Global cost optimization method based on deep-sea mining operation multi-system safety

Abstract

The invention discloses a global cost optimization method based on deep-sea mining operation system safety. The method comprises the following steps: determining design parameters of each system based on global cost optimization and a coupling relationship between the systems; materials are selected based on light load-stability, a self-cleaning nozzle is carried, and a crawler walking system is optimized; a two-way hydraulic-pneumatic energy conversion mechanism is used for optimizing a buoyancy compensation system; a hybrid power efficiency optimization algorithm is adopted to optimize a water surface support system based on a safety burden mitigation technology; a planetary gear is used for amplifying torque, and an electrolyzed water trigger type release mechanism is adopted in the laying and recycling process, so that a laying and recycling system is optimized; based on the principle of low energy consumption, a three-axis IMU data fusion technology is applied, and a three-degree-of-freedom active-passive combined heave compensation system is optimized; the method can effectively solve the problem of high cost of deep-sea mining, realizes safety, burden reduction, cost reduction and benefit increase of a deep-sea mining system, and optimizes the global cost of deep-sea mining operation.

Description

Technical Field

[0001] The present invention relates to the technical field of deep-sea mining operations, and in particular to a global cost optimization method based on multi-system safety of deep-sea mining operations. Background Art

[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 rich in variety and huge in quantity. Polymetallic nodules, polymetallic sulfides, cobalt-rich crusts, and rare earth-rich muds are the main types of mineral resources found on the global seabed. They are rich in manganese, copper, nickel, cobalt, rare earth elements, gold, silver, zirconium, chromium, tungsten, tin, molybdenum, antimony, lithium, and other metal elements. These metals have important applications in electronics, renewable energy, and aerospace, making the efficient collection of deep-sea minerals while minimizing environmental disturbance a major research challenge. As a comprehensive technological framework for developing seabed mineral resources, deep-sea mining systems have evolved from early exploration and testing to an intelligent system centered around surface vessels, subsea mining vehicles, and pipeline hoisting systems. However, these systems still face numerous challenges, including adaptability to extreme high-pressure environments, risks of ecological damage, and high operating costs. Therefore, the deep-sea mining industry urgently needs a global cost optimization method based on the safety of multiple systems in deep-sea mining operations. This approach can improve mining efficiency and energy utilization, reduce energy consumption, and achieve safer mining operations. Furthermore, it can reduce costs and increase efficiency across multiple mining vehicle systems, thereby lowering the overall cost of mining operations.

[0003] Based on the above practical problems, existing technologies have carried out certain designs and optimizations on deep-sea mining operation systems, but the following problems still exist: 1. The independent optimization mode of local subsystems is mostly adopted, and there is a lack of multi-system integration and collaborative optimization mechanism, resulting in the coupling effect between subsystems not being effectively utilized and the overall operating cost not reaching the optimal state; 2. Traditional deep-sea mining equipment uses high-density structural materials, which leads to excessively high bearing stress of the travel mechanism, resulting in a significant increase in power energy consumption, which limits the efficiency of ore collection operations; 3. A dynamic resource scheduling mechanism based on multi-source sensor data streams has not been established, resulting in insufficient matching between energy allocation strategies and working conditions; 4. The three-degree-of-freedom active-passive combined heave compensation system has not been optimized, resulting in low compensation accuracy and high energy consumption; 5. Data sharing between various systems in deep-sea mining operations has not been achieved, and the synergy between various systems cannot be well coordinated, resulting in unnecessary cost consumption. Summary of the Invention

[0004] The present invention aims to overcome at least one of the defects of the above-mentioned prior art and provide a global cost optimization method based on the multi-system safety of deep-sea mining operations to solve the problems of high cost, low mining efficiency, low performance ratio, high energy consumption and low energy utilization of deep-sea mining operations.

[0005] The application provides a global cost optimization method based on multi-system safety of deep-sea mining operation, comprising the following steps: S1: determining the design parameters of each system based on global cost optimization and the coupling relationship between systems; S11, constructing a function-load-energy mapping table of buoyancy compensation, track walking, heave compensation, deployment and recovery, and water surface support system, quantifying the energy transmission path and mechanical coupling relationship between each subsystem, specifically: S111: determining the core functions of each system; The core functions of each system are: Track walking system: driving mining vehicles through pressure-resistant and corrosion-resistant track structure, adapting to complex seabed topography and ensuring mobile reliability; Buoyancy compensation system: dynamically adjusting the buoyancy of the equipment, adapting to the change of deep-sea pressure and maintaining stable hovering or working posture; Water surface support system: providing power, communication, navigation and emergency support, coordinating each subsystem to achieve efficient operation in the whole process; Deployment and recovery system: safely deploying and recovering deep-sea equipment, integrating real-time monitoring and emergency mechanism to reduce operational risk; Heave compensation system: offsetting the heave motion of the water surface ship body to ensure the stability of the underwater equipment position and the continuity of the operation.

[0006] S112: defining load types, including static load, dynamic load and fatigue load; S113: mechanically coupling between each system to establish the coupling relationship between each system; S114: constructing a function-load-energy mapping table to determine the coupling influence coefficient between each system; S12, using QFD quality function deployment method to convert mining efficiency, system reliability and cost control requirements into design parameters of each subsystem, specifically: S121: clearly defining optimization requirements, including mining efficiency, system reliability and cost control; S122: converting requirements into technical parameters; S123: constructing a quality house, first analyzing the correlation strength of requirements and technical parameters by establishing a relationship matrix; secondly, conducting competitive analysis to compare industry benchmarks and setting target values for technical parameters; finally, constructing a roof matrix to analyze the conflict between technical parameters; S124: Determine the converted technical parameter target value, mining efficiency: continuous operation time is improved by 40%~60%, production capacity reaches 120~150 tons / hour, automation level is improved by 30%~40%; System reliability: maintain 95% or more of the mean time between failures, reduce the redundancy design ratio by 40%~80%, extend the maintenance interval by 40%~50%; Cost control: material cost reduction of 20%~30%, energy consumption reduction of 20%~50%, system integration improvement of 30%~50%, decompose each technical parameter to each subsystem design; Solve the conflict between each system design parameter, optimize the design scheme based on the optimization of the global cost of mining; S2: Based on light load-stability, optimize the track walking system, deep sea mining vehicle selects titanium alloy material, track selects light composite structure composed of hollow titanium alloy material and carbon fiber reinforced polyimide track plate, and sprays super-hydrophobic nano material on the surface, carries self-cleaning nozzle to clean the track adhered mud, further reduces the load of the mining vehicle, reduces the energy consumption of the mining vehicle by more than 20% and the operation cost by more than 25%; Based on light load-stability, determine material selection, mining vehicle selects titanium alloy material and track selects light composite structure composed of hollow titanium alloy material and carbon fiber reinforced polyimide track plate, and sprays super-hydrophobic nano material on the surface; The nozzle is made of titanium alloy material, and the surface is sprayed with titanium nitride coating to reduce the load of the mining vehicle by 30%~60%; Carry out nozzle design, the nozzle selects a circular nozzle, the internal flow channel adopts a contraction-expansion structure, and a self-rotating filter screen is arranged inside to automatically remove large particle impurities, thereby accelerating the water flow and enhancing the impact force. According to the functional requirements, the performance indicators of the nozzle are set, including: the jet pressure is controllable and adjustable within the range of 30~70 Bar, the coverage area covers at least 90% of the track surface, the cleaning period is 15 minutes / once, dynamic adjustment is carried out, and the single jet energy consumption is ≤0.5kWh; Select the nozzle angle 25°~35°, diameter 6~12 mm, spacing 20~50 cm, and nozzle position arranged horizontally around the outside of the mining vehicle track; Set the nozzle trigger mechanism and pressure adjustment mode, the nozzle trigger mechanism is dynamic trigger, which is started immediately when the travel resistance coefficient is greater than 0.2 through torque sensor feedback; The viscosity of the sediment is obtained through the vehicle-mounted camera and AI image recognition, and the water pressure is adjusted in real time through the proportional valve, so as to automatically match the best pressure; The travel resistance coefficient is a dimensionless parameter for quantifying the relationship between the total resistance and the positive pressure when moving on the seabed, reflecting the comprehensive resistance characteristics between the vehicle body and the seabed sediment and the water body, which is used here to define the self-cleaning nozzle start threshold.

[0007] The clogging state of the nozzle is monitored in real time through the flow sensor, and when clogging occurs, the reverse flushing mode is triggered to realize self-diagnosis of nozzle failure; S3: Perform buoyancy compensation system optimization, use a bidirectional hydraulic-pneumatic energy conversion mechanism to establish an energy recovery mechanism, with an energy recovery efficiency of more than 38%, reducing energy consumption by 20%-30%, and reducing the full life cycle cost by more than 30%; The hydraulic cylinder is a double-acting hydraulic cylinder, the piston is connected to the gas bag at both ends, the cylinder body is made of titanium alloy material (compressive strength ≥ 150 MPa), and the sealing ring is made of fluorine rubber (pressure resistance 55-65 MPa); The gas bag group selects a variable volume gas bag group, the gas bag is a layered structure, the inner layer is a silicone rubber mold with a thickness of 0.8-1 mm, the outer layer is a Kevlar fiber woven layer (tear strength ≥ 500 N / mm), and the gas bag is divided into four independent compartments, each compartment is equipped with an electromagnetic valve for segmented control (response time ≤ 10 ms); The energy storage unit uses a high-pressure gas tank (working pressure 30 MPa) and a hydraulic energy accumulator (pressure range 0-60 MPa) in parallel, uses a high-pressure gas-liquid converter, and adjusts the communication between the oil and gas circuits through a proportional valve group; Set the energy recovery mechanism, during the sinking stage of the deep-sea mining vehicle, perform energy recovery; The energy recovery includes: ① Seawater pressure drive: When the mining vehicle sinks, the external seawater pressure acts on the rodless cavity of the hydraulic cylinder, pushing the piston to compress the gas in the gas bag and store energy; ② Gas compression energy storage: The compressed gas enters the high-pressure gas tank through the one-way valve and stores energy; ③ Hydraulic energy storage: Hydraulic oil flows from the rod cavity into the energy accumulator to store pressure energy.

[0008] Set the energy release mechanism, during the floating stage of the deep-sea mining vehicle, perform energy release; The energy release includes: ① Gas expansion work: Release the gas in the high-pressure gas tank, push the piston to move in the opposite direction, and assist the gas bag to expand to provide buoyancy; ② Hydraulic energy assistance: The accumulator releases hydraulic oil to drive the hydraulic cylinder to enhance the upward thrust.

[0009] Evaluate the working efficiency of the mechanism, including the energy recovery rate index and the dynamic response index: ① Energy recovery rate index: , the target value is required to be ≥ 45%; wherein : the energy recovered; : the energy consumed.

[0010] ② Dynamic response index: Stage response adjustment time < 0.8s; S4: Based on the safety reduction technology, the water surface support system is optimized, the hybrid power efficiency optimization algorithm is adopted, the multi-energy dynamic scheduling of the water surface support system is realized, the energy distribution ratio is optimized in real time, the system overall energy efficiency is improved, the fuel can be saved by 20%-28%, the lithium battery temperature fluctuation is reduced by 25%-35%, the carbon emission is reduced by 30%-35%, the system overall energy efficiency is improved by 20%-30%, and the whole life cycle cost is reduced by more than 35%; S41, construct a hybrid power efficiency optimization algorithm: S411: input real-time data, including lithium battery state of charge (SOC), diesel generator load rate, power demand of current task stage (walking, collecting, recycling) and environmental parameters (sea state level, sea water temperature, environmental temperature and humidity); S412: input the predicted future one-hour operation power demand and lithium battery health state attenuation model to predict the lithium battery life, and the lithium battery life prediction model is as follows:

[0011] Among them : change amount of discharge depth; : activation energy (related to battery chemical properties); : gas constant, : battery temperature; : attenuation rate constant; SOH: health state of lithium battery; S413: optimize the objective function of the hybrid power efficiency optimization algorithm, and the objective function is as follows:

[0012] Among them : diesel consumption cost; : lithium battery cycle loss cost; carbon emission penalty cost; : dynamic weight coefficient, adjusted according to task priority; S414: determine the constraint condition: 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 overdischarging, and the load rate of the diesel generator is greater than 30%; S42, establish a sensor network, deploy current and voltage sensors (accuracy ±0.5%), oil consumption flow meters (accuracy ±0.1%), and environmental sensors (temperature, humidity and wave height), use precise time PTP protocol to align the sensor time, and ensure the consistency of multi-source data time; S43, dynamically optimize the model according to the real-time received sensor data, and set a rolling optimization window to optimize once every fifteen minutes; S44, set real-time decision logic, determine mode switching rules, reduce operating energy consumption, the mode switching rules are as follows: ① Pure electric mode: lithium battery power SOC > 60% and load less than 200 KW, only use lithium battery power supply; ② Hybrid mode: lithium battery power SOC is 20% ~ 60% or load is greater than 600 KW, use lithium battery and diesel generator combined power supply; ③ Fuel priority mode: severe sea conditions (wave height > 3 m) or emergency tasks, use diesel engine main power supply, lithium battery backup; S45, receive algorithm results, realize multi-energy dynamic scheduling of water surface support system, real-time optimize energy distribution ratio, reduce deep sea mining operation cost by reducing energy consumption; S5: optimize the deployment and recovery system, based on the light load of the mining car to reduce energy consumption in the deployment and recovery process, use planetary gear set to drive winch to realize high torque cable deployment and recovery in the deployment and recovery stage, reduce energy consumption by more than 30%, and arrange electrolytic water trigger type release mechanism to realize emergency floating or load release of the deployment and recovery equipment in emergency, improve operation safety and avoid equipment damage; The deployment and recovery system includes deployment and recovery of conveying hose and deployment and recovery of deep sea mining car.

[0013] Adopt planetary gear set as recovery gear to amplify torque by more than 125 times, gear material adopts carburizing and quenching alloy steel, surface hardness ≥ 60 HRC, wear resistance is improved by 30%.

[0014] The working process of planetary gear set is as follows: ① First stage transmission, power input shaft drives sun gear, sun gear and planetary gear mesh, planetary gear meshes with fixed inner ring at the same time, forming speed reduction and torque increase effect; ② The second stage transmission planetary carrier is the output end, which transmits power to the next stage planetary gear set through the transmission shaft, realizing multi-stage torque amplification; ③ Receive torque sensor feedback, dynamically adjust motor input speed to avoid overload.

[0015] Design electrolytic cell and sensor inside the collection ship, including positive and negative electrodes and ion exchange membrane, electrode spacing is controlled at 1 ~ 2 mm, voltage is usually below 12V, to ensure low energy consumption and fast response (trigger time < 1s); Store the hydrogen and oxygen generated by electrolysis in high-pressure cavity with pressure resistance > 30MPa, control the flow direction through one-way valve; Receive sensor data; The sensor signal is judged, the sensor detects an emergency signal (cable tension exceeds limit or system failure), the circuit is triggered to be powered on, the gas pushes the piston to cut off the mechanical connection (umbilical cable connection), and the loss and operation and maintenance cost are reduced.

[0016] S6: Perform three-degree-of-freedom active-passive combined heave compensation system optimization, integrate servo motor driven active Z-axis compensation mechanism, magneto-rheological damper driven passive XY-axis compensation mechanism, and three-axis IMU data fusion technology, the three-axis IMU data fusion technology provides real-time feedback for heave compensation effect, the active-passive collaborative control makes the heave compensation accuracy improve from the traditional ±0.5 m to ±0.15 m, dynamic resource allocation is performed, the system comprehensive energy consumption is reduced by more than 40% compared with the full active scheme, the IMU data fusion technology makes the attitude solution error <0.1°, and the failure rate <0.1 times per thousand hours; High-precision servo motors (precision ±0.01 mm) are combined with ball screws (including servo motors, ball screws, feedback devices, controllers, and drivers) to adjust the Z-axis displacement in real time, provide dynamic adjustment capability, and compensate for the heave motion of the mother ship. Specifically: ①Input target parameters, the controller converts the instructions into electrical signals, and sends them to the servo driver through a communication protocol; ②The driver receives the control signal and adjusts the three-phase current to drive the motor; ③The screw rotation of the ball screw is converted into the linear motion of the nut (load platform); ④The encoder records the angular displacement of the motor rotor, and the grating ruler directly detects the load position. The controller compares the target value with the actual value, dynamically calculates the correction amount, adjusts the output signal through the PID algorithm, and eliminates the steady-state error; ⑤When external load mutation or vibration triggers feedback anomaly, the controller increases current output or reduces speed to maintain constant thrust, and dynamically adjusts by predicting trajectory error; ⑥After the load reaches the target, the servo motor maintains torque, the ball screw self-locking feature prevents reverse sliding, and after receiving the stop command, the controller stops according to the preset deceleration curve to avoid mechanical impact.

[0017] The driver adopts multi-loop control: the current loop controls the motor torque, and the response time is the fastest (ms level); the speed loop adjusts the rotating speed based on the encoder feedback; and the position loop ensures the final positioning accuracy. The servo motor rotor rotates, the output shaft is rigidly connected with the ball screw through a shaft coupling to directly drive the ball screw to rotate. The structure of the ball screw includes a screw and a nut. The screw surface has a precision thread raceway that pushes the ball to circulate when rotating. The ball groove in the nut is engaged with the screw, which can convert rotation into linear motion.

[0018] The viscosity of the magnetorheological fluid is adjusted by current (1-50 KN·S / m of damping force corresponding to 0-5 A of current), the XY axis damping is adjusted in real time according to the sway acceleration, high-frequency vibration is absorbed through the damping characteristics, and the response speed and stability are considered; The master-slave cooperative strategy is optimized, and the master-slave joint dynamic resource allocation is implemented: ① Low-intensity motion stage with wave height less than 1 m: start the passive damping dominant mode, passive damping accounts for 70%~80%, and active compensation accounts for 20%~30%; ② Moderate-intensity motion stage with wave height greater than 2 m and less than 3 m: start the active compensation dominant mode, active compensation accounts for 60%~80%, and passive compensation accounts for 20%~40%; ③ High-intensity motion stage with wave height greater than 3 m: preferentially enable active compensation, switch to the passive damping dominant mode in the stable stage, and reduce energy consumption; If the active system fails, the passive damping automatically switches to the maximum damping state, maintaining more than 50% compensation ability for at least 30 minutes; The three-axis IMU data fusion technology integrates the data of gyroscopes, accelerometers, and magnetometers, and generates high-precision attitude information by combining filtering algorithms and attitude solving methods to provide real-time feedback for heave compensation. The working process of the three-axis IMU data fusion technology is as follows: T1: Calibrate and preprocess the sensor; T2: Use the data fusion algorithm, complementary filtering: fuse the high-frequency characteristics of the gyroscope and the low-frequency characteristics of the accelerometer, the formula is:

[0019] Wherein is the weight coefficient, generally 0.96~0.98, dynamically adjusted to balance noise and drift; T3: Perform attitude solving and coordinate system conversion, use Runge-Kutta method to update quaternion, avoid Euler angle gimbal lock problem, formula is:

[0020] Wherein represents quaternion multiplication, is the angular velocity vector; convert the IMU body coordinate system to the geographic coordinate system through the rotation matrix to ensure that the compensation command is consistent with the heave direction; T4: Ensure real-time and synchronization optimization, use multi-thread processing, IMU data acquisition (1KHz), filtering (100Hz), and solving (100Hz) are run in separate threads, the delay is controlled within 2ms, align the time base of IMU and heave compensation controller through hardware trigger signal, avoid phase error, real-time feedback to the heave compensation system; T5: Monitor IMU data, realize three-axis IMU data fusion technology diagnosis and self-healing, maintain long-term feedback to the heave compensation system, and optimize the whole life cycle cost by more than 25%; Among them, the IMU data is monitored in real time, and a three-level response mechanism is triggered: first level: switch to backup filtering algorithm; second level: disable the faulty sensor channel and rely on redundant IMU data; third level: start the emergency lifting program.

[0021] Finally, based on the light load-stability optimized track walking system, the buoyancy compensation system, the safety weight reduction technology system of the deployment and recovery system and the water surface support system is proposed, the low-energy three-degree-of-freedom active-passive combined heave compensation system is developed, and the global cost optimization is realized; the sensor is arranged to receive deep sea mining system data in real time, and the multi-system data sharing and resource dynamic allocation are realized, the global cost is optimized, and compared with the traditional independent control system, the overall energy efficiency can be improved by more than 20%, and the global cost of deep sea mining operation system can be reduced by more than 30%; The beneficial effects of the present application are: (1) The present application is designed and optimized based on the light load-stability track walking system, high-strength composite materials are used as the main structure of the mining vehicle, and a track self-cleaning nozzle system is configured, so that double-effect weight reduction is realized; the self-weight of the vehicle body is reduced under the premise of maintaining the structural strength, and the running resistance is reduced by more than 20% by removing the adhesion of the track in real time; the comprehensive scheme makes the running cost of deep sea mining operation reduce by more than 25%, and the passing stability of complex seabed terrain is significantly improved; (2) The present application adopts a bidirectional hydraulic-pneumatic energy conversion mechanism to establish an energy recovery mechanism, for the buoyancy compensation system, energy recovery is carried out during the sinking stage, the recovery efficiency is greater than or equal to 38%, and the recovered energy is maximized during the lifting stage; the technology makes the overall energy consumption of the system reduce by 20%~30%, and the whole cycle cost of the mining operation system reduce by more than 30%; (3) Based on the safety weight reduction technology system, a hybrid power efficiency optimization algorithm is used to dynamically schedule multiple energies in the water surface support system, and the energy distribution ratio is optimized in real time; the algorithm makes the comprehensive energy utilization rate improve by more than 30%, under the premise of ensuring the reliability of the system, the fuel consumption reduces by 20%~28%, and the lithium battery cycle loss reduces by 25%~35%, which has a cascading effect on reducing the overall operation cost of the whole life cycle by more than 35%; (4) The present application reduces the energy consumption in the deployment and recovery process by using the walking motor of the mining vehicle to drive the winch in reverse and using the planetary gear set to amplify the torque 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, which significantly improves the safety redundancy of deep sea operation and ensures safety while avoiding equipment damage; (5) The application adopts three-axis IMU data fusion technology to optimize the three-degree-of-freedom active-passive combined heave compensation system, so that the heave compensation accuracy is improved from the traditional ±0.5 m to ±0.15 m, dynamic resource allocation is performed, the system comprehensive energy consumption is reduced by more than 40% compared with the full active scheme, the IMU data fusion technology makes the attitude solution error <0.1°, and the failure rate <0.1 times per thousand hours. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 A global cost optimization method flowchart based on deep-sea mining operation multi-system safety is provided. Figure 2 A mechanical coupling relationship diagram between each system is provided. Figure 3 A servo motor combined with a ball screw working flowchart is provided. DETAILED DESCRIPTION

[0023] The accompanying drawings in the embodiments of the present application are used to describe the technical solutions in the embodiments of the present application in more detail. In the accompanying drawings, the same or similar reference signs represent the same or similar elements or elements with the same or similar functions throughout. The described embodiments are part of the embodiments of the present application, rather than all the embodiments. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application. The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0024] It should be noted that if the present application has any directional indication (such as up, down, left, right, front, back, etc.), the directional indication is only used to explain the relative position relationship, motion condition, etc. between the components in a certain posture (as shown in the drawings), if the certain posture changes, the directional indication also changes accordingly.

[0025] In addition, if the present application has any description of "first", "second", etc., the description of "first", "second", etc. is only for description purpose, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features limited by "first" and "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions of each embodiment can be combined with each other, but it must be based on the realization of ordinary skilled in the art, when the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist, and is not within the scope of protection claimed by the present application.

[0026] Embodiment 1 A global cost optimization method based on multi-system safety of deep-sea mining operations, as shown in Figure 1 includes the following steps: Step S1: Based on global cost optimization and the coupling relationship between systems, determine the design parameters of each system; S11, construct the function-load-energy mapping table of buoyancy compensation, track walking, heave compensation, deployment and recovery, and surface support system, quantify the energy transmission path and mechanical coupling relationship between each subsystem, specifically: S111: Determine the core function of each system; S112: Define load types, including static load, dynamic load and fatigue load; S113: Perform mechanical coupling between systems, as shown in Figure 2 , establish the coupling relationship between each system; S114: Build a function-load-energy mapping table to determine the coupling influence coefficient between each system; S12, using QFD quality function deployment method, the mining efficiency, system reliability and cost control requirements are converted into design parameters of each subsystem, specifically: S121: Clearly define optimization requirements, including mining efficiency, system reliability and cost control; S122: Convert requirements into technical parameters; S123: Build a quality house. First, analyze the correlation strength of requirements and technical parameters by establishing a relationship matrix; second, perform competitive analysis to compare industry benchmarks and set target values for technical parameters; finally, build a roof matrix to analyze conflicts between technical parameters; S124: Determine the target values of each technical parameter converted, mining efficiency: continuous operation time improved by 60%, production capacity reached 150 tons / hour, automation level improved by 40%; System reliability: maintain 95% or more average trouble-free time, reduce redundancy design ratio by 80%, extend maintenance interval by 50%; Cost control: reduce material cost by 30%, reduce energy consumption by 50%, increase system integration by 50%, decompose each technical parameter to each subsystem design; Solve the conflict between each system design parameter, optimize the design scheme based on the optimization of the global cost of mining; S2: Based on light load-stability, optimize the track walking system, deep-sea mining vehicle selects titanium alloy material, track selects light composite structure composed of hollow titanium alloy material and carbon fiber reinforced polyimide track plate, and sprays super-hydrophobic nano material on the surface, increases the stability of mining through high-strength material, and carries self-cleaning nozzle to further reduce the load of mining vehicle. Clean the adhesion of the track to the soil; Based on light load-stability to determine material selection, mining truck selects titanium alloy material and track selects light composite structure composed of hollowed-out titanium alloy material and carbon fiber reinforced polyimide track plate, and sprays super-hydrophobic nano material on the surface; the nozzle selects titanium alloy material, and the surface is sprayed with titanium nitride coating to reduce the load of the mining truck by 40%; The nozzle is designed to be a circular nozzle, the internal flow channel adopts a contraction-expansion structure, and a self-rotating filter screen is built in to automatically remove large particle impurities, thereby accelerating the water flow and enhancing the impact force. The performance indicators of the nozzle are set according to the functional requirements, including: the spray pressure is controllable and adjustable within the range of 30~70 Bar, the coverage area covers at least 90% of the track surface, the cleaning cycle is 10 minutes / time, dynamic adjustment is performed, and the energy consumption of single spraying is ≤0.5 kWh; The nozzle angle is selected to be 30°, the diameter is 10 mm, the spacing is 40 cm, and the nozzle position is arranged horizontally around the outside of the track of the mining truck; The nozzle trigger mechanism and pressure adjustment mode are set. The nozzle trigger mechanism is dynamic triggering, which is fed back through a torque sensor, and the cleaning is started immediately when the travel resistance coefficient is >0.2. The sediment viscosity is obtained through the vehicle-mounted camera and AI image recognition, and the water pressure is adjusted in real time through a proportional valve to automatically match the optimal pressure; The nozzle blockage state is monitored in real time through a flow sensor, and a reverse flushing mode is triggered when it is blocked to realize self-diagnosis of nozzle failure; The track walking system can reduce the load by 70% by optimizing the material and carrying the self-cleaning nozzle to flush the attached sediment of the track, thereby reducing the energy consumption of mining operations by 20% and the operating cost by 25% of the mining truck; S3: The buoyancy compensation system is optimized, and a bidirectional hydraulic-pneumatic energy conversion mechanism is used to establish an energy recovery mechanism for the buoyancy compensation system; The hydraulic cylinder selects a double-acting hydraulic cylinder, the hydraulic oil flows through both sides of the piston, the piston rod end is rigidly connected with the air bag, the cylinder body material is titanium alloy material (compressive strength ≥ 150 MPa), and the sealing ring selects fluorine rubber (pressure resistance 55~65 MPa); The air bag group selects a variable volume air bag group, the air bag has a layered structure, the inner layer is a silicone rubber mold with a thickness of 1 mm, the outer layer is a Kevlar fiber woven layer (tear strength ≥ 500 N / mm), and the air bag is divided into four independent cabins, each cabin is equipped with an electromagnetic valve for segmented control (response time ≤10 ms); The energy storage unit adopts a high-pressure gas tank (working pressure 30 MPa) and a hydraulic energy accumulator (pressure range 0~60 MPa) in parallel, uses a high-pressure gas-liquid converter, and adjusts the communication between the oil circuit and the gas circuit through a proportional valve group; Set up energy recovery mechanism, deep-sea mining car sinking stage, energy recovery: when the mining car sinks, the external seawater pressure acts on the rodless cavity 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 cavity into the energy accumulator to store pressure energy; Set up energy release mechanism, deep-sea mining car floating stage, energy release: release high-pressure gas tank gas, push the piston to move reversely, auxiliary air bag inflation to provide buoyancy; energy accumulator releases hydraulic oil to drive hydraulic cylinder to enhance the upward thrust; Evaluate the working efficiency of the mechanism, including energy recovery rate index (ratio of recovered energy and consumed energy) and dynamic response index: ① Energy recovery rate index: , the target value is required to be greater than or equal to 45%; wherein : recovered energy; : consumed energy.

[0027] ② Dynamic response index: stage response adjustment time < 0.8s; Through the optimization of the buoyancy compensation system by the energy conversion mechanism, the energy recovery efficiency is more than 60%, thereby reducing the energy consumption of mining by 33% and reducing the full life cycle cost by 42%; S4: Based on the safety weight reduction technology, optimize the water surface support system, adopt hybrid power efficiency optimization algorithm, realize multi-energy dynamic scheduling of water surface support system, realize real-time optimization of energy distribution ratio, and improve the overall energy efficiency of the system; S41, construct hybrid power efficiency optimization algorithm: S411: input real-time data, including lithium battery state of charge (SOC), diesel generator load rate, power demand of current task stage (walking, collecting, recycling) and environmental parameters (sea state level, seawater temperature, environmental temperature and humidity); S412: input the predicted future one-hour operation power demand and lithium battery health state decay model to predict the lithium battery life, the lithium battery life prediction model:

[0028] Wherein : change amount of discharge depth; : activation energy (related to battery chemical properties); : gas constant, : battery temperature; : decay rate constant; related to battery type; SOH: state of health of lithium battery; S413: optimize the objective function of the hybrid power efficiency optimization algorithm, the objective function is as follows:

[0029] wherein : diesel consumption cost; : lithium battery cycle loss cost; : carbon emission penalty cost; : dynamic weight coefficient, adjusted according to task priority; bring the obtained data into the objective function, which can save 28% of fuel, reduce lithium battery temperature fluctuation by 35%, and reduce carbon emissions by 30%; S414: determine the constraint condition: 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 overdischarging, and the load rate of the diesel generator is greater than 30%; S42, establish a sensor network, deploy current and voltage sensors (precision ±0.5%), oil consumption flow meters (precision ±0.1%), and environmental sensors (temperature, humidity, and wave height), and use the precise time PTP protocol to align the sensor time to ensure the consistency of multi-source data time; S43, dynamically optimize the model according to the real-time received sensor data, and set a rolling optimization window to optimize once every fifteen minutes; S44, set real-time decision logic to determine mode switching rules to reduce energy consumption, and the mode switching rules are as follows: ① Pure electric mode: when the lithium battery power SOC is greater than 60% and the load is less than 200 KW, only use the lithium battery to supply power; ② Hybrid mode: when the lithium battery power SOC is between 20% and 60% or the load is greater than 600 KW, use the lithium battery and diesel generator to supply power jointly; ③ Fuel priority mode: in severe sea conditions (wave height > 3 m) or emergency tasks, use the diesel engine to supply power mainly, and the lithium battery is used as backup; S45, receive the algorithm result to realize multi-energy dynamic scheduling of the water surface support system, real-time optimize the energy distribution ratio, reduce the energy consumption to reduce the deep sea mining operation cost, improve the overall energy efficiency of the system by 30%, and reduce the whole life cycle cost by more than 35%; S5: optimize the deployment and recovery system, based on the light load of the mining car to reduce energy consumption in the deployment and recovery process, use the planetary gear set to drive the winch to realize high-torque cable deployment and recovery in the deployment and recovery stage, reduce energy consumption by more than 30%, and arrange the electrolytic water trigger type release mechanism to realize the emergency floating or load release of the deployment and recovery equipment in emergency, improve the operation safety and avoid equipment damage; Use the planetary gear set as the recovery gear to amplify the torque by 140 times, the gear material is carburized and quenched alloy steel, the surface hardness is ≥60 HRC, the wear resistance is improved by 30%, and the energy consumption is reduced by 35%; An electrolytic cell and sensor are designed inside the collection vessel, containing positive and negative electrodes and an ion exchange membrane. The electrode spacing is controlled to 1-2 mm, and the voltage is typically below 12V, ensuring low energy consumption and fast response (trigger time <1 s). 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. Receive sensor data; The system judges sensor signals. When the sensor detects an emergency signal (excessive cable tension or system failure), it triggers the circuit to energize, and the gas pushes the piston to cut the mechanical connection (umbilical cable connection), reducing losses by 68% and operating and maintenance costs by 47%; S6: Optimize the three-degree-of-freedom active-passive combined heave compensation system, integrating a servo motor-driven active Z-axis compensation mechanism, a magnetorheological damper-driven passive XY-axis compensation mechanism, and three-axis IMU data fusion technology. The three-axis IMU data fusion technology provides real-time feedback on the heave compensation effect. It uses 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, provide dynamic adjustment capabilities, and compensate for the heave and sinking movement of the mother ship. Figure 3 As shown, the following steps are included: ① Input the target parameters, the controller converts the instructions into electrical signals and sends them to the servo drive through the communication protocol; ②The driver receives the control signal and adjusts the three-phase current to drive the motor; ③The ball screw's rotary motion is converted into the linear motion of the nut (load platform); ④ The encoder records the angular displacement of the motor rotor, the grating ruler directly detects the load position, the controller compares the target and actual values, dynamically calculates the correction value, and adjusts the output signal through the PID algorithm to eliminate steady-state errors; ⑤ When the external load changes suddenly or the vibration triggers abnormal feedback, the controller increases the current output or reduces the speed to maintain constant thrust, and makes dynamic adjustments by predicting the trajectory error; ⑥ After the load reaches the target, the servo motor maintains torque, and the self-locking feature of the ball screw prevents reverse sliding. After receiving the stop command, the controller stops according to the preset deceleration curve to avoid mechanical shock; 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). The XY axis damping is adjusted in real time according to the sway acceleration. The damping characteristics absorb high-frequency vibrations, balancing response speed and stability. Optimize active-passive coordination strategies and implement active-passive joint dynamic resource allocation: ① Low-intensity motion stage with wave height less than 1 m: passive damping dominant mode is started, passive damping accounts for 70-80%, and active compensation accounts for 20-30%; ② Moderate-intensity motion stage with wave height greater than 2 m and less than 3 m: active compensation dominant mode is started, active compensation accounts for 60-80%, and passive compensation accounts for 20-40%; ③ High-intensity motion stage with wave height greater than 3 m: active compensation is preferentially started, and passive damping dominant mode is switched to in stable stage to reduce energy consumption; If the active system fails, passive damping automatically switches to maximum damping state, maintaining more than 50% compensation ability for at least 30 minutes; The active-passive collaborative control improves the heave compensation accuracy from the traditional ±0.5 m to ±0.15 m, performs dynamic resource allocation, and reduces the system comprehensive energy consumption by 45% compared with the full-active scheme; The three-axis IMU data fusion technology integrates the data of gyroscopes, accelerometers, and magnetometers, combines filtering algorithms and attitude solving methods to generate high-precision attitude information, and provides real-time feedback for heave compensation. The working process of the three-axis IMU data fusion technology is as follows: T1: Calibrate and preprocess the sensor; T2: Use data fusion algorithm, complementary filtering: fuse the high-frequency characteristics of gyroscope and low-frequency characteristics of accelerometer, formula is:

[0030] Wherein is the weight coefficient (0.96-0.98 throughout), which is dynamically adjusted to balance noise and drift; T3: Perform attitude solving and coordinate system conversion, use Runge-Kutta method to update quaternion, avoid Euler angle gimbal lock problem, formula is:

[0031] Wherein represents quaternion multiplication, is the angular velocity vector; convert the IMU body coordinate system to geographic coordinate system through rotation matrix to ensure that the compensation command is consistent with the heave direction; T4: Ensure real-time and synchronization optimization, use multi-thread processing, IMU data acquisition (1KHz), filtering (100Hz), and solving (100Hz) are run in separate threads, delay control is within 2ms, align the time reference of IMU and heave compensation controller through hardware trigger signal to avoid phase error, and provide real-time feedback to the heave compensation system; T5: Monitor IMU data, implement diagnosis and self-healing with three-axis IMU data fusion technology, and maintain long-term feedback to the heave compensation system. IMU data fusion technology reduces attitude solution error to less than 0.1° and failure rate to less than 0.1 times / 1,000 hours, optimizing the lifecycle cost by 25%. Finally, based on the light load-stability optimization of the crawler walking system and buoyancy compensation system, a safety load-reducing technology system for the deployment and recovery system and the surface support system was proposed. A low-energy three-degree-of-freedom active-passive combined heave compensation system was developed to achieve global cost optimization. Deploy sensors to receive real-time data from deep-sea mining systems, enable multi-system data sharing and dynamic resource allocation, and optimize overall costs. Compared with traditional independent control systems, this can improve overall energy efficiency by 47%, reduce energy consumption by 43%, and reduce the overall cost of deep-sea mining operations by 52%.

[0032] Based on existing deep-sea mining operation indicators, the following table shows the optimization results after implementation of Example 1 of the present invention:

[0033] Example 2 The global cost optimization method based on multi-system safety of deep-sea mining operations differs from Example 1 in that: S124: The target values ​​of each technical parameter determined and converted are: 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 at above 95%, redundant design ratio reduced by 60%, maintenance interval extended by 45%; cost control: material cost reduced by 30%, energy consumption reduced by 40%, system integration improved by 45%, and each technical parameter is broken down into subsystem designs; The nozzle design uses a circular nozzle with a contraction-expansion structure for the internal flow channel. A built-in spin filter automatically rotates to scrape off large impurities, thereby accelerating the water flow and enhancing the impact force. The nozzle's performance indicators are set according to functional requirements, including: spray pressure adjustable within the range of 30-70 bar, coverage of at least 90% of the track surface, a cleaning cycle of 13 minutes per time with dynamic adjustment, and energy consumption per spray of ≤0.5 kWh. The nozzles were selected with an angle of 25°, a diameter of 8 mm, a spacing of 50 cm, and were arranged horizontally around the outside of the mining vehicle tracks. Set the nozzle trigger mechanism and pressure adjustment mode. The nozzle trigger mechanism is dynamic triggering, and through torque sensor feedback, cleaning starts immediately when the travel resistance coefficient is greater than 0.2. The sediment viscosity is obtained through the on-board 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 flow sensor monitors the nozzle blockage status in real time and triggers the reverse flushing mode when blockage occurs, thus realizing nozzle fault self-diagnosis. The crawler travel system can reduce the load by 60% through material optimization and the installation of self-cleaning nozzles to flush deposits attached to the crawler tracks, thereby reducing the mining vehicle's energy consumption by 17% and operating costs by 22%. Evaluate the working efficiency of the mechanism, including energy recovery rate index (ratio of recovered energy to consumed energy) and dynamic response index: ①Energy recovery rate index: , the target value is required to be ≥45%; : Recovered energy; : Energy consumed.

[0034] ②Dynamic response index: stage response adjustment time <0.8s; By optimizing the buoyancy compensation system through this energy conversion mechanism, the energy recovery efficiency reaches over 54%, thereby reducing mining energy consumption by 31% and reducing the total life cycle cost by 35%; S41. Construct hybrid power efficiency optimization algorithm: S411: Input real-time data, including lithium battery state of charge (SOC), diesel generator load rate, power requirements for the current mission phase (travel, collection, recovery), and environmental parameters (sea state level, sea temperature, ambient temperature and humidity); S412: Input the predicted power demand for the next one hour and the lithium battery health state decay model to predict the lithium battery life. The lithium battery life prediction model:

[0035] in : Change in discharge depth; : activation energy (related to battery chemistry); : gas constant, : battery temperature; : Decay rate constant; related to battery type; SOH: State of health of lithium battery; S413: Optimizing the hybrid power efficiency optimization algorithm objective function, the objective function is as follows:

[0036] in : diesel consumption cost; : lithium battery cycle loss cost; Carbon emission penalty costs; : Dynamic weight coefficient, adjusted according to task priority; by substituting the obtained data into the objective function, it can save fuel by 25%, reduce lithium battery temperature fluctuation by 27%, and reduce carbon emissions by 27%; S45, receiving algorithm results, realizing multi-energy dynamic scheduling of the water surface support system, optimizing energy distribution ratio in real time, reducing energy consumption to reduce deep sea mining operation cost, improving system overall energy efficiency by 27%, reducing whole life cycle cost by 30%; Adopting planetary gear set as recovery gear to amplify torque by 130 times, gear material adopting carburizing and quenching alloy steel, surface hardness ≥60 HRC, wear resistance improved by 30%, energy consumption reduced by 32%; Judging sensor signals, triggering circuit energization when the sensor detects emergency signals (cable tension exceeds limit or system failure), gas pushing piston cutting off mechanical connection (umbilical connection), reducing loss by 68% and operation and maintenance cost by 47%; Optimizing active-passive collaborative strategy, implementing active-passive joint dynamic resource allocation: ① Low-intensity motion stage with wave height less than 1 m: starting passive damping dominant mode, passive damping accounting for 70%~80%, active compensation accounting for 20%~30%; ② Medium-intensity motion stage with wave height greater than 2 m and less than 3 m: starting active compensation dominant mode, active compensation accounting for 60%~80%, passive compensation accounting for 20%~40%; ③ High-intensity motion stage with wave height greater than 3 m: preferentially starting active compensation, switching to passive damping dominant mode in stable stage, reducing energy consumption; If the active system fails, the passive damping automatically switches to the maximum damping state, maintaining more than 50% compensation ability for at least 30 minutes; Active-passive collaborative control improves heave compensation accuracy from traditional ±0.5 m to ±0.15 m, performs dynamic resource allocation, and reduces system comprehensive energy consumption by 43% compared with the full active scheme; T5: monitoring IMU data, realizing diagnosis and self-recovery of three-axis IMU data fusion technology, maintaining long-term feedback to the heave compensation system, IMU data fusion technology makes attitude solution error <0.1°, failure rate <0.1 times / thousand hours, optimizing whole life cycle cost by 24%; Deploying sensors to receive deep sea mining system data in real time, realizing multi-system data sharing and resource dynamic allocation, optimizing global cost, improving overall energy efficiency by 38% compared with traditional independent control system, reducing energy consumption by 35% and global cost of deep sea mining operation system by 43%.

[0037] According to the existing deep sea mining operation index, the following table is the optimization result after the implementation of embodiment 2 of the present application:

[0038] Embodiment 3 The global cost optimization method based on the multi-system safety of deep-sea mining operations is different from embodiment 1 in that: S124: The determined converted technical parameter target value, mining efficiency: continuous operation time is improved by 40%, production capacity reaches 130 tons / hour, and automation level is improved by 35%; system reliability: average failure-free time is maintained above 90%, redundancy design ratio is reduced by 40%, and maintenance interval is extended by 40%; cost control: material cost is reduced by 20%, energy consumption is reduced by 30%, and system integration is improved by 30%, and each technical parameter is decomposed to each subsystem design; The nozzle is designed, the circular nozzle is selected, the internal flow channel adopts the shrinkage-expansion structure, and the self-rotating filter screen is built in to automatically rotate and remove large particle impurities, thereby accelerating the water flow and enhancing the impact force, the performance indicators of the nozzle are set according to the functional requirements, including: the jet pressure is controllable and adjustable in the range of 30-70 Bar, the coverage area covers at least 90% of the track surface, the cleaning period is 15 minutes / time, dynamic adjustment is performed, and the single jet energy consumption is ≤0.5 kWh; The nozzle angle is selected as 35°, the diameter is 10 mm, the spacing is 45 cm, and the nozzle position is arranged horizontally around the outside of the track of the mining vehicle; The nozzle triggering mechanism and pressure adjustment mode are set, the nozzle triggering mechanism is dynamic triggering, the torque sensor is fed back, and when the travel resistance coefficient is >0.2, the cleaning is immediately started; the sediment viscosity is obtained through the vehicle-mounted camera and AI image recognition, and the water pressure is adjusted in real time through the proportional valve, so as to automatically match the best pressure; The nozzle clogging state is monitored in real time through the flow sensor, and when clogging occurs, the reverse flushing mode is triggered to realize self-diagnosis of the nozzle fault; The track walking system can reduce the load by 58% through material optimization and carrying self-cleaning nozzles to flush the attached sediments of the track, thereby reducing the energy consumption of the mining vehicle by 15% and the operating cost by 20%; The working efficiency of the mechanism is evaluated, including the energy recovery rate index (the ratio of recovered energy to consumed energy) and the dynamic response index: ①Energy recovery rate index: , the target value is required to be ≥45%; wherein : recovered energy; : consumed energy.

[0039] ②Dynamic response index: stage response adjustment time <0.8s; The buoyancy compensation system is optimized through the energy conversion mechanism, the energy recovery efficiency reaches 50%, thereby reducing the mining energy consumption by 30% and the whole life cycle cost by 33%; S41, construct a hybrid power efficiency optimization algorithm: S411: input real-time data, including lithium battery state of charge (SOC), diesel generator load rate, current task phase (walking, collecting, recycling) power demand and environmental parameters (sea state level, sea water temperature, ambient temperature and humidity); S412: input predicted future one-hour operation power demand and lithium battery health state decay model to predict lithium battery life, lithium battery life prediction model:

[0040] wherein : change in depth of discharge; : activation energy (related to battery chemical properties); : gas constant, : battery temperature; : decay rate constant; related to battery type; SOH: state of health of lithium battery; S413: optimize the hybrid power efficiency optimization algorithm objective function, which is as follows:

[0041] wherein : diesel consumption cost; : lithium battery cycle loss cost; carbon emission penalty cost; : dynamic weight coefficient, adjusted according to task priority; the obtained data is brought into the objective function, which can save 21% of fuel, reduce lithium battery temperature fluctuation by 27%, and reduce carbon emissions by 24%; S45, receive algorithm results, realize multi-energy dynamic scheduling of surface support system, optimize energy distribution ratio in real time, reduce deep sea mining operation cost by reducing energy consumption, improve system overall energy efficiency by 24%, and reduce whole life cycle cost by 26%; Adopt planetary gear set as recycling gear to amplify torque by 125 times, gear material adopts carburizing and quenching alloy steel, surface hardness ≥ 60 HRC, wear resistance is improved by 30%, and energy consumption is reduced by 30%; Judge the sensor signal, the sensor detects an emergency signal (cable tension exceeds the limit or system failure), triggers the circuit to be powered on, the gas pushes the piston to cut off the mechanical connection (umbilical connection), reduces the loss by 68% and the operation and maintenance cost by 47%; Optimize the active-passive cooperative strategy and implement active-passive joint dynamic resource allocation: ① Low-intensity motion phase with wave height less than 1 m: start passive damping dominant mode, passive damping accounts for 70%~80%, active compensation accounts for 20%~30%; ② Moderate intensity motion stage: wave height is greater than 2 m and less than 3 m: start active compensation dominant mode, active compensation ratio is 60%~80%, passive compensation ratio is 20%~40%; ③ High intensity motion stage: wave height is greater than 3 m: preferentially start active compensation, switch to passive damping dominant mode in stable stage, reduce energy consumption; If the active system fails, the passive damping automatically switches to the maximum damping state, maintaining more than 50% compensation ability for at least 30 minutes; The active-passive collaborative control makes the heave compensation accuracy improve from the traditional ±0.5 m to ±0.15 m, performs dynamic resource allocation, and reduces the system comprehensive energy consumption by 40% compared with the full active scheme; T5: Monitor IMU data, realize diagnosis and self-recovery of three-axis IMU data fusion technology, maintain long-term feedback to the heave compensation system, the IMU data fusion technology makes the attitude solution error <0.1°, the failure rate <0.1 times / thousand hours, and optimizes the whole life cycle cost by 22%; Deploy sensors to receive deep-sea mining system data in real time, realize multi-system data sharing and dynamic resource allocation, optimize global cost, and improve overall energy efficiency by 36% compared with the traditional independent control system, reduce energy consumption by 32%, and reduce the global cost of the deep-sea mining operation system by 40%.

[0042] According to the existing deep-sea mining operation index, the following table is the optimization result after the implementation of the embodiment 3 of the present application:

[0043] Comparative example 1 The global cost optimization method based on the safety of the multi-system of deep-sea mining operation is different from the embodiment 1 in that only the structural design of the light load-stability tracked walking system is performed.

[0044] According to the existing deep-sea mining operation index, the following table is the optimization result after the implementation of the comparative example 1 of the present application:

[0045] Comparative example 2 The global cost optimization method based on the safety of the multi-system of deep-sea mining operation is different from the embodiment 1 in that only the water surface support system is optimized based on the safety reduction technology.

[0046] According to the existing deep-sea mining operation index, the following table is the optimization result after the implementation of the comparative example 2 of the present application:

[0047] From the optimization results of Examples 1, 2 and 3 above, it can be seen that the global cost optimization method based on the multi-system safety of deep-sea mining operation of the present application has achieved relatively obvious effects in the optimization results of fuel consumption, carbon emission, mining vehicle load, operating cost, energy efficiency, energy consumption and global cost.

[0048] It is found by comparison that the optimization results of Comparative Examples 1 and 2 are lower than those of the examples, which shows that the global cost optimization using the method can better meet the needs of low energy consumption and low cost of deep-sea mining operation.

[0049] In addition to the above examples, the present application can also have other implementation manners; any technical solution formed by equivalent replacement or equivalent transformation falls within the protection scope required by the present application.

[0050] For those skilled in the art, the present application is obviously not limited to the details of the foregoing specific examples. It can be implemented in other forms without departing from the essence of the present application. Therefore, the examples should be regarded as exemplary rather than limiting. It should be noted that the description is described in a specific implementation, but is not limited to a single scheme; this expression is for clarity only. The skilled person should understand the description as a whole and can combine the technical solutions in the examples to deduce other feasible implementation manners.

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 crawler 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 comprises the following steps: S1: Determine the design parameters of the deep-sea mining operation system based on global cost optimization and the coupling relationship between deep-sea mining operation systems; S2: Optimize the crawler system based on light load and stability; S3: Utilize a bidirectional hydraulic-pneumatic energy conversion mechanism to establish an energy recovery mechanism and an energy release mechanism to optimize the buoyancy compensation system; S4: Optimize the surface support system based on safety and load reduction technology; S5: Optimize the deployment and recovery system based on the light load of the mining vehicle to reduce energy consumption during the deployment and recovery process; S6: Optimize the three-degree-of-freedom active-passive combined heave compensation system; In step S1, the method for determining the design parameters of the deep-sea mining operation system is: S11: Construct a function-load-energy consumption mapping table for the buoyancy compensation system, tracked travel 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: Use the quality function deployment method to convert mining efficiency, system reliability and cost control requirements into design parameters for each subsystem; In step S2, the crawler walking system optimization includes: material selection, nozzle design, setting the nozzle trigger mechanism and pressure adjustment mode, and nozzle fault self-diagnosis design; In step S3, the buoyancy compensation system optimization includes: the design of the hydraulic cylinder, the airbag group, the energy storage unit, the energy recovery and release mechanism, and the design of the work efficiency evaluation mechanism; In step S4, the surface support system optimization method is: S41: Construct hybrid efficiency optimization algorithm; S42: Establishing a sensor network; S43: Dynamically optimize the model based on the sensor data received in real time; S44: Set real-time decision logic, determine mode switching rules, and reduce operating energy consumption; S45: Receive the algorithm results, realize the dynamic scheduling of multiple energy sources of the surface support system, and optimize the energy distribution ratio in real time; In step S5, the deployment and recovery system optimization method is as follows: a planetary gear set is used to drive a winch to achieve high-torque cable deployment; an electrolytic cell and a sensor are designed inside the collection vessel, including positive and negative electrodes and an ion exchange membrane; 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 includes: an active Z-axis compensation mechanism driven by a servo motor, a passive XY-axis compensation mechanism driven by a magnetorheological damper, active-passive collaborative control technology, and three-axis IMU data fusion technology.

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

3. The global cost optimization method based on deep-sea mining operation system safety according to claim 1 is characterized in that: In step S2, the material selection specifically includes: the deep-sea mining vehicle is made of titanium alloy material, the crawler is made of a lightweight composite structure consisting of a hollow titanium alloy material and a carbon fiber reinforced polyimide track plate, and the surface is sprayed with super-hydrophobic nanomaterials and equipped with a self-cleaning nozzle; the nozzle is made of titanium alloy material and the surface is sprayed with a titanium nitride coating; The nozzle is designed as follows: a circular nozzle is used, the internal flow channel adopts a contraction-expansion structure, and a built-in self-spinning filter screen rotates automatically. The nozzle angle is 25° to 35°, the diameter is 6 to 12 mm, the spacing is 20 to 50 cm, and the nozzle position is arranged horizontally around the outer side of the mining vehicle track; The nozzle triggering mechanism and pressure adjustment mode are specifically set as follows: the nozzle triggering mechanism is dynamic, and through torque sensor feedback, cleaning is immediately started when the travel resistance coefficient is greater than 0.2; the sediment viscosity is obtained through the on-board 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: the nozzle blockage status is monitored in real time through the flow sensor, and the reverse flushing mode is triggered when blockage occurs to realize nozzle fault self-diagnosis.

4. The global cost optimization method based on deep-sea mining operation system safety according to claim 1 is characterized in that: In step S3, the hydraulic cylinder uses a double-acting hydraulic cylinder, hydraulic oil is passed through both sides of the piston, the piston rod end is rigidly connected to the airbag, the cylinder body material is titanium alloy, and the sealing ring is fluororubber; the airbag group uses a variable volume airbag group, the airbag has 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. The airbag is divided into four independent compartments, each compartment is equipped with a solenoid valve for segmented control; the energy storage unit uses a high-pressure gas tank and a hydraulic accumulator in parallel, a high-pressure gas-liquid converter, and the oil circuit and the gas circuit are connected through a proportional valve group; The energy recovery mechanism is: Seawater pressure drive: When the mining vehicle sinks, the external seawater pressure acts on the rodless cavity of the hydraulic cylinder, pushing the piston to compress the gas in the airbag to store energy; Gas compression energy storage: The compressed gas enters the 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: Gas expansion work: releasing the high-pressure gas tank gas, pushing the piston to move in the opposite direction, and assisting the airbag to expand and provide buoyancy; Hydraulic energy assistance: the accumulator releases hydraulic oil to drive the hydraulic cylinder to enhance the upward thrust; The evaluation mechanism of work efficiency includes energy recovery rate index and dynamic response index, among which the energy recovery rate index is: , the target value is required to be ≥45%; dynamic response index: stage response adjustment time is <0.8s; in : Recovered energy; : Energy consumed.

5. The global cost optimization method based on deep-sea mining operation system safety according to claim 1 is characterized in that: The method for constructing the hybrid power efficiency optimization algorithm in step S41 is: S411: Input real-time data, including lithium battery charge status, diesel generator load rate, power requirements of the current mission phase, and environmental parameters; S412: Input the predicted power demand for the next one hour and the lithium battery health state decay model to predict the lithium battery life. The lithium battery life prediction model: in : Change in discharge depth; : activation energy, which is related to the battery chemistry; : gas constant, : battery temperature; : decay rate constant; SOH: state of health of lithium battery; S413: Optimizing the hybrid power efficiency optimization algorithm objective function, the objective function is as follows: in : diesel consumption cost; : lithium battery cycle loss cost; Carbon emission penalty costs; : Dynamic weight coefficient, adjusted according to task priority; S414: Determine the constraints: the total system power output is greater than the current load demand, the lithium battery state of charge (SOC) is maintained between 20% and 90% to prevent overcharging and over-discharging, and the diesel generator load factor 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 aligning the sensor times using the precise time PTP protocol; The dynamic optimization in step S43 is to set the rolling optimization window to be optimized 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 to provide power. Fuel priority mode: In severe sea conditions (wave height > 3 m) or emergency missions, the diesel engine is used as the primary power source, with the lithium battery as a backup.

6. The global cost optimization method based on deep-sea mining operation system safety according to claim 1 is characterized in that: In step S5, the gear material of the planetary gear set is carburized and quenched alloy steel with a surface hardness of ≥60 HRC; the distance between the positive and negative electrodes is controlled to be 1-2 mm, and the voltage is generally below 12 V; hydrogen and oxygen 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; the sensor data is analyzed as follows: when the sensor detects excessive cable tension or system failure, the circuit is triggered to energize, and the gas pushes the piston to cut the mechanical connection.

7. The global cost optimization method based on deep-sea mining operation system safety according to claim 1 is 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 servo motor has an accuracy of ±0.01 mm, and the ball screw includes a servo motor, a ball screw, a feedback device, a controller, and a driver; The steps to implement the active Z-axis compensation mechanism driven by the servo motor are as follows: Input the target parameters, the controller converts the instructions into electrical signals and sends them to the servo drive through the communication protocol; The driver receives the control signal and adjusts the three-phase current to drive the motor; The screw rotation 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, and the controller compares the target and actual values, dynamically calculates the correction value, and adjusts the output signal through the PID algorithm to eliminate steady-state errors. When the external load suddenly changes or the vibration triggers abnormal feedback, the controller increases the current output or reduces the speed to maintain constant thrust. It makes dynamic adjustments by predicting the trajectory error in advance. After the load reaches the target, the servo motor maintains torque, and the self-locking feature of the ball screw prevents reverse sliding. After receiving the stop command, the controller stops according to the preset deceleration curve to avoid mechanical shock.

8. The global cost optimization method based on deep-sea mining operation system safety according to claim 1 is characterized in that: In step S6, the passive XY-axis compensation mechanism driven by the magnetorheological damper is implemented by regulating 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 by the damping characteristics.

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

10. The global cost optimization method based on deep-sea mining operation system safety according to claim 1, characterized in that: In step S6, the implementation method of the three-axis IMU data fusion technology is: T1: Calibrate and preprocess the sensor; T2: Using data fusion algorithm, complementary filtering: integrating the high-frequency characteristics of the gyroscope and the low-frequency characteristics of the accelerometer, the formula is: in The weight coefficient is generally 0.96~0.98, and is dynamically adjusted to balance noise and drift; T3: Perform attitude calculation and coordinate system conversion, and use the Runge-Kutta method to update the quaternion to avoid the Euler angle universal lock problem. The formula is: in represents quaternion multiplication, is the angular velocity vector; the IMU body coordinate system is converted to the geographic coordinate system through the rotation matrix to ensure that the compensation instruction is consistent with the heave direction; T4: Multi-threaded processing is used, including IMU data acquisition, filtering, and solution execution in separate threads. Latency is controlled within 2ms. Hardware trigger signals are used to align the time bases of the IMU and heave compensation controller to avoid phase errors, providing real-time feedback to the heave compensation system. T5: Monitor IMU data, implement diagnosis and self-healing of three-axis IMU data fusion technology, and maintain long-term feedback to the heave compensation system.

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