Combined fleet power distribution method and system based on intelligent control

Abstract

The invention provides a combined fleet power distribution method and system based on intelligent control, and relates to the technical field of data processing, and the method comprises the steps: predicting the total power demand of a fleet at a future moment according to effective operation data and historical operation data; calculating the power output capability data of each ship based on the maximum power output limit, the current load condition and the operation efficiency of the ship by combining the operation state of each ship; based on the power margin, the power efficiency and the task priority of each ship, the power demand is distributed among the ships; a corresponding power instruction is sent to a power system of each ship, and power output of each ship is adjusted; monitoring the actual operation state of the fleet, obtaining operation feedback data, comparing the operation feedback data with the power demand data, and if the deviation exceeds a preset deviation threshold value, updating the effective operation data; the autonomy and accuracy of power distribution of the combined fleet are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a combined fleet power distribution method and system based on intelligent control. Background Art

[0002] In the ship power system, the power distribution of the combined fleet is one of the key links. In the prior art, a rule-based fixed distribution method or a simple load balancing strategy is usually adopted to distribute power resources among the ships in the fleet. This type of method relies on the historical operating data and preset parameters of the ship, and performs static or semi-static power distribution through a pre-set power distribution ratio. Some systems also combine PID controllers or fuzzy control algorithms to achieve a certain degree of adaptive adjustment. In specific applications, the scheduling system mainly distributes power based on factors such as the ship's load, speed requirements, and navigation path, so as to meet the requirements of the overall navigation mission.

[0003] However, the existing combined fleet power distribution methods have certain limitations in practical applications. Taking the port tugboat fleet as an example, tugboats usually need to adjust power output according to real-time load changes during collaborative operations. Existing methods lack the ability to deeply analyze real-time data, resulting in a lag in power distribution adjustment. Especially when dealing with sudden load changes or severe sea conditions, the system often cannot respond quickly, and it is easy for some tugboats to be overloaded while other tugboats have redundant power. This not only affects the overall power economy of the fleet, but may also cause abnormal wear of tugboat equipment and shorten the service life of the equipment. Therefore, how to achieve more accurate and efficient power distribution based on intelligent control methods has become a problem that current technology urgently needs to solve. Summary of the invention

[0004] The purpose of the present invention is to provide a combined fleet power distribution method and system based on intelligent control, aiming to solve the problems mentioned in the background technology.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] In a first aspect, a combined fleet power distribution method based on intelligent control, the method comprising:

[0007] Based on effective operation data and historical operation data, the total power demand of the fleet at future times is predicted to obtain power demand data;

[0008] According to the power demand data, combined with the operating status of each ship, the power output capacity data of each ship is calculated based on the maximum power output limit of the ship, the current load condition and the operating efficiency;

[0009] According to the power demand data and the power output capacity data, based on the power margin, power efficiency and mission priority of each ship, the power demand is allocated among the ships to generate power allocation data;

[0010] According to the power distribution data, send corresponding power instructions to the power system of each ship to adjust the power output of each ship;

[0011] Monitor the actual operating status of the fleet, obtain operating feedback data, compare the operating feedback data with the power demand data, and if the deviation exceeds the preset deviation threshold, update the effective operating data and power demand data, and redistribute power.

[0012] Preferably, the total power demand of the fleet at a future time is predicted based on the effective operation data and the historical operation data to obtain the power demand data, including:

[0013] Construct time series of effective operation data, extract dynamic features including position change rate, speed change gradient and load fluctuation range, and form feature sequence data;

[0014] The sliding window method is used to divide the historical operation data, and the data in each sliding window is matched with the characteristic sequence data to capture the change trend in the historical data and obtain trend data;

[0015] Based on the trend data, a demand forecasting model with a multi-stage input structure is constructed to calculate the short-term power demand value and the medium-term power demand value respectively;

[0016] The short-term power demand value and the medium-term power demand value are weighted and integrated to obtain the power demand data.

[0017] Preferably, according to the power demand data, combined with the operating status of each ship, based on the maximum power output limit of the ship, the current load condition and the operating efficiency, the power output capacity data of each ship is calculated, including:

[0018] Based on the propulsion system specifications of the ship in the current voyage, the maximum output power parameters are determined and a maximum output parameter set is generated;

[0019] Based on the current load and operating efficiency of the ship, the actual available output ratio is calculated through the current load ratio, propulsion resistance and unit power consumption to form the output adjustment coefficient;

[0020] Match the maximum output parameter set with the output adjustment coefficient on a ship-by-ship basis, calculate the corresponding available output power, and form preliminary power output capacity data;

[0021] Based on the historical operation data, the ship's operation stability within a preset time period is calculated, so that the preliminary power output capacity data is corrected to generate power output capacity data.

[0022] Preferably, according to the power demand data and the power output capacity data, based on the power margin, power efficiency and task priority of each ship, the power demand is allocated among the ships to generate power allocation data, including:

[0023] Calculate the power margin of each ship according to the power output capacity data to generate a margin data set, wherein the power margin is the power output capacity data minus the power required for basic navigation;

[0024] The surplus data set is normalized, and the allocation priority matrix is ​​generated by combining the unit power consumption rate of the corresponding ship and the weight of the current operation task;

[0025] According to the allocation priority matrix, the power demand data is allocated step by step from top to bottom according to the task priority to generate the initial allocation data;

[0026] The initial allocation data is adjusted to ensure that the allocation value of a single ship does not exceed the upper limit of its power output capacity data, and the power allocation data is obtained.

[0027] Preferably, the actual operation status of the fleet is monitored, operation feedback data is obtained, the operation feedback data is compared with the power demand data, and if the deviation exceeds a preset deviation threshold, the effective operation data and the power demand data are updated, and the power is redistributed, including:

[0028] Obtaining real-time operational feedback data for each ship, including speed deviation, actual power consumption value, load, and actual and expected deviation of task completion time;

[0029] According to the operational feedback data, the actual power demand of the current ship is calculated based on the current speed, load and actual power consumption;

[0030] Compare the actual power demand with the power demand data, and if the deviation exceeds a preset deviation threshold, trigger a dynamic update process;

[0031] In the dynamic update process, according to the deviation between the actual power demand and the expected power demand, the effective operation data is adjusted, the effective operation data and power demand data are regenerated, and the power is redistributed.

[0032] Preferably, the operation stability of the ship within a preset time period is calculated based on the historical operation data, so as to correct the preliminary power output capacity data and generate the power output capacity data, including:

[0033] Based on historical operation data, the speed fluctuation, load change and power consumption fluctuation of each ship within a preset time period are extracted to generate a stability analysis data set;

[0034] Calculate the stability index of each ship within a preset time period based on the stability analysis data set;

[0035] The stability index of each ship is normalized and correlated with the preliminary power output capacity data to obtain the stability correction factor;

[0036] According to the stability correction factor, the preliminary power output capacity data is adjusted to generate the power output capacity data.

[0037] Preferably, the surplus data set is normalized, and combined with the unit power consumption rate of the corresponding ship and the current operation task weight, an allocation priority matrix is ​​generated, including:

[0038] According to the residual data set, the data is normalized to a uniform range to form a normalized residual data set;

[0039] According to the unit power consumption rate of the ship, combined with the urgency and importance of the current operation task, the task weight coefficient of each ship is calculated to generate a task priority data set;

[0040] According to the task priority data set and the normalized margin data set, the normalized power margin is multiplied by the task weight coefficient to obtain the priority weight data of each ship and generate the allocation priority matrix.

[0041] In a second aspect, a combined fleet power distribution system based on intelligent control, the system comprising:

[0042] The data acquisition module is used to collect fleet operation data, including the real-time position, speed, load information and power consumption rate of each ship, to form initial operation data;

[0043] The data preprocessing module is used to preprocess the initial operation data, remove abnormal data and complete missing data to obtain valid operation data;

[0044] The power demand calculation module is used to predict the total power demand of the fleet at a future time based on the effective operation data and historical operation data, and obtain the power demand data;

[0045] A power output calculation module is used to calculate the power output capacity data of each ship based on the power demand data, combined with the operating status of each ship, based on the maximum power output limit of the ship, the current load condition and the operating efficiency;

[0046] The power distribution planning module is used to distribute the power demand among the ships and generate power distribution data based on the power demand data and power output capacity data, the power margin, power efficiency and mission priority of each ship;

[0047] The power distribution execution module is used to send corresponding power instructions to the power system of each ship according to the power distribution data to adjust the power output of each ship;

[0048] The feedback adjustment module is used to monitor the actual operating status of the fleet, obtain operating feedback data, compare the operating feedback data with the power demand data, and if the deviation exceeds the preset deviation threshold, update the effective operating data and power demand data, and redistribute the power.

[0049] The above solution of the present invention includes at least the following beneficial effects:

[0050] First, by collecting data such as the fleet's real-time position, speed, load, and power consumption rate, the present invention can generate accurate initial operating data, and remove abnormal data and complete missing data through preprocessing to ensure data integrity and reliability. This step improves the system's ability to perceive the actual operation of the fleet and provides solid data support for subsequent power demand prediction.

[0051] Secondly, the present invention adopts a time series analysis method in power demand forecasting, combining the fleet's historical operation data and real-time data to more accurately predict future power demand. This method overcomes the drawbacks of static allocation in the prior art, especially when dealing with sudden load changes and severe sea conditions. The present invention can adjust power allocation in real time, avoiding overload or redundancy caused by system lag, and ensuring efficient operation of the fleet under complex conditions.

[0052] Furthermore, the present invention combines the actual operating status, maximum power output limit, load condition and operating efficiency of each ship to calculate the power output capacity of each ship, and accurately allocates the power demand based on this. By dynamically adjusting the power output capacity of each ship, the present invention can effectively avoid power imbalance between ships, reduce energy waste, avoid overload loss of equipment, and significantly extend the service life of ship equipment.

[0053] In addition, the system also monitors feedback data in real time to promptly detect and respond to operational deviations, ensuring that power distribution is automatically adjusted when the deviation exceeds the preset threshold. This feedback mechanism improves the adaptability of the fleet system, allowing ships to flexibly adjust according to actual operating conditions and changes in the external environment, further optimizing the fleet's power distribution and power economy.

[0054] In general, the present invention not only improves the accuracy and flexibility of fleet power distribution, but also effectively reduces energy consumption, reduces equipment wear, ensures the efficiency, economy and safety of fleet operation, and has significant technical advantages and application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flowchart of a combined fleet power distribution method based on intelligent control provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0057] like Figure 1 As shown, an embodiment of the present invention proposes a combined fleet power distribution method based on intelligent control, the method comprising:

[0058] S100, collect fleet operation data, including the real-time position, speed, load information and power consumption rate of each ship, to form initial operation data;

[0059] S200, preprocessing the initial operation data, removing abnormal data and completing missing data to obtain valid operation data;

[0060] S300, predicting the total power demand of the fleet at a future time according to the effective operation data and the historical operation data, and obtaining power demand data;

[0061] S400, calculating the power output capacity data of each ship according to the power demand data, in combination with the operating status of each ship, based on the maximum power output limit of the ship, the current load condition and the operating efficiency;

[0062] S500, according to the power demand data and the power output capacity data, based on the power margin, power efficiency and task priority of each ship, the power demand is allocated among the ships to generate power allocation data;

[0063] S600, sending corresponding power instructions to the power systems of each ship according to the power distribution data, and adjusting the power output of each ship;

[0064] S700, monitor the actual operating status of the fleet, obtain operating feedback data, compare the operating feedback data with the power demand data, and if the deviation exceeds a preset deviation threshold, update the effective operating data and power demand data, and redistribute power.

[0065] In the embodiment of the present invention, by collecting the real-time operation data of the fleet, including the speed, position, load information and power consumption rate of each ship, a comprehensive and accurate data basis is provided for further processing and analysis. The initial data is preprocessed to remove outliers and fill in missing values, thereby ensuring the reliability and accuracy of subsequent data analysis. This process can effectively eliminate errors caused by incomplete or inaccurate data, thereby enhancing the system's ability to predict the total power demand of the fleet.

[0066] Furthermore, by combining effective operating data and historical data, the present invention can predict the total power demand of the fleet at future times. This prediction not only takes into account the current operating status of the fleet, but also integrates past data trends, making the future power demand prediction more accurate and avoiding the deviation that may be caused by a single data source. Accurate power demand prediction enables each ship to reasonably allocate power output according to its specific mission requirements, thereby optimizing the operating efficiency of the entire fleet.

[0067] During the power distribution process, the system automatically adjusts the power output according to the power reserve, power efficiency and mission priority of each ship, avoiding energy waste and unbalanced loads between ships. This dynamic allocation mechanism ensures the optimal use of fleet resources, reduces unnecessary energy consumption and reduces the risk of ship failure. By monitoring the fleet's operating status in real time, obtaining feedback data in a timely manner, and comparing it with the predicted power demand, the system can adjust the power distribution when the deviation exceeds the preset threshold to ensure that the fleet is always in the best operating state.

[0068] Among them, the fleet operation data is collected, including the real-time position, speed, load information and power consumption rate of each ship, to form the initial operation data. Specifically:

[0069] First, the data acquisition system collects the operation data of each ship in the fleet in real time, including but not limited to the real-time position, speed, load information and power consumption rate of each ship.

[0070] Real-time location: The real-time location coordinates (such as longitude and latitude) of each ship at sea or in other navigation areas can be accurately obtained through the Global Positioning System (GPS) or other positioning devices. This data can help the system understand the exact location of the ship in the water in real time, providing a basis for subsequent navigation route planning, sailing time estimation, and fleet collaborative operations.

[0071] Speed: The speed of a ship is an important parameter to measure the speed of the ship, which is usually measured in real time by radar, GPS or other ship speed sensors. This data helps to calculate the possible distance a ship can complete within a specific time, and can also be used to adjust power demand to ensure the timeliness and accuracy of power distribution.

[0072] Load information: The ship's load data is obtained through a load cell or a ship's load management system. Load is a key factor affecting a ship's power consumption, speed, and required power, so accurate load information is essential for calculating a ship's power requirements.

[0073] Power consumption rate: The power consumption data of a ship is usually monitored in real time through sensors, reflecting the fuel consumption or power consumption of the ship during navigation. This data helps the system evaluate the energy efficiency of the ship and avoid excessive energy consumption when allocating power.

[0074] Through the collection of these real-time data, the system can form initial operating data and provide reliable input data for subsequent power demand forecasting and allocation. These data are further pre-processed to remove outliers and fill in missing data to ensure the accuracy and completeness of the data, laying a solid foundation for subsequent analysis and decision-making.

[0075] In a preferred embodiment of the present invention, the total power demand of the fleet at a future time is predicted based on the effective operation data and the historical operation data to obtain the power demand data, including:

[0076] Construct time series of effective operation data, extract dynamic features including position change rate, speed change gradient and load fluctuation range, and form feature sequence data;

[0077] The sliding window method is used to divide the historical operation data, and the data in each sliding window is matched with the characteristic sequence data to capture the change trend in the historical data and obtain trend data;

[0078] Based on the trend data, a demand forecasting model with a multi-stage input structure is constructed to calculate the short-term power demand value and the medium-term power demand value respectively;

[0079] The short-term power demand value and the medium-term power demand value are weighted and integrated to obtain the power demand data; among them, , : Final total power demand, indicating the total power demand of the fleet;

[0080] , : Weighting coefficient, satisfying , which respectively control the impact of short-term and medium-term demand on total power demand;

[0081] : Short-term power demand, based on speed changes, power consumption changes and load fluctuations;

[0082] : Medium-term power demand, based on long-term trends in speed, power consumption, and load changes;

[0083] , , : Weight coefficients in the short-term power demand calculation, which respectively control the impact of speed change, power consumption change and load fluctuation on the short-term power demand;

[0084] : The rate of change of speed, reflecting the change of ship speed;

[0085] : Power consumption change rate, reflecting the rate of change of ship power consumption;

[0086] : Load change rate, reflecting the rate of change of ship load;

[0087] : speed;

[0088] : Power consumption;

[0089] : load;

[0090] : time period;

[0091] , , : Weight coefficients of speed, power consumption and load-carrying change trends in the medium-term demand model;

[0092] , , : Respectively represent the trend strength of speed, power consumption and load change;

[0093] : Maximum change in speed;

[0094] : Maximum change in power consumption;

[0095] : Maximum change in load;

[0096] : wind speed disturbance correction coefficient;

[0097] : correction coefficient of ocean current disturbance;

[0098] : Segment length, which refers to the distance the ship is expected to sail;

[0099] : average speed;

[0100] : Segment correction factor;

[0101] , , , : Unit conversion coefficient, used to convert , , , Convert to dimensionless and unify the dimensions of calculation formulas.

[0102] In the embodiment of the present invention, when predicting the total power demand of the fleet, the present invention uses a time series analysis method to construct effective operation data, and matches the historical operation data with the current feature sequence data through a sliding window method, so as to identify the dynamic change trend in the operation of the ship. This process can capture the dynamic change characteristics of the ship's position, speed and load, and predict future power demand based on these characteristics. This method overcomes the limitations of traditional static prediction models, can more accurately reflect the changes in the actual operation of the ship, and improves the flexibility and real-time performance of power demand prediction.

[0103] Through the demand forecasting model with a multi-stage input structure, the short-term and medium-term power demand values ​​can be calculated separately, and the final power demand data can be obtained by weighted fusion of these two demand values. This method combines the predicted demand in different time periods, allowing the system to better adapt to changes in different voyages and tasks, thereby improving the flexibility and adaptability of fleet operations.

[0104] The core advantage of this method lies in dynamic adjustment and real-time optimization, which makes the energy distribution among ships more reasonable, avoids waste or shortage of resources, and further improves the overall economy and efficiency of the fleet.

[0105] Among them, the effective operation data is constructed into a time series, and dynamic features including the position change rate, speed change gradient and load fluctuation range are extracted to form feature sequence data. Specifically:

[0106] For effective operation data, firstly, the time series construction method is used to arrange the collected ship operation data in chronological order to form a time series. The data at each time point will include information such as the ship's real-time position, speed, load and power consumption. Time series data provides strong support for subsequent trend analysis and demand forecasting.

[0107] Through dynamic feature extraction, the system can identify and extract key changes in ship operation, including but not limited to:

[0108] Position change rate: By analyzing the displacement of a ship within a specific period of time, the ship's position change rate can be calculated. This feature reveals the speed at which the ship's position changes during navigation and is crucial for understanding the ship's motion characteristics.

[0109] Speed ​​gradient: The speed gradient reflects the fluctuation of the ship's speed over a period of time and can reveal the acceleration and deceleration trend of the ship during operation. This feature helps analyze the ship's adaptability to environmental changes and plays an important role in dynamically adjusting power.

[0110] Load fluctuation range: Through the time series analysis of the ship load information, the fluctuation range of the ship load can be extracted. The fluctuation of load may directly affect the power demand of the ship, so this feature is crucial for accurately estimating the power demand of the ship.

[0111] The features extracted above form feature sequence data, which can reflect the changes of various dynamic factors of the ship during navigation and provide a key basis for subsequent power demand prediction.

[0112] Among them, the sliding window method is used to divide the historical operation data, and the data in each sliding window is matched with the characteristic sequence data to capture the change trend in the historical data and obtain trend data. Specifically:

[0113] In order to improve the prediction accuracy of ship operation trends, the present invention adopts a sliding window method to divide the historical operation data. The sliding window method refers to setting a fixed-length window in the data sequence, and the data in the window will slide forward over time, and the data points in the window will be updated sequentially according to the time series.

[0114] Specifically, the time series in the historical operation data will be divided into multiple subsequences according to the fixed window size. In each window, the system matches the feature sequence data with the historical operation data. In this way, the system can capture the changing trends in the data, such as the acceleration and deceleration trends of the speed, the changing patterns of the load, etc.

[0115] The data in each sliding window is called a local data segment. In this way, the system can not only identify the trend of each local time period, but also observe the periodic characteristics of ship operation from a longer time dimension. This method enables the system to accurately predict the future operation status of the ship based on the actual situation of time and environmental changes.

[0116] Among them, according to the trend data, a demand forecasting model with a multi-stage input structure is constructed to calculate the short-term power demand value and the medium-term power demand value respectively. Specifically:

[0117] Based on the trend data obtained by the sliding window method, the present invention further constructs a demand forecasting model with a multi-stage input structure. The model combines historical data from multiple stages with current dynamic characteristics, and can accurately predict the power demand of the fleet based on the actual operating status of the ship, environmental factors, and mission requirements.

[0118] Short-term power demand value: This value is mainly calculated based on the data trend in the current time period, reflecting the power demand of the ship in the short term. This demand forecast can quickly respond to dynamic changes in the ship, such as sudden changes in speed or sudden increase in load, to ensure that the power distribution system can make timely adjustments to avoid insufficient or overloaded ships.

[0119] Medium-term power demand value: By analyzing historical data over a longer period of time, the system can predict the power demand of the ship in the future. This prediction can take into account the long-term operating trends of the ship, including cyclical changes in navigation, load fluctuations and other factors, providing the system with a more robust power demand prediction result.

[0120] These two power demand values ​​(short-term and medium-term) are calculated separately and weighted together according to different weights, ultimately generating accurate power demand data, providing an accurate basis for the fleet's power allocation.

[0121] Among them, the short-term power demand value and the medium-term power demand value are weighted and integrated to obtain the power demand data. Specifically:

[0122] In the present invention, after the short-term power demand value and the medium-term power demand value are calculated respectively, the system will perform weighted fusion on the two values ​​according to the actual situation. The purpose of weighted fusion is to adjust the reliability of the prediction results according to the degree of data fluctuation in different time periods.

[0123] Short-term power demand values ​​are usually more sensitive to changes in real-time data, so they need to be given a higher weight during calculation to ensure that the system can respond quickly to rapid changes in the ship's status.

[0124] The medium-term power demand value takes into account the trend over a longer period of time, so its weight is relatively low. It is used to smooth the system's forecast results and avoid short-term fluctuations from having too much impact on the forecast results.

[0125] Through weighted fusion, the system can combine short-term and medium-term power demand forecast results to obtain more accurate and balanced power demand data, thereby making more reasonable decisions in the power allocation process.

[0126] In a preferred embodiment of the present invention, according to the power demand data, combined with the operating status of each ship, based on the maximum power output limit of the ship, the current load condition and the operating efficiency, the power output capacity data of each ship is calculated, including:

[0127] Based on the propulsion system specifications of the ship in the current voyage, the maximum output power parameters are determined and a maximum output parameter set is generated;

[0128] Based on the current load and operating efficiency of the ship, the actual available output ratio is calculated through the current load ratio, propulsion resistance and unit power consumption to form the output adjustment coefficient;

[0129] Match the maximum output parameter set with the output adjustment coefficient on a ship-by-ship basis, calculate the corresponding available output power, and form preliminary power output capacity data;

[0130] Based on the historical operation data, the ship's operation stability within a preset time period is calculated, so that the preliminary power output capacity data is corrected to generate power output capacity data.

[0131] In the embodiment of the present invention, the present invention calculates the maximum power output capacity of the ship, combines the load condition and operation efficiency of the ship, and accurately calculates the power output capacity data of each ship. Through data such as the current load ratio, propulsion resistance and unit power consumption, the system can generate an output adjustment coefficient to further correct the preliminary power output capacity data. In this way, the system not only relies on theoretical calculations, but also combines the actual operating environment and the specific conditions of the ship, providing a more accurate basis for power distribution.

[0132] By calculating the stability index of the ship within a preset time period based on historical operating data, the system can correct the power output capacity according to the ship's operating stability. This method improves the ship's adaptability under complex sea conditions and load changes, ensuring that the ship can provide sufficient and stable power output under any given working conditions.

[0133] This power output capacity adjustment based on stability correction not only improves the operating efficiency of the ship, but also reduces failures and equipment losses caused by insufficient power or overload, extends the service life of the ship, and improves the long-term economic benefits of the fleet.

[0134] Among them, based on the propulsion system specifications of the ship in the current section, its maximum output power parameters are determined and the maximum output parameter set is generated. Specifically:

[0135] In order to achieve accurate power distribution during the operation of the combined fleet, the maximum power that each ship can provide in the current segment must be determined first. This maximum power depends on factors such as the propulsion system specifications, load status, and segment operating conditions of each ship.

[0136] The specifications of the propulsion system include the rated output power of the engine, the efficiency of the propeller, the characteristics of the transmission system, etc. These parameters are usually given by the ship designer or manufacturer at the time of delivery. In actual operation, these specifications need to be used in combination with the current navigation environment and mission requirements. Based on the current section, the propulsion capacity of the ship needs to be corrected. For example, when the ship is sailing against the current or in a section with increased wind resistance, its actual output power will be affected to a certain extent.

[0137] In addition, the load status of the ship will also affect the output performance of the propulsion system. When the ship is fully loaded, the power output required by the propulsion system is relatively high, and the maximum output capacity at this time needs to consider the power conversion efficiency and stability constraints of the system; while in the light load or no-load state, the propulsion system may not be able to operate at rated power for a long time, so its maximum output power should be reduced accordingly.

[0138] By comprehensively analyzing and processing the above-mentioned multiple influencing factors, the system can generate a set of maximum output power parameter sets for each ship that can be dynamically updated over time. This set is the maximum output parameter set. In the subsequent power distribution process, all distribution upper limits must be constrained by this parameter set to ensure safe and efficient navigation capabilities.

[0139] In a preferred embodiment of the present invention, according to the power demand data and the power output capacity data, based on the power margin, power efficiency and task priority of each ship, the power demand is allocated among the ships to generate power allocation data, including:

[0140] Calculate the power margin of each ship according to the power output capacity data to generate a margin data set, wherein the power margin is the power output capacity data minus the power required for basic navigation;

[0141] The surplus data set is normalized, and the allocation priority matrix is ​​generated by combining the unit power consumption rate of the corresponding ship and the weight of the current operation task;

[0142] According to the allocation priority matrix, the power demand data is allocated step by step from top to bottom according to the task priority to generate the initial allocation data;

[0143] The initial allocation data is adjusted to ensure that the allocation value of a single ship does not exceed the upper limit of its power output capacity data, and the power allocation data is obtained.

[0144] In the embodiment of the present invention, during the power distribution process, the system first calculates the power margin of each ship based on the power output capacity data and power demand data of each ship. The power margin refers to the remaining available power of the ship under the current load and sailing conditions, in addition to the power required to complete the basic sailing mission. By calculating the power margin and generating a margin data set, the system can perform effective load balancing according to the power status of each ship, ensuring that the power resources of the fleet are reasonably distributed among the ships.

[0145] By normalizing the surplus data set and combining the unit power consumption rate of the ship with the urgency and importance of the current task, the system can generate an allocation priority matrix. This matrix helps the system determine which ships should be given priority in reducing their burdens and which ships should take on more power tasks, thereby optimizing the task execution efficiency and power utilization efficiency of the entire fleet.

[0146] During this process, the system allocates power demand data from top to bottom according to the mission priority, ensuring that each ship is reasonably dispatched according to the mission urgency and available capacity. The generated initial allocation data is then adjusted to ensure that the power distribution of each ship does not exceed its maximum power output capacity, avoiding power overload or imbalance. Through this optimized power distribution method, the fleet can complete the task efficiently while minimizing energy waste, improving navigation economy and extending the service life of ship equipment.

[0147] Among them, the initial allocation data is adjusted to ensure that the allocation value of a single ship does not exceed the upper limit of its power output capacity data, and the power allocation data is obtained. Specifically:

[0148] In order to achieve intelligent and dynamic power resource control, after initially allocating the fleet's power requirements, the initial allocation results need to be adjusted to ensure that they meet the physical output capacity limitations of each ship. This step is an indispensable part of the power allocation process and has both binding and safety functions.

[0149] During the implementation process, the system first reads the power output capacity data corresponding to each ship, which is the result of a comprehensive evaluation based on the ship's propulsion system specifications, operating efficiency, stability and other parameters. Then, the system will compare the initial allocated power value with the corresponding power output capacity limit for each ship. If it is found that the allocated value of a ship exceeds its capacity limit, the adjustment mechanism will be triggered immediately.

[0150] During the adjustment process, the system will give priority to reducing the allocation value of the ship to the range that its output capacity can carry, to avoid the risk of system alarm, reduced power conversion efficiency, increased component wear, etc. due to overload operation. At the same time, in order to avoid waste of resources caused by changes in the overall allocation, the system will also reallocate this part of the excess power among other ships with surplus output capacity to achieve partial redistribution of power resources.

[0151] Finally, through this adjustment step, power distribution data that meets all constraints is generated to ensure that each ship operates within its physical carrying capacity, thereby improving the overall operational stability and energy efficiency of the fleet.

[0152] In a preferred embodiment of the present invention, the actual operation status of the fleet is monitored, operation feedback data is obtained, the operation feedback data is compared with the power demand data, and if the deviation exceeds a preset deviation threshold, the effective operation data and the power demand data are updated, and the power is redistributed, including:

[0153] Obtaining real-time operational feedback data for each ship, including speed deviation, actual power consumption value, load, and actual and expected deviation of task completion time;

[0154] According to the operational feedback data, the actual power demand of the current ship is calculated based on the current speed, load and actual power consumption;

[0155] Compare the actual power demand with the power demand data, and if the deviation exceeds a preset deviation threshold, trigger a dynamic update process;

[0156] In the dynamic update process, according to the deviation between the actual power demand and the expected power demand, the effective operation data is adjusted, the effective operation data and power demand data are regenerated, and the power is redistributed.

[0157] In the embodiment of the present invention, by obtaining the operational feedback data of each ship in real time, including the speed deviation, the actual power consumption value and the deviation between the task completion time and the expected time, the system can timely obtain the actual operation status of the ship. These feedback data are compared with the original power demand data. If the deviation exceeds the set threshold, the system will automatically trigger the dynamic update process.

[0158] During the dynamic update process, the system will correct the original effective operation data and regenerate the power demand data according to the deviation between the actual power demand and the expected power demand. Through this process, the system can adapt to unpredictable changes in ship operation, such as sudden climate change, speed fluctuations or other external factors, to ensure that the fleet can always maintain efficient and stable operation.

[0159] The core of this process is that the introduction of real-time dynamic feedback ensures that each ship obtains reasonable power instructions according to its own real-time situation, which not only ensures the efficient operation of the ship, but also effectively avoids the waste of resources and energy consumption caused by data lag or prediction errors. Through continuous optimization and adjustment, the fleet management system of the present invention can adapt to complex and changing environmental conditions, and improve the overall operational flexibility and resource utilization efficiency of the fleet.

[0160] In a preferred embodiment of the present invention, the ship operation stability within a preset time period is calculated based on historical operation data, thereby correcting the preliminary power output capacity data to generate the power output capacity data, including:

[0161] Based on historical operation data, the speed fluctuation, load change and power consumption fluctuation of each ship within a preset time period are extracted to generate a stability analysis data set;

[0162] Calculate the stability index of each ship within a preset time period based on the stability analysis data set;

[0163] The stability index of each ship is normalized and correlated with the preliminary power output capacity data to obtain the stability correction factor;

[0164] According to the stability correction coefficient, the preliminary power output capacity data is adjusted to generate the power output capacity data; wherein, , : Corrected power output capacity, indicating the actual available power of the ship;

[0165] : The maximum output power of the ship is determined by the design of the ship's propulsion system;

[0166] : Output adjustment coefficient;

[0167] : stability correction factor;

[0168] : Propulsion resistance ratio, which indicates the ratio of the actual resistance of the ship under the current load and environment to the design resistance;

[0169] : Unit load power consumption rate, indicating the power consumption per unit load;

[0170] : Current load ratio, which indicates the ratio of actual load to maximum rated load;

[0171] , , : Respectively represent the influence coefficients of resistance, power consumption and load on efficiency; these coefficients need to be determined through empirical data or model training;

[0172] , , : represent the standard deviation of speed, power consumption and load, respectively, which describe the fluctuation of the ship within a preset time period;

[0173] : Maximum stability tolerance value, which is the tolerance threshold set for the stability of the ship during operation;

[0174] , , : Weighted coefficient of stability factor, used to weight the stability effects of speed, power consumption and load fluctuations;

[0175] : Unit conversion factor, used to convert Convert to dimensionless.

[0176] In the embodiment of the present invention, the ship's operational stability correction is achieved by analyzing historical operational data and extracting key data such as speed fluctuation, load change, and power consumption fluctuation of each ship within a preset time period. By generating a stability analysis data set, the system can gain an in-depth understanding of the performance of each ship under different operating conditions, thereby more accurately calculating the ship's stability index.

[0177] The stability index is normalized and correlated with the preliminary power output data to generate a stability correction factor. This correction factor can dynamically adjust the power output based on the ship's historical performance to ensure the stability and efficiency of the ship during operation. Through this method, the system can adjust the power output of the ship in real time according to its actual operating performance, thereby improving the performance of the ship under various complex conditions.

[0178] This stability correction mechanism improves the safety and predictability of ships during navigation, and reduces failures and maintenance costs caused by power imbalance or overload. It ensures that ships maintain stable operation during long-term navigation, which not only improves the navigation efficiency of ships, but also extends the service life of ships and reduces maintenance and repair costs.

[0179] In a preferred embodiment of the present invention, the surplus data set is normalized, and the allocation priority matrix is ​​generated by combining the unit power consumption rate of the corresponding ship and the current operation task weight, including:

[0180] According to the residual data set, the data is normalized to a uniform range to form a normalized residual data set;

[0181] According to the unit power consumption rate of the ship, combined with the urgency and importance of the current operation task, the task weight coefficient of each ship is calculated to generate a task priority data set;

[0182] According to the task priority data set and the normalized margin data set, the normalized power margin is multiplied by the task weight coefficient to obtain the priority weight data of each ship and generate the allocation priority matrix.

[0183] In an embodiment of the present invention, by normalizing the power reserve of each ship to form a reserve data set within a unified range, the system can more intuitively compare the available power of each ship to ensure that each ship can be reasonably allocated according to its remaining power and mission requirements.

[0184] On this basis, combined with the urgency and importance of the current operation tasks, the system calculates the task weight coefficient of each ship and generates a task priority data set. This data set not only takes into account the current status of the ship, but also comprehensively evaluates the urgency and importance of the ship's tasks, thereby providing a more scientific basis for subsequent power allocation.

[0185] According to the mission priority data set and the normalized margin data set, the system multiplies the power margin of each ship by the mission weight coefficient to obtain the priority weight data of each ship and generate the final allocation priority matrix. This priority matrix ensures the efficient completion of the mission while avoiding waste of power resources or overload of any ship. Through this optimized allocation strategy, the fleet can complete the mission efficiently and in a balanced manner, maximize the use of existing power resources, and improve the overall efficiency and economy of navigation.

[0186] Among them, according to the unit power consumption rate of the ship, combined with the urgency and importance of the current operation task, the task weight coefficient of each ship is calculated to generate the task priority data set. Specifically:

[0187] In the process of fleet collaborative operation, the tasks undertaken by each ship often have differentiated time sensitivity and strategic importance. Therefore, in order to achieve accurate scheduling of power resources, it is necessary to establish a comprehensive evaluation model to generate a differentiated task priority data set.

[0188] The process first evaluates the energy efficiency level of each ship based on its unit power consumption rate. The unit power consumption rate usually refers to the amount of power consumed by a ship for every ton of cargo it carries, reflecting the energy efficiency performance of the ship under a specific load. Ships with high consumption rates have higher energy usage costs. If the mission is less urgent, their power allocation should be reduced first to optimize power economy.

[0189] Secondly, the system introduces evaluation indicators for task urgency. For example, in the port tugboat collaboration scenario, the tugboat responsible for assisting large cargo ships in berthing has a limited task time window and is of high urgency. However, for ships performing routine cruises or fleet shifts, their tasks can be delayed and are of low urgency. The system will assign urgency weights to each task based on indicators such as scheduling plans, arrival time, and operation sequence.

[0190] In addition, the importance of the task needs to be evaluated, that is, whether the task belongs to the critical path and whether it affects the execution efficiency of the entire navigation task or operation cycle. For example, some tasks are not urgent but have important dependencies on subsequent operations and should also be given priority in allocation.

[0191] The system normalizes the above multiple indicators to form a unified task weight coefficient as the core reference for power allocation sorting. Finally, a clear task priority label is established for each ship in the fleet to form a complete data set to guide subsequent allocation strategy optimization, resource pre-adjustment and energy consumption control.

[0192] An embodiment of the present invention further provides a combined fleet power distribution system based on intelligent control, the system comprising:

[0193] The data acquisition module is used to collect fleet operation data, including the real-time position, speed, load information and power consumption rate of each ship, to form initial operation data;

[0194] The data preprocessing module is used to preprocess the initial operation data, remove abnormal data and complete missing data to obtain valid operation data;

[0195] The power demand calculation module is used to predict the total power demand of the fleet at a future time based on the effective operation data and historical operation data, and obtain the power demand data;

[0196] A power output calculation module is used to calculate the power output capacity data of each ship based on the power demand data, combined with the operating status of each ship, based on the maximum power output limit of the ship, the current load condition and the operating efficiency;

[0197] The power distribution planning module is used to distribute the power demand among the ships and generate power distribution data based on the power demand data and power output capacity data, the power margin, power efficiency and mission priority of each ship;

[0198] The power distribution execution module is used to send corresponding power instructions to the power system of each ship according to the power distribution data to adjust the power output of each ship;

[0199] The feedback adjustment module is used to monitor the actual operating status of the fleet, obtain operating feedback data, compare the operating feedback data with the power demand data, and if the deviation exceeds the preset deviation threshold, update the effective operating data and power demand data, and redistribute the power.

[0200] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0201] The embodiment of the present invention further provides a computing device, including: a processor, a memory storing a computer program, and when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0202] The embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the above method. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0203] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A combined fleet power distribution method based on intelligent control, characterized in that: The method comprises: Based on effective operation data and historical operation data, the total power demand of the fleet at future times is predicted to obtain power demand data; According to the power demand data, combined with the operating status of each ship, the power output capacity data of each ship is calculated based on the maximum power output limit of the ship, the current load condition and the operating efficiency; According to the power demand data and the power output capacity data, based on the power margin, power efficiency and mission priority of each ship, the power demand is allocated among the ships to generate power allocation data; According to the power distribution data, send corresponding power instructions to the power system of each ship to adjust the power output of each ship; Monitor the actual operating status of the fleet, obtain operating feedback data, compare the operating feedback data with the power demand data, and if the deviation exceeds the preset deviation threshold, update the effective operating data and power demand data, and redistribute power.

2. The method for allocating power of a combined fleet based on intelligent control according to claim 1, characterized in that: Based on effective operation data and historical operation data, the total power demand of the fleet at future times is predicted to obtain power demand data, including: Construct time series of effective operation data, extract dynamic features including position change rate, speed change gradient and load fluctuation range, and form feature sequence data; The sliding window method is used to divide the historical operation data, and the data in each sliding window is matched with the characteristic sequence data to capture the change trend in the historical data and obtain trend data; Based on the trend data, a demand forecasting model with a multi-stage input structure is constructed to calculate the short-term power demand value and the medium-term power demand value respectively; The short-term power demand value and the medium-term power demand value are weighted and integrated to obtain the power demand data.

3. The method for allocating power of a combined fleet based on intelligent control according to claim 2 is characterized in that: Based on the power demand data, combined with the operating status of each ship, the power output capacity data of each ship is calculated based on the ship's maximum power output limit, current load conditions and operating efficiency, including: Based on the propulsion system specifications of the ship in the current voyage, the maximum output power parameters are determined and a maximum output parameter set is generated; Based on the current load and operating efficiency of the ship, the actual available output ratio is calculated through the current load ratio, propulsion resistance and unit power consumption to form the output adjustment coefficient; Match the maximum output parameter set with the output adjustment coefficient on a ship-by-ship basis, calculate the corresponding available output power, and form preliminary power output capacity data; Based on the historical operation data, the ship's operation stability within a preset time period is calculated, so that the preliminary power output capacity data is corrected to generate power output capacity data.

4. The method for allocating power of a combined fleet based on intelligent control according to claim 3 is characterized in that: According to the power demand data and power output capacity data, based on the power margin, power efficiency and mission priority of each ship, the power demand is allocated among the ships to generate power allocation data, including: Calculate the power margin of each ship according to the power output capacity data to generate a margin data set, wherein the power margin is the power output capacity data minus the power required for basic navigation; The surplus data set is normalized, and the allocation priority matrix is ​​generated by combining the unit power consumption rate of the corresponding ship and the weight of the current operation task; According to the allocation priority matrix, the power demand data is allocated step by step from top to bottom according to the task priority to generate the initial allocation data; The initial allocation data is adjusted to ensure that the allocation value of a single ship does not exceed the upper limit of its power output capacity data, and the power allocation data is obtained.

5. The method for allocating power to a combined fleet based on intelligent control according to claim 4 is characterized in that: Monitor the actual operation status of the fleet, obtain operation feedback data, compare the operation feedback data with the power demand data, and if the deviation exceeds the preset deviation threshold, update the effective operation data and power demand data, and redistribute the power, including: Obtaining real-time operational feedback data for each ship, including speed deviation, actual power consumption value, load, and actual and expected deviation of task completion time; According to the operational feedback data, the actual power demand of the current ship is calculated based on the current speed, load and actual power consumption; Compare the actual power demand with the power demand data, and if the deviation exceeds a preset deviation threshold, trigger a dynamic update process; In the dynamic update process, according to the deviation between the actual power demand and the expected power demand, the effective operation data is adjusted, the effective operation data and power demand data are regenerated, and the power is redistributed.

6. The method for allocating power to a combined fleet based on intelligent control according to claim 2, characterized in that: Based on the historical operation data, the ship operation stability within the preset time period is calculated, so as to correct the preliminary power output capacity data and generate the power output capacity data, including: Based on historical operation data, the speed fluctuation, load change and power consumption fluctuation of each ship within a preset time period are extracted to generate a stability analysis data set; Calculate the stability index of each ship within a preset time period based on the stability analysis data set; The stability index of each ship is normalized and correlated with the preliminary power output capacity data to obtain the stability correction factor; According to the stability correction factor, the preliminary power output capacity data is adjusted to generate the power output capacity data.

7. The method for allocating power to a combined fleet based on intelligent control according to claim 6, characterized in that: The surplus data set is normalized, and combined with the unit power consumption rate of the corresponding ship and the current operation task weight, an allocation priority matrix is ​​generated, including: According to the residual data set, the data is normalized to a uniform range to form a normalized residual data set; According to the unit power consumption rate of the ship, combined with the urgency and importance of the current operation task, the task weight coefficient of each ship is calculated to generate a task priority data set; According to the task priority data set and the normalized margin data set, the normalized power margin is multiplied by the task weight coefficient to obtain the priority weight data of each ship and generate the allocation priority matrix.

8. A combined fleet power distribution system based on intelligent control, characterized in that: Applied to the method according to any one of claims 1 to 7, the system comprises: The data acquisition module is used to collect fleet operation data, including the real-time position, speed, load information and power consumption rate of each ship, to form initial operation data; The data preprocessing module is used to preprocess the initial operation data, remove abnormal data and complete missing data to obtain valid operation data; The power demand calculation module is used to predict the total power demand of the fleet at a future time based on the effective operation data and historical operation data, and obtain the power demand data; A power output calculation module is used to calculate the power output capacity data of each ship based on the power demand data, combined with the operating status of each ship, based on the maximum power output limit of the ship, the current load condition and the operating efficiency; The power distribution planning module is used to distribute the power demand among the ships and generate power distribution data based on the power demand data and power output capacity data, the power margin, power efficiency and mission priority of each ship; The power distribution execution module is used to send corresponding power instructions to the power system of each ship according to the power distribution data to adjust the power output of each ship; The feedback adjustment module is used to monitor the actual operating status of the fleet, obtain operating feedback data, compare the operating feedback data with the power demand data, and if the deviation exceeds the preset deviation threshold, update the effective operating data and power demand data, and redistribute the power.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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