A green ship energy efficiency and emission collaborative optimization method and system

Abstract

The application discloses a kind of green ship energy efficiency and emission collaborative optimization method and system, it is related to green ship energy efficiency management technical field, including, in ship engine deployment sensor collection engine signal calculation engine power and fuel flow rate, synchronous acquisition ship navigation data divides navigation section space grid, calculate the fuel consumption and emission of navigation section and space grid;According to ship navigation data, construct navigation mechanism model and calibrate model parameters by least square method, select monitoring site to obtain observation data and map into emission observation matrix and construct uncertainty matrix, use positive definite matrix factor decomposition to solve source contribution matrix and source component spectrum matrix, calculate ship source contribution concentration, based on spatial convolution model, obtain ship source contribution space field.The application realizes from emission source to receptor response again to the closed-loop optimization of ship operation strategy, provides high-precision ship contribution space field and optimization optimal navigation instruction, significantly improves ship navigation energy efficiency.

Description

Technical Field

[0001] This invention relates to the field of green ship energy efficiency management technology, and in particular to a method and system for synergistic optimization of green ship energy efficiency and emissions. Background Technology

[0002] With the International Maritime Organization (IMO) successively issuing mandatory standards such as the Energy Efficiency Design Index (EEDI), the Existing Ship Energy Efficiency Index (EEXI), and the Carbon Intensity Index (CII), green shipping and low-carbon ship operation have become the core development direction of the shipping and maritime industry. Regarding the control of energy consumption and pollutant emissions, the industry has developed several technological approaches: one focuses on improving the energy efficiency of main engines, auxiliary engines, and propulsion systems, such as main engine performance monitoring, speed optimization, and hull form and propeller matching optimization; another focuses on emission inventories and atmospheric environmental impact assessments, constructing regional emission inventories using AIS-based ship activity data and emission factors, and combining numerical models or empirical models to assess the impact on air quality in coastal and port areas. The development of these technologies has provided a certain foundation for green ship operation, but overall it still mainly remains at the relatively separate level of "energy efficiency management" and "emission assessment". It has limited characterization of the spatial differences in emissions and the real contribution to the shore-based atmospheric environment. Traditional emission inventories mostly rely on static emission factors and activity data, lacking deep coupling with real-time sensor signals of ship engines and power-fuel mechanism models, and cannot effectively reflect the fine differences in emission intensity under different operating conditions and control strategies. Summary of the Invention

[0003] In view of the aforementioned existing problems, the present invention is proposed.

[0004] Therefore, this invention provides a method and system for synergistic optimization of energy efficiency and emissions of green ships, which solves the problem that existing technologies rely heavily on static emission factors and activity data, lack deep coupling with real-time sensor signals of ship engines and power-fuel mechanism models, and cannot effectively reflect the fine differences in emission intensity under different operating conditions and control strategies.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for synergistic optimization of energy efficiency and emissions of green ships, comprising, Sensors are deployed on ship engines to collect engine signals, calculate engine power and fuel flow rate, and simultaneously acquire ship navigation data to divide the navigation segment into a spatial grid, and calculate the fuel consumption and emissions of the navigation segment and the spatial grid. Based on ship navigation data, a navigation mechanism model is constructed and the model parameters are calibrated by the least squares method. Monitoring stations are selected to obtain observation data, which is mapped to the emission observation matrix and an uncertainty matrix is ​​constructed. Positive definite matrix factorization is used to solve the source contribution matrix and the source component spectrum matrix, the ship source contribution concentration is calculated, and the ship source contribution spatial field is obtained based on the spatial convolution model. Decision variables are defined based on ship navigation data, and a segment operating condition vector is constructed. The spatially sensitive weighted emission index is calculated by normalizing the spatial field of ship source contribution. An energy efficiency-emission integrated objective function is constructed and navigation constraints are added. The optimal decision variables are output by numerical solution through SQP.

[0006] As a preferred embodiment of the green ship energy efficiency and emission synergistic optimization method described in this invention, the step of deploying sensors on the ship's engine to collect engine signals and calculate engine power and fuel flow rate, simultaneously acquiring ship navigation data to divide the navigation segment into a spatial grid, and calculating the fuel consumption and emissions of the navigation segment and the spatial grid refers to deploying high-precision sensors on the ship's main engine and auxiliary engines to calculate the instantaneous effective power of the main engine and the instantaneous effective power of the auxiliary engine. The total fuel flow rate is calculated by combining the fuel flow rates of the ship's main engine and auxiliary engines; The system reads ship navigation data via GPS, including route, latitude and longitude, ground speed and heading angle, obtains ship draft based on the draft gauge, obtains relative wind speed through the anemometer, and collects ship cargo weight. Collect ship flue gas monitoring data to read the concentration of pollutant p and flue gas volume flow rate to estimate pollutant emissions; The route is divided into Y segments according to spatial distance, and a spatial grid is divided using regular latitude and longitude to calculate instantaneous fuel consumption; The fuel consumption for each leg of the journey is calculated by summing the time taken by the ship at each leg. With emissions Simultaneously calculate the ship's fuel consumption in each grid. With emissions .

[0007] As a preferred embodiment of the green ship energy efficiency and emission synergistic optimization method of the present invention, wherein: the step of constructing a navigation mechanism model based on ship navigation data and calibrating the model parameters by the least squares method refers to constructing a propulsion-resistance mechanism model based on ship navigation data, including a still water resistance model and a wind resistance model; A total fuel flow rate model was constructed based on the ship manufacturer's SFOC curve; Simultaneously construct a model of pollutant stationary emission factors; Construct an objective function by combining the total fuel flow rate model and the pollutant stationary emission factor model. ; Minimize the objective function using the least squares method. The parameters of the navigation mechanism model are output to form an optimized navigation mechanism model.

[0008] As a preferred embodiment of the green ship energy efficiency and emission synergistic optimization method described in this invention, the following steps are taken: selecting monitoring stations to obtain observation data, mapping it to an emission observation matrix and constructing an uncertainty matrix, using positive definite matrix factorization to solve the source contribution matrix and source component spectrum matrix, calculating the ship source contribution concentration refers to selecting J environmental monitoring stations in the ship's navigation area to monitor pollutant concentrations, defining the monitoring time as T, the number of monitoring samples as N, and mapping the station-time two-dimensional index to a one-dimensional sample index i; The concentrations of monitored pollutants are mapped to the emission observation matrix X based on the sample index; Construct an uncertainty matrix U for each observation in the emissions observation matrix; The observation matrix is ​​decomposed by positive definite matrix factorization; Define the objective function and solve for the source contribution matrix and source component spectrum matrix using the least squares method; The objective function is solved using the least squares method, and the decomposed source contribution matrix is ​​output. Source component spectral matrix ; The ship label score is defined for each source by the source component spectrum matrix obtained through positive definite matrix factorization. ; The source with the highest ship mark score is selected as the ship source factor. The corresponding source contribution concentration is extracted through the source contribution matrix. Contribution concentration from ship sources And calculate the average ship source contribution concentration within the monitoring time T.

[0009] As a preferred embodiment of the green ship energy efficiency and emission synergistic optimization method described in this invention, wherein: the spatial field of ship source contribution obtained based on the spatial convolution model refers to selecting L virtual nodes within the ship's navigation area. Define the Gaussian kernel function; Constructing the space field of ship source contribution using Gaussian kernel weighted superposition .

[0010] As a preferred embodiment of the green ship energy efficiency and emission synergistic optimization method described in this invention, the step of defining decision variables based on ship navigation data and constructing a segment operating condition vector, and normalizing the spatial field of ship source contribution to calculate the spatially sensitive weighted emission index, refers to defining decision variables for segment i based on ship navigation data, including segment speed. and control vector ; Constructing the segment working condition vector ; Input the segment operation vector into the navigation mechanism model to obtain the total fuel flow rate. and pollutant emissions Calculate the total fuel consumption of the route; Calculate the spatially sensitive weights and further calculate the spatially sensitive weighted emission index H.

[0011] As a preferred embodiment of the green ship energy efficiency and emission synergistic optimization method of the present invention, wherein: the construction of the energy efficiency-emission integrated objective function and the addition of navigation constraints, and the output of the optimal decision variable through SQP numerical solution, refers to the construction of the energy efficiency-emission integrated objective function B based on the total fuel consumption of the route and the space-sensitive weighted emission index; The optimization objective is defined as minimizing the energy efficiency-emissions integrated objective function B, and constraints are added to the optimization process, including speed constraints, regional emission regulations constraints, and flight time constraints. The objective function is solved by SQP. All decision variables are arranged into a high-dimensional vector in sequence and an initial solution is selected to start the SQP iteration to construct a quadratic programming problem. After solving the quadratic programming problem, the optimal decision variables are output and the optimal decision variables are output to form the ship navigation control command.

[0012] Secondly, this invention provides a green ship energy efficiency and emission synergistic optimization system, comprising, The ship data acquisition module is used to deploy sensors on the ship's engine to collect engine signals, calculate engine power and fuel flow rate, simultaneously acquire ship navigation data to divide the navigation segment into a spatial grid, and calculate the fuel consumption and emissions of the navigation segment and the spatial grid. The pollution contribution analysis module is used to construct a navigation mechanism model based on ship navigation data and calibrate the model parameters using the least squares method. It selects monitoring stations to obtain observation data, maps it to an emission observation matrix, and constructs an uncertainty matrix. It uses positive definite matrix factorization to solve the source contribution matrix and source component spectrum matrix, calculates the ship source contribution concentration, and obtains the ship source contribution spatial field based on the spatial convolution model. The energy efficiency and emission optimization module defines decision variables based on ship navigation data and constructs a segment operating condition vector. It normalizes the spatial field of ship source contribution to calculate spatially sensitive weighted emission indicators, constructs an energy efficiency-emission integrated objective function and adds navigation constraints, and outputs the optimal decision variables through SQP numerical solution.

[0013] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the green ship energy efficiency and emission synergistic optimization method as described in the first aspect of the present invention.

[0014] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the green ship energy efficiency and emission synergistic optimization method as described in the first aspect of the present invention.

[0015] The beneficial effects of this invention are as follows: This invention calculates power and fuel flow rate by deploying sensors on ship engines to collect signals, simultaneously acquires navigation data to divide the route into segments and spatial grids, and accurately calculates fuel consumption and emissions; further, it constructs a ship source contribution spatial field based on a navigation mechanism model calibrated by the least squares method, positive definite matrix factorization, and spatial convolution model, and on this basis defines a spatially sensitive weighted emission index, constructs an energy efficiency-emission integrated objective function, and outputs the optimal decision variables under navigation constraints through SQP, realizing closed-loop optimization and control from emission source to receptor response and back to ship operation strategy, providing a high-precision ship contribution spatial field and optimized optimal navigation commands, significantly improving ship navigation energy efficiency. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0017] Figure 1 This is a flowchart of the green ship energy efficiency and emission synergistic optimization method in Example 1.

[0018] Figure 2 This is a structural diagram of the green ship energy efficiency and emission synergistic optimization system in Example 1. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0022] Example 1, referring to Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides a method for synergistic optimization of energy efficiency and emissions of green ships, including the following steps: S1. Deploy sensors on the ship's engine to collect engine signals, calculate engine power and fuel flow rate, simultaneously acquire ship navigation data to divide the navigation segment into a spatial grid, and calculate the fuel consumption and emissions of the navigation segment and the spatial grid. Specifically, sensors are deployed on ship engines to collect engine signals and calculate engine power and fuel flow rate. Simultaneously, ship navigation data is acquired to divide the navigation segment into a spatial grid, calculating fuel consumption and emissions for each segment and grid. High-precision sensors, including speed, torque, and flow sensors, are deployed on the ship's main engine and auxiliary engines to calculate instantaneous main engine effective power and instantaneous auxiliary engine effective power. in and For the speed of the main unit and auxiliary unit, and For the torque of the main unit and auxiliary unit, and The instantaneous effective power of the main unit and the instantaneous effective power of the auxiliary unit. For time; The total fuel flow rate is calculated by combining the fuel flow rates of the main engine and auxiliary engines of the ship. in For total fuel flow rate, For the main engine fuel flow rate, For the number of auxiliary machines, Let be the fuel flow rate of the j-th auxiliary machine; The system reads ship navigation data via GPS, including route, latitude and longitude, ground speed and heading angle, obtains ship draft based on the draft gauge, obtains relative wind speed through the anemometer, and collects ship cargo weight. Collect ship flue gas monitoring data to read the concentration of pollutants p (SO2, NO2, PM, etc.) and flue gas volumetric flow rate to estimate pollutant emissions: in The concentration of pollutant p, The flue gas volumetric flow rate, For unit and temperature / pressure conversion factors; The flight route is divided into Y segments based on spatial distance, and a spatial grid is created using regular latitude and longitude coordinates. Instantaneous fuel consumption is then calculated. in This refers to instantaneous fuel consumption. For time intervals; The fuel consumption for each leg of the journey is calculated by summing the time taken by the ship at each leg. With emissions Simultaneously calculate the ship's fuel consumption in each grid. With emissions .

[0023] High-precision sensors for speed, torque, and flow rate are simultaneously deployed on the main engine and all auxiliary engines. Based on these sensors, the effective power and corresponding fuel flow rate at each moment are calculated, and then superimposed on a unified time axis to obtain the total fuel flow rate. Essentially, this forms a refined dynamic characterization of the ship's propulsion load and auxiliary loads. This concept breaks through the existing practice of relying on nameplate power and average fuel consumption parameters for overall estimation, transforming fuel consumption calculation from "static assumptions" to "actual operating condition measurements." Therefore, on the one hand, it effectively solves the technical problem that traditional methods have long weakened or roughly calculated auxiliary engine loads, leading to a systematic underestimation of fuel consumption and emissions during berthing and standby phases; on the other hand, it provides basic data with high temporal resolution and accuracy for subsequent energy consumption and emission decomposition by segment and spatial grid. Through a flue gas online monitoring system, pollutant concentration and flue gas volumetric flow rate are collected in real time, and a unified temperature and pressure conversion is introduced to transform the monitoring results into pollutant mass emission rates. This invention upgrades the emission estimation process from the traditional "fuel consumption multiplied by an empirical factor" model to a "source concentration measurement-driven" model. This design extends environmental monitoring technology to the emission source, implementing dual constraints of fuel-side and flue gas-side information in the model structure. This effectively overcomes the shortcomings of relying solely on empirical emission factors, which struggle to reflect factors such as fuel quality fluctuations, combustion efficiency changes, and different after-treatment conditions in a timely manner. Especially under complex operating conditions such as frequent load fluctuations or fuel switching, flue gas monitoring can synchronously reflect instantaneous changes in emission intensity, thus ensuring a high degree of consistency between the emission time series and actual operating conditions.

[0024] S2. Construct a navigation mechanism model based on ship navigation data and calibrate the model parameters using the least squares method. Select monitoring stations to obtain observation data, map it into an emission observation matrix, and construct an uncertainty matrix. Use positive definite matrix factorization to solve the source contribution matrix and source component spectrum matrix, calculate the ship source contribution concentration, and obtain the ship source contribution spatial field based on the spatial convolution model. Specifically, a navigation mechanism model is constructed based on ship navigation data, and the model parameters are calibrated using the least squares method. This involves constructing a propulsion-resistance mechanism model based on the ship navigation data, including a hydrostatic resistance model and a wind resistance model. The hydrostatic resistance model is constructed using quadratic-cubic polynomials and is expressed as: in For ship speed, For the ship's draft, and The coefficient of frictional resistance of the hull is determined by the least squares method; The wind resistance model is expressed as: in Relative wind speed, The drag coefficient is determined using the least squares method. The total resistance is constructed using hydrostatic resistance and wind resistance models. : Total resistance Mapped to ship propulsion power demand and mapped to ship output power : in To improve efficiency, To improve the power matching coefficient, it is calibrated using the least squares method; Construct a total fuel flow rate model based on the ship manufacturer's SFOC curve: in The total fuel flow rate is the output of the model, and SFOC is the specific fuel consumption curve provided by the manufacturer. Simultaneously construct a model of pollutant stationary emission factors: in For pollutant emissions, The emission factors of pollutants p (SO2, NO2, PM, etc.) are determined experimentally; Construct an objective function by combining the total fuel flow rate model and the pollutant stationary emission factor model. : in For the fuel component weight, Weights for each pollutant; Minimize the objective function using the least squares method. The optimized navigation mechanism model is formed by outputting navigation mechanism model parameters, including propulsion-drag mechanism model, total fuel flow rate model and pollutant fixed emission factor model.

[0025] In terms of propulsion resistance modeling, this invention employs a hydrostatic resistance polynomial composed of the square and cube of ship speed, and superimposes a wind resistance model based on the square of relative wind speed, identifying parameters through actual navigation data. This approach does not simply apply empirical formulas, but extends the resistance characteristics of traditional test tank phases to the long-term navigation data environment of in-service vessels, recalibrating the resistance coefficients through statistical methods. Since the calibration data covers different drafts, speeds, and wind conditions, the resulting model maintains high prediction accuracy under comprehensive ship operating conditions, thus solving the problem of significant fuel consumption deviations caused by "replacing actual resistance of in-service vessels with test coefficients" in existing technologies. Regarding power mapping and total fuel flow rate construction, this invention derives propulsion power requirements by correlating total resistance with speed and propulsion efficiency, and further uses matching coefficients to establish a linear mapping between propulsion power and actual output power, while embedding the manufacturer's specific fuel consumption curve to construct a total fuel flow rate model. This continuous mapping chain extends from external resistance to fuel flow rate, unifying hydrodynamics, propulsion system performance, and engine room measured power and fuel consumption data within the same framework. In constructing the emission factor model and joint objective function, this invention incorporates both fuel consumption and pollutant emissions into a unified fitting objective. The difference between the total fuel flow rate model output and the sensor-measured fuel flow rate, as well as the difference between the emission factor model output and the emissions monitored in the flue gas duct, are jointly used in parameter optimization. This joint calibration approach ensures that the mechanistic model not only approximates real-world operating conditions in terms of energy balance but also maintains consistency with monitoring data in terms of environmental emissions. This solves the problem in existing technologies where "fuel models and emission models are calibrated independently, leading to difficulties in unifying them." By introducing adjustable weights into the objective function, a trade-off between energy-saving accuracy and emission accuracy can be struck for different application scenarios.

[0026] Furthermore, the observation data obtained from selected monitoring stations are mapped to an emission observation matrix, and an uncertainty matrix is ​​constructed. Positive definite matrix factorization is used to solve for the source contribution matrix and source component spectrum matrix. The ship source contribution concentration is calculated by selecting J environmental monitoring stations in the ship's navigation area to monitor pollutant concentrations. The monitoring time is defined as T, and the number of monitoring samples is N = J * T. The station-time two-dimensional index is mapped to a one-dimensional sample index i. in For site indexing, For time indexing; Based on the sample index, the monitored pollutant concentrations are mapped to the emission observation matrix X: in Let the monitored concentration of pollutant k at the j-th station at time t be denoted as . Let K be the monitoring concentration of pollutant k for the i-th sample, where K is the total number of pollutants. Construct an uncertainty matrix U for each observation in the emissions observation matrix: in Let be the uncertainty of the i-th sample for contaminant k; The uncertainty is determined by an algorithm, including: For each pollutant k, a pre-defined instrument detection limit is given. and relative measurement error ratio For each observation ,like If the signal is significant, then the uncertainty is set as: in The baseline noise standard deviation of the instrument; like If it is, then it is considered a low signal, and the uncertainty is set as: like If no signal is detected, the uncertainty is set as follows: The observation matrix is ​​decomposed using positive definite matrix factorization: in This refers to the number of source types (e.g., shipping sources, industrial sources, transportation sources, etc.). The contribution concentration of the s-th source in the i-th sample. Let be the characteristic spectrum of the s-th source for the k-th pollutant, and force... , ; Define the objective function and solve for the source contribution matrix and source component spectrum matrix using the least squares method: Where Q is the total weighted residual, and the source contribution matrix after decomposition is output by solving the objective function using the least squares method. Source component spectral matrix ; The ship label score is defined for each source by the source component spectrum matrix obtained through positive definite matrix factorization. : in k is the source index, and k is the pollutant index; The source with the highest ship mark score is selected as the ship source factor. The corresponding source contribution concentration is extracted through the source contribution matrix. Contribution concentration from ship sources And calculate the average ship source contribution concentration during the monitoring time T: in The average ship-source contribution concentration at monitoring station j during the monitoring period. Let be the sample index corresponding to site j at time t.

[0027] By mapping a two-dimensional site-time index to a one-dimensional sample index and constructing a unified observation matrix and uncertainty matrix, composite data from all sites and at all times are represented within the same matrix framework. This approach allows subsequent positive definite matrix factorization to be performed directly on the sample dimension, eliminating the need for separate modeling of each site or time period, thus solving the problem of traditional methods struggling to perform unified source analysis across multiple sites and time series. Since each sample carries both spatial location and temporal segment information, the source contributions calculated by the model can be easily back-mapped to specific sites and times, giving the source analysis results inherent spatiotemporal resolution. Through positive definite matrix factorization, the observation matrix is ​​decomposed into a source contribution matrix and a source component spectrum matrix, enabling independent analysis of pollution sources such as ships, industry, and transportation. This crucial step significantly improves the accuracy of source analysis, avoiding the inability of traditional methods to clearly distinguish the contributions of different pollution sources. This method allows for a detailed understanding of the contribution characteristics of each pollution source to different pollutants, while ensuring the physical rationality of the calculation process (e.g., both source contribution and source composition spectra are non-negative). By calculating the concentration of ship source contributions, this invention can accurately identify the impact of ship sources on pollutant concentrations within a specific area. Ship source factors are labeled and scored using the source composition spectrum matrix, and the corresponding source contribution concentrations are extracted from the source contribution matrix, providing a more accurate quantitative basis for ship source emissions. By calculating the average concentration of ship sources over the monitoring period, the pollutant contribution of ships at different times and on different routes can be comprehensively assessed.

[0028] Furthermore, based on the spatial convolution model, the spatial field index of the ship source contribution is obtained by selecting L virtual nodes within the ship's navigation area. Nodes can be evenly distributed within the region or coincide with grid points. Define the Gaussian kernel function: Where r is the spatial distance. The scaling parameter of the kernel function; Constructing the space field of ship source contribution using Gaussian kernel weighted superposition : in The overall mean parameter is obtained by averaging the overall ship contribution concentration. The weight coefficient of the l-th virtual node is... For location.

[0029] By introducing virtual nodes, a relatively regular and controllable spatial basis can be formed across the entire region. Then, existing ship source contribution information is used to infer the weights of each node, resulting in a more balanced and smoother spatial representation within the region. This step provides a structured solution to the technical problem of severe spatial field distortion caused by sparse and uneven distribution of measurement points, laying the foundation for any subsequent field distribution-based analysis. A Gaussian kernel is used as the convolution kernel function to diffuse the influence of virtual nodes into the surrounding continuous space. Compared to simple linear interpolation or inverse distance weighting, the Gaussian kernel is mathematically smooth, continuous, and differentiable at all orders, naturally reflecting the gradual spatial trend of pollutant concentration. An overall mean parameter is explicitly introduced into the spatial field representation, and an offset composed of virtual nodes and their weights is superimposed on top of this. The starting point of this concept is to model the regional average level separately from local spatial differences: the overall mean is responsible for reflecting the background contribution level of ship sources on a large scale, while the combination of node weights is used to characterize local spatial fluctuations and hotspot areas. The spatial field of ship source contributions obtained using a convolutional model can expand the ship source contribution information, which was originally only visible at monitoring points or on a limited grid, into a continuously queryable distribution over the entire region. Thus, when assessing the environmental impact of a port area, waterway, or sensitive nearshore area, it is not necessary to add a large number of monitoring stations; the ship source contribution level at the corresponding location can be directly read through this field function.

[0030] S3. Based on ship navigation data, define decision variables and construct a segment operating condition vector. Normalize the spatial field of ship source contribution to calculate the spatially sensitive weighted emission index. Construct an energy efficiency-emission integrated objective function and add navigation constraints. Output the optimal decision variables through SQP numerical solution. Specifically, decision variables are defined based on ship navigation data, and a segment operating condition vector is constructed. The spatially sensitive weighted emission index is calculated by normalizing the spatial field of ship source contributions. Decision variables are defined for segment i based on ship navigation data, including segment speed. and control vector , Including components It can represent the on / off status of energy efficiency devices, the load distribution ratio between main and auxiliary equipment, etc. Constructing the segment working condition vector : in Let i be the average draft of segment i. Let be the average wind speed for flight segment i; Input the segment operation vector into the navigation mechanism model to obtain the total fuel flow rate. and pollutant emissions Calculate the total fuel consumption of the route: in The travel time for the i-th segment is obtained by dividing the segment distance by the speed. Total fuel consumption for the route; Calculate the spatially sensitive weights and further calculate the spatially sensitive weighted emission index H: in For grid weight coefficients, Spatial sensitive weights, Pollutant emissions for grid g are calculated based on the time a ship spends navigating within the grid.

[0031] By combining segment operating conditions (including speed, draft, wind speed, etc.) with control vectors (such as the on / off status of energy efficiency devices, main and auxiliary engine load distribution, etc.), variables for optimization decisions are defined. The key to this step is that it not only considers traditional ship speed but also incorporates ship energy efficiency control factors into the decision variables. This innovative method can optimize ship fuel consumption and emissions by adjusting the ship's actual operating conditions (such as engine load, wind assistance, etc.). Through the construction of these decision variables, ships can dynamically adjust their operating strategies according to specific circumstances in different segments, thereby maximizing energy efficiency and minimizing emissions while ensuring navigational safety. Especially when facing complex navigation environments (such as complex wind speeds, varying channel depths, etc.), the total fuel flow rate and pollutant emissions for each segment are calculated using a navigation mechanism model, leveraging segment operating condition vectors and decision variables. The beneficial effect of this step is that it can accurately model the ship's fuel consumption and emissions for each segment, avoiding the shortcomings of traditional methods that rely solely on empirical data or simple extrapolations. By accurately calculating fuel flow rates and emissions for different navigation segments, ship operators can precisely assess energy efficiency and pollutant emissions under various conditions and operating conditions, enabling targeted optimization and adjustments. The spatially sensitive weighted emission index, by combining spatial weighting coefficients and pollutant emissions, can generate targeted emission indicators based on the regional characteristics and sensitivity of ship navigation. The innovation of this method lies in its inclusion of spatial factors beyond just ship-sourced emissions, accurately reflecting the intensity of ship emissions' impact on different regions (such as ports and waterways).

[0032] Furthermore, an integrated energy efficiency-emission objective function is constructed and navigation constraints are added. The optimal decision variable is output through SQP numerical solution. The integrated energy efficiency-emission objective function B is constructed based on the total fuel consumption of the route and space-sensitive weighted emission indicators. in The weighting is based on a combination of fuel consumption and emissions. , and It is a dimensionless parameter; The optimization objective is defined as minimizing the energy efficiency-emissions integrated objective function B, and constraints are added to the optimization process, including speed constraints, regional emission regulations constraints, and flight time constraints. The travel time constraint is as follows: in Maximum permissible sailing time; The speed constraint is: in and These are the minimum safe speed and the maximum permissible speed, respectively. The regional emission regulations are as follows: The ECA is a regulatory-bound area. For pollutant emissions in the i-th flight segment, To restrict pollutant emissions in areas subject to regulatory constraints; The objective function is solved using SQP (Sequential Quadratic Programming). All decision variables are arranged into a high-dimensional vector in sequence, and an initial solution is selected to start the SQP iteration to construct the quadratic programming problem. After solving the quadratic programming problem, the optimal decision variables are output, and the optimal decision variables are output to form the ship navigation control command.

[0033] By incorporating speed constraints, regional emission regulations, and navigation time constraints, this invention ensures that the optimized solution not only meets technical and environmental requirements but also adapts to various limitations in actual operation. Speed ​​constraints ensure that ships avoid excessive fuel consumption and unnecessary emissions while remaining within their maximum speed; time constraints ensure that navigation meets timeliness requirements, which is crucial for routes with strict time constraints (such as cargo and passenger transport); regional emission regulations address the problem of ships being unable to meet stringent regulations in special emission control areas. The introduction of these constraints makes the optimized solution more operable and practically valuable, enabling a multi-faceted balance in complex navigation environments. The SQP (Sequential Quadratic Programming) method is used to solve the objective function, effectively handling constrained nonlinear optimization problems. Its application in ship navigation optimization allows for iterative updates to find the optimal solution, while considering the interplay of multiple factors such as speed, emissions, and fuel consumption. Compared to traditional heuristic algorithms or simple linear programming methods, SQP exhibits higher accuracy and convergence when facing complex constraints and nonlinear relationships, providing superior optimization results.

[0034] This embodiment also provides a green ship energy efficiency and emission synergistic optimization system, including: The ship data acquisition module is used to deploy sensors on the ship's engine to collect engine signals, calculate engine power and fuel flow rate, simultaneously acquire ship navigation data to divide the navigation segment into a spatial grid, and calculate the fuel consumption and emissions of the navigation segment and the spatial grid. The pollution contribution analysis module is used to construct a navigation mechanism model based on ship navigation data and calibrate the model parameters using the least squares method. It selects monitoring stations to obtain observation data, maps it to an emission observation matrix, and constructs an uncertainty matrix. It uses positive definite matrix factorization to solve the source contribution matrix and source component spectrum matrix, calculates the ship source contribution concentration, and obtains the ship source contribution spatial field based on the spatial convolution model. The energy efficiency and emission optimization module defines decision variables based on ship navigation data and constructs a segment operating condition vector. It normalizes the spatial field of ship source contribution to calculate spatially sensitive weighted emission indicators, constructs an energy efficiency-emission integrated objective function and adds navigation constraints, and outputs the optimal decision variables through SQP numerical solution.

[0035] This embodiment also provides a computer device applicable to the method of synergistic optimization of energy efficiency and emissions for green ships, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the method of synergistic optimization of energy efficiency and emissions for green ships as proposed in the above embodiment. The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0036] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for synergistic optimization of energy efficiency and emissions for green ships as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0037] In summary, this invention utilizes sensors deployed on ship engines to collect signals and calculate power and fuel flow rate, simultaneously acquiring navigation data to divide the route into segments and spatial grids, and precisely calculating fuel consumption and emissions. Furthermore, it constructs a ship source contribution spatial field based on a least-squares calibrated navigation mechanism model, positive definite matrix factorization, and spatial convolution model. On this basis, it defines spatially sensitive weighted emission indices, constructs an energy efficiency-emission integrated objective function, and outputs optimal decision variables under navigation constraints using SQP. This achieves closed-loop optimization and control from emission sources to receptor responses and back to ship operation strategies, providing a high-precision ship contribution spatial field and optimized navigation commands, significantly improving ship navigation energy efficiency.

[0038] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for synergistic optimization of energy efficiency and emissions of green ships, characterized in that: include, Sensors are deployed on ship engines to collect engine signals, calculate engine power and fuel flow rate, and simultaneously acquire ship navigation data to divide the navigation segment into a spatial grid, and calculate the fuel consumption and emissions of the navigation segment and the spatial grid. Based on ship navigation data, a navigation mechanism model is constructed and the model parameters are calibrated by the least squares method. Monitoring stations are selected to obtain observation data, which is mapped to the emission observation matrix and an uncertainty matrix is ​​constructed. Positive definite matrix factorization is used to solve the source contribution matrix and the source component spectrum matrix, the ship source contribution concentration is calculated, and the ship source contribution spatial field is obtained based on the spatial convolution model. Decision variables are defined based on ship navigation data, and a segment operating condition vector is constructed. The spatially sensitive weighted emission index is calculated by normalizing the spatial field of ship source contribution. An energy efficiency-emission integrated objective function is constructed and navigation constraints are added. The optimal decision variables are output by SQP numerical solution. The navigation mechanism model includes a propulsion-drag mechanism model, a total fuel flow rate model, and a pollutant fixed emission factor model. The propulsion-drag mechanism model includes a hydrostatic drag model and a wind drag model. The total drag is constructed by the hydrostatic drag model and the wind drag model and mapped to the ship's output power. The total fuel flow rate model is as follows: in The model outputs the total fuel flow rate, and SFOC is the specific fuel consumption curve provided by the manufacturer. For ship output power; The pollutant fixed emission factor model is as follows: in For pollutant emissions, The emission factor of pollutant p. This refers to instantaneous fuel consumption. Construct an objective function by combining the total fuel flow rate model and the pollutant stationary emission factor model. : in For the fuel component weight, For each pollutant weight, For total fuel flow rate, For pollutant emissions; Minimize the objective function using the least squares method. It also outputs the navigation mechanism model parameters to form an optimized navigation mechanism model; The decision variables include segment speed. and control vector , Including components ; The flight segment operating condition vector for: in Let i be the average draft of segment i. Let be the average wind speed for flight segment i; The space-sensitive weighted emission index H is: in For grid weight coefficients, Spatial sensitive weights For grid g Pollutant emissions are calculated based on the ship's navigation time within the grid. Contribute space field to ship-borne sources; The comprehensive objective function for energy efficiency and emissions is: in The weighting is based on a combination of fuel consumption and emissions. For the total fuel consumption of the route, and It is a dimensionless parameter.

2. The method for synergistic optimization of energy efficiency and emissions of green ships as described in claim 1, characterized in that: The process of deploying sensors on ship engines to collect engine signals, calculate engine power and fuel flow rate, simultaneously acquire ship navigation data to divide the navigation segment into a spatial grid, and calculate the fuel consumption and emissions of the navigation segment and the spatial grid refers to deploying high-precision sensors on the ship's main engine and auxiliary engines to calculate the instantaneous effective power of the main engine and the instantaneous effective power of the auxiliary engine. The total fuel flow rate is calculated by combining the fuel flow rates of the ship's main engine and auxiliary engines; The system reads ship navigation data via GPS, including route, latitude and longitude, ground speed and heading angle, obtains ship draft based on the draft gauge, obtains relative wind speed through the anemometer, and collects ship cargo weight. Collect ship flue gas monitoring data to read the concentration of pollutant p and flue gas volume flow rate to estimate pollutant emissions; The route is divided into Y segments according to spatial distance, and a spatial grid is divided using regular latitude and longitude to calculate instantaneous fuel consumption; The fuel consumption for each leg of the journey is calculated by summing the time taken by the ship at each leg. With emissions Simultaneously calculate the ship's fuel consumption in each grid. With emissions .

3. The method for synergistic optimization of energy efficiency and emissions of green ships as described in claim 2, characterized in that: The selected monitoring stations obtain observation data and map them into an emission observation matrix and construct an uncertainty matrix. Positive definite matrix factorization is used to solve the source contribution matrix and source component spectrum matrix. The ship source contribution concentration is calculated by selecting J environmental monitoring stations in the ship navigation area to monitor pollutant concentrations. The monitoring time is defined as T and the number of monitoring samples is N. The station-time two-dimensional index is mapped to a one-dimensional sample index i. The concentrations of monitored pollutants are mapped to the emission observation matrix X based on the sample index; Construct an uncertainty matrix U for each observation in the emissions observation matrix; The observation matrix is ​​decomposed by positive definite matrix factorization; Define the objective function and solve for the source contribution matrix and source component spectrum matrix using the least squares method; The objective function is solved using the least squares method, and the decomposed source contribution matrix is ​​output. Source component spectral matrix ; The ship label score is defined for each source by the source component spectrum matrix obtained through positive definite matrix factorization. ; The source with the highest ship mark score is selected as the ship source factor. The corresponding source contribution concentration is extracted through the source contribution matrix. Contribution concentration from ship sources And calculate the average ship source contribution concentration within the monitoring time T.

4. The method for synergistic optimization of energy efficiency and emissions of green ships as described in claim 3, characterized in that: The spatial field index for obtaining the ship source contribution based on the spatial convolution model selects L virtual nodes within the ship's navigation area. Define the Gaussian kernel function; Constructing the space field of ship source contribution using Gaussian kernel weighted superposition .

5. The method for synergistic optimization of energy efficiency and emissions of green ships as described in claim 4, characterized in that: The process of defining decision variables based on ship navigation data and constructing a segment operating condition vector, and normalizing the spatial field of ship source contributions to calculate the spatially sensitive weighted emission index, refers to defining decision variables for segment i based on ship navigation data, including segment speed. and control vector ; Constructing the segment working condition vector ; Input the segment operation vector into the navigation mechanism model to obtain the total fuel flow rate. and pollutant emissions Calculate the total fuel consumption of the route; Calculate the spatially sensitive weights and further calculate the spatially sensitive weighted emission index H.

6. The method for synergistic optimization of energy efficiency and emissions of green ships as described in claim 5, characterized in that: The construction of the energy efficiency-emission integrated objective function and the addition of navigation constraints, and the output of the optimal decision variables through SQP numerical solution, refer to the construction of the energy efficiency-emission integrated objective function B based on the total fuel consumption of the route and the space-sensitive weighted emission index; The optimization objective is defined as minimizing the energy efficiency-emissions integrated objective function B, and constraints are added to the optimization process, including speed constraints, regional emission regulations constraints, and flight time constraints. The objective function is solved by SQP. All decision variables are arranged into a high-dimensional vector in sequence and an initial solution is selected to start the SQP iteration to construct a quadratic programming problem. After solving the quadratic programming problem, the optimal decision variables are output and the optimal decision variables are output to form the ship navigation control command.

7. A green ship energy efficiency and emission synergistic optimization system, based on the green ship energy efficiency and emission synergistic optimization method according to any one of claims 1 to 6, characterized in that: include, The ship data acquisition module is used to deploy sensors on the ship's engine to collect engine signals, calculate engine power and fuel flow rate, simultaneously acquire ship navigation data to divide the navigation segment into a spatial grid, and calculate the fuel consumption and emissions of the navigation segment and the spatial grid. The pollution contribution analysis module is used to construct a navigation mechanism model based on ship navigation data and calibrate the model parameters using the least squares method. It selects monitoring stations to obtain observation data, maps it to an emission observation matrix, and constructs an uncertainty matrix. It uses positive definite matrix factorization to solve the source contribution matrix and source component spectrum matrix, calculates the ship source contribution concentration, and obtains the ship source contribution spatial field based on the spatial convolution model. The energy efficiency and emission optimization module defines decision variables based on ship navigation data and constructs a segment operating condition vector. It normalizes the spatial field of ship source contribution to calculate spatially sensitive weighted emission indicators, constructs an energy efficiency-emission integrated objective function and adds navigation constraints, and outputs the optimal decision variables through SQP numerical solution.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the green ship energy efficiency and emission synergistic optimization method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the green ship energy efficiency and emission synergistic optimization method according to any one of claims 1 to 6.

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