Power pre-distribution method based on intelligent trailing suction hopper dredger

Abstract

The invention relates to the technical field of dredging equipment power management, and provides a power pre-distribution method based on an intelligent trailing suction dredger, which comprises the following steps: setting a pre-distribution coefficient for each load according to different working conditions, the pre-distribution coefficient comprising a basic coefficient and a floating coefficient; introducing the pre-distribution coefficient into a trailing suction dredger power balance equation to obtain an updated power balance equation; and calculating the use power of each power consumption under the current working condition, obtaining the remaining available power of the power grid, determining the power distribution priority of each load according to the working condition, and proportionally distributing the remaining distributable power to each load according to the priority sequence and the pre-distribution coefficient of each load. The requirement for power pre-distribution of all dredging equipment is met by setting basic power and floating power, stable construction under the current working condition can be met, floating adjustment can be conducted according to the deviation between the real-time state and the target state of a ship, and the purpose of finding a stable and efficient balance point is achieved.

Description

Technical Field

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

[0002] Dredging refers to the earthwork carried out underwater using dredgers or other equipment, as well as manual labor, to widen and deepen waterways. Dredging involves excavating and disposing of mud, sand, and rock from the bottom of a waterway or harbor within a specified area and depth.

[0003] Currently, dredging vessels use conventional ship power grid architecture and management methods. Load management strategies often follow a first-come, first-served basis, failing to rationally allocate power based on operating conditions. When grid power exceeds a certain threshold, power limiting is triggered, often employing a "one-size-fits-all" restriction strategy that fails to achieve smooth optimization.

[0004] Therefore, it is urgent to design a power pre-allocation method that can manage and optimize power from the perspective of dredging efficiency and energy consumption. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides a power pre-distribution 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, which includes a basic coefficient and a floating coefficient; the loads include thrusters, high-pressure flushing pumps, mud pumps and bow thrusters;

[0007] (2) The pre-distribution coefficient is introduced 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 consumption under the current working 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 operating conditions, and distribute the remaining allocable power to each load in proportion according to the priority order and the pre-allocation coefficient of each load.

[0010] Furthermore, the basic coefficient is obtained manually based on experience or by using 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 for the optimal power pre-allocation coefficient.

[0011] Furthermore, the calculation process of the floating coefficient is:

[0012] The target production P at the next moment is calculated and predicted by the dredging control system of the intelligent trailing suction hopper dredgerDT And the actual output P at the moment DA , calculate the yield 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 course D according to the working line and current ship position provided by the dredging track and profile display system (DTPM). T , target speed S T , the target speed of the ship S T 、Target heading D T and actual speed S A , actual heading D A By comparison, we can obtain the speed difference ΔS1 and heading difference ΔD1;

[0015]

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

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

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

[0019] The dynamic positioning and dynamic tracking system (DP / DT) calculates the influence of wind, wave and current model on the ship state. Various forces acting on the hull will affect the ship's heading and track. This process is achieved through the hull model M SHIP and rake arm model M P The deviation matrix between the actual state and the target state of the ship is obtained:

[0020]

[0021] Among them, ΔS2 is the hull model M SHIP and rake arm model M P The speed change per unit time, ΔD2 is the speed of the ship model M SHIP and rake arm model M P The amount of heading change per unit time; F WIND F is the force exerted by the wind on the hull, which is calculated from the wind speed measured by the anemometer and the windward area of ​​the hull; WAVEis the force exerted by waves on the hull, which is calculated from wind force and water wave parameters; F FLOW is the force exerted by the flow on the hull, which is calculated from the wind force and water wave parameters; F HEAD is the drag force of the rake head, measured by the dynamometer;

[0022] The difference between the target and actual speed ΔS, the difference between the target and actual heading ΔD, and the difference between the target and the actual output at the moment ΔP D They are:

[0023]

[0024] Among them, P DT is the predicted target output at the next moment (instantaneous); P DA is the actual output at the moment;

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

[0026]

[0027] The floating coefficient of each load is:

[0028]

[0029] Among them, k1, k2, k3 and k4 are the floating limit coefficients of each load, among which k1 reflects the influence of propulsion through the rudder on the ship's heading; k2 reflects the influence of the bow thruster on the ship's heading; k3 reflects the influence of the mud pump on the production; k4 reflects the influence of the high-pressure flushing pump on the production; k1+k2=1, k3+k4=1; ΔP CPP Indicates the power fluctuation range of the propeller; ΔP BT Indicates the power floating range of the bow thruster; ΔP DP Indicates the power floating range of the mud pump; P DPe Indicates the rated power of the mud pump; ΔP JP Indicates the power floating range of the high-pressure flushing pump; P JPe Indicates the rated power of the high-pressure flushing pump; P USE Indicates the remaining available power of the grid; ΔX CPP Indicates the floating coefficient of the thruster, ΔX DP is the floating coefficient of the mud pump, ΔX JP is the floating coefficient of the high-pressure flushing pump, P CPPe is the propeller rated power; P DPe is the rated power of the mud pump; P JPe is the rated power of the high-pressure flushing pump, P BTe is the rated power of the bow thruster;

[0030] When ΔS is negative, it indicates that the propulsion has a tendency to slow down, which will release more available power, and its effect will be reflected in the next calculation cycle;

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

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

[0033] The distribution coefficient is introduced into the power balance equation of the trailing suction hopper dredger, which 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 position, 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 MEis the host power; P CPPe is the propeller rated power; P DPe is the rated power of the mud pump; P JPe is the rated power of the high-pressure flushing pump, P BTe is the rated power of the bow thruster; P MT Main transformer power; P HY is the hydraulic pump power; P GP is the sealing water pump power; XCPP is the basic coefficient of the thruster; ΔXCPP is the floating coefficient of the thruster; 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] The remaining available power P of the power grid in actual calculation USE When , in the first cycle, the host power is subtracted from the product of the rated power of each load and the basic coefficient, and the expression is:

[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 the subsequent cycle, the remaining available power of the grid is P USE The actual feedback power of each load is deducted from the host power, and its expression 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 is the rated power of the host, P CPPa is the actual feedback power of the thruster, P DPa is the actual feedback power of the mud pump, P JPais the actual feedback power of the high-pressure flushing pump, P BTa is the actual feedback power of the bow thruster.

[0046] Furthermore, the calculated remaining available power P of the grid is USE Then distributed to each load, the expression 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 needs, the available power of the high-pressure flushing pump and the mud pump will be reduced first; the thruster and the bow thruster will be given priority to ensure power supply.

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

[0052] (1) The present invention sets power pre-distribution coefficients for different working conditions and combines the power balance equation of the trailing suction hopper dredger to achieve reasonable pre-distribution and dynamic adjustment of the power of each load, thereby improving the ship's operating efficiency and energy utilization efficiency;

[0053] (2) The present invention achieves the power pre-distribution requirement for each dredging equipment by setting the basic power plus the floating power, which can meet the stable construction under the current working conditions and can also perform floating adjustment according to the deviation between the real-time state of the ship and the target state, aiming to find a balance point between stability and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0055] Figure 2 This is a schematic diagram of power pre-distribution in the present invention.

[0056] Figure 3 It is a flow chart of the floating coefficient calculation in the present invention.

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

[0058] Figure 5 This is a schematic diagram of the power pre-distribution module for dredging and bow blowing conditions, and the pre-distribution coefficients set for each load according to different soil types.

[0059] Figure 6 It is a schematic diagram of the power balance equation of a trailing suction hopper dredger. DETAILED DESCRIPTION

[0060] The technical solution of the present invention is further described in detail below in conjunction with specific embodiments, but this embodiment is not intended to limit the present invention. All similar structures and similar variations of the present invention should be included in the scope of protection of the present invention. The semicolons in the present invention represent the relationship of and, 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] Among them, the dredging control system (DCS) includes the dredging trajectory and profile display system (DTPM), the data acquisition and monitoring control system (SCADA), and the human-machine interface device (HMI). The dredging trajectory and profile display system (DTPM) is mainly responsible for ship positioning, construction planning, construction terrain reconstruction and display. The data acquisition and monitoring control system (SCADA) is mainly responsible for collecting and displaying the process data of the dredging control system. The dredging control system is designed and issues control instructions through the human-machine interface device HMI to control mud pumps, high-pressure flushing pumps, water sealing pumps, A-frames, winches, rake heads, rake pipes, mud chambers, mud doors, chamber doors, gate valves and other equipment to realize dredging processes such as dredging, mud throwing and filling.

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

[0064] The power management system (PMS) is primarily responsible for managing power plant components such as shaft generators, auxiliary generators, and emergency generators. It switches power distribution modes based on operating conditions, distributes loads among multiple generators after parallel operation, conducts overload assessments and inquiries before starting large loads, limits power to specific equipment when the grid is highly loaded, and performs power recovery procedures in the event of partial or complete power outages on distribution boards.

[0065] The Intelligent Monitoring and Integrated Management Platform (HD-ICMP) achieves intelligent monitoring and integrated management of the entire ship's equipment through the coordinated operation of various systems. The Intelligent Integration Platform (IMP) utilizes computers, local area networks, and other technologies to acquire information and data from various ship systems, enabling standardized shipwide data management, unified construction of common modules across all systems, and network data security. The platform provides standardized interfaces, enabling data sharing among subsystems through the IMP, providing the hardware platform for intelligent dredging across the entire ship.

[0066] The dredging control system includes the Basic Dredging Control System (BDS) and the Intelligent Dredging Control System (IDS). The Basic Dredging Control System utilizes sensors, PLCs, an operator interface, and computer network technologies to monitor and control dredging equipment such as the hydraulic system, pump system, rake arm system, mud chamber, bow blower, and anchor winch. The Intelligent Dredging Control System utilizes automated and intelligent technologies to achieve fully automatic one-touch dredging control, active rake lip control, intelligent mud pump control, dredging speed control, and automatic chamber pumping gate valve control.

[0067] The dredging track and profile display system (DTPM) allows operators to import terrain files into DTPM and design construction plans (working lines). It also accesses signals from the differential global positioning system (DGPS), gyrocompass, automatic ship identification system (AIS), tide telemeter, etc. to display construction data such as the coordinates, heading, track line, water depth, and profile of the hull in the construction area. After construction, the terrain file can be calculated and reconstructed based on the track, rake track, and cutting amount.

[0068] The Dredging Trajectory and Profile Display System (DTPM) has an interface to output the construction plan (working 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 INS automatically recommends speeds for dredging and free navigation, autonomous track planning and recommended routes, collision warnings, enhanced visual assistance, and a digital twin of dredging operations and three-dimensional terrain visualization. The DP / DT system collects parameters such as the vessel's floating state, power system, dredging operating conditions, and water environment to enable dynamic positioning and tracking of the vessel.

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

[0071] S1 sets a pre-allocation coefficient for each load based on different operating conditions. The pre-allocation coefficients include a basic coefficient and a floating coefficient. Loads include thrusters, high-pressure water pumps, dredge pumps, and bow thrusters. The basic coefficient reflects the typical power usage of this equipment under specific operating conditions and soil conditions. The floating coefficient and floating power range reflect the power range required to compensate for deviations from the target value caused by external factors interfering with ship operation. The basic power plus the floating power achieve the power pre-allocation requirements for each dredging equipment. This allows for stable operation under current operating conditions while also allowing for floating adjustments based on deviations between the ship's real-time status and the target state, 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 a single-objective genetic algorithm to optimize the instantaneous production value 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 large amounts of data and recommend a series of operation parameter sets to provide initial targets for real-time optimization. The speed target and power pre-allocation coefficients (basic coefficients) provide a reference for the intelligent power management system to allocate power to subsea pumps, in-cabin pumps, and high-pressure flushing pumps. The optimization steps are as follows.

[0073] Step 1: The intelligent integrated platform collects historical operating data through the dredging control system, including control parameters: ship speed, dredge pump speed and power (submersible pump, in-cabin pump), high-pressure flushing pump speed and power, dredging depth, and overflow tube height. Process parameters include concentration, flow rate, and vacuum. Operating condition parameters include soil quality, designed dredging depth, and row spacing. The intelligent energy efficiency management system collects the main engine fuel consumption rate, the real-time power of the dredge pump, high-pressure flushing pump, and propeller, and the total power. Historical data sets under different typical operating conditions are filtered and divided according to tags such as soil quality, designed dredging depth, and row spacing.

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

[0075] Step 3: To obtain the speed target and power pre-allocation coefficient (basic coefficient), the prediction model is used for intelligent optimization. There are two optimization modes: maximum instantaneous output mode and economic output mode.

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

[0077] Determining the feasible domain: The feasible domain of the power pre-allocation coefficient (basic coefficient) is also derived from statistical analysis of historical data. This historical data includes control parameters: ship speed, mud pump speed and power (underwater pumps, in-cabin pumps), high-pressure flushing pump speed and power, excavation depth, and overflow tube height; process parameters: concentration, flow rate, and vacuum; and operating parameters: soil quality, designed excavation depth, and row spacing. The intelligent energy efficiency management system collects the main engine fuel consumption rate, the real-time power of the mud pump, high-pressure flushing pump, and propeller, and the total power. Historical data sets under different typical operating conditions are filtered and divided based on labels such as soil quality, designed excavation depth, and row spacing. The system analyzes the historical power data of each pump and their proportional relationship. Combined with the total power limit, it 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] Calculation of power pre-allocation coefficient (basic coefficient) in maximum instantaneous production mode: The single-objective genetic algorithm optimizes the power value of each pump to maximize the instantaneous production. Once the optimal power distribution of each pump is determined, the corresponding power pre-allocation coefficient (basic coefficient) is calculated and used as the recommended basic coefficient in this mode. For example, if the optimal power of the underwater pump, cabin pump, and high-pressure flushing pump is P1, P2, and P3 respectively, the basic coefficient can be expressed as the ratio between them to the total main engine power (P ME ) ratio P1 / P ME , P2 / P ME , P3 / P ME .

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

[0080] like Figure 3 As shown in the figure, the calculation process of the floating coefficient is as follows: the dredging control system (IDS) calculates the target production, the intelligent navigation control system (INS) calculates the target course and target speed, and the intelligent dredging control system (IDS) builds the key operation process and the dredging system automatic controller and intelligent controller for the dredging loading, mud dumping, and mud pumping and shore discharge conditions through the full automatic control of the dredging process and historical data mining and analysis, and predicts the production P at the next moment.DT The target yield is primarily predicted using the LSTM prediction model built into the Intelligent Integration Platform (IMP), using two different optimization modes to determine the target yield. Maximum instantaneous yield mode: In this mode, the system's core objective is to maximize the amount of dredged earthwork per unit time. The intelligent integration platform utilizes a single-objective genetic algorithm, using the "instantaneous yield" value predicted by the LSTM model as the individual fitness function. Through the genetic algorithm's iterative and evolutionary operations (such as selection, crossover, and mutation), it continuously searches for and optimizes a set of controllable construction parameters (such as ship speed and pump power) to achieve the theoretical maximum instantaneous yield output by the LSTM model. This maximized instantaneous yield is the target yield under this mode. Economic yield mode: This mode seeks the optimal balance between instantaneous yield and fuel consumption per 10,000 cubic meters of earthwork. The IMP utilizes the NSGA-II multi-objective optimization genetic algorithm. After initializing a set of recommended parameters (including ship speed, concentration range, flow rate range, and basic power pre-allocation coefficients), the algorithm inputs these parameters into the LSTM model to obtain the corresponding predicted values ​​for instantaneous yield and fuel consumption per 10,000 cubic meters of earthwork. Based on these two predicted values, NSGA-II performs non-dominated sorting, calculates congestion, and performs genetic operations for iterative optimization. Ultimately, the algorithm generates a series of Pareto-optimal solutions, each representing a different equilibrium between "instantaneous production" and "fuel consumption per 10,000 cubic meters of soil." Construction personnel can select one of these solutions based on their actual needs (e.g., pursuing high production while accepting slightly higher fuel consumption, or pursuing extreme economy with moderate production). The corresponding instantaneous production becomes the target production for the current working conditions.

[0081] The Basic Dredging Control System (BDS) calculates the current instantaneous production P based on the collected mud density, flow rate and other data. DA ;

[0082]

[0083] ΔP D =P DT -P DA ;

[0084] Where N is the mud concentration, ρ C To measure density, ρ S is the density of seawater, ρ G 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 course D according to the working line and current ship 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 ACalculate the heading deviation ΔD1 and speed deviation ΔS1, and output 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, which includes two optimization modes and relies 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 operating conditions (based on soil quality, designed excavation depth, and row spacing). The intersection of the top 80% of intervals with high data distribution probability is used as the feasible region for the speed parameter during the optimization process, ensuring that the recommended speed is within a range that has been proven effective and safe by historical experience. Optimization in the maximum instantaneous yield mode: In this mode, a single-objective genetic algorithm uses speed as one of the variables to be optimized. The algorithm searches within the feasible region of speed, adjusting combinations of speed and other relevant parameters and using an LSTM model to predict their impact on instantaneous yield to find the speed value that maximizes instantaneous yield. This optimal speed becomes the target speed for this mode. Optimization in the Economic Yield Model: In this model, the NSGA-II multi-objective optimization algorithm uses speed as one of the recommended parameters to be optimized and searches within its feasible domain. The algorithm iterates to find speed values ​​that achieve different optimal balances between "instantaneous yield" and "fuel consumption per 10,000 cubic meters of soil." The resulting Pareto optimal solution set includes a recommended target speed for each solution, allowing construction personnel to select the appropriate speed based on the overall strategy.

[0086] The target speed of the ship S T 、Target heading D T and target output and actual speed S A , actual heading D A , actual production comparison, obtain speed difference ΔS1, heading difference ΔD1 and production difference;

[0087] Right now,

[0088] The power pre-distribution module of the intelligent trailing suction hopper dredger sends the operation instructions to the corresponding execution equipment according to the speed difference ΔS1, heading difference ΔD1 and production difference as well as the dredging conditions;

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

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

[0091] like Figure 4 As shown in the figure, the dynamic positioning and dynamic tracking system (DP / DT) calculates the influence of the wind, wave and current model on the ship state. Various forces acting on the hull will affect the ship's heading and track. This process is achieved through the hull model MSHIP and rake arm model M P The deviation matrix between the actual state and the target state of the ship is obtained:

[0092]

[0093] Among them, P DT is the predicted target output at the next moment (instantaneous); P DA is the actual output at the moment; F WIND F is the force exerted by the wind on the hull, which is calculated from the wind speed measured by the anemometer and the windward area of ​​the hull; WAVE is the force exerted by waves on the hull, which is calculated from wind force and water wave parameters; F FLOW is the force exerted by the flow on the hull, which is calculated from the wind force and water wave parameters; F HEAD is the drag force of the rake head, measured by the dynamometer;

[0094] The difference between the target and actual speed ΔS, the difference between the target and actual heading ΔD, and the difference between the target and the actual output at the moment ΔP D They are:

[0095]

[0096] Among them, P DT is the predicted target output at the next moment (instantaneous); P DA is the actual output at the moment;

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

[0098]

[0099] The floating coefficient of each load is:

[0100]

[0101] Among them, k1, k2, k3 and k4 are the floating limit coefficients of each load, among which k1 reflects the influence of propulsion through the rudder on the ship's heading; k2 reflects the influence of the bow thruster on the ship's heading; k3 reflects the influence of the mud pump on the production; k4 reflects the influence of the high-pressure flushing pump on the production; k1+k2=1, k3+k4=1; ΔP CPP Indicates the power fluctuation range of the propeller; ΔP BT Indicates the power floating range of the bow thruster; ΔP DP Indicates the power floating range of the mud pump; P DPe Indicates the rated power of the mud pump; ΔP JPIndicates the power floating range of the high-pressure flushing pump; P JPe Indicates the rated power of the high-pressure flushing pump; P USE Indicates the remaining available power of the grid; ΔX CPP Indicates the floating coefficient of the thruster, ΔX DP is the floating coefficient of the mud pump, ΔX JP is the floating coefficient of the high-pressure flushing pump, P CPPe is the propeller rated power; P DPe is the rated power of the mud pump; P JPe is the rated power of the high-pressure flushing pump, P BTe is the rated power of the bow thruster;

[0102] When ΔS is negative, it indicates that the propulsion has a tendency to slow down, which will release more available power, and its effect will be reflected in the next calculation cycle;

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

[0104] Since the loads of free navigation, mud-carrying navigation and mud-door unloading are relatively simple, we will not discuss them in detail. Figure 5 As shown in the figure, the power pre-distribution module sets a pre-distribution coefficient for each load according to the dredging and bow blowing conditions and different soil types. Taking the dredging condition - soil type 1 as an example, the basic coefficient of the propeller is X CPP , the floating coefficient is ΔX CPP .

[0105] S2, introduce the pre-distribution coefficient into the power balance equation of the trailing suction hopper dredger to obtain the updated power balance equation. The specific process is:

[0106] like Figure 6 As shown in Figure 2, 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 position, 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 is the host power; P CPPe is the propeller rated power; P DPe is the rated power of the mud pump; P JPe is the rated power of the high-pressure flushing pump, P BTe is the rated power of the bow thruster; P MT Main transformer power; P HY is the hydraulic pump power; P GP is the sealing water pump power; XCPP is the basic coefficient of the thruster; ΔXCPP is the floating coefficient of the thruster; 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 consumption under the current working conditions, and obtain the remaining available power P of the power grid USE The specific process is:

[0112] The remaining available power P of the power grid in actual calculation 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 optimizing the historical data and is used as the starting value of 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 the subsequent cycle, the remaining available power of the grid is P USE The actual feedback power of each load is deducted from the host power, and its expression 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 and the value of rated power * basic coefficient should converge and be approximate.

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

[0118] S4, determining the power allocation priority of each load according to the operating conditions, and allocating the remaining allocable power to each load in proportion according to the priority order and the pre-allocation coefficient of each load.

[0119] The calculated remaining available power P of the grid USE Then distributed to each load, the expression 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 basic coefficient is the mean of the high-efficiency range, and the floating coefficient is the deviation value. The basic coefficient solves the problem of whether it can work, and the floating coefficient solves the problem of whether it can work well. Therefore, in the available power P USE When redistributing, the floating coefficient is used for calculation.

[0124] Because trailing suction hopper dredgers are navigational construction vessels and require collision avoidance in narrow waterways, propulsion takes precedence. When an override is manually activated or the load increases beyond the host engine threshold, power is allocated according to the power allocation priority. When power adjustment is required, the available power of the high-pressure flushing pump and dredge pump is prioritized for reduction; the thruster and bow thruster are prioritized for guaranteed power supply.

[0125] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

Claims

1. A power pre-distribution 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, which includes a basic coefficient and a floating coefficient; the loads include thrusters, high-pressure flushing pumps, mud pumps and bow thrusters; (2) The pre-distribution coefficient is introduced 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 consumption under the current working conditions and obtain the remaining available power P of the power grid USE ; (4) Determine the power allocation priority of each load according to the operating conditions, and distribute the remaining allocable power to each load in proportion according to the priority order and the pre-allocation coefficient of each load.

2. The power pre-distribution method based on an intelligent trailing suction hopper dredger according to claim 1, characterized in that: The basic coefficients are obtained manually based on experience or by using the NSGA-II multi-objective optimization genetic algorithm or the single-objective genetic algorithm to optimize the instantaneous output value and / or the oil consumption per 10,000 cubic meters of soil.

3. The power pre-distribution method based on an intelligent trailing suction hopper dredger according to claim 1, characterized in that: The calculation process of the floating coefficient is: The target production P at the next moment is calculated and predicted by the dredging control system of the intelligent trailing suction hopper dredger DT And the actual output P at the moment DA , calculate the yield difference ΔP D : ΔP D =P DT -P DA ; The intelligent navigation system of the intelligent trailing suction hopper dredger automatically calculates the target course D according to the dredging track and the working line and current ship position provided by the profile display system. T , target speed S T , the target speed of the ship S T 、Target heading D T and actual speed S A , actual heading D A By comparison, we can obtain the speed difference ΔS1 and heading difference ΔD1; The power pre-distribution module of the intelligent trailing suction hopper dredger is based on the speed difference ΔS1, heading difference ΔD1 and production difference ΔP D And the dredging condition sends the operation instructions to the corresponding execution equipment; The execution equipment controls the operation of the left propulsion, right propulsion, side thrust device and dredging equipment according to the instructions; Under the condition of environmental interference, collect the current actual speed, actual heading, and actual output, and repeat the above steps; The dynamic positioning and dynamic tracking system calculates the influence of wind, wave and current model on the ship state. Various forces acting on the hull will affect the ship's heading and track. This process is achieved through the hull model M SHIP and rake arm model M P The deviation matrix between the actual state and the target state of the ship is obtained: Among them, ΔS2 is the hull model M SHIP and rake arm model M P The speed change per unit time, ΔD2 is the speed of the ship model M SHIP and rake arm model M P The amount of heading change per unit time; F WIND F is the force exerted by the wind on the hull; WAVE is the force exerted by the wave on the hull; F FLOW is the force exerted by the flow on the hull; F HEAD is the drag force of the rake head; The difference between the target and actual speed ΔS, the difference between the target and actual heading ΔD, and the difference between the target and the actual output at the moment ΔP D They are: The floating power range of each device is calculated based on the deviation matrix, and the floating limit range of each load is as follows: The floating coefficient of each load is: Among them, k1, k2, k3 and k4 are the floating limit coefficients of each load, k1+k2=1, k3+k4=1; ΔP CPP Indicates the power floating range of the propeller; ΔP BT Indicates the power floating range of the bow thruster; ΔP DP Indicates the power floating range of the mud pump; P DPe Indicates the rated power of the mud pump; ΔP JP Indicates the power floating range of the high-pressure flushing pump; P JPe Indicates the rated power of the high-pressure flushing pump; P USE Indicates the remaining available power of the grid; ΔX CPP Indicates the floating coefficient of the thruster, ΔX DP is the floating coefficient of the mud pump, ΔX JP is the floating coefficient of the high-pressure flushing pump, P CPPe is the propeller rated power; P DPe is the rated power of the mud pump; P JPe is the rated power of the high-pressure flushing pump, P BTe is the rated power of the bow thruster; When ΔS is negative, it indicates that the propulsion has a tendency to slow down, which will release more available power, and its effect will be reflected in the next calculation cycle; When ΔP D When it is negative, the surface mud pump and high-pressure flushing pump tend to slow down, releasing more available power, and its effect will be reflected in the next calculation cycle.

4. The power pre-distribution method based on an intelligent trailing suction hopper dredger according to claim 1, characterized in that: The specific process of the updated power balance equation in step (2) is: The distribution coefficient is introduced into the power balance equation of the trailing suction hopper dredger, which is expressed as: After adjusting the coefficient position, the power balance equation is expressed as: Among them, P ME is the host power; P CPPe is the propeller rated power; P DPe is the rated power of the mud pump; P JPe is the rated power of the high-pressure flushing pump, P BTe is the rated power of the bow thruster; P MT Main transformer power; P HY is the hydraulic pump power; P GP is the sealing water pump power; X CPP is the basic coefficient of the propeller; ΔX CPP is the floating coefficient of the propeller; X DP is the basic coefficient of the mud pump; ΔX DP is the floating coefficient of the mud pump; X BT is the basic coefficient of the bow thruster; ΔX BT is the floating coefficient of the bow thruster; X JP is the basic coefficient of the high-pressure flushing pump; ΔX JP It is the floating coefficient of the high-pressure flushing pump.

5. The power pre-distribution method based on an intelligent trailing suction hopper dredger according to claim 4, characterized in that: The specific process of step (3) is: The remaining available power P of the power grid in actual calculation USE When , in the first cycle, the host power is subtracted from the product of the rated power of each load and the basic coefficient, and the expression is: 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 ); In the subsequent cycle, the remaining available power of the grid is P USE The actual feedback power of each load is deducted from the host power, and its expression is: P USE =P MEe -(P CPPa +P DPa +P JPa +P BTa )-(P MT +P HY +P GP ); Among them, P MEe is the rated power of the host, P CPPa is the actual feedback power of the thruster, P DPa is the actual feedback power of the mud pump, P JPa is the actual feedback power of the high-pressure flushing pump, P BTa is the actual feedback power of the bow thruster.

6. The power pre-distribution method based on an intelligent trailing suction hopper dredger according to claim 5, characterized in that: The calculated remaining available power P of the grid USE Then distribute it to each load, the expression is: P USE =(P CPPe *ΔX CPP +P DPe *ΔX DP +P JPe *ΔX JP +P BTe *ΔX BT ); 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 ; Then P USE =(ΔP CPP +ΔP DP +ΔP JP +ΔP BT ).

7. The power pre-distribution method based on an intelligent trailing suction hopper dredger according to claim 1, characterized in that: The power allocation priority is: when facing the need for power adjustment, the available power of the high-pressure flushing pump and mud pump will be reduced first; the thruster and bow thruster will be given priority to ensure power supply.

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