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

Abstract

Disclosed in the present invention is an intelligent energy efficiency management system for an LNG dual-fuel powered trailing suction hopper dredger, comprising: an online intelligent LNG energy efficiency monitoring module, configured to collect energy efficiency key factor parameters of a main energy consumption device of the LNG dual-fuel powered trailing suction hopper dredger to form a unified LNG powered trailing suction hopper dredger energy efficiency data dictionary; an intelligent energy efficiency analysis and evaluation module, configured to construct, on the basis of the LNG powered trailing suction hopper dredger energy efficiency data dictionary, an operating condition identification model, an energy efficiency indicator calculation model, a carbon emission calculation model, an energy efficiency level model and a periodic energy efficiency analysis model to form an energy efficiency analysis and evaluation system; and a dredging operation energy efficiency auxiliary decision-making module, configured to construct an energy consumption prediction model for ten-thousand cubic meters of soil on the basis of soil type, design dredging depth, construction parameters and historical fuel consumption data, and recommending a dredging construction operation parameter combination by means of an optimization algorithm. Also disclosed is an intelligent energy efficiency management method for an LNG dual-fuel powered trailing suction hopper dredger. The present invention increases the energy efficiency.

Description

An intelligent energy efficiency management system and method for an LNG dual-fuel power drag suction dredger TECHNICAL FIELD

[0001] The present application relates to the dredging technical field, in particular to an intelligent energy efficiency management system and method for an LNG (liquefied natural gas) dual-fuel power drag suction dredger. BACKGROUND

[0002] The international "decarbonization" pace and measures are constantly strengthened. The IMO (Marine Environment Protection Committee) introduces the ship energy efficiency rules through the MARPOL (International Convention for the Prevention of Pollution from Ships) amendment, and for the first time, determines two ship energy efficiency standards, the ship energy efficiency design index EEDI and the ship energy efficiency management plan SEEMP, as the global mandatory carbon emission reduction rules. Driven by the international shipping energy saving and emission reduction background, China has introduced relevant policies such as "Green Development Action Plan for Shipbuilding Industry" and "2030 Carbon Peak Action Plan" to guide the green development of the shipbuilding industry. In order to implement the national strategic goal of "carbon peak and carbon neutrality", fill the gap in the use of clean energy on dredging ships, and strengthen the design concept of green energy saving and environmental protection and sustainable development, dredging engineering ships have adopted LNG driving forms. At present, there are several LNG-powered dredgers in production and use at home and abroad.

[0003] Informationization and intelligentization are the development trend of modern dredging engineering ships and one of the important factors to measure the advancement of dredging ships. With the increasing application of LNG clean energy on dredgers, higher requirements are put forward for the monitoring, management, analysis and evaluation, and auxiliary decision-making and suggestions of ship energy efficiency. In view of the actual construction of LNG dual-fuel drag suction dredgers, the intelligent energy efficiency management system realizes energy efficiency optimization through intelligent means, improves energy efficiency, and provides effective technical means for energy saving and emission reduction of dredgers.

[0004] Therefore, an intelligent energy efficiency management system and method for an LNG dual-fuel power drag suction dredger are provided. SUMMARY

[0005] The present application aims to overcome the existing defects and provide an intelligent energy efficiency management system and method for an LNG dual-fuel power drag suction dredger, which improves energy efficiency.

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

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

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

[0009] An intelligent energy efficiency analysis and evaluation module is configured to construct a working condition identification model, an energy efficiency index calculation model, a carbon emission calculation model, an energy efficiency level model, and a periodic performance efficiency analysis model according to an LNG trailing suction dredger energy efficiency data dictionary, and form an energy efficiency analysis and evaluation system;

[0010] An auxiliary decision-making module for dredging operation is configured to construct a cubic yard soil energy consumption prediction model according to the soil type, the design dredging depth, the construction parameters, and the historical data of fuel consumption, and recommend a dredging construction operation parameter combination through an optimization algorithm.

[0011] A speed intelligent optimization module is configured to optimize the speed during the dispatching process and the dredging construction operation process.

[0012] An energy efficiency data management module is configured to store and manage various types of data of the LNG dual-fuel trailing suction dredger.

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

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

[0015] A working condition identification unit is configured to identify the construction parameters through the constructed working condition identification model according to the process requirements of the trailing suction dredger, and then identify various working conditions, wherein the working conditions include five conditions of dredging, dumping, sailing, blowing on the shore, and others.

[0016] An energy efficiency index calculation unit is configured to automatically calculate the energy efficiency index of the voyage and the LNG trailing suction dredger energy efficiency data according to the energy efficiency index calculation model.

[0017] A carbon emission calculation unit is configured 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] An energy efficiency level unit is configured to evaluate the overall energy consumption level of the dredging operation according to the energy efficiency level model, display the energy consumption level of the dredging operation, and evaluate the cubic yard soil energy consumption.

[0019] A periodic performance efficiency analysis unit is configured to calculate the energy consumption of the single voyage dredging period, the dredging and dumping period, the dredging and blowing period, and the sailing period according to the periodic performance efficiency analysis model, automatically analyze and calculate the energy efficiency index of each period according to the voyage of the dredger and the time range, calculate the key factors affecting the 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 index includes but is not limited to real-time energy consumption, working condition energy consumption, unit time energy consumption, unit nautical mile energy consumption and ten thousand square meters of soil energy consumption, wherein,

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

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

[0023] The unit time energy consumption M total is the ratio of the total energy consumption M 总 of a single voyage and the total operation time T 总 , as follows:

[0024] The unit nautical mile energy consumption M n refers to the energy consumption M total of the ship per nautical mile distance during the sailing stage, and the calculation formula is as follows:

[0025] The ten thousand square meters of soil energy consumption W d refers to the LNG or fuel consumption when the excavated soil volume is 10000 square meters, and the calculation formula is as follows:

[0026] Preferably, in the carbon emission calculation unit,

[0027] The calculation formula of the energy efficiency operation index EEOI of a single voyage is as follows:

[0028] In the formula, C Fj is the CO2 emission factor of LNG and fuel, D is the sailing distance of the ship, FC j is the total consumption of LNG and fuel of the dredger, m 土方量 is the soil volume of a single voyage, and j is the fuel type;

[0029] The calculation formula of the carbon intensity index CII of a single voyage is as follows:

[0030] In the formula, W is the transport work, and the calculation formula is as follows: W=soil volume x D.

[0031] Preferably, in the dredging operation energy efficiency auxiliary decision module, the recommended dredging operation parameters include but are not limited to dredging speed, mud pump speed, high-pressure water flushing pump speed, rake lip angle, overflow cylinder height and optimal loading time;

[0032] The algorithm execution is divided into two steps:

[0033] Firstly, by establishing a LSTM neural network based on time series data driving, a Wanfang soil energy consumption prediction model based on big data online learning is realized, and construction parameters and Wanfang soil energy consumption are taken as input and output;

[0034] Secondly, according to the particle swarm optimization algorithm and the fitness function, the prediction model is inversely calculated to obtain the best construction parameter combination under a specific external environment, and the energy efficiency auxiliary decision based on the Wanfang soil energy consumption optimization is realized.

[0035] Preferably, the intelligent speed optimization module comprises:

[0036] The dispatch speed optimization unit is used for constructing a ship speed-energy efficiency model based on data driving according to the energy efficiency and speed history data in the process of heavy load navigation and light load navigation, and then solving the theoretical speed point of the model with the optimal energy efficiency according to the optimization algorithm, and automatically calculating the predicted fuel consumption and predicted arrival time under the remaining navigation distance;

[0037] The dredging construction speed optimization unit is used for constructing a speed optimization model based on the Wanfang soil energy consumption optimization as the target according to the construction and energy efficiency data, and then calculating the best speed by the optimization algorithm with the Wanfang soil energy consumption as the optimization target.

[0038] Preferably, the energy efficiency data management module comprises:

[0039] The database is used for storing various data of the LNG dual-fuel drag suction dredger, including but not limited to energy efficiency data and index data;

[0040] The data query unit is used for automatically querying historical data according to the ship name, voyage or time period;

[0041] The report generation unit is used for forming an energy efficiency report according to the queried energy efficiency data and index data, and exporting data in the form of a.csv file.

[0042] The second LNG dual-fuel power drag suction dredger intelligent energy efficiency management method comprises:

[0043] Step S1, the energy efficiency key factor parameters of the main energy consumption equipment of the LNG dual-fuel drag suction dredger are collected to form a unified LNG drag suction dredger energy efficiency data dictionary;

[0044] Step S2, a working condition recognition model, an energy efficiency index calculation model, a carbon emission calculation model, an energy efficiency level model and a periodic performance analysis model are constructed according to the LNG drag suction dredger energy efficiency data dictionary to form an energy efficiency analysis and evaluation system;

[0045] Step S3, according to the soil type, design depth, construction parameters and historical data of fuel consumption, a soil energy consumption prediction model is constructed, and through an optimization algorithm, dredging operation parameter combinations are recommended;

[0046] Step S4, the speed during the dispatching process and the dredging operation process is optimized;

[0047] Step S5, data management is performed on the LNG dual-fuel drag suction dredger, and energy efficiency data and report data are queried, exported and data reported.

[0048] Preferably, in step S1, the energy efficiency key factor parameters include but are not limited to LNG consumption, oil consumption, power, fuel switching state, and ship floating state, dredging operation and water environment;

[0049] The step S2 includes:

[0050] Step S21, according to the process requirements of the drag suction dredger, the construction parameters are identified through the constructed working condition identification model, and then various working states are identified, wherein the working states include dredging, dumping, sailing, blowing shore and other five states;

[0051] Step S22, according to the energy efficiency index calculation model, the energy efficiency index of the voyage and the energy efficiency data of the LNG drag suction dredger are automatically calculated;

[0052] Step S23, according to the carbon emission calculation model, the energy efficiency operation index EEOI and the ship carbon intensity index CII of the LNG drag suction dredger are calculated;

[0053] Step S24, according to the energy efficiency level model, the overall energy consumption level of the dredging operation is evaluated, the energy consumption level of the dredging operation is displayed, and the evaluation is based on the soil energy consumption;

[0054] Step S25, according to the periodic performance analysis model, the energy consumption of single dredging cycle, dredging and dumping cycle, dredging and blowing cycle, sailing cycle is calculated, the energy efficiency index of each cycle is automatically analyzed and calculated according to the dredging ship cycle, time range, the key factors affecting energy consumption are calculated through the Pearson correlation coefficient algorithm, and finally the energy efficiency report is formed;

[0055] In step S3, the recommended dredging operation parameters include but are not limited to dredging speed, mud pump speed, high-pressure water jet pump speed, rake lip angle, overflow cylinder height and optimal loading time;

[0056] The algorithm execution is divided into two steps:

[0057] Firstly, a LSTM neural network based on time series data driving is established, a soil energy consumption prediction model based on big data online learning is realized, and construction parameters and soil energy consumption are taken as input and output;

[0058] Secondly, according to the particle swarm optimization algorithm and the fitness function, the prediction model is inversely calculated to obtain the optimal construction parameter combination under a specific external environment, and the energy efficiency auxiliary decision based on the optimal energy consumption of the ten thousand square meters of soil is realized;

[0059] In the step S4, the ship speed-energy efficiency model based on data driving is constructed according to the energy efficiency and the speed history data in the process of heavy load navigation and light load navigation, and the theoretical speed point of the model energy efficiency optimization is solved according to the optimization algorithm, and the predicted fuel consumption and the predicted arrival time under the remaining navigation mileage are automatically calculated;

[0060] According to the construction and energy efficiency data, the speed optimization model based on the optimal energy consumption of the ten thousand square meters of soil is constructed, and then the optimal speed of the ten thousand square meters of soil is calculated by the optimization algorithm.

[0061] The beneficial effects of the present application are: the present application is aimed at the particularity of the LNG dual-fuel drag suction dredger power equipment, through the monitoring of the energy consumption data of the main engine, auxiliary engine, hot water heater and other main energy consumption equipment, such as LNG, fuel oil, ignition oil, and the monitoring of the parameters of ship navigation, water environment, construction parameters and the like, the comprehensive energy efficiency, emission, ten thousand square meters of soil energy consumption and the like are calculated in real time, the energy efficiency system database is established, the multi-working condition of the dredger is analyzed and evaluated, the auxiliary decision suggestion of the dredging operation equipment operation parameter is provided, the intelligent energy efficiency management system of the LNG dual-fuel drag suction dredger is completed, the energy efficiency is improved, and the effective technical means for the energy saving and emission reduction of the dredger is provided. BRIEF DESCRIPTION OF DRAWINGS

[0062] Fig. 1 is a module diagram of an intelligent energy efficiency management system of an LNG dual-fuel power drag suction dredger according to the present application;

[0063] Fig. 2 is a specific module diagram of an intelligent energy efficiency analysis and evaluation module in the present application;

[0064] Fig. 3 is a specific module diagram of a speed intelligent optimization module in the present application;

[0065] Fig. 4 is a specific module diagram of an energy efficiency data management module in the present application;

[0066] Fig. 5 is a flow chart of an intelligent energy efficiency management method of an LNG dual-fuel power drag suction dredger according to the present application;

[0067] Fig. 6 is a specific flow chart of forming an energy efficiency analysis and evaluation system in the present application. DETAILED DESCRIPTION

[0068] The technical solutions of the present application will be described clearly and completely in combination with the drawings. In the description of the present application, it should be noted that the orientations or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", "third" are only for descriptive purposes and cannot be understood as indicating or implying opposite importance.

[0069] The present application will be further described in combination with the drawings.

[0070] As shown in FIG. 1, an intelligent energy efficiency management system of an LNG dual-fuel trailing suction dredger includes 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.

[0071] The LNG energy efficiency online intelligent monitoring module 1 is used to collect energy efficiency key factor parameters of main energy consumption equipment of the LNG dual-fuel trailing suction dredger, and form a unified LNG trailing suction dredger energy efficiency data dictionary.

[0072] In the embodiment, the energy efficiency key factor parameters include but are not limited to LNG consumption, oil consumption, power, fuel switching state, and ship floating state, dredging construction operation, and water environment.

[0073] Specific data includes:

[0074] Inlet / outlet flow, power, fuel temperature, pressure fuel, LNG and fuel / oil ignition fuel switching state of main energy consumption equipment (main engine, auxiliary diesel engine, boiler);

[0075] 400V and 220V main power equipment power parameters;

[0076] Working state of main dredging equipment (mud pump, high-pressure water flushing pump, side thrust, etc.), including power, pressure, temperature, speed, etc. parameters;

[0077] Speed, power, pitch, etc. parameters of propulsion equipment;

[0078] Environmental parameters such as wind direction, wind power, water flow, dredging soil quality, etc.

[0079] Construction parameters such as speed, pitch, ship draft, earthwork volume, rake head depth, rake head angle to ground, overflow cylinder height, etc.

[0080] The intelligent energy efficiency analysis and evaluation module 2 is used for constructing a working condition recognition model, an energy efficiency index calculation model, a carbon emission calculation model, an energy efficiency level model and a periodic performance efficiency analysis model according to an LNG drag suction dredger energy efficiency data dictionary, and forming an energy efficiency analysis and evaluation system.

[0081] As shown in FIG. 2, the intelligent energy efficiency analysis and evaluation module 2 comprises a working condition recognition unit 21, an energy efficiency index calculation unit 22, a carbon emission calculation unit 23, an energy efficiency level unit 24 and a periodic performance efficiency analysis unit 25.

[0082] The working condition recognition unit 21 is used for recognizing construction parameters through the constructed working condition recognition model according to the process requirements of the drag suction dredger, and then identifying various working conditions, wherein the working conditions are divided into five conditions of dredging, mud throwing, sailing, blowing shore and others.

[0083] The energy efficiency index calculation unit 22 is used for automatically calculating energy efficiency indexes of a voyage and the LNG drag suction dredger energy efficiency data according to the energy efficiency index calculation model.

[0084] In the embodiment, the energy efficiency indexes include but are not limited to real-time energy consumption, working condition energy consumption, unit time energy consumption, unit nautical mile energy consumption and ten-thousand-square-meter soil energy consumption, wherein,

[0085] The real-time energy consumption mainly includes real-time fuel consumptions of the main engine, auxiliary engine and hot water heater;

[0086] The working condition energy consumption is the fuel consumptions and time lengths of LNG, oil consumption and ignition oil of the five working conditions of dredging, mud throwing, sailing, blowing shore and others recognized by the working condition recognition unit 21;

[0087] The unit time energy consumption M total is the ratio of the total energy consumption M 总 of a single voyage and the total operation time T 总 , and the calculation formula is as follows:

[0088] The unit nautical mile energy consumption M n refers to the energy consumption M total of the ship per nautical mile distance in the sailing stage, and the calculation formula is as follows:

[0089] The ten-thousand-square-meter soil energy consumption W d refers to the LNG or fuel consumption when the excavated soil volume is 10,000 square meters, and the calculation formula is as follows:

[0090] The carbon emission calculation unit 23 is used for calculating the energy efficiency operation index EEOI and the ship carbon intensity index CII of the LNG drag suction dredger according to the carbon emission calculation model.

[0091] In the embodiment,

[0092] The calculation formula of the energy efficiency operation index EEOI of a ship is:

[0093] In the formula, C Fj is the CO2 emission factor of LNG and fuel oil, D is the voyage distance of the ship, FC j is the total amount of LNG and fuel oil consumption of the dredger, m 土方量 is the earthwork volume of a single ship, and j is the fuel type.

[0094] The calculation formula of the carbon intensity index CII of a single ship is:

[0095] In the formula, W is the work done for transportation, and the calculation formula is as follows: W = earthwork volume x D.

[0096] The energy efficiency level unit 24 is configured to evaluate the overall energy consumption level of the dredging operation according to an energy efficiency level model, display the energy consumption level of the dredging operation, and the evaluation basis is the energy consumption per million cubic meters of earthwork.

[0097] The periodic performance efficiency analysis unit 25 is configured to calculate the energy consumption of the dredging period, the dredging and dumping period, the dredging and blowing period, and the voyage period according to a periodic performance efficiency analysis model, automatically analyze and calculate the energy efficiency index of each period according to the ship, time range, calculate the key factors affecting the energy consumption through the Pearson correlation coefficient algorithm, and finally form an energy efficiency report.

[0098] The dredging operation energy efficiency auxiliary decision module 3 is configured to construct a million cubic meters of earthwork energy consumption prediction model according to the soil type, design dredging depth, construction parameters, and historical data of fuel consumption, and recommend a dredging construction operation parameter combination through an optimization algorithm.

[0099] In the embodiment, the recommended dredging construction operation parameters include but are not limited to dredging speed, mud pump speed, high-pressure water flushing pump speed, rake lip angle, overflow cylinder height, and optimal loading time.

[0100] The algorithm execution is divided into two steps:

[0101] First, a LSTM neural network based on time series data is established to realize a million cubic meters of earthwork energy consumption prediction model based on big data online learning, and construction parameters and million cubic meters of earthwork energy consumption are taken as input and output.

[0102] Second, the prediction model is calculated inversely according to the particle swarm optimization algorithm and the fitness function to obtain the best construction parameter combination under a specific external environment, and the energy efficiency auxiliary decision is realized based on the optimal million cubic meters of earthwork energy consumption.

[0103] The speed intelligent optimization module 4 is configured to optimize the speed during the dispatching process and the dredging construction operation process.

[0104] As shown in FIG. 3, the intelligent speed optimization module 4 includes a dispatch speed optimization unit 41 and a dredging construction speed optimization unit 42.

[0105] The dispatch speed optimization unit 41 is configured 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 to automatically calculate the predicted fuel consumption and predicted arrival time under the remaining navigation distance according to the optimal solution of the model energy efficiency.

[0106] The dredging construction speed optimization unit 42 is configured to construct a speed optimization model based on the construction and energy efficiency data, and then calculate the optimal speed by optimizing the energy consumption of cubic meters of soil.

[0107] The energy efficiency data management module 5 is configured to store and manage various types of data of the LNG dual-fuel drag suction dredger.

[0108] As shown in FIG. 4, the energy efficiency data management module 5 includes a database 51, a data query unit 52 and a report generation unit 53.

[0109] The database 51 is configured to store various types of data of the LNG dual-fuel drag suction dredger, including but not limited to energy efficiency data and index data.

[0110] The data query unit 52 is configured to automatically query historical data according to the ship name, voyage or time period.

[0111] The report generation unit 53 is configured to form an energy efficiency report according to the queried energy efficiency data and index data, and to export data in.csv format.

[0112] As shown in FIG. 5, an intelligent energy efficiency management method for an LNG dual-fuel drag suction dredger includes:

[0113] Step S1, collect energy efficiency key factor parameters of main energy consumption equipment of the LNG dual-fuel drag suction dredger to form a unified LNG drag suction dredger energy efficiency data dictionary.

[0114] In the embodiment, the energy efficiency key factor parameters include but are not limited to LNG consumption, oil consumption, power, fuel switching state, and ship floating state, dredging construction operation and water environment.

[0115] Step S2, construct a working condition recognition model, an energy efficiency index calculation model, a carbon emission calculation model, an energy efficiency level model and a periodic performance analysis model according to the LNG drag suction dredger energy efficiency data dictionary to form an energy efficiency analysis and evaluation system.

[0116] As shown in FIG. 6, step S2 specifically includes:

[0117] Step S21, according to the process requirements of the cutter suction dredger, the construction parameters are identified through the constructed working condition identification model, and then various working conditions are identified, wherein the working conditions include dredging, mud throwing, sailing, blowing shore and other five conditions.

[0118] Step S22, according to the energy efficiency index calculation model, the energy efficiency index of the voyage and the LNG cutter suction dredger is automatically calculated.

[0119] Step S23, according to the carbon emission calculation model, the energy efficiency operation index EEOI and the ship carbon intensity index CII of the LNG cutter suction dredger are calculated.

[0120] Step S24, according to the energy efficiency level model, the overall energy consumption level of dredging operation is evaluated, and the energy consumption level of dredging operation is displayed. The evaluation basis is ten thousand tons of soil energy consumption.

[0121] Step S25, according to the periodic performance analysis model, the energy consumption of single ship dredging period, dredging and throwing period, dredging and blowing period, sailing period is calculated, and the energy efficiency index of each period is automatically analyzed and calculated according to the ship trip, time range of the dredger. The key factors affecting energy consumption are calculated by Pearson correlation coefficient algorithm, and finally the energy efficiency report is formed.

[0122] Step S3, according to the soil type, design dredging depth, construction parameters and historical data of fuel consumption, a ten thousand tons of soil energy consumption prediction model is constructed, and through optimization algorithm, the dredging construction parameter combination is recommended.

[0123] In the embodiment, the recommended dredging construction parameters include but are not limited to dredging speed, mud pump speed, high pressure water pump speed, rake lip angle, overflow cylinder height and optimal loading time;

[0124] The algorithm execution is divided into two steps:

[0125] Firstly, through the establishment of LSTM neural network based on time series data driving, the ten thousand tons of soil energy consumption prediction model based on big data online learning is realized, and the construction parameters and ten thousand tons of soil energy consumption are taken as input and output;

[0126] Secondly, according to the particle swarm optimization algorithm and the fitness function, the prediction model is calculated inversely to obtain the best construction parameter combination under the specific external environment, and the energy efficiency auxiliary decision based on the optimal ten thousand tons of soil energy consumption is realized.

[0127] Step S4, the sailing speed in the dispatching process and the dredging construction process is optimized.

[0128] In the embodiments, a ship speed-energy efficiency model based on data driving is constructed according to the historical data of energy efficiency and speed during heavy load navigation and light load navigation, and a theoretical speed point with optimal energy efficiency of the model is solved according to an optimization algorithm, so as to automatically calculate the predicted fuel consumption and predicted arrival time under the remaining navigation distance;

[0129] A speed optimization model based on the optimal energy consumption of tons of oil is constructed according to the construction and energy efficiency data, and then the optimal speed with the optimal energy consumption of tons of oil as the optimization target is calculated through the optimization algorithm.

[0130] In step S5, the data management is performed on the LNG dual-fuel drag suction dredger, and the energy efficiency data and report data are queried, exported and reported.

[0131] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit the present application; although the present application is described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the above embodiments can be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent energy efficiency management system for an LNG dual-fuel trailing suction hopper dredger, characterized in that, The application relates to an intelligent energy efficiency monitoring system for a LNG dual-fuel cutter suction dredger. The system comprises: an LNG energy efficiency online intelligent monitoring module for collecting energy efficiency key factor parameters of main energy consumption equipment of the LNG dual-fuel cutter suction dredger, and forming a unified LNG cutter suction dredger energy efficiency data dictionary; an intelligent energy efficiency analysis and evaluation module for constructing a working condition recognition model, an energy efficiency index calculation model, a carbon emission calculation model, an energy efficiency level model and a periodic performance efficiency analysis model according to the LNG cutter suction dredger energy efficiency data dictionary, and forming an energy efficiency analysis and evaluation system; a dredging operation energy efficiency auxiliary decision module for constructing a cubic meter of soil energy consumption prediction model according to soil type, design dredging depth, construction parameters and historical fuel consumption data, and recommending dredging operation parameter combinations through an optimization algorithm; a speed intelligent optimization module for optimizing the speed during the dispatching process and the dredging operation process; 2. The intelligent energy efficiency management system for an LNG dual-fuel dredger as claimed in claim 1, characterized in that, an energy efficiency data management module for storing and managing various data of the LNG dual-fuel cutter suction dredger.

3. The intelligent energy efficiency management system for an LNG dual-fuel tractor dredger according to claim 1, characterized in that, In the LNG energy efficiency online intelligent monitoring module, the energy efficiency key factor parameters include but are not limited to LNG consumption, oil consumption, power, fuel switching state and ship floating state, dredging operation and water environment. The intelligent energy efficiency analysis and evaluation module comprises: a working condition recognition unit for recognizing construction parameters through the constructed working condition recognition model according to the process requirements of the cutter suction dredger, and then identifying various working conditions, wherein the working conditions are divided into five conditions, i.e. dredging, mud throwing, sailing, blowing and other conditions; an energy efficiency index calculation unit for automatically calculating the energy efficiency index of a voyage and the energy efficiency data of the LNG cutter suction dredger according to the energy efficiency index calculation model; a carbon emission calculation unit for calculating the energy efficiency operation index EEOI and the ship carbon intensity index CII of the LNG cutter suction dredger according to the carbon emission calculation model; an energy efficiency level unit for evaluating the overall energy consumption level of the dredging operation according to the energy efficiency level model, displaying the dredging operation energy consumption level and evaluating the cubic meter of soil energy consumption; 4. The intelligent energy efficiency management system of the LNG dual-fuel tractor dredger according to claim 3, characterized in that, a periodic performance efficiency analysis unit for calculating the energy consumption of a single ship cycle, a dredging and throwing cycle, a dredging and blowing cycle and a sailing cycle according to the periodic performance efficiency analysis model, automatically analyzing and calculating the energy efficiency index of each cycle according to the ship cycle and time range of the cutter suction dredger, calculating the key factors affecting the energy consumption through a Pearson correlation coefficient algorithm and finally forming an energy efficiency report. 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, unit time energy consumption, unit nautical mile energy consumption and cubic meter of soil energy consumption, wherein the real-time energy consumption mainly includes the real-time fuel consumption of the main engine, auxiliary engine and hot water heater; Unit time energy consumption M total The calculation of the total energy consumption M 总 and the total operation time T 总 The ratio is as follows: Unit energy consumption in nautical miles M n Refers to the energy consumption M of the ship per nautical mile distance during the navigation phase total The calculation formula is as follows: W d The amount of LNG or fuel consumed when excavating 10,000 cubic meters of earth is referred to as the energy consumption of 10,000 cubic meters of earth, and the calculation formula is as follows:

5. The intelligent energy efficiency management system for an LNG dual-fuel tractor dredger according to claim 3, characterized in that, the working condition energy consumption is the fuel consumption amount and time of LNG, oil consumption and ignition oil for the five working conditions, i.e. dredging, mud throwing, sailing, blowing and other conditions, which are recognized by the working condition recognition unit; The formula for calculating the energy efficiency operational index EEOI of a ship is: where C Fj is the CO2 emission factor of LNG and fuel oil, D is the voyage distance of the ship, FC j is the total consumption of LNG and fuel oil of the dredger, m 土方量 is the earthwork volume of a single voyage, j is the fuel type; The formula for calculating the carbon intensity indicator CII for a ship voyage is: in the carbon emission calculation unit, wherein W is the transportation work, and the calculation formula is as follows:

6. The intelligent energy efficiency management system for an LNG dual-fuel dredger as claimed in claim 1, characterized in that, W = cubic meter of soil * D. In the dredging operation energy efficiency auxiliary decision module, the recommended dredging operation parameters include but are not limited to dredging speed, mud pump speed, high-pressure water flushing pump speed, rake lip angle, overflow cylinder height and optimal loading time; the algorithm execution is divided into two steps: Firstly, by establishing a LSTM neural network based on time series data, a Wanfang soil energy consumption prediction model based on online learning of big data is realized, taking construction parameters and Wanfang soil energy consumption as input and output; Secondly, according to the particle swarm optimization algorithm and the fitness function, the prediction model is inverted to obtain the best combination of construction parameters under specific external environment, and the energy efficiency auxiliary decision is realized based on the optimal Wanfang soil energy consumption.

7. The intelligent energy efficiency management system for an LNG-dual-fuel tractor dredger according to claim 1, characterized in that, The intelligent speed optimization module comprises: The dispatch speed optimization unit is used for constructing a ship speed-energy efficiency model based on data driving according to the energy efficiency and speed history data in the process of heavy load navigation and light load navigation, and then solving the optimal theoretical speed point of the model according to the optimization algorithm to automatically calculate the predicted fuel consumption and predicted arrival time under the remaining navigation distance; The dredging construction speed optimization unit is used for constructing a speed optimization model based on the optimal Wanfang soil energy consumption as the target according to the construction and energy efficiency data, and then calculating the best speed by the optimization algorithm with Wanfang soil energy consumption as the optimization target.

8. The intelligent energy efficiency management system for an LNG dual-fuel tractor dredger according to claim 1, characterized in that, The energy efficiency data management module comprises: The database is used for storing various data of the LNG dual-fuel drag suction dredger, including but not limited to energy efficiency data and index data; The data query unit is used for automatically querying historical data according to the ship name, voyage or time period; The report generation unit is used for forming an energy efficiency report according to the queried energy efficiency data and index data, and exporting data in the form of a.csv file.

9. An intelligent energy efficiency management method for an LNG dual-fuel trailing suction dredger, characterized in that, It comprises: Step S1, collecting the energy efficiency key factor parameters of the main energy consumption equipment of the LNG dual-fuel drag suction dredger to form a unified LNG drag suction dredger energy efficiency data dictionary; Step S2, 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 performance analysis model according to the LNG drag suction dredger energy efficiency data dictionary to form an energy efficiency analysis and evaluation system; Step S3, constructing a Wanfang soil energy consumption prediction model according to the historical data of soil type, design dredging depth, construction parameters and fuel consumption, and recommending the dredging operation parameter combination through the optimization algorithm; Step S4, optimizing the speed during the dispatch process and the dredging operation process; Step S5, managing the data of the LNG dual-fuel drag suction dredger, and querying, exporting and reporting the energy efficiency data and report data.

10. The intelligent energy efficiency management method for an LNG dual-fuel tractor dredger according to claim 9, characterized in that, In the step S1, the energy efficiency key 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; The step S2 comprises: Step S21, identifying the construction parameters through the constructed working condition recognition model according to the process requirements of the drag suction dredger, and then identifying various working states, wherein the working states include five states of dredging, dumping, navigation, blowing shore and others; Step S22, automatically calculating the energy efficiency index of the voyage and the LNG drag suction dredger 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 drag suction dredger according to the carbon emission calculation model; Step S24, the total energy consumption level of the dredging operation is evaluated according to the energy efficiency level model, and the energy consumption level of the dredging operation is displayed, and the evaluation basis is the energy consumption of ten thousand tons of soil; Step S25, according to the periodic performance analysis model, the energy consumption of single ship cycle, dredging cycle, dredging and blowing cycle, sailing cycle is counted, the energy efficiency index of each cycle is automatically analyzed and calculated according to the ship cycle, time range of dredger, the key factors affecting energy consumption are calculated through Pearson correlation coefficient algorithm, and finally the energy efficiency report is formed; In step S3, the recommended dredging construction operation parameters include but are not limited to dredging speed, mud pump speed, high-pressure water pump speed, rake lip angle, overflow cylinder height and optimal loading time; The algorithm execution is divided into two steps: Firstly, by establishing a LSTM neural network based on time series data driving, a big data online learning based ten-thousand-ton soil energy consumption prediction model is realized, and the construction parameters and ten-thousand-ton soil energy consumption are taken as input and output; Secondly, according to the particle swarm optimization algorithm and the fitness function, the prediction model is calculated inversely, the best construction parameter combination under the specific external environment is obtained, and the energy efficiency auxiliary decision based on the ten-thousand-ton soil energy consumption optimization is realized; In step S4, according to the energy efficiency and sailing speed historical data in the process of heavy load sailing and light load sailing, a ship speed-energy efficiency model based on data driving is constructed, and then the optimal theoretical sailing speed point of the model is solved according to the optimization algorithm, and the predicted fuel consumption and predicted arrival time under the remaining sailing distance are automatically calculated; According to the construction and energy efficiency data, a speed optimization model based on the ten-thousand-ton soil energy consumption optimization is constructed, and then the best speed with the ten-thousand-ton soil energy consumption as the optimization target is calculated through the optimization algorithm.

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