize the lightweight and economy of the cable while meeting the strength requirements.

[0093] The constraint is set as follows: the predicted equivalent stress output by the quadratic polynomial regression model for the combination of cable parameters to be evaluated. s pred Not exceeding the allowable stress s ],Right now: s pred ≤[ s ].

[0094] Allowable stress s The tensile strength of the cable material s b Divided by the preset structural safety factor k str get:[ s ]= s b / k str .

[0095] Taking aramid fiber cable as an example, the tensile strength of aramid fiber cable... s b ≈2800MPa, take the structural safety factor k str =5, then: [ s =52800=560MPa.

[0096] Structural safety factor k str Specifically designed to withstand uncertainties in strength, such as the dispersion of material strength, accumulation of fatigue damage during long-term service, and manufacturing defects, resulting in a structural safety factor. k str Environmental safety factor for amplifying environmental dynamic tension k envThey are independent of each other and do not offset each other, together forming a complete two-layer safety guarantee mechanism from extreme environmental loads to the allowable capacity of materials.

[0097] Step 1542: Solve the quadratic polynomial regression model within the target value range of each cable structure parameter to obtain the combination of independent variables that satisfies the constraints and minimizes the result of the optimization objective function. Use the combination of independent variables as the optimal combination of cable parameters.

[0098] The system has an equivalent elastic modulus X 1∈[60,200]GPa, cable diameter X 2∈[88,188]mm, number of cable strands X Within the target value range of 3∈[6,12] (taking positive integers), the numerical optimization solver is invoked to traverse and find the optimal value that satisfies the condition. s pred ≤560MPa constraint condition and make the cable diameter X 2. The combination of independent variables with the smallest actual value is determined as the optimal combination of cable parameters.

[0099] Taking a sea operation scenario with a water depth of 3000m as an example, the coded value corresponding to the optimal combination of cable parameters is obtained by solving: X 1 = -0.12 (corresponding to an equivalent elastic modulus of approximately 121.6 GPa, belonging to aramid-type materials). X 2 = -0.95 (corresponding to a cable diameter of approximately 92.5 mm). X 3 = +0.08 (corresponding to approximately 9.24 shares, rounded to 9). Substituting the optimal cable parameter combination into the quadratic polynomial regression model, the predicted equivalent stress is obtained. s pred =548.6MPa, which satisfies s pred ≤[ s The constraint condition is 560MPa.

[0100] Through the above steps, by quantitatively mapping the range ratio of the dominant sea state parameters to the target value range of the cable structure parameters, the correlation between the environmental layer analysis results and the structural layer optimization input is realized. This overcomes the fundamental defects of traditional methods, where the two links are separated and the range of structural parameters depends on subjective experience. It gives the two-layer optimization process a clear physical basis and significantly improves the objectivity and credibility of the optimization results.

[0101] In one possible implementation, this embodiment further discloses a specific method for finite element verification and closed-loop adaptive iterative correction after obtaining the optimal cable parameter combination. The system continuously corrects the prediction deviation between the quadratic polynomial regression model and the high-fidelity finite element physical model through a closed-loop iterative correction process.

[0102] Step 210: Input the optimal cable parameter combination into the preset three-dimensional solid finite element model of the cable, apply the ultimate load and perform finite element solution calculation to obtain the finite element verification equivalent stress.

[0103] The system inputs the optimal cable parameter combination (equivalent elastic modulus, cable diameter, and number of strands) obtained in step 1542 into the pre-established three-dimensional solid finite element model of the cable in the ABAQUS finite element calculation environment, and applies the ultimate load according to the mesh generation criteria, contact properties, boundary condition settings, and material constitutive relations specified in step 152. F ult The finite element method was completed, and the maximum equivalent stress in the three-dimensional solid finite element model of the cable was extracted as the finite element verification equivalent stress. s FEM .

[0104] Taking a 3000m water depth operation scenario as an example, the optimal combination of cable parameters (equivalent elastic modulus 122GPa, cable diameter 92.5mm, number of strands 9) is substituted into the three-dimensional solid finite element model of the cable. After applying an ultimate load of 12380kN, the finite element solution yields the finite element verification equivalent stress. s FEM =557.3MPa.

[0105] Step 220: Calculate the relative error between the finite element verification equivalent stress and the predicted equivalent stress output by the quadratic polynomial regression model for the optimal cable parameter combination.

[0106] The system calculates the equivalent stress of the finite element verification according to the following formula. s FEM Equivalent effect predicted by quadratic polynomial regression model s pred relative error between d : d =| s FEM - s pred | / s FEM ×100%; relative error d The quantitative measurement of the local prediction accuracy of the quadratic polynomial regression model at the optimal parameter point is a core criterion for determining whether to initiate an iterative correction process. Taking an operational scenario in a sea area with a water depth of 3000m as an example, the calculation yields: d =|557.3-548.6| / 557.3×100%=8.7 / 557.3×100%≈1.56%.

[0107] Step 230: When the relative error is greater than the preset error threshold, the optimal cable parameter combination and its corresponding finite element verification equivalent stress are added as new sample points to the original sample set to obtain the updated sample set.

[0108] The system will use relative error d Compare with a preset error threshold (set to 5%). When d When the accuracy exceeds 5%, the prediction accuracy of the quadratic polynomial regression model at the optimal parameter point does not meet the engineering accuracy requirements. The system will then use the optimal combination of cable parameters (equivalent elastic modulus, cable diameter, number of strands) and the corresponding finite element verification equivalent stress. s FEM A new sample point is created and incorporated into the original 17 sample sets to form an updated sample set containing the new high-fidelity calculation results. The new sample point is located in the region where the quadratic polynomial regression model has the lowest prediction accuracy (i.e., near the optimal parameter point). Including the new sample point in the training set can specifically correct the fitting bias of the quadratic polynomial regression model in this local region.

[0109] when d When the accuracy is ≤5%, the prediction accuracy of the quadratic polynomial regression model meets the engineering accuracy requirements. The system directly outputs the current optimal cable parameter combination as the final design result, without needing to initiate an iterative correction process. Taking an operation scenario with a water depth of 3000m in a certain sea area as an example, the initial verification yielded... d =1.56%<5%, which meets the convergence condition. There is no need to enter the iterative correction process. The optimal cable parameter combination (equivalent elastic modulus 122GPa, nominal diameter 93mm, 9-strand structure) is directly output as the final design.

[0110] Step 240: Reconstruct the parameter prediction model based on the updated sample set, and re-solve the structural optimization problem in the reconstructed parameter prediction model based on the ultimate load until the relative error obtained by the re-solved problem is less than or equal to the preset error threshold.

[0111] Based on the updated sample set, the system reconstructs the parameter prediction model using the same quadratic polynomial least squares fitting method as in step 153. The reconstructed parameter prediction model incorporates high-fidelity data points provided by the finite element verification, resulting in a targeted improvement in fitting accuracy in the region near the optimal parameter points. The system then re-executes steps 1541, 1542, 210, and 220 based on the reconstructed parameter prediction model to obtain a new round of optimal cable parameter combinations and their corresponding relative errors. This process is repeated until the relative error is less than or equal to a preset error threshold of 5%, at which point the final optimal cable parameter combination that meets the accuracy requirements is output.

[0112] Furthermore, this embodiment further discloses a specific method for monitoring the number of iterations and the rate of change of relative error during the closed-loop iteration process and forcibly outputting the results based on the termination condition.

[0113] Step 310: Monitor the number of iterations and the rate of change of the relative error between two consecutive iterations.

[0114] After each round of step 240 is completed, the system automatically records the total number of iterations performed and calculates the relative error of the current round. d n Relative error compared to the previous round d n-1 rate of change between or : or =| d n-1 - d n | / d n-1 ×100%; Relative error rate of change or This reflects the improvement in the prediction accuracy of the quadratic polynomial regression model over two consecutive iterations. or If the deviation remains small, it indicates that the marginal contribution of continued iteration to improving prediction accuracy is approaching saturation, and further iteration can no longer significantly improve the convergence state.

[0115] Step 320: When the number of iterations reaches the preset maximum number of iterations, or when the relative error change rate of two consecutive iterations is less than the preset change rate threshold and the current relative error is still greater than the preset error threshold, stop the iteration calculation and output the optimal cable parameter combination obtained from the last iteration calculation.

[0116] The system is set to a maximum of 3 iterations, a preset error threshold of 5%, and a preset rate of change threshold of 10%. The system will forcibly terminate the iterative calculation and output the results when any of the following termination conditions are met: Termination condition 1: The number of iterations reaches the preset maximum number of iterations, 3, regardless of the current relative error. d If the preset error threshold is not met, the process will be forcibly terminated, and the optimal combination of cable parameters obtained from the third iteration will be output.

[0117] Termination condition two: If the number of iterations has not reached 3, the relative error change rate between two consecutive iterations. or <10% and current relative error dIf the error is greater than 5%, it indicates that continued iteration can no longer effectively improve the accuracy of the surrogate model. The system forcibly terminates the iterative calculation, outputs the optimal cable parameter combination obtained from the last iteration, and issues an error warning to the engineering designers, indicating that the relative error of the current output result has not reached the preset accuracy requirement of 5%, prompting the engineering designers to pay attention to it when making engineering decisions.

[0118] The aforementioned dual termination conditions together constitute the safety boundary of the iterative process, ensuring that the iterative computation terminates within limited computing resources and avoiding the risk of exhausting computing resources due to infinite iteration.

[0119] Reference Figure 7 , Figure 7 This is a schematic diagram of the dual-layer optimization system for mineral hoisting cable parameters under complex sea conditions provided by the present invention. The system includes: The data acquisition module is used to acquire cable tension data of the cable hoisting system under various preset sea state parameter combinations; The data analysis module is used to determine the target sea state parameter combination that maximizes the cable tension, the ultimate load of the cable under the target sea state parameter combination, and the range of the influence of each target sea state parameter on the cable tension based on the cable tension data. The parameter filtering module is used to determine at least one dominant sea state parameter from the combination of target sea state parameters based on the range of each target sea state parameter. The data processing module is used to determine the cable structure parameters associated with any dominant sea state parameter, and to scale the baseline values ​​of the cable structure parameters according to the range ratio of the dominant sea state parameter to obtain the target value range of the cable structure parameters; the range ratio is the ratio of the range of the dominant sea state parameter to the sum of the ranges of all dominant sea state parameters. The optimization solution module is used to select target structural parameters from various cable structure parameters as optimization targets. Within the target value range of each cable structure parameter, it performs structural optimization solution based on the ultimate load to obtain the optimal combination of cable parameters that meets the preset constraints.

[0120] It should be noted that the mineral hoisting cable parameter dual-layer optimization system under complex sea conditions provided by the present invention can execute the mineral hoisting cable parameter dual-layer optimization method under complex sea conditions of any of the above embodiments during specific operation, which will not be elaborated in this embodiment.

[0121] Figure 8 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 8As shown, the electronic device may include a processor 810, a communication interface 820, a memory 830, and a communication bus 840. The processor 810, communication interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions from the memory 830 to execute the dual-layer optimization method for mineral hoisting cable parameters under complex sea conditions provided in the above embodiments.

[0122] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part 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 of the various embodiments of the present invention. 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.

[0123] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by the computer, the computer is able to execute the two-layer optimization method for mineral lifting cable parameters under complex sea conditions provided in the above embodiments.

[0124] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the two-layer optimization method for mineral lifting cable parameters under complex sea conditions provided in the above embodiments.

[0125] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0126] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A two-layer optimization method for mineral hoisting cable parameters under complex sea conditions, characterized in that, include: Acquire cable tension data of the cable hoisting system under various preset sea state parameter combinations; Based on the cable tension data, determine the target sea state parameter combination that maximizes the cable tension, the ultimate load of the cable under the target sea state parameter combination, and the range of the influence of each target sea state parameter on the cable tension in the target sea state parameter combination. Based on the magnitude of the range of each of the target sea state parameters, at least one dominant sea state parameter is determined from the combination of the target sea state parameters; For any of the dominant sea state parameters, determine the cable structure parameters associated with the dominant sea state parameters, and scale the reference values ​​of the cable structure parameters according to the range ratio of the dominant sea state parameters to obtain the target value range of the cable structure parameters. The range ratio is the ratio of the range of the dominant sea state parameter to the sum of the ranges of all dominant sea state parameters; From the cable structure parameters, a target structural parameter is selected as the optimization target. Within the target value range of each cable structure parameter, structural optimization is performed based on the ultimate load to obtain the optimal combination of cable parameters that satisfies the preset constraints.

2. The method according to claim 1, characterized in that, The dominant sea state parameters include wave height, current velocity, and wind speed; For any of the dominant sea state parameters, determining the cable structure parameters associated with the dominant sea state parameter includes: The equivalent elastic modulus of the cable is determined as a cable structural parameter associated with the wave height. The diameter of the cable is determined as a cable structure parameter associated with the flow velocity; The number of strands in the cable is determined as a cable structure parameter associated with the wind speed.

3. The method according to claim 2, characterized in that, The step of scaling the baseline value of the cable structure parameter according to the range ratio to obtain the target value range of the cable structure parameter includes: The target value range for each of the cable structure parameters is calculated using the following formula: ； ； ； in, This is the lower limit boundary of the equivalent elastic modulus of the cable. This represents the upper limit boundary of the equivalent elastic modulus of the cable. This is the reference value for the equivalent elastic modulus. The range ratio of the wave height; This is the lower limit boundary of the diameter of the cable. This is the upper limit boundary of the diameter of the cable. This is the reference value for the diameter. This represents the range ratio of the flow rates; This represents the lower limit boundary of the number of strands in the cable. This represents the upper limit boundary of the number of strands in the cable. This is the baseline value for the number of shares. This represents the range ratio of the wind speeds. round This is the integer division operator.

4. The method according to claim 1, characterized in that, Determining the ultimate load of the cable under the target sea state parameter combination includes: Extract the maximum effective tension during the simulation of the target sea state parameter combination; The ultimate load is obtained by multiplying the maximum effective tension by a preset environmental safety factor.

5. The method according to claim 1, characterized in that, Within the target value range of each of the cable structure parameters, structural optimization is performed based on the ultimate load to obtain the optimal combination of cable parameters that satisfies preset constraints, including: Within the target value range of each of the cable structure parameters, a test sample set containing multiple sets of structure parameters is generated; For each set of structural parameters in the test sample set, a cable solid model corresponding to the structural parameter is established, and the maximum equivalent stress of the cable solid model under the ultimate load is taken as the sample equivalent stress corresponding to the set of structural parameters. Using the structural parameters of each group and the corresponding sample equivalent stress, a quadratic polynomial regression model with equivalent stress as the dependent variable and each of the cable structural parameters as the independent variable is fitted and constructed. Numerical optimization is performed based on the quadratic polynomial regression model to obtain the optimal combination of cable parameters that satisfies the preset constraints.

6. The method according to claim 5, characterized in that, The numerical optimization solution based on the quadratic polynomial regression model, which yields the optimal combination of cable parameters that satisfies the preset constraints, includes: The objective function is set according to the target structural parameters, with the constraint that the predicted equivalent stress output by the quadratic polynomial regression model is less than or equal to the allowable stress. Solve the quadratic polynomial regression model within the target value range of each of the cable structure parameters to obtain the combination of independent variables that satisfies the constraints and minimizes the result of the optimization objective function. Use the combination of independent variables as the optimal combination of cable parameters.

7. The method according to claim 5, characterized in that, After obtaining the optimal combination of cable parameters that satisfies the preset constraints, the following steps are also included: The optimal cable parameter combination is input into a preset three-dimensional solid finite element model of the cable, and the ultimate load is applied to perform finite element solution calculation to obtain the finite element verification equivalent stress. Calculate the relative error between the finite element verification equivalent stress and the predicted equivalent stress output by the quadratic polynomial regression model for the optimal cable parameter combination; When the relative error is greater than the preset error threshold, the optimal cable parameter combination and its corresponding finite element verification equivalent stress are added as new sample points to the original sample set to obtain an updated sample set. Based on the updated sample set, the parameter prediction model is reconstructed, and the structural optimization solution is re-solved in the reconstructed parameter prediction model based on the ultimate load until the relative error obtained by the re-solution is less than or equal to the preset error threshold.

8. The method according to claim 7, characterized in that, The process of resolving the structural optimization problem based on the ultimate load in the reconstructed parametric prediction model also includes: Monitor the number of iterations and the rate of change of the relative error between two consecutive iterations; When the number of iterations reaches the preset maximum number of iterations, or when the rate of change of the relative error between two consecutive iterations is less than a preset rate of change threshold and the current relative error is still greater than the preset error threshold, the iteration calculation is stopped and the optimal cable parameter combination obtained from the last iteration calculation is output.

9. The method according to claim 1, characterized in that, The acquisition of cable tension data for the cable lifting system under various preset sea state parameter combinations includes: Extract multiple preset environmental factors and multiple numerical levels corresponding to each environmental factor; The multiple environmental factors and their corresponding numerical levels are cross-combined using orthogonal experimental design to generate the various preset sea state parameter combinations; The preset sea state parameters are sequentially input into a pre-established system dynamics simulation model containing the mining vessel, cable, and ore collection bag for simulation and solution. The maximum effective tension value of the cable within the preset simulation period is extracted as the cable tension data.

10. A dual-layer optimization system for mineral hoisting cable parameters under complex sea conditions, characterized in that, include: The data acquisition module is used to acquire cable tension data of the cable hoisting system under various preset sea state parameter combinations; The data analysis module is used to determine, based on the cable tension data, the target sea state parameter combination that maximizes the cable tension, the ultimate load of the cable under the target sea state parameter combination, and the range of the influence of each target sea state parameter on the cable tension. The parameter filtering module is used to determine at least one dominant sea state parameter from the combination of target sea state parameters based on the magnitude of the range of each target sea state parameter. The data processing module is used to determine the cable structure parameters associated with any of the dominant sea state parameters, and to scale the reference values ​​of the cable structure parameters according to the range ratio of the dominant sea state parameters to obtain the target value range of the cable structure parameters. The range ratio is the ratio of the range of the dominant sea state parameter to the sum of the ranges of all dominant sea state parameters; The optimization solution module is used to select target structural parameters from the cable structure parameters as optimization targets, and perform structural optimization solution based on the ultimate load within the target value range of each cable structure parameter to obtain the optimal combination of cable parameters that meets the preset constraints.

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