Intelligent energy efficiency management system and method for LNG dual-fuel power trailing suction dredger

Abstract

The invention discloses an intelligent energy efficiency management system for an LNG dual-fuel power trailing suction dredger, and the system comprises an LNG energy efficiency online intelligent monitoring module which is used for collecting energy efficiency key factor parameters of main energy consumption equipment of the LNG dual-fuel trailing suction dredger, and forming a unified LNG trailing suction dredger energy efficiency data dictionary; the intelligent energy efficiency analysis and evaluation module is used for constructing a working condition recognition model, an energy efficiency index calculation model, a carbon emission calculation model, an energy efficiency grade model and a periodic energy efficiency analysis model according to the energy efficiency data dictionary of the LNG trailing suction dredger to form an energy efficiency analysis and evaluation system; and the dredging operation energy efficiency auxiliary decision-making module is used for constructing a thousand-square soil energy consumption prediction model according to the historical data of the soil texture type, the designed excavation depth, the construction parameters and the fuel consumption, and recommending a dredging construction operation parameter combination through an optimization algorithm. The invention further discloses an intelligent energy efficiency management method for the LNG dual-fuel power trailing suction hopper dredger. The energy efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of dredging, and in particular to an intelligent energy efficiency management system and method for an LNG (liquefied natural gas) dual-fuel powered trailing suction dredger. Background Art

[0002] The pace and intensity of "decarbonization" measures are constantly increasing internationally. The IMO (Maritime Environment Protection Committee) introduced ship energy efficiency rules through the MARPOL (International Convention for the Prevention of Pollution from Ships) amendment, and for the first time identified two ship energy efficiency standards, the Ship Energy Efficiency Design Index EEDI and the Ship Energy Efficiency Management Plan SEEMP, as global mandatory carbon emission reduction rules.

[0003] Informatization and intelligence are the development trend of modern dredging engineering ships, and are also one of the important factors to measure the advancement of dredging ships. With the increasing application of LNG clean energy in dredgers, higher requirements are put forward for the monitoring, management, analysis and evaluation, and auxiliary decision-making suggestions of ship energy efficiency. In the actual construction of LNG dual-fuel trailing suction dredgers, the intelligent energy efficiency management system optimizes energy efficiency through intelligent means, improves energy efficiency, and provides effective technical means for dredgers to save energy and reduce emissions.

[0004] Therefore, an intelligent energy efficiency management system and method for an LNG dual-fuel powered trailing suction hopper dredger is provided. Summary of the invention

[0005] The purpose of the present invention is to overcome the existing defects and provide an intelligent energy efficiency management system and method for an LNG dual-fuel powered trailing suction dredger, thereby improving energy efficiency.

[0006] The technical solution to achieve the above purpose is:

[0007] One of the present invention is an intelligent energy efficiency management system for an LNG dual-fuel powered trailing suction dredger, comprising:

[0008] LNG energy efficiency online intelligent monitoring module, used to collect key energy efficiency factor parameters of main energy-consuming equipment of LNG dual-fuel trailing suction hopper dredger, and form a unified LNG trailing suction hopper dredger energy efficiency data dictionary;

[0009] Intelligent energy efficiency analysis and evaluation module, which is used to construct the working condition identification model, energy efficiency index calculation model, carbon emission calculation model, energy efficiency grade model and periodic energy efficiency analysis model according to the energy efficiency data dictionary of LNG trailing suction dredger, and form an energy efficiency analysis and evaluation system;

[0010] The dredging operation energy efficiency auxiliary decision-making module is used to build a Wancu soil energy consumption prediction model based on soil type, designed digging depth, construction parameters and historical data of fuel consumption, and recommend dredging construction operation parameter combinations through optimization algorithms;

[0011] Intelligent speed optimization module, used to optimize the speed during the dispatch process and dredging operation;

[0012] Energy efficiency data management module, used to store and manage various data of LNG dual-fuel trailing suction hopper dredger.

[0013] Preferably, in the LNG energy efficiency online intelligent monitoring module, key energy efficiency factor parameters include but are not limited to LNG consumption, fuel consumption, power, fuel switching status, and ship buoyancy, dredging operations and water environment.

[0014] Preferably, the intelligent energy efficiency analysis and evaluation module includes:

[0015] The working condition identification unit is used to identify the construction parameters through the constructed working condition identification model according to the process requirements of the trailing suction hopper dredger, and then distinguish various working conditions, among which the working conditions are divided into five conditions: dredging, mud throwing, navigation, shore blowing and others;

[0016] Energy efficiency index calculation unit, used to automatically calculate energy efficiency index for voyage and LNG trailing suction dredger energy efficiency data according to energy efficiency index calculation model;

[0017] A carbon emission calculation unit is used to calculate the energy efficiency operation index EEOI and the ship carbon intensity index CII of the LNG trailing suction dredger according to the carbon emission calculation model;

[0018] Energy efficiency grade unit, used to evaluate the overall energy consumption level of dredging operations according to the energy efficiency grade model, and to display the energy consumption level of dredging operations. The evaluation basis is the energy consumption per 10,000 cubic meters of soil.

[0019] The cycle performance efficiency analysis unit is used to count the energy consumption of a single ship dredging cycle, dredging and throwing cycle, dredging and blowing cycle, and navigation cycle according to the cycle performance efficiency analysis model, automatically analyze and calculate the energy efficiency indicators of each cycle according to the dredging ship number and time range, calculate the key factors affecting energy consumption through the Pearson correlation coefficient algorithm, and finally form an energy efficiency report.

[0020] Preferably, in the energy efficiency index calculation unit, the energy efficiency indexes include but are not limited to real-time energy consumption, working condition energy consumption, energy consumption per unit time, energy consumption per unit nautical mile and energy consumption per ten thousand cubic meters of land, wherein:

[0021] Real-time energy consumption mainly includes real-time fuel consumption of main engine, auxiliary engine and hot water heater;

[0022] The working condition energy consumption is the fuel consumption and duration of LNG, fuel consumption and ignition oil for dredging, mud dumping, navigation, shore blowing and other five working conditions respectively according to the working condition identification unit;

[0023] Energy consumption per unit time Mtotal The calculation is the total energy consumption of a single ship M 总 and total operation time T 总 The ratio is as follows:

[0024]

[0025] Energy consumption per nautical mile M n Refers to the energy consumption of the ship per nautical mile during the navigation phase M total , the calculation formula is as follows:

[0026]

[0027] Energy consumption per 10,000 cubic meters of soil d Refers to the amount of LNG or fuel consumed when the excavation volume is 10,000 cubic meters. The calculation formula is as follows:

[0028]

[0029] Preferably, in the carbon emission calculation unit,

[0030] The calculation formula of the energy efficiency operation index EEOI of a ship is:

[0031]

[0032] In the formula, C Fj is the CO2 emission factor of LNG and fuel oil, D is the ship's mileage, FC j Total LNG and fuel consumption of dredger, m 土方量 is the earthwork volume per ship, j is the fuel type;

[0033] The calculation formula for the carbon intensity index CII of a ship is:

[0034]

[0035] Where W is the work done in transportation, and its calculation formula is as follows:

[0036] W = earthwork volume × D.

[0037] Preferably, in the dredging operation energy efficiency auxiliary decision-making module, the recommended dredging operation parameters include but are not limited to dredging speed, mud pump speed, high-pressure flushing pump speed, rake lip to ground angle, overflow tube height and optimal loading time;

[0038] The algorithm is executed in two steps:

[0039] Firstly, by establishing an LSTM neural network driven by time series data, a prediction model for the energy consumption of 10,000 cubic meters of soil based on big data online learning is realized, with construction parameters and energy consumption of 10,000 cubic meters of soil as input and output;

[0040] Secondly, the prediction model is inversely calculated based on the particle swarm optimization algorithm and the fitness function to obtain the optimal combination of construction parameters under a specific external environment, thus realizing energy-efficient auxiliary decision-making based on the goal of optimizing the energy consumption of ten thousand cubic meters of soil.

[0041] Preferably, the speed intelligent optimization module includes:

[0042] The dispatching speed optimization unit is used to build a data-driven ship speed-energy efficiency model based on the energy efficiency and speed history data during heavy-load and light-load navigation, and then solve the theoretical speed point with the best energy efficiency of the model according to the optimization algorithm, and automatically calculate the estimated fuel consumption and estimated arrival time under the remaining sailing mileage;

[0043] The dredging construction speed optimization unit is used to construct a speed optimization model based on the optimal energy consumption per 10,000 cubic meters of soil according to past construction and energy efficiency data, and then calculate the optimal speed with the energy consumption per 10,000 cubic meters of soil as the optimization target through the optimization algorithm.

[0044] Preferably, the energy efficiency data management module includes:

[0045] A database for storing various data of the LNG dual-fuel trailing suction hopper dredger, including but not limited to energy efficiency data and index data;

[0046] Data query unit, used to automatically query historical data according to ship name, ship number or time period;

[0047] The report generation unit is used to generate an energy efficiency report based on the queried energy efficiency data and indicator data, and export the data in a .csv format file.

[0048] The second aspect of the present invention is an intelligent energy efficiency management method for an LNG dual-fuel powered trailing suction dredger, comprising:

[0049] Step S1, collecting the key energy efficiency factor parameters of the main energy-consuming equipment of the LNG dual-fuel trailing suction hopper dredger to form a unified LNG trailing suction hopper dredger energy efficiency data dictionary;

[0050] Step S2, constructing a working condition identification model, an energy efficiency index calculation model, a carbon emission calculation model, an energy efficiency grade model and a periodic energy efficiency analysis model according to the LNG trailing suction dredger energy efficiency data dictionary to form an energy efficiency analysis and evaluation system;

[0051] Step S3, based on soil type, designed excavation depth, construction parameters and historical data of fuel consumption, a Wancuum soil energy consumption prediction model is constructed, and a combination of dredging construction operation parameters is recommended through an optimization algorithm;

[0052] Step S4, optimizing the speed during the dispatching process and the dredging operation process;

[0053] Step S5, managing the data of the LNG dual-fuel trailing suction hopper dredger, querying, exporting and reporting the energy efficiency data and report data.

[0054] Preferably, in step S1, the key energy efficiency factor parameters include but are not limited to LNG consumption, fuel consumption, power, fuel switching state, and ship floating state, dredging operation and water environment;

[0055] The step S2 comprises:

[0056] Step S21, according to the process requirements of the trailing suction hopper dredger, the construction parameters are identified through the constructed working condition identification model, and then various working states are distinguished, wherein the working states are divided into five states: dredging, mud throwing, sailing, shore blowing and others;

[0057] Step S22, automatically calculating the energy efficiency index for the voyage and the LNG trailing suction dredger energy efficiency data according to the energy efficiency index calculation model;

[0058] Step S23, calculating the energy efficiency operation index EEOI and the ship carbon intensity index CII of the LNG trailing suction dredger according to the carbon emission calculation model;

[0059] Step S24, evaluating the overall energy consumption level of the dredging operation according to the energy efficiency grade model, and displaying the energy consumption level of the dredging operation, the evaluation basis being the energy consumption per ten thousand cubic meters of soil;

[0060] Step S25, according to the cycle performance efficiency analysis model, the energy consumption of a single ship dredging cycle, dredging and throwing cycle, dredging and blowing cycle, and navigation cycle is counted, and the energy efficiency index of each cycle is automatically analyzed and calculated according to the dredging ship number and time range, and the key factors affecting energy consumption are calculated by the Pearson correlation coefficient algorithm, and finally an energy efficiency report is formed;

[0061] In step S3, the recommended dredging operation parameters include but are not limited to dredging speed, mud pump speed, high-pressure flushing pump speed, rake lip to ground angle, overflow tube height and optimal loading time;

[0062] The algorithm is executed in two steps:

[0063] Firstly, by establishing an LSTM neural network driven by time series data, a prediction model for the energy consumption of 10,000 cubic meters of soil based on big data online learning is realized, with construction parameters and energy consumption of 10,000 cubic meters of soil as input and output;

[0064] Secondly, the prediction model is inversely calculated based on the particle swarm optimization algorithm and fitness function to obtain the optimal combination of construction parameters under specific external conditions, thus achieving energy efficiency auxiliary decision-making based on the goal of optimizing the energy consumption of ten thousand cubic meters of soil;

[0065] In step S4, a data-driven ship speed-energy efficiency model is constructed based on the energy efficiency and speed history data during heavy-load navigation and light-load navigation, and then the theoretical speed point with the best energy efficiency of the model is solved according to the optimization algorithm, and the estimated fuel consumption and estimated arrival time under the remaining navigation mileage are automatically calculated;

[0066] According to the construction and energy efficiency data, a speed optimization model based on the optimal energy consumption of ten thousand cubic meters of soil is constructed, and then the optimal speed with the energy consumption of ten thousand cubic meters of soil as the optimization target is calculated through the optimization algorithm.

[0067] The beneficial effects of the present invention are as follows: the present invention targets the particularity of the power equipment of the LNG dual-fuel trailing suction hopper dredger, monitors the energy consumption data of LNG, fuel oil, ignition oil and other energy-consuming equipment such as the main engine, auxiliary engine, hot water heater and other main energy-consuming equipment, as well as the parameters such as ship navigation, water environment and construction parameters, calculates the comprehensive energy efficiency, emissions, energy consumption per 10,000 cubic meters of soil and other indicators in real time, establishes an energy efficiency system database, analyzes and evaluates multiple working conditions of the dredger, provides auxiliary decision-making suggestions for the operating parameters of the dredging operation equipment, completes the intelligent energy efficiency management system of the LNG dual-fuel trailing suction hopper dredger, improves energy efficiency, and provides effective technical means for energy conservation and emission reduction of dredgers. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a module diagram of an intelligent energy efficiency management system for an LNG dual-fuel powered trailing suction dredger according to the present invention;

[0069] Figure 2 It is a specific module diagram of the intelligent energy efficiency analysis and evaluation module in the present invention;

[0070] Figure 3 It is a specific module diagram of the intelligent speed optimization module in the present invention;

[0071] Figure 4 It is a specific module diagram of the energy efficiency data management module in the present invention;

[0072] Figure 5 This is a flow chart of an intelligent energy efficiency management method for an LNG dual-fuel powered trailing suction dredger of the present invention;

[0073] Figure 6 It is a specific flow chart of the energy efficiency analysis and evaluation system formed in the present invention. DETAILED DESCRIPTION

[0074] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. In the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0075] The present invention will be further described below in conjunction with the accompanying drawings.

[0076] like Figure 1 As shown, an intelligent energy efficiency management system for an LNG dual-fuel powered trailing suction dredger comprises: an LNG energy efficiency online intelligent monitoring module 1, an intelligent energy efficiency analysis and evaluation module 2, a dredging operation energy efficiency auxiliary decision-making module 3, a speed intelligent optimization module 4 and an energy efficiency data management module 5.

[0077] The LNG energy efficiency online intelligent monitoring module 1 is used to collect the key energy efficiency factor parameters of the main energy-consuming equipment of the LNG dual-fuel trailing suction hopper dredger to form a unified LNG trailing suction hopper dredger energy efficiency data dictionary.

[0078] In the embodiment, the key energy efficiency factor parameters include but are not limited to LNG consumption, fuel consumption, power, fuel switching status, and ship floating state, dredging operations and water environment.

[0079] Specific data include:

[0080] Import / export flow, power, fuel temperature, pressure fuel, LNG and fuel oil / ignition oil fuel switching status of main energy-consuming equipment (main engine, auxiliary diesel engine, boiler);

[0081] Power parameters of main electrical equipment of 400V and 220V;

[0082] Working status of main dredging equipment (mud pump, high-pressure flushing pump, thruster, etc.), including parameters such as power, pressure, temperature, and speed;

[0083] Parameters of propulsion equipment such as speed, power, pitch, etc.;

[0084] Environmental parameters such as wind direction, wind force, water flow, dredged soil quality, etc.

[0085] Construction parameters such as speed, longitudinal and transverse inclination, ship draft, earthwork volume, drag head depth, drag head angle to the ground, overflow tube height, etc.

[0086] The intelligent energy efficiency analysis and evaluation module 2 is used to construct a working condition identification model, an energy efficiency index calculation model, a carbon emission calculation model, an energy efficiency grade model and a periodic energy efficiency analysis model according to the energy efficiency data dictionary of the LNG trailing suction dredger, so as to form an energy efficiency analysis and evaluation system.

[0087] like Figure 2 As shown, the intelligent energy efficiency analysis and evaluation module 2 includes: an operating condition identification unit 21, an energy efficiency index calculation unit 22, a carbon emission calculation unit 23, an energy efficiency grade unit 24 and a periodic energy efficiency analysis unit 25.

[0088] The working condition identification unit 21 is used to identify the construction parameters through the constructed working condition identification model according to the process requirements of the trailing suction hopper dredger, and then distinguish various working states, wherein the working states are divided into five states: dredging, mud throwing, navigation, shore blowing and others.

[0089] The energy efficiency index calculation unit 22 is used to automatically calculate the energy efficiency index for the voyage and the LNG trailing suction dredger energy efficiency data according to the energy efficiency index calculation model.

[0090] In the embodiment, the energy efficiency index includes but is not limited to real-time energy consumption, working condition energy consumption, energy consumption per unit time, energy consumption per unit nautical mile and energy consumption per ten thousand cubic meters of land, among which:

[0091] Real-time energy consumption mainly includes real-time fuel consumption of main engine, auxiliary engine and hot water heater;

[0092] The working condition energy consumption is the fuel consumption and duration of LNG, fuel consumption and ignition oil for dredging, mud dumping, navigation, shore blowing and other five working conditions respectively according to the working condition identification unit 21;

[0093] Energy consumption per unit time M total The calculation is the total energy consumption of a single ship M 总 and total operation time T 总 The ratio is as follows:

[0094]

[0095] Energy consumption per nautical mile M n Refers to the energy consumption of the ship per nautical mile during the navigation phase M total , the calculation formula is as follows:

[0096]

[0097] Energy consumption per 10,000 cubic meters of soil d Refers to the amount of LNG or fuel consumed when the excavation volume is 10,000 cubic meters. The calculation formula is as follows:

[0098]

[0099] The carbon emission calculation unit 23 is used to calculate the energy efficiency operation index EEOI and the ship carbon intensity index CII of the LNG trailing suction dredger according to the carbon emission calculation model.

[0100] In the embodiment,

[0101] The calculation formula of the energy efficiency operation index EEOI of a ship is:

[0102]

[0103] In the formula, C Fj is the CO2 emission factor of LNG and fuel oil, D is the ship's mileage, FC j Total LNG and fuel consumption of dredger, m 土方量 is the earthwork volume per ship, j is the fuel type;

[0104] The calculation formula for the carbon intensity index CII of a ship is:

[0105]

[0106] Where W is the work done in transportation, and its calculation formula is as follows:

[0107] W = earthwork volume × D.

[0108] The energy efficiency rating unit 24 is used to evaluate the overall energy consumption level of the dredging operation according to the energy efficiency rating model, and to display the energy consumption level of the dredging operation. The evaluation basis is the energy consumption per ten thousand cubic meters of soil.

[0109] The cycle performance efficiency analysis unit 25 is used to calculate the energy consumption of a single ship dredging cycle, a dredging and throwing cycle, a dredging and blowing cycle, and a navigation cycle according to the cycle performance efficiency analysis model, automatically analyze and calculate the energy efficiency index of each cycle according to the dredging ship number and time range, calculate the key factors affecting energy consumption through the Pearson correlation coefficient algorithm, and finally form an energy efficiency report.

[0110] The dredging operation energy efficiency auxiliary decision-making module 3 is used to build a 10,000-cubic-meter soil energy consumption prediction model based on soil type, designed excavation depth, construction parameters and historical data of fuel consumption, and recommend dredging construction operation parameter combinations through an optimization algorithm.

[0111] In the embodiment, the recommended dredging operation parameters include but are not limited to dredging speed, mud pump speed, high-pressure flushing pump speed, rake lip to ground angle, overflow tube height and optimal loading time;

[0112] The algorithm is executed in two steps:

[0113] Firstly, by establishing an LSTM neural network driven by time series data, a prediction model for the energy consumption of 10,000 cubic meters of soil based on big data online learning is realized, with construction parameters and energy consumption of 10,000 cubic meters of soil as input and output;

[0114] Secondly, the prediction model is inversely calculated based on the particle swarm optimization algorithm and the fitness function to obtain the optimal combination of construction parameters under a specific external environment, thus realizing energy-efficient auxiliary decision-making based on the goal of optimizing the energy consumption of ten thousand cubic meters of soil.

[0115] The intelligent speed optimization module 4 is used to optimize the speed during the dispatching process and the dredging operation process.

[0116] like Figure 3 As shown, the intelligent speed optimization module 4 includes: a dispatching speed optimization unit 41 and a dredging construction speed optimization unit 42.

[0117] The dispatching speed optimization unit 41 is used to construct a data-driven ship speed-energy efficiency model based on the energy efficiency and speed history data during heavy-load navigation and light-load navigation, and then solve the theoretical speed point with the optimal energy efficiency of the model according to the optimization algorithm, and automatically calculate the estimated fuel consumption and estimated arrival time under the remaining navigation mileage.

[0118] The dredging construction speed optimization unit 42 is used to construct a speed optimization model based on the optimal energy consumption per 10,000 cubic meters of soil according to the past construction and energy efficiency data, and then calculate the optimal speed with the energy consumption per 10,000 cubic meters of soil as the optimization target through an optimization algorithm.

[0119] The energy efficiency data management module 5 is used to store and manage various data of the LNG dual-fuel trailing suction dredger.

[0120] like Figure 4 As shown, the energy efficiency data management module 5 includes: a database 51 , a data query unit 52 and a report generation unit 53 .

[0121] The database 51 is used to store various data of the LNG dual-fuel trailing suction hopper dredger, including but not limited to energy efficiency data and index data.

[0122] The data query unit 52 is used to automatically query historical data according to the ship name, ship number or time period.

[0123] The report generation unit 53 is used to generate an energy efficiency report according to the queried energy efficiency data and indicator data, and export the data in a .csv format file.

[0124] like Figure 5 As shown, an intelligent energy efficiency management method for an LNG dual-fuel powered trailing suction dredger comprises:

[0125] Step S1, collecting the key energy efficiency factor parameters of the main energy-consuming equipment of the LNG dual-fuel trailing suction hopper dredger to form a unified LNG trailing suction hopper dredger energy efficiency data dictionary.

[0126] In the embodiment, the key energy efficiency factor parameters include but are not limited to LNG consumption, fuel consumption, power, fuel switching status, and ship floating state, dredging operations and water environment.

[0127] Step S2, constructing a working condition identification model, an energy efficiency index calculation model, a carbon emission calculation model, an energy efficiency grade model and a periodic energy efficiency analysis model according to the LNG trailing suction dredger energy efficiency data dictionary to form an energy efficiency analysis and evaluation system.

[0128] like Figure 6 As shown, step S2 specifically includes:

[0129] Step S21, according to the process requirements of the trailing suction hopper dredger, the construction parameters are identified through the constructed working condition identification model, and then various working states are distinguished, wherein the working states are divided into five states: dredging, mud throwing, navigation, shore blowing and others.

[0130] Step S22, automatically calculating the energy efficiency index for the voyage and the LNG trailing suction dredger energy efficiency data according to the energy efficiency index calculation model.

[0131] Step S23, calculating the energy efficiency operation index EEOI and the ship carbon intensity index CII of the LNG trailing suction dredger according to the carbon emission calculation model.

[0132] Step S24, evaluating the overall energy consumption level of the dredging operation according to the energy efficiency grade model, and displaying the energy consumption level of the dredging operation, with the evaluation basis being the energy consumption per ten thousand cubic meters of soil.

[0133] Step S25, according to the cycle performance efficiency analysis model, the energy consumption of a single ship dredging cycle, dredging and throwing cycle, dredging and blowing cycle, and navigation cycle is counted, and the energy efficiency index of each cycle is automatically analyzed and calculated according to the dredging ship number and time range. The key factors affecting energy consumption are calculated by the Pearson correlation coefficient algorithm, and finally an energy efficiency report is formed.

[0134] Step S3, based on the soil type, designed excavation depth, construction parameters and historical data of fuel consumption, a Wancu soil energy consumption prediction model is constructed, and a combination of dredging construction operation parameters is recommended through an optimization algorithm.

[0135] In the embodiment, the recommended dredging operation parameters include but are not limited to dredging speed, mud pump speed, high-pressure flushing pump speed, rake lip to ground angle, overflow tube height and optimal loading time;

[0136] The algorithm is executed in two steps:

[0137] Firstly, by establishing an LSTM neural network driven by time series data, a prediction model for the energy consumption of 10,000 cubic meters of soil based on big data online learning is realized, with construction parameters and energy consumption of 10,000 cubic meters of soil as input and output;

[0138] Secondly, the prediction model is inversely calculated based on the particle swarm optimization algorithm and the fitness function to obtain the optimal combination of construction parameters under a specific external environment, thus realizing energy-efficient auxiliary decision-making based on the goal of optimizing the energy consumption of ten thousand cubic meters of soil.

[0139] Step S4, optimizing the speed during the dispatching process and the dredging operation process.

[0140] In the embodiment, according to the energy efficiency and speed history data during heavy-load navigation and light-load navigation, a data-driven ship speed-energy efficiency model is constructed, and then the theoretical speed point with the best energy efficiency of the model is solved according to the optimization algorithm, and the estimated fuel consumption and estimated arrival time under the remaining navigation mileage are automatically calculated;

[0141] According to the construction and energy efficiency data, a speed optimization model based on the optimal energy consumption of ten thousand cubic meters of soil is constructed, and then the optimal speed with the energy consumption of ten thousand cubic meters of soil as the optimization target is calculated through the optimization algorithm.

[0142] Step S5, managing the data of the LNG dual-fuel trailing suction hopper dredger, querying, exporting and reporting the energy efficiency data and report data.

[0143] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some or all of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent energy efficiency management system for an LNG dual-fuel powered trailing suction hopper dredger, characterized in that: include: LNG energy efficiency online intelligent monitoring module, used to collect key energy efficiency factor parameters of main energy-consuming equipment of LNG dual-fuel trailing suction hopper dredger, and form a unified LNG trailing suction hopper dredger energy efficiency data dictionary; Intelligent energy efficiency analysis and evaluation module, which is used to construct the working condition identification model, energy efficiency index calculation model, carbon emission calculation model, energy efficiency grade model and periodic energy efficiency analysis model according to the energy efficiency data dictionary of LNG trailing suction dredger, and form an energy efficiency analysis and evaluation system; The dredging operation energy efficiency auxiliary decision-making module is used to build a Wancu soil energy consumption prediction model based on soil type, designed digging depth, construction parameters and historical data of fuel consumption, and recommend dredging construction operation parameter combinations through optimization algorithms; Intelligent speed optimization module, used to optimize the speed during the dispatching process and dredging operation; Energy efficiency data management module, used to store and manage various data of LNG dual-fuel trailing suction hopper dredger.

2. According to claim 1, an intelligent energy efficiency management system for an LNG dual-fuel powered trailing suction hopper dredger is characterized in that: In the LNG energy efficiency online intelligent monitoring module, key energy efficiency factor parameters include but are not limited to LNG consumption, fuel consumption, power, fuel switching status, and ship floating state, dredging operations and water environment.

3. The intelligent energy efficiency management system for an LNG dual-fuel powered trailing suction hopper dredger according to claim 1 is characterized in that: The intelligent energy efficiency analysis and evaluation module includes: The working condition identification unit is used to identify the construction parameters through the constructed working condition identification model according to the process requirements of the trailing suction hopper dredger, and then distinguish various working conditions, among which the working conditions are divided into five conditions: dredging, mud throwing, navigation, shore blowing and others; Energy efficiency index calculation unit, used to automatically calculate energy efficiency index for voyage and LNG trailing suction dredger energy efficiency data according to energy efficiency index calculation model; A carbon emission calculation unit is used to calculate the energy efficiency operation index EEOI and the ship carbon intensity index CII of the LNG trailing suction dredger according to the carbon emission calculation model; Energy efficiency grade unit, used to evaluate the overall energy consumption level of dredging operations according to the energy efficiency grade model, and to display the energy consumption level of dredging operations. The evaluation basis is the energy consumption per 10,000 cubic meters of soil. The cycle performance efficiency analysis unit is used to count the energy consumption of a single ship dredging cycle, dredging and throwing cycle, dredging and blowing cycle, and navigation cycle according to the cycle performance efficiency analysis model, automatically analyze and calculate the energy efficiency indicators of each cycle according to the dredging ship number and time range, calculate the key factors affecting energy consumption through the Pearson correlation coefficient algorithm, and finally form an energy efficiency report.

4. The intelligent energy efficiency management system for an LNG dual-fuel powered trailing suction hopper dredger according to claim 3 is characterized in that: In the energy efficiency index calculation unit, the energy efficiency index includes but is not limited to real-time energy consumption, working condition energy consumption, energy consumption per unit time, energy consumption per unit nautical mile and energy consumption per ten thousand cubic meters of land, wherein: Real-time energy consumption mainly includes real-time fuel consumption of main engine, auxiliary engine and hot water heater; The working condition energy consumption is the fuel consumption and duration of LNG, fuel consumption and ignition oil for dredging, mud dumping, navigation, shore blowing and other five working conditions respectively according to the working condition identification unit; Energy consumption per unit time M total The calculation is the total energy consumption of a single ship M 总 and total operation time T 总 The ratio is as follows: Energy consumption per nautical mile M n Refers to the energy consumption of the ship per nautical mile during the navigation phase M total , the calculation formula is as follows: Energy consumption per 10,000 cubic meters of soil d Refers to the amount of LNG or fuel consumed when the excavation volume is 10,000 cubic meters. The calculation formula is as follows:

5. The intelligent energy efficiency management system for an LNG dual-fuel powered trailing suction hopper dredger according to claim 3 is characterized in that: In the carbon emission calculation unit, The calculation formula of the energy efficiency operation index EEOI of a ship is: In the formula, C Fj is the CO2 emission factor of LNG and fuel oil, D is the ship's mileage, FC j Total LNG and fuel consumption of dredger, m 土方量 is the earthwork volume per ship, j is the fuel type; The calculation formula for the carbon intensity index CII of a ship is: Where W is the work done in transportation, and its calculation formula is as follows: W = earthwork volume × D.

6. The intelligent energy efficiency management system for an LNG dual-fuel powered trailing suction hopper dredger according to claim 1, characterized in that: In the dredging operation energy efficiency auxiliary decision-making module, recommended dredging operation parameters include but are not limited to dredging speed, mud pump speed, high-pressure flushing pump speed, rake lip to ground angle, overflow tube height and optimal loading time; The algorithm is executed in two steps: Firstly, by establishing an LSTM neural network driven by time series data, a prediction model for the energy consumption of 10,000 cubic meters of soil based on big data online learning is realized, with construction parameters and energy consumption of 10,000 cubic meters of soil as input and output; Secondly, the prediction model is inversely calculated based on the particle swarm optimization algorithm and the fitness function to obtain the optimal combination of construction parameters under a specific external environment, thus realizing energy-efficient auxiliary decision-making based on the goal of optimizing the energy consumption of ten thousand cubic meters of soil.

7. The intelligent energy efficiency management system for an LNG dual-fuel powered trailing suction hopper dredger according to claim 1, characterized in that: The speed intelligent optimization module includes: The dispatching speed optimization unit is used to build a data-driven ship speed-energy efficiency model based on the energy efficiency and speed history data during heavy-load and light-load navigation, and then solve the theoretical speed point with the best energy efficiency of the model according to the optimization algorithm, and automatically calculate the estimated fuel consumption and estimated arrival time under the remaining sailing mileage; The dredging construction speed optimization unit is used to construct a speed optimization model based on the optimal energy consumption per 10,000 cubic meters of soil according to past construction and energy efficiency data, and then calculate the optimal speed with the energy consumption per 10,000 cubic meters of soil as the optimization target through the optimization algorithm.

8. The intelligent energy efficiency management system for an LNG dual-fuel powered trailing suction hopper dredger according to claim 1, characterized in that: The energy efficiency data management module includes: A database for storing various data of the LNG dual-fuel trailing suction hopper dredger, including but not limited to energy efficiency data and index data; Data query unit, used to automatically query historical data according to ship name, ship number or time period; The report generation unit is used to generate an energy efficiency report based on the queried energy efficiency data and indicator data, and export the data in a .csv format file.

9. An intelligent energy efficiency management method for an LNG dual-fuel powered trailing suction hopper dredger, characterized in that: include: Step S1, collecting the key energy efficiency factor parameters of the main energy-consuming equipment of the LNG dual-fuel trailing suction hopper dredger to form a unified LNG trailing suction hopper dredger energy efficiency data dictionary; Step S2, constructing a working condition identification model, an energy efficiency index calculation model, a carbon emission calculation model, an energy efficiency grade model and a periodic energy efficiency analysis model according to the LNG trailing suction dredger energy efficiency data dictionary to form an energy efficiency analysis and evaluation system; Step S3, based on soil type, designed excavation depth, construction parameters and historical data of fuel consumption, a Wancuum soil energy consumption prediction model is constructed, and a combination of dredging construction operation parameters is recommended through an optimization algorithm; Step S4, optimizing the speed during the dispatching process and the dredging operation process; Step S5, managing the data of the LNG dual-fuel trailing suction hopper dredger, querying, exporting and reporting the energy efficiency data and report data.

10. The intelligent energy efficiency management method for an LNG dual-fuel powered trailing suction hopper dredger according to claim 9, characterized in that: In step S1, the key energy efficiency factor parameters include but are not limited to LNG consumption, fuel consumption, power, fuel switching status, ship floating state, dredging operation and water environment; The step S2 comprises: Step S21, according to the process requirements of the trailing suction hopper dredger, the construction parameters are identified through the constructed working condition identification model, and then various working states are distinguished, wherein the working states are divided into five states: dredging, mud throwing, sailing, shore blowing and others; Step S22, automatically calculating the energy efficiency index for the voyage and the LNG trailing suction dredger energy efficiency data according to the energy efficiency index calculation model; Step S23, calculating the energy efficiency operation index EEOI and the ship carbon intensity index CII of the LNG trailing suction dredger according to the carbon emission calculation model; Step S24, evaluating the overall energy consumption level of the dredging operation according to the energy efficiency grade model, and displaying the energy consumption level of the dredging operation, the evaluation basis being the energy consumption per ten thousand cubic meters of soil; Step S25, according to the cycle performance efficiency analysis model, the energy consumption of a single ship dredging cycle, dredging and throwing cycle, dredging and blowing cycle, and navigation cycle is counted, and the energy efficiency index of each cycle is automatically analyzed and calculated according to the dredging ship number and time range, and the key factors affecting energy consumption are calculated by the Pearson correlation coefficient algorithm, and finally an energy efficiency report is formed; In step S3, the recommended dredging operation parameters include but are not limited to dredging speed, mud pump speed, high-pressure flushing pump speed, rake lip to ground angle, overflow tube height and optimal loading time; The algorithm is executed in two steps: Firstly, by establishing an LSTM neural network driven by time series data, a prediction model for the energy consumption of 10,000 cubic meters of soil based on big data online learning is realized, with construction parameters and energy consumption of 10,000 cubic meters of soil as input and output; Secondly, the prediction model is inversely calculated based on the particle swarm optimization algorithm and fitness function to obtain the optimal combination of construction parameters under specific external conditions, thus achieving energy efficiency auxiliary decision-making based on the goal of optimizing the energy consumption of ten thousand cubic meters of soil; In step S4, a data-driven ship speed-energy efficiency model is constructed based on the energy efficiency and speed history data during heavy-load navigation and light-load navigation, and then the theoretical speed point with the best energy efficiency of the model is solved according to the optimization algorithm, and the estimated fuel consumption and estimated arrival time under the remaining navigation mileage are automatically calculated; According to the construction and energy efficiency data, a speed optimization model based on the optimal energy consumption of ten thousand cubic meters of soil is constructed, and then the optimal speed with the energy consumption of ten thousand cubic meters of soil as the optimization target is calculated through the optimization algorithm.

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