A Global Cost Optimization Method Based on Multi-System Safety in Deep-Sea Mining Operations

Abstract

This invention discloses a global cost optimization method for the safety of deep-sea mining operation systems. The method includes the following steps: determining the design parameters of each system based on global cost optimization and the coupling relationship between systems; optimizing the tracked walking system by selecting materials based on light load and stability and incorporating self-cleaning nozzles; optimizing the buoyancy compensation system using a bidirectional hydraulic-pneumatic energy conversion mechanism; optimizing the surface support system based on safety-reducing technology and employing a hybrid power efficiency optimization algorithm; optimizing the deployment and recovery system by utilizing planetary gears to amplify torque and employing an electrolytic water-triggered release mechanism during deployment and recovery; and optimizing the three-degree-of-freedom active-passive combined heave compensation system based on the principle of low energy consumption and using three-axis IMU data fusion technology. This method effectively addresses the problem of high costs in deep-sea mining, achieving safety-reducing and cost-efficiency-enhancing effects for deep-sea mining systems, and optimizing the overall cost of deep-sea mining operations.

Description

Technical Field

[0001] This invention relates to the field of deep-sea mining operation technology, and specifically to a global cost optimization method based on the safety of multiple systems in deep-sea mining operations. Background Technology

[0002] As global demand for resources continues to increase, deep-sea mineral resources are considered to have strategic potential and have attracted increasing attention. Deep-sea mineral resources are abundant and diverse, with polymetallic nodules, polymetallic sulfides, cobalt-rich crusts, and rare-earth-rich mud being the main types of mineral resources found on the global seabed. These resources are rich in various metallic elements such as manganese, copper, nickel, cobalt, rare earth elements, gold, silver, zirconium, chromium, tungsten, tin, molybdenum, antimony, and lithium. These metals have important applications in electronics, renewable energy, and aerospace, making the effective collection of deep-sea minerals while minimizing environmental impact a significant research challenge.

[0003] Deep-sea mining systems, as a comprehensive technological system for developing seabed mineral resources, have gradually evolved from early exploration and testing into an intelligent system centered on surface mother ships, subsea mining vehicles, and pipeline hoisting systems. However, this system still faces many challenges, including adaptability to extreme high-pressure environments, ecological damage risks, and high operating costs. Therefore, the deep-sea mining field urgently needs a global cost optimization method based on the safety of multiple systems in deep-sea mining operations to improve mining efficiency and energy utilization, reduce energy consumption, achieve safety reduction in mining operations, and reduce costs and increase efficiency for multiple mining vehicle systems, thereby lowering the overall cost of mining operations.

[0004] Based on the above-mentioned practical problems, existing technologies have made some design and optimization to deep-sea mining operation systems, but the following problems still exist: 1. Most of them adopt independent optimization mode of local subsystems, lacking a multi-system integration and collaborative optimization mechanism, resulting in the ineffective utilization of the coupling effect between subsystems and the overall operating cost not reaching the optimal state; 2. The use of high-density structural materials in traditional deep-sea mining equipment leads to excessive stress on the traveling mechanism, resulting in a significant increase in power consumption and limiting the efficiency of ore collection operations; 3. The lack of a dynamic resource scheduling mechanism based on multi-source sensor data flow leads to insufficient matching between energy allocation strategy and working conditions; 4. The failure to optimize the three-degree-of-freedom active-passive combined heave compensation system results in low compensation accuracy and high energy consumption; 5. The lack of data sharing between various systems in deep-sea mining operations prevents good coordination of the collaborative effects between systems, resulting in unnecessary cost consumption. Summary of the Invention

[0005] The present invention aims to overcome at least one of the defects of the prior art and provide a global cost optimization method based on the safety of multiple systems in deep-sea mining operations, so as to solve the problems of high cost, low efficiency, low efficiency ratio, high energy consumption and low energy utilization rate in deep-sea mining operations.

[0006] This invention provides a global cost optimization method based on the safety of multiple systems in deep-sea mining operations, comprising the following steps:

[0007] S1: Based on global cost optimization and the coupling relationship between various systems, determine the design parameters of each system;

[0008] S11. Construct a function-load-energy consumption mapping table for buoyancy compensation, tracked movement, heave compensation, deployment and recovery, and surface support systems. Quantify the energy transfer paths and mechanical coupling relationships between each subsystem. Specifically:

[0009] S111: Determine the core functions of each system;

[0010] The core functions of each system are as follows:

[0011] Tracked walking system: The mining vehicle is driven by a pressure-resistant and corrosion-resistant track structure, which can adapt to complex seabed terrain and ensure reliable movement.

[0012] Buoyancy compensation system: dynamically adjusts the buoyancy of the equipment to adapt to changes in deep-sea pressure and maintain a stable hovering or operating posture;

[0013] Surface support system: Provides power, communication, navigation and emergency support, and coordinates various subsystems to achieve efficient operation throughout the entire process;

[0014] Deployment and recovery system: Safely deploys and recovers deep-sea equipment, integrating real-time monitoring and emergency mechanisms to reduce operational risks;

[0015] Heave compensation system: counteracts the heave motion of the ship's hull on the water surface, ensuring the stability of underwater equipment and the continuity of operations.

[0016] S112: Defines the load type, which is divided into static load, dynamic load and fatigue load;

[0017] S113: Perform mechanical coupling between the systems and establish the coupling relationship between them;

[0018] S114: Construct a function-load-energy consumption mapping table and determine the coupling influence coefficients between each system;

[0019] S12. Using the Quality Function Deployment (QFD) method, mining efficiency, system reliability, and cost control requirements are transformed into design parameters for each subsystem. Specifically:

[0020] S121: Define optimization requirements, including mining efficiency, system reliability, and cost control;

[0021] S122: Translate requirements into technical parameters;

[0022] S123: Construct a House of Quality. First, analyze the correlation between requirements and technical parameters by establishing a relationship matrix; second, conduct competitive analysis to compare with industry benchmarks and set target values ​​for technical parameters; finally, construct a roof matrix to analyze conflicts between technical parameters.

[0023] S124: Determine the target values ​​for each technical parameter to be transformed, including: Mining efficiency: 40%~60% increase in continuous operation time, production capacity reaching 120~150 tons / hour, and automation level increasing by 30%~40%; System reliability: Maintain mean time between failures (MTBF) above 95%, reduce redundancy design ratio by 40%~80%, and extend maintenance interval by 40%~50%; Cost control: Reduce material costs by 20%~30%, reduce energy consumption by 20%~50%, and increase system integration by 30%~50%. Decompose each technical parameter into the design of each subsystem; Resolve conflicts between system design parameters, and optimize the design scheme based on optimizing the overall mining cost.

[0024] S2: Based on light load and stability, the tracked walking system is optimized. The deep-sea mining vehicle uses titanium alloy material, and the track is a lightweight composite structure composed of hollow titanium alloy material and carbon fiber reinforced polyimide track plate. The surface is sprayed with superhydrophobic nanomaterials and equipped with self-cleaning nozzles to clean the mud adhering to the track, further reducing the load of the mining vehicle and reducing the energy consumption of mining operations by more than 20% and the operating cost by more than 25%.

[0025] Based on the principle of light load and stability, the material selection is determined by the following: the mining vehicle is made of titanium alloy, and the tracks are made of a lightweight composite structure composed of hollow titanium alloy and carbon fiber reinforced polyimide track plates, with superhydrophobic nanomaterials sprayed on the surface; the nozzles are made of titanium alloy and coated with titanium nitride, reducing the load on the mining vehicle by 30% to 60%.

[0026] The nozzle is designed to be circular with a contraction-expansion structure in the internal flow channel. It also has a built-in self-rotating filter that automatically scrapes away large particles of impurities, thereby accelerating the water flow and enhancing the impact force. The performance indicators of the nozzle are set according to functional requirements, including: controllable adjustment of spray pressure within the range of 30~70 Bar, coverage area of ​​at least 90% of the track surface, cleaning cycle of 15 minutes / time with dynamic adjustment, and energy consumption of ≤0.5kWh per spray.

[0027] Select nozzles with an angle of 25°~35°, a diameter of 6~12 mm, a spacing of 20~50 cm, and arrange the nozzles horizontally around the outer side of the mining vehicle track.

[0028] The nozzle triggering mechanism and pressure regulation mode are set. The nozzle triggering mechanism is dynamic triggering, which is based on feedback from the torque sensor. Cleaning is started immediately when the travel resistance coefficient is >0.2. The viscosity of the sediment is obtained through the vehicle camera and AI image recognition, and the water pressure is adjusted in real time through the proportional valve to automatically match the optimal pressure. The travel resistance coefficient is a dimensionless parameter used to quantify the relationship between the total resistance and the positive pressure when the vehicle moves on the seabed. It reflects the comprehensive resistance characteristics between the vehicle body and the seabed sediment and water body. Here, it is used to define the self-cleaning nozzle start threshold.

[0029] The nozzle clogging status is monitored in real time by a flow sensor. When clogging occurs, a reverse flushing mode is triggered to achieve self-diagnosis of nozzle malfunctions.

[0030] S3: Optimize the buoyancy compensation system and establish an energy recovery mechanism using a two-way hydraulic-pneumatic energy conversion mechanism. The energy recovery efficiency reaches more than 38%, reducing energy consumption by 20% to 30% and reducing the total life cycle cost by more than 30%.

[0031] The hydraulic cylinder is a double-acting hydraulic cylinder with hydraulic oil flowing through both sides of the piston. The piston rod end is rigidly connected to the air bladder. The cylinder body is made of titanium alloy (compressive strength ≥150MPa), and the sealing ring is made of fluororubber (pressure resistance 55~65 MPa).

[0032] The airbag assembly uses a variable volume airbag assembly with a layered structure. The inner layer is a silicone rubber mold with a thickness of 0.8~1 mm, and the outer layer is a Kevlar fiber braided layer (tear resistance ≥500 N / mm). The airbag is divided into four independent compartments, each equipped with a solenoid valve for segmented control (response time ≤10 ms).

[0033] The energy storage unit uses a high-pressure gas tank (working pressure 30 MPa) and a hydraulic energy storage device (pressure range 0~60 MPa) connected in parallel, and adopts a high-pressure gas-liquid converter to regulate the connection between the oil circuit and the gas circuit through a proportional valve group;

[0034] An energy recovery mechanism is set up so that energy can be recovered during the descent phase of the deep-sea mining vehicle;

[0035] Energy recovery includes:

[0036] ① Seawater pressure drive: When the mining vehicle sinks, the external seawater pressure acts on the rodless chamber of the hydraulic cylinder, pushing the piston to compress the gas in the airbag to store energy;

[0037] ②Gas compression energy storage: The compressed gas enters the high-pressure gas tank through a one-way valve to store energy;

[0038] ③ Hydraulic energy storage: Hydraulic oil flows from the rod chamber into the accumulator to store pressure energy.

[0039] An energy release mechanism is set up so that energy is released during the ascent phase of the deep-sea mining vehicle.

[0040] Energy release includes:

[0041] ① Gas expansion does work: The gas in the high-pressure tank is released, which pushes the piston to move in the opposite direction, assisting the expansion of the airbag to provide buoyancy;

[0042] ② Hydraulic energy assistance: The accumulator releases hydraulic oil to drive the hydraulic cylinder, enhancing the upward thrust.

[0043] The effectiveness of an organization's operations is evaluated, including energy recovery rate indicators and dynamic response indicators:

[0044] ① Energy recovery rate index: The target value is required to be ≥45%; among which : Recovered energy; Energy consumed.

[0045] ② Dynamic response index: Stage response adjustment time < 0.8s;

[0046] S4: Based on safety and load reduction technology, the surface support system is optimized. A hybrid power efficiency optimization algorithm is adopted to realize dynamic scheduling of multiple energy sources in the surface support system, optimize the energy distribution ratio in real time, improve the overall energy efficiency of the system, save fuel by 20%~28%, reduce lithium battery temperature fluctuation by 25%~35%, reduce carbon emissions by 30%~35%, improve the overall energy efficiency of the system by 20%~30%, and reduce the total life cycle cost by more than 35%.

[0047] S41. Construct a hybrid power efficiency optimization algorithm:

[0048] S411: Input real-time data, including lithium battery state of charge (SOC), diesel generator load rate, power requirements for the current mission phase (walking, data collection, recovery), and environmental parameters (sea state, sea temperature, ambient temperature and humidity).

[0049] S412: Input the predicted power demand for the next hour of operation and the lithium battery health status degradation model to predict lithium battery life. Lithium battery life prediction model:

[0050]

[0051] in : Change in depth of discharge; Activation energy (related to battery chemical properties); Gas constant Battery temperature; : Degradation rate constant; SOH: State of health of lithium battery;

[0052] S413: Optimize the objective function of the hybrid power efficiency optimization algorithm, wherein the objective function is as follows:

[0053]

[0054] in Diesel fuel consumption cost; : Lithium battery cycle loss cost; Carbon emission penalty costs; Dynamic weighting coefficients, adjusted according to task priority;

[0055] S414: Define the constraints: the total power output of the system is greater than the current load demand, the state of charge (SOC) of the lithium battery is maintained at 20%~90% to prevent overcharging and over-discharging, and the load rate of the diesel generator is greater than 30%.

[0056] S42. Establish a sensor network and deploy current and voltage sensors (accuracy ±0.5%), oil consumption and flow meters (accuracy ±0.1%), and environmental sensors (temperature, humidity, and wave height). Use the Precision Time Protocol (PTP) to align the sensor times and ensure consistency of time for multi-source data.

[0057] S43. Optimize the model dynamically based on the real-time received sensor data, and set the rolling optimization window to optimize once every fifteen minutes;

[0058] S44. Set real-time decision-making logic, determine mode switching rules, and reduce operating energy consumption. The mode switching rules are as follows:

[0059] ① Pure electric mode: When the lithium battery SOC is greater than 60% and the load is less than 200 KW, only the lithium battery is used for power supply;

[0060] ② Hybrid mode: When the lithium battery SOC is between 20% and 60% or the load is greater than 600 KW, the lithium battery and diesel generator are used together for power supply;

[0061] ③ Fuel priority mode: In severe sea conditions (wave height > 3 m) or emergency missions, the diesel engine is used as the main power source, with the lithium battery as a backup;

[0062] S45. Receive algorithm results, realize dynamic scheduling of multiple energy sources in the surface support system, optimize energy allocation ratio in real time, and reduce the cost of deep-sea mining operations by reducing energy consumption.

[0063] S5: Optimize the deployment and retrieval system. Based on the light load of the mining vehicle, reduce energy consumption during deployment and retrieval. During the deployment and retrieval phase, use a planetary gear set to drive the winch to achieve high-torque cable deployment and retrieval, reducing energy consumption by more than 30%. An electrolysis-triggered release mechanism is also installed to enable emergency floating or load release of the deployment and retrieval equipment in case of emergencies, improving operational safety and preventing equipment damage. The deployment and retrieval system includes the deployment and retrieval of the delivery hose and the deployment and retrieval of the deep-sea mining vehicle.

[0064] Planetary gear sets are used as recovery gears to amplify torque by more than 125 times. The gear material is carburized and quenched alloy steel with a surface hardness of ≥60 HRC, which improves wear resistance by 30%.

[0065] The working process of the planetary gear set is as follows:

[0066] ① In the first stage of transmission, the power input shaft drives the sun gear, the sun gear meshes with the planet gears, and the planet gears simultaneously mesh with the fixed internal gear ring, forming a speed reduction and torque increase effect;

[0067] ②The secondary transmission planetary carrier serves as the output end, transmitting power to the next stage planetary gear set via the transmission wheel shaft, thereby achieving multi-stage torque amplification;

[0068] ③ Receive feedback from the torque sensor and dynamically adjust the motor input speed to avoid overload.

[0069] Electrolyzers and sensors are designed inside the collection vessel, including positive and negative electrodes and ion exchange membranes. The electrode spacing is controlled at 1~2 mm, and the voltage is usually below 12V to ensure low energy consumption and fast response (trigger time <1s).

[0070] The hydrogen and oxygen produced by electrolysis are stored in a high-pressure chamber with a pressure resistance of >30MPa, and the flow direction is controlled by a one-way valve;

[0071] Receive sensor data;

[0072] The sensor signals are analyzed. If the sensor detects an emergency signal (cable tension exceeding the limit or system failure), the trigger circuit is energized, and the gas pushes the piston to cut off the mechanical connection (umbilical cable connection), reducing losses and operating and maintenance costs.

[0073] S6: Optimize the three-degree-of-freedom active-passive combined heave compensation system by integrating an active Z-axis compensation mechanism driven by a servo motor, a passive XY-axis compensation mechanism driven by a magnetorheological damper, and three-axis IMU data fusion technology. The three-axis IMU data fusion technology provides real-time feedback on the heave compensation effect. The active-passive coordinated control improves the heave compensation accuracy from the traditional ±0.5 m to ±0.15 m. Dynamic resource allocation is performed. Compared with the fully active solution, the overall system energy consumption is reduced by more than 40%. The IMU data fusion technology makes the attitude calculation error <0.1° and the failure rate <0.1 times / thousand hours.

[0074] A high-precision servo motor (accuracy ±0.01 mm) combined with a ball screw (including servo motor, ball screw, feedback device, controller, and driver) is used to adjust the Z-axis displacement in real time, providing dynamic adjustment capability to compensate for the heave motion of the mother ship. Specifically:

[0075] ① Input the target parameters, and the controller will convert the instructions into electrical signals and send them to the servo driver through the communication protocol;

[0076] ② The driver receives control signals and adjusts the three-phase current to drive the motor;

[0077] ③ The rotational motion of the ball screw is converted into the linear motion of the nut (load platform);

[0078] ④ The encoder records the angular displacement of the motor rotor, the grating ruler directly detects the load position, the controller compares the target value with the actual value, dynamically calculates the correction amount, and adjusts the output signal through the PID algorithm to eliminate steady-state error;

[0079] ⑤ When the external load changes suddenly or the vibration triggers abnormal feedback, the controller increases the current output or decreases the speed to maintain constant thrust and makes dynamic adjustments by predicting the trajectory error;

[0080] ⑥ After the load reaches the target, the servo motor maintains torque, and the ball screw's self-locking characteristic prevents reverse slippage. Upon receiving a stop command, the controller stops the machine according to the preset deceleration curve to avoid mechanical shock.

[0081] The driver employs a multi-loop control system: the current loop controls the motor torque with the fastest response time (ms-level); the speed loop adjusts the rotational speed based on encoder feedback; and the position loop ensures final positioning accuracy. The servo motor rotor rotates, and the output shaft is rigidly connected to the ball screw via a coupling, directly driving the ball screw's rotation. The ball screw structure consists of a screw and a nut. The screw surface has precision threaded raceways that drive the balls to circulate during rotation. The ball grooves inside the nut mesh with the screw, converting rotation into linear motion.

[0082] The viscosity of the magnetorheological fluid is adjusted by current (0-5 A current corresponds to a damping force of 1-50 KN·S / m), and the XY axis damping is adjusted in real time according to the sway acceleration. The damping characteristics are used to absorb high-frequency vibrations, taking into account both response speed and stability.

[0083] Optimize the active-passive collaborative strategy and implement joint active-passive dynamic resource allocation:

[0084] ① In the low-intensity motion phase with wave height less than 1m: activate the passive damping-dominant mode, with passive damping accounting for 70%~80% and active compensation accounting for 20%~30%;

[0085] ②In the moderate-intensity motion phase with wave height greater than 2 m and less than 3 m:Activate the active compensation-dominant mode, with active compensation accounting for 60%~80% and passive compensation accounting for 20%~40%;

[0086] ③ During high-intensity motion phases with wave heights greater than 3 m: Active compensation is prioritized, while passive damping-dominated mode is switched during stable phases to reduce energy consumption;

[0087] If the active system fails, the passive damping will automatically switch to the maximum damping state and maintain more than 50% of the compensation capacity for at least 30 minutes.

[0088] A three-axis IMU data fusion technology is used to integrate data from gyroscopes, accelerometers, and magnetometers, and combined with filtering algorithms and attitude calculation methods to generate high-precision attitude information, providing real-time feedback for heave compensation. The workflow of the three-axis IMU data fusion technology is as follows:

[0089] T1: Calibrate and preprocess the sensor;

[0090] T2: Employs a data fusion algorithm and complementary filtering: fuses the high-frequency characteristics of the gyroscope and the low-frequency characteristics of the accelerometer, using the following formula:

[0091]

[0092] in The weighting coefficient is typically 0.96 to 0.98, and is dynamically adjusted to balance noise and drift.

[0093] T3: Perform attitude calculation and coordinate system transformation, using the Runge-Kutta method to update quaternions and avoid Euler angle gimbal lock issues. The formula is:

[0094]

[0095] in To represent quaternion multiplication, The angular velocity vector is used; the IMU body coordinate system is converted to the geographic coordinate system using a rotation matrix to ensure that the compensation command is consistent with the heave direction;

[0096] T4: To ensure real-time performance and synchronization optimization, multi-threaded processing is adopted. IMU data acquisition (1KHz), filtering (100Hz), and calculation (100Hz) are run in separate threads, with the delay controlled within 2ms. The time base of the IMU and the heave compensation controller is aligned through hardware trigger signals to avoid phase errors and to provide real-time feedback to the heave compensation system.

[0097] T5: Monitors IMU data, realizes diagnosis and self-healing of three-axis IMU data fusion technology, maintains long-term feedback to the heave compensation system, and optimizes the total life cycle cost by more than 25%.

[0098] The system monitors IMU data anomalies in real time and triggers a three-level response mechanism: Level 1: Switch to backup filtering algorithm; Level 2: Disable faulty sensor channel and rely on redundant IMU data; Level 3: Initiate emergency ascent procedure.

[0099] Ultimately, based on the light load-stability optimization of the tracked walking system and buoyancy compensation system, a safety-reducing technology system for the deployment and recovery system and the surface support system was proposed. A low-energy-consumption three-degree-of-freedom active-passive combined heave compensation system was developed to achieve global cost optimization. Sensors were deployed to receive data from the deep-sea mining system in real time, enabling multi-system data sharing and dynamic resource allocation, thus optimizing global costs. Compared with traditional independent control systems, this can improve overall energy efficiency by more than 20% and reduce the global cost of the deep-sea mining operation system by more than 30%.

[0100] The beneficial effects of this invention are:

[0101] (1) The present invention is based on the design optimization of a light load-stability tracked walking system. By using high-strength composite materials as the main structure of the mining vehicle and configuring a self-cleaning nozzle system for the track, a dual-effect load reduction is achieved. The vehicle weight is reduced while maintaining structural strength, and the running resistance is reduced by more than 20% by removing track attachments in real time. This comprehensive solution reduces the operating cost of deep-sea mining operations by more than 25% and significantly improves the stability of passing through complex seabed terrain.

[0102] (2) The present invention adopts a bidirectional hydraulic-pneumatic energy conversion mechanism to establish an energy recovery mechanism. For the buoyancy compensation system, energy is recovered during the sinking stage with a recovery efficiency of ≥38%, and the recovered energy is maximized during the floating stage. This technology reduces the overall energy consumption of the system by 20%~30% and reduces the total cost of the mining operation system by more than 30%.

[0103] (3) Based on the safety and burden reduction technology system, the present invention uses a hybrid power efficiency optimization algorithm to dynamically schedule multiple energy sources in the water surface support system and optimize the energy allocation ratio in real time. This algorithm improves the comprehensive energy utilization rate by more than 30%. Under the premise of ensuring system reliability, fuel consumption is reduced by 20% to 28%, lithium battery cycle loss is reduced by 25% to 35%, and the associated effect reduces the overall operating cost of the whole life cycle by more than 35%.

[0104] (4) Based on the light load, the energy consumption during the deployment and recovery process is reduced, and the mining vehicle's walking motor drives the winch in reverse and the planetary gear set is used to amplify the torque, reducing energy consumption by more than 30%; during the deployment and recovery process, an electrolytic water trigger-type release mechanism is arranged to realize emergency release in case of emergency, significantly improving the safety redundancy of deep-sea operations, ensuring safety while avoiding equipment damage.

[0105] (5) The present invention uses three-axis IMU data fusion technology to optimize the three-degree-of-freedom active-passive joint heave compensation system, thereby improving the heave compensation accuracy from the traditional ±0.5m to ±0.15m, and performing dynamic resource allocation. Compared with the fully active solution, the overall energy consumption of the system is reduced by more than 40%. The IMU data fusion technology makes the attitude calculation error <0.1° and the failure rate <0.1 times / thousand hours. Attached Figure Description

[0106] Figure 1 Flowchart for implementing a global cost optimization method based on multi-system safety in deep-sea mining operations;

[0107] Figure 2 This diagram illustrates the mechanical coupling relationships between the various systems.

[0108] Figure 3 A flowchart illustrating the process of combining a servo motor with a ball screw. Detailed Implementation

[0109] The accompanying drawings illustrate the technical solutions of the embodiments of the present invention in more detail. Throughout the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions. The described embodiments are some, but not all, embodiments of the present invention. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0110] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0111] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0112] Example 1

[0113] A global cost optimization method based on the safety of multiple systems in deep-sea mining operations, such as Figure 1 As shown, it includes the following steps:

[0114] Step S1: Based on global cost optimization and the coupling relationship between various systems, determine the design parameters of each system;

[0115] S11. Construct a function-load-energy consumption mapping table for buoyancy compensation, tracked movement, heave compensation, deployment and recovery, and surface support systems. Quantify the energy transfer paths and mechanical coupling relationships between each subsystem. Specifically:

[0116] S111: Determine the core functions of each system;

[0117] S112: Defines the load type, which is divided into static load, dynamic load and fatigue load;

[0118] S113: To perform mechanical coupling between various systems, such as Figure 2 As shown, establish the coupling relationships between the various systems;

[0119] S114: Construct a function-load-energy consumption mapping table and determine the coupling influence coefficients between each system;

[0120] S12. Using the Quality Function Deployment (QFD) method, mining efficiency, system reliability, and cost control requirements are transformed into design parameters for each subsystem. Specifically:

[0121] S121: Define optimization requirements, including mining efficiency, system reliability, and cost control;

[0122] S122: Translate requirements into technical parameters;

[0123] S123: Construct a House of Quality. First, analyze the correlation between requirements and technical parameters by establishing a relationship matrix; second, conduct competitive analysis to compare with industry benchmarks and set target values ​​for technical parameters; finally, construct a roof matrix to analyze conflicts between technical parameters.

[0124] S124: Determine the target values ​​for each technical parameter to be transformed: Mining efficiency: increase continuous operation time by 60%, reach a production capacity of 150 tons / hour, and increase automation level by 40%; System reliability: maintain mean time between failures (MTBF) above 95%, reduce redundancy design ratio by 80%, and extend maintenance interval by 50%; Cost control: reduce material costs by 30%, reduce energy consumption by 50%, and increase system integration by 50%. Decompose each technical parameter into the design of each subsystem; resolve conflicts between system design parameters, and optimize the design scheme based on optimizing the overall mining cost.

[0125] S2: Based on light load and stability, the tracked walking system is optimized. The deep-sea mining vehicle uses titanium alloy material, and the track is a lightweight composite structure composed of hollow titanium alloy material and carbon fiber reinforced polyimide track plate. Superhydrophobic nanomaterials are sprayed on the surface. While increasing mining stability through high-strength materials, the self-cleaning nozzle is equipped to clean the mud adhering to the track and further reduce the load of the mining vehicle.

[0126] Based on the principle of light load and stability, the material selection for the mining vehicle is determined by titanium alloy and the tracks are made of a lightweight composite structure composed of hollow titanium alloy and carbon fiber reinforced polyimide track plates, with superhydrophobic nanomaterials sprayed on the surface; the nozzles are made of titanium alloy and coated with titanium nitride, reducing the load on the mining vehicle by 40%.

[0127] The nozzle is designed to be circular with a contraction-expansion structure in the internal flow channel. It also has a built-in self-rotating filter that automatically scrapes away large particles of impurities, thereby accelerating the water flow and enhancing the impact force. The performance indicators of the nozzle are set according to functional requirements, including: controllable adjustment of spray pressure within the range of 30~70 Bar, coverage area of ​​at least 90% of the track surface, cleaning cycle of 10 minutes / time with dynamic adjustment, and energy consumption of ≤0.5 kWh per spray.

[0128] Select nozzles with an angle of 30°, a diameter of 10 mm, a spacing of 40 cm, and position them horizontally around the outside of the mining vehicle tracks;

[0129] The nozzle triggering mechanism and pressure regulation mode are set. The nozzle triggering mechanism is dynamic, and cleaning is started immediately when the travel resistance coefficient is greater than 0.2, based on feedback from the torque sensor. The viscosity of the sediment is obtained through the vehicle camera and AI image recognition, and the water pressure is adjusted in real time through the proportional valve to automatically match the optimal pressure.

[0130] The nozzle clogging status is monitored in real time by a flow sensor. When clogging occurs, a reverse flushing mode is triggered to achieve self-diagnosis of nozzle malfunctions.

[0131] By optimizing materials and incorporating self-cleaning nozzles to flush away deposits on the tracks, the tracked walking system can reduce the load by 70%, thereby reducing energy consumption and operating costs of mining vehicles by 20% and 25% respectively.

[0132] S3: Optimize the buoyancy compensation system by establishing an energy recovery mechanism for the buoyancy compensation system using a two-way hydraulic-pneumatic energy conversion mechanism;

[0133] The hydraulic cylinder is a double-acting hydraulic cylinder with hydraulic oil flowing through both sides of the piston. The piston rod end is rigidly connected to the air bladder. The cylinder body is made of titanium alloy (compressive strength ≥150 MPa), and the sealing ring is made of fluororubber (pressure resistance 55~65 MPa).

[0134] The airbag system uses a variable volume airbag system with a layered structure. The inner layer is a silicone rubber mold with a thickness of 1 mm, and the outer layer is a Kevlar fiber braided layer (tear resistance ≥500 N / mm). The airbag is divided into four independent compartments, each equipped with a solenoid valve for segmented control (response time ≤10 ms).

[0135] The energy storage unit uses a high-pressure gas tank (working pressure 30 MPa) and a hydraulic energy storage device (pressure range 0~60 MPa) connected in parallel, and adopts a high-pressure gas-liquid converter to regulate the connection between the oil circuit and the gas circuit through a proportional valve group;

[0136] An energy recovery mechanism is set up so that energy can be recovered during the sinking phase of the deep-sea mining vehicle: When the mining vehicle sinks, the external seawater pressure acts on the rodless chamber of the hydraulic cylinder, pushing the piston to compress the gas in the air bag to store energy; the compressed gas enters the high-pressure gas tank through the one-way valve to store energy; the hydraulic oil flows from the rod chamber into the energy storage device to store pressure energy.

[0137] An energy release mechanism is set up so that during the ascent phase of the deep-sea mining vehicle, energy is released: gas from the high-pressure tank is released to drive the piston to move in the opposite direction, assisting the airbag to expand and provide buoyancy; the accumulator releases hydraulic oil to drive the hydraulic cylinder, enhancing the ascent thrust.

[0138] The effectiveness of an organization is evaluated, including energy recovery rate (the ratio of recovered energy to consumed energy) and dynamic response indicators.

[0139] ① Energy recovery rate index: The target value is required to be ≥45%; among which : Recovered energy; Energy consumed.

[0140] ② Dynamic response index: Stage response adjustment time < 0.8s;

[0141] By optimizing the buoyancy compensation system through this energy conversion mechanism, the energy recovery efficiency reaches over 60%, thereby reducing energy consumption during extraction by 33% and reducing the total life cycle cost by 42%.

[0142] S4: Based on safety and load reduction technology, the surface support system is optimized. A hybrid power efficiency optimization algorithm is adopted to realize dynamic scheduling of multiple energy sources in the surface support system, optimize the energy allocation ratio in real time, and improve the overall energy efficiency of the system.

[0143] S41. Construct a hybrid power efficiency optimization algorithm:

[0144] S411: Input real-time data, including lithium battery state of charge (SOC), diesel generator load rate, power requirements for the current mission phase (walking, data collection, recovery), and environmental parameters (sea state, sea temperature, ambient temperature and humidity).

[0145] S412: Input the predicted power demand for the next hour of operation and the lithium battery health status degradation model to predict lithium battery life. Lithium battery life prediction model:

[0146]

[0147] in : Change in depth of discharge; Activation energy (related to battery chemical properties); Gas constant Battery temperature; : Degradation rate constant; related to battery type; SOH: State of health of lithium battery;

[0148] S413: Optimize the objective function of the hybrid power efficiency optimization algorithm, wherein the objective function is as follows:

[0149]

[0150] in Diesel fuel consumption cost; : Lithium battery cycle loss cost; Carbon emission penalty costs; Dynamic weighting coefficients are adjusted according to task priority; by substituting the obtained data into the objective function, fuel consumption can be reduced by 28%, lithium battery temperature fluctuations by 35%, and carbon emissions by 30%.

[0151] S414: Define the constraints: the total power output of the system is greater than the current load demand, the state of charge (SOC) of the lithium battery is maintained at 20%~90% to prevent overcharging and over-discharging, and the load rate of the diesel generator is greater than 30%.

[0152] S42. Establish a sensor network and deploy current and voltage sensors (accuracy ±0.5%), oil consumption and flow meters (accuracy ±0.1%), and environmental sensors (temperature, humidity, and wave height). Use the Precision Time Protocol (PTP) to align the sensor times and ensure consistency of time for multi-source data.

[0153] S43. Optimize the model dynamically based on the real-time received sensor data, and set the rolling optimization window to optimize once every fifteen minutes;

[0154] S44. Set real-time decision-making logic, determine mode switching rules, and reduce operating energy consumption. The mode switching rules are as follows:

[0155] ① Pure electric mode: When the lithium battery SOC is greater than 60% and the load is less than 200 KW, only the lithium battery is used for power supply;

[0156] ② Hybrid mode: When the lithium battery SOC is between 20% and 60% or the load is greater than 600 KW, the lithium battery and diesel generator are used together for power supply;

[0157] ③ Fuel priority mode: In severe sea conditions (wave height > 3 m) or emergency missions, the diesel engine is used as the main power source, with the lithium battery as a backup;

[0158] S45. Receive algorithm results, realize dynamic scheduling of multiple energy sources in the surface support system, optimize the energy allocation ratio in real time, reduce energy consumption to reduce the cost of deep-sea mining operations, improve the overall energy efficiency of the system by 30%, and reduce the total life cycle cost by more than 35%.

[0159] S5: Optimize the deployment and retrieval system. Based on the light load of the mining vehicle, reduce energy consumption during deployment and retrieval. In the deployment and retrieval phase, use a winch driven by a planetary gear set to achieve high-torque cable deployment and retrieval, reducing energy consumption by more than 30%. Also, install an electrolysis water-triggered release mechanism to enable emergency floating or load release of the deployment and retrieval equipment in case of emergencies, improving operational safety and avoiding equipment damage.

[0160] Using a planetary gear set as the recovery gear amplifies the torque by 140 times. The gear material is carburized and quenched alloy steel with a surface hardness of ≥60 HRC, improving wear resistance by 30% and reducing energy consumption by 35%.

[0161] Electrolyzers and sensors are designed inside the collection vessel, including positive and negative electrodes and ion exchange membranes. The electrode spacing is controlled at 1~2 mm, and the voltage is usually below 12V to ensure low energy consumption and fast response (trigger time <1 s).

[0162] The hydrogen and oxygen produced by electrolysis are stored in a high-pressure chamber with a pressure resistance of >30 MPa, and the flow direction is controlled by a one-way valve;

[0163] Receive sensor data;

[0164] The sensor signals are analyzed. If the sensor detects an emergency signal (cable tension exceeding the limit or system failure), the trigger circuit is energized, and the gas pushes the piston to cut off the mechanical connection (umbilical cable connection), reducing losses by 68% and operating and maintenance costs by 47%.

[0165] S6: Optimize the three-degree-of-freedom active-passive combined heave compensation system by integrating an active Z-axis compensation mechanism driven by a servo motor, a passive XY-axis compensation mechanism driven by a magnetorheological damper, and three-axis IMU data fusion technology. The three-axis IMU data fusion technology provides real-time feedback on the heave compensation effect.

[0166] It employs a high-precision servo motor (accuracy ±0.01 mm) combined with a ball screw (including servo motor, ball screw, feedback device, controller, and driver) to adjust the Z-axis displacement in real time, providing dynamic adjustment capability to compensate for the heave motion of the mother ship. Specifically, as shown below... Figure 3 As shown, it includes the following steps:

[0167] ① Input the target parameters, and the controller will convert the instructions into electrical signals and send them to the servo driver through the communication protocol;

[0168] ② The driver receives control signals and adjusts the three-phase current to drive the motor;

[0169] ③ The rotational motion of the ball screw is converted into the linear motion of the nut (load platform);

[0170] ④ The encoder records the angular displacement of the motor rotor, the grating ruler directly detects the load position, the controller compares the target value with the actual value, dynamically calculates the correction amount, and adjusts the output signal through the PID algorithm to eliminate steady-state error;

[0171] ⑤ When the external load changes suddenly or the vibration triggers abnormal feedback, the controller increases the current output or decreases the speed to maintain constant thrust and makes dynamic adjustments by predicting the trajectory error;

[0172] ⑥ After the load reaches the target, the servo motor maintains torque, and the ball screw's self-locking characteristic prevents reverse slippage. Upon receiving a stop command, the controller stops the machine according to the preset deceleration curve to avoid mechanical shock.

[0173] The viscosity of the magnetorheological fluid is adjusted by current (0-5 A current corresponds to a damping force of 1-50 KN·S / m), and the XY axis damping is adjusted in real time according to the sway acceleration. The damping characteristics are used to absorb high-frequency vibrations, taking into account both response speed and stability.

[0174] Optimize the active-passive collaborative strategy and implement joint active-passive dynamic resource allocation:

[0175] ① In the low-intensity motion phase with wave height less than 1 m: the passive damping-dominant mode is activated, with passive damping accounting for 70%~80% and active compensation accounting for 20%~30%;

[0176] ②In the moderate-intensity motion phase with wave height greater than 2 m and less than 3 m:Activate the active compensation-dominant mode, with active compensation accounting for 60%~80% and passive compensation accounting for 20%~40%;

[0177] ③ During high-intensity motion phases with wave heights greater than 3 m: Active compensation is prioritized, while passive damping-dominated mode is switched during stable phases to reduce energy consumption;

[0178] If the active system fails, the passive damping will automatically switch to the maximum damping state and maintain more than 50% of the compensation capacity for at least 30 minutes.

[0179] Active-passive coordinated control improves heave compensation accuracy from the traditional ±0.5 m to ±0.15 m, enables dynamic resource allocation, and reduces overall system energy consumption by 45% compared to a fully active solution.

[0180] A three-axis IMU data fusion technology is used to integrate data from gyroscopes, accelerometers, and magnetometers, and combined with filtering algorithms and attitude calculation methods to generate high-precision attitude information, providing real-time feedback for heave compensation. The workflow of the three-axis IMU data fusion technology is as follows:

[0181] T1: Calibrate and preprocess the sensor;

[0182] T2: Employs a data fusion algorithm and complementary filtering: fuses the high-frequency characteristics of the gyroscope and the low-frequency characteristics of the accelerometer, using the following formula:

[0183]

[0184] in The weighting coefficients (general length 0.96~0.98) are dynamically adjusted to balance noise and drift.

[0185] T3: Perform attitude calculation and coordinate system transformation, using the Runge-Kutta method to update quaternions and avoid Euler angle gimbal lock issues. The formula is:

[0186]

[0187] in To represent quaternion multiplication, The angular velocity vector is used; the IMU body coordinate system is converted to the geographic coordinate system using a rotation matrix to ensure that the compensation command is consistent with the heave direction;

[0188] T4: To ensure real-time performance and synchronization optimization, multi-threaded processing is adopted. IMU data acquisition (1KHz), filtering (100Hz), and calculation (100Hz) are run in separate threads, with the delay controlled within 2ms. The time base of the IMU and the heave compensation controller is aligned through hardware trigger signals to avoid phase errors and to provide real-time feedback to the heave compensation system.

[0189] T5: Monitors IMU data, enables diagnosis and self-healing of three-axis IMU data fusion technology, maintains long-term feedback to the heave compensation system, and achieves attitude calculation error <0.1°, failure rate <0.1 times / thousand hours, and optimizes the total life cycle cost by 25%;

[0190] Ultimately, based on the light load-stability optimization of the tracked walking system and buoyancy compensation system, a safety load reduction technology system for the deployment and recovery system and the surface support system is proposed. A low-energy-consumption three-degree-of-freedom active-passive combined heave compensation system is developed to achieve global cost optimization.

[0191] Deploying sensors to receive data from the deep-sea mining system in real time enables data sharing and dynamic resource allocation across multiple systems, optimizing overall costs. Compared to traditional independent control systems, it can improve overall energy efficiency by 47%, reduce energy consumption by 43%, and lower the overall cost of the deep-sea mining operation system by 52%.

[0192] Based on existing deep-sea mining operation indicators, the following table shows the optimization results after implementing Example 1 of the present invention:

[0193]

[0194] Example 2

[0195] The global cost optimization method based on multi-system safety in deep-sea mining operations differs from Example 1 in that:

[0196] S124: The target values ​​of each technical parameter determined and transformed, mining efficiency: continuous operation time increased by 50%, production capacity reached 140 tons / hour, automation level increased by 35%; system reliability: mean time between failures maintained above 95%, redundancy design ratio reduced by 60%, maintenance interval extended by 45%; cost control: material cost reduced by 30%, energy consumption reduced by 40%, system integration increased by 45%, decomposing each technical parameter into the design of each subsystem;

[0197] The nozzle is designed to be circular with a contraction-expansion structure in the internal flow channel. It also has a built-in self-rotating filter that automatically scrapes away large particles of impurities, thereby accelerating the water flow and enhancing the impact force. The performance indicators of the nozzle are set according to functional requirements, including: controllable adjustment of spray pressure within the range of 30~70 Bar, coverage area of ​​at least 90% of the track surface, cleaning cycle of 13 minutes / time with dynamic adjustment, and energy consumption of ≤0.5 kWh per spray.

[0198] Select nozzles with an angle of 25°, a diameter of 8 mm, a spacing of 50 cm, and arrange them horizontally around the outside of the mining vehicle tracks.

[0199] The nozzle triggering mechanism and pressure regulation mode are set. The nozzle triggering mechanism is dynamic, and cleaning is started immediately when the travel resistance coefficient is greater than 0.2, based on feedback from the torque sensor. The viscosity of the sediment is obtained through the vehicle camera and AI image recognition, and the water pressure is adjusted in real time through the proportional valve to automatically match the optimal pressure.

[0200] The nozzle clogging status is monitored in real time by a flow sensor. When clogging occurs, a reverse flushing mode is triggered to achieve self-diagnosis of nozzle malfunctions.

[0201] By optimizing materials and incorporating self-cleaning nozzles to flush away deposits on the tracks, the tracked walking system can reduce the load by 60%, thereby reducing energy consumption and operating costs of mining vehicles by 17% and 22%;

[0202] The effectiveness of an organization is evaluated, including energy recovery rate (the ratio of recovered energy to consumed energy) and dynamic response indicators.

[0203] ① Energy recovery rate index: The target value is required to be ≥45%; among which : Recovered energy; Energy consumed.

[0204] ② Dynamic response index: Stage response adjustment time < 0.8s;

[0205] By optimizing the buoyancy compensation system through this energy conversion mechanism, the energy recovery efficiency reaches over 54%, thereby reducing energy consumption during extraction by 31% and reducing the total life cycle cost by 35%.

[0206] S41. Construct a hybrid power efficiency optimization algorithm:

[0207] S411: Input real-time data, including lithium battery state of charge (SOC), diesel generator load rate, power requirements for the current mission phase (walking, data collection, recovery), and environmental parameters (sea state, sea temperature, ambient temperature and humidity).

[0208] S412: Input the predicted power demand for the next hour of operation and the lithium battery health status degradation model to predict lithium battery life. Lithium battery life prediction model:

[0209]

[0210] in : Change in depth of discharge; Activation energy (related to battery chemical properties); Gas constant Battery temperature; : Degradation rate constant; related to battery type; SOH: State of health of lithium battery;

[0211] S413: Optimize the objective function of the hybrid power efficiency optimization algorithm, wherein the objective function is as follows:

[0212]

[0213] in Diesel fuel consumption cost; : Lithium battery cycle loss cost; Carbon emission penalty costs; Dynamic weighting coefficients are adjusted according to task priority; by substituting the obtained data into the objective function, fuel consumption can be reduced by 25%, lithium battery temperature fluctuations by 27%, and carbon emissions by 27%.

[0214] S45. Receive algorithm results to realize dynamic scheduling of multiple energy sources in the surface support system, optimize energy allocation ratio in real time, reduce energy consumption to reduce deep-sea mining operation costs, improve overall system energy efficiency by 27%, and reduce total life cycle cost by 30%;

[0215] Using a planetary gear set as the recovery gear amplifies the torque by 130 times. The gear material is carburized and quenched alloy steel with a surface hardness of ≥60 HRC, improving wear resistance by 30% and reducing energy consumption by 32%.

[0216] The sensor signals are analyzed. If the sensor detects an emergency signal (cable tension exceeding the limit or system failure), the trigger circuit is energized, and the gas pushes the piston to cut off the mechanical connection (umbilical cable connection), reducing losses by 68% and operating and maintenance costs by 47%.

[0217] Optimize the active-passive collaborative strategy and implement joint active-passive dynamic resource allocation:

[0218] ① In the low-intensity motion phase with wave height less than 1 m: the passive damping-dominant mode is activated, with passive damping accounting for 70%~80% and active compensation accounting for 20%~30%;

[0219] ②In the moderate-intensity motion phase with wave height greater than 2 m and less than 3 m:Activate the active compensation-dominant mode, with active compensation accounting for 60%~80% and passive compensation accounting for 20%~40%;

[0220] ③ During high-intensity motion phases with wave heights greater than 3 m: Active compensation is prioritized, while passive damping-dominated mode is switched during stable phases to reduce energy consumption;

[0221] If the active system fails, the passive damping will automatically switch to the maximum damping state and maintain more than 50% of the compensation capacity for at least 30 minutes.

[0222] Active-passive coordinated control improves heave compensation accuracy from the traditional ±0.5 m to ±0.15 m, enables dynamic resource allocation, and reduces overall system energy consumption by 43% compared to a fully active solution.

[0223] T5: Monitors IMU data, enables diagnosis and self-healing of three-axis IMU data fusion technology, maintains long-term feedback to the heave compensation system, and the IMU data fusion technology achieves attitude calculation error <0.1°, failure rate <0.1 times / thousand hours, and optimizes the total life cycle cost by 24%.

[0224] Deploying sensors to receive data from the deep-sea mining system in real time enables data sharing and dynamic resource allocation across multiple systems, optimizing overall costs. Compared to traditional independent control systems, it can improve overall energy efficiency by 38%, reduce energy consumption by 35%, and lower the overall cost of the deep-sea mining operation system by 43%.

[0225] Based on existing deep-sea mining operation indicators, the following table shows the optimization results after implementing Example 2 of the present invention:

[0226]

[0227] Example 3

[0228] The global cost optimization method based on multi-system safety in deep-sea mining operations differs from Example 1 in that:

[0229] S124: The target values ​​of each technical parameter determined and transformed, mining efficiency: continuous operation time increased by 40%, production capacity reached 130 tons / hour, automation level increased by 35%; system reliability: mean time between failures maintained above 90%, redundancy design ratio reduced by 40%, maintenance interval extended by 40%; cost control: material cost reduced by 20%, energy consumption reduced by 30%, system integration increased by 30%, decomposing each technical parameter into the design of each subsystem;

[0230] The nozzle is designed to be circular with a contraction-expansion structure in the internal flow channel. It also has a built-in self-rotating filter that automatically scrapes away large particles of impurities, thereby accelerating the water flow and enhancing the impact force. The performance indicators of the nozzle are set according to functional requirements, including: controllable adjustment of spray pressure within the range of 30~70 Bar, coverage area of ​​at least 90% of the track surface, cleaning cycle of 15 minutes / time with dynamic adjustment, and energy consumption of ≤0.5 kWh per spray.

[0231] Select nozzles with an angle of 35°, a diameter of 10 mm, a spacing of 45 cm, and position them horizontally around the outside of the mining vehicle tracks;

[0232] The nozzle triggering mechanism and pressure regulation mode are set. The nozzle triggering mechanism is dynamic, and cleaning is started immediately when the travel resistance coefficient is greater than 0.2, based on feedback from the torque sensor. The viscosity of the sediment is obtained through the vehicle camera and AI image recognition, and the water pressure is adjusted in real time through the proportional valve to automatically match the optimal pressure.

[0233] The nozzle clogging status is monitored in real time by a flow sensor. When clogging occurs, a reverse flushing mode is triggered to achieve self-diagnosis of nozzle malfunctions.

[0234] By optimizing materials and incorporating self-cleaning nozzles to flush away deposits on the tracks, the tracked walking system can reduce load by 58%, thereby reducing energy consumption and operating costs of mining vehicles by 15% and 20%, respectively.

[0235] The effectiveness of an organization is evaluated, including energy recovery rate (the ratio of recovered energy to consumed energy) and dynamic response indicators.

[0236] ① Energy recovery rate index: The target value is required to be ≥45%; among which : Recovered energy; Energy consumed.

[0237] ② Dynamic response index: Stage response adjustment time < 0.8s;

[0238] By optimizing the buoyancy compensation system through this energy conversion mechanism, the energy recovery efficiency reaches 50%, thereby reducing energy consumption during extraction by 30% and reducing the total life cycle cost by 33%.

[0239] S41. Construct a hybrid power efficiency optimization algorithm:

[0240] S411: Input real-time data, including lithium battery state of charge (SOC), diesel generator load rate, power requirements for the current mission phase (walking, data collection, recovery), and environmental parameters (sea state, sea temperature, ambient temperature and humidity).

[0241] S412: Input the predicted power demand for the next hour of operation and the lithium battery health status degradation model to predict lithium battery life. Lithium battery life prediction model:

[0242]

[0243] in : Change in depth of discharge; Activation energy (related to battery chemical properties); Gas constant Battery temperature; : Degradation rate constant; related to battery type; SOH: State of health of lithium battery;

[0244] S413: Optimize the objective function of the hybrid power efficiency optimization algorithm, wherein the objective function is as follows:

[0245]

[0246] in Diesel fuel consumption cost; : Lithium battery cycle loss cost; Carbon emission penalty costs; Dynamic weighting coefficients are adjusted according to task priority; by substituting the obtained data into the objective function, fuel consumption can be reduced by 21%, lithium battery temperature fluctuations by 27%, and carbon emissions by 24%.

[0247] S45. Receive algorithm results, realize dynamic scheduling of multiple energy sources in the surface support system, optimize energy allocation ratio in real time, reduce energy consumption to reduce deep-sea mining operation costs, improve overall system energy efficiency by 24%, and reduce total life cycle cost by 26%;

[0248] Using a planetary gear set as the recovery gear amplifies the torque by 125 times. The gear material is carburized and quenched alloy steel with a surface hardness of ≥60 HRC, improving wear resistance by 30% and reducing energy consumption by 30%.

[0249] The sensor signals are analyzed. If the sensor detects an emergency signal (cable tension exceeding the limit or system failure), the trigger circuit is energized, and the gas pushes the piston to cut off the mechanical connection (umbilical cable connection), reducing losses by 68% and operating and maintenance costs by 47%.

[0250] Optimize the active-passive collaborative strategy and implement joint active-passive dynamic resource allocation:

[0251] ① In the low-intensity motion phase with wave height less than 1 m: the passive damping-dominant mode is activated, with passive damping accounting for 70%~80% and active compensation accounting for 20%~30%;

[0252] ②In the moderate-intensity motion phase with wave height greater than 2 m and less than 3 m:Activate the active compensation-dominant mode, with active compensation accounting for 60%~80% and passive compensation accounting for 20%~40%;

[0253] ③ During high-intensity motion phases with wave heights greater than 3 m: Active compensation is prioritized, while passive damping-dominated mode is switched during stable phases to reduce energy consumption;

[0254] If the active system fails, the passive damping will automatically switch to the maximum damping state and maintain more than 50% of the compensation capacity for at least 30 minutes.

[0255] Active-passive coordinated control improves heave compensation accuracy from the traditional ±0.5 m to ±0.15 m, enables dynamic resource allocation, and reduces overall system energy consumption by 40% compared to a fully active solution.

[0256] T5: Monitors IMU data, enables diagnosis and self-healing of three-axis IMU data fusion technology, maintains long-term feedback to the heave compensation system, and the IMU data fusion technology achieves attitude calculation error <0.1°, failure rate <0.1 times / thousand hours, and optimizes the total life cycle cost by 22%;

[0257] Deploying sensors to receive data from the deep-sea mining system in real time enables data sharing and dynamic resource allocation across multiple systems, optimizing overall costs. Compared to traditional independent control systems, it can improve overall energy efficiency by 36%, reduce energy consumption by 32%, and lower the overall cost of the deep-sea mining operation system by 40%.

[0258] Based on existing deep-sea mining operation indicators, the following table shows the optimization results after implementing Example 3 of the present invention:

[0259]

[0260] Comparative Example 1

[0261] The global cost optimization method based on the safety of multiple systems in deep-sea mining operations differs from Example 1 in that it only performs the structured design of the light-load-stability tracked walking system.

[0262] Based on existing deep-sea mining operation indicators, the following table shows the optimization results after implementing Comparative Example 1 of this invention:

[0263]

[0264] Comparative Example 2

[0265] The global cost optimization method based on the safety of multiple systems in deep-sea mining operations differs from Example 1 in that it only optimizes the surface support system based on safety burden reduction technology.

[0266] Based on existing deep-sea mining operation indicators, the following table shows the optimization results after implementing Comparative Example 2 of this invention:

[0267]

[0268] As can be seen from the optimization results of Examples 1, 2, and 3 above, the global cost optimization method based on the multi-system safety of deep-sea mining operations of this invention has achieved significant results in optimizing indicators such as fuel consumption, carbon emissions, mining vehicle load, operating costs, energy efficiency, energy consumption, and global costs.

[0269] The comparison revealed that the optimization results of Comparative Examples 1 and 2 were lower than those of the Implementation Example in all aspects, indicating that using this method for global cost optimization can better meet the requirements of low energy consumption and low cost in deep-sea mining operations.

[0270] In addition to the above embodiments, the present invention may have other implementation methods; all technical solutions formed by equivalent substitution or equivalent transformation fall within the protection scope claimed by the present invention.

[0271] It will be apparent to those skilled in the art that the present invention is not limited to the details of the specific embodiments described above. Other forms can be used without departing from the spirit of the invention. Therefore, the embodiments should be considered exemplary rather than restrictive. It should also be noted that although the specification describes specific implementations, it is not intended to limit the scope to a single solution; this description is for clarity only. Those skilled in the art should understand the specification in its entirety and can derive other feasible implementations by combining the technical solutions in the embodiments.

Claims

1. A global cost optimization method based on the safety of a deep-sea mining operation system, wherein the deep-sea mining operation system includes a tracked walking system, a buoyancy compensation system, a surface support system, a deployment and recovery system, and a heave compensation system; characterized in that, The global cost optimization method includes the following steps: S1: Based on global cost optimization and the coupling relationship between deep-sea mining operation systems, determine the design parameters of the deep-sea mining operation system; S2: Optimization of tracked walking system based on light load and stability; S3: Utilize a bidirectional hydraulic-pneumatic energy conversion mechanism to establish an energy recovery and energy release mechanism, and optimize the buoyancy compensation system; S4: Optimize the surface support system based on safety load reduction technology; S5: Optimize the deployment and recovery system by reducing energy consumption during deployment and recovery based on the light load of mining vehicles; S6: Optimize the three-degree-of-freedom active-passive combined heave compensation system; The method for determining the design parameters of the deep-sea mining operation system in step S1 is as follows: S11: Construct a function-load-energy consumption mapping table for the buoyancy compensation system, tracked walking system, heave compensation system, deployment and recovery system, and surface support system, and quantify the energy transfer path and mechanical coupling relationship between each subsystem; S12: Employ the quality function deployment method to transform mining efficiency, system reliability, and cost control requirements into design parameters for each subsystem; In step S2, the tracked walking system optimization includes: material selection, nozzle design, setting the nozzle triggering mechanism and pressure regulation mode, and nozzle fault self-diagnosis design. In step S3, the buoyancy compensation system optimization includes: the design of hydraulic cylinders, airbag assembly, energy storage unit, energy recovery and release mechanism, and the design of work efficiency evaluation mechanism. In step S4, the method for optimizing the surface support system is as follows: S41: Construct a hybrid power efficiency optimization algorithm; S42: Establish a sensor network; S43: Dynamically optimize the model based on real-time received sensor data; S44: Set real-time decision-making logic, determine mode switching rules, and reduce operating energy consumption; S45: Receives algorithm results, realizes dynamic scheduling of multiple energy sources in the surface support system, and optimizes the energy allocation ratio in real time; In step S5, the optimization method for the deployment and recovery system is as follows: high-torque cable deployment and retrieval are achieved by driving a winch with a planetary gear set; an electrolytic cell and sensors are designed inside the collection vessel, including positive and negative electrodes and ion exchange membranes; hydrogen and oxygen generated by electrolysis are stored; and sensor data is received and analyzed. In step S6, the three-degree-of-freedom active-passive combined heave compensation system consists of: an active Z-axis compensation mechanism driven by a servo motor, a passive XY-axis compensation mechanism driven by a magnetorheological damper, active-passive cooperative control technology, and three-axis IMU data fusion technology.

2. The global cost optimization method based on the safety of deep-sea mining operation systems according to claim 1, characterized in that, The step S11, which quantifies the energy transfer paths and mechanical coupling relationships between the subsystems, is as follows: S111: Determine the core functions of each system; S112: Defines the load type, which is divided into static load, dynamic load and fatigue load; S113: Perform mechanical coupling between the systems and establish the coupling relationship between them; S114: Construct a function-load-energy consumption mapping table and determine the coupling influence coefficients between each system; The quality function deployment method in step S12 is specifically as follows: S121: Define optimization requirements, including mining efficiency, system reliability, and cost control; S122: Translate requirements into technical parameters; S123: Construct a house of quality, analyze the correlation between requirements and technical parameters by establishing a relationship matrix; secondly, conduct competitive analysis to compare with industry benchmarks and set target values ​​for technical parameters; Finally, the roof matrix analysis technique parameters were used to identify conflicts. S124: Determine the target values ​​of each technical parameter to be transformed, decompose each technical parameter into each subsystem, and resolve the conflicts between the design parameters of each system.

3. The global cost optimization method based on the safety of deep-sea mining operation systems according to claim 1, characterized in that, In step S2, the material selection specifically includes: the deep-sea mining vehicle is made of titanium alloy, the track is made of hollow titanium alloy and carbon fiber reinforced polyimide track plate, which is a lightweight composite structure, and the surface is sprayed with superhydrophobic nanomaterials and equipped with self-cleaning nozzles; the nozzle is made of titanium alloy and the surface is sprayed with titanium nitride coating. The nozzle is designed as follows: the nozzle is a circular nozzle with a contraction-expansion structure in the internal flow channel and a built-in self-rotating filter screen that rotates automatically. The nozzle angle is 25°~35°, the diameter is 6~12 mm, the spacing is 20~50 cm, and the nozzle position is arranged horizontally around the outside of the mining vehicle track. The nozzle triggering mechanism and pressure regulation mode are set as follows: the nozzle triggering mechanism is dynamic, and cleaning is started immediately when the travel resistance coefficient is greater than 0.2, based on feedback from the torque sensor; the viscosity of the sediment is obtained through the vehicle camera and AI image recognition, and the water pressure is adjusted in real time through the proportional valve to automatically match the optimal pressure; The nozzle fault self-diagnosis method is as follows: the nozzle blockage status is monitored in real time by a flow sensor, and a reverse flushing mode is triggered when blockage occurs to achieve nozzle fault self-diagnosis.

4. The global cost optimization method based on the safety of deep-sea mining operation systems according to claim 1, characterized in that, In step S3, a double-acting hydraulic cylinder is selected, with hydraulic oil flowing through both sides of the piston. The piston rod end is rigidly connected to the airbag. The cylinder body is made of titanium alloy, and the sealing ring is made of fluororubber. The airbag assembly is a variable volume airbag assembly. The airbag has a layered structure, with an inner layer of silicone rubber mold with a thickness of 0.8~1mm and an outer layer of Kevlar fiber braided layer. The airbag is divided into four independent compartments, each equipped with a solenoid valve for segmented control. The energy storage unit uses a high-pressure gas tank and a hydraulic energy storage device connected in parallel, and a high-pressure gas-liquid converter is used to adjust the connection between the oil circuit and the gas circuit through a proportional valve group. The energy recovery mechanism is as follows: Seawater pressure drive: When the mining vehicle sinks, the external seawater pressure acts on the rodless chamber of the hydraulic cylinder, pushing the piston to compress the gas in the air bladder to store energy; Gas compression energy storage: Compressed gas enters a high-pressure gas tank through a one-way valve to store energy; Hydraulic energy storage: Hydraulic oil flows from the rod chamber into the accumulator to store pressure energy; The energy release mechanism is as follows: Gas expansion does work: releasing gas from the high-pressure tank pushes the piston to move in the opposite direction, assisting the airbag to expand and provide buoyancy; Hydraulic energy assistance: The accumulator releases hydraulic oil to drive the hydraulic cylinder, enhancing the upward thrust; The performance evaluation mechanism includes energy recovery rate indicators and dynamic response indicators, among which the energy recovery rate indicator is: The target value is required to be ≥45%; dynamic response indicator: stage response adjustment time <0.8s; in : Recovered energy; Energy consumed.

5. The global cost optimization method based on the safety of deep-sea mining operation systems according to claim 1, characterized in that, The method for constructing the hybrid power efficiency optimization algorithm in step S41 is as follows: S411: Input real-time data, including lithium battery status of charge, diesel generator load rate, power requirements for the current task phase, and environmental parameters; S412: Input the predicted power demand for the next hour of operation and the lithium battery health status degradation model to predict lithium battery life. Lithium battery life prediction model: ； in : Change in depth of discharge; Activation energy, which is related to the chemical properties of the battery; Gas constant Battery temperature; : Decay rate constant; SOH: State of Health of the lithium battery; S413: Optimize the objective function of the hybrid power efficiency optimization algorithm, wherein the objective function is as follows: ； in Diesel fuel consumption cost; : Lithium battery cycle loss cost; Carbon emission penalty costs; Dynamic weighting coefficients, adjusted according to task priority; S414: Define the constraints: the total power output of the system is greater than the current load demand, the state of charge (SOC) of the lithium battery is maintained at 20%~90% to prevent overcharging and over-discharging, and the load rate of the diesel generator is greater than 30%. The establishment of the sensor network in step S42 includes deploying current and voltage sensors, fuel consumption flow meters, and environmental sensors, and using the Precision Time Protocol (PTP) to align the sensor times. The dynamic optimization in step S43 is to set the scrolling optimization window to optimize once every fifteen minutes. The mode switching rules in step S44 include: pure electric mode, hybrid mode, and fuel priority mode; wherein... Pure electric mode: When the lithium battery SOC is greater than 60% and the load is less than 200KW, only the lithium battery is used for power supply; Hybrid mode: When the lithium battery SOC is between 20% and 60% or the load is greater than 600 KW, the lithium battery and diesel generator are used together for power supply. Fuel Priority Mode: In severe sea conditions (wave height > 3 m) or emergency missions, diesel engines are used as the primary power source, with lithium batteries as backup.

6. The global cost optimization method based on the safety of deep-sea mining operation systems according to claim 1, characterized in that, In step S5, the planetary gear set uses carburized and quenched alloy steel as the gear material, with a surface hardness ≥60 HRC; the distance between the positive and negative electrodes is controlled at 1~2 mm, and the voltage is usually below 12 V; hydrogen and oxygen are stored in a high-pressure chamber with a pressure resistance >30 MPa, and their flow direction is controlled by a one-way valve; the sensor data analysis method is as follows: when the sensor detects that the cable tension exceeds the limit or the system malfunctions, the trigger circuit is energized, and the gas pushes the piston to cut off the mechanical connection.

7. The global cost optimization method based on the safety of deep-sea mining operation systems according to claim 1, characterized in that, In step S6, the active Z-axis compensation mechanism driven by the servo motor includes a servo motor combined with a ball screw to adjust the Z-axis displacement in real time, providing dynamic adjustment capability to compensate for the heave motion of the mother ship; wherein the accuracy of the servo motor is ±0.01 mm, and the ball screw includes a servo motor, a ball screw, a feedback device, a controller, and a driver. The implementation steps of the servo motor driven active Z-axis compensation mechanism are as follows: When the target parameters are input, the controller converts the instructions into electrical signals and sends them to the servo driver via a communication protocol. The driver receives control signals and adjusts the three-phase current to drive the motor. The rotational motion of the ball screw is converted into the linear motion of the nut load platform; The encoder records the angular displacement of the motor rotor, the grating ruler directly detects the load position, the controller compares the target value with the actual value, dynamically calculates the correction amount, and adjusts the output signal through the PID algorithm to eliminate steady-state error; When the external load changes abruptly or vibration triggers abnormal feedback, the controller increases the current output or decreases the speed to maintain constant thrust, and makes dynamic adjustments by predicting trajectory errors in advance. Once the load reaches the target, the servo motor maintains torque, and the ball screw's self-locking characteristic prevents reverse slippage. Upon receiving a stop command, the controller stops the machine according to a preset deceleration curve to avoid mechanical shock.

8. The global cost optimization method based on the safety of deep-sea mining operation systems according to claim 1, characterized in that, In step S6, the passive XY axis compensation mechanism driven by the magnetorheological damper is implemented by adjusting the viscosity of the magnetorheological fluid by current, adjusting the XY axis damping in real time according to the sway acceleration, and absorbing high-frequency vibrations through the damping characteristics.

9. The global cost optimization method based on the safety of deep-sea mining operation systems according to claim 1, characterized in that, In step S6, the active-passive cooperative control technology includes the following steps: When the wave height is less than 1 m and the motion is low-intensity: the passive damping-dominant mode is activated, with passive damping accounting for 70%~80% and active compensation accounting for 20%~30%; During the moderate-intensity motion phase with wave heights greater than 2 m but less than 3 m: the active compensation-dominant mode is activated, with active compensation accounting for 60%~80% and passive compensation accounting for 20%~40%; During high-intensity motion phases with wave heights greater than 3 m: active compensation is prioritized, while passive damping-dominated mode is switched during stable phases to reduce energy consumption. If the active system fails, the passive damping automatically switches to the maximum damping state, maintaining more than 50% of the compensation capacity for at least 30 minutes.

10. The global cost optimization method based on the safety of deep-sea mining operation systems according to claim 1, characterized in that, In step S6, the implementation method of the three-axis IMU data fusion technology is as follows: T1: Calibrate and preprocess the sensor; T2: Employs a data fusion algorithm and complementary filtering: fuses the high-frequency characteristics of the gyroscope and the low-frequency characteristics of the accelerometer, using the following formula: ； in The attitude angle after fusion. From the perspective of fusion at the previous moment, The angular velocity measured by the gyroscope. The sampling time interval, The attitude angles are calculated directly from accelerometer data. The weighting coefficient is typically 0.96 to 0.98, and is dynamically adjusted to balance noise and drift. T3: Perform attitude calculation and coordinate system transformation, using the Runge-Kutta method to update quaternions and avoid Euler angle gimbal lock issues. The formula is: ； in Let k+1 be the attitude quaternion. The sampling time interval, Let k be the pose quaternion at time step k. To represent quaternion multiplication, The angular velocity vector is used; the IMU body coordinate system is converted to the geographic coordinate system using a rotation matrix to ensure that the compensation command is consistent with the heave direction; T4: Employs multi-threaded processing, including IMU data acquisition, filtering, and calculation running in separate threads, with latency controlled within 2ms. It aligns the time base of the IMU and the heave compensation controller through hardware trigger signals to avoid phase errors and provides real-time feedback to the heave compensation system. T5: Monitors IMU data, enables diagnosis and self-healing of three-axis IMU data fusion technology, and maintains long-term feedback to the heave compensation system.

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