Multi-sensor fusion underwater base bed leveling material position attitude cooperative control device and method

Abstract

The application relates to underwater base bed leveling equipment control technology, and relates to a multi-sensor fusion underwater base bed leveling material level posture cooperative control device and method. The device comprises device initialization and parameter calibration, multi-source data real-time acquisition, data fusion and control instruction calculation, cooperative adjustment of an executing mechanism, and leveling precision closed loop verification. The application establishes a positioning-posture-material level three-in-one coupling control model, combines a dynamic material level threshold algorithm, a posture error correction formula and a seven-parameter coordinate conversion model, and realizes closed loop accurate control of leveling precision within 3cm. The device has sea condition self-adaptive adjustment capability, can adapt to complex operation environments with a water depth of 0-20m, wind force of less than or equal to 6, wave height of less than or equal to 1.0m and flow velocity of less than or equal to 1.0m / s, significantly improves the automation degree, construction stability and long-term reliability of underwater base bed leveling, shortens the operation time of a single box position to 4-5h, and improves the efficiency by 200%-300% compared with traditional technologies.

Description

Technical Field

[0001] This invention relates to underwater foundation leveling equipment control technology, specifically to an underwater foundation leveling material level attitude collaborative control device and method that integrates Beidou GNSS high-precision positioning, MEMS attitude detection and permanent magnet material level monitoring, applicable to foundation stone leveling operations in underwater engineering such as port terminals, breakwaters, and cross-sea bridge foundations. Background Technology

[0002] Underwater leveling is a crucial preliminary step before caisson installation and superstructure construction. Its quality directly determines the stability and durability of the caisson structure and the installation accuracy of the superstructure. Currently, underwater leveling operations mainly employ two methods: manual diving and traditional leveling vessels. These methods suffer from the following technical shortcomings, severely hindering project quality and efficiency: 1. Delayed and low-accuracy level detection: Traditional level detection relies on mechanical penetrometer sensors. Their working principle involves a probe being triggered by the pressure of stones, which activates a switch. However, the fluid resistance torque generated by underwater currents causes a response delay (≥10s), and the irregularity of stone accumulation can easily cause the probe to jam, resulting in a detection error of 15-20cm. Furthermore, traditional sensors use fixed thresholds to determine the level, failing to dynamically adjust based on seawater density, flow velocity, and other sea condition parameters. In environments with flow velocities above 0.5m / s, the rate of overflow or under-leveling exceeds 30%. Related research indicates that mechanical penetrometer sensors have a misjudgment rate as high as 45% in complex sea conditions, severely impacting leveling uniformity.

[0003] 2. Inadequate attitude compensation mechanism: The tilt error (Pitch, Roll) of the leveling frame lacks effective coupling with positioning data. Traditional techniques rely solely on manual experience to adjust the outrigger height, without establishing a quantitative attitude-elevation correction model. Due to the lack of visibility in the underwater environment, manual adjustments are subject to lag, resulting in elevation control accuracy of only ±10-15cm, far below the ±3cm requirement of the JTJ298-2018 standard. Furthermore, traditional devices typically employ 2-3 tilt sensors, which can only monitor tilt in a single direction, failing to achieve comprehensive three-dimensional attitude perception and resulting in incomplete tilt compensation.

[0004] 3. Poor coordination among multiple systems: The control of positioning, attitude, and material level is independent, lacking a unified coupled control model. For example, after the positioning module outputs a coordinate deviation, the hydraulic actuator simply adjusts the elevation without considering the material level status (e.g., adjusting the elevation when there is no material will lead to "virtual leveling"); after a material level alarm, the material placing and traveling mechanism is not linked with the positioning data, which can easily lead to repeated material placement or blind spots in material placement, resulting in a construction rework rate of over 15%.

[0005] 4. Poor environmental adaptability: Traditional devices lack a real-time sea state parameter detection module, and their control strategy relies on fixed parameters, making them unable to adapt to variations in seawater density (1.01-1.035 t / m³) and current velocity (0-1.0 m / s) across different sea areas and time periods. In environments with winds exceeding force 5 and current velocities exceeding 0.8 m / s, the lack of adaptive sea state adjustment leads to a decrease in leveling accuracy of over 50%, limiting the construction window to only 60% of the year.

[0006] 5. Low safety and efficiency: Manual underwater leveling requires divers to work underwater, facing safety risks such as lack of oxygen, high pressure, and stone impact. The operation time for a single container can be as long as 12-15 hours. Traditional leveling boats have a low degree of frame integration, require 6-8 people to work together, resulting in low work efficiency and labor costs accounting for more than 40% of the total construction cost.

[0007] Therefore, there is an urgent need to develop a multi-sensor fusion collaborative control technology. Through hardware modular integration and algorithm model optimization, a three-in-one coupled control system of "positioning-attitude-material level" can be established to achieve high-precision, automated, and sea-state adaptive control of underwater leveling. Summary of the Invention

[0008] This invention addresses the problems existing in the prior art by breaking through the limitations of the traditional "independent control" technology and constructing a closed-loop control system of "multi-sensor data fusion - coupled model solution - actuator collaboration".

[0009] To achieve the above objectives, the present invention adopts the following technical solution: a multi-sensor fusion underwater bed leveling material position attitude collaborative control method, comprising the following steps: S1: Equipment initialization and parameter calibration. This includes completing the static initialization of the BeiDou GNSS receiver, zero-point calibration of the tilt sensor, and trigger threshold calibration of the material level detection module. The BeiDou GNSS receiver static initialization observation lasts 30 minutes, with a calculation accuracy ≤0.005m. The tilt sensor zero-point calibration temperature compensation is adjusted to a 25℃ reference. Input seven-parameter coordinate transformation parameters, the design elevation H0 of the construction area, and sea state reference parameters. The sea state reference parameters include: initial seawater density ρ0 = 1.02 t / m³. 3 The initial flow velocity v0 = 0 m / s; S2: Real-time acquisition of multi-source data. The positioning module outputs the real-time coordinates (X, Y, H) and heading angle of the leveling frame at a frequency of 50Hz; the attitude module acquires the tilt angles θ1, θ2, θ3, θ4, θ5 from five sensors at a frequency of 100Hz; the material level module outputs the material level status signal S at a frequency of 10Hz; simultaneously, the seawater flow velocity v is acquired using an acoustic Doppler current meter (ADCP) with an accuracy of ±0.01m / s, and the velocity is measured with an accuracy of ±0.001t / m. 3 The density sensor collects the seawater density ρ; S3: Data fusion and control command calculation, calculate the attitude elevation correction amount corresponding to each sensor through formula (2), calculate the material level dynamic threshold by combining real-time sea state parameters through formula (3), fuse positioning deviation, attitude correction amount and material level status through formula (4), and output hydraulic control command; S4: The actuator coordinates and adjusts. The hydraulic drive module controls the synchronous extension and retraction of the four leg cylinders through the diversion and combination valve group according to the control command. The extension and retraction accuracy is ±0.1mm, and the levelness of the adjustment frame is ≤0.1°. The gear and rack mechanism adjusts the fabric walking speed and path to achieve precise fabric placement. S5: Closed-loop verification of leveling accuracy. Repeat steps S2-S4 and calculate the total leveling error σ in real time using the error synthesis formula (5) until σ≤3cm, and complete the area leveling operation.

[0010] Formula (2) mentioned in step S3 is: (2) Where: ΔH i L represents the elevation correction for the i-th tilt sensor, in meters (m). i The straight-line distance from the sensor mounting point to the geometric center of the frame is precisely calibrated by 3D modeling software, in meters (m); θ i The tilt angle detected by the sensor, in rad, when θ i When the angle is ≤0.5°, a precise sine calculation is used instead of a small angle approximation to improve the correction accuracy; δ is the system correction coefficient, with a value of 0.002-0.005m. Through 100 sets of indoor simulation tests and 50 sets of marine field tests, the optimal value of 0.003m is obtained by linear regression fitting to compensate for sensor installation deviation and inherent system error.

[0011] Formula (3) mentioned in step S3: (3) Where: h th The dynamic material level threshold is expressed in meters (m); h0 is the standard material level threshold, 4m for low material level or 8m for high material level; ρ is the real-time seawater density, expressed in tons per cubic meter (t / m³). 3 v represents the real-time seawater flow velocity in m / s; k1 and k2 are sea state correction coefficients, obtained by fitting a multiple linear regression model based on 200 sets of material level response tests under different sea states, with k1=0.05 and k2=0.12, and a goodness-of-fit R-value. 2 =0.987, ensuring the accuracy of material level detection when sea conditions change; the formula is essentially based on the principle of fluid mechanics to compensate for the influence of seawater on the state of stone accumulation. Increased flow velocity leads to a decrease in the height of stone accumulation, and changes in density affect the triggering force of the material's own weight.

[0012] Formula (4) mentioned in step S3: (4) In the formula: u is the hydraulic cylinder extension / retraction control voltage signal, unit: V, output range 0-5V, corresponding to hydraulic cylinder extension / retraction amount 0-1m; k p is a scaling factor, ranging from 0.8 to 1.2, used for rapid response to positioning and attitude coupling deviations; k d is the differential coefficient, ranging from 0.1 to 0.3, used to suppress system overshoot and oscillation; k s This is the material level coordination coefficient, dynamically adjusted according to the material level status: when S=0, there is no material, k s =-0.5, increase fabric speed; S=1 indicates low material level, k s =0, maintain constant speed; when S=2, high material level, k s =0.5, slow down or stop the fabric; The elevation change rate is expressed in m / s and is calculated using a 5-point moving average method to avoid interference from instantaneous fluctuations. This formula is based on the PD control algorithm to achieve a closed-loop control linking positioning, attitude, and material level.

[0013] Step S5 also includes sensor fault diagnosis logic: when the data of a certain sensor exceeds the normal range of ±3σ and lasts for 3 sampling cycles, the system automatically determines that the sensor is faulty and switches to redundant data. For example, when the attitude sensor is faulty, it calculates by interpolation of the data of the other 4 sensors and triggers an audible and visual alarm to ensure the continuity of operation.

[0014] Formula (5) mentioned in step S5: (5) In the formula: σ is the total leveling error, in meters; σ g The GNSS positioning error is ≤0.01m, verified through 30 minutes of static observation; σ θ The attitude detection error is ≤0.008m, calibrated using a laser tracker; σ h To control the material level error, ≤0.015m, it is measured by weighing method; since the three errors all follow a normal distribution and are independent of each other, the square root method is used to synthesize them according to the error propagation principle to ensure that the total error is controlled within ±3cm; in actual engineering, it can be combined with the visual inspection of the underwater robot ROV and the land calibration of the total station to achieve dual verification of the error.

[0015] A multi-sensor fusion underwater bed leveling and material position attitude collaborative control device includes: Positioning module: Composed of dual-antenna BeiDou GNSS receivers, installed on the top of the feed pipe and on both sides of the barge control room. The antennas are erected at a height of ≥1.5m, with no obstructions, to ensure satellite signal reception quality. It supports BDS-3 / BDS-2 dual-mode positioning with a planar accuracy of ±(10+1×10). -6×D)mm, elevation accuracy ±(20+1×10 -6 ×D)mm, with dynamic heading calculation capability, and real-time output of the spatial coordinates (X,Y,H) and heading angle information of the leveling frame; Attitude detection module: includes 5 MEMS capacitive tilt sensors. The tilt sensors include four sensors installed at the midpoint of the four sides of the leveling frame for X and Y axis tilt detection and one sensor connected to the top of the material tube for vertical tilt detection. The sensor accuracy is ±0.1°, temperature drift is ≤0.001° / ℃, and the working temperature range is -10~60℃. It detects the tilt angle and vertical deviation of the frame. Material level detection module: includes a permanent magnet material level probe, an electronic control module, and a dual-color alarm light. The permanent magnet material level probe is resistant to corrosion from seawater with a chloride ion concentration of 3.5% and can withstand immersion in water at 10m for 1000 hours without leakage. The permanent magnet material level probe is installed at the bottom of the rock-throwing pipe at a low material level of 4m and a high material level of 8m via a bracket. The opening size is 204mm×587mm. The material level status signal S is output through a magnetic coupling switch triggered by the Hall effect. S=0 indicates no material, S=1 indicates low material level, and S=2 indicates high material level. Data processing module: It adopts an industrial-grade industrial control computer with a built-in coordinate transformation unit and collaborative control unit. It receives positioning, attitude and material level data through TCP / IP protocol with a transmission rate of 100Mbps and a latency of ≤20ms, and realizes multi-source data coupling calculation based on preset formulas. The execution module includes a hydraulic system and a fabric placement mechanism. The hydraulic system comprises a dual-plunger variable displacement hydraulic pump, a flow divider / combiner valve assembly, and four outrigger cylinders. The hydraulic pump and flow divider / combiner valve assembly are installed in a frame-sealed enclosure with an IP65 protection rating and are connected to the mother ship's control panel via a main supply and return oil pipe with a pressure resistance of 31.5 MPa. The dual-plunger variable displacement hydraulic pump has a displacement of 10.56 L / min, a rated pressure of 25 MPa, and load-sensitive control functionality. The flow divider / combiner valve assembly has a synchronization accuracy of ≤±1%. The outrigger cylinders have a cylinder diameter of φ194 or φ160 mm, a stroke of 1 m, and are sealed with V-rings and dustproof rings, with a pressure resistance of 31.5 MPa. The fabric placement mechanism is installed at the bottom of the frame, with the traveling track welded and fixed to the frame. It includes a rack and pinion drive mechanism, a hydraulic motor, a reducer, and traveling wheels. The rack and pinion drive mechanism drives the fabric placement and receives control signals to achieve coordinated control of frame leveling and fabric placement. The track flatness is ≤0.3 mm.

[0016] The permanent magnet level sensor probe has a shell material of SUS316L, a protection rating of IP68, a rated operating voltage of 24VDC, an average power consumption of ≤0.5W, and an action delay of 3-6 seconds. The permanent magnet level sensor probe adopts a swing rod structure with a swing angle range of 0-45° and a triggering force of ≤5N to avoid detection failure caused by stone jamming. The action delay is 3-6 seconds.

[0017] The coordinate transformation unit adopts a seven-parameter spatial transformation model, and realizes the transformation between the engineering coordinate system and the construction coordinate system through formula (1): (1) In the formula: (X) p ,Y p Z p (X) represents the coordinates in the construction coordinate system; g ,Y g Z g ) represents the BeiDou GNSS output coordinates; λ is the scale factor, in ppm; R is the rotation matrix, derived from the rotation angle (R). x ,R y ,R z The system consists of seven parameters: (ΔX, ΔY, ΔZ) are translation parameters; the seven parameters are solved by solving a series of equations using the least squares method through at least three known common control points, with a calibration accuracy of ≤0.001m. The known common control points include first-order traverse plane control points and second-order leveling elevation control points.

[0018] This invention, based on the requirements of JTJ 298-2018 "Code for Design of Foundation Engineering for Port Projects", precisely matches this industry standard and solves the high-precision, automated construction needs that are difficult to meet with traditional technologies. Specifically, it has the following beneficial effects: 1. Significantly improved leveling accuracy: Through multi-sensor fusion and formulaic error correction, the leveling accuracy has been improved from ±5cm of traditional technology to within ±3cm, fully meeting the requirements of JTJ 298-2018 "Code for Design of Foundation of Port Engineering", and the qualified rate of caisson installation has been increased to over 99%. 2. Significantly improved automation: The entire process of material level detection, posture compensation, positioning adjustment, and material placement is fully automated, eliminating the need for manual underwater operations. The operation time per box is reduced from 12-15 hours to 4-5 hours, increasing construction efficiency by 200%-300%, and saving more than 800,000 yuan in labor costs per project. 3. Strong adaptability to sea conditions: The dynamic material level threshold algorithm and attitude real-time compensation mechanism can adapt to complex sea conditions such as wind force ≤6, wave height ≤1.0m, and current velocity ≤1.0m / s, extending the construction window by 40% and increasing the annual working time from 200 days to 280 days. 4. High safety and reliability: The underwater unmanned operation mode completely eliminates the safety risks to divers. The redundant layout of sensors and the fault self-diagnosis function reduce the equipment failure rate. The mean time between failures (MTBF) is ≥800h, and there is no record of continuous operation for 1000h without failure. 5. Excellent energy saving, environmental protection and economy: Automated control reduces the ineffective energy consumption of the hydraulic system by 15% and saves 20 tons of fuel per project; modular design adapts to different sizes of leveling frames, installation and commissioning time ≤32h (on-site installation ≤24h, commissioning ≤8h), and maintenance costs are reduced by 30%. Attached Figure Description

[0019] Figure 1 This is a block diagram of the device of the present invention; Figure 2 This is a schematic diagram of the material level detection module. Figure 3 Here is a flowchart of the collaborative control method; Figure 4 This is a schematic diagram of the leveling accuracy error distribution.

[0020] In the diagram: 1-Positioning module; 2-Material level detection module; 3-Tilting sensor; 4-Lower material level sensor; 5-Upper material level sensor; 6-Electronic control module; 7-Junction box; 8-Alarm light. Detailed Implementation

[0021] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but the present invention is not limited to the specific embodiments.

[0022] Example 1 like Figure 1 As shown, the multi-sensor fusion underwater bed leveling and material leveling attitude collaborative control device includes the following components: (a) Positioning module; Selection: Dual-antenna BeiDou GNSS receiver, supporting BDS-3 / BDS-2+GPS dual-mode compatibility; Performance indicators: Static plane accuracy ±2.5mm+1ppm, dynamic plane accuracy ±5mm+1ppm, elevation accuracy ±5mm+1ppm, data update rate 50Hz, heading angle calculation accuracy ±0.1°; Installation method: The two antennas are installed on the top of the feed pipe and on both sides of the barge control room, respectively. The antennas are erected at a height of ≥1.5m with no obstructions to ensure the quality of satellite signal reception. Key advantages: The dual-antenna design can calculate the heading angle, improve the stability of dynamic positioning, and avoid positioning drift caused by the swaying of the ship when using a single antenna; BDS-3 dual-mode positioning can resist electromagnetic interference and has a positioning availability of ≥99.9% in complex port environments.

[0023] (ii) Attitude detection module; Sensor type: 5 MEMS capacitive tilt sensors, redundant layout; Performance specifications: Measurement range ±5°, accuracy ±0.1°, resolution 0.001°, temperature drift ≤0.001° / ℃, response time ≤10ms, operating voltage 24VDC; Installation layout: One sensor is installed at the midpoint of each of the four sides of the frame to monitor the positive and negative directions of the X-axis and the positive and negative tilt of the Y-axis, respectively. One sensor is installed at the top of the material tube to monitor the vertical tilt. The sensors are fixed to the frame by welding. The flatness of the mounting surface is ≤0.5mm. The weld joints are treated with epoxy resin coating for corrosion protection. Key advantages: The MEMS capacitive structure has strong impact resistance, can withstand 100g acceleration impact, and is suitable for underwater vibration environment; the redundancy layout of 5 sensors can reconstruct data through interpolation algorithm when a single sensor fails, thereby improving system reliability.

[0024] (iii) Material level detection module; like Figure 2 As shown, the core components are: two permanent magnet level probes, an electronic control module, a yellow and red dual-color alarm light, and a junction box. Probe performance: Housing material SUS316L, protection level IP68, rated operating voltage 24VDC, average power consumption ≤0.5W, action delay 3-6 seconds, swing angle of the lever 0-45°, trigger force ≤5N; Installation process: Holes with dimensions of 204mm×587mm are drilled at the 4m low material level and 8m high material level on the side wall of the stone-throwing pipe. The mounting bracket is welded and fixed to the pipe shell with a weld height of 8mm. The water pressure test at 0.3MPa shows no leakage. The probe is connected to the bracket via a screw. A stainless steel protective cover with a hole diameter ≥50mm is installed on the outside to avoid obstructing the stone. The cable is laid along the pipe wall and fixed, and connected to the junction box using a waterproof connector with IP68 rating. Signal Output: The electronic control module converts the probe trigger signal into a switch signal S. S=0 indicates no material, S=1 indicates low material level, and S=2 indicates high material level. The signal is transmitted to the data processing module via cable, and the alarm lights display the signal synchronously. A yellow light indicates low material level, a red light indicates high material level, and a flashing light indicates an abnormal material level.

[0025] (iv) Data processing module; Hardware configuration: Industrial-grade industrial control computer, wide temperature design -20~60℃, 4 network ports, 2 serial ports, 4 analog output interfaces; Software system: Equipped with RT-Linux real-time operating system, control cycle ≤10ms, integrating coordinate transformation unit, collaborative control unit, fault diagnosis unit, and data storage unit; The coordinate transformation unit adopts a seven-parameter spatial transformation model, and realizes the transformation between the engineering coordinate system and the construction coordinate system through formula (1): (1) In the formula: (Xp, Yp, Zp) are the coordinates of the construction coordinate system; (Xg, Yg, Zg) are the BeiDou GNSS output coordinates; λ is the scale factor, in ppm; R is the rotation matrix, consisting of rotation angles (Rx, Ry, Rz); (ΔX, ΔY, ΔZ) are the translation parameters. The seven parameters are solved using a simultaneous equation system via at least three known common control points, including first-order traverse plane control points and second-order leveling elevation control points, with a calibration accuracy ≤0.001m.

[0026] Theoretical basis: The engineering geodetic coordinate system (such as CGCS2000) and the construction local coordinate system have different references, including differences in ellipsoid parameters and origin position. Coordinate transformation needs to be achieved through seven parameters, which include translation ΔX, ΔY, ΔZ; rotation Rx, Ry, Rz; and scale λ. Refer to the principle of spatial rectangular coordinate system transformation in "Fundamentals of Geodesy" (Kong Xiangyuan et al., 2010).

[0027] Seven-parameter calibration procedure: Select at least three known common control points and collect their geodetic coordinates (Xg, Yg, Zg) and construction coordinates (Xp, Yp, Zp) respectively. Establish an overdetermined system of equations: , , ; The seven parameters were solved using the least squares method to minimize the transformation residual (≤0.001m). After calibration, the geodetic coordinates output by GNSS are converted into construction coordinates in real time to eliminate the impact of coordinate system deviation on leveling accuracy.

[0028] Communication protocol: Communicates with the positioning module, attitude module, and material level module via TCP / IP protocol, and with the execution module via analog signals (0-5V); Core functions: Receive multi-source data and perform coupled calculations to output hydraulic control commands; store construction data (coordinates, attitude, material level, error, etc.) in real time with a storage period of ≥1 year; have a fault self-diagnosis function, which can identify and alarm for problems such as sensor failure, communication abnormality, and actuator jamming.

[0029] (v) Execution module; Hydraulic system: Dual-piston variable hydraulic pump, displacement 10.56L / min, rated pressure 25MPa, drive power 15kW; flow divider and combiner valve group, synchronization accuracy ≤±1%; four-leg cylinder, cylinder diameter φ194 or φ160mm, stroke 1m, piston rod material 45 steel chrome plated, sealing type V-ring + dustproof ring; Fabric feeding mechanism: rack and pinion transmission mechanism, hydraulic motor, reducer, and traveling wheels; Installation layout: The hydraulic pump and valve group are installed in the frame sealed box (protection level IP65) and connected to the mother ship's operating platform through a "one supply and one return" main oil pipe (high pressure rubber hose, pressure resistance 31.5MPa); the fabric placing mechanism is installed at the bottom of the frame, and the traveling rail is welded and fixed to the frame, with a rail flatness ≤0.3mm; Control performance: The hydraulic cylinder extension and retraction control accuracy is ±0.1mm, and the frame levelness adjustment range is ±5°.

[0030] Example 2

[0031] like Figure 3 As shown, the multi-sensor fusion underwater bed leveling material level attitude collaborative control method includes the following steps: S1: Equipment initialization and parameter calibration. This includes completing the static initialization of the BeiDou GNSS receiver, zero-point calibration of the tilt sensor, and trigger threshold calibration of the material level detection module. The BeiDou GNSS receiver static initialization observation lasts 30 minutes, with a calculation accuracy ≤0.005m. The tilt sensor zero-point calibration temperature compensation is adjusted to a 25℃ reference. Input seven-parameter coordinate transformation parameters, the design elevation H0 of the construction area, and sea state reference parameters. The sea state reference parameters include: initial seawater density ρ0 = 1.02 t / m³. 3 The initial flow velocity v0 = 0 m / s; The seven-parameter space transformation model, i.e., Formula 1: Theoretical basis: The reference of the engineering geodetic coordinate system (such as CGCS2000) and the construction local coordinate system are different (the ellipsoid parameters and the origin position are different). Coordinate transformation needs to be achieved through seven parameters (translation ΔX, ΔY, ΔZ; rotation Rx, Ry, Rz; scale λ). Refer to the principle of spatial rectangular coordinate system transformation in "Fundamentals of Geodesy" (Kong Xiangyuan et al., 2010).

[0032] Seven-parameter calibration procedure: Select at least three known common control points and collect their geodetic coordinates (Xg, Yg, Zg) and construction coordinates (Xp, Yp, Zp) respectively. Establish an overdetermined system of equations: , , ; The seven parameters were solved using the least squares method to minimize the transformation residual (≤0.001m). After calibration, the geodetic coordinates output by GNSS are converted into construction coordinates in real time to eliminate the impact of coordinate system deviation on leveling accuracy.

[0033] S2: Real-time acquisition of multi-source data. The positioning module outputs the real-time coordinates (X, Y, H) and heading angle of the leveling frame at a frequency of 50Hz; the attitude module acquires the tilt angles (θ1, θ2, θ3, θ4, θ5) of five sensors at a frequency of 100Hz; the material level module outputs the material level status signal S at a frequency of 10Hz; simultaneously, the seawater flow velocity v is acquired through an acoustic Doppler current meter (ADCP) with an accuracy of ±0.01m / s, and the velocity is measured with an accuracy of ±0.001t / m. 3 The density sensor collects the seawater density ρ; S3: Data fusion and control command calculation, calculate the attitude elevation correction amount corresponding to each sensor through formula (2), calculate the material level dynamic threshold by combining real-time sea state parameters through formula (3), fuse positioning deviation, attitude correction amount and material level status through formula (4), and output hydraulic control command; The formula (2) is: (2) Where: ΔH i This represents the elevation correction amount corresponding to the i-th tilt sensor, in meters. The straight-line distance from the sensor mounting point to the geometric center of the frame is precisely calibrated by 3D modeling software, in meters. The tilt angle detected by the sensor, in rad, when θ ᵢ When the angle is ≤0.5°, a precise sine calculation is used instead of a small angle approximation to improve the correction accuracy; δ is the system correction coefficient, with a value of 0.002-0.005m. Through 100 sets of indoor simulation tests and 50 sets of marine field tests, the optimal value of 0.003m is obtained by linear regression fitting to compensate for sensor installation deviation and inherent system error. Physical Model: Due to the right triangle's side-angle relationship: ΔH≈L·sinθ, when the leveling frame is tilted, the actual elevation of the sensor installation point will deviate from the theoretical elevation. The deviation is measured by the distance from the sensor to the center of the frame. With tilt angle The decision must be made while compensating for inherent system deviations, including installation errors and sensor zero drift.

[0034] Derivation of Formula (2): Ideally, the elevation correction amount ; The inherent deviation δ of the system is caused by uneven mounting surface, zero drift of sensor, etc. Considering the inherent deviation δ of the system, δ=0.003m is obtained by fitting experimental data; Final revised formula: ,in Accurate calibration using 3D modeling software (such as SolidWorks) with an error ≤0.001m.

[0035] Correction logic: Sum the correction values ​​from the five sensors. The total elevation correction of the entire frame is obtained, which is used to compensate for the leveling error caused by tilting.

[0036] Formula (3): (3) Where: h th The dynamic material level threshold is expressed in meters (m); h0 is the standard material level threshold, 4m for low material level or 8m for high material level; ρ is the real-time seawater density, expressed in tons per cubic meter (t / m³). 3 v represents the real-time seawater flow velocity in m / s; k1 and k2 are sea state correction coefficients, obtained by fitting a multiple linear regression model based on 200 sets of material level response tests under different sea states, with k1=0.05 and k2=0.12, and a goodness-of-fit R-value. 2 =0.987, ensuring the accuracy of material level detection when sea conditions change; the formula is essentially based on the principle of fluid mechanics to compensate for the influence of seawater on the state of stone accumulation. Increased flow velocity leads to a decrease in the height of stone accumulation, and changes in density affect the triggering force of the material's own weight.

[0037] Mechanism of influence: The density of seawater ρ affects the weight of the stone. The higher the density, the greater the triggering force of the material on the probe, and the easier it is to trigger prematurely. The fluid resistance generated by the seawater flow velocity v will change the accumulation state of the stone in the stone throwing tube. The higher the flow velocity, the lower the accumulation height, and the easier it is to delay the triggering. This effect needs to be compensated by dynamic threshold.

[0038] Experimental fit: A sea state simulation test bench was built to simulate different densities and current velocities ranging from 0 to 1.0 m / s within the range of 1.01-1.035 t / m³. Measure the actual material level thresholds under different sea conditions and establish a regression model. ; By fitting 200 sets of experimental data, we obtained k1=0.05, k2=0.12, and the goodness of fit R²=0.987, indicating that the model can accurately reflect the influence of sea state on the material level threshold.

[0039] Application logic: Real-time collection of ρ and v, dynamic adjustment of material level threshold to avoid misjudgment of material level caused by changes in sea state. For example, when v=1.0m / s and ρ=1.03t / m³, the high material level threshold is adjusted to 8×(1+0.05×1.03+0.12×1.0)=9.052m.

[0040] Formula (4): (4) In the formula: u is the hydraulic cylinder extension / retraction control voltage signal, unit: V, output range 0-5V, corresponding to hydraulic cylinder extension / retraction amount 0-1m; k pis a scaling factor, ranging from 0.8 to 1.2, used for rapid response to positioning and attitude coupling deviations; k d is the differential coefficient, ranging from 0.1 to 0.3, which suppresses system overshoot and oscillation; ks is the material level coordination coefficient, which is dynamically adjusted according to the material level status: when S=0 there is no material, ks=-0.5, which speeds up the material distribution speed; when S=1 there is a low material level, ks=0, which maintains a constant speed; when S=2 there is a high material level, ks=0.5, which slows down or stops the material distribution. The elevation change rate is expressed in m / s and is calculated using a 5-point moving average method to avoid interference from instantaneous fluctuations. This formula is based on the PD control algorithm to achieve a closed-loop control linking positioning, attitude, and material level.

[0041] Control objectives: fuse positioning deviation H0-H and total attitude correction. In conjunction with the material level status S, hydraulic control commands are output to achieve coordinated adjustment of the frame level and the material feeding speed.

[0042] Algorithm Design: Proportional Link : Fast response to positioning and attitude coupling deviation, k p The value is 0.8-1.2, and should be adjusted according to the construction stage. 1.2 is used in the initial stage, and 0.8 is used when the accuracy is close to the requirement. Differential element Suppress system overshoot and avoid frequent frame vibration, k d Values ​​range from 0.1 to 0.3; Material level coordination The control strategy is dynamically adjusted based on the material level status to achieve positioning-material level linkage. Command output: The control voltage signal u ranges from 0 to 5V, corresponding to the cylinder extension and retraction of 0 to 1m. It is output to the hydraulic system through an analog interface, and at the same time controls the cloth walking speed. The larger u is, the slower the walking speed, to ensure uniform cloth distribution.

[0043] S4: The actuator coordinates and adjusts. The hydraulic drive module controls the synchronous extension and retraction of the four leg cylinders through the diversion and combination valve group according to the control command. The extension and retraction accuracy is ±0.1mm, and the levelness of the adjustment frame is ≤0.1°. The gear and rack mechanism adjusts the fabric walking speed and path to achieve precise fabric placement. S5: Closed-loop verification of leveling accuracy. Repeat steps S2-S4, and calculate the total leveling error σ in real time using the error synthesis formula (5) until σ≤3cm, completing the leveling operation for this area. The formula (5) is as follows: (5) In the formula: σ is the total leveling error, in meters; σ g The GNSS positioning error is ≤0.01m, verified through 30 minutes of static observation; σ θThe attitude detection error is ≤0.008m, calibrated using a laser tracker; σ h To control the material level error, ≤0.015m, it is measured by weighing method; since the three errors all follow a normal distribution and are independent of each other, the square root method is used to synthesize them according to the error propagation principle to ensure that the total error is controlled within ±3cm; in actual engineering, it can be combined with the visual inspection of the underwater robot ROV and the land calibration of the total station to achieve dual verification of the error.

[0044] Sources of error: Total leveling error is caused by GNSS positioning error σ g Attitude detection error σ θ Material level control error σ h The three errors are independent of each other and all follow a normal distribution.

[0045] Synthesis Principle: According to the error propagation theory, the synthesis of independent normally distributed errors adopts the root sum of squares method (refer to "Error Theory and Fundamentals of Measurement Adjustment", Wuhan University, 2019), that is: .

[0046] Accuracy Guarantee: Through hardware selection and algorithm optimization, ensure σ g ≤0.01m, σ θ ≤0.008m, σ h ≤0.015m, substituting into the formula, we get: ≈0.0207m≤0.03m Meets industry standard requirements. A schematic diagram of the leveling accuracy error distribution is shown below. Figure 4 As shown.

[0047] The specific concept of this invention is as follows: Hardware redundancy integration: The dual-antenna BeiDou GNSS + multi-sensor array layout ensures the comprehensiveness and reliability of positioning, attitude and material level data, while integrating a sea state parameter detection module to provide data support for environmental adaptation; Model quantification coupling: Establish a seven-parameter coordinate transformation model, attitude-elevation correction model, sea state-level threshold model and multi-parameter collaborative control model to transform dispersed physical quantities into quantifiable control parameters; Dynamic algorithm optimization: PD control algorithm, dynamic threshold algorithm and error synthesis verification algorithm are introduced to realize real-time adjustment of control strategy and accuracy closed-loop verification; Engineering adaptation: Through modular design, stringent protection levels and installation processes, the stability and applicability of the device in complex underwater environments are ensured.

[0048] Example 3

[0049] This embodiment takes the underwater subgrade leveling operation at berth #1 of a port as an example. The project overview is as follows: Construction area: Berth 1, container bays 1-3, each container bay is 20m×15m in size, design elevation H0=-12.5m; Operating environment: water depth 15m, sea state 3-4, wind force 4-5, seawater current velocity v=0.5m / s, seawater density ρ=1.025t / m³; Quality requirements: Leveling elevation error ≤ ±3cm, plane flatness ≤ 5mm / m.

[0050] S1: Equipment initialization and parameter calibration: Module installation: Positioning module: Dual antennas are installed on the top of the feed pipe (1.8m high) and the left side of the barge control room (3.0m high), with the antennas facing the sky without obstruction; Attitude sensor: The frame is fixed by welding at the midpoint of the four sides and the top of the tube. The flatness of the mounting surface is ≤0.4mm. The weld is coated with epoxy resin for corrosion protection. Material level detection module: holes (204mm×587mm) are made at heights of 4m and 8m in the stone-throwing pipe. After welding the bracket, a water pressure test of 0.3MPa shows no leakage. The probe is installed at an angle of 30° (angle with the pipe axis). The protective cover is firmly fixed. Data processing module: installed in the barge control room, with IP address set to (192.168.1.254), port 4005, and establishes communication connection with each module; Execution module: The hydraulic pump and valve group are installed in the frame sealing box. After the oil pipe is connected, a pressure test is performed (25MPa without leakage). The flatness of the cloth placing mechanism's travel track is ≤0.2mm.

[0051] Parameter calibration: Seven-parameter calibration: Three first-order traverse control points (A, B, C) were selected, and their geodetic coordinates and construction coordinates were collected. The seven parameters were solved using the least squares method: ΔX=194.4128m, ΔY=163.1989m, ΔZ=54.8726m, Rx=-3.155514°, Ry=8.080020°, Rz=-0.743276°, λ=5.83072459ppm, and the calibration residual was 0.0008m. Sensor calibration: 30 minutes of static observation with BeiDou GNSS, with a solution accuracy of 0.004m; zero-point calibration of tilt sensor, temperature compensation to 25℃, zero drift ≤0.001°; material level module trigger threshold calibration, S=0 in empty material state, S=1 for low material level (4m) trigger, and S=2 for high material level (8m) trigger.

[0052] S2: Real-time acquisition of multi-source data: The positioning module outputs the following real-time coordinates: X=4314824.610m, Y=512190.496m, H=-12.52m; Attitude sensor output angles: θ1=0.2° (positive X-axis), θ2=-0.15° (negative X-axis), θ3=0.08° (positive Y-axis), θ4=-0.05° (negative Y-axis), θ5=0.03° (vertical direction); Material level module output: S=1 (low material level); Sea state parameters: v=0.5m / s, ρ=1.025t / m³.

[0053] S3: Data fusion and control command calculation: Attitude correction calculation (Formula 2): L1=L2=L3=L4=2.25m (frame side length 4.5m, distance from midpoint to center), L5=3.0m (distance from top of pipe to center). ΔH1=2.25×sin(0.2°×π / 180)+0.003≈0.0088m; ΔH2=2.25×sin(-0.15°×π / 180)+0.003≈0.0014m; ΔH3=2.25×sin(0.08°×π / 180)+0.003≈0.0060m; ΔH4=2.25×sin(-0.05°×π / 180)+0.003≈0.0049m; ΔH5=3.0×sin(0.03°×π / 180)+0.003≈0.0046m; Total correction =0.0088+0.0014+0.0060+0.0049+0.0046≈0.0257m; Material level dynamic threshold calculation (Formula 3): According to Table 1, the sea state for this construction area is 3-4, the current velocity is 0.5 m / s, and the density is 1.025 t / m³. 3 The coefficients are k1=0.04 and k2=0.09. The coefficients in the table have been calibrated using measured data from multiple ports in the East China Sea and South China Sea, and are suitable for the sea state characteristics of this region. The calculated values ​​are: h th =4×(1+0.04×1.025+0.09×0.5)=4×1.066=4.264m; Table 1. Calibration Table of Formula Coefficients under Different Sea States

[0054] Note: The coefficients in Table 1 are calibrated based on measured sea condition data from multiple ports in the East China Sea and South China Sea, and can be fine-tuned according to the statistical characteristics of sea conditions in specific sea areas.

[0055] Hydraulic control command output (Formula 4): k p =1.0, k d =0.2, k s =0 (S=1), =0.005m / s (rate of change of elevation); u=1.0×(-12.5 - (-12.52) - 0.0257) + 0.2×0.005 + 0×1≈0.0043V.

[0056] S4: Coordination and Adjustment of Implementing Agencies The hydraulic system receives the command and controls the four leg cylinders to extend synchronously by 0.0086m (0.0043V corresponds to the extension amount), and the levelness of the frame after adjustment is ≤0.08°; The fabric travels at a speed of 1.05 m / min, following a serpentine path (overlap length 8 cm).

[0057] S5: Leveling accuracy closed-loop verification: The elevation after leveling was determined to be -12.49m using a total station (accuracy ±0.5mm) and an underwater robot (ROV) in combination. Total error calculation (Formula 5): σ= ≈0.0207m≤0.03m, which meets the requirements.

[0058] Example 4

[0059] Sea state adaptive adjustment case: When the sea state deteriorates to level 5 (wind force 6, v=1.0m / s, ρ=1.03t / m³) during the operation: Material level dynamic threshold adjustment: Based on Table 1, sea state level 5, flow velocity 1.0 m / s, density 1.03 t / m³. 3 The operating condition matching coefficients k1=0.05 and k2=0.12 are the standard calibration values ​​for severe sea states in the table, adapted to the hydrodynamic characteristics of this operating condition. The calculated values ​​are: h th (High material level) = 8 × (1 + 0.05 × 1.03 + 0.12 × 1.0) = 8 × 1.1715 = 9.372m, to avoid false alarms of high material level caused by water flow impact; Attitude correction adjustment: θ1=0.3°, ΔH1=2.25×sin(0.3°×π / 180)+0.003≈0.0148m, the hydraulic system compensates in real time, and the frame levelness is maintained at ≤0.1°; Fabric speed adjustment: u=0.9×(-12.5 - (-12.53) - 0.0312) + 0.2×0.003 + 0×1≈0.0078V, the fabric speed is reduced to 0.9m / min to ensure uniform fabric distribution.

[0060] Example 5

[0061] Troubleshooting Case: During operation, the tilt sensor (θ5) at the top of the feed tube malfunctioned (data exceeded ±3σ): The system automatically diagnoses and triggers an audible and visual alarm. At the same time, it calculates θ5≈(θ1+θ2+θ3+θ4) / 4≈0.02° by interpolating the data from the other four sensors. The attitude correction amount ΔH5 = 3.0 × sin(0.02° × π / 180) + 0.003 ≈ 0.0040m, which does not affect the overall leveling accuracy; After the construction was completed, the maintenance personnel replaced the faulty sensor (replacement time ≤ 1 hour), and the system returned to normal.

[0062] This invention proposes a three-in-one coupled control model of "positioning-attitude-material level," breaking through the limitations of traditional independent control technologies and achieving deep fusion and linkage control of multi-source data. A dynamic material level threshold algorithm based on sea state parameters (density, flow velocity) is designed, and the accuracy of material level detection under complex sea conditions is improved by fitting and correcting coefficients through multiple linear regression. A redundant layout of dual-antenna BeiDou GNSS and five MEMS tilt sensors is adopted, combined with a seven-parameter coordinate transformation and attitude error correction formula, to ensure high precision in positioning and attitude detection. The PD control algorithm and material level coordination coefficient are integrated to achieve closed-loop control of the hydraulic actuator, balancing response speed and stability, with a leveling accuracy within ±3cm. Modular design and stringent protection processes adapt to complex underwater environments, improving the reliability and industrial applicability of the equipment.

[0063] The device and method of this invention have been successfully tested and applied in multiple port underwater engineering projects, verifying their high precision, high automation, and high adaptability. The device adopts a modular design, allowing for flexible adjustment based on different container sizes and water depths. Installation and commissioning are convenient, and maintenance costs are low. The control method achieves precise control through formulaic and quantitative approaches, eliminating reliance on manual experience and facilitating engineering implementation. The application of this invention can significantly improve the construction quality and efficiency of underwater subgrade leveling, reduce safety risks and engineering costs, and is of great significance for promoting the automation and intelligent development of underwater engineering equipment.

Claims

1. A multi-sensor fusion method for coordinated control of material leveling and attitude in underwater subgrade beds, characterized in that, Includes the following steps: S1: Equipment initialization and parameter calibration. This includes completing the static initialization of the BeiDou GNSS receiver, zero-point calibration of the tilt sensor, and trigger threshold calibration of the material level detection module. The BeiDou GNSS receiver static initialization observation lasts 30 minutes, with a calculation accuracy ≤0.005m. The tilt sensor zero-point calibration temperature compensation is adjusted to a 25℃ reference. Input seven-parameter coordinate transformation parameters, the design elevation H0 of the construction area, and sea state reference parameters. The sea state reference parameters include: initial seawater density ρ0 = 1.02 t / m³. 3 The initial flow velocity v0 = 0 m / s; S2: Real-time acquisition of multi-source data. The positioning module outputs the real-time coordinates (X, Y, H) and heading angle of the leveling frame at a frequency of 50Hz; the attitude module acquires the tilt angles θ1, θ2, θ3, θ4, θ5 from five sensors at a frequency of 100Hz; the material level module outputs the material level status signal S at a frequency of 10Hz; simultaneously, the seawater flow velocity v is acquired using an acoustic Doppler current meter (ADCP) with an accuracy of ±0.01m / s, and the velocity is measured with an accuracy of ±0.001t / m. 3 The density sensor collects the seawater density ρ; S3: Data fusion and control command calculation, calculate the attitude elevation correction amount corresponding to each sensor through formula (2), calculate the material level dynamic threshold by combining real-time sea state parameters through formula (3), fuse positioning deviation, attitude correction amount and material level status through formula (4), and output hydraulic control command; S4: The actuator coordinates and adjusts. The hydraulic drive module controls the synchronous extension and retraction of the four leg cylinders through the diversion and combination valve group according to the control command. The extension and retraction accuracy is ±0.1mm, and the levelness of the adjustment frame is ≤0.1°. The gear and rack mechanism adjusts the fabric walking speed and path to achieve precise fabric placement. S5: Closed-loop verification of leveling accuracy. Repeat steps S2-S4 and calculate the total leveling error σ in real time using the error synthesis formula (5) until σ≤3cm, and complete the area leveling operation.

2. The multi-sensor fusion underwater bed leveling and material position attitude collaborative control method according to claim 1, characterized in that, The formula (2) mentioned in step S3 is （2） Where: ΔH i This represents the elevation correction amount corresponding to the i-th tilt sensor, in meters. L i The straight-line distance from the sensor mounting point to the geometric center of the frame is precisely calibrated by 3D modeling software, in meters. θ i The tilt angle detected by the sensor, in rad, when θ i When the angle is ≤0.5°, precise sine calculation is used instead of small angle approximation to improve the correction accuracy; δ is the system correction coefficient, ranging from 0.002 to 0.005m. Through 100 sets of indoor simulation tests and 50 sets of marine field tests, the optimal value of 0.003m was obtained by linear regression fitting to compensate for sensor installation deviation and inherent system error.

3. The multi-sensor fusion underwater bed leveling and material position attitude collaborative control method according to claim 1, characterized in that, Formula (3) mentioned in step S3: （3） Where: h th The dynamic material level threshold is expressed in meters (m); h0 is the standard material level threshold, 4m for low material level or 8m for high material level; ρ is the real-time seawater density, expressed in tons per cubic meter (t / m³). 3 v represents the real-time seawater flow velocity in m / s; k1 and k2 are sea state correction coefficients, obtained by fitting a multiple linear regression model based on 200 sets of material level response tests under different sea states, with k1=0.05 and k2=0.12, and a goodness-of-fit R-value. 2 =0.987, ensuring the accuracy of material level detection when sea conditions change; the formula is essentially based on the principle of fluid mechanics to compensate for the influence of seawater on the state of stone accumulation. Increased flow velocity leads to a decrease in the height of stone accumulation, and changes in density affect the triggering force of the material's own weight.

4. The multi-sensor fusion underwater bed leveling material level attitude collaborative control method according to claim 1, characterized in that, Formula (4) mentioned in step S3: （4） In the formula: u is the hydraulic cylinder extension / retraction control voltage signal, unit: V, output range 0-5V, corresponding to hydraulic cylinder extension / retraction amount 0-1m; k p is a scaling factor, ranging from 0.8 to 1.2, used for rapid response to positioning and attitude coupling deviations; k d is the differential coefficient, ranging from 0.1 to 0.3, used to suppress system overshoot and oscillation; k s This is the material level coordination coefficient, dynamically adjusted according to the material level status: when S=0, there is no material, k s =-0.5, increase fabric speed; S=1 indicates low material level, k s =0, maintain constant speed; when S=2, high material level, k s =0.5, slow down or stop the fabric; The elevation change rate is expressed in m / s and is calculated using a 5-point moving average method to avoid interference from instantaneous fluctuations. This formula is based on the PD control algorithm to achieve a closed-loop control linking positioning, attitude, and material level.

5. The multi-sensor fusion underwater subgrade leveling material level attitude collaborative control method according to claim 1, characterized in that, Step S5 also includes sensor fault diagnosis logic: when the data of a certain sensor exceeds the normal range of ±3σ and lasts for 3 sampling cycles, the system automatically determines that the sensor is faulty and switches to redundant data. For example, when the attitude sensor is faulty, it calculates by interpolation of the data of the other 4 sensors and triggers an audible and visual alarm to ensure the continuity of operation.

6. The multi-sensor fusion underwater bed leveling and material position attitude collaborative control method according to claim 1, characterized in that, Formula (5) mentioned in step S5: （5） In the formula: σ is the total leveling error, in meters (m); σ g The GNSS positioning error is ≤0.01m, verified through 30 minutes of static observation; σ θ The attitude detection error is ≤0.008m, calibrated using a laser tracker; σ h To control the material level error, ≤0.015m, it is measured by weighing method; since the three errors all follow a normal distribution and are independent of each other, the square root method is used to synthesize them according to the error propagation principle to ensure that the total error is controlled within ±3cm; in actual engineering, it can be combined with the visual inspection of the underwater robot ROV and the land calibration of the total station to achieve dual verification of the error.

7. A multi-sensor fusion underwater bed leveling and material position attitude collaborative control device, characterized in that, include: Positioning module: Composed of dual-antenna BeiDou GNSS receivers, installed on the top of the feed pipe and on both sides of the barge control room. The antennas are erected at a height of ≥1.5m, with no obstructions, to ensure satellite signal reception quality. It supports BDS-3 / BDS-2 dual-mode positioning with a planar accuracy of ±(10+1×10). -6 ×D)mm, elevation accuracy ±(20+1×10 -6 ×D)mm, with dynamic heading calculation capability, and real-time output of the spatial coordinates (X,Y,H) and heading angle information of the leveling frame; Attitude detection module: includes 5 MEMS capacitive tilt sensors. The tilt sensors include four sensors installed at the midpoint of the four sides of the leveling frame for X and Y axis tilt detection and one sensor connected to the top of the material tube for vertical tilt detection. The sensor accuracy is ±0.1°, temperature drift is ≤0.001° / ℃, and the working temperature range is -10~60℃. It detects the tilt angle and vertical deviation of the frame. Material level detection module: includes a permanent magnet material level probe, an electronic control module, and a dual-color alarm light. The permanent magnet material level probe is resistant to corrosion from seawater with a chloride ion concentration of 3.5% and can withstand immersion in water at 10m for 1000 hours without leakage. The permanent magnet material level probe is installed at the bottom of the rock-throwing pipe at a low material level of 4m and a high material level of 8m via a bracket. The opening size is 204mm×587mm. The material level status signal S is output through a magnetic coupling switch triggered by the Hall effect. S=0 indicates no material, S=1 indicates low material level, and S=2 indicates high material level. Data processing module: It adopts an industrial-grade industrial control computer with a built-in coordinate transformation unit and collaborative control unit. It receives positioning, attitude and material level data through TCP / IP protocol with a transmission rate of 100Mbps and a latency of ≤20ms, and realizes multi-source data coupling calculation based on preset formulas. The execution module includes a hydraulic system and a fabric placement mechanism. The hydraulic system comprises a dual-plunger variable displacement hydraulic pump, a flow divider / combiner valve assembly, and four outrigger cylinders. The hydraulic pump and flow divider / combiner valve assembly are installed in a frame-sealed enclosure with an IP65 protection rating and are connected to the mother ship's control panel via a main supply and return oil pipe with a pressure resistance of 31.5 MPa. The dual-plunger variable displacement hydraulic pump has a displacement of 10.56 L / min, a rated pressure of 25 MPa, and load-sensitive control functionality. The flow divider / combiner valve assembly has a synchronization accuracy of ≤±1%. The outrigger cylinders have a cylinder diameter of φ194 or φ160 mm, a stroke of 1 m, and are sealed with V-rings and dustproof rings, with a pressure resistance of 31.5 MPa. The fabric placement mechanism is installed at the bottom of the frame, with the traveling track welded to the frame. It includes a rack and pinion drive mechanism, a hydraulic motor, a reducer, and traveling wheels. The rack and pinion drive mechanism drives the fabric placement movement and receives control signals to achieve coordinated control of frame leveling and fabric movement. The track flatness is ≤0.3 mm.

8. The multi-sensor fusion underwater bed leveling and material position attitude collaborative control device according to claim 7, characterized in that, The permanent magnet level sensor probe has a shell material of SUS316L, a protection rating of IP68, a rated operating voltage of 24VDC, an average power consumption of ≤0.5W, and an action delay of 3-6 seconds. The permanent magnet level sensor probe adopts a swing rod structure with a swing angle range of 0-45° and a triggering force of ≤5N to avoid detection failure caused by stone jamming. The action delay is 3-6 seconds.

9. The multi-sensor fusion underwater bed leveling and material position attitude collaborative control device according to claim 7, characterized in that, The coordinate transformation unit adopts a seven-parameter spatial transformation model, and realizes the transformation between the engineering coordinate system and the construction coordinate system through formula (1): （1） In the formula: (X) p ,Y p Z p (X) represents the coordinates in the construction coordinate system; g ,Y g Z g ) represents the BeiDou GNSS output coordinates; λ is the scale factor, in ppm; R is the rotation matrix, derived from the rotation angle (R). x ,R y ,R z The system consists of seven parameters: (ΔX, ΔY, ΔZ) are translation parameters; the seven parameters are solved by solving a series of equations using the least squares method through at least three known common control points, with a calibration accuracy of ≤0.001m. The known common control points include first-order traverse plane control points and second-order leveling elevation control points.

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