Intelligent evaluation method and system for energy efficiency level of wind power installation ship

Abstract

The invention discloses an intelligent evaluation method and system for the energy efficiency level of a wind power installation ship, and relates to the technical field of evaluation of the energy efficiency level of the wind power installation ship, and the method comprises the steps: carrying out the preprocessing of real-time multi-source data, and obtaining the processed multi-source data; constructing an initial energy efficiency level evaluation model, and performing calculation and analysis to obtain a key energy efficiency index; obtaining a real-time energy efficiency evaluation result based on the key energy efficiency index and the environmental meteorological prediction boundary analysis calculation; inputting the initial energy efficiency level evaluation model for analysis and verification to obtain a target energy efficiency level evaluation model; the ship operation situation is intelligently analyzed, and a ship energy efficiency optimization strategy is obtained; and intelligently analyzing the key energy efficiency index and the real-time energy efficiency evaluation result to obtain an initial ship energy efficiency level quantitative evaluation result, and inputting the initial ship energy efficiency level quantitative evaluation result to a target energy efficiency level evaluation model for verification to obtain an optimized ship energy efficiency level quantitative evaluation result. According to the method, energy efficiency strategy intelligent analysis is sensed through real-time data, and multi-dimension, evaluation and optimization of ship energy efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of energy efficiency evaluation technology for wind turbine installation vessels, and more specifically, to an intelligent evaluation method and system for the energy efficiency of wind turbine installation vessels. Background Technology

[0002] With the global shipping industry increasingly focused on energy consumption, improving ship energy efficiency and reducing energy waste has become a critical issue that urgently needs to be addressed. Particularly in the operation of specialized vessels such as wind turbine installation ships, accurately assessing and optimizing ship energy efficiency in complex marine environments to avoid unnecessary resource waste is crucial for improving the ship's economic and environmental performance. Traditional ship energy efficiency assessment methods mainly rely on static models and empirical rules, typically making preliminary estimates based on only a few parameters such as speed and load. While these methods can provide some initial energy efficiency assessments, they are ill-suited to the complexity and dynamic changes of actual operating environments.

[0003] Traditional methods rely on static models that cannot cope with dynamic changes in environmental conditions and struggle to reflect the impact of factors such as weather and ocean currents on ship energy efficiency in real time. Without real-time external meteorological data, traditional models cannot accurately predict energy efficiency for different sea states, wind speeds, and other factors, resulting in poor accuracy in energy efficiency assessments and failing to provide truly valuable decision support for crew members or dispatchers.

[0004] Furthermore, as ships operate over long periods, changes in environmental factors and the ship's own condition can lead to the accumulation of assessment errors. The lack of adaptive correction capabilities affects the model's effectiveness. Traditional methods typically lack real-time data acquisition and feedback mechanisms, making it difficult for crew and management to understand energy efficiency status promptly and to make quick adjustments when problems arise, resulting in suboptimal energy efficiency during ship operation.

[0005] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0006] To address the problems in related technologies, this invention proposes an intelligent evaluation method and system for the energy efficiency level of wind power installation vessels, in order to overcome the aforementioned technical problems existing in the existing related technologies.

[0007] Therefore, the specific technical solution adopted by the present invention is as follows: Firstly, this invention proposes an intelligent evaluation method for the energy efficiency level of wind power installation vessels, including: S1. Real-time multi-source data is acquired based on various sensors on the wind turbine installation vessel, and the real-time multi-source data is preprocessed to obtain processed multi-source data; S2. Construct an initial energy efficiency level assessment model, input the processed multi-source data into the initial energy efficiency level assessment model for calculation and analysis, and obtain key energy efficiency indicators; S3. Based on key energy efficiency indicators and environmental meteorological prediction boundaries, perform analysis and calculation to obtain real-time energy efficiency assessment results; and input the real-time energy efficiency assessment results into the initial energy efficiency level assessment model for analysis and verification to obtain the target energy efficiency level assessment model. S4. Using the target energy efficiency level assessment model, intelligent analysis of the ship's operating status is conducted to obtain ship energy efficiency optimization strategies. S5. Utilize ship energy efficiency optimization strategies to perform intelligent analysis on key energy efficiency indicators and real-time energy efficiency assessment results to obtain initial quantitative assessment results of ship energy efficiency levels; and input the initial quantitative assessment results of ship energy efficiency levels into the target energy efficiency level assessment model for evaluation and verification to obtain optimized quantitative assessment results of ship energy efficiency levels.

[0008] Furthermore, based on the various sensors on the wind turbine installation vessel, real-time multi-source data is acquired, and the real-time multi-source data is preprocessed to obtain the processed multi-source data, including: S11. Based on various types of sensors deployed on wind turbine installation vessels, collect real-time multi-source data covering the entire process of ship operation; S12. The collected real-time data is transmitted to the data management platform for aggregation and processing via the ship's internal network; S13. Perform format parsing, missing data completion, outlier detection and removal, and duplicate data removal and cleaning operations on the real-time multi-source data after aggregation processing to obtain the cleaned data. S14. Perform format unification, standardization conversion, and time alignment on the cleaned data to obtain processed multi-source data.

[0009] Furthermore, an initial energy efficiency assessment model was constructed, and the processed multi-source data was input into the initial energy efficiency assessment model for calculation and analysis, resulting in key energy efficiency indicators including: S21. Based on the operational characteristics of wind turbine installation vessels, ship design drawings, and rated performance parameters of main engines, define a key energy efficiency indicator system that needs to be evaluated. S22. Construct an initial energy efficiency level assessment model based on a key energy efficiency indicator system; S23. Calculate the processed multi-source data using the initial energy efficiency level assessment model to obtain key energy efficiency indicators.

[0010] Furthermore, the processed multi-source data were calculated using the initial energy efficiency level assessment model to obtain key energy efficiency indicators, including: S231. Based on the processed multi-source data, select data segments that meet the definition of the baseline working condition, and group data with similar environmental conditions. S232. Use a data correction mechanism to correct the grouped multi-source data in real time to eliminate the influence of random interference and obtain the actual energy efficiency intermediate results. S233. Use an aggregation algorithm to aggregate intermediate energy efficiency results according to time windows, generate key energy efficiency indicators for evaluation and decision-making, and output them to the database and visualization platform.

[0011] Furthermore, based on key energy efficiency indicators and environmental meteorological prediction boundaries, analysis and calculations are performed to obtain real-time energy efficiency assessment results. These results are then input into the initial energy efficiency level assessment model for analysis and verification, resulting in the target energy efficiency level assessment model, which includes: S31. Obtain forecast meteorological data using an external meteorological data interface, and use the forecast meteorological data as boundary conditions to input into the initial energy efficiency level assessment model for scenario analysis to obtain real-time energy efficiency assessment results. S32. The initial energy efficiency level assessment model is modified using an adaptive correction mechanism to obtain the modified initial energy efficiency level assessment model. S33. The modified initial energy efficiency level assessment model is iteratively optimized based on the real-time energy efficiency assessment results to obtain the target energy efficiency level assessment model.

[0012] Furthermore, the initial energy efficiency assessment model is modified using an adaptive correction mechanism, resulting in the modified initial energy efficiency assessment model, which includes: S321. Obtain forecast meteorological data using an external meteorological data interface, and simultaneously collect historical and real-time operational data from the database. Clean, align, and convert the forecast meteorological data and the historical and real-time operational data. S322. Input the processed predicted meteorological data and historical and real-time operational data into the initial energy efficiency level assessment model to obtain simulation results; S323. Compare and analyze the simulation results with the measured data, and use the calibration algorithm to automatically adjust the key parameters in the initial energy efficiency level assessment model to generate a corrected initial energy efficiency level assessment model with real characteristics.

[0013] Furthermore, the processed predicted meteorological data and historical and real-time operational data are input into the initial energy efficiency level assessment model, and the simulation result formula is obtained as follows: ; In the formula, F Fuel consumption is expressed through the nonlinear relationship between speed, load, and environmental factors; P Expresses the ship's power output; ; In the formula, F Expressing fuel consumption β 0 represents a constant term. β 1, β 2 and β 3 represents the regression coefficient. S Indicates speed, L Indicates load, W Indicates meteorological factors, This indicates the error term.

[0014] Furthermore, by utilizing the target energy efficiency level assessment model, intelligent analysis of the ship's operational status is conducted, resulting in ship energy efficiency optimization strategies, including: S41. Collect and integrate environmental data, navigation data, and main engine and auxiliary engine operation data in real time to obtain the ship's operational status; S42. Input the ship's operational status into the intelligent optimization algorithm in the trained target energy efficiency level assessment model to perform multi-objective optimization and obtain the optimized ship's operational status. S43. Based on the optimized ship operation status, the ship energy efficiency optimization strategy is obtained; S44. Ship energy efficiency optimization strategies include speed adjustment, route selection, and load optimization, and the ship energy efficiency optimization strategies are adaptively adjusted through an automated platform.

[0015] Furthermore, intelligent analysis is performed on key energy efficiency indicators and real-time energy efficiency assessment results using ship energy efficiency optimization strategies to obtain initial quantitative assessment results of ship energy efficiency levels. These initial quantitative assessment results are then input into the target energy efficiency level assessment model for evaluation and verification, resulting in optimized quantitative assessment results of ship energy efficiency levels, including: S51. Utilize ship energy efficiency optimization strategies to jointly analyze key energy efficiency indicators and real-time energy efficiency assessment results. The intelligent analysis process simulates different operating scenarios to obtain the initial quantitative assessment results of ship energy efficiency levels. S52. Input the initial quantitative assessment results of ship energy efficiency level into the target energy efficiency level assessment model for evaluation and verification, and obtain the optimized quantitative assessment results of ship energy efficiency level. S53. Using interactive energy efficiency analysis tools, based on the optimized quantitative assessment results of ship energy efficiency level, simulate the energy efficiency performance under different operating conditions, and view the optimized quantitative assessment results of ship energy efficiency level in various scenarios in real time.

[0016] Secondly, the present invention also provides an intelligent evaluation system for the energy efficiency level of a wind turbine installation vessel, comprising: The data processing module is used to acquire real-time multi-source data based on various sensors on the wind turbine installation vessel, and to preprocess the real-time multi-source data to obtain processed multi-source data. The model calculation module is used to construct an initial energy efficiency level assessment model and input the processed multi-source data into the initial energy efficiency level assessment model for calculation and analysis to obtain key energy efficiency indicators. The model optimization module is used to perform analysis and calculation based on key energy efficiency indicators and environmental meteorological prediction boundaries to obtain real-time energy efficiency assessment results; and input the real-time energy efficiency assessment results into the initial energy efficiency level assessment model for analysis and verification to obtain the target energy efficiency level assessment model. The strategy analysis module is used to intelligently analyze the ship's operational status using the target energy efficiency level assessment model, and obtain ship energy efficiency optimization strategies. The evaluation result optimization module is used to intelligently analyze key energy efficiency indicators and real-time energy efficiency evaluation results using ship energy efficiency optimization strategies to obtain initial quantitative evaluation results of ship energy efficiency level; and input the initial quantitative evaluation results of ship energy efficiency level into the target energy efficiency level evaluation model for evaluation and verification to obtain optimized quantitative evaluation results of ship energy efficiency level.

[0017] The beneficial effects of this invention are as follows: 1) This invention establishes an end-to-end intelligent analysis process from real-time data perception to energy efficiency strategy generation, thereby realizing multi-dimensional and refined evaluation and optimization of ship energy efficiency in complex navigation environments. This enables comprehensive modeling of complex environmental factors such as wind, electricity, and current, and improves the adaptability of the energy efficiency assessment model to actual operating conditions.

[0018] 2) This invention constructs a closed-loop feedback mechanism that includes strategy simulation verification, thereby ensuring that each optimization strategy undergoes rigorous performance evaluation and risk filtering, significantly improving the reliability and executability of the strategies. Furthermore, by integrating external weather forecasts and real-time flight data, it enables forward-looking prediction and pre-optimization of energy efficiency requirements for future flight segments, elevating energy efficiency management from passive response to proactive planning.

[0019] 3) This invention dynamically adjusts the initial energy efficiency assessment model through an adaptive correction mechanism, which solves the problem that traditional static models cannot cope with changes in the environment and ship operating conditions during actual operation, thereby ensuring the real-time performance and accuracy of energy efficiency assessment. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of an intelligent evaluation method for the energy efficiency level of a wind power installation vessel according to an embodiment of the present invention; Figure 2 This is a schematic diagram of an intelligent evaluation system for the energy efficiency level of a wind power installation vessel according to an embodiment of the present invention. Detailed Implementation

[0022] To further illustrate the various embodiments, the present invention provides accompanying drawings, which are part of the disclosure of the present invention. These drawings are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these drawings, those skilled in the art should be able to understand other possible implementation methods and the advantages of the present invention.

[0023] According to an embodiment of the present invention, a method and system for intelligent evaluation of the energy efficiency level of a wind power installation vessel are proposed.

[0024] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 1 As shown, an intelligent evaluation method for the energy efficiency level of a wind turbine installation vessel according to an embodiment of the present invention includes: Step S1: Acquire real-time multi-source data based on various sensors on the wind turbine installation vessel, and preprocess the real-time multi-source data to obtain processed multi-source data; Step S2: Construct an initial energy efficiency level assessment model, and input the processed multi-source data into the initial energy efficiency level assessment model for calculation and analysis to obtain key energy efficiency indicators; Step S3: Based on key energy efficiency indicators and environmental meteorological prediction boundaries, perform analysis and calculation to obtain real-time energy efficiency assessment results; and input the real-time energy efficiency assessment results into the initial energy efficiency level assessment model for analysis and verification to obtain the target energy efficiency level assessment model. Step S4: Using the target energy efficiency level assessment model, intelligent analysis of the ship's operating status is conducted to obtain ship energy efficiency optimization strategies; Step S5: Utilize ship energy efficiency optimization strategies to perform intelligent analysis on key energy efficiency indicators and real-time energy efficiency assessment results to obtain initial quantitative assessment results of ship energy efficiency levels; input the initial quantitative assessment results of ship energy efficiency levels into the target energy efficiency level assessment model for evaluation and verification to obtain optimized quantitative assessment results of ship energy efficiency levels.

[0025] In this optional embodiment, real-time multi-source data is acquired based on various sensors on the wind turbine installation vessel, and the real-time multi-source data is preprocessed to obtain processed multi-source data, including: S11. Based on various types of sensors deployed on wind turbine installation vessels, collect real-time multi-source data covering the entire process of ship operation; S12. The collected real-time multi-source data is transmitted to the data management platform for aggregation and processing via the ship's internal network; S13. Perform format parsing, missing data completion, outlier detection and removal, and duplicate data removal and cleaning operations on the real-time multi-source raw data after aggregation processing to obtain the cleaned data. S14. Perform format unification, standardization conversion, and time alignment on the cleaned data to obtain processed multi-source data.

[0026] Specifically, a sensor network deployed throughout the ship collects real-time data from over a hundred measuring points throughout the entire ship's operation, including key parameters such as latitude and longitude, speed and heading, wind speed and direction, main engine speed, instantaneous fuel flow, and generator power. The shipbuilding Ethernet network transmits data from each subsystem to the central data management platform in real time. The aggregated raw data undergoes comprehensive cleaning, using linear interpolation to fill in missing data caused by signal interruptions, employing anomaly detection algorithms to identify and remove outliers significantly exceeding reasonable ranges, and eliminating duplicate data records caused by network retransmissions. The cleaned, valid data is then standardized, including unifying unit systems, normalizing numerical data, and strictly aligning all monitoring parameters to a unified timestamp. Finally, after format unification, standardization conversion, and time alignment, the processed multi-source data is obtained.

[0027] In this optional embodiment, an initial energy efficiency level assessment model is constructed, and the processed multi-source data is input into the initial energy efficiency level assessment model for calculation and analysis to obtain key energy efficiency indicators, including: S21. Based on the operational characteristics of wind turbine installation vessels, ship design drawings, and rated performance parameters of main engines, define a key energy efficiency indicator system that needs to be evaluated. S22. Construct an initial energy efficiency level assessment model based on a key energy efficiency indicator system; S23. Calculate the processed multi-source data using the initial energy efficiency level assessment model to obtain key energy efficiency indicators.

[0028] In this optional embodiment, the processed multi-source data is calculated using an initial energy efficiency level assessment model to obtain key energy efficiency indicators, including: S231. Based on the processed multi-source data, select data segments that meet the definition of the baseline working condition, and group data with similar environmental conditions. S232. Use a data correction mechanism to correct the grouped multi-source data in real time to eliminate the influence of random interference and obtain the actual energy efficiency intermediate results. S233. Use an aggregation algorithm to aggregate intermediate energy efficiency results according to time windows, generate key energy efficiency indicators for evaluation and decision-making, and output them to the database and visualization platform.

[0029] Specifically, based on the operational characteristics and design parameters of wind turbine installation vessels, an evaluation system is established that includes key indicators such as energy efficiency operation index, unit operating energy consumption, and main engine energy efficiency ratio. Simultaneously, an initial evaluation model integrating ship hydrodynamic principles and equipment performance curves is constructed. The preprocessed multi-source data undergoes benchmark condition screening, extracting standard operational data segments for wind speed and wave height. Random interference is eliminated using a moving average algorithm to obtain median energy consumption values ​​for each operating condition. Using an hourly time window, a weighted aggregation algorithm is employed to generate key energy efficiency indicators, including average energy efficiency operation, which are then stored in real-time in a database and pushed to a visualization platform, providing a reliable basis for subsequent energy efficiency optimization decisions.

[0030] In this optional embodiment, based on key energy efficiency indicators and environmental meteorological prediction boundaries, analysis and calculation are performed to obtain real-time energy efficiency assessment results; these results are then input into an initial energy efficiency level assessment model for analysis and verification to obtain a target energy efficiency level assessment model, including: S31. Obtain forecast meteorological data using an external meteorological data interface, and use the forecast meteorological data as boundary conditions to input into the initial energy efficiency level assessment model for scenario analysis to obtain real-time energy efficiency assessment results. S32. The initial energy efficiency level assessment model is modified using an adaptive correction mechanism to obtain the modified initial energy efficiency level assessment model. S33. The modified initial energy efficiency level assessment model is iteratively optimized based on the real-time energy efficiency assessment results to obtain the target energy efficiency level assessment model.

[0031] In this optional embodiment, the initial energy efficiency level assessment model is modified using an adaptive correction mechanism to obtain the modified initial energy efficiency level assessment model, which includes: S321. Obtain forecast meteorological data using an external meteorological data interface, and simultaneously collect historical and real-time operational data from the database. Clean, align, and convert the forecast meteorological data and the historical and real-time operational data. S322. Input the processed predicted meteorological data and historical and real-time operational data into the initial energy efficiency level assessment model to obtain simulation results; S323. Compare and analyze the simulation results with the measured data, and use the calibration algorithm to automatically adjust the key parameters in the initial energy efficiency level assessment model to generate a corrected initial energy efficiency level assessment model with real characteristics.

[0032] Specifically, the system acquires future meteorological data, including environmental factors such as wind speed, ocean currents, and temperature, through an external meteorological data interface. This data is then used as boundary conditions input into the initial energy efficiency assessment model for scenario analysis, yielding preliminary real-time energy efficiency assessment results. For example, based on meteorological forecast data, the system derives key energy efficiency indicators such as fuel consumption, speed, and load for a ship at a given moment. Building upon this, the system utilizes an adaptive correction mechanism to refine the initial energy efficiency assessment model, enabling it to more accurately reflect the ship's energy efficiency level based on real-time data and meteorological forecasts.

[0033] Specifically, the system acquires forecasted meteorological data from an external meteorological data interface and simultaneously collects historical and real-time ship operation data from a database. The collected historical and real-time ship operation data and forecasted meteorological data are cleaned, aligned, and format-converted to ensure accuracy and consistency. This data is then input into an initial energy efficiency assessment model for simulation calculations, yielding preliminary energy efficiency assessment results, such as fuel consumption, speed, and operational efficiency. The simulation results are compared with actual measured data. The system automatically adjusts key parameters in the model using a calibration algorithm to eliminate discrepancies between model predictions and measured data, generating a revised initial energy efficiency assessment model that better reflects reality. Based on the revised model, the system iteratively optimizes using real-time energy efficiency assessment results. By continuously inputting new data feedback, the system gradually adjusts the model's parameters and algorithms, enabling the model to adapt to different meteorological conditions and ship operating conditions. This generates a more accurate target energy efficiency assessment model, providing real-time energy efficiency assessment results for ships under various meteorological conditions, and providing a scientific basis for optimizing ship operations. For example, an optimized model can determine a ship's optimal fuel consumption and energy efficiency ratio at a given moment and make dynamic adjustments based on real-time data, providing intuitive energy efficiency information to help adjust operational strategies in real time to achieve optimal energy efficiency.

[0034] In this optional embodiment, the processed predicted meteorological data and historical and real-time operational data are input into the initial energy efficiency level assessment model, and the simulation result formula is obtained as follows: ; In the formula, F Fuel consumption is expressed through the nonlinear relationship between speed, load, and environmental factors; P Expresses the ship's power output; ; In the formula, FExpressing fuel consumption β 0 represents a constant term. β 1, β 2 and β 3 represents the regression coefficient. S Indicates speed, L Indicates load, W Indicates meteorological factors, This indicates the error term.

[0035] In this optional embodiment, the target energy efficiency level assessment model is used to intelligently analyze the ship's operational status, resulting in ship energy efficiency optimization strategies, including: S41. Collect and integrate environmental data, navigation data, and main engine and auxiliary engine operation data in real time to obtain the ship's operational status; S42. Input the ship's operational status into the intelligent optimization algorithm in the trained target energy efficiency level assessment model to perform multi-objective optimization and obtain the optimized ship's operational status. S43. Based on the optimized ship operation status, the ship energy efficiency optimization strategy is obtained; S44. Ship energy efficiency optimization strategies include speed adjustment, route selection, and load optimization, and the ship energy efficiency optimization strategies are adaptively adjusted through an automated platform.

[0036] Specifically, the system aggregates and integrates multi-source data from meteorological sensors, navigation equipment, and the power system in real time to construct a comprehensive understanding of the current ship's operating environment, including navigation status, environmental conditions, and equipment operating conditions. This operational status is input into a fully trained target energy efficiency assessment model. The model's intelligent optimization algorithm simultaneously calculates multiple objectives, including energy efficiency, operational efficiency, and safety requirements. Through complex multi-objective optimization calculations, it generates the optimal combination of operating parameters while ensuring operational safety and timeliness. Based on the optimization calculation results, the system automatically generates actionable energy efficiency optimization schemes, encompassing improvements in multiple dimensions, such as speed optimization, economical route selection, and rational allocation of power equipment load. Through an integrated automated control platform, the optimization strategy can be dynamically adjusted based on actual operational feedback, forming a self-adaptive continuous optimization mechanism to ensure the ship maintains optimal energy efficiency under different operating conditions.

[0037] In this optional embodiment, a ship energy efficiency optimization strategy is used to intelligently analyze key energy efficiency indicators and real-time energy efficiency assessment results to obtain an initial quantitative assessment result of the ship's energy efficiency level. This initial quantitative assessment result is then input into a target energy efficiency level assessment model for evaluation and verification, resulting in an optimized quantitative assessment result of the ship's energy efficiency level, including: S51. Utilize ship energy efficiency optimization strategies to jointly analyze key energy efficiency indicators and real-time energy efficiency assessment results. The intelligent analysis process simulates different operating scenarios to obtain the initial quantitative assessment results of ship energy efficiency levels. S52. Input the initial quantitative assessment results of ship energy efficiency level into the target energy efficiency level assessment model for evaluation and verification, and obtain the optimized quantitative assessment results of ship energy efficiency level. S53. Using interactive energy efficiency analysis tools, based on the optimized quantitative assessment results of ship energy efficiency level, simulate the energy efficiency performance under different operating conditions, and view the optimized quantitative assessment results of ship energy efficiency level in various scenarios in real time.

[0038] Specifically, based on ship energy efficiency optimization strategies and combining key energy efficiency indicators with real-time energy efficiency assessment results, initial quantitative assessment results of ship energy efficiency levels are obtained through multi-scenario simulation analysis. These initial assessment results are then input into a validated target energy efficiency level assessment model. The model's built-in validation algorithm performs credibility analysis and accuracy correction on the initial results, generating optimized quantitative assessment results of ship energy efficiency levels. Finally, an interactive energy efficiency analysis tool is used to simulate energy efficiency performance under different sea states, loads, and other operational conditions, displaying the optimized quantitative assessment results in real time to ensure higher accuracy in the final optimized quantitative assessment results of ship energy efficiency levels.

[0039] like Figure 2 As shown, according to another embodiment of the present invention, a smart evaluation system for the energy efficiency level of a wind turbine installation vessel is also provided, comprising: Data processing module 201 is used to acquire real-time multi-source data based on various sensors on the wind power installation vessel, and to preprocess the real-time multi-source data to obtain processed multi-source data. The model calculation module 202 is used to construct an initial energy efficiency level assessment model. The processed multi-source data is input into the initial energy efficiency level assessment model for calculation and analysis to obtain key energy efficiency indicators. The model optimization module 203 is used to perform analysis and calculation based on key energy efficiency indicators and environmental meteorological prediction boundaries to obtain real-time energy efficiency assessment results; and input the real-time energy efficiency assessment results into the initial energy efficiency level assessment model for analysis and verification to obtain the target energy efficiency level assessment model. The strategy analysis module 204 is used to intelligently analyze the ship's operating status using the target energy efficiency level assessment model to obtain ship energy efficiency optimization strategies. The evaluation result optimization module 205 is used to intelligently analyze key energy efficiency indicators and real-time energy efficiency evaluation results using ship energy efficiency optimization strategies to obtain initial quantitative evaluation results of ship energy efficiency level; and inputs the initial quantitative evaluation results of ship energy efficiency level into the target energy efficiency level evaluation model for evaluation and verification to obtain optimized quantitative evaluation results of ship energy efficiency level.

[0040] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A wind power installation ship energy efficiency level intelligent evaluation method, characterized in that, The method comprises the following steps: S1, acquiring real-time multi-source data based on various sensors of a wind power installation ship, and preprocessing the real-time multi-source data to obtain processed multi-source data; S2, constructing an initial energy efficiency level evaluation model, inputting the processed multi-source data into the initial energy efficiency level evaluation model for calculation and analysis, and obtaining key energy efficiency indicators; S3, based on the key energy efficiency indicators and the environmental meteorological prediction boundary, performing analysis and calculation to obtain real-time energy efficiency evaluation results; inputting the real-time energy efficiency evaluation results into the initial energy efficiency level evaluation model for analysis and verification to obtain a target energy efficiency level evaluation model; S4, using the target energy efficiency level evaluation model to intelligently analyze the ship operation situation to obtain a ship energy efficiency optimization strategy; S5, using the ship energy efficiency optimization strategy to intelligently analyze the key energy efficiency indicators and the real-time energy efficiency evaluation results to obtain an initial ship energy efficiency level quantitative evaluation result; and inputting the initial ship energy efficiency level quantitative evaluation result into the target energy efficiency level evaluation model for evaluation and verification to obtain an optimized ship energy efficiency level quantitative evaluation result.

2. The intelligent evaluation method for energy efficiency level of a wind power installation vessel according to claim 1, characterized in that, The method comprises the following steps: S11, based on the various types of sensors deployed on the wind power installation ship, collecting real-time multi-source data covering the entire ship operation process; S12, transmitting the collected real-time multi-source data to a data management platform through a ship internal network for aggregation processing; S13, performing format analysis, missing data completion, outlier detection and elimination, and repeated data removal and cleaning operations on the aggregated real-time multi-source data to obtain cleaned data; S14, performing format unification, standardization conversion and time alignment processing on the cleaned data to obtain processed multi-source data.

3. The method according to claim 1, characterized in that, The method comprises the following steps: S21, based on the operation characteristics of the wind power installation ship, the ship design drawings and the main engine rated performance parameters, and defining a key energy efficiency indicator system to be evaluated; S22, constructing an initial energy efficiency level evaluation model based on the key energy efficiency indicator system; S23, using the initial energy efficiency level evaluation model to calculate the processed multi-source data to obtain key energy efficiency indicators.

4. The intelligent evaluation method for energy efficiency level of a wind power installation vessel according to claim 3, characterized in that, The method comprises the following steps: S231, based on the processed multi-source data, screening out data segments that meet the definition of the reference working condition, and grouping data with similar environmental conditions; S232, using a data correction mechanism to correct the grouped multi-source data in real time to eliminate the influence of non-random interference, and obtaining energy efficiency intermediate results of the actual situation; S233, using an aggregation algorithm to aggregate the energy efficiency intermediate results according to a time window to generate key energy efficiency indicators for evaluation and decision making, and outputting to a database and a visualization platform.

5. The intelligent evaluation method for energy efficiency level of a wind power installation vessel according to claim 1, characterized in that, The method comprises the following steps: And input the real-time energy efficiency evaluation result into the initial energy efficiency level evaluation model for analysis and verification to obtain a target energy efficiency level evaluation model, including: S31, obtaining predicted meteorological data by using an external meteorological data interface, and inputting the predicted meteorological data as a boundary condition into the initial energy efficiency level evaluation model for scene analysis to obtain a real-time energy efficiency evaluation result; S32, correcting the initial energy efficiency level evaluation model by using an adaptive correction mechanism to obtain a corrected initial energy efficiency level evaluation model; S33, iteratively optimizing the corrected initial energy efficiency level evaluation model according to the real-time energy efficiency evaluation result to obtain a target energy efficiency level evaluation model.

6. The intelligent evaluation method for energy efficiency level of a wind power installation vessel according to claim 5, characterized in that, The adaptive correction mechanism includes: S321, obtaining predicted meteorological data by using an external meteorological data interface, and collecting historical and real-time operation data from a database, and performing cleaning, alignment and format conversion processing on the predicted meteorological data and the historical and real-time operation data; S322, inputting the processed predicted meteorological data and the historical and real-time operation data into the initial energy efficiency level evaluation model to obtain a simulation result; S323, comparing and analyzing the simulation result with measured data, and automatically adjusting key parameters in the initial energy efficiency level evaluation model by using a calibration algorithm to generate a corrected initial energy efficiency level evaluation model with real characteristics.

7. The intelligent evaluation method for energy efficiency level of a wind power installation vessel according to claim 6, characterized in that, The formula for inputting the processed predicted meteorological data and the historical and real-time operation data into the initial energy efficiency level evaluation model to obtain a simulation result is: ； In the formula, F expressing the fuel consumption, obtained by a non-linear relationship of the speed, the load and the environmental factors; P expressing the power output of the ship; ； wherein F expressing fuel consumption, β 0 represents a constant term, β 1, β 2 and β 3 represent regression coefficients, S represents a speed, L represents a load, W represents a meteorological factor, represents an error term. 8.The intelligent evaluation method for energy efficiency level of a wind power installation vessel according to claim 1, characterized in that, The target energy efficiency level evaluation model is used to intelligently analyze a ship operation situation to obtain a ship energy efficiency optimization strategy, including: S41, obtaining a ship operation situation by real-time collection and fusion of environmental data, navigation data and main engine and auxiliary engine operation data; S42, inputting the ship operation situation into an intelligent optimization algorithm in the trained target energy efficiency level evaluation model for multi-objective optimization to obtain an optimized ship operation situation; S43, obtaining a ship energy efficiency optimization strategy according to the optimized ship operation situation; S44, the ship energy efficiency optimization strategy includes speed adjustment, route selection and load optimization, and the ship energy efficiency optimization strategy is adaptively adjusted by an automatic platform.

9. The intelligent evaluation method for energy efficiency level of a wind power installation vessel according to claim 1, characterized in that, The ship energy efficiency optimization strategy is used to intelligently analyze key energy efficiency indicators and real-time energy efficiency evaluation results to obtain an initial ship energy efficiency level quantitative evaluation result; And input the initial ship energy efficiency level quantitative evaluation result into the target energy efficiency level evaluation model for evaluation and verification to obtain an optimized ship energy efficiency level quantitative evaluation result, including: S51, jointly analyzing key energy efficiency indicators and real-time energy efficiency evaluation results by using the ship energy efficiency optimization strategy, and simulating different operation scenarios in an intelligent analysis process to obtain an initial ship energy efficiency level quantitative evaluation result; S52, inputting the initial ship energy efficiency level quantitative evaluation result into the target energy efficiency level evaluation model for evaluation and verification to obtain an optimized ship energy efficiency level quantitative evaluation result; S53, using the interactive energy efficiency analysis tool, simulating the energy efficiency performance under different operation conditions according to the optimized ship energy efficiency level quantitative evaluation result parameters, and real-time viewing the ship energy efficiency level quantitative evaluation result of the ship under various situations.

10. An intelligent evaluation system for energy efficiency level of a wind power installation vessel, for implementing the intelligent evaluation method for energy efficiency level of a wind power installation vessel according to any one of claims 1-9, characterized in that, Comprise: a data processing module, configured to acquire real-time multi-source data based on various sensors of a wind power installation ship, and pre-process the real-time multi-source data to obtain processed multi-source data; a model calculation module, configured to construct an initial energy efficiency level evaluation model, and input the processed multi-source data into the initial energy efficiency level evaluation model for calculation and analysis to obtain key energy efficiency indicators; a model optimization module, configured to perform analysis and calculation based on the key energy efficiency indicators and an environmental meteorological prediction boundary to obtain real-time energy efficiency evaluation results; input the real-time energy efficiency evaluation results into the initial energy efficiency level evaluation model for analysis and verification to obtain a target energy efficiency level evaluation model; a strategy analysis module, configured to use the target energy efficiency level evaluation model to intelligently analyze a ship operation situation to obtain a ship energy efficiency optimization strategy; an evaluation result optimization module, configured to use the ship energy efficiency optimization strategy to intelligently analyze the key energy efficiency indicators and the real-time energy efficiency evaluation results to obtain an initial ship energy efficiency level quantitative evaluation result; and input the initial ship energy efficiency level quantitative evaluation result into the target energy efficiency level evaluation model for evaluation and verification to obtain an optimized ship energy efficiency level quantitative evaluation result.

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