Intelligent load balancing and precise positioning system of ocean platform floating crane

Abstract

The invention discloses an intelligent load balancing and precise positioning system of an ocean platform floating crane, and particularly relates to the technical field of ocean engineering equipment, which comprises an intelligent load balancing module, a precise positioning module, a dynamic compensation unit and a central control platform, the intelligent load balancing module collects lifting point load and boom attitude data through a multi-dimensional sensor group, and dynamically adjusts the output of an execution mechanism by using an improved particle swarm optimization algorithm in combination with environmental parameters, so that the mean square error of the load is less than or equal to 6%. According to the intelligent load balancing and accurate positioning system of the ocean platform floating crane, dynamic load balancing (the mean square error is smaller than or equal to 6%) and millimeter-level positioning (the error is smaller than or equal to 2.5 mm) under the complex ocean condition are achieved. The system integrates environment sensing and dynamic compensation functions, the wave compensation efficiency under the six-level sea condition is larger than or equal to 94%, the precision and safety of offshore hoisting operation are remarkably improved, and the system is suitable for the fields of deep sea resource development, cross-sea engineering construction and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of marine engineering equipment, and in particular to an intelligent load balancing and precise positioning system for a marine platform floating crane. Background Art

[0002] Floating cranes are needed to transport and load and unload cargo at sea. The offshore platform floating crane system is mainly a ship crane used on offshore platforms. It is a special crane that performs transportation operations at sea. It is widely used in offshore wind power installation platforms and offshore drilling platforms. It is also used for tasks such as offshore replenishment, transportation and transfer of cargo between ships, and deployment and recovery of underwater operating equipment.

[0003] Chinese patent publication number CN210795583U discloses a floating crane system for an offshore platform capable of 360-degree rotation, comprising support piles, a placement platform fixedly mounted on the upper surface of the support piles, a support rod fixedly mounted on the upper surface of the placement platform, a guide rail fixedly mounted on the upper surface of the support rod, a secondary winding group of a linear motor fixedly mounted on the upper surface of the guide rail, secondary coils fixedly mounted on the inner and outer side walls of the guide rail, a rotating rail sleeved on the outer wall of the guide rail, a primary winding group of a linear motor fixedly mounted on the upper end of the inner wall of the rotating rail, a guide electromagnet fixedly mounted in the middle of the inner wall of the rotating rail, and a primary coil fixedly mounted on the lower end of the inner wall of the rotating rail. The system enables the rotating rail to rotate 360 ​​degrees on the guide rail, placing heavy objects without angle restrictions, and the repulsive force between the electromagnets to make the rotating rail free on the guide rail to eliminate friction damage caused by mutual contact. However, in actual use, the system has the following defects:

[0004] 1. Load balancing relies on manual experience or fixed parameter control and cannot respond to environmental changes such as waves and wind speed in real time, which can easily lead to local overload or lifting instability;

[0005] 2. The positioning system is affected by the platform's shaking and sensor noise, and single GPS or inertial navigation cannot meet millimeter-level accuracy requirements;

[0006] 3. Due to the lack of a dynamic environmental compensation mechanism, operational efficiency significantly decreases in sea conditions above level 6. Therefore, a new control system with intelligent decision-making, multi-source fusion, and dynamic compensation capabilities is urgently needed. Summary of the Invention

[0007] The main purpose of the present invention is to provide an intelligent load balancing and precise positioning system for an offshore platform floating crane, which can effectively solve the problem that load balancing relies on manual experience or fixed parameter control, cannot respond to environmental changes such as waves and wind speed in real time, and easily leads to local overload or lifting instability.

[0008] To achieve the above object, the technical solution adopted by the present invention is:

[0009] An intelligent load balancing and precise positioning system for an offshore platform floating crane includes an intelligent load balancing module, a precise positioning module, a dynamic compensation unit, and a central control platform. The intelligent load balancing module collects lifting point load and boom posture data through a multi-dimensional sensor group, combines environmental parameters, and dynamically adjusts the actuator output using an improved particle swarm optimization algorithm to achieve a load mean square error of ≤6%.

[0010] Preferably, the precise positioning module includes a Beidou / GPS dual-mode receiver, a MEMS inertial measurement unit and a static magnetic grating displacement measurement unit, and fuses the three types of sensor data through a Kalman filter algorithm to output three-dimensional coordinates with a positioning error of ≤3mm.

[0011] Preferably, the dynamic compensation unit is equipped with a fuzzy neural network wave compensation algorithm, which predicts the wave height period based on the ultrasonic wave height meter data, drives the variable amplitude cylinder to compensate for the vertical displacement of the boom, and the compensation accuracy is ≥94% under level 6 sea conditions.

[0012] Preferably, the central control platform adopts a dual-redundant PLC control system, integrates a signal isolator and a power filter, has electromagnetic interference suppression and fault self-diagnosis functions, and the system's mean time between failures is ≥9000 hours.

[0013] Preferably, the multi-dimensional sensor group of the intelligent load balancing module includes a weighing sensor with an accuracy of ±0.2% FS, an inclination sensor with a resolution of 0.01°, and an ultrasonic wave height meter with a range of 0-55m and an accuracy of ±6cm.

[0014] Compared with the prior art, the present invention has the following beneficial effects:

[0015] 1. In the present invention, through multi-sensor real-time feedback and intelligent algorithms, the load distribution unevenness is reduced from 16% of the traditional system to below 6%, thereby improving the load balancing accuracy.

[0016] 2. In the present invention, the Beidou / GPS, IMU and static magnetostatic grid data are integrated to achieve a land positioning accuracy of ≤ 2.5mm and an underwater positioning accuracy of ≤ 0.2% of the ranging distance, thereby improving the positioning performance.

[0017] 3. In the present invention, the wave compensation efficiency is ≥94% under level 6 sea conditions, the operation time is increased by 25% compared with the traditional system, and the environmental adaptability is improved.

[0018] 4. In the present invention, dual redundant control and anti-interference design meet the high stability requirements of long-term offshore operations and improve the reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is the core operation flow chart of the marine platform floating crane intelligent load balancing and precise positioning system of the present invention. DETAILED DESCRIPTION

[0020] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0021] Embodiment: This embodiment discloses an intelligent load balancing and precise positioning system for an offshore platform floating crane, comprising an intelligent load balancing module, a precise positioning module, a dynamic compensation unit, and a central control platform. The intelligent load balancing module collects the lifting point load and boom posture data through a multi-dimensional sensor group, combines environmental parameters, and uses an improved particle swarm optimization algorithm to dynamically adjust the output of the actuator to achieve a load mean square deviation of ≤6%.

[0022] In order to fuse multi-source positioning data, achieve millimeter-level precision calculation of the absolute position and relative displacement of the floating crane, and provide a benchmark for lifting operations, the precise positioning module includes a Beidou / GPS dual-mode receiver, a MEMS inertial measurement unit, and a static magnetic grid displacement measurement unit. It fuses the three types of sensor data through the Kalman filter algorithm and outputs three-dimensional coordinates with a positioning error of ≤3mm.

[0023] Specifically, the precise positioning module consists of a multi-source fusion positioning system and an underwater auxiliary positioning subsystem. The multi-source fusion positioning system:

[0024] Satellite positioning unit: Beidou / GPS dual-mode receiver (positioning accuracy ≤15cm), providing the absolute position coordinates of the floating crane.

[0025] Inertial Navigation Unit: High-precision MEMS inertial measurement unit (IMU, angular velocity accuracy 0.02° / s, acceleration accuracy 15μg), real-time calculation of the floating platform's six-degree-of-freedom motion parameters.

[0026] Magnetostatic grating displacement measurement unit: A magnetostatic scale (resolution 0.1mm) is deployed on the trolley track and cooperated with the on-board magnetic head to achieve high-precision measurement of relative displacement.

[0027] Kalman filter fusion algorithm:

[0028] Establish the state equation: X(k)=AX(k-1)+BU(k)+W(k)

[0029] Measurement equation: Z(k)=HX(k)+V(k)

[0030] The three types of sensor data are integrated to output three-dimensional coordinates with a positioning error of ≤3mm.

[0031] Underwater auxiliary positioning subsystem: equipped with a long baseline (LBL) hydroacoustic positioning module, it can locate underwater targets within a range of 30km by deploying seabed transponders, with an accuracy of ≤0.2% of the ranging distance.

[0032] In order to achieve real-time response to marine environmental disturbances, compensate for hull swaying and load fluctuations, and ensure operational stability and safety, the dynamic compensation unit is equipped with a fuzzy neural network wave compensation algorithm, which predicts the wave height period based on ultrasonic wave height meter data and drives the variable amplitude cylinder to compensate for the vertical displacement of the boom. The compensation accuracy is ≥94% under level 6 sea conditions.

[0033] Specifically, the dynamic compensation unit consists of a wave compensation algorithm and an anti-interference control strategy.

[0034] Wave compensation algorithm: Based on fuzzy neural network (FNN), wave height and period are predicted to drive the luffing cylinder to compensate for the vertical displacement of the boom, with compensation accuracy ≥94% (under level 6 sea conditions).

[0035] Anti-interference control strategy: A dual-redundant PLC control system is used, electromagnetic interference is suppressed through signal isolators (isolation voltage ≥ 2kV) and power filters, and fault self-diagnosis and reset are achieved with a hardware watchdog. The system's mean time between failures (MTBF) is ≥ 9000 hours.

[0036] In order to achieve integrated data display, human-computer interaction, system management and remote operation and maintenance, it serves as the "nerve center" of the entire system. The central control platform adopts a dual-redundant PLC control system, integrated signal isolators and power filters, and has electromagnetic interference suppression and fault self-diagnosis functions. The system's average time between failures is ≥9000 hours.

[0037] Specifically, the central control platform consists of hardware and software.

[0038] Hardware: Industrial grade reinforced computer (working temperature -35℃ ～ +75℃, protection grade IP67), integrated 15-inch anti-glare touch screen.

[0039] Software: A human-computer interaction interface developed based on Qt, which displays load distribution, positioning coordinates, and environmental parameters in real time, supports manual / automatic mode switching, and has operation path planning and energy consumption optimization functions.

[0040] To monitor the load distribution and boom posture at the lifting points in real time, dynamically adjust the actuator output, and ensure load balancing and operational safety, the intelligent load balancing module's multi-dimensional sensor suite includes a load cell with an accuracy of ±0.2% FS, an inclination sensor with a resolution of 0.01°, and an ultrasonic wave height meter with a range of 0-55m and an accuracy of ±6cm.

[0041] Specifically, the intelligent load balancing module consists of a multi-dimensional sensor group, an environmental data acquisition unit and an adaptive load distribution algorithm.

[0042] Multi-dimensional sensor group: including load cells (accuracy ±0.2% FS), pin-type force sensors, and boom inclination sensors (resolution 0.01°) installed at the lifting points, which collect real-time force data at each lifting point and boom posture data.

[0043] Environmental data acquisition unit: Integrated ultrasonic wave height meter (range 0-55m, accuracy ±6cm) and six-axis anemometer (wind speed range 0-75m / s, accuracy ±0.2m / s) to obtain environmental parameters such as wave period, wind speed and direction.

[0044] Adaptive load distribution algorithm: Using improved particle swarm optimization (IPSO) algorithm to establish load balancing objective function

[0045] Among them, Fi is the real-time load of each hanging point, F total is the total load, θ i is the arm inclination angle, is the attitude balance weight coefficient, and the load mean square deviation is achieved by dynamically adjusting the output of each actuator (luffing cylinder, winch) to ≤6%.

[0046] In summary, the specific implementation method is as follows:

[0047] 1. Data collection and preprocessing stage

[0048] 1. Real-time data collection from multi-dimensional sensors

[0049] (1) Load and posture data:

[0050] A pin-type force sensor (accuracy ±0.06% FS) and a fiber Bragg grating strain gauge at the suspension point monitor the single-point load in real time (sampling rate 100 Hz), and a 3-axis force sensor simultaneously collects X, Y, and Z forces.

[0051] The dual-axis tilt sensor (resolution 0.01°) and IMU (angular velocity accuracy 0.01° / s) at the root of the boom measure the boom pitch and roll angles and the six-degree-of-freedom motion parameters of the hull.

[0052] (2) Environmental parameter collection:

[0053] Ultrasonic wave height meters (range 0-55m, accuracy ±6cm) are integrated with lidar to measure wave height and wave period (the fusion algorithm dynamically assigns weights, with lidar data being prioritized in severe weather).

[0054] The six-axis anemometer (accuracy ±0.2m / s) collects wind speed and direction in real time and outputs them to the central controller.

[0055] (3) Positioning data collection:

[0056] Beidou / GPS dual-mode receiver (accuracy ≤12cm) provides the absolute coordinates (latitude, longitude, altitude) of the floating crane;

[0057] The static magnetic grating displacement measurement unit (resolution 0.05mm) measures the relative displacement of the trolley along the track in real time through the track magnetic scale and the onboard magnetic head;

[0058] The underwater USBL positioning module calculates the relative position of underwater targets (accuracy ≤ 0.4m@1000m ranging) through underwater acoustic transducers and seabed transponders.

[0059] 2. Data preprocessing and synchronization

[0060] (1) Time alignment: Nanosecond-level synchronization (error ≤ 12ns) is achieved through the IEEE1588 precision clock protocol, and the timestamps of each sensor are unified (Beidou 1Hz, IMU 100Hz, and magnetostatic grid 50Hz);

[0061] (2) Outlier elimination: Apply the 3σ criterion to filter abnormal data (for example, when the load mutation exceeds 3 times the standard deviation of the mean, the weighted average of the first 3 valid values ​​is used instead);

[0062] (3) Coordinate conversion: The Beidou longitude and latitude are converted into the local Cartesian coordinate system (UTM projection), and the relative displacement of the magnetostatic grid is converted into absolute position coordinates.

[0063] 2. Intelligent Load Balancing Control Process

[0064] 1. Real-time evaluation of load status

[0065] (1) Total load calculation: Summarize the force sensor data of each lifting point to obtain the total load F total =∑Fi, and calculate the load mean square error

[0066] (2) Boom attitude analysis: Combine the tilt sensor and IMU data to determine whether the boom pitch angle θ and roll angle φ exceed the safety threshold (such as ±15°).

[0067] 2. Adaptive load distribution algorithm runs

[0068] (1) Objective function optimization: Using the improved particle swarm optimization (IPSO) algorithm to minimize the load unevenness and posture deviation:

[0069] in is the attitude balance weight (dynamically adjusted according to sea conditions, set to 0.8 in sea conditions level 6);

[0070] (2) Actuator control: Send speed adjustment instructions to the winch (stepless speed regulation range 0.1-25m / min) to balance the loads at each lifting point (mean square error ≤ 3%); drive the boom cylinder (direct drive motor + ball screw, response time ≤ 55ms) to adjust the boom angle and compensate for the load offset caused by the hull shaking.

[0071] 3. Dynamic security threshold verification

[0072] Real-time monitoring of whether the load at each lifting point exceeds 80% of the rated value and whether the arm stress exceeds 60% of the material yield strength (calculated through strain gauge data);

[0073] If the overload warning is triggered, it will automatically switch to "safe mode": suspend the operation, maintain the current posture, and issue an audible and visual alarm to the operator.

[0074] 3. Precise Positioning Solution Process

[0075] 1. Multi-source positioning data fusion

[0076] Kalman filter core calculation:

[0077] Prediction step: Based on the previous state X(k-1), predict the current state X(K|K-1) = A·X(K-1) + B·U, and update the covariance matrix P(k|k-1) = A·P(k-1)A T +Q;

[0078] Update step: Fuse BeiDou / GPS, IMU, and magnetostatic grid measurement data (Z(k)), calculate the Kalman gain K(k), and obtain the optimal estimate X(k) = X(k|k-1) + K(k)·(Z(k)-H·X(k|k-1));

[0079] Dynamic weight adjustment: When the Beidou signal is lost (the number of satellites is less than 5), the IMU and magnetostatic grid weights are automatically increased (for example, the IMU weight is increased from 0.25 to 0.75), and visual SLAM-assisted positioning is enabled (feature point matching error ≤ 5cm).

[0080] 2. Positioning result output and calibration

[0081] Output the fused 3D coordinates (accuracy ≤ 3mm) to the central control platform and send them to the dynamic compensation unit as a position reference;

[0082] After each underwater operation (such as docking a submerged pipe), the long baseline (LBL) positioning results are used to calibrate the system error, reset the Kalman filter covariance matrix P(k), and eliminate the accumulated drift.

[0083] 4. Dynamic Compensation and Collaborative Control Process

[0084] 1. Wave motion prediction and compensation

[0085] (1) Wave height period analysis: The wave height meter data is processed by fuzzy neural network (FNN) to predict the wave motion within the next 10 seconds (the period and amplitude prediction error is ≤6%);

[0086] (2) Vertical displacement compensation: Based on the prediction results, the boom cylinder is driven to compensate for the boom heave movement in real time (compensation accuracy ≥98% under level 8 sea conditions), ensuring that the vertical displacement fluctuation of the lifting point is ≤2cm.

[0087] 2. Anti-interference and redundant control

[0088] (1) Dual redundant PLC system: The main and standby controllers are hot-swapped in real time (switching time ≤ 25ms), and electromagnetic interference is suppressed by signal isolators (isolation voltage ≥ 2kV) and power filters;

[0089] (2) Hardware Watchdog: Monitors the system operating status every 55ms. If a software freeze or communication interruption is detected, it automatically triggers a hardware reset to ensure control signal continuity (MTBF ≥ 9000 hours).

[0090] 5. Human-computer interaction and monitoring process

[0091] 1. Real-time data visualization

[0092] The central control platform's 15-inch anti-glare touch screen dynamically displays:

[0093] Load distribution cloud diagram (real-time load and mean square deviation of each hanging point);

[0094] Positioning coordinate curve (XYZ three-axis displacement fluctuation ≤ 2mm);

[0095] Environmental parameter dashboard (wave height, wind speed, hull attitude angle);

[0096] Device health status (sensor operating temperature, battery charge, filter error statistics).

[0097] 2. Operation mode switching and decision support

[0098] Manual mode: The operator controls the boom luffing and winch raising and lowering through the handle, and the system displays the safety margin in real time (such as the maximum allowable inclination angle ±15°);

[0099] Automatic mode: After inputting the target coordinates, the system automatically plans the operation path and uses the MPC algorithm to optimize the control parameters (energy consumption is reduced by 12%).

[0100] AR assistance function: Through the head-mounted AR device, the virtual lifting path and the actual position deviation are superimposed and displayed (accuracy ≤ 6mm), helping operators to predict risks.

[0101] 6. Closed-loop feedback and optimization

[0102] 1. Data closed-loop optimization

[0103] After the operation is completed, the system automatically records key data (load curve, positioning error, environmental parameters) and stores them in the local database;

[0104] Use machine learning algorithms to analyze historical data, optimize the load balancing algorithm weight coefficient and Kalman filter noise parameters, and form a "collection-control-optimization" closed loop.

[0105] 2. Fault diagnosis and maintenance

[0106] Vibration Acoustic Monitoring (VAM) detects winch bearing wear in real time (frequency resolution ≤ 2Hz), and oil spectral analysis provides early warning of hydraulic system impurities (alarm when particle size > 6μm);

[0107] Support remote operation and maintenance: The device status is sent to the shore-based monitoring center via 4G / satellite communication, and experts can adjust control parameters or trigger system reset online.

[0108] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent load balancing and precise positioning system for an offshore platform floating crane, characterized by: It includes an intelligent load balancing module, a precise positioning module, a dynamic compensation unit and a central control platform. The intelligent load balancing module collects the suspension point load and boom posture data through a multi-dimensional sensor group, combines environmental parameters, and uses an improved particle swarm optimization algorithm to dynamically adjust the actuator output to achieve a load mean square deviation of ≤6%.

2. The intelligent load balancing and precise positioning system for an offshore platform floating crane according to claim 1, characterized in that: The precise positioning module includes a Beidou / GPS dual-mode receiver, a MEMS inertial measurement unit and a static magnetic grid displacement measurement unit. It fuses the three types of sensor data through the Kalman filter algorithm and outputs three-dimensional coordinates with a positioning error of ≤3mm.

3. The intelligent load balancing and precise positioning system for an offshore platform floating crane according to claim 1 is characterized by: The dynamic compensation unit is equipped with a fuzzy neural network wave compensation algorithm, which predicts the wave height period based on the data of the ultrasonic wave height meter, drives the variable amplitude cylinder to compensate for the vertical displacement of the boom, and the compensation accuracy is ≥94% under level 6 sea conditions.

4. The intelligent load balancing and precise positioning system for an offshore platform floating crane according to claim 1 is characterized by: The central control platform adopts a dual-redundant PLC control system, integrates signal isolators and power filters, and has electromagnetic interference suppression and fault self-diagnosis functions. The system's mean time between failures is ≥9,000 hours.

5. The intelligent load balancing and precise positioning system for an offshore platform floating crane according to claim 1 is characterized in that: The multi-dimensional sensor group of the intelligent load balancing module includes a weighing sensor with an accuracy of ±0.2% FS, an inclination sensor with a resolution of 0.01°, and an ultrasonic wave height meter with a range of 0-55m and an accuracy of ±6cm.

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