Power pre-allocation method based on intelligent drag suction dredger

Abstract

The present application relates to the technical field of power management of dredging equipment, and provides a power pre-distribution method based on an intelligent cutter suction dredger, which comprises the following steps: setting a pre-distribution coefficient for each load according to different working conditions, wherein the pre-distribution coefficient comprises a basic coefficient and a floating coefficient; introducing the pre-distribution coefficient into a power balance equation of the cutter suction dredger to obtain an updated power balance equation; calculating the use power of each power consumption under the current working condition to obtain the remaining available power of the power grid; determining the power distribution priority of each load according to the working condition; and distributing the remaining distributable power to each load in proportion according to the pre-distribution coefficient of each load in the order of priority. The demand for power pre-distribution of each dredging equipment is met by setting the basic power and the floating power, that is, stable construction under the current working condition can be met, and floating adjustment can be made according to the deviation between the real-time state and the target state of the ship, so as to find a stable and efficient balance point.

Description

Technical Field

[0001] This invention relates to the technical field of power management for dredging equipment, and in particular to a power pre-allocation method based on an intelligent trailing suction hopper dredger. Background Technology

[0002] Dredging is an earthwork project that uses dredgers or other machinery and manual labor to excavate underwater, in order to widen and deepen waterways. Dredging is an engineering project that excavates and treats the mud, sand, and rocks at the bottom of waterways or port areas within a specified range and depth.

[0003] Currently, dredging vessels employ conventional shipboard power grid architecture and management methods. Load management strategies often follow a first-come, first-served approach, failing to allocate power rationally based on operating conditions. When grid power consumption exceeds a certain threshold, power limiting is triggered, often employing a "one-size-fits-all" approach that cannot achieve smooth optimization.

[0004] Therefore, there is an urgent need to design a power pre-allocation method that manages and optimizes power from the perspective of dredging efficiency and energy consumption. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a power pre-allocation method based on an intelligent trailing suction hopper dredger, comprising:

[0006] (1) According to different working conditions, a pre-allocation coefficient is set for each load. The pre-allocation coefficient includes a basic coefficient and a floating coefficient. The loads include the propeller, high-pressure flushing pump, mud pump and bow thruster.

[0007] (2) Introduce the pre-allocation coefficient into the power balance equation of the trailing suction hopper dredger to obtain the updated power balance equation;

[0008] (3) Calculate the power consumption of each power source under the current operating conditions, and obtain the remaining available power P of the power grid. USE ;

[0009] (4) Determine the power allocation priority of each load according to the working conditions, and allocate the remaining allocable power to each load proportionally according to the priority order and the pre-allocation coefficient of each load.

[0010] Furthermore, the basic coefficients are obtained manually based on experience or by using the NSGA-II multi-objective optimization genetic algorithm or single-objective genetic algorithm to optimize the instantaneous output value and / or the oil consumption per 10,000 cubic meters of soil.

[0011] Furthermore, the calculation process for the floating coefficient is as follows:

[0012] The target output P at the next moment is calculated and predicted by the dredging control system of the intelligent trailing suction hopper dredger.DT And the current instantaneous actual output P DA Calculate the production difference ΔP D :

[0013] ΔP D =P DT -P DA ;

[0014] The Intelligent Navigation System (INS) of the intelligent trailing suction hopper dredger automatically calculates the target heading D based on the working line and current position provided by the dredging trajectory and profile display system (DTPM). T Target speed S T The target speed S of the ship T Target heading D T With actual speed S A Actual heading D A By comparison, the speed difference ΔS1 and the heading difference ΔD1 are obtained;

[0015]

[0016] The power pre-distribution module of the intelligent trailing suction hopper dredger is based on the speed difference ΔS1, the heading difference ΔD1, and the production difference ΔP. D And the dredging operation will send the operation instructions to the corresponding execution equipment;

[0017] The actuator controls the operation of the left propulsion, right propulsion, side thruster, and dredging equipment according to instructions;

[0018] Under environmental interference, collect the current actual speed, actual course, and actual output, and repeat the above steps;

[0019] The dynamic positioning and tracking system (DP / DT) calculates the impact of wind, wave, and current models on the ship's state. Various forces act on the hull, affecting the ship's course and trajectory. This process is mediated by the hull model M. SHIP and rake arm model M P This is reflected in the deviation matrix between the actual state and the target state of the ship:

[0020]

[0021] Where ΔS2 is the hull model M SHIP and rake arm model M P The change in speed per unit time, ΔD2, is the hull model M. SHIP and rake arm model M P Change in heading per unit time; F WIND The force exerted by the wind on the hull is calculated from the wind speed measured by an anemometer and the windward area of ​​the hull; F WAVEThe force exerted by the wave on the hull is calculated from wind and water ripple parameters; F FLOW The force exerted by the flow on the hull is calculated from wind and water ripple parameters; F HEAD The drag force of the rake head is measured by a force gauge.

[0022] The difference between the target speed and the actual speed ΔS, the difference between the target speed and the actual course ΔD, and the difference between the target speed and the actual output at the instant ΔP. D They are respectively:

[0023]

[0024] Among them, P DT P is the predicted target output for the next instantaneous moment; DA This refers to the actual output at any given instant.

[0025] Based on the deviation matrix, the floating power range of each device is calculated, and the floating limit range of each load is as follows:

[0026]

[0027] The fluctuation factor for each load is:

[0028]

[0029] Where k1, k2, k3, and k4 are all floating limit coefficients for each load, with k1 reflecting the impact of propulsion via the rudder on the bow direction; k2 reflecting the impact of the bow thruster on the bow direction; k3 reflecting the impact of the mud pump on output; and k4 reflecting the impact of the high-pressure flushing pump on output; k1+k2=1, k3+k4=1; ΔP CPP Indicates the power fluctuation range of the thruster; ΔP BT Indicates the power fluctuation range of the bow thruster; ΔP DP Indicates the power fluctuation range of the mud pump; P DPe Indicates the rated power of the mud pump; ΔP JP Indicates the power fluctuation range of the high-pressure flushing pump; P JPe Indicates the rated power of the high-pressure water pump; P USE Indicates the remaining available power of the power grid; ΔX CPP Indicates the thruster's float coefficient, ΔX DP The floating coefficient of the mud pump, ΔX JP For the high-pressure flushing pump's float coefficient, P CPPe P is the rated power of the thruster. DPe P is the rated power of the mud pump. JPe P is the rated power of the high-pressure flushing pump. BTe This refers to the rated power of the bow thruster;

[0030] When ΔS is negative, it indicates that the propulsion tends to decelerate, which will release more available power, and its effect will be reflected in the next calculation cycle.

[0031] When ΔP D When the value is negative, the surface mud pump and high-pressure flushing pump tend to slow down, releasing more available power, the effect of which will be reflected in the next calculation cycle.

[0032] Furthermore, the specific process of updating the power balance equation in step (2) is as follows:

[0033] Introducing the distribution coefficient into the power balance equation of a trailing suction hopper dredger, it is expressed as:

[0034] P ME =P CPPe *(X CPP +ΔX CPP )+P MT +P DPe *(X DP +ΔX DP )

[0035] +P JPe *(X JP +ΔX JP )+P BTe *(X BT +ΔX BT )+P HY +P GP ;

[0036] After adjusting the coefficient positions, the power balance equation is expressed as:

[0037] P ME =(P CPPe *X CPP +P DPe *X DP +P JPe *X JP +P BTe *X BT )+(P MT +P HY +P GP )

[0038] +(P CPPe *ΔX CPP +P DPe *ΔX DP +P JPe *ΔX JP +P BTe *ΔX BT );

[0039] Among them, P MEP represents the main unit power. CPPe P is the rated power of the thruster. DPe P is the rated power of the mud pump. JPe P is the rated power of the high-pressure flushing pump. BTe P is the rated power of the bow thruster. MT Main transformer power; P HY P represents the power of the hydraulic pump. GP XCPP is the power of the sealing pump; ΔXCPP is the basic coefficient of the propeller; ΔXCPP is the floating coefficient of the propeller; XDP is the basic coefficient of the mud pump; ΔXDP is the floating coefficient of the mud pump; XBT is the basic coefficient of the bow thruster; ΔXBT is the floating coefficient of the bow thruster; XJP is the basic coefficient of the high-pressure flushing pump; ΔXJP is the floating coefficient of the high-pressure flushing pump.

[0040] Furthermore, the specific process of step (3) is as follows:

[0041] In actual calculations, the remaining available power P of the power grid USE In the first cycle, the product of the rated power of each load and the basic coefficient is subtracted from the host power. The expression is as follows:

[0042] P USE =P MEe -(P CPPe *X CPP +P DPe *X DP +P JPe *X JP +P BTe *X BT )-(P MT +P HY +P GP );

[0043] In subsequent cycles, the remaining available power P of the power grid USE The expression for subtracting the actual feedback power of each load from the host power is:

[0044] P USE =P MEe -(P CPPa +P DPa +P JPa +P BTa )-(P MT +P HY +P GP );

[0045] Among them, P MEe P is the rated power of the host. CPPa P represents the actual feedback power of the thruster. DPa P represents the actual feedback power of the mud pump. JPaP represents the actual feedback power of the high-pressure flushing pump. BTa This represents the actual feedback power of the bow thruster.

[0046] Furthermore, the calculated remaining available power P of the power grid USE The expression for redistribution to each load is:

[0047] P USE =(P CPPe *ΔX CPP +P DPe *ΔX DP +P JPe *ΔX JP +P BTe *ΔX BT );

[0048] Let ΔP CPP =P CPPe *ΔX CPP ;ΔP DP =P DPe *ΔX DP ;ΔP JP =P JPe *ΔX JP ;ΔP BT =P BTe *ΔX BT ;

[0049] Then P USE =(ΔP) CPP +ΔP DP +ΔP JP +ΔP BT ).

[0050] Furthermore, the power allocation priority is as follows: when faced with power adjustment requirements, the available power of the high-pressure flushing pump and mud pump will be reduced first; the propeller and bow thruster will be given priority in ensuring power supply.

[0051] The present invention has the following beneficial effects:

[0052] (1) This invention sets power pre-allocation coefficients for different working conditions and combines them with the power balance equation of trailing suction hopper dredgers to achieve reasonable pre-allocation and dynamic adjustment of the power of each load, so as to improve the efficiency of ship operation and energy utilization.

[0053] (2) This invention achieves the power pre-allocation requirement of each dredging equipment by setting a basic power plus floating power, which can meet the stable construction under the current working conditions and can also make floating adjustments according to the deviation between the real-time state of the ship and the target state, aiming to find a balance between stability and efficiency. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of a trailing suction hopper dredger system.

[0055] Figure 2 This is a schematic diagram of power pre-allocation in this invention.

[0056] Figure 3 This is a flowchart of the floating coefficient calculation in this invention.

[0057] Figure 4 This is a schematic diagram illustrating the impact of the dynamic positioning and tracking system (DP / DT) on the ship's state by calculating the wind, wave, and current model.

[0058] Figure 5 This is a diagram showing the pre-allocation coefficients set for each load in the power pre-allocation module for dredging and unloading operations, as well as for different soil conditions.

[0059] Figure 6 This is a schematic diagram of the power balance equation for a trailing suction hopper dredger. Detailed Implementation

[0060] The technical solution of the present invention will be further described in detail below with reference to specific embodiments. However, these embodiments are not intended to limit the present invention. Any similar structures and similar variations of the present invention should be included in the protection scope of the present invention. The commas in the present invention all indicate the relationship between and. The English letters in the present invention are case-sensitive.

[0061] like Figure 1 As shown, the trailing suction hopper dredger includes a dredging control system (DCS), a power management system (PMS), and an engine room monitoring and alarm system (AMS).

[0062] The dredging control system (DCS) comprises a dredging trajectory and profile display system (DTPM), a data acquisition and monitoring control system (SCADA), and a human-machine interface (HMI). The DTPM is primarily responsible for vessel positioning, construction planning, and the reconstruction and display of the construction terrain. The SCADA system is mainly responsible for collecting and displaying the process data of the dredging control system. The dredging control system uses the HMI to design and issue control commands to control equipment such as mud pumps, high-pressure flushing pumps, sealing pumps, A-frames, winches, rake heads, rake pipes, mud hoppers, mud gates, extraction hatches, and gate valves to achieve dredging processes including dredging, mud dumping, and reclamation.

[0063] The engine room monitoring and alarm system (AMS) is mainly responsible for monitoring and alarming all equipment on the ship, including diesel engine monitoring, generator monitoring, cabinet monitoring, hydraulic pump station monitoring, auxiliary equipment monitoring, dredging equipment monitoring, etc.

[0064] The Power Management System (PMS) is mainly responsible for the management of power plants such as shaft generators, auxiliary generators, and emergency generators. It switches the power distribution mode according to the operating conditions, distributes the load among multiple generators after paralleling, performs heavy load judgment and inquiry before starting a large load, limits the power of specific equipment when the grid is under high load, and performs power failure recovery procedures when some or all of the switchboards lose power.

[0065] The Intelligent Monitoring and Integrated Management Platform (HD-ICMP) enables intelligent monitoring and comprehensive management of all shipboard equipment through the collaborative operation of various systems. The Intelligent Integration Platform (IMP) utilizes computer and local area network technologies to acquire information data from various ship systems, achieving standardized data management across the entire ship, unified construction of common modules across systems, and network data security. The platform provides standardized interfaces, allowing subsystems to share data. This provides the hardware platform for intelligent dredging across the entire ship.

[0066] The dredging control system includes a basic dredging control system (BDS) and an intelligent dredging control system (IDS). The basic dredging control system uses sensors, PLCs, user interfaces, and computer network technology to monitor and control dredging equipment such as hydraulic systems, pump systems, rake arm systems, mud hoppers, bow blowdown devices, and anchor winches. The intelligent dredging control system employs automation and intelligent technologies to achieve one-button fully automatic dredging control, active rake lip control, intelligent mud pump control, dredging speed control, and automatic hopper removal valve control.

[0067] The Dredging Track and Profile Display System (DTPM) allows operators to import terrain files and design construction plans (work lines). It can also connect to signals from Differential Global Positioning System (DGPS), gyrocompass, Automatic Identification System (AIS), and tide meter to display construction data such as the ship's coordinates, heading, track, water depth, and profile in the construction area. After construction, the terrain file can be calculated and reconstructed based on the track, raking track, and cutting amount.

[0068] The Dredging Track and Profile Display System (DTPM) has an interface to output the construction plan (work line) to the Intelligent Navigation System (INS) for construction reference.

[0069] The intelligent navigation control system includes an intelligent navigation system (INS) and a dynamic positioning and dynamic tracking system (DP / DT). The intelligent navigation system enables automatic speed recommendation, autonomous trajectory planning and route recommendation, collision warning, visual enhancement assistance, digital twin of dredging operations, and 3D terrain visualization during dredging and free navigation. The dynamic positioning and dynamic tracking system collects parameters such as the ship's buoyancy, propulsion system, dredging operation conditions, and water environment to achieve dynamic positioning and tracking of the ship.

[0070] like Figure 2As shown, the present invention provides a power pre-allocation method based on an intelligent trailing suction hopper dredger, comprising:

[0071] S1 sets pre-allocation coefficients for each load based on different working conditions. These pre-allocation coefficients include a basic coefficient and a floating coefficient. The loads include the propeller, high-pressure water pump, mud pump, and bow thruster. The basic coefficient reflects the normal power usage of this equipment under specific working conditions and soil conditions. The floating coefficient and floating power range reflect the power range required to compensate for deviations caused by external factors interfering with the vessel's operation and thus deviating from the target value. The basic power plus the floating power achieves the pre-allocation of power for each dredging device; this satisfies the need for stable operation under current conditions while allowing for floating adjustments based on deviations between the vessel's real-time status and the target status, aiming to find a balance between stability and efficiency.

[0072] The basic coefficients are obtained manually based on experience or by using the NSGA-II multi-objective optimization genetic algorithm or single-objective genetic algorithm to optimize instantaneous production values ​​and / or fuel consumption per 10,000 cubic meters of soil. The Intelligent Integrated Platform (IMP) is an intelligent optimization analysis based on historical data. It uses neural networks to learn from a large amount of data and recommends a series of operational parameters, providing initial targets for real-time optimization. The target speed and the power pre-allocation coefficients (basic coefficients) provide a reference for power allocation of underwater pumps, cabin pumps, and high-pressure flushing pumps to the intelligent power management system. The optimization steps are as follows.

[0073] Step 1: The intelligent integrated platform collects historical operational data through the dredging control system, including control parameters: speed, mud pump speed and power (submersible pump, in-cabin pump), high-pressure flushing pump speed and power, dredging depth, and overflow height. Process parameters: concentration, flow rate, and vacuum. Operating condition parameters: soil type, design dredging depth, and discharge distance. The intelligent energy efficiency management system collects data on main engine fuel consumption rate, real-time power of the mud pump, high-pressure flushing pump, and propeller, as well as total power. Historical datasets under different typical operating conditions are filtered and categorized based on tags such as soil type, design dredging depth, and discharge distance.

[0074] Step 2: To reflect the impact of short-term time-series dynamics of parameters on current output and fuel consumption, the intelligent integration platform constructs a joint prediction model of controllable construction parameters and power allocation based on LSTM for each typical working condition. Its inputs are construction parameters such as concentration, flow rate, high-pressure flushing pump pressure, overflow cylinder height, and speed, as well as the power of underwater pump, in-cabin pump, and high-pressure flushing pump. The outputs are instantaneous output and fuel consumption per 10,000 cubic meters of soil.

[0075] Step 3: To obtain the target speed and power pre-allocation coefficients (basic coefficients), intelligent optimization is performed using a predictive model. There are two optimization modes: maximum instantaneous output mode and economic output mode.

[0076] The specific steps for confirming the power pre-allocation factor (basic factor) are as follows:

[0077] Feasible Region Determination: The feasible region of the power pre-allocation coefficient (basic coefficient) is also obtained based on statistical analysis of historical data. Historical data includes control parameters: speed, mud pump speed and power (submersible pump, in-cabin pump), high-pressure flushing pump speed and power, dredging depth, and overflow height; process parameters: concentration, flow rate, and vacuum; and operating condition parameters: soil type, design dredging depth, and discharge distance. The system collects real-time power and total power of the main engine fuel consumption rate, mud pump, high-pressure flushing pump, and propeller through an intelligent energy efficiency management system. Historical datasets under different typical operating conditions are filtered and segmented based on labels such as soil type, design dredging depth, and discharge distance. The system analyzes the historical power data of each pump and their proportional relationships, and, combined with total power constraints, determines the range of variation for the power pre-allocation coefficient (basic coefficient), typically taking the intersection of the top 80% intervals with the highest data distribution probability.

[0078] Calculating the power pre-allocation coefficient (basic coefficient) under the maximum instantaneous output mode: A single-objective genetic algorithm optimizes the power values ​​of each pump to maximize instantaneous output. Once the optimal power allocation for each pump is determined, the corresponding power pre-allocation coefficient (basic coefficient) is calculated as the recommended basic coefficient for this mode. For example, if the optimal power values ​​of the submersible pump, the in-tank pump, and the high-pressure flushing pump are P1, P2, and P3 respectively, then the basic coefficient can be expressed as the ratio of their respective power values ​​to the total main engine power (P...). ME The ratio of P1 / P ME P2 / P ME P3 / P ME .

[0079] Direct Optimization under Economic Output Mode: In this mode, the NSGA-II multi-objective optimization algorithm uses "power pre-allocation basic coefficients" as recommended parameters to be optimized. The algorithm searches within the feasible region of these coefficients and evaluates the impact of different coefficient combinations on instantaneous output and oil consumption per 10,000 cubic meters of soil using an LSTM model. After iterative optimization, NSGA-II outputs a series of Pareto optimal solutions, each containing a set of recommended power pre-allocation basic coefficients. These coefficients represent the optimal power allocation strategy under different output and oil consumption balance points, providing construction personnel with a choice and initial allocation reference for the intelligent power management system.

[0080] like Figure 3 As shown, the calculation process of the floating coefficient is as follows: the dredging control system (IDS) calculates the target yield, the intelligent navigation control system (INS) calculates the target course and target speed, and the intelligent dredging control system (IDS), for dredging, loading, dumping, and decanting operations, constructs key operation processes and automatic and intelligent controllers for the dredging system through fully automated control of the dredging process and historical data mining and analysis, and predicts the yield P at the next moment.DT The target output is primarily predicted using the LSTM prediction model built into the Intelligent Integrated Platform (IMP), combined with two different optimization modes. **Maximum Instantaneous Output Mode:** In this mode, the core objective is to maximize the volume of dredged earth per unit time. The Intelligent Integrated Platform utilizes a single-objective genetic algorithm, directly using the "instantaneous output" value predicted by the LSTM model as the individual fitness function. Through iterative and evolutionary operations (such as selection, crossover, and mutation) of the genetic algorithm, a set of controllable construction parameters (such as speed and pump power) are continuously searched and optimized to ensure that the instantaneous output of the LSTM model reaches its theoretical maximum value. This maximized instantaneous output is the target output under this mode. **Economic Output Mode:** This mode seeks the optimal balance between instantaneous output and oil consumption per 10,000 cubic meters of earth. The IMP employs the NSGA-II multi-objective optimization genetic algorithm. After initializing a set of recommended parameters (including speed, concentration range, flow velocity range, and power pre-allocation basic coefficients), the algorithm inputs them into the LSTM model to obtain the corresponding predicted values ​​for instantaneous output and oil consumption per 10,000 cubic meters of earth. Based on these two predictions, NSGA-II performs non-dominated sorting, calculates crowding, and executes genetic operations for iterative optimization. Ultimately, the algorithm generates a set of Pareto optimal solutions, each representing a different balance between "instantaneous output" and "oil consumption per 10,000 cubic meters." Construction personnel can select one of these solutions based on actual needs (e.g., pursuing higher output while accepting slightly higher oil consumption, or pursuing extreme economy with moderate output), and the corresponding instantaneous output becomes the target output under the current working conditions.

[0081] The basic dredging control system (BDS) calculates the current instantaneous production rate P based on collected data such as mud density and flow velocity. DA ;

[0082]

[0083] ΔP D =P DT -P DA ;

[0084] Where N is the mud concentration, ρ C To measure density, ρ S ρ is the density of seawater. G Where S is the dry soil density, S is the flow velocity, and D is the diameter of the mud pipe.

[0085] The Intelligent Navigation System (INS) automatically calculates the target heading D based on the working line and current position provided by the Dredging Track and Profile Display System (DTPM). T Target speed S T And measure the actual heading D in real time. A Actual speed S AThe system calculates the heading deviation ΔD1 and speed deviation ΔS1 and outputs these deviations to the power pre-allocation module of the Intelligent Power Management System (IPMS). Within the intelligent navigation system, the target speed is automatically calculated through optimization, including two optimization modes, relying on historical data analysis and an LSTM prediction model. First, based on historical operating data, the system statistically analyzes the distribution of speed parameters for different typical working conditions (divided according to soil type, design excavation depth, and row spacing). The intersection of the top 80% of the data distribution probabilities is taken as the feasible region for the speed parameters in the optimization process, ensuring that the recommended speed is within a range proven effective and safe through historical experience. Optimization under the maximum instantaneous output mode: In this mode, a single-objective genetic algorithm treats speed as one of the variables to be optimized. This algorithm searches within the feasible region of speed, adjusting the combination of speed and other relevant parameters, and using an LSTM model to predict their impact on instantaneous output to find the speed value that maximizes instantaneous output. This optimal speed is the target speed under this mode. Optimization under the Economic Output Model: In this model, the NSGA-II multi-objective optimization algorithm uses speed as one of the recommended parameters to be optimized, searching within its feasible region. Through iteration, the algorithm finds speed values ​​that achieve different optimal balance points between "instantaneous output" and "fuel consumption per 10,000 cubic meters of soil." The final Pareto optimal solution set output includes a recommended target speed for each solution, allowing construction personnel to select an appropriate speed based on the overall strategy.

[0086] The ship's target speed S T Target heading D T And target production and actual speed S A Actual heading D A By comparing actual production output, we can obtain the speed difference ΔS1, the heading difference ΔD1, and the production difference.

[0087] Right now,

[0088] The power pre-allocation module of the intelligent trailing suction hopper dredger sends operating instructions to the corresponding execution equipment based on the speed difference ΔS1, the heading difference ΔD1, the production difference, and the dredging conditions.

[0089] The actuator controls the operation of the left propulsion, right propulsion, side thruster, and dredging equipment according to instructions;

[0090] Under environmental interference, collect the current actual speed, actual course, and actual output, and repeat the above steps;

[0091] like Figure 4 As shown, the influence of the wind, wave, and current model on the ship's state is calculated by the dynamic positioning and dynamic tracking system (DP / DT). Various forces act on the hull, affecting the ship's course and trajectory. This process is mediated by the hull model M.SHIP and rake arm model M P This is reflected in the deviation matrix between the actual state and the target state of the ship:

[0092]

[0093] Among them, P DT P is the predicted target output for the next instantaneous moment; DA F represents the actual output at the instant; WIND The force exerted by the wind on the hull is calculated from the wind speed measured by an anemometer and the windward area of ​​the hull; F WAVE The force exerted by the wave on the hull is calculated from wind and water ripple parameters; F FLOW The force exerted by the flow on the hull is calculated from wind and water ripple parameters; F HEAD The drag force of the rake head is measured by a force gauge.

[0094] The difference between the target speed and the actual speed ΔS, the difference between the target speed and the actual course ΔD, and the difference between the target speed and the actual output at the instant ΔP. D They are respectively:

[0095]

[0096] Among them, P DT P is the predicted target output for the next instantaneous moment; DA This refers to the actual output at any given instant.

[0097] Based on the deviation matrix, the floating power range of each device is calculated, and the floating limit range of each load is as follows:

[0098]

[0099] The fluctuation factor for each load is:

[0100]

[0101] Where k1, k2, k3, and k4 are all floating limit coefficients for each load, with k1 reflecting the impact of propulsion via the rudder on the bow direction; k2 reflecting the impact of the bow thruster on the bow direction; k3 reflecting the impact of the mud pump on output; and k4 reflecting the impact of the high-pressure flushing pump on output; k1+k2=1, k3+k4=1; ΔP CPP Indicates the power fluctuation range of the thruster; ΔP BT Indicates the power fluctuation range of the bow thruster; ΔP DP Indicates the power fluctuation range of the mud pump; P DPe Indicates the rated power of the mud pump; ΔP JPIndicates the power fluctuation range of the high-pressure flushing pump; P JPe Indicates the rated power of the high-pressure water pump; P USE Indicates the remaining available power of the power grid; ΔX CPP Indicates the thruster's float coefficient, ΔX DP The floating coefficient of the mud pump, ΔX JP For the high-pressure flushing pump's float coefficient, P CPPe P is the rated power of the thruster. DPe P is the rated power of the mud pump. JPe P is the rated power of the high-pressure flushing pump. BTe This refers to the rated power of the bow thruster;

[0102] When ΔS is negative, it indicates that the propulsion tends to decelerate, which will release more available power, and its effect will be reflected in the next calculation cycle.

[0103] When ΔP D When the value is negative, the surface mud pump and high-pressure flushing pump tend to slow down, releasing more available power, the effect of which will be reflected in the next calculation cycle.

[0104] Since the load conditions for free navigation, mud-carrying navigation, and mud gate unloading are relatively simple, they will not be discussed in detail. Figure 5 As shown, the power pre-allocation module sets pre-allocation coefficients for both dredging and bow-blown unloading operations, and also for different soil types, based on different soil conditions. Taking dredging operation - soil type 1 as an example, the basic coefficient of the thruster is X. CPP The fluctuation coefficient is ΔX CPP .

[0105] S2, Introducing the pre-allocation coefficient into the power balance equation of the trailing suction hopper dredger, we obtain the updated power balance equation. The specific process is as follows:

[0106] like Figure 6 As shown, the distribution coefficient is introduced into the power balance equation of the trailing suction hopper dredger, which is expressed as:

[0107] P ME =P CPPe *(X CPP +ΔX CPP )+P MT +P DPe *(X DP +ΔX DP )+P JPe *(X JP +ΔX JP )+P BTe *(X BT +ΔX BT )+P HY +P GP ;

[0108] After adjusting the coefficient positions, the power balance equation is expressed as:

[0109] P ME =(P CPPe *X CPP +P DPe *X DP +P JPe *X JP +P BTe *X BT )+(P MT +P HY +P GP )+(P CPPe *ΔX CPP +P DPe *ΔX DP +P JPe *ΔX JP +P BTe *ΔX BT );

[0110] Among them, P ME P represents the main unit power. CPPe P is the rated power of the thruster. DPe P is the rated power of the mud pump. JPe P is the rated power of the high-pressure flushing pump. BTe P is the rated power of the bow thruster. MT Main transformer power; P HY P represents the power of the hydraulic pump. GP XCPP is the power of the sealing pump; ΔXCPP is the basic coefficient of the propeller; ΔXCPP is the floating coefficient of the propeller; XDP is the basic coefficient of the mud pump; ΔXDP is the floating coefficient of the mud pump; XBT is the basic coefficient of the bow thruster; ΔXBT is the floating coefficient of the bow thruster; XJP is the basic coefficient of the high-pressure flushing pump; ΔXJP is the floating coefficient of the high-pressure flushing pump.

[0111] S3, calculate the power consumption of each power source under the current operating conditions, and obtain the remaining available power P of the power grid. USE The specific process is as follows:

[0112] In actual calculations, the remaining available power P of the power grid USE In the first cycle, the product of the rated power of each load and the basic coefficient is subtracted from the host power. The basic coefficient is obtained by optimization through historical data and serves as the starting value for this control. Its expression is:

[0113] P USE =P MEe -(P CPPe *X CPP +P DPe *X DP+P JPe *X JP +P BTe *X BT )-(P MT +P HY +P GP );

[0114] In subsequent cycles, the remaining available power P of the power grid USE The expression for subtracting the actual feedback power of each load from the host power is:

[0115] P USE =P MEe -(P CPPa +P DPa +P JPa +P BTa )-(P MT +P HY +P GP );

[0116] After multiple cycles, the actual power value of each load should converge and approximate the value of rated power * basic coefficient.

[0117] Among them, P MEe P is the rated power of the host. CPPa P represents the actual feedback power of the thruster. DPa P represents the actual feedback power of the mud pump. JPa P represents the actual feedback power of the high-pressure flushing pump. BTa This represents the actual feedback power of the bow thruster.

[0118] S4 determines the power allocation priority of each load according to the working conditions, and allocates the remaining allocable power to each load proportionally according to the priority order and the pre-allocation coefficient of each load.

[0119] The calculated remaining available power P of the power grid USE The expression for redistribution to each load is:

[0120] P USE =(P CPPe *ΔX CPP +P DPe *ΔX DP +P JPe *ΔX JP +P BTe *ΔX BT );

[0121] Let ΔP CPP =P CPPe *ΔX CPP ;ΔP DP =P DPe*ΔX DP ;ΔP JP =P JPe *ΔX JP ;ΔP BT =P BTe *ΔX BT ;

[0122] Then P USE =(ΔP) CPP +ΔP DP +ΔP JP +ΔP BT ).

[0123] When allocating power to equipment, the base factor is the average value within the high-efficiency range, and the fluctuation factor is the deviation value. Therefore, the base factor addresses whether the equipment can operate, while the fluctuation factor addresses whether it can operate well. Thus, within the available power P... USE During redistribution, calculations are performed using a floating coefficient.

[0124] Since trailing suction hopper dredgers are navigational construction vessels with a need to avoid collisions in narrow waterways, propulsion has the highest priority. When manual over-control is triggered or the load increases beyond the main engine threshold, power allocation is carried out according to the following priority: when power adjustment is required, the available power of the high-pressure flushing pump and mud pump will be reduced first; the propeller and bow thruster will be given priority in ensuring power supply.

[0125] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

Claims

1. A power pre-allocation method based on an intelligent trailing suction hopper dredger, characterized in that, include: (1) According to different working conditions, a pre-allocation coefficient is set for each load. The pre-allocation coefficient includes a basic coefficient and a floating coefficient. The loads include the propeller, high-pressure flushing pump, mud pump and bow thruster. (2) Introduce the pre-allocation coefficient into the power balance equation of the trailing suction hopper dredger to obtain the updated power balance equation; (3) Calculate the power consumption of each power source under the current operating conditions to obtain the remaining available power of the power grid. ; (4) Determine the power allocation priority of each load according to the working conditions, and allocate the remaining allocable power to each load proportionally according to the priority order and the pre-allocation coefficient of each load. The basic coefficients are obtained manually based on experience or by using the NSGA-II multi-objective optimization genetic algorithm or single-objective genetic algorithm to optimize the instantaneous output value and / or the oil consumption per 10,000 cubic meters of soil. The calculation process for the floating coefficient is as follows: The target output for the next moment is calculated and predicted by the dredging control system of the intelligent trailing suction hopper dredger. And the current instantaneous actual output Calculate the production difference : ； The intelligent navigation system of the intelligent trailing suction hopper dredger automatically calculates the target course based on the working line and current position provided by the dredging trajectory and profile display system. Target speed The target speed of the ship Target heading Compared with actual speed Actual heading By comparison, the speed difference was obtained. Heading difference ; ； The power pre-distribution module of the intelligent trailing suction hopper dredger is based on the speed difference. Heading difference Difference between output and production And the dredging operation will send the operation instructions to the corresponding execution equipment; The actuator controls the operation of the left propulsion, right propulsion, side thruster, and dredging equipment according to instructions; Under environmental interference, collect the current actual speed, actual course, and actual output, and repeat the above steps; The dynamic positioning and tracking system calculates the impact of wind, wave, and current models on the ship's state. Various forces act on the hull, affecting the ship's course and trajectory. This process is mediated by the hull model. and rake arm model This is reflected in the deviation matrix between the actual state and the target state of the ship: ； in, For ship hull model and rake arm model Change in speed per unit time For ship hull model and rake arm model Change in heading per unit time; The force exerted by the wind on the hull; The force exerted by the wave on the hull; The force exerted by the flow on the hull; For the drag force of the rake head; The difference between the target speed and the actual speed The difference between the target and the actual course And the difference between the target and the actual output at any given moment. They are respectively: ； Based on the deviation matrix, the floating power range of each device is calculated, and the floating limit range of each load is as follows: ； The fluctuation factor for each load is: ; ； ； ； in, , , and These are all the floating limit coefficients for each load. + =1, + =1; Indicates the power fluctuation range of the thruster; This indicates the power fluctuation range of the bow thruster; This indicates the power fluctuation range of the mud pump; Indicates the rated power of the mud pump; This indicates the power fluctuation range of the high-pressure flushing pump; This indicates the rated power of the high-pressure water pump; Indicates the remaining available power in the power grid; Indicates the float coefficient of the thruster, The float coefficient for the bow thruster; For the mud pump's float coefficient, For the float coefficient of the high-pressure flushing pump, This refers to the rated power of the thruster; This refers to the rated power of the mud pump. This refers to the rated power of the high-pressure flushing pump. This refers to the rated power of the bow thruster; when When the value is negative, it indicates that the propulsion is slowing down, which will release more available power, and the effect will be reflected in the next calculation cycle. when When the value is negative, the surface mud pump and high-pressure flushing pump tend to slow down, releasing more available power, the effect of which will be reflected in the next calculation cycle.

2. The power pre-allocation method based on an intelligent trailing suction hopper dredger according to claim 1, characterized in that, The specific process of obtaining the updated power balance equation in step (2) is as follows: Introducing the pre-allocation coefficient into the power balance equation of a trailing suction hopper dredger, it is expressed as: ； After adjusting the coefficient positions, the power balance equation is expressed as: ； in, This refers to the power of the main unit; This refers to the rated power of the thruster; This refers to the rated power of the mud pump. This refers to the rated power of the high-pressure flushing pump. This refers to the rated power of the bow thruster; Main transformer power; Power of the hydraulic pump; For the power of the sealing water pump; X CPP These are the basic coefficients of the thruster; X CPP The float coefficient of the thruster; X DP For the basic coefficients of the mud pump; X DP This is the float coefficient of the mud pump; X BT These are the basic coefficients for the bow thruster; X BT The float coefficient for the bow thruster; X JP These are the basic coefficients for high-pressure flushing pumps; X JP This is the float coefficient of the high-pressure flushing pump.

3. The power pre-allocation method based on an intelligent trailing suction hopper dredger according to claim 2, characterized in that, The specific process of step (3) is as follows: In actual calculations, the remaining available power of the power grid In the first cycle, the product of the rated power of each load and the basic coefficient is subtracted from the host power. The expression is as follows: ； In subsequent cycles, the remaining available power of the power grid The expression for subtracting the actual feedback power of each load from the host power is: ； in, The rated power of the main unit, This represents the actual feedback power of the thruster. This represents the actual feedback power of the mud pump. This represents the actual feedback power of the high-pressure flushing pump. This represents the actual feedback power of the bow thruster.

4. The power pre-allocation method based on an intelligent trailing suction hopper dredger according to claim 3, characterized in that, The calculated remaining available power of the power grid The expression for redistribution to each load is: ； make ; ; ; ; but .

5. The power pre-allocation method based on an intelligent trailing suction hopper dredger according to claim 1, characterized in that, The power allocation priority is as follows: when faced with power adjustment requirements, the available power of the high-pressure flushing pump and mud pump will be reduced first; the propeller and bow thruster will be given priority in ensuring power supply.

Images