A fleet power collaborative management and distribution system

Abstract

The present invention relates to the technical field of fleet power distribution, and discloses a fleet power collaborative management and distribution system, comprising a data acquisition unit, an analysis unit, an association unit, a power distribution unit and a determination unit. Aiming at the static scheduling defects of traditional architectures, dynamic power management is achieved through the collaboration of multiple units. The data acquisition and analysis unit captures fleet formation changes and regional resistance characteristics in real time. The association unit integrates equipment operation and environmental parameters to generate precise power demand. The distribution unit dynamically allocates power based on generator power and relative position. The determination unit ensures the rationality of distribution through triple thresholds and triggers graded corrections, so that the system can sensitively respond to load fluctuations caused by formation changes, avoid supply and demand imbalance, improve the power distribution balance and navigation reliability of the fleet in complex inland river environments, and make the fleet's power collaborative management and distribution more reasonable.

Description

Technical Field

[0001] The present invention relates to the technical field of fleet power distribution, and in particular to a fleet power collaborative management and distribution system. Background Art

[0002] At present, when cargo ships sail long distances on inland rivers, the fleet's power supply is still centered on diesel generators, which drive the generators by burning diesel, thereby providing power support for the entire fleet. In the power management and distribution link, a distributed energy management architecture is still adopted. This architecture implements power scheduling based on a pre-established sailing plan. Specifically, during the voyage, power resources are allocated according to the preset priority sequence based on the real-time load requirements of each cargo ship, so as to achieve basic management and scheduling of the fleet's power system. This management model can guarantee the basic power demand of each ship under steady-state sailing scenarios. Its technical advantages lie in its mature architecture, low implementation cost, and the ability to achieve relatively independent power control of each ship.

[0003] However, during actual long-distance inland navigation, fleets often need to adjust their formations according to the real-time navigation environment. The core flaw of the traditional distributed management architecture is the static nature of its scheduling strategy. This architecture relies on preset navigation plans for power distribution and lacks the ability to perceive and respond to dynamic load fluctuations caused by real-time formation changes. When formation changes cause nonlinear changes in the power demand of each ship, this management and distribution method can easily cause an imbalance in the fleet's power supply and demand, resulting in unreasonable power management and distribution. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present invention provides a fleet power collaborative management and distribution system to solve the above problems.

[0005] The above technical objectives of the present invention are achieved through the following technical solutions:

[0006] A fleet power collaborative management and distribution system, comprising:

[0007] The data collection unit is used to divide the fleet's navigation route into multiple navigation areas and collect the navigation data of each cargo ship in the fleet, the operating data of the load equipment, the environmental data, the historical navigation data and the historical sea condition data in real time;

[0008] An analysis unit is used to obtain the relative position matrix between the cargo ships based on the navigation data, and analyze the relative position matrix to obtain the current fleet formation type. When the fleet formation is adjusted, the regional resistance coefficient of each cargo ship is calculated based on the navigation data, environmental data and navigation area;

[0009] The correlation unit is used to extract the operating data of the load equipment of each cargo ship to obtain the equipment operation characteristic matrix, and to correlate the equipment operation characteristic matrix, the formation type and the regionalized resistance coefficient to obtain the real-time power demand parameters of each cargo ship;

[0010] The power distribution unit integrates and analyzes the real-time power demand parameters, the real-time output power of the diesel generators, and the relative position matrix to obtain the power distribution parameters between the cargo ships;

[0011] The determination unit redistributes the fleet power in the current navigation area according to the power distribution parameters.

[0012] Furthermore, the fleet's sailing routes are divided into multiple sailing areas, including:

[0013] Analyze navigation data and environmental data to obtain the resistance value and sea condition value of the current navigation area;

[0014] Calculate historical navigation data and historical sea condition data to obtain resistance value thresholds and sea condition value thresholds;

[0015] The resistance value and the sea condition value of the current navigation area are compared with the resistance value threshold and the sea condition value threshold respectively, and the current navigation area is divided into one of the resistance area and the sea condition area.

[0016] Furthermore, based on the navigation data, the relative position matrix between the cargo ships is obtained, including:

[0017] Process the navigation data of each cargo ship to generate position and direction data;

[0018] According to the position and direction data, it is assumed that the fleet has several cargo ships. The cargo ship is used as the reference ship, and the azimuth and relative distance of the remaining cargo ships relative to the reference ship are calculated to obtain the relative position parameters of the single ship centered on the reference ship;

[0019] Traverse each cargo ship and use it as a reference ship in turn to obtain the relative position parameters of each ship;

[0020] The relative position parameters of all cargo ships are collected, sorted and arranged in order of the cargo ship numbers to form a relative position matrix between the cargo ships.

[0021] Furthermore, the relative position matrix is ​​analyzed to obtain the current fleet formation type, including:

[0022] Extract the relative position matrix to obtain the formation characteristic parameters of the fleet's compactness and directional consistency;

[0023] Analyze the common formations of cargo ships and obtain the range of formation characteristic parameters;

[0024] Match the formation characteristic parameters with the formation characteristic parameter range to obtain the formation type of the current fleet.

[0025] Furthermore, when the fleet formation is adjusted, the regional resistance coefficient of each cargo ship is calculated based on the navigation data, environmental data and navigation area, including:

[0026] Calculate the rate of change of the relative position matrix to generate formation stability parameters;

[0027] Based on the current navigation area type, set the formation stability threshold, and compare the formation stability parameter with the formation stability threshold to determine whether the fleet formation needs to be adjusted;

[0028] When the fleet formation is adjusted, the navigation data and environmental data are extracted based on the current navigation area type to obtain the basic resistance parameters;

[0029] Analyze the relative position matrix between cargo ships to generate interference correction parameters for the formation;

[0030] The basic resistance parameters and interference correction parameters are integrated to obtain the regional resistance coefficient of each cargo ship.

[0031] Furthermore, the operating data of each cargo ship's load equipment is extracted to obtain the equipment operation feature matrix, including:

[0032] Extract the load equipment operation data of each cargo ship and generate basic equipment operation parameters;

[0033] Based on the type of navigation area of ​​the cargo ship, the start and stop frequency of the equipment in the navigation area is analyzed to obtain the equipment area response parameters;

[0034] The basic equipment operating parameters and equipment regional response parameters are integrated and arranged according to the cargo ship number and equipment type to form an equipment operating characteristic matrix.

[0035] Furthermore, the equipment operation characteristic matrix, formation type, and regionalized resistance coefficient are correlated to obtain the real-time power demand parameters of each cargo ship, including:

[0036] The basic operating parameters of the equipment are integrated with the regional response parameters of the equipment to generate the equipment-related feature vector;

[0037] The influence of formation type on the resistance distribution of each cargo ship is analyzed, and the formation resistance distribution matrix is ​​obtained;

[0038] The formation resistance distribution matrix and the regional resistance coefficient are integrated to obtain the resistance variation factor caused by the position difference of each cargo ship under different formations.

[0039] The equipment-associated feature vector and the resistance variation factor are weightedly fused to generate the real-time power demand parameters of each cargo ship.

[0040] Furthermore, the real-time power demand parameters, the real-time output power of the diesel generators, and the relative position matrix are integrated and analyzed to obtain the power distribution parameters between the cargo ships, including:

[0041] Compare the real-time power demand parameters of each cargo ship with the real-time output power of the diesel generator to generate the power surplus or shortage coefficient of each ship;

[0042] Analyze the relative position matrix and formation type to generate the formation coordination efficiency factor matrix;

[0043] The power surplus / shortage coefficient of a single ship is coupled with the formation coordination efficiency factor matrix to generate a dynamic weight vector for power distribution.

[0044] According to the total available surplus electricity of the fleet and the dynamic weight vector of electricity distribution, the electricity transfer amount between ships is calculated to obtain the electricity distribution parameters between cargo ships.

[0045] Furthermore, the fleet power in the current navigation area is redistributed according to the power distribution parameters, including:

[0046] Analyze the navigation area type, equipment operation characteristic matrix and power distribution parameters of each cargo ship to generate the power security parameters of each cargo ship;

[0047] By comparing the relative position matrix of each cargo ship before and after power distribution and the impact of the real-time output power change of the diesel generator on the speed, the formation coordination efficiency maintenance coefficient is obtained;

[0048] According to the current navigation area type, regional characteristic response parameters are extracted from navigation data and environmental data;

[0049] Analyze the regional characteristic response parameters, the actual response capabilities of each ship's diesel generator output power and the real-time capacity of the battery pack after power allocation, and generate regional adaptability evaluation indicators;

[0050] The equipment operation characteristic matrix of each cargo ship, the formation coordination efficiency factor matrix and the current navigation area type are analyzed respectively to obtain the power security threshold, the formation coordination efficiency maintenance coefficient threshold and the regional adaptability evaluation index threshold.

[0051] The power security parameters, formation coordination efficiency maintenance coefficient and regional adaptability assessment index of each cargo ship are compared with three thresholds respectively. If any of the power security parameters, formation coordination efficiency maintenance coefficient and regional adaptability assessment index does not reach the threshold, the corresponding command is triggered.

[0052] Furthermore, if any of the power security parameter, formation coordination efficiency maintenance coefficient, and regional adaptability evaluation index does not reach the threshold, the corresponding instructions are triggered, including:

[0053] When the power security parameters of each cargo ship When the power security threshold is reached, the power security is determined to be unreasonable, triggering the first correction instruction;

[0054] When the formation coordination efficiency maintenance coefficient If the formation coordination efficiency maintenance coefficient threshold is exceeded, the coordination efficiency is judged to be unreasonable and the second correction instruction is triggered;

[0055] When regional adaptability evaluation indicators If the regional adaptability evaluation index threshold is exceeded, the regional adaptability is judged to be unreasonable, triggering the third correction instruction;

[0056] The priority order is: First correction instruction > Third correction instruction > Second correction instruction;

[0057] When multiple instructions are triggered at the same time, they are executed one by one according to their priority, and the next instruction will only be executed if the remaining power meets the power requirement of the next instruction.

[0058] In summary, the present invention mainly has the following beneficial effects:

[0059] A dynamic response mechanism was constructed through multi-dimensional data collection and real-time analysis. The data collection unit divides the navigation route into resistance and sea condition areas, and combines historical data with real-time environmental parameters to generate regionalized resistance coefficients for each cargo ship, accurately quantifying the energy consumption characteristics of different navigation areas. The analysis unit dynamically captures formation changes through the relative position matrix, identifies the current formation type based on characteristic parameters such as compactness and directional consistency, and evaluates in real time the changes in resistance distribution caused by formation adjustments. The association unit further integrates the equipment operation characteristic matrix with the formation resistance change factor, and generates real-time power demand parameters through a weighted algorithm, enabling the system to sensitively respond to nonlinear load fluctuations caused by formation changes. For example, when the fleet changes from a single column to a goose formation, the system can quickly identify the increased resistance of the flanking ships due to water flow impact, and synchronously adjust the power fluctuation coefficients of their auxiliary equipment, avoiding local power surplus or shortage caused by formation changes in traditional static allocation, and ensuring dynamic matching between power demand calculation and actual energy consumption scenarios.

[0060] By constructing a dynamic power allocation scheme based on formation coordination, the power allocation unit integrates real-time power demand parameters, diesel generator output power, and a relative position matrix to generate a dynamic weight vector consisting of individual ship power surplus / shortage coefficients and formation coordination efficiency factors, achieving global optimal allocation of power resources. Specifically, the system first calculates surplus / shortage coefficients based on each ship's real-time load and generator output capacity, identifying ships with surplus and shortage power. It then analyzes the coordination efficiency of adjacent ships based on the formation type to form differentiated allocation weights. When a barge experiences a sudden surge in power demand due to increased cargo load, the system prioritizes the surplus power of the main propulsion vessels in the same formation. The optimal transmission path is calculated using the relative position matrix, minimizing transmission losses while avoiding overloading individual generators. Compared to traditional preset priority allocation, this mechanism not only balances the loads of each ship in real time but also improves overall energy efficiency through the formation coordination efficiency factor. For example, when navigating complex river bends, the system dynamically adjusts the allocation strategy based on the formation stability parameter to reduce allocation fluctuations caused by frequent formation changes, ensuring that the diesel generators operate within the efficient range and reducing overall fuel consumption for the fleet.

[0061] By designing a triple judgment mechanism and hierarchical correction strategy including power security, collaborative efficiency, and regional adaptation, the judgment unit first determines the minimum power maintenance threshold of each ship in different areas through the equipment operation characteristic matrix, and generates power security parameters based on the actual allocated power and demand parameters to ensure the basic power priority of key ship types such as main propulsion ships and auxiliary function ships. Secondly, by analyzing the position stability and power coordination before and after the formation changes, the formation collaborative efficiency maintenance coefficient is evaluated to avoid formation instability caused by power distribution. For complex river conditions, the system further extracts regional characteristic response parameters to evaluate the regional adaptability of each ship's battery capacity and generator output. When the power security does not meet the standard, the system will give priority to triggering the first correction instruction and cut off Non-essential loads are cut off to ensure core power consumption. If the collaborative efficiency decreases, the allocation strategy is optimized based on the common formation. In the face of extreme sea conditions, the power allocation ratio is dynamically adjusted through regional adaptive correction. This hierarchical response mechanism not only solves the drawbacks of the traditional one-size-fits-all allocation, but also ensures the robustness of the system in key scenarios through priority sorting. For example, in the event of a sudden strong countercurrent, the system can automatically increase the power allocation ratio of the main ship to ensure the power output of the fleet and avoid navigation accidents caused by local power shortages. It significantly improves the safety and reliability of long-distance inland navigation, and can quickly perceive and respond to dynamic load fluctuations caused by real-time formation changes, ensuring the balance of power supply and demand of the fleet, making the management and allocation of power more practical and more reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a block diagram of the fleet power collaborative management and distribution system of the present invention. DETAILED DESCRIPTION

[0063] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0064] refer to Figure 1 , a fleet power collaborative management and distribution system, comprising:

[0065] The data collection unit is used to divide the fleet's navigation route into multiple navigation areas, which are divided into resistance areas and sea condition areas, and collect the navigation data, load equipment operation data, environmental data and historical navigation data of each cargo ship in the fleet in real time;

[0066] An analysis unit is used to obtain the relative position matrix between the cargo ships based on the navigation data, and analyze the relative position matrix to obtain the current fleet formation type. When the fleet formation is adjusted, the regional resistance coefficient of each cargo ship is calculated based on the navigation data, environmental data and navigation area;

[0067] The correlation unit is used to extract the operating data of the load equipment of each cargo ship to obtain the equipment operation characteristic matrix, and to correlate the equipment operation characteristic matrix, the formation type and the regionalized resistance coefficient to obtain the real-time power demand parameters of each cargo ship;

[0068] The power distribution unit integrates and analyzes the real-time power demand parameters, the real-time output power of the diesel generators, and the relative position matrix to obtain the power distribution parameters between the cargo ships;

[0069] The determination unit redistributes the fleet power in the current navigation area according to the power distribution parameters.

[0070] The navigation data includes: longitude and latitude, speed, heading angle, real-time output power of diesel generators, current draft of each ship, hull surface roughness, navigation resistance, stop time, roll angle, pitch angle, wave direction angle, cargo ship number, ship's windward area, etc.

[0071] The operating data of the load equipment includes: main propulsion motor power, auxiliary engine energy consumption, real-time battery capacity, equipment start and stop frequency, total available surplus power of the fleet, and equipment type;

[0072] Environmental data include: water velocity, direction, water depth, wind speed, wind direction, wave height, ocean temperature and salinity data, etc.

[0073] The data acquisition unit comprehensively collects multi-dimensional information such as navigation data, load equipment operation data and environmental data, providing an accurate data basis for subsequent power management. The analysis unit can accurately analyze the fleet formation type and calculate the regional resistance coefficient. The correlation unit associates the equipment operation characteristics and other data based on this to obtain the real-time power demand parameters of each cargo ship. The power distribution unit integrates multiple parameters for fusion analysis to achieve accurate power distribution. The system can also use the judgment unit to promptly determine whether the power distribution is reasonable and trigger the power redistribution mechanism, effectively ensuring the rationality and efficiency of power distribution during the fleet's navigation, reducing power waste, improving the economy and stability of the fleet's navigation, and enhancing the fleet's adaptability and collaborative operation efficiency in complex navigation environments.

[0074] In one case of this embodiment, the fleet's sailing route is divided into multiple sailing areas, including:

[0075] Analyze navigation data and environmental data to obtain the resistance value and sea state value of the current navigation area. Specifically, the following methods are used: Using the resistance calculation method, multiply the air density by the windward area of ​​the ship, multiply by the wind speed squared, and multiply by 0.5 to obtain the wind resistance value. The wind resistance value is added to the navigation resistance to obtain the resistance value. A multi-parameter weighted fusion algorithm is used to normalize the wind speed, wave height, and current speed, assigning weight coefficients of 0.4, 0.4, and 0.2 respectively, and performing linear superposition to obtain the sea state value.

[0076] The historical navigation data and historical sea state data were calculated to obtain the resistance value threshold and the sea state value threshold. Specifically, the following steps were taken: the sum of the wind resistance value and the navigation resistance value in the same area of ​​the historical navigation data was collected, and the average value plus 2 times the standard deviation was taken as the resistance value threshold. The wind speed, wave height, and current speed in the historical sea state data were weighted and fused with weights of 0.4, 0.4, and 0.2, and normalized and linearly superimposed to obtain a large number of historical sea state values. These sea state values ​​were then sorted, and the 85th percentile was taken as the sea state value threshold.

[0077] The resistance value and sea condition value of the current navigation area are compared with the resistance value threshold and the sea condition value threshold respectively, and the current navigation area is divided into one of the resistance area and the sea condition area, specifically including: if the resistance value of the current navigation area is greater than or equal to the resistance value threshold, it is directly judged as the resistance area; if the resistance value does not exceed the threshold, the sea condition value is compared; when the sea condition value is greater than or equal to the sea condition value threshold, it is divided into the sea condition area; when the sea condition value does not exceed the threshold, it is divided into the resistance area; when both the resistance value and the sea condition value exceed the threshold, it is still divided into the resistance area.

[0078] Through the resistance calculation method and multi-parameter weighted fusion algorithm, the resistance value and sea condition value of the current sailing area can be accurately obtained, providing reliable data support for subsequent analysis. In terms of threshold determination, based on historical navigation data and sea condition data, the resistance value threshold and sea condition value threshold are set to make the threshold more in line with the actual sailing situation. When dividing the area, clear comparison rules can accurately determine whether the current sailing area belongs to the resistance area or the sea condition area, so that the system can clearly understand the navigation environment of the fleet. This series of operations helps to achieve coordinated management and reasonable allocation of the fleet's electricity. According to the characteristics of different sailing areas, the electricity use strategy can be optimized, the electricity utilization efficiency can be improved, the fleet's endurance and navigation safety under different sea conditions and resistance conditions can be guaranteed, and the overall operational efficiency and management level of the fleet can be improved.

[0079] In one case of this embodiment, a relative position matrix between cargo ships is obtained based on navigation data, including:

[0080] The navigation data of each cargo ship is processed to generate position and direction data, specifically including: converting the longitude and latitude of each cargo ship into plane rectangular coordinates using the UTM projection algorithm to obtain position data; using the Kalman filter algorithm to denoise the heading angle data; decomposing the velocity vector along the heading angle into coordinates in a plane coordinate system; representing the ship's motion direction in the form of a unit vector to generate direction data; and correlating and integrating the position data and direction data to obtain position and direction data;

[0081] According to the position and direction data, it is assumed that the fleet has several cargo ships. The cargo ship is used as the reference ship, and the azimuth and relative distance of the remaining cargo ships relative to the reference ship are calculated to obtain the relative position parameters of the single ship centered on the reference ship. Specifically, The cargo ship is the reference ship, and its coordinates are ( ), the remaining cargo ships The coordinates are ( ), calculate cargo ship Relative reference ship The horizontal coordinate difference and the vertical coordinate difference are squared and the square root of the two is taken to get the relative distance. The north direction of the reference ship is 0° and the clockwise direction is the positive direction. Relative reference ship The azimuth is calculated by the inverse tangent function based on the horizontal and vertical coordinate differences. The angle range is determined by the coordinate quadrant and converted to 0-360° to obtain the relative position parameters of the single ship centered on the reference ship.

[0082] Traverse each cargo ship and use it as a reference ship in turn to obtain the relative position parameters of each ship;

[0083] The relative position parameters of all cargo ships are collected and sorted and arranged in the order of cargo ship numbers to form a relative position matrix between cargo ships. Specifically, the relative position matrix corresponding to the number of cargo ships is established based on the relative position parameters of the single ships generated during the traversal process. Matrix frame, row index and column index are in the order of cargo ship number ( to ) are arranged, when the When a cargo ship is used as a reference ship, compare it with the Cargo ships ( to ) are stored in the matrix in the corresponding relative distance and azimuth parameters. Row, No. Column position, for The diagonal position of can be filled with zero values ​​because the relative distance is zero and the azimuth angle has no practical meaning. According to this rule, fill it row by row and column by column to form a relative position matrix with the cargo ship number as the index and the rows and columns corresponding to the reference ship and the target ship respectively.

[0084] Through precise processing and analysis of cargo ship navigation data, the UTM projection algorithm is used to convert longitude and latitude into plane rectangular coordinates in the data preprocessing stage, which effectively solves the problem of conversion accuracy from geographic coordinates to plane coordinates. The heading angle data is denoised by the Kalman filter algorithm, which significantly improves the anti-interference ability of the direction data and ensures the reliability and real-time performance of the position and direction data. In the calculation of the relative position parameters of a single ship, the relative distance is calculated by taking the square root of the sum of the squares of the coordinate differences. The azimuth is determined by the inverse tangent function combined with the coordinate quadrant and converted to the range of 0-360°. The azimuth relationship in the actual navigation of the ship is fully considered, and the angle calculation error is avoided. The relative position parameters of a single ship can accurately reflect the spatial orientation and distance information between ships, which helps the fleet to grasp the spatial distribution status of each cargo ship in real time, provides an accurate position reference for avoiding collision risks and optimizing the formation, and improves the safety and coordination of the fleet navigation.

[0085] By traversing each cargo ship as a reference ship, the relative position parameters of each ship are generated and integrated into an N×N matrix. This matrix comprehensively and systematically presents the relative position relationships between cargo ships in the fleet. The matrix is ​​indexed by ship number, with rows and columns corresponding to the reference and target ships, respectively. This clearly defines the relative distance and azimuth between each pair of ships. The rule of filling the diagonal with zero values ​​is both physically valid and convenient for data processing. This matrix reflects the dynamic spatial structure of the fleet in real time, providing a spatial decision-making basis for optimizing power allocation strategies. The system can adjust the coordinated propulsion strategy based on the relative distance between ships, optimize power allocation for close-proximity ships, and reduce the additional power consumption caused by navigation resistance or sea conditions. Azimuth information is used to predict the effects of airflow or wave interference between ships, allowing preemptive compensation adjustments to the power systems of affected ships. This power collaborative management model based on the relative position matrix enables refined control of the fleet's overall energy consumption, improves power utilization efficiency, and enhances the fleet's power endurance in complex navigation environments.

[0086] In one case of this embodiment, the relative position matrix is ​​analyzed to obtain the formation type of the current fleet, including:

[0087] The relative position matrix is ​​extracted to obtain the formation characteristic parameters of the fleet's compactness and direction consistency, specifically including: extracting the relative distance values ​​of all non-diagonal elements from the relative position matrix to form a distance data set, calculating the average value of the distance data set, and then calculating the sum of the squares of the differences between each distance value and the average value, dividing the sum of squares by the number of distance values ​​and taking the square root to obtain the standard deviation, which is the compactness parameter. The smaller the value, the denser the distribution of the fleet. Extracting the heading angle after Kalman filtering from the direction data of each cargo ship to form a heading angle data set, calculating the average value of the heading angle data set, and then calculating the sum of the squares of the differences between each heading angle and the average value, dividing the sum of squares by the number of heading angles and taking the square root to obtain the standard deviation, which is the direction consistency parameter. The smaller the value, the more similar the headings of the cargo ships are. The compactness parameter and the direction consistency parameter are normalized and weighted fused to obtain the formation characteristic parameters.

[0088] The common formations of cargo ships were analyzed to obtain the range of formation characteristic parameters. Common formations of cargo ships include: push formation, towing formation, mixed formation, parallel formation and barge formation. Specifically, the historical navigation data sets of each formation were collected, and sample data were generated after UTM projection and Kalman filtering. For the sample data of each formation, the compactness parameter and directional consistency parameter of all samples were calculated respectively. The descriptive statistical method was used to calculate the mean and standard deviation of the parameters of each formation. times the standard deviation to construct the characteristic parameter range of the formation;

[0089] Match the formation characteristic parameters with the formation characteristic parameter range to obtain the formation type of the current fleet, specifically including: traversing the formation categories one by one, and for each fleet formation, checking whether the current formation characteristic parameters fall into the formation characteristic parameter range at the same time. If they only fall into the formation characteristic parameter range of a certain formation, then it is determined to be that formation type. If they fall into multiple formation intervals, calculate the Euclidean distance between the current formation characteristic parameters and each formation characteristic parameter, and select the formation with the smallest distance as the matching result. If there is no matching interval, it will prompt you to manually mark the formation.

[0090] By using standard deviation as a measure of compactness and directional consistency, the compactness of the fleet's spatial distribution and its heading coordination can be intuitively reflected. The compactness parameter quantifies the density of the formation through the discrete degree of distance data, and the directional consistency parameter measures the level of convergence of the navigation direction based on the standard deviation of the heading angle. The formation characteristic parameters formed by the normalized weighted fusion of the two effectively integrate the dual-dimensional information of spatial distribution and motion state. In the process of historical data processing, the sample data is standardized and preprocessed through UTM projection and Kalman filtering, and the characteristic parameter range of each formation is constructed using descriptive statistical methods. This not only ensures the reliability of the sample data, but also provides a statistically significant judgment basis for subsequent matching, enabling the system to accurately capture the typical characteristic differences of different formations, laying a solid data foundation for real-time identification of fleet formations under complex sea conditions.

[0091] A preliminary screening is performed through interval inclusiveness judgment. If a unique match occurs, the formation type is directly determined, ensuring recognition efficiency. If it falls into multiple formation intervals, the optimal match is selected by calculating the Euclidean distance, effectively solving the fuzzy discrimination problem when feature parameters overlap. For the manual marking prompt mechanism for no-match situations, the system retains flexibility and adaptability, avoiding misjudgment due to insufficient data coverage. This multi-level matching strategy enables the system to accurately identify common formations such as push-type and towing-type in real time, providing key formation morphology information for power collaborative management. Based on the formation type, the system can optimize the power allocation strategy in a targeted manner. For example, for compact parallel formations, the propulsion power is coordinated to reduce the additional energy consumption caused by airflow interference between ships. For mixed formations with low directional consistency, the power reserves of each ship are dynamically adjusted to ensure balanced endurance under complex formation structures. This significantly improves the power utilization efficiency of the fleet under different formation modes and enhances the system's ability to cope with diverse navigation scenarios.

[0092] In one case of this embodiment, when the fleet formation is adjusted, the regionalized resistance coefficient of each cargo ship is calculated based on the navigation data, environmental data, and navigation area, including:

[0093] The rate of change of the relative position matrix is ​​calculated to generate the formation stability parameter. Specifically, the method includes: taking one of the reference ships in the fleet formation as the origin, constructing the relative position matrix of each cargo ship in the inertial coordinate system (the elements are the horizontal and vertical relative coordinates). Based on the navigation data of the discrete time series, the central difference method is used to calculate the first-order derivative of each coordinate element in the matrix with respect to time to obtain the relative position change rate matrix. By calculating the mean square error of the relative velocity vectors of each ship in the matrix, the formation stability parameter is generated. The smaller the parameter value, the smoother the relative position change of the formation and the higher the formation stability.

[0094] Based on the current navigation area type, a formation stability threshold is set, and the formation stability parameter is compared with the formation stability threshold to determine whether the fleet formation needs to be adjusted. Specifically, the formation stability threshold is set. If the formation stability parameter is greater than the formation stability threshold, the formation stability is determined to be unsatisfactory and the formation adjustment is triggered. If the formation stability parameter is less than or equal to the formation stability threshold, the current formation is maintained.

[0095] When the fleet formation is adjusted, based on the current navigation area type, the navigation data and environmental data are extracted to obtain the basic resistance parameters. Specifically, if the current navigation area type is a resistance area, the additional resistance of the upstream flow is calculated based on the relative angle between the water velocity and the ship speed. If the current navigation area type is a sea state area, the wind pressure is calculated according to the angle between the wind direction and the heading, and the wave slamming resistance is calculated in combination with the wave height period. The wind pressure and the wave slamming resistance are normalized and added together to obtain the sea state resistance. Depending on whether the navigation area is a resistance area or a sea state area, the resistance of one area and the navigation resistance are normalized and added item by item to obtain the basic resistance parameter.

[0096] The relative position matrix between each cargo ship is analyzed to generate interference correction parameters for the formation, specifically including: constructing a relative position matrix in the inertial coordinate system with the reference ship as the origin, extracting the transverse and longitudinal relative coordinates of each cargo ship, calculating the transverse and longitudinal spacing for each pair of ships, and combining the main dimensions of the ships to obtain the transverse spacing ratio (transverse spacing divided by ship length) and longitudinal spacing ratio (longitudinal spacing divided by ship width), according to the spacing ratio and azimuth (the heading angle relative to the reference ship), first linearly interpolating between adjacent data points of the transverse spacing ratio, then linearly interpolating between adjacent interpolation results of the longitudinal spacing ratio, and finally radially interpolating within the angle interval according to the azimuth to obtain the wave-making interference coefficient between each pair of ships, and dividing the relative positions of the two ships into three areas according to the longitudinal spacing ratio, and respectively according to Based on the speed of the reference ship and the longitudinal distance between the two ships, the resistance increase in three regions is calculated using the empirical law that wake velocity decays with distance (the closer the distance, the greater the influence of wake velocity). When the longitudinal spacing ratio is less than or equal to 0.8 times the length of the reference ship, it is considered the near field, with a correction factor of 1.2. When the longitudinal spacing ratio is between 0.8 and 1.2 times the length of the reference ship, it is considered the midfield, with a correction factor of 1. When the longitudinal spacing ratio is greater than 1.2 times the length of the reference ship, it is considered the far field, where the influence of the wake is considered negligible and a correction factor of 1 is used. The corresponding correction factor is directly matched to the region to which the current longitudinal spacing belongs. After obtaining the wake resistance correction factor, for each pair of ships in the formation, the wave-making interference coefficient and the correction factor are weighted and fused, using the ship tonnage as the weight factor, to obtain the interference correction parameter for the formation.

[0097] The basic resistance parameters and interference correction parameters are integrated to obtain the regional resistance coefficient of each cargo ship, including: The ship is Basic drag coefficient under different sailing attitudes Divide by The reciprocal square root of the wave load factor for the region , to obtain the basic resistance correction term ; The current speed of the ship With the The average suitable speed in the area The absolute difference Divide by The result and base unit value Add together to get the speed correction term ; Set the base resistance modifier and speed correction Multiply by The complex function of the water flow velocity in the region changing with time , to obtain the corrected drag coefficient term ; Sum the corrected resistance coefficient terms for all sea state and current combinations to obtain the comprehensive dynamic corrected resistance , use it as the numerator, and then calculate the The ship is Basic resistance coefficient for all sea conditions and current combinations in the navigation area The maximum value of the sum and the base unit value The larger value of , taking it as the denominator, divide the numerator by the denominator to obtain the regionalized resistance coefficient , when applied specifically, it can be achieved through the following calculation formula, for example:

[0098] ;

[0099] Where, Indicates the The ship is The regional drag coefficient of each navigation area, Indicates the The ship is The basic drag coefficient under the sailing attitude is Indicates the The wave load factor for each region, Indicates the The current speed of the ship, Indicates the The average suitable speed for the region, represents the complex function of the water velocity in the nth region changing with time, and V represents the The total number of different sea state and current combinations within a navigation area;

[0100] The calculation formula of complex variable function is as follows:

[0101] ;

[0102] Where, represents the velocity component of the water flow in the longitudinal direction, Represents the velocity component of water flow in the latitudinal direction.

[0103] The formation stability parameters are calculated by combining the central difference method with the mean square error, which can quantify the dynamic changes of the relative position of the formation in real time, ensure timely identification of formation fluctuations in complex sea conditions or power adjustments, and provide a reasonable quantitative basis for triggering the formation adjustment mechanism. In the calculation of basic resistance parameters, the countercurrent additional resistance and wind pressure are introduced according to the different characteristics of the resistance area and the sea condition area. The coupled calculation method of wave impact resistance not only takes into account the vector relationship between water direction and ship speed, but also integrates the synergistic effect of multiple environmental factors such as wind, waves and currents, so that the basic resistance parameters can accurately reflect the dominant resistance causes in different navigation areas. This regional modeling method avoids the limitations of traditional unified resistance calculations and significantly improves the adaptability of resistance parameters to complex navigation scenarios.

[0104] Through the fusion algorithm of interference correction parameters and regionalized resistance coefficient, the quantification problem of fluid dynamic interference between ships in the formation is effectively solved. By converting the relative position matrix into the lateral and longitudinal spacing ratio, and combining the azimuth angle for three-dimensional interpolation to calculate the wave-making interference coefficient, and dividing the near field, midfield and far field based on the wake attenuation law and matching the correction coefficient, the system comprehensively considers the nonlinear effects of ship spacing, azimuth and scale on resistance, especially using ship tonnage as a weight factor for weighted fusion, so that the correction parameters can reflect the actual force differences of different ship types in the formation, which improves the engineering practicality of interference resistance calculation. Finally, through complex variable The function embeds the dynamic characteristics of water velocity and combines the normalization processing of wave load factor and speed deviation to form a regionalized resistance coefficient. It not only integrates multi-source data of environmental load, formation structure and ship motion status, but also ensures the horizontal comparability of resistance coefficients between different navigation areas and different ship types through the normalization design of the benchmark resistance in the denominator. This mechanism provides a refined resistance assessment for the coordinated distribution of fleet power, and enables the system to dynamically optimize the power distribution of each ship according to the real-time formation form and environmental load, thereby reducing energy consumption while ensuring the overall endurance of the formation, and significantly improving the fleet operation efficiency and management accuracy under complex working conditions.

[0105] In one case of this embodiment, the operating data of each cargo ship's load equipment is extracted to obtain an equipment operating characteristic matrix, including:

[0106] The load equipment operating data of each cargo ship is extracted to generate basic equipment operating parameters. Specifically, the following steps are performed: For the real-time operating data of each cargo ship's load equipment, the signal is denoised using a Butterworth low-pass filter. The sliding window algorithm is used to calculate the signal mean, variance, and root mean square value window by window to obtain time domain statistics. For periodic motor equipment, the time domain signal is converted to the frequency domain using a fast Fourier transform. The amplitude and phase of the fundamental frequency and the fifth harmonic are extracted to obtain frequency domain spectrum characteristics. The number of equipment starts and stops and the cumulative operating time are counted using a current threshold trigger mechanism (startup is determined when the current is greater than 20% of the rated value) to obtain state parameters. The time domain statistics, frequency domain spectrum characteristics, and state parameters are integrated according to the equipment ID and timestamp to generate the basic equipment operating parameters.

[0107] Based on the type of the cargo ship's navigation area, the start and stop frequency of the equipment in the navigation area is analyzed to obtain the equipment area response parameters, including: based on the type of the cargo ship's navigation area (sea condition area, resistance area), the time window is divided by area type, and for each device, the start and stop event timestamp is extracted from the state parameter, and the time window matching algorithm is used to count the number of starts and stops in each area time window, the start and stop frequency in the sea condition area Number of starts and stops in this area Duration of the zone, frequency of start and stop in the resistance zone Number of starts and stops in this area The duration of the region is calculated by constructing a two-dimensional feature vector through the start-stop frequency in the sea state region and the start-stop frequency in the resistance region. The dimension effect is eliminated through normalization processing to generate the device regional response parameters that reflect the response characteristics of the device in different regions.

[0108] The basic operating parameters of the equipment and the regional response parameters of the equipment are integrated and arranged according to the cargo ship number and equipment type to form an equipment operation feature matrix. Specifically, the following steps are taken: a first-level index is established according to the cargo ship number (MMSI code), a second-level index is established for the equipment in each cargo ship according to the type (propulsion motor, pump group, etc.), and the basic operating parameters of the equipment (time domain statistics, frequency domain characteristics and state parameters) are internally connected and associated with the regional response parameters of the equipment (start and stop frequency of the sea state area and resistance area) through the equipment ID to ensure the alignment of the timestamps. A feature splicing algorithm is used to horizontally arrange the dimensions of the basic operating parameters (mean, variance and fundamental frequency amplitude) and the two-dimensional vectors of the regional response parameters with the basic parameters first and the regional parameters second, so that each row corresponds to a unique cargo ship. A two-dimensional matrix of equipment combinations, each column corresponding to a feature, and the matrix row label is the cargo ship number Device Type The device ID and column labels are feature names (such as voltage mean, fifth harmonic phase, or start / stop frequency), generating a structured device operation feature matrix.

[0109] The equipment operation characteristic matrix constructed through multi-dimensional data processing provides precise support for status analysis and power management of cargo ship load equipment. For basic parameter extraction, a Butterworth low-pass filter is used for noise reduction and a sliding window algorithm is used to extract time-domain features. Fast Fourier transforms are then used to analyze the frequency-domain characteristics of motor-type equipment. A current threshold mechanism is also used to calculate start and stop states. This generates three-dimensional basic data covering the time, frequency, and operating states, ensuring the comprehensiveness and reliability of equipment operation information. For regional response analysis, time windows are divided based on the navigation area type. The start and stop frequencies of equipment in sea conditions and resistance areas are counted and constructed into a two-dimensional feature vector. After normalization, this effectively maps equipment behavior with the external environment, clearly reflecting the load response characteristics of equipment in different areas. Finally, a matrix is ​​formed through multi-level indexing and feature splicing techniques, aligning basic operating parameters with regional response parameters in time and space. This provides a structured basis for equipment energy consumption assessment for coordinated power management. The system can dynamically adjust power allocation strategies, optimize power supply priorities for high-load equipment, provide early warnings of abnormal operating conditions, and accurately allocate power, significantly improving equipment energy efficiency and power system stability. This provides a data foundation for intelligent power allocation for fleets under complex operating conditions.

[0110] In one case of this embodiment, the equipment operation characteristic matrix, the formation type, and the regionalized resistance coefficient are associated to obtain the real-time power demand parameters of each cargo ship, including:

[0111] The basic operating parameters of the equipment are integrated with the regional response parameters of the equipment to generate the equipment-related feature vector. Specifically, the basic operating parameters of the equipment and the normalized regional response parameters of the equipment are aligned according to the equipment ID and timestamp. Then, through the feature splicing algorithm, the dimensions of the basic parameters (such as mean, variance, fundamental frequency amplitude, etc.) and the two-dimensional vectors of the regional response parameters are arranged horizontally in sequence to form the basic parameters. Device-associated feature vectors of sequential concatenation of regional parameters;

[0112] The influence of formation type on the resistance distribution of each cargo ship is analyzed to obtain the formation resistance distribution matrix. Specifically, the matrix is ​​constructed with the unique serial numbers (MMSI codes) of all cargo ships as the row index and column index of the matrix. The number of rows and columns is equal to the total number of cargo ships. For each pair of ships in the formation, the interference correction parameters calculated based on their relative position matrix are used to fill the matrix according to the following rules: the row index corresponds to the number of cargo ships. Number, column index corresponds to the cargo ship The matrix element value is the cargo ship With cargo ship The interference correction parameter between When (same ship), the matrix element value is set to 0. In the final square matrix, each non-diagonal element reflects the resistance interference intensity of the corresponding two ships under the current formation. The row and column indexes are directly related to the cargo ships, forming a formation resistance distribution matrix.

[0113] The formation resistance distribution matrix and the regionalized resistance coefficient are integrated to obtain the resistance variation factors of each cargo ship due to position differences in different formations. Specifically, the following steps are taken: for each cargo ship's corresponding row vector in the formation resistance distribution matrix (excluding diagonal 0 values), the interference correction parameters between the ship and other ships are extracted, and weighted average is performed with the corresponding ship tonnage as the weight to obtain the position interference coefficient of the ship in the current formation. The position interference coefficient and the regionalized resistance coefficient of the ship are normalized and differed. Specifically, the position interference coefficient is subtracted from the regionalized resistance coefficient and then divided by the regionalized resistance coefficient to eliminate the dimension and generate a dimensionless resistance variation factor.

[0114] The equipment-associated feature vector and the resistance variation factor are weighted and integrated to generate the real-time power demand parameters of each cargo ship, including: calculating the sum of the power fluctuation coefficients of the w-th auxiliary equipment of the i-th ship divided by the total number of auxiliary equipment And with the base unit value Add together to get the power fluctuation coefficient of auxiliary equipment , the rated power of the propulsion motor of the i-th ship Multiply it by the power fluctuation coefficient of the auxiliary equipment to get the basic power demand item ; Regional resistance coefficient of the ship in the current area With unit value The difference is multiplied by the The relative position parameters of the ships relative to the formation reference ship The absolute difference, divided by After the base unit value Add together to get the position resistance correction term , multiply the basic power demand term by the position resistance correction term to obtain the corrected power demand term ; Calculate the The current real-time capacity of the battery pack of the ship Compared to the fleet's average battery capacity The absolute difference is divided by the maximum battery capacity of the fleet , and obtain the deviation result , subtract the deviation result from 1 to get the battery capacity deviation term ; The deviation between the current ambient temperature and the rated operating temperature of the equipment Divide by the device's rated operating temperature range Then compare with the base unit value Add together to get the temperature correction term , multiply the temperature correction term by the battery capacity deviation term to obtain the comprehensive correction coefficient , divide the corrected power demand item by the comprehensive correction coefficient to obtain the real-time power demand parameter , when applied specifically, it can be achieved through the following calculation formula, for example:

[0115] ;

[0116] Where, Indicates the The ship is Real-time power demand parameters for each analysis period, Indicates the Rated power of the ship's main propulsion motor, Indicates the The power fluctuation coefficient of the w-th auxiliary equipment of the ship, Indicates the The relative position parameters of the ships relative to the formation reference ship, Indicates the The regional resistance coefficient of a ship in the current area, Indicates the The current real-time capacity of the battery pack of the ship, represents the fleet average battery capacity, Indicates the fleet's maximum battery capacity, Indicates the deviation between the current ambient temperature and the rated operating temperature of the device. Indicates the rated operating temperature range of the device. Indicates the The total number of different auxiliary equipment on board.

[0117] The Butterworth low-pass filter is used to reduce the noise of the real-time signal of the equipment and effectively filter out high-frequency interference. The sliding window algorithm is combined to extract time domain statistics such as mean and variance window by window to ensure that the dynamic details of the equipment operation are captured. For periodic loads such as motors, the fast Fourier transform is used to analyze the amplitude and phase of the fundamental frequency and the fifth harmonic to achieve in-depth mining of frequency domain characteristics and provide key spectrum basis for equipment fault prediction. The current threshold trigger mechanism accurately counts the number of starts and stops and the cumulative duration, forming a three-dimensional basic operating parameter system including time domain, frequency domain, and state parameters. It not only covers the characteristic dimensions of normal equipment operation, but also provides multi-perspective data support for abnormal operating condition identification, ensuring the comprehensiveness and reliability of equipment status analysis from the bottom up and avoiding evaluation deviations caused by single parameters.

[0118] By dividing the time window according to the type of navigation area and constructing a two-dimensional feature vector through the start-stop frequency statistics, the equipment operation behavior is directly linked to the external environmental load. The high-frequency start-stop in the sea state area reflects the response intensity of the equipment to changes in waves and wind speed, and the start-stop characteristics in the resistance area reflect the degree of influence of water flow resistance on the power system. After normalization, the dimensional differences are eliminated, making the regional adaptability of different equipment horizontally comparable. The final multi-level index feature matrix is ​​aligned in time and space through feature splicing technology. The system can quickly locate the operating mode of each ship's equipment in a specific area, and then dynamically optimize the power distribution strategy: for example, stable power supply is prioritized for pump groups that start and stop frequently in the sea state area, and energy consumption fluctuations of propulsion motors running at high load in the resistance area are monitored in real time. Abnormal conditions are warned in combination with the basic operating parameters of the equipment to avoid overload or inefficient operation. This mechanism significantly improves the energy efficiency of equipment and reduces power loss in complex environments.

[0119] In one case of this embodiment, the real-time power demand parameter, the real-time output power of the diesel generator, and the relative position matrix are integrated and analyzed to obtain the power distribution parameters between the cargo ships, including:

[0120] Compare the real-time power demand parameters of each cargo ship with the real-time output power of the diesel generator to generate the power surplus and shortage coefficient of each ship, including: The cargo ship After normalizing the real-time power demand parameter and the real-time output power of the diesel generator, the real-time power demand parameter is subtracted from the real-time output power of the diesel generator and divided by the real-time output power of the diesel generator to obtain the single-ship power surplus or shortage coefficient.

[0121] The relative position matrix and formation type are analyzed to generate a formation coordination efficiency factor matrix. Specifically, the following steps are performed: feature extraction is performed on the relative position matrix, and the lateral spacing ratio, longitudinal spacing ratio, and azimuth angle of each ship are calculated to form a formation topology structure matrix. According to the formation type (push formation, towing formation, mixed formation, parallel formation, and barge formation), a corresponding coordination efficiency basic weight is set for each formation. The correction coefficient corresponding to each weight is calculated based on the spacing ratio and azimuth angle of each ship in the current formation using a bilinear interpolation algorithm. The correction coefficients of all ship pairs are filled according to the formation topology structure matrix to form the non-diagonal elements of the coordination efficiency factor matrix. The diagonal elements are set to the ship's own baseline efficiency value of 1. The coordination efficiency factor matrix is ​​then normalized so that the sum of the elements in each row is equal to 1, thereby obtaining the final formation coordination efficiency factor matrix.

[0122] The power surplus / shortage coefficient of a single ship is coupled with the formation coordination efficiency factor matrix to generate a dynamic weight vector for power distribution. Specifically, for one cargo ship, its power surplus / shortage coefficient is multiplied by each element of the corresponding row in the coordination efficiency factor matrix (reflecting the coordination efficiency of the ship with other ships) to obtain the weighted value of the row. The weighted values ​​of each row are then normalized so that the sum of the elements in each row is 1, thus generating a dynamic weight vector for power distribution.

[0123] Based on the total available surplus electricity of the fleet and the dynamic weight vector of electricity allocation, the electricity transfer amount between ships is calculated to obtain the electricity allocation parameters between cargo ships. Specifically, the total available surplus electricity of the fleet is calculated, that is, the positive value of the sum of the real-time output power of all diesel generators minus the sum of the real-time electricity demand of each cargo ship. Then, for each cargo ship, according to the corresponding weight in its dynamic weight vector of electricity allocation, the total available surplus electricity is allocated to each ship according to the weight ratio, and the weight value is multiplied by the total surplus electricity to obtain the amount of electricity transferred from the ship to other ships or received from other ships, thereby obtaining the electricity allocation parameters between cargo ships.

[0124] By normalizing the real-time power demand and generator output power, the power supply and demand status of each ship is accurately quantified, and an intuitive evaluation benchmark is established for cross-ship power allocation. The formation coordination efficiency factor matrix innovatively integrates the relative position matrix and formation type, and dynamically corrects the coordination efficiency weights of different formations (push type, parallel type, etc.) based on the bilinear interpolation algorithm, fully considering the physical influence of ship spacing and azimuth angle on power coordination. After normalization, the coordination efficiency under different formation structures is horizontally comparable, effectively solving the power allocation adaptation problem in complex formation scenarios.

[0125] By coupling the surplus and shortage coefficient with the dynamic weight vector generated by the synergy efficiency factor, the system can balance the power supply and demand with the synergy benefits of the fleet in real time. For example, surplus ships will give priority to transmitting electricity to adjacent power-deficient ships according to the synergy efficiency, reducing ineffective transmission losses. Power-deficient ships will obtain reasonable supplies based on the importance of their position in the formation to ensure the stable operation of key equipment. By combining the proportional allocation algorithm of the total surplus electricity, the on-demand allocation of electricity resources is realized, the unnecessary cross-ship electricity flow is suppressed, and the overall efficiency of the fleet power system is significantly improved. In actual applications, it can reduce the redundant loss of generators, reduce the battery charging and discharging load, enhance the endurance and power supply reliability in long-duration multi-formation mode, and provide technical support for energy consumption optimization and intelligent management of green shipping.

[0126] In one case of this embodiment, the power of the fleet in the current navigation area is redistributed according to the power distribution parameter, including:

[0127] According to the type of navigation area of ​​each cargo ship, the equipment operation characteristic matrix of each cargo ship is extracted to obtain the minimum power maintenance threshold of each cargo ship when operating in the corresponding area. Specifically, if the navigation area of ​​each cargo ship is a resistance area, the power consumption data corresponding to different resistances is extracted from the equipment operation characteristic matrix, and the power consumption value under the resistance condition is interpolated between known resistance points according to the current resistance through linear interpolation method to calculate the minimum power maintenance threshold of each cargo ship when operating in the corresponding area. If the navigation area of ​​each cargo ship is a sea condition area, the operation data of the equipment under the sea condition (such as the power regulation characteristics and fuel consumption rate data of the equipment) is extracted from the equipment operation characteristic matrix, the operation data is sorted and the average value is calculated, which is the minimum power maintenance threshold of each cargo ship when operating in the corresponding area.

[0128] A triple matching analysis is performed on the actual power obtained by each ship in the power allocation parameters, the real-time power demand parameters, and the minimum power maintenance threshold to generate the power security parameters of each cargo ship. Specifically, the actual power obtained in the power allocation parameters of each cargo ship is divided by the real-time power demand parameters to obtain the real-time satisfaction rate, and the real-time satisfaction rate is divided by the minimum power maintenance threshold to obtain the basic security rate. The minimum value of the real-time satisfaction rate and the basic security rate is then taken as the power security parameter to generate the power security parameter of each cargo ship.

[0129] Compare the coordinate offset and heading angle deviation of the relative position matrix of each cargo ship before and after power distribution to obtain the position stability parameter of the formation, calculate the impact of the real-time output power change of the diesel generator on the speed, generate the power coordination parameter, and fuse the position stability parameter with the power coordination parameter to obtain the formation stability change parameter, which specifically includes: extracting the coordinate value and heading angle of each cargo ship, calculating the average value of the absolute value of the horizontal and longitudinal coordinate offset of each ship as the position offset parameter, calculating the average value of the absolute value of the heading angle deviation as the heading stability parameter, and taking the weighted sum of the position offset parameter and the heading stability parameter to obtain the position stability parameter, and extracting the diesel generator power of each ship from the equipment operation characteristic matrix. Speed ​​mapping relationship, calculate the speed change corresponding to the power change, calculate the standard deviation of the speed change of all cargo ships, take the reciprocal and normalize it to generate the power coordination parameter, add the normalized position stability parameter and the power coordination parameter to obtain the formation stability change parameter;

[0130] The formation stability change parameters and the formation coordination efficiency factor matrix are analyzed to obtain the formation coordination efficiency maintenance coefficient. Specifically, the following steps are performed: the formation stability change parameters are normalized to make them consistent with the dimensions of the elements of the formation coordination efficiency factor matrix; each non-diagonal element is weighted and corrected using the normalized formation stability change parameter as a weight to obtain the adjusted inter-ship coordination efficiency value; the average value of all adjusted non-diagonal elements is calculated; the inter-ship coordination efficiency value and the average value are weighted averaged (the weight of the average value of the non-diagonal elements is the proportion of the number of ships in the formation, and the weight of the diagonal elements is the proportion of the number of ships); finally, the formation coordination efficiency maintenance coefficient is obtained;

[0131] According to the current navigation area type, regional characteristic response parameters are extracted from navigation data and environmental data. If the current navigation area is a resistance area, the regional characteristic response parameters are water flow resistance response parameters. If the current navigation area is a sea condition area, the regional characteristic response parameters are water flow wave load response parameters. Specifically, if the current navigation area is a resistance area, the speed and heading of each cargo ship are extracted from the navigation data, and the water flow speed and direction are extracted from the environmental data. The relative speed between the water flow and the cargo ship is calculated by the vector synthesis algorithm, and the relative speed, direction and waterline area ratio of the cargo ship are distributed. Water resistance: Calculate the resistance of each ship and take the average value as the water resistance response parameter. If it is a sea condition area, obtain the wave height, period and direction related data from the environmental data, use the sliding average algorithm to smooth the relevant data, match the wave height with the ship's orientation according to the length, width, height and draft of the ship, divide the waves into different frequency bands according to the period, assign a weight to the wave height in each frequency band, combine the weighted wave height with the ship parameters and calculate the wave impact force on each ship. Finally, calculate the average value of the wave impact force of all cargo ships as the water wave load response parameter;

[0132] The regional characteristic response parameters, the actual response capabilities of each ship's diesel generator output power and battery pack real-time capacity after power allocation are analyzed to generate a regional adaptability evaluation index. Specifically, for each ship, the diesel generator output power and battery pack real-time capacity are divided by the regional characteristic response parameters to obtain the power response coefficient and capacity response coefficient. The average power response coefficient and capacity response coefficient of all cargo ships are then calculated, and the minimum value of the two is taken as the regional adaptability evaluation index.

[0133] The equipment operation characteristic matrix of each cargo ship, the formation collaborative efficiency factor matrix and the current navigation area type are analyzed respectively to obtain the power security threshold, the formation collaborative efficiency maintenance coefficient threshold and the regional adaptability evaluation index threshold. Specifically, for the equipment operation characteristic matrix of each cargo ship, the corresponding historical power consumption data is extracted according to the navigation area type. When the navigation area is a resistance area, the historical value after linear interpolation is taken. For the sea condition area, the average value of the operation data is taken. The combined median of the two types of data is calculated as the power security threshold. When the navigation area is a resistance area, the non-diagonal elements of the collaborative efficiency factor matrix when the fleet is in the resistance area in all historical records are extracted to form an independent data set, and the data set is sorted from small to large. Sorting, using linear interpolation to calculate the 25th percentile value of the data set as the threshold value of the formation coordination efficiency maintenance coefficient. If it is a sea state area, extract the non-diagonal elements of the coordination efficiency factor matrix when the fleet is in the sea state area in all historical records and form an independent data set. The data set is sorted from small to large, and the 30th percentile value of the independent data set is calculated by linear interpolation as the threshold value of the formation coordination efficiency maintenance coefficient. If the current navigation area is a resistance area, use a sliding window to take the average value of the data of the past 10 cycles from the historical navigation resistance data as the threshold value of the regional adaptability evaluation index. If it is a sea state area, calculate the median of the data of the past 10 cycles from the historical wave data as the threshold value of the regional adaptability evaluation index.

[0134] The power security parameters, formation coordination efficiency maintenance coefficient and regional adaptability assessment index of each cargo ship are compared with three thresholds respectively. If any of the power security parameters, formation coordination efficiency maintenance coefficient and regional adaptability assessment index does not reach the threshold, the corresponding command is triggered.

[0135] By constructing a multi-dimensional power allocation rationality assessment method, a systematic guarantee mechanism is provided for the stable power supply of the fleet in complex navigation environments. In the power security analysis, linear interpolation and data averaging methods are used to extract the minimum power maintenance threshold based on the differentiated characteristics of the resistance area and the sea condition area, respectively, to accurately fit the actual energy consumption characteristics of the equipment in different areas. Through the dual calculation of real-time satisfaction rate and basic guarantee rate and minimum value determination, the power security parameters formed can intuitively reflect the degree to which the power allocation of each ship meets the basic operating needs, avoiding the risk of equipment downtime due to insufficient power. In the formation stability assessment link, the coordinate offset, heading angle deviation and power coordination parameters are integrated to consider the dynamic changes of the formation's spatial structure and quantify the impact of diesel generator power adjustment on speed consistency. The formation stability change parameters can comprehensively characterize the actual effect of power allocation on the formation's coordinated navigation, providing a key basis for judging whether power allocation will cause the risk of formation loss of control.

[0136] By extracting the water flow resistance response parameters of the resistance area and the wave load response parameters of the sea state area, and combining the regional adaptability evaluation index constructed by the diesel generator output power and battery pack capacity, it is possible to dynamically measure the degree of matching between the power distribution plan and the current environmental load, ensuring the reasonable inclination of power resources between key equipment and basic operations. The regional threshold setting based on historical data not only utilizes statistical laws to ensure the scientific nature of the threshold, but also realizes dynamic adaptation to real-time working conditions through sliding window and linear interpolation technology. When any indicator of power security, collaborative efficiency maintenance coefficient or regional adaptability does not meet the standard, a reallocation mechanism is triggered, forming an evaluation determination The revised closed-loop management model effectively avoids the one-sidedness of single parameter evaluation, improves the robustness of the system under extreme sea conditions or sudden load changes, and provides core technical support for the optimal allocation of power resources and safe endurance of the fleet when sailing across regions.

[0137] In one case of this embodiment, if any one of the power security parameter, the formation coordination efficiency maintenance coefficient, and the regional adaptability evaluation index does not reach a threshold, a corresponding instruction is triggered, including:

[0138] When the power security parameters of each cargo ship When the power security threshold is reached, the power security is judged to be unreasonable, triggering the first correction instruction. The first correction instruction is: with the basic power demand of each ship as the core, the power supply of all cargo ships is prioritized, and the power security priority is main propulsion ship > auxiliary function ship > barge;

[0139] When the formation coordination efficiency maintenance coefficient If the formation coordination efficiency maintenance coefficient threshold is exceeded, the coordination efficiency is judged to be unreasonable, triggering the second correction instruction, which is to adaptively distribute the power of each cargo ship based on the common formation of the fleet;

[0140] When regional adaptability evaluation indicators If the regional adaptability evaluation index threshold is exceeded, the regional adaptability is determined to be unreasonable, triggering the third correction instruction, which is: adaptively allocating the power of each cargo ship based on the navigation area;

[0141] The priority order is: First correction instruction > Third correction instruction > Second correction instruction;

[0142] When multiple instructions are triggered at the same time, they are executed one by one according to the priority, and the next instruction will be executed only when the remaining power meets the power requirement of the next instruction.

[0143] Through hierarchical threshold comparison and priority instruction execution strategy, an intelligent and reliable decision-making framework is provided for the dynamic optimization of the fleet's power system. When the power security parameter is lower than the threshold, the system will prioritize triggering the first correction instruction to reconstruct the power distribution with the power security priority of main propulsion ship > auxiliary function ship > barge, ensuring the basic power demand of key equipment and fundamentally avoiding the navigation safety risks caused by power shortages. If the formation coordination efficiency maintenance coefficient is unreasonable, the second correction instruction will dynamically adapt the power distribution based on common formation characteristics to ensure the stability of the formation structure and power coordination. For the third correction instruction with insufficient regional adaptability, the power will be accurately allocated based on the resistance or sea conditions and regional characteristics to improve the equipment's response to environmental loads.

[0144] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A fleet power collaborative management and distribution system, characterized by: include: The data collection unit is used to divide the fleet's navigation route into multiple navigation areas and collect the navigation data of each cargo ship in the fleet, the operating data of the load equipment, the environmental data, the historical navigation data and the historical sea condition data in real time; The analysis unit is used to obtain the relative position matrix between the cargo ships based on the navigation data, including: Process the navigation data of each cargo ship to generate position and direction data; According to the position and direction data, it is assumed that the fleet has several cargo ships. The cargo ship is used as the reference ship, and the azimuth and relative distance of the remaining cargo ships relative to the reference ship are calculated to obtain the relative position parameters of the single ship centered on the reference ship; Traverse each cargo ship and use it as a reference ship in turn to obtain the relative position parameters of each ship; Collect the relative position parameters of all cargo ships, sort them and arrange them in order of ship numbers to form a relative position matrix between the cargo ships; And analyze the relative position matrix to obtain the current fleet formation type, including: Extract the relative position matrix to obtain the formation characteristic parameters of the fleet's compactness and directional consistency; Analyze the common formations of cargo ships and obtain the range of formation characteristic parameters; Match the formation characteristic parameters with the formation characteristic parameter range to obtain the formation type of the current fleet; When the fleet formation is adjusted, the regional resistance coefficient of each cargo ship is calculated based on the navigation data, environmental data and navigation area, including: Calculate the rate of change of the relative position matrix to generate formation stability parameters; Based on the current navigation area type, set the formation stability threshold, and compare the formation stability parameter with the formation stability threshold to determine whether the fleet formation needs to be adjusted; When the fleet formation is adjusted, the navigation data and environmental data are extracted based on the current navigation area type to obtain the basic resistance parameters; Analyze the relative position matrix between cargo ships to generate interference correction parameters for the formation; The basic resistance parameters are combined with the interference correction parameters to obtain the regional resistance coefficient of each cargo ship; The correlation unit is used to extract the operating data of the load equipment of each cargo ship to obtain the equipment operation characteristic matrix, and to correlate the equipment operation characteristic matrix, the formation type and the regionalized resistance coefficient to obtain the real-time power demand parameters of each cargo ship; The power distribution unit integrates and analyzes the real-time power demand parameters, the real-time output power of the diesel generators, and the relative position matrix to obtain the power distribution parameters between the cargo ships; The determination unit redistributes the fleet power in the current navigation area according to the power distribution parameters.

2. A fleet power collaborative management and distribution system according to claim 1, characterized in that: The fleet's sailing routes are divided into multiple sailing areas, including: Analyze navigation data and environmental data to obtain the resistance value and sea condition value of the current navigation area; Calculate historical navigation data and historical sea condition data to obtain resistance value thresholds and sea condition value thresholds; The resistance value and the sea condition value of the current navigation area are compared with the resistance value threshold and the sea condition value threshold respectively, and the current navigation area is divided into one of the resistance area and the sea condition area.

3. A fleet power collaborative management and distribution system according to claim 2, characterized in that: The operating data of each cargo ship's load equipment is extracted to obtain the equipment operation feature matrix, including: Extract the load equipment operation data of each cargo ship and generate basic equipment operation parameters; Based on the type of navigation area of ​​the cargo ship, the start and stop frequency of the equipment in the navigation area is analyzed to obtain the equipment area response parameters; The basic equipment operating parameters and equipment regional response parameters are integrated and arranged according to the cargo ship number and equipment type to form an equipment operating characteristic matrix.

4. A fleet power collaborative management and distribution system according to claim 3, characterized in that: By correlating the equipment operation characteristic matrix, formation type, and regionalized resistance coefficient, the real-time power demand parameters of each cargo ship are obtained, including: The basic operating parameters of the equipment are integrated with the regional response parameters of the equipment to generate the equipment-related feature vector; The influence of formation type on the resistance distribution of each cargo ship is analyzed, and the formation resistance distribution matrix is ​​obtained; The formation resistance distribution matrix and the regional resistance coefficient are integrated to obtain the resistance variation factor caused by the position difference of each cargo ship under different formations. The equipment-associated feature vector and the resistance variation factor are weightedly fused to generate the real-time power demand parameters of each cargo ship.

5. A fleet power collaborative management and distribution system according to claim 4, characterized in that: The real-time power demand parameters, the real-time output power of the diesel generators, and the relative position matrix are integrated and analyzed to obtain the power distribution parameters between the cargo ships, including: Compare the real-time power demand parameters of each cargo ship with the real-time output power of the diesel generator to generate the power surplus or shortage coefficient of each ship; Analyze the relative position matrix and formation type to generate the formation coordination efficiency factor matrix; The power surplus / shortage coefficient of a single ship is coupled with the formation coordination efficiency factor matrix to generate a dynamic weight vector for power distribution. According to the total available surplus electricity of the fleet and the dynamic weight vector of electricity distribution, the electricity transfer amount between ships is calculated to obtain the electricity distribution parameters between cargo ships.

6. A fleet power collaborative management and distribution system according to claim 5, characterized in that: Redistribute the fleet power in the current navigation area according to the power distribution parameters, including: Analyze the navigation area type, equipment operation characteristic matrix and power distribution parameters of each cargo ship to generate the power security parameters of each cargo ship; By comparing the relative position matrix of each cargo ship before and after power distribution and the impact of the real-time output power change of the diesel generator on the speed, the formation coordination efficiency maintenance coefficient is obtained; According to the current navigation area type, regional characteristic response parameters are extracted from navigation data and environmental data; Analyze the regional characteristic response parameters, the actual response capabilities of each ship's diesel generator output power and the real-time capacity of the battery pack after power allocation, and generate regional adaptability evaluation indicators; The equipment operation characteristic matrix of each cargo ship, the formation coordination efficiency factor matrix and the current navigation area type are analyzed respectively to obtain the power security threshold, the formation coordination efficiency maintenance coefficient threshold and the regional adaptability evaluation index threshold. The power security parameters, formation coordination efficiency maintenance coefficient and regional adaptability assessment index of each cargo ship are compared with three thresholds respectively. If any of the power security parameters, formation coordination efficiency maintenance coefficient and regional adaptability assessment index does not reach the threshold, the corresponding command is triggered.

7. A fleet power collaborative management and distribution system according to claim 6, characterized in that: If any of the power security parameter, formation coordination efficiency maintenance coefficient, and regional adaptability assessment index does not reach the threshold, the corresponding instructions will be triggered, including: When the power security parameter of each cargo ship is less than the power security threshold, the power security is determined to be unreasonable, triggering the first correction instruction; When the formation coordination efficiency maintenance coefficient is less than the formation coordination efficiency maintenance coefficient threshold, the coordination efficiency is determined to be unreasonable, and the second correction instruction is triggered; When the regional adaptability evaluation index is less than the regional adaptability evaluation index threshold, the regional adaptability is determined to be unreasonable, and the third correction instruction is triggered; The priority order is: First correction instruction > Third correction instruction > Second correction instruction; When multiple instructions are triggered at the same time, they are executed one by one according to their priority, and the next instruction will only be executed if the remaining power meets the power requirement of the next instruction.

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