A deep-sea mining equipment design method based on light weight and dynamic load

Abstract

This invention relates to the field of deep-sea mining equipment technology, and proposes a design method for deep-sea mining equipment based on lightweighting and dynamic load, thereby improving the product quality and reliability of deep-sea mining equipment. The method includes the following steps: S100: Obtaining deep-sea mining operation parameters; S200: Classifying lightweighting levels and dynamic load types; S300: Integrating lightweighting levels, dynamic load parameters, and deep-sea mining operation parameters to establish an initial parameter input matrix for the mining equipment; S400: Constructing a mining capacity assessment model based on the initial parameter input matrix; S500: Constructing a mining equipment reliability assessment model based on the initial parameter input matrix; S600: Dynamically selecting an optimization mode, integrating the structural characteristics of the mining equipment and system parameters, and performing multi-objective collaborative optimization design; S700: Updating the optimized parameter combination to the matrix, and verifying the capacity and reliability of the mining equipment after multi-objective collaborative optimization; S800: Evaluating, optimizing, and verifying the results, and establishing an optimal output matrix.

Description

Technical Field

[0001] This invention relates to the field of deep-sea mining equipment technology, specifically to a design method for deep-sea mining equipment based on lightweight and dynamic load. Background Technology

[0002] The deep sea is rich in mineral resources, including polymetallic nodules, cobalt-rich crusts, and polymetallic sulfides. Against the backdrop of increasingly scarce terrestrial resources and surging demand from emerging industries, it has become a new focal point of global resource strategic competition. The advancement of deep-sea mining technology and equipment is crucial for the commercial exploitation of these resources. However, existing technologies face bottlenecks in areas such as lightweight equipment performance and environmental adaptability under dynamic loads, necessitating innovative design methods to improve the high productivity and reliability of deep-sea mining equipment.

[0003] Current research primarily focuses on deep-sea mining equipment such as mining vehicles, hoisting equipment, and mining vessels. While advancements are made in technologies related to data collection, transportation, and support platforms, they still fall short of meeting the high-capacity and high-reliability operational requirements of mining equipment under conditions of high lightweight design and extreme deep-sea environments. Currently, there are certain gaps and shortcomings in the design methods for high-capacity and high-reliability deep-sea mining equipment, mainly including the following points:

[0004] 1. Disconnect between lightweight design and dynamic load adaptability: Traditional lightweight design does not fully consider the impact of dynamic load on equipment performance, resulting in reduced structural strength and reliability; while dynamic load analysis does not take into account lightweight objectives, which increases energy consumption and cost due to redundant equipment design.

[0005] 2. The optimization methods are singular and lack synergy: Existing optimization methods mostly use a single algorithm, relying only on topology optimization or genetic algorithms, which cannot simultaneously meet the multi-objective optimization needs of production capacity and reliability, and have not made algorithm improvements for the special working conditions of deep-sea mining equipment;

[0006] 3. Incomplete verification and output system: Traditional design lacks a systematic verification process and does not closely link the results of capacity and reliability verification with design parameters. The output parameters are difficult to guide actual engineering applications. Summary of the Invention

[0007] One of the objectives of this invention is to propose a design method for deep-sea mining equipment based on lightweight and dynamic load, thereby improving the product quality and reliability of deep-sea mining equipment.

[0008] The technical solution of the present invention is as follows:

[0009] A design method for deep-sea mining equipment based on lightweight design and dynamic load, comprising the following steps:

[0010] S100: Obtain parameters for deep-sea mining operations;

[0011] S200: Classifies lightweight levels and dynamic load types;

[0012] S300: Overall Lightweight Rating Dynamic load parameters Based on deep-sea mining operation parameters, establish the initial parameter input matrix for mining equipment: ,in H For the target operating water depth, T For the target operation cycle, T t For the target equipment self-sufficiency time, Q t For target mining volume, K w The target unit time weight-to-productivity ratio;

[0013] S400: Input matrix based on the initial parameters Construct a mining capacity assessment model;

[0014] S500: Input matrix based on the initial parameters Construct a reliability assessment model for mining equipment;

[0015] S600: Based on the capacity and reliability determination results of steps S400 and S500, the optimization mode is dynamically selected. Taking into account the structural characteristics of mining equipment and system parameters, topology optimization and multi-objective genetic algorithm are used to carry out multi-objective collaborative optimization design.

[0016] S700: The optimized version Parameter combination updated to The matrix was used to verify the capacity and reliability of the mining equipment after multi-objective collaborative optimization.

[0017] S800: Based on the results of capacity and reliability assessment, optimization, and verification, an optimal output matrix for high capacity and high reliability of deep-sea mining equipment based on lightweight design and dynamic load is established.

[0018] ,in L i * For optimal lightweight level, F j * For optimal dynamic load parameters, H * For optimal operating water depth, T * For optimal work cycle, T t * For optimal equipment self-sufficiency time, Q t* For optimal mining quantity, K w * This represents the optimal weight-to-production ratio per unit time.

[0019] Furthermore, step S200 includes:

[0020] S210: Based on the strength and weight reduction targets of deep-sea mining equipment, different lightweighting levels are classified. The lightweight level includes:

[0021] Specific strength ≥320MPa·cm³ / g, target weight loss ≥60%;

[0022] Specific strength is 260~320MPa·cm³ / g, and the target weight reduction is 30%~60%;

[0023] Specific strength is 200~260 MPa·cm³ / g, and the target weight reduction is 15%~30%;

[0024] Specific strength <200MPa·cm³ / g, target weight loss ≤15%;

[0025] S220: Identify the dynamic load types of deep-sea mining equipment under typical operating conditions The equipment's operational mechanism and parameter range are further refined, and the dynamic load types include:

[0026] Operating depth water pressure load: static pressure value range of 40~60MPa at a water depth of 4000~6000m;

[0027] Underwater inlet resistance load, travel speed 0.5~1.1m / s, load amplitude ≤180kN;

[0028] : Earth resistance load in rugged terrain, load amplitude ≤200kN;

[0029] Submarine impact-subsidence-slippage composite load, load amplitude ≤400kN;

[0030] : Reaction force of jet disturbance flow field in polymetallic nodule mining vehicle, load amplitude ≤130kN, jet velocity ≤12m / s;

[0031] The cutting impact load of the cobalt-rich crust mining vehicle is ≤280kN.

[0032] : Impact load of polymetallic sulfide mining vehicle crushing, load amplitude ≤250kN;

[0033] Umbilical cable tension-torsion mixed load, load amplitude ≤400kN, torsional angular velocity 0~6° / s, vibration frequency <0.2Hz;

[0034] The hull-lifting system coupled disturbance load has a load amplitude of ≤100kN and a fluctuation frequency of ≤0.3Hz.

[0035] Furthermore, the mining capacity in step S400 is determined by the extraction efficiency. The equipment's propulsion speed v and conveying efficiency η are jointly determined, and the specific capacity model relationship is as follows:

[0036] The capacity model relationship is as follows:

[0037] ,in Lightweight level With dynamic load type The coupling parameter pair;

[0038] The impact of lightweighting of mining equipment on mining capacity is as follows:

[0039] When lightweight level At that time, the collection efficiency Increase by 8%~12%, equipment propulsion speed v increases by 20%~30%, conveying efficiency η increases by 6%~10%, and overall production capacity Q increases by ≥30%;

[0040] When lightweight level At that time, the collection efficiency Increase by 5%~8%, equipment propulsion speed v increases by 10%~20%, conveying efficiency η increases by 4%~7%, and overall production capacity Q increases by 15%~30%;

[0041] When lightweight level At that time, the collection efficiency Increase by 2%~5%, equipment propulsion speed v increases by 5%~10%, conveying efficiency η increases by 2%~4%, and overall production capacity Q increases by 5%~15%;

[0042] When lightweight level At that time, the collection efficiency The improvement rate is ≤2%, the improvement rate of equipment propulsion speed v is ≤5%, the improvement rate of conveying efficiency η is ≤2%, and the improvement rate of overall production capacity Q is ≤5%;

[0043] The impact of dynamic load type on mining equipment on mining capacity is as follows:

[0044] When dynamic load type At that time, the collection efficiency The decrease is ≤2%, the decrease in propulsion speed v and conveying efficiency η is ≤2%, and the decrease in overall production capacity Q is ≤5%;

[0045] When dynamic load type At that time, the collection efficiency If the decrease is ≤5%, the propulsion speed v decreases by 5%~10%, the conveying efficiency η decreases by ≤2%, and the overall production capacity Q decreases by 5%~12%;

[0046] When dynamic load type At that time, the collection efficiency A decrease of 5%~8%, a decrease in propulsion speed v of 10%~20%, a decrease in conveying efficiency η of ≤5%, and an overall decrease in production capacity Q of 15%~25%;

[0047] When dynamic load type At that time, the collection efficiency The overall production capacity (Q) decreased by 20% to 35%, with a decrease of 8% to 12%, a decrease of 15% to 25%, a decrease of 5% to 10%, and a decrease of 8% to 12%.

[0048] When dynamic load type At that time, the collection efficiency The overall production capacity Q will decrease by 10% to 15%, with a decrease of 5% to 10% in the propulsion speed v and a decrease of ≤5% in the conveying efficiency η and a decrease of ≤2% in the conveying efficiency.

[0049] Dynamic load type At that time, the collection efficiency The overall production capacity Q decreased by 15% to 20%, with a decrease of 6% to 9%, a decrease of 10% to 15%, a decrease of ≤2% in conveying efficiency η, and a decrease of 6% to 9% in conveying speed v.

[0050] When dynamic load type At that time, the collection efficiency The overall production capacity Q decreased by 15% to 20%, with a decrease of 5% to 8%, a decrease of 10% to 15%, a decrease of ≤2% in conveying efficiency η, and a decrease of 15% to 20%.

[0051] When dynamic load type At that time, the collection efficiency The decrease is ≤2%, the decrease in propulsion speed v is ≤5%, the decrease in conveying efficiency η is 3%~7%, and the decrease in overall production capacity Q is 5%~12%;

[0052] When dynamic load type At that time, the collection efficiency Both the conveying efficiency η and the advancing speed v decrease by 3% - 6%, and the overall production capacity Q decreases by 8% - 15%.

[0053] The comprehensive discrimination system for mining production capacity is as follows:

[0054] When the mining production capacity Q ≥ 120 t / h and the weight - production - capacity ratio K ≤ 0.18, it is determined that the current mining equipment has high - production - capacity support ability, and the existing combination of lightweight level Li and dynamic load type Fj should be maintained, and directly enter the reliability assessment stage;

[0055] When 90 ≤ Q < 120 t / h and 0.18 < K ≤ 0.30 for the mining production capacity, it is determined that there is a bottleneck in the mining efficiency of the current mining equipment, and enter the multi - objective collaborative optimization stage;

[0056] When the mining production capacity Q < 90 t / h or the weight - production - capacity ratio K > 0.30, it is determined that the production capacity of the current mining equipment is limited and does not meet the target operation requirements, and step S2 should be returned to reset the lightweight level and the dynamic load type range, and reconstruct the initial parameter input matrix .

[0057] Furthermore, the reliability of the mining equipment in step S500 is jointly determined by the structural strength margin index , the fatigue life factor and the operation stability level . The relationship of the reliability assessment model is:

[0058] , where refers to the coupling parameter pair of the lightweight level and the dynamic load type ;

[0059] The influence of the lightweight level of the mining equipment on mining reliability is as follows:

[0060] When the lightweight level , the structural strength margin decreases by 15% - 25%, the fatigue life factor decreases by 20% - 30%, the operation stability decreases by 5% - 10%, and the comprehensive reliability R decreases by 20% - 35%;

[0061] When the lightweight level , the structural strength margin decreases by 5% - 10%, the fatigue life factor decreases by 8% - 15%, the operation stability decreases by 3% - 6%, and the comprehensive reliability Decrease of 10% to 20%;

[0062] When lightweight level At that time, structural strength margin A decrease of 2% to 5% in fatigue life factor Decrease of 5%~8%, operational stability A decrease of 2% to 4%, and a decrease in overall reliability R of 5% to 10%;

[0063] When lightweight level At that time, structural strength margin The decrease is ≤2%, fatigue life factor Decrease rate ≤3%, operational stability The decrease in reliability R is ≤2%, and the decrease in overall reliability R is ≤5%.

[0064] The impact of dynamic load type on mining equipment on mining reliability is as follows:

[0065] When dynamic load type At that time, structural strength margin The decrease is ≤5%, fatigue life factor Decrease rate ≤5%, operational stability The decrease in reliability R is ≤3%, and the overall reliability R decrease is ≤8%;

[0066] When the dynamic load type Fj=F2, structural strength margin The decrease is ≤8%, fatigue life factor Decrease of 8%~12%, operational stability If the decrease is ≤5%, the overall reliability R decreases by 8%~15%;

[0067] When dynamic load type At that time, structural strength margin A decrease of 10% to 15% in fatigue life factor Decrease of 15%~20%, operational stability Decreases by 8% to 12%, and overall reliability (R) decreases by 15% to 22%.

[0068] When dynamic load type At that time, structural strength margin A 15% to 25% decrease in fatigue life factor Decrease of 20%~30%, operational stability A decrease of 10%~15%, and a decrease in overall reliability R of 20%~30%;

[0069] When dynamic load type At that time, structural strength margin A decrease of 5% to 8% in fatigue life factor Decrease of 8%~12%, operational stability If the decrease is ≤5%, the overall reliability R decreases by 8%~15%;

[0070] When dynamic load type At that time, structural strength margin A decrease of 8% to 12% in fatigue life factor Decrease of 12%~18%, operational stability A decrease of 5% to 8%, and a decrease in overall reliability R of 12% to 20%;

[0071] Dynamic load type At that time, structural strength margin A decrease of 10% to 14% in fatigue life factor Decrease of 12%~18%, operational stability A decrease of 5% to 8%, and a decrease in overall reliability R of 12% to 20%;

[0072] Dynamic load type At that time, structural strength margin A decrease of 5% to 8% in fatigue life factor Decrease of 8%~12%, operational stability A decrease of 8% to 12%, and an overall reliability (R) decrease of 8% to 15%;

[0073] When dynamic load type At that time, structural strength margin A decrease of 8% to 12% in fatigue life factor Decrease of 8%~12%, operational stability A decrease of 5% to 10%, and a decrease in overall reliability R of 8% to 15%;

[0074] The comprehensive assessment system for mining reliability is as follows:

[0075] When the reliability index R ≥ 0.85 and the equipment self-sufficiency time At that time, it was determined that the current mining equipment possessed a high level of reliability and that the existing lightweight design should be maintained. and dynamic load type The combination allows for direct entry into a high-capacity, high-reliability parameter output stage.

[0076] When the reliability index is 0.70 ≤ R < 0.85 and the equipment self-sufficiency time When the time is T~1.5T, the reliability of the current mining equipment is judged to be at a medium level, and it should enter the multi-objective collaborative optimization stage;

[0077] When the reliability index R < 0.70 or the equipment endurance time If it is determined that the current mining equipment has a major reliability defect and does not meet the target operation requirements, the process should return to step S200 and reset the lightweight level. With dynamic load type Range, reconstruct the initial parameter input matrix And reassess.

[0078] Furthermore, the optimization mode in step S600 is as follows:

[0079] Mode A: Capacity optimization, triggered by the following conditions in step S400: 90 ≤ Q < 120 and 0.18 <K≤0.30;

[0080] Mode B: Reliability optimization, triggered by the condition in step S500: 0.70 ≤ R < 0.85 and ;

[0081] Mode C: Composite optimization, enabled when both Mode A and Mode B are triggered simultaneously;

[0082] The specific steps for optimizing the mode include:

[0083] S610: Select parameters from modes A, B, and C that are highly sensitive to mining capacity Q and structural reliability R as design variables to form a design variable set:

[0084] ;

[0085] Among them, each variable covers the lightweight level Directly related structural cross-sections, material density, and material layout proportions; and dynamic load types. Related structural topology connection forms and reinforcement of boundary area layout;

[0086] S620: Employs a differentiated topology optimization strategy to solve for the optimal configuration at the structural level, specifically including:

[0087] S621: Input matrix according to parameters Based on the geometry and performance parameters of the mining equipment and the design variable set X, a finite element model is constructed, targeting the lightweight level. Set design space volume constraints :

[0088] When lightweight level At that time, design space volume constraints (Original volume of the structure);

[0089] When lightweight level At that time, design space volume constraints ;

[0090] When lightweight level At that time, design space volume constraints ;

[0091] When lightweight level At that time, design space volume constraints ;

[0092] S622: For dynamic load types Set boundary conditions:

[0093] When dynamic load type for When static load boundary conditions are applied;

[0094] When dynamic load type for At that time, frequency domain excitation load boundary conditions are used;

[0095] When dynamic load type for At that time, transient dynamic boundary conditions are used;

[0096] S623: Set the objective function for different optimization modes:

[0097] Mode A: The objective function is to maximize the disturbance resistance stiffness and the equipment propulsion speed v;

[0098] Mode B: The objective function is to maximize the structural strength margin. With fatigue life factor ;

[0099] Mode C: The objective function is a weighted optimization of static stiffness, frequency stiffness, and impact stiffness;

[0100] S624: Introduces the Continuous Density Method (SIMP) to parametrically control the density of structural elements, achieving continuous discretization of material distribution in lightweight structural elements, targeting lightweight grades. Minimum density threshold for different materials for:

[0101] When lightweight level hour, ;

[0102] When lightweight level hour, ;

[0103] When lightweight level hour, ;

[0104] When lightweight level hour, ;

[0105] S625: Output topology-optimized structure, wherein the topology-optimized structure is an initial lightweight structure with high stiffness and low mass bit characteristics;

[0106] S630: A multi-objective genetic algorithm is used to collaboratively optimize the topology structure, specifically including:

[0107] S631: Segment the structural configuration, module layout, and control parameters, embed optimization mode labels (A / B / C), and construct a multi-source parameter composite coding structure;

[0108] S632: The initial population is matched to the high-productivity, high-reliability regions (Q≥120t / h and R≥0.85) in steps S400 and S500. Combining the results of topology optimization in step S620 with the core of the solution, we can form an initial solution with structure-function synergy.

[0109] S633: A multi-objective fitness function set is adopted, and the solution set is filtered through Pareto sorting and crowding mechanism. The multi-objective fitness function and filtering mechanism adapted to different optimization modes are as follows:

[0110] Mode A: The fitness function is F=0.8Q+0.2R, with constraints R≥0.85 and K≤0.30. Pareto sort selects the solution with the higher Q value, and the crowding calculation adds a penalty term for K.

[0111] Mode B: Fitness function is F = 0.2Q + 0.8R, constraint is Q ≥ 120. ,choose and An equilibrium solution eliminates vibration-sensitive configurations;

[0112] Mode C: The fitness function is F=0.5Q+0.5R, with constraints of Q≥120 and R≥0.85. The elite retention strategy is triggered when the Pareto front coverage is >90%.

[0113] S634: According to Combined adaptive adjustment of genetic parameters, including mutation rate, crossover method and population size;

[0114] (High-frequency shock): Mutation rate is 0.25~0.30, crossover method is three-point crossover, and population size is 100~120;

[0115] (Transient shock): Mutation rate 0.20~0.25, crossover method is arithmetic crossover, population size is 80~100;

[0116] (Steady-state load): Mutation rate 0.10~0.15, crossover method uniform crossover, population size 60~80;

[0117] (Composite load): Mutation rate 0.15~0.20, crossover method is simulated binary crossover, population size is 70~90;

[0118] S635: Set the convergence criterion as the objective function variance < ε and the Pareto boundary stability, ensuring that the optimization process focuses on... The feasible solution set for the scenario;

[0119] S636: Output Pareto solution set Each solution set comes with a corresponding... and The identifier is used for subsequent solution decisions and operational condition adaptability verification.

[0120] Furthermore, the mining equipment capacity verification in step S700 includes:

[0121] S710: Substitute the Pareto optimal solution set from S636 into the mining capacity assessment model constructed in step S400, and calculate the corresponding... and Mining capacity Q:

[0122] If the optimized solution satisfies Q≥120t / h and K≤0.18, then the optimized solution is in the corresponding... and It possesses both high production capacity and low energy consumption, and its production capacity has been verified.

[0123] If a solution does not meet this condition, it is necessary to return to step S6, adjust the range of design variables and optimize the algorithm parameters, and then perform collaborative optimization again.

[0124] Reliability verification includes:

[0125] S720: Substitute the Pareto optimal solution set from S6 into the mining equipment reliability assessment model constructed in step S500 to calculate the reliability index R:

[0126] If all solutions satisfy R≥0.85 and This indicates that the designed structure is currently... and It possesses high reliability and self-sustaining time, and reliability verification has been passed.

[0127] If a solution does not meet this condition, the relevant design variables and structural configuration parameters are adjusted accordingly, and the process is returned to step S600 for optimization.

[0128] Furthermore, in step S100, when acquiring operational parameters, the operational water depth H and water pressure change data are monitored in real time through a deep-sea sensor network. The sensor network includes pressure sensors and depth sensors, and the sensor accuracy error range is controlled within ±0.5%.

[0129] Furthermore, in step S210, when classifying lightweight levels, the lightweight level is... The weight reduction target is achieved by using a gradient composite process of carbon fiber reinforced composite materials and titanium alloy, and the strength of the composite structure is ensured by electron beam welding technology.

[0130] Furthermore, in step S210, the dynamic load type is identified. At that time, multiphysics coupling simulation software was used to simulate composite loads. By setting fluid-solid-thermal coupling boundary conditions, the resultant force range and the proportion of each load were accurately calculated.

[0131] Furthermore, the extraction efficiency in the mining capacity assessment model of step S400 The equipment propulsion speed v and conveying efficiency η are dynamically predicted using a deep learning model. This deep learning model is trained based on historical operation data, and the input is the lightweight level. Dynamic load type Including real-time environmental parameters, outputting the predicted values ​​of the corresponding production capacity impact parameters;

[0132] In step S500, the reliability assessment model for mining equipment incorporates acoustic emission monitoring technology to acquire real-time data on structural fatigue crack propagation, which is used to correct the fatigue life factor. The computational model;

[0133] In the topology optimization method of step S620, an adaptive mesh generation technique is used to automatically refine the mesh in the stress concentration region, thereby improving the calculation accuracy of the finite element model. The refinement ratio is not less than 1 / 4 of the basic mesh.

[0134] In the multi-objective genetic algorithm of step S630, a dynamic load weight adjustment module is set to dynamically adjust the weight coefficients of the capacity and reliability objectives in the Pareto sorting, with a weight range of 0.3-0.7.

[0135] In step S700, accelerated life testing is used to obtain life data of key components of mining equipment in a short time by increasing the test stress level. Combined with Chaboche's nonlinear damage accumulation theory, the reliability of the equipment within the design life cycle is verified.

[0136] Step S800 Output Matrix In this study, confidence intervals were set for each parameter, and the impact of parameter fluctuations on production capacity and reliability was calculated using the Monte Carlo simulation method.

[0137] The beneficial effects of this invention are as follows:

[0138] 1. This invention establishes a design method for deep-sea mining equipment that couples multiple factors such as lightweight level and dynamic load type, achieving a deep integration of lightweighting and dynamic load adaptability. In the lightweighting design process, the impact of dynamic load on equipment performance is fully considered. At the same time, the lightweighting target is combined in the dynamic load analysis, breaking through the limitations of traditional single-factor design. It effectively solves the main contradiction between equipment weight reduction and disturbance resistance in the extreme environment of deep sea, avoids redundant design and reduces energy consumption and cost, and improves the overall performance of deep-sea mining equipment.

[0139] 2. This invention constructs a mining capacity and reliability assessment model, quantifying the impact of various factors on capacity and reliability based on an initial parameter input matrix. By establishing a clear comprehensive judgment system for capacity and reliability, it accurately assesses the capacity and reliability performance of equipment under different parameter combinations, providing a more scientific and comprehensive assessment basis for the design of deep-sea mining equipment, effectively guiding the adjustment of high-capacity and high-reliability design directions, and avoiding design deviations and resource waste caused by inaccurate parameter assessment.

[0140] 3. This invention employs a collaborative optimization design method based on topology optimization and multi-objective genetic algorithms, taking into account lightweight level and dynamic load type. It dynamically selects the optimization mode according to the capacity and reliability determination results, customizes topology optimization strategies for different lightweight levels and dynamic load types, and utilizes key technologies such as the continuous density method to achieve high-precision structural optimization. Combined with the segmented encoding, parameter adjustment, and solution set screening of the multi-objective genetic algorithm, it quickly obtains the optimal design scheme with high capacity and high reliability. Compared to traditional single optimization algorithms, this invention significantly improves optimization efficiency and quality, effectively meeting the multi-objective optimization needs of deep-sea mining equipment.

[0141] 4. This invention integrates multiple key technologies, including high-precision sensor networks, adaptive grid optimization, and dynamic algorithm strategies, to achieve rapid design and output in complex deep-sea environments to guide actual deep-sea mining engineering applications. This significantly shortens the R&D cycle of deep-sea mining equipment and promotes the coordinated iterative upgrade of deep-sea mining equipment towards high lightweight, high productivity, and high reliability. Attached Figure Description

[0142] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0143] Figure 1 This is a flowchart of the design method for high-yield and high-reliability deep-sea mining equipment according to the present invention.

[0144] Figure 2 This is a schematic diagram of the dynamic load type of the deep-sea mining equipment of the present invention. Detailed Implementation

[0145] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0146] like Figure 1-2 As shown, this embodiment provides a design method for high-capacity and high-reliability deep-sea mining equipment based on lightweight design and dynamic load, including the following steps:

[0147] S100: Obtain parameters for deep-sea mining operations;

[0148] The acquired deep-sea mining operation parameters include the operating water depth H (m), operation cycle T (h), equipment mass M (t), and target equipment self-sufficiency time. Equipment load amplitude F (kN), target mining volume Target unit time weight-to-productivity ratio .

[0149] S200: Classifies lightweight levels and dynamic load types;

[0150] Specifically, step S200 includes:

[0151] S210: Based on the strength and weight reduction targets of deep-sea mining equipment, different lightweighting levels are classified. Lightweight grades include:

[0152] Specific strength ≥320MPa·cm³ / g, target weight loss ≥60%;

[0153] Specific strength is 260~320MPa·cm³ / g, and the target weight reduction is 30%~60%;

[0154] Specific strength is 200~260 MPa·cm³ / g, and the target weight reduction is 15%~30%;

[0155] Specific strength <200MPa·cm³ / g, target weight loss ≤15%;

[0156] S220: Identify the dynamic load types of deep-sea mining equipment under typical operating conditions The equipment's operational mechanism and parameter range are refined, and dynamic load types include:

[0157] Operating depth water pressure load: static pressure value range of 40~60MPa at a water depth of 4000~6000m;

[0158] Underwater inlet resistance load, travel speed 0.5~1.1m / s, load amplitude ≤180kN;

[0159] : Earth resistance load in rugged terrain, load amplitude ≤200kN;

[0160] Submarine impact-subsidence-slippage composite load, load amplitude ≤400kN;

[0161] : Reaction force of jet disturbance flow field in polymetallic nodule mining vehicle, load amplitude ≤130kN, jet velocity ≤12m / s;

[0162] The cutting impact load of the cobalt-rich crust mining vehicle is ≤280kN.

[0163] : Impact load of polymetallic sulfide mining vehicle crushing, load amplitude ≤250kN;

[0164] Umbilical cable tension-torsion mixed load, load amplitude ≤400kN, torsional angular velocity 0~6° / s, vibration frequency <0.2Hz;

[0165] The hull-lifting system coupled disturbance load has a load amplitude of ≤100kN and a fluctuation frequency of ≤0.3Hz.

[0166] S300: Overall Lightweight Rating Dynamic load parameters Based on deep-sea mining operation parameters, establish the initial parameter input matrix for mining equipment: ,in H For the target operating water depth, T For the target operation cycle, T t For the target equipment self-sufficiency time, Q t For target mining volume, K w The target unit time weight-to-productivity ratio;

[0167] Matrix processing can transform multi-dimensional parameters ( , The parameters (including environmental parameters and target parameters) are structured and integrated, so that the subsequent capacity assessment model (S400) and reliability assessment model (S500) can directly call the associated parameters, avoiding assessment bias caused by isolated parameters, and providing a standardized input format for finite element analysis.

[0168] S400: Input matrix based on initial parameters Construct a mining capacity assessment model;

[0169] Specifically, the mining capacity in step S400 is determined by the extraction efficiency. The equipment's propulsion speed v and conveying efficiency η are jointly determined, and the specific capacity model relationship is as follows:

[0170] ,in Lightweight level With dynamic load type The coupling parameter pair, actual production capacity calculation is based on and Based on coupling;

[0171] The impact of lightweighting of mining equipment on mining capacity is as follows:

[0172] When lightweight level At that time, the collection efficiency Increase by 8%~12%, equipment propulsion speed v increases by 20%~30%, conveying efficiency η increases by 6%~10%, and overall production capacity Q increases by ≥30%;

[0173] When lightweight level At that time, the collection efficiency Increase by 5%~8%, equipment propulsion speed v increases by 10%~20%, conveying efficiency η increases by 4%~7%, and overall production capacity Q increases by 15%~30%;

[0174] When lightweight level At that time, the collection efficiency Increase by 2%~5%, equipment propulsion speed v increases by 5%~10%, conveying efficiency η increases by 2%~4%, and overall production capacity Q increases by 5%~15%;

[0175] When lightweight level At that time, the collection efficiency The improvement rate is ≤2%, the improvement rate of equipment propulsion speed v is ≤5%, the improvement rate of conveying efficiency η is ≤2%, and the improvement rate of overall production capacity Q is ≤5%;

[0176] The impact of dynamic load type on mining equipment on mining capacity is as follows:

[0177] When dynamic load type At that time, the collection efficiency The decrease is ≤2%, the decrease in propulsion speed v and conveying efficiency η is ≤2%, and the decrease in overall production capacity Q is ≤5%;

[0178] When dynamic load type At that time, the collection efficiency If the decrease is ≤5%, the propulsion speed v decreases by 5%~10%, the conveying efficiency η decreases by ≤2%, and the overall production capacity Q decreases by 5%~12%;

[0179] When dynamic load type At that time, the collection efficiency A decrease of 5%~8%, a decrease in propulsion speed v of 10%~20%, a decrease in conveying efficiency η of ≤5%, and an overall decrease in production capacity Q of 15%~25%;

[0180] When dynamic load type At that time, the collection efficiency The overall production capacity (Q) decreased by 20% to 35%, with a decrease of 8% to 12%, a decrease of 15% to 25%, a decrease of 5% to 10%, and a decrease of 8% to 12%.

[0181] When dynamic load type At that time, the collection efficiency The overall production capacity Q will decrease by 10% to 15%, with a decrease of 5% to 10% in the propulsion speed v and a decrease of ≤5% in the conveying efficiency η and a decrease of ≤2% in the conveying efficiency.

[0182] Dynamic load type At that time, the collection efficiency The overall production capacity Q decreased by 15% to 20%, with a decrease of 6% to 9%, a decrease of 10% to 15%, a decrease of ≤2% in conveying efficiency η, and a decrease of 6% to 9% in overall production capacity Q.

[0183] When dynamic load type At that time, the collection efficiency The overall production capacity Q decreased by 15% to 20%, with a decrease of 5% to 8%, a decrease of 10% to 15%, a decrease of ≤2% in conveying efficiency η, and a decrease of 15% to 20%.

[0184] When dynamic load type At that time, the collection efficiency The decrease is ≤2%, the decrease in propulsion speed v is ≤5%, the decrease in conveying efficiency η is 3%~7%, and the decrease in overall production capacity Q is 5%~12%;

[0185] When dynamic load type At that time, the collection efficiency With a decrease in both conveying efficiency η and propulsion speed v, the overall production capacity Q decreases by 8% to 15%.

[0186] The comprehensive discrimination system for mining production capacity is as follows:

[0187] When the mining production capacity Q ≥ 120 t / h and the weight production capacity ratio K ≤ 0.18, it is determined that the current mining equipment has the ability to support high production capacity, and the existing lightweight level L i and the dynamic load type F j should be combined and directly enter the reliability assessment stage;

[0188] When the mining production capacity 90 ≤ Q < 120 t / h and the weight production capacity ratio 0.18 < K ≤ 0.30, it is determined that there is a bottleneck in the mining efficiency of the current mining equipment, and it enters the multi-objective collaborative optimization stage;

[0189] When the mining production capacity Q < 90 t / h or the weight production capacity ratio K > 0.30, it is determined that the production capacity of the current mining equipment is limited and does not meet the target operation requirements. It should return to step S2 to reset the lightweight level and the dynamic load type range, and reconstruct the initial parameter input matrix .

[0190] Specifically, S500: Based on the initial parameter input matrix , construct a reliability assessment model for mining equipment;

[0191] The reliability of the mining equipment in step S500 is jointly determined by the structural strength margin index , the fatigue life factor and the operation stability level . The relationship of the reliability assessment model is:

[0192] , where refers to the coupling parameter pair of the lightweight level and the dynamic load type . The actual reliability calculation is based on the coupling effect of and ;

[0193] The influence of the lightweight level of mining equipment on mining reliability is as follows:

[0194] When the lightweight level , the structural strength margin decreases by 15% - 25%, the fatigue life factor decreases by 20% - 30%, the operation stability decreases by 5% - 10%, and the comprehensive reliability R decreases by 20% - 35%;

[0195] When the lightweight level , the structural strength margin decreases by 5% - 10%, the fatigue life factor Decrease of 8%~15%, operational stability Decreased by 3% to 6%, overall reliability Decrease of 10% to 20%;

[0196] When lightweight level At that time, structural strength margin A decrease of 2% to 5% in fatigue life factor Decrease of 5%~8%, operational stability A decrease of 2% to 4%, and a decrease in overall reliability R of 5% to 10%;

[0197] When lightweight level At that time, structural strength margin The decrease is ≤2%, fatigue life factor Decrease rate ≤3%, operational stability The decrease in reliability R is ≤2%, and the decrease in overall reliability R is ≤5%.

[0198] The impact of dynamic load type on mining equipment on mining reliability is as follows:

[0199] When dynamic load type At that time, structural strength margin The decrease is ≤5%, fatigue life factor Decrease rate ≤5%, operational stability The decrease in reliability R is ≤3%, and the overall reliability R decrease is ≤8%;

[0200] When dynamic load type F j When =F2, structural strength margin The decrease is ≤8%, fatigue life factor Decrease of 8%~12%, operational stability If the decrease is ≤5%, the overall reliability R decreases by 8%~15%;

[0201] When dynamic load type At that time, structural strength margin A 10% to 15% decrease in fatigue life factor Decrease of 15%~20%, operational stability Decreases by 8% to 12%, and overall reliability (R) decreases by 15% to 22%.

[0202] When dynamic load type At that time, structural strength margin A 15% to 25% decrease in fatigue life factor Decrease of 20%~30%, operational stability A decrease of 10%~15%, and a decrease in overall reliability R of 20%~30%;

[0203] When dynamic load type At that time, structural strength margin A decrease of 5% to 8% in fatigue life factor Decrease of 8%~12%, operational stability If the decrease is ≤5%, the overall reliability R decreases by 8%~15%;

[0204] When dynamic load type At that time, structural strength margin A decrease of 8% to 12% in fatigue life factor Decrease of 12%~18%, operational stability A decrease of 5% to 8%, and a decrease in overall reliability R of 12% to 20%;

[0205] Dynamic load type At that time, structural strength margin A decrease of 10% to 14% in fatigue life factor Decrease of 12%~18%, operational stability A decrease of 5% to 8%, and a decrease in overall reliability R of 12% to 20%;

[0206] Dynamic load type At that time, structural strength margin A decrease of 5% to 8% in fatigue life factor Decrease of 8%~12%, operational stability A decrease of 8% to 12%, and an overall reliability (R) decrease of 8% to 15%;

[0207] When dynamic load type At that time, structural strength margin A decrease of 8% to 12% in fatigue life factor Decrease of 8%~12%, operational stability A decrease of 5% to 10%, and a decrease in overall reliability R of 8% to 15%;

[0208] The comprehensive assessment system for mining reliability is as follows:

[0209] When the reliability index R ≥ 0.85 and the equipment self-sufficiency time At that time, it was determined that the current mining equipment possessed a high level of reliability and that the existing lightweight design should be maintained. and dynamic load type The combination allows for direct entry into a high-capacity, high-reliability parameter output stage.

[0210] When the reliability index is 0.70 ≤ R < 0.85 and the equipment self-sufficiency time When the time is T~1.5T, the reliability of the current mining equipment is judged to be at a medium level, and it should enter the multi-objective collaborative optimization stage;

[0211] When the reliability index R < 0.70 or the equipment endurance time If it is determined that the current mining equipment has a major reliability defect and does not meet the target operation requirements, the process should return to step S200 and reset the lightweight level. With dynamic load type Range, reconstruct the initial parameter input matrix And reassess.

[0212] S600: Based on the capacity and reliability determination results of steps S400 and S500, the optimization mode is dynamically selected. Taking into account the structural characteristics of mining equipment and system parameters, topology optimization and multi-objective genetic algorithm are used to carry out multi-objective collaborative optimization design.

[0213] Specifically, the optimization mode in step S600 is as follows:

[0214] Mode A: Capacity optimization, triggered by the following conditions in step S400: 90 ≤ Q < 120 and 0.18 <K≤0.30;

[0215] Mode B: Reliability optimization, triggered by the condition in step S500: 0.70 ≤ R < 0.85 and ;

[0216] Mode C: Composite optimization, enabled when both Mode A and Mode B are triggered simultaneously;

[0217] The specific steps for optimizing the mode include:

[0218] S610: Select parameters from modes A, B, and C that are highly sensitive to mining capacity Q and structural reliability R as design variables to form a design variable set:

[0219] ;

[0220] Among them, each variable covers the lightweight level Directly related structural cross-sections, material density, and material layout proportions; and dynamic load types. Related structural topology connection forms and reinforcement of boundary area layout;

[0221] S620: Employs a differentiated topology optimization strategy to solve for the optimal configuration at the structural level, specifically including:

[0222] S621: Input matrix according to parameters Based on the geometry and performance parameters of the mining equipment and the design variable set X, a finite element model is constructed, targeting the lightweight level. Set design space volume constraints :

[0223] When lightweight level At that time, design space volume constraints (Original volume of the structure);

[0224] When lightweight level At that time, design space volume constraints ;

[0225] When lightweight level At that time, design space volume constraints ;

[0226] When lightweight level At that time, design space volume constraints ;

[0227] This embodiment sets differentiated design space volume constraints for different lightweight levels. In order to match the weight reduction target and specific strength requirements of each level, balance lightweight and dynamic load resistance, provide accurate boundaries for topology optimization and adapt to subsequent material density threshold parameters.

[0228] S622: For dynamic load types Set boundary conditions:

[0229] When dynamic load type for When static load boundary conditions are applied;

[0230] When dynamic load type for At that time, frequency domain excitation load boundary conditions are used;

[0231] When dynamic load type for At that time, transient dynamic boundary conditions are used;

[0232] S623: Set the objective function for different optimization modes:

[0233] Mode A: The objective function is to maximize the disturbance resistance stiffness and the equipment propulsion speed v;

[0234] Mode B: The objective function is to maximize the structural strength margin. With fatigue life factor ;

[0235] Mode C: The objective function is a weighted optimization of static stiffness, frequency stiffness, and impact stiffness;

[0236] S624: Introduces the Continuous Density Method (SIMP) to parametrically control the density of structural elements, achieving continuous discretization of material distribution in lightweight structural elements, targeting lightweight grades. Minimum density threshold for different materials for:

[0237] When lightweight level hour, ;

[0238] When lightweight level hour, ;

[0239] When lightweight level hour, ;

[0240] When lightweight level hour, ;

[0241] S625: Outputs a topology-optimized structure, which is an initial lightweight structure with high stiffness and low mass bit characteristics;

[0242] S630: Employs a multi-objective genetic algorithm to collaboratively optimize the topology structure, specifically including:

[0243] S631: Segment the structural configuration, module layout, and control parameters, embed optimization mode labels (A / B / C), and construct a multi-source parameter composite coding structure;

[0244] S632: The initial population is matched to the high-productivity, high-reliability regions (Q≥120t / h and R≥0.85) in steps S400 and S500. Combining the results of topology optimization in step S620 with the core of the solution, we can form an initial solution with structure-function synergy.

[0245] S633: Employs a multi-objective fitness function set and uses Pareto sorting and crowding mechanisms for solution set selection. The multi-objective fitness functions and selection mechanisms adapted to different optimization modes are as follows:

[0246] Mode A: The fitness function is F=0.8Q+0.2R, with constraints R≥0.85 and K≤0.30. Pareto sort selects the solution with the higher Q value, and the crowding calculation adds a penalty term for K.

[0247] Mode B: Fitness function is F = 0.2Q + 0.8R, constraint is Q ≥ 120. ,choose and An equilibrium solution eliminates vibration-sensitive configurations;

[0248] Mode C: The fitness function is F=0.5Q+0.5R, with constraints of Q≥120 and R≥0.85. The elite retention strategy is triggered when the Pareto front coverage is >90%.

[0249] S634: According to Combined adaptive adjustment of genetic parameters, including mutation rate, crossover method and population size;

[0250] (High-frequency shock): Mutation rate is 0.25~0.30, crossover method is three-point crossover, and population size is 100~120;

[0251] (Transient shock): Mutation rate 0.20~0.25, crossover method is arithmetic crossover, population size is 80~100;

[0252] (Steady-state load): Mutation rate 0.10~0.15, crossover method uniform crossover, population size 60~80;

[0253] (Composite load): Mutation rate 0.15~0.20, crossover method is simulated binary crossover, population size is 70~90;

[0254] S635: Set the convergence criterion as the objective function variance < ε and the Pareto boundary stability, ensuring that the optimization process focuses on... The feasible solution set for the scenario;

[0255] S636: Output Pareto solution set Each solution set comes with a corresponding... and The identifier is used for subsequent solution decisions and operational condition adaptability verification.

[0256] S700: The optimized version Parameter combination updated to The matrix was used to verify the capacity and reliability of the mining equipment after multi-objective collaborative optimization.

[0257] Specifically, the mining equipment capacity verification in step S700 includes:

[0258] S710: Substitute the Pareto optimal solution set from S636 into the mining capacity assessment model constructed in step S400, and calculate the corresponding... and Mining capacity Q:

[0259] If the optimized solution satisfies Q≥120t / h and K≤0.18, then the optimized solution is in the corresponding... and It possesses both high production capacity and low energy consumption, and its production capacity has been verified.

[0260] If a solution does not meet this condition, it is necessary to return to step S6, adjust the range of design variables and optimize the algorithm parameters, and then perform collaborative optimization again.

[0261] Reliability verification includes:

[0262] S720: Substitute the Pareto optimal solution set from S6 into the mining equipment reliability assessment model constructed in step S500 to calculate the reliability index R:

[0263] If all solutions satisfy R≥0.85 and This indicates that the designed structure is currently... and It possesses high reliability and self-sustaining time, and reliability verification has been passed.

[0264] If a solution does not meet this condition, the relevant design variables and structural configuration parameters are adjusted accordingly, and the process is returned to step S600 for optimization.

[0265] S800: Based on the results of capacity and reliability assessment, optimization, and verification, an optimal output matrix for high capacity and high reliability of deep-sea mining equipment based on lightweight design and dynamic load is established.

[0266] .

[0267] in L i * For optimal lightweight level, F j * For optimal dynamic load parameters, H * For optimal operating water depth, T * For optimal work cycle, T t * For optimal equipment self-sufficiency time, Q t * For optimal mining quantity, K w * This represents the optimal weight-to-production ratio per unit time.

[0268] Furthermore, in step S100 of this embodiment, when acquiring operational parameters, the operational water depth H and water pressure change data are monitored in real time through a deep-sea sensor network. The sensor network includes pressure sensors and depth sensors, and the sensor accuracy error range is controlled within ±0.5%. The sensor network can capture subtle changes in water pressure and depth in real time, providing a reliable data foundation for the initial parameter input matrix (step S300). At the same time, the distributed arrangement of the sensor network can cover key parts of the equipment, avoiding data loss caused by single-point failures, and ensuring the continuity and integrity of parameter acquisition.

[0269] In step S210, when classifying lightweight levels, the lightweight level is... The weight reduction target is achieved by using a gradient composite process of carbon fiber reinforced composite materials and titanium alloy, and the strength of the composite structure is ensured by electron beam welding technology.

[0270] Identifying the dynamic load type in step S210 At that time, multiphysics coupling simulation software was used to simulate composite loads. By setting fluid-solid-thermal coupling boundary conditions, the resultant force range and the proportion of each load were accurately calculated.

[0271] Extraction efficiency in step S400 mining capacity assessment model The equipment propulsion speed v and conveying efficiency η are dynamically predicted using a deep learning model. The deep learning model is trained based on historical operation data, and the input is the lightweight level. Dynamic load type It also outputs the predicted values ​​and degree of impact of real-time environmental parameters on the corresponding production capacity.

[0272] In step S500, the reliability assessment model for mining equipment incorporates acoustic emission monitoring technology to acquire real-time data on structural fatigue crack propagation, which is used to correct the fatigue life factor. Calculation model; structural strength margin index Fatigue life factor and operational stability level Similarly, a deep learning model is used to make dynamic predictions and to give the impact of the lightweight level of mining equipment and the dynamic load type on mining reliability.

[0273] In step S620, the topology optimization method employs an adaptive mesh generation technique to automatically refine the mesh in stress concentration regions, improving the computational accuracy of the finite element model. The refinement ratio is no less than 1 / 4 of the basic mesh. A ratio greater than or equal to 1 / 4 ensures the accuracy of stress gradient capture while avoiding convergence problems caused by excessive mesh differences, thus adapting to the iterative requirements of topology optimization.

[0274] In the multi-objective genetic algorithm of step S630, a dynamic load weight adjustment module is set up to dynamically adjust the weight coefficients of capacity and reliability objectives in Pareto sorting, with a weight range of 0.3-0.7. The weight range of 0.3-0.7 ensures that both capacity and reliability are within the effective range, preventing excessive pursuit of capacity at the expense of reliability, and avoiding excessive conservatism that violates the lightweight objective.

[0275] In step S700, accelerated life testing is used to obtain life data of key components of mining equipment in a short time by increasing the test stress level. Combined with Chaboche's nonlinear damage accumulation theory, the reliability of the equipment within the design life cycle is verified.

[0276] Step S800 Output Matrix In this study, confidence intervals were set for each parameter, and the impact of parameter fluctuations on production capacity and reliability was calculated using the Monte Carlo simulation method.

[0277] Using the above embodiments and incorporating specific variables, the following case study is designed:

[0278] Design Case 1

[0279] S1. Obtain the operating parameters of the A# type 6000-meter-class polymetallic nodule mining vehicle in the Eastern Pacific Ocean, including operating water depth H=6000m, operating cycle T=72h, equipment mass M(t)=14.5t, and target equipment self-sufficiency time. Equipment load amplitude F=85kN, target mining volume Target unit time weight-to-productivity ratio ;

[0280] When acquiring operational parameters, the changes in operational water depth H and water pressure are monitored in real time through a deep-sea sensor network. The sensor network includes pressure sensors and depth sensors, and the accuracy error range of the sensors is controlled within ±0.5%.

[0281] S2. Classify the lightweight level and dynamic load type of A# type mining vehicles, including:

[0282] Lightweight level is It adopts high-strength titanium alloy and composite materials; the dynamic load type is ;

[0283] Identify dynamic load types At that time, multiphysics coupling simulation software was used to simulate composite loads. By setting fluid-solid-thermal coupling boundary conditions, the resultant force range and the proportion of each load were accurately calculated.

[0284] S3, Overall Lightweight Rating Dynamic load parameters And deep - sea mining operation parameters, establish an initial parameter input matrix for the mining vehicle. The initial parameter input matrix is

[0285] ;

[0286] S4. Based on the initial parameter input matrix , construct a mining production capacity evaluation model. The mining production capacity is jointly determined by the collection efficiency , the equipment propulsion speed v, and the conveying efficiency η. The relationship of the production capacity model is:

[0287] ;

[0288] 1) Influence of the lightweight level of the mining vehicle on the mining production capacity:

[0289] Lightweight level , the collection efficiency C h increases by 8% - 12%, the equipment propulsion speed v increases by 20% - 30%, the conveying efficiency η increases by 6% - 10%, and the overall production capacity Q increases by ≥30%;

[0290] 2) Influence of the dynamic load type of the mining vehicle on the mining production capacity:

[0291] Dynamic load type , the collection efficiency decreases by 5% - 10%, the propulsion speed v decreases by ≤5%, the conveying efficiency η decreases by ≤2%, and the overall production capacity Q decreases by 10% - 15%;

[0292] In the mining production capacity evaluation model, the collection efficiency , the equipment propulsion speed v, and the conveying efficiency η are dynamically predicted through a deep - learning model. The deep - learning model is trained based on historical operation data. The inputs are the lightweight level , the dynamic load type and real - time environmental parameters, and the output is the predicted value of the corresponding production capacity influence parameter.

[0293] Among them, the jet collection efficiency , the propulsion speed v = 5.6 m / min, the conveying efficiency η = 0.85, and the output mining volume per unit time is 108 t / h. The production capacity is significantly lower than the target , not meeting the commercial mining requirements.

[0294] 3) Comprehensive discrimination system for mining production capacity:

[0295] When the mining production capacity 90 ≤ Q < 120 t / h and the weight - to - production - capacity ratio 0.18 < K ≤ 0.30, enter the multi - objective collaborative optimization stage;

[0296] S5. Based on the initial parameter input matrix A reliability assessment model for mining vehicles was constructed, and the reliability of mining equipment was determined by the structural strength margin index. Fatigue life factor and operational stability level The reliability assessment model relationship is jointly determined as follows:

[0297] ;

[0298] In the reliability assessment model of mining vehicles, acoustic emission monitoring technology is introduced to obtain real-time data on structural fatigue crack propagation, which is used to correct the fatigue life factor. The computational model;

[0299] 1) The impact of lightweight mining vehicles on mining reliability:

[0300] Lightweight level Structural strength margin S m A 15% to 25% decrease in fatigue life factor Decrease of 20%~30%, operational stability A decrease of 5% to 10%, and a decrease in overall reliability R of 20% to 35%;

[0301] 2) The impact of dynamic load type of mining vehicles on mining reliability:

[0302] Dynamic load type Overall structural strength margin A decrease of 5% to 8% in fatigue life factor Decreased by 8%~12%, operational stability R s A decrease of 10%~15%, and a decrease in overall reliability R of 20%~30%;

[0303] Among them, structural strength margin index =1.25, fatigue life factor =0.72, operational stability level =0.92, and the output reliability is 0.828;

[0304] 3) Comprehensive assessment system for mining reliability:

[0305] Reliability index R≥0.85 and equipment self-sufficiency time At that time, it was determined that the current mining vehicle possessed a high level of reliability;

[0306] S6. Based on the capacity and reliability determination results of steps S4 and S5, dynamically select the optimization mode, comprehensively consider the structural characteristics and system parameters of the mining equipment, and use topology optimization and multi-objective genetic algorithms to perform multi-objective collaborative optimization design. The optimization modes include:

[0307] Mode A: Capacity optimization, triggered by the condition that in S4, 90 ≤ Q < 120 and 0.18 <K≤0.30;

[0308] S6.1 Select the parameters in Mode A that are highly sensitive to mining capacity Q and structural reliability R as design variables to form a design variable set:

[0309] ;

[0310] Among them, each variable covers the lightweight level Directly related structural cross-sections, material density, and material layout proportions; and dynamic load types. Related structural topology connection forms and reinforcement of boundary area layout;

[0311] S6.2. Employing a differentiated topology optimization strategy to solve for the optimal configuration at the structural level, specifically including:

[0312] 1) Input matrix according to parameters Based on the geometry and performance parameters of the mining equipment and the design variable set X, a finite element model is constructed to determine the lightweight level. Design space volume constraints (Original volume of the structure);

[0313] 2) For dynamic load types Set boundary conditions:

[0314] Dynamic load type for At that time, transient dynamic boundary conditions are comprehensively adopted;

[0315] 3) Set the objective function for different optimization modes:

[0316] Mode A: The objective function is to maximize the disturbance resistance stiffness and the equipment propulsion speed v;

[0317] 4) Introducing the Continuous Density Method (SIMP) to parametrically control the density of structural units, achieving continuous discretization of the material distribution of lightweight structural units, and setting minimum density thresholds for different materials. ;

[0318] 5) Output the topology-optimized structure, which is an initial lightweight structure with high stiffness and low mass bit characteristics;

[0319] In the topology optimization method, an adaptive mesh generation technique is used to automatically refine the mesh in stress concentration regions, thereby improving the calculation accuracy of the finite element model. The refinement ratio is no less than 1 / 4 of the basic mesh.

[0320] S6.3. A multi-objective genetic algorithm is used to collaboratively optimize the topology structure, specifically including:

[0321] 1) The structural configuration, module layout, and control parameters are segmented and encoded, and optimization mode labels (A) are embedded to construct a multi-source parameter composite coding structure;

[0322] 2) The initial population was matched to the high-productivity, high-reliability region (Q≥120t / h and R≥0.85) of S4 / S5. Combining the results of topology optimization in step S6.2 with the core of the solution, an initial solution with structure-function synergy is formed by integrating the results of topology optimization in step S6.2.

[0323] 3) A multi-objective fitness function set is adopted, and the solution set is filtered through Pareto sorting and crowding mechanism. The multi-objective fitness function and filtering mechanism adapted to different optimization modes are as follows:

[0324] The fitness function is F = 0.8Q + 0.2R, with constraints R ≥ 0.85 and K ≤ 0.30. Pareto sort selects the solution with the higher Q value, and the crowding calculation adds a penalty term for K.

[0325] 4) According to The genetic parameters are adaptively adjusted in combination, with a mutation rate of 0.25~0.30, a three-point crossover method, and a population size of 100~120.

[0326] 5) Set the convergence criterion as the objective function variance < ε and the Pareto boundary stability, ensuring that the optimization process focuses on the objective function. The feasible solution set for the scenario;

[0327] 6) Output the Pareto solution set Each solution set comes with a corresponding... and The identifier is used for subsequent solution decisions and operational condition adaptability verification;

[0328] When optimizing the multi-objective genetic algorithm, a dynamic load weight adjustment module is set up to dynamically adjust the weight coefficients of the capacity and reliability objectives in the Pareto sort, with a weight range of 0.3-0.7.

[0329] S7, after optimization Parameter combination updated to The matrix was used to verify the capacity and reliability of the mining equipment after multi-objective collaborative optimization.

[0330] 1) Capacity Verification: Substitute the Pareto optimal solution set from S6 into the mining capacity assessment model constructed in S4, and calculate... Lower mining capacity , If Q ≥ 120 t / h and K ≤ 0.18, then the optimized scheme is... It possesses high production capacity and low energy consumption performance, and its production capacity has been verified.

[0331] 2) Reliability Verification: Substitute the Pareto optimal solution set from S6 into the mining equipment reliability assessment model constructed in S5 to calculate the reliability index. , The condition R ≥ 0.85 and This indicates that the designed structure is currently in L * 1-F * It exhibits high reliability and self-sustaining time under conditions 5, and its reliability has been verified.

[0332] During the verification process, accelerated life testing was adopted. By increasing the test stress level, life data of key components of mining equipment were obtained in a short time. Combined with Chaboche's nonlinear damage accumulation theory, the reliability of the equipment within the design life cycle was verified.

[0333] S8. Based on the results of capacity and reliability assessment, optimization, and verification, establish the optimal output matrix for high capacity and high reliability of deep-sea mining equipment based on lightweight design and dynamic load. The parameter output matrix is ​​as follows:

[0334] ;

[0335] Output matrix At that time, confidence intervals were set for each parameter, and the impact of parameter fluctuations on production capacity and reliability was calculated using the Monte Carlo simulation method.

[0336]

[0337] Design Case 2

[0338] S1. Obtain the operating parameters of the Type B# 5000-meter-class North Pacific cobalt-rich crust mining vehicle, including operating water depth H=5000m, operating cycle T=80h, equipment mass M(t)=15.0t, and target equipment self-sufficiency time. Equipment load amplitude F=42.5kN, target mining volume Target unit time weight-to-productivity ratio ;

[0339] S2. Classify the lightweight level and dynamic load type of B# type mining vehicles, including:

[0340] Lightweight level is It adopts a multi-system composite structure design using high-strength aluminum-lithium alloy and carbon fiber reinforced composite materials; the dynamic load type is ;

[0341] S3, Overall Lightweight Rating Dynamic load parameter F j Based on the deep-sea mining operation parameters, an initial parameter input matrix for the mining vehicle is established. The initial parameter input matrix is ​​as follows:

[0342] ;

[0343] S4. Input matrix based on initial parameters The capacity model relationship is as follows:

[0344] ;

[0345] 1) The impact of lightweight mining vehicles on mining productivity:

[0346] Lightweight level Collection efficiency Increase by 5%~8%, equipment propulsion speed v increases by 10%~20%, conveying efficiency η increases by 4%~7%, and overall production capacity Q increases by 15%~30%;

[0347] 2) The impact of dynamic load type of mining vehicles on mining production capacity:

[0348] Dynamic load type Acquisition efficiency C h The overall production capacity Q decreased by 15% to 20%, with a decrease of 6% to 9%, a decrease of 10% to 15%, a decrease of ≤2% in conveying efficiency η, and a decrease of 6% to 9% in conveying speed v.

[0349] Among them, cutting acquisition efficiency With a propulsion speed v = 1.60 m / min, a conveying efficiency η = 0.93, and an output mining volume of 135 t / h per unit time, the production capacity exceeds the target Q. t =130t / h, meeting the needs of commercial mining.

[0350] 3) Comprehensive Mining Capacity Assessment System:

[0351] Mining capacity Q≥120t / h and weight-to-capacity ratio K≤0.18, enters the reliability assessment stage;

[0352] S5. Input matrix based on initial parameters The reliability assessment model relationship is as follows:

[0353] ;

[0354] 1) The impact of lightweight mining vehicles on mining reliability:

[0355] Lightweight level Structural strength margin A decrease of 5% to 10% in fatigue life factor Decrease of 8%~15%, operational stability A decrease of 3% to 6%, and a decrease in overall reliability R of 10% to 20%;

[0356] 2) The impact of dynamic load type of mining vehicles on mining reliability:

[0357] Dynamic load type Structural strength margin A decrease of 8% to 12% in fatigue life factor Decrease of 12%~18%, operational stability A decrease of 5% to 8%, and a decrease in overall reliability R of 12% to 20%;

[0358] Among them, structural strength margin index Fatigue life factor Operational stability level The output reliability is 0.72;

[0359] 3) Comprehensive assessment system for mining reliability:

[0360] Reliability index 0.70≤R<0.85 and equipment self-sufficiency time When the time is T~1.5T, it enters the multi-objective collaborative optimization stage;

[0361] S6. Based on the capacity and reliability determination results of steps S4 and S5, dynamically select the optimization mode, comprehensively consider the structural characteristics and system parameters of the mining equipment, and use topology optimization and multi-objective genetic algorithms to perform multi-objective collaborative optimization design. The optimization modes include:

[0362] Mode B: Reliability optimization, triggered by the condition that 0.70 ≤ R < 0.85 in S5 and ;

[0363] S6.1 Select the parameters in Mode B that are highly sensitive to mining capacity Q and structural reliability R as design variables to form a design variable set:

[0364] ;

[0365] Among them, each variable covers the lightweight level Directly related structural cross-sections, material density, and material layout proportions; and dynamic load types. Related structural topology connection forms and reinforcement of boundary area layout;

[0366] S6.2. Employing a differentiated topology optimization strategy to solve for the optimal configuration at the structural level, specifically including:

[0367] 1) Input matrix according to parameters Based on the geometry and performance parameters of the mining vehicle and the design variable set X, a finite element model is constructed to determine the lightweight level. Design space volume constraints ;

[0368] 2) Dynamic load type Frequency domain excitation load boundary conditions are adopted;

[0369] 3) Set the objective function for different optimization modes:

[0370] Mode B: The objective function is to maximize the structural strength margin. With fatigue life factor ;

[0371] 4) Introducing the Continuous Density Method (SIMP) to parametrically control the density of structural units, achieving continuous discretization of the material distribution of lightweight structural units, targeting lightweight levels. , ;

[0372] 5) Output the topology-optimized structure, which is an initial lightweight structure with high stiffness and low mass bit characteristics;

[0373] S6.3. A multi-objective genetic algorithm is used to collaboratively optimize the topology structure, specifically including:

[0374] 1) The structural configuration, module layout, and control parameters are segmented and encoded, and optimization mode labels (B) are embedded to construct a multi-source parameter composite coding structure;

[0375] 2) The initial population was matched to the high-productivity, high-reliability region (Q≥120t / h and R≥0.85) of S4 / S5. Combining the results of topology optimization in step S6.2 with the core of the solution, an initial solution with structure-function synergy is formed by integrating the results of topology optimization in step S6.2.

[0376] 3) A multi-objective fitness function set is adopted, and the solution set is filtered through Pareto sorting and crowding mechanism. The multi-objective fitness function and filtering mechanism adapted to different optimization modes are as follows:

[0377] The fitness function is F = 0.2Q + 0.8R, and the constraint is Q ≥ 120. ,choose and An equilibrium solution eliminates vibration-sensitive configurations;

[0378] 4) According to The genetic parameters are adaptively adjusted in combination, with a mutation rate of 0.20~0.25, an arithmetic crossover method, and a population size of 80~100.

[0379] 5) Set the convergence criterion as the objective function variance < ε and the Pareto boundary stability to ensure that the optimization process focuses on the engineering feasible solution set under the L2-F6 scenario;

[0380] 6) Output the Pareto solution set Each solution set comes with a corresponding... and The identifier is used for subsequent solution decisions and operational condition adaptability verification;

[0381] S7, after optimization Parameter combination updated to The matrix was used to verify the mining vehicle's capacity and reliability after multi-objective collaborative optimization.

[0382] 1) Capacity Verification: Substitute the Pareto optimal solution set from S6 into the mining capacity assessment model constructed in S4, and calculate... Lower mining capacity , If Q ≥ 120 t / h and K ≤ 0.18, then the optimized scheme is... It possesses high production capacity and low energy consumption performance, and its production capacity has been verified.

[0383] 2) Reliability Verification: Substitute the Pareto optimal solution set from S6 into the mining equipment reliability assessment model constructed in S5 to calculate the reliability index. , The condition R ≥ 0.85 and This indicates that the designed structure is currently... It possesses high reliability and self-sustaining time, and reliability verification has been passed.

[0384] S8. Based on the results of capacity and reliability assessment, optimization, and verification, establish the optimal output matrix for high capacity and high reliability of deep-sea mining equipment based on lightweight design and dynamic load. The parameter output matrix is ​​as follows:

[0385] ;

[0386]

[0387] Design Case 3:

[0388] S1. Obtain the operating parameters of the C# type 4000-meter-class North Atlantic polymetallic sulfide mining vehicle, including operating water depth H=4000m, operating cycle T=90h, equipment mass M(t)=20.5t, and target equipment self-sufficiency time. Equipment load amplitude F=96kN, target mining volume Target unit time weight-to-productivity ratio ;

[0389] S2. Divide the lightweight levels and dynamic load types of C# type mining vehicles, including:

[0390] ① The lightweight level is , and high-strength aluminum alloy and carbon fiber composite materials are used;

[0391] ② The dynamic load type is ;

[0392] S3. Based on the comprehensive lightweight level , dynamic load parameters and deep-sea mining operation parameters, establish an initial parameter input matrix for mining equipment. The initial parameter input matrix is

[0393] ;

[0394] S4. Based on the initial parameter input matrix , the production capacity model relationship is:

[0395] ;

[0396] 1) Influence of the lightweight level of the mining vehicle on the mining production capacity:

[0397] For the lightweight level , the collection efficiency increases by 2% - 5%, the equipment propulsion speed v increases by 5% - 10%, the conveying efficiency η increases by 2% - 4%, and the overall production capacity Q increases by 5% - 15%;

[0398] 2) Influence of the dynamic load type of the mining vehicle on the mining production capacity:

[0399] For the dynamic load type , the collection efficiency decreases by 5% - 8%, the propulsion speed v decreases by 10% - 15%, the decrease range of the conveying efficiency η ≤ 2%, and the overall production capacity Q decreases by 15% - 20%;

[0400] Among them, the crushing collection efficiency , the propulsion speed v = 1.95 m / min, the conveying efficiency η = 0.85, the mined amount per unit time is 115 t / h, and the production capacity is lower than the target , not meeting the commercial mining requirements.

[0401] 3) Comprehensive discrimination system for mining production capacity:

[0402] When the mining production capacity 90 ≤ Q < 120 t / h and the weight-production capacity ratio 0.18 < K ≤ 0.30 t / (t / h), enter the multi-objective collaborative optimization stage;

[0403] S5. Based on the initial parameter input matrix , the reliability evaluation model relationship is:

[0404] ;

[0405] 1) The impact of lightweight mining equipment on mining reliability:

[0406] Lightweight level Structural strength margin A decrease of 2% to 5% in fatigue life factor Decrease of 5%~8%, operational stability A decrease of 2% to 4%, and a decrease in overall reliability R of 5% to 10%;

[0407] 2) The impact of dynamic load type of mining equipment on mining reliability:

[0408] Dynamic load type Structural strength margin A decrease of 10% to 14% in fatigue life factor Decrease of 12%~18%, operational stability A decrease of 5% to 8%, and a decrease in overall reliability R of 12% to 20%;

[0409] Among them, structural strength margin index Fatigue life factor Operational stability level The output reliability is 0.72;

[0410] 3) Comprehensive assessment system for mining reliability:

[0411] Reliability index 0.70≤R<0.85 and equipment self-sufficiency time When the time is T~1.5T, it enters the multi-objective collaborative optimization stage;

[0412] S6. Based on the capacity and reliability determination results of steps S4 and S5, dynamically select the optimization mode, comprehensively consider the structural characteristics and system parameters of the mining equipment, and use topology optimization and multi-objective genetic algorithms to perform multi-objective collaborative optimization design. The optimization modes include:

[0413] Mode C: Composite optimization, enabled when both Mode A and Mode B are triggered simultaneously;

[0414] S6.1 Select the parameters in Mode B that are highly sensitive to mining capacity Q and structural reliability R as design variables to form a design variable set:

[0415] ;

[0416] Among them, the variables include structural cross-section, material density, and material layout ratio variables directly related to the lightweight level L3; and dynamic load type. Related structural topology connection forms and reinforcement of boundary area layout;

[0417] S6.2. Employing a differentiated topology optimization strategy to solve for the optimal configuration at the structural level, specifically including:

[0418] 1) Input matrix according to parameters Based on the geometry and performance parameters of the mining equipment and the design variable set X, a finite element model is constructed to determine the lightweight level. Design space volume constraints ;

[0419] 2) Dynamic load type Transient dynamic boundary conditions are adopted;

[0420] 3) Set the objective function for different optimization modes:

[0421] Mode C: The objective function is a weighted optimization of static stiffness, frequency stiffness, and impact stiffness;

[0422] 4) Introducing the Continuous Density Method (SIMP) to parametrically control the density of structural units, achieving continuous discretization of the material distribution of lightweight structural units, targeting lightweight levels. , ;

[0423] 5) Output the topology-optimized structure, which is an initial lightweight structure with high stiffness and low mass bit characteristics;

[0424] S6.3. A multi-objective genetic algorithm is used to collaboratively optimize the topology structure, specifically including:

[0425] 1) The structural configuration, module layout, and control parameters are segmented and encoded, and optimization mode labels (C) are embedded to construct a multi-source parameter composite coding structure;

[0426] 2) The initial population was matched to the high-productivity, high-reliability region (Q≥120t / h and R≥0.85) of S4 / S5. Combining the results of topology optimization in step S6.2 with the core of the solution, an initial solution with structure-function synergy is formed by integrating the results of topology optimization in step S6.2.

[0427] 3) A multi-objective fitness function set is adopted, and the solution set is filtered through Pareto sorting and crowding mechanism. The multi-objective fitness function and filtering mechanism adapted to different optimization modes are as follows:

[0428] The fitness function is F=0.5Q+0.5R, with constraints of Q≥120 and R≥0.85. The elite retention strategy is triggered when the Pareto front coverage is >90%.

[0429] 4) According to The genetic parameters are adaptively adjusted in combination, with a mutation rate of 0.10~0.15, a uniform crossover method, and a population size of 60~80.

[0430] 5) Set the convergence criterion as the objective function variance < ε and the Pareto boundary stability to ensure that the optimization process focuses on the objective function. The feasible solution set for the scenario;

[0431] 6) Output the Pareto solution set S6 output ={X i Q i ,R i Each solution group is accompanied by corresponding L3 and F7 identifiers for subsequent scheme decision-making and working condition adaptability verification.

[0432] S7, after optimization Parameter combination updated to The matrix was used to verify the capacity and reliability of the mining equipment after multi-objective collaborative optimization.

[0433] 1) Capacity Verification: Substitute the Pareto optimal solution set from S6 into the mining capacity assessment model constructed in S4, and calculate... Lower mining capacity , If Q ≥ 120 t / h and K ≤ 0.18, then the optimized scheme is... It possesses both high production capacity and low energy consumption, and its production capacity has been verified.

[0434] 2) Reliability Verification: Substitute the Pareto optimal solution set from S6 into the mining equipment reliability assessment model constructed in S5 to calculate the reliability index. , The condition R ≥ 0.85 and This indicates that the designed structure is currently... It possesses high reliability and self-sustaining time, and reliability verification has been passed.

[0435] S8. Based on the results of capacity and reliability assessment, optimization, and verification, establish the optimal output matrix for high capacity and high reliability of deep-sea mining equipment based on lightweight design and dynamic load. The parameter output matrix is ​​as follows:

[0436] ;

[0437]

[0438] Comparative Example 1:

[0439] The main technical indicators of the deep-sea polymetallic nodule mining vehicle were optimized using a safety factor assessment method based on linear static analysis. The safety factor method ignores many aspects such as dynamic load influence, structural-material nonlinear response, system coupling characteristics, and life-stability assessment.

[0440]

[0441] Comparative Example 2:

[0442] These are the main technical specifications of the existing foreign deep-sea polymetallic nodule mining vehicle D.

[0443]

[0444] Comparative Example 3:

[0445] These are the main technical specifications of existing domestic conventional deep-sea polymetallic nodule mining vehicles.

[0446]

[0447] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A design method for deep-sea mining equipment based on lightweight design and dynamic load, characterized in that, It includes the steps: S100: Obtain deep - sea mining operation parameters; S200: Divide the lightweight level and dynamic load type; S300: Overall Lightweight Rating Dynamic load type Based on deep-sea mining operation parameters, establish the initial parameter input matrix for mining equipment: ,in H For the target operating water depth, T For the target operation cycle, T t For the target equipment self-sufficiency time, Q t For target mining volume, K w The target unit time weight-to-productivity ratio; S400: Input matrix based on the initial parameters Construct a mining capacity assessment model; S5 00: Input matrix based on the initial parameters Construct a reliability assessment model for mining equipment; S600: According to the production capacity and reliability determination results of steps S400 and S500, dynamically select the optimization mode, comprehensively consider the structural characteristics and system parameters of the mining equipment, and use topology optimization and multi - objective genetic algorithm to conduct multi - objective collaborative optimization design; S700: The optimized version Parameter combination updated to The matrix was used to verify the capacity and reliability of the mining equipment after multi-objective collaborative optimization. S800: Based on the results of capacity and reliability assessment, optimization, and verification, an optimal output matrix for high capacity and high reliability of deep-sea mining equipment based on lightweight design and dynamic load is established. ,in L i * For optimal lightweight level, F j * For optimal dynamic load type, H * For optimal operating water depth, T * For optimal work cycle, T t * For optimal equipment self-sufficiency time, Q t * For optimal mining quantity, K w * This represents the optimal weight-to-production ratio per unit time.

2. The deep-sea mining equipment design method based on lightweight and dynamic load as described in claim 1, characterized in that, Step S200 includes: S210: Based on the strength and weight reduction targets of deep-sea mining equipment, different lightweighting levels are classified. The lightweight level includes: Specific strength ≥320MPa·cm³ / g, target weight loss ≥60%; Specific strength is 260~320MPa·cm³ / g, and the target weight reduction is 30%~60%; Specific strength is 200~260 MPa·cm³ / g, and the target weight reduction is 15%~30%; Specific strength <200MPa·cm³ / g, target weight loss ≤15%; S220: Identify the dynamic load types of deep-sea mining equipment under typical operating conditions The equipment's operational mechanism and parameter range are further refined, and the dynamic load types include: Operating depth water pressure load: static pressure value range of 40~60MPa at a water depth of 4000~6000m; Underwater inlet resistance load, travel speed 0.5~1.1m / s, load amplitude ≤180kN; : Earth resistance load in rugged terrain, load amplitude ≤200kN; Submarine impact-subsidence-slippage composite load, load amplitude ≤400kN; : Reaction force of jet disturbance flow field in polymetallic nodule mining vehicle, load amplitude ≤130kN, jet velocity ≤12m / s; The cutting impact load of the cobalt-rich crust mining vehicle is ≤280kN. : Impact load of polymetallic sulfide mining vehicle crushing, load amplitude ≤250kN; Umbilical cable tension-torsion mixed load, load amplitude ≤400kN, torsional angular velocity 0~6° / s, vibration frequency <0.2Hz; The hull-lifting system coupled disturbance load has a load amplitude of ≤100kN and a fluctuation frequency of ≤0.3Hz.

3. The deep-sea mining equipment design method based on lightweight and dynamic load as described in claim 2, characterized in that, The mining capacity in step S400 is determined by the extraction efficiency. The equipment's propulsion speed v and conveying efficiency η are jointly determined, and the specific capacity model relationship is as follows: Lightweight level With dynamic load type The coupling parameter pair; Among them, the influence of the lightweight level of mining equipment on mining production capacity is as follows: When lightweight level At that time, the collection efficiency Increase by 8%~12%, equipment propulsion speed v increases by 20%~30%, conveying efficiency η increases by 6%~10%, and overall production capacity Q increases by ≥30%; When lightweight level At that time, the collection efficiency Increase by 5%~8%, equipment propulsion speed v increases by 10%~20%, conveying efficiency η increases by 4%~7%, and overall production capacity Q increases by 15%~30%; When lightweight level At that time, the collection efficiency Increase by 2%~5%, equipment propulsion speed v increases by 5%~10%, conveying efficiency η increases by 2%~4%, and overall production capacity Q increases by 5%~15%; When lightweight level At that time, the collection efficiency Increase ≤2%, equipment propulsion speed v increases ≤5%, conveying efficiency η increases ≤2%, overall production capacity Q increases ≤5%; The influence of the dynamic load type of mining equipment on mining production capacity is as follows: When dynamic load type At that time, the collection efficiency A decrease of ≤2% in propulsion speed v and conveying efficiency η, and a decrease of ≤2% in overall production capacity Q, will result in a decrease of ≤5% in total production capacity Q. When dynamic load type At that time, the collection efficiency A decrease of ≤5% in the propulsion speed v is 5%~10% decrease, a decrease of ≤2% in the conveying efficiency η is 5%~12% decrease, and an overall capacity Q is 5%~12% decrease. When dynamic load type At that time, the collection efficiency A decrease of 5%~8%, a decrease in propulsion speed v of 10%~20%, a decrease in conveying efficiency η of ≤5%, and a decrease in overall production capacity Q of 15%~25%; When dynamic load type At that time, the collection efficiency The overall production capacity (Q) decreased by 20% to 35%, with a decrease of 8% to 12%, a decrease of 15% to 25%, a decrease of 5% to 10%, and a decrease of 8% to 12%. When dynamic load type At that time, the collection efficiency A decrease of 5%~10%, a decrease of ≤5% in propulsion speed v, a decrease of ≤2% in conveying efficiency η, and a decrease of 10%~15% in overall production capacity Q; Dynamic load type At that time, the collection efficiency The overall production capacity Q decreased by 15% to 20%, with a decrease of 6% to 9%, a decrease of 10% to 15%, a decrease of ≤2%, and a decrease of 6% to 9% in the propulsion speed v. When dynamic load type At that time, the collection efficiency The overall production capacity Q decreased by 15% to 20%, with a decrease of 5% to 8%, a decrease of 10% to 15%, a decrease of ≤2%, and a decrease of 5% to 8% in the propulsion speed v. When dynamic load type At that time, the collection efficiency The decrease is ≤2%, the decrease in propulsion speed v is ≤5%, the decrease in conveying efficiency η is 3%~7%, and the decrease in overall production capacity Q is 5%~12%; When dynamic load type At that time, the collection efficiency With a decrease in both conveying efficiency η and propulsion speed v, the overall production capacity Q decreases by 8% to 15%. The comprehensive discrimination system of mining production capacity is as follows: When the mining capacity Q ≥ 120 t / h and the weight-to-capacity ratio K ≤ 0.18, the current mining equipment is deemed to have high capacity support capability and should maintain its current lightweight design. and dynamic load type The combination allows for direct entry into the reliability assessment phase; When the mining production capacity 90t / h ≤ Q < 120t / h and the weight - to - production - capacity ratio 0.18 < K ≤ 0.30, it is determined that there is a bottleneck in the mining efficiency of the current mining equipment, and it enters the multi - objective collaborative optimization stage; When the mining capacity Q < 90 t / h or the weight-to-capacity ratio K > 0.30, it is determined that the current mining equipment capacity is limited and does not meet the target operation requirements. The process should return to step S200 to reset the lightweighting level. and dynamic load type Range, reconstruct the initial parameter input matrix .

4. The deep-sea mining equipment design method based on lightweight and dynamic load as described in claim 3, characterized in that, The reliability of the mining equipment in step S500 is determined by the structural strength margin index. Fatigue life factor and operational stability level The reliability assessment model relationship is jointly determined as follows: ,in Lightweight level With dynamic load type The coupling parameter pair; The influence of the lightweight level of mining equipment on mining reliability is as follows: When lightweight level At that time, structural strength margin index A 15% to 25% decrease in fatigue life factor A decrease of 20%~30%, operational stability level A decrease of 5% to 10%, and a decrease in overall reliability R of 20% to 35%; When lightweight level At that time, structural strength margin index A decrease of 5% to 10% in fatigue life factor Decrease of 8%~15%, operational stability level A decrease of 3% to 6%, and a decrease in overall reliability R of 10% to 20%; When lightweight level At that time, structural strength margin index A decrease of 2% to 5% in fatigue life factor Decrease of 5%~8%, operational stability level A decrease of 2% to 4%, and a decrease in overall reliability R of 5% to 10%; When lightweight level At that time, structural strength margin index A decrease of ≤2% in fatigue life factor Decrease ≤3%, operational stability level The overall reliability R decreases by ≤5% with a decrease of ≤2%. The influence of the dynamic load type of mining equipment on mining reliability is as follows: When dynamic load type At that time, structural strength margin index A decrease of ≤5% in fatigue life factor Decrease ≤5%, operational stability level The overall reliability R decreases by ≤8% with a decrease of ≤3%. When dynamic load type At that time, structural strength margin index A decrease of ≤8% indicates a fatigue life factor. Decrease of 8%~12%, operational stability level A decrease of ≤5% results in a decrease in overall reliability R of 8%~15%; When dynamic load type At that time, structural strength margin index A decrease of 10% to 15% in fatigue life factor Decrease of 15%~20%, operational stability level Decreases by 8% to 12%, and overall reliability (R) decreases by 15% to 22%. When dynamic load type At that time, structural strength margin index A 15% to 25% decrease in fatigue life factor A decrease of 20%~30%, operational stability level A decrease of 10%~15%, and a decrease in overall reliability R of 20%~30%; When dynamic load type At that time, structural strength margin index A decrease of 5% to 8% in fatigue life factor Decrease of 8%~12%, operational stability level A decrease of ≤5% results in a decrease in overall reliability R of 8%~15%; When dynamic load type At that time, structural strength margin index A decrease of 8% to 12% in fatigue life factor Decrease of 12%~18%, operational stability level A decrease of 5% to 8%, and a decrease in overall reliability R of 12% to 20%; Dynamic load type At that time, structural strength margin index A decrease of 10% to 14% in fatigue life factor Decrease of 12%~18%, operational stability level A decrease of 5% to 8%, and a decrease in overall reliability R of 12% to 20%; Dynamic load type At that time, structural strength margin index A decrease of 5% to 8% in fatigue life factor Decrease of 8%~12%, operational stability level A decrease of 8% to 12%, and an overall reliability (R) decrease of 8% to 15%; When dynamic load type At that time, structural strength margin index A decrease of 8% to 12% in fatigue life factor Decrease of 8%~12%, operational stability level A decrease of 5% to 10%, and a decrease in overall reliability R of 8% to 15%; The comprehensive discrimination system of mining reliability is as follows: When the reliability index R ≥ 0.85 and the equipment self-sufficiency time At that time, it was determined that the current mining equipment possessed a high level of reliability and that the existing lightweight design should be maintained. and dynamic load type The combination allows for direct entry into a high-capacity, high-reliability parameter output stage. When the reliability index is 0.70 ≤ R < 0.85 and the equipment self-sufficiency time When the time is T~1.5T, the reliability of the current mining equipment is judged to be at a medium level, and it should enter the multi-objective collaborative optimization stage; When the reliability index R < 0.70 or the equipment endurance time If it is determined that the current mining equipment has a major reliability defect and does not meet the target operation requirements, the process should return to step S200 and reset the lightweight level. With dynamic load type Range, reconstruct the initial parameter input matrix And reassess.

5. The deep-sea mining equipment design method based on lightweight and dynamic load as described in claim 4, characterized in that, The optimization modes in step S600 are as follows: Mode A: Production capacity optimization, the triggering condition is in step S400: 90t / h ≤ Q < 120t / h and 0.18 < K ≤ 0.30; Mode B: Reliability optimization, triggered by the condition in step S500: 0.70 ≤ R < 0.85 and ; Mode C: Composite optimization, enabled when both mode A and mode B are triggered simultaneously; The specific steps of the optimization mode include: S610: Select parameters from modes A, B, and C that are highly sensitive to mining capacity Q and overall reliability R as design variables. This constitutes the design variable set: ; Among them, each variable covers the lightweight level Directly related structural cross-sections, material density, and material layout proportions; and dynamic load types. Related structural topology connection forms and reinforcement of boundary area layout; S620: Adopt a differential topology optimization strategy to solve the optimal configuration at the structural level, specifically including: S621: Input matrix according to initial parameters Based on the geometry and performance parameters of the mining equipment and the design variable set X, a finite element model is constructed, targeting the lightweight level. Set design space volume constraints : When lightweight level At that time, design space volume constraints ; When lightweight level At that time, design space volume constraints ; When lightweight level At that time, design space volume constraints ; When lightweight level At that time, design space volume constraints ; S622: For dynamic load types Set boundary conditions: When dynamic load type for When static load boundary conditions are applied; When dynamic load type for At that time, frequency domain excitation load boundary conditions are used; When dynamic load type for At that time, transient dynamic boundary conditions are used; S623: For different optimization modes, set the objective function: Mode A'': The objective function is to maximize the anti - disturbance stiffness and the equipment propulsion speed v; Mode B'': The objective function is to maximize the structural strength margin index. With fatigue life factor ; Mode C'': The objective function is the weighted optimization of static stiffness, frequency stiffness and impact stiffness; S624: Introduces a continuous density method to parametrically control the density of structural elements, achieving continuous discretization of material distribution in lightweight structural elements, targeting lightweight grades. Minimum density threshold for different materials for: When lightweight level hour, ; When lightweight level hour, ; When lightweight level hour, ; When lightweight level hour, ; S625: Output the topology - optimized structure, and the topology - optimized structure is an initial lightweight structure with high stiffness and low mass - ratio characteristics; S630: Use the multi - objective genetic algorithm to conduct collaborative optimization solution for the topology - optimized structure, specifically including: S631: Segmentally encode the structural configuration, module layout, and control parameters, embed the optimization mode label, and construct a multi - source parameter composite coding structure; S632: The initial population is matched with the high-productivity-high-reliability regions in steps S400 and S500. Combining the results of topology optimization in step S620 with the core of the solution, we can form an initial solution with structure-function synergy. S633: Adopt a multi - objective fitness function group, and conduct solution set screening through the Pareto sorting and crowding mechanism. The multi - objective fitness functions and screening mechanisms adapted to different optimization modes are: Mode A': The fitness function is F = 0.8Q + 0.2R, the constraint conditions are R ≥ 0.85, K ≤ 0.30, select the solution with a high Q value in the Pareto sorting, and increase the penalty term for K in the crowding degree calculation; Mode B': Fitness function is F = 0.2Q + 0.8R, constraint is Q ≥ 120 t / h. ,choose and An equilibrium solution eliminates vibration-sensitive configurations; Mode C': The fitness function is F = 0.5Q + 0.5R, the constraint conditions are Q ≥ 120 t / h, R ≥ 0.85, and trigger the elite retention strategy when the Pareto front coverage rate > 90%; S634: According to Combined adaptive adjustment of genetic parameters, including mutation rate, crossover method and population size; The mutation rate is 0.25~0.30, the crossover method is three-point crossover, and the population size is 100~120. The mutation rate is 0.20~0.25, the crossover method is arithmetic crossover, and the population size is 80~100. The mutation rate is 0.10~0.15, the crossover method is uniform crossover, and the population size is 60~80. The mutation rate is 0.15~0.20, the crossover method is simulated binary crossover, and the population size is 70~90. S635: Set the convergence criterion as the objective function variance < ε and the Pareto boundary stability, ensuring that the optimization process focuses on the objective function. The feasible solution set for the scenario; S636: Output Pareto solution set Each solution set comes with a corresponding... and The identifier is used for subsequent solution decisions and operational condition adaptability verification.

6. The deep-sea mining equipment design method based on lightweight and dynamic load as described in claim 5, characterized in that, The verification of the production capacity of the mining equipment in step S700 includes: S710: Substitute the Pareto optimal solution set from S636 into the mining capacity assessment model constructed in step S400, and calculate the corresponding... and Mining capacity Q: If the optimized solution satisfies Q≥120t / h and K≤0.18, then the optimized solution is in the corresponding... and It possesses both high production capacity and low energy consumption, and its production capacity has been verified. If there is a solution that does not meet the conditions, it is necessary to return to step S600, adjust the design variable range and optimization algorithm parameters, and conduct collaborative optimization again; The reliability verification includes: S720: Substitute the Pareto optimal solution set in S600 into the mining equipment reliability evaluation model constructed in step S500, and calculate the reliability index R: If all solutions satisfy R≥0.85 and This indicates that the designed structure is currently... and It possesses high reliability and self-sustaining time, and reliability verification has been passed. If any proposed solution does not meet the requirements, the relevant design variables and structural configuration parameters are adjusted accordingly, and the process is returned to step S600 for targeted optimization.

7. The deep-sea mining equipment design method based on lightweight and dynamic load as described in claim 6, characterized in that, When acquiring operation parameters in step S100, the operation water depth H and water pressure change data are monitored in real time through a deep-sea sensor network. The sensor network includes pressure sensors and depth sensors, and the sensor accuracy error range is controlled within ±0.5%.

8. The deep-sea mining equipment design method based on lightweight and dynamic load as described in claim 7, characterized in that, In step S210, when classifying lightweight levels, the lightweight level is... The weight reduction target is achieved by using a gradient composite process of carbon fiber reinforced composite materials and titanium alloy, and the strength of the composite structure is ensured by electron beam welding technology.

9. A design method for deep-sea mining equipment based on lightweight and dynamic load as described in claim 8, characterized in that, Identifying the dynamic load type in step S220 At that time, multiphysics coupling simulation software was used to simulate composite loads. By setting fluid-solid-thermal coupling boundary conditions, the resultant force range and the proportion of each load were accurately calculated.

10. A design method for deep-sea mining equipment based on lightweight and dynamic load as described in claim 9, characterized in that: Extraction efficiency in step S400 mining capacity assessment model The equipment propulsion speed v and conveying efficiency η are dynamically predicted using a deep learning model. This deep learning model is trained based on historical operation data, and the input is the lightweight level. Dynamic load type Including real-time environmental parameters, outputting the predicted values ​​of the corresponding production capacity impact parameters; In step S500, the reliability assessment model for mining equipment incorporates acoustic emission monitoring technology to acquire real-time data on structural fatigue crack propagation, which is used to correct the fatigue life factor. The computational model; In step S620, the topology optimization strategy adopts an adaptive mesh generation technique to automatically refine the mesh in stress concentration areas, thereby improving the calculation accuracy of the finite element model. The refinement ratio is no less than 1 / 4 of the basic mesh. In the multi-objective genetic algorithm of step S630, a dynamic load weight adjustment module is set to dynamically adjust the weight coefficients of the capacity and reliability objectives in the Pareto sorting, with a weight range of 0.3-0.

7. In step S700, accelerated life testing is used to obtain life data of key components of mining equipment in a short time by increasing the test stress level. Combined with Chaboche's nonlinear cumulative damage theory, the reliability of the equipment within the design life cycle is verified. Step S800 Output Matrix In this study, confidence intervals were set for each parameter, and the impact of parameter fluctuations on production capacity and reliability was calculated using the Monte Carlo simulation method.

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